Experimental adaptive on‐line optimization of cellular productivity of a continuous bakers' yeast culture

AbstractAn adaptive on‐line optimization method that utilizes dynamic model identification has been applied to maximize the cellular productivity of a continuous bakers' yeast culture. Experiments were conducted on a sophisticated computerized fermentation system. Experimental results show that the adaptive on‐line optimization method requires very little a priori information, is easy to implement, converges quickly, adapts to changes in the process, and is stable even when operational difficulties are encountered.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

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Title: Experimental adaptive on‐line optimization of cellular productivity of a continuous bakers' yeast culture
Abstract: AbstractAn adaptive on‐line optimization method that utilizes dynamic model identification has been applied to maximize the cellular productivity of a continuous bakers' yeast culture. Experiments were conducted on a sophisticated computerized fermentation system. Experimental results show that the adaptive on‐line optimization method requires very little a priori information, is easy to implement, converges quickly, adapts to changes in the process, and is stable even when operational difficulties are encountered.

[Assistant] Methodology


Experimental multivariable adaptive optimization of the steady‐state cellular productivity of a continuous baker's yeast culture

AbstractA multivariable adaptive optimization algorithm that uses transient data to improve the optimization speed was successfully implemented on‐line to maximize the steady‐state cellular productivity of a continuous culture of baker's yeast. The algorithm was shown to be stable even during periods of oscillatory growth and was able to reoptimize the culture when planned disturbances were introduced. Although adaptive tuning of the forgetting factor improved the performance, further refinements in the adaptive forgetting factor algorithm are necessary for completely satisfactory results.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

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Title: Experimental multivariable adaptive optimization of the steady‐state cellular productivity of a continuous baker's yeast culture
Abstract: AbstractA multivariable adaptive optimization algorithm that uses transient data to improve the optimization speed was successfully implemented on‐line to maximize the steady‐state cellular productivity of a continuous culture of baker's yeast. The algorithm was shown to be stable even during periods of oscillatory growth and was able to reoptimize the culture when planned disturbances were introduced. Although adaptive tuning of the forgetting factor improved the performance, further refinements in the adaptive forgetting factor algorithm are necessary for completely satisfactory results.

[Assistant]
The primary contribution is the development and experimental implementation of a multivariable adaptive optimization algorithm. The yeast culture serves as the experimental validation context, while the main focus is the proposed optimization methodology.

Methodology

Explanation: The primary contribution is the development and experimental implementation of a multivariable adaptive optimization algorithm. The yeast culture serves as the experimental validation context, while the main focus is the proposed optimization methodology.


[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Experimental multivariable adaptive optimization of the steady‐state cellular productivity of a continuous baker's yeast culture
Abstract: AbstractA multivariable adaptive optimization algorithm that uses transient data to improve the optimization speed was successfully implemented on‐line to maximize the steady‐state cellular productivity of a continuous culture of baker's yeast. The algorithm was shown to be stable even during periods of oscillatory growth and was able to reoptimize the culture when planned disturbances were introduced. Although adaptive tuning of the forgetting factor improved the performance, further refinements in the adaptive forgetting factor algorithm are necessary for completely satisfactory results.

[Assistant] Methodology


Influence Diffusion in Social Networks

Recently influence diffusion in social networks has become a hot topic in research communities. In this paper, we outline the techniques used in optimizing or facilitating information diffusion in social networks. We begin with an overview of social networks identifying its significance and characteristics. Then among various problems that are related to diffusion of information in social networks, we study the two fundamental problems using multi-scale analysis, namely (a) maximizing the influence spread and (b) minimizing the spread of misinformation in social networks. Research trends of this topic are also detected and discussed.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Influence Diffusion in Social Networks
Abstract: Recently influence diffusion in social networks has become a hot topic in research communities. In this paper, we outline the techniques used in optimizing or facilitating information diffusion in social networks. We begin with an overview of social networks identifying its significance and characteristics. Then among various problems that are related to diffusion of information in social networks, we study the two fundamental problems using multi-scale analysis, namely (a) maximizing the influence spread and (b) minimizing the spread of misinformation in social networks. Research trends of this topic are also detected and discussed.

[Assistant] Review


Systems Biology Approaches to Enzyme Kinetics: Analyzing Network Models of Drug Metabolism

Intracellular drug metabolism involves transport, bioactivation, conjugation, and other biochemical steps. The dynamics of these steps are each dependent on a number of other cellular factors that can ultimately lead to unexpected behavior. In this review, we discuss the confounding processes and coupled reactions within bioactivation networks that require a systems-level perspective in order to fully understand the time-varying behavior. When converting known in vitro characteristics of drug-enzyme interactions into descriptions of cellular systems, features such as substrate availability, cell-to-cell variability, and intracellular redox state deserve special focus. An example of doxorubicin bioactivation is used for discussing points of consideration when constructing and analyzing network models of drug metabolism.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Systems Biology Approaches to Enzyme Kinetics: Analyzing Network Models of Drug Metabolism
Abstract: Intracellular drug metabolism involves transport, bioactivation, conjugation, and other biochemical steps. The dynamics of these steps are each dependent on a number of other cellular factors that can ultimately lead to unexpected behavior. In this review, we discuss the confounding processes and coupled reactions within bioactivation networks that require a systems-level perspective in order to fully understand the time-varying behavior. When converting known in vitro characteristics of drug-enzyme interactions into descriptions of cellular systems, features such as substrate availability, cell-to-cell variability, and intracellular redox state deserve special focus. An example of doxorubicin bioactivation is used for discussing points of consideration when constructing and analyzing network models of drug metabolism.

[Assistant] Review


Experimental Evaluation of ACO for Continuous Domains to Solve Function Optimization Problems

A new Ant Colony Optimization \(\text {ACO}_\text {B}\) to solve function optimization problems (FOP) is evaluated experimentally by using ten standard multimodal test functions such as Michaelwicz’s function. In \(\text {ACO}_\text {B}\), ants search for solutions in binary search space and can improve the accuracy of solutions by the stepwise localization of search space. Experiments show that \(\text {ACO}_\text {B}\) can keep the balance between accuracy and efficiency to search for optimum solutions, and that it can reduce the population size of \(\text {ACO}_\text {R}\), which is a preceding ACO based on real search space. It is also shown that Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) is superior in computational time but lacks the accuracy of solutions, and that Genetic Algorithm (GA) is superior in the ratio of getting the optimum solutions but weak in the performance.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Experimental Evaluation of ACO for Continuous Domains to Solve Function Optimization Problems
Abstract: A new Ant Colony Optimization \(\text {ACO}_\text {B}\) to solve function optimization problems (FOP) is evaluated experimentally by using ten standard multimodal test functions such as Michaelwicz’s function. In \(\text {ACO}_\text {B}\), ants search for solutions in binary search space and can improve the accuracy of solutions by the stepwise localization of search space. Experiments show that \(\text {ACO}_\text {B}\) can keep the balance between accuracy and efficiency to search for optimum solutions, and that it can reduce the population size of \(\text {ACO}_\text {R}\), which is a preceding ACO based on real search space. It is also shown that Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) is superior in computational time but lacks the accuracy of solutions, and that Genetic Algorithm (GA) is superior in the ratio of getting the optimum solutions but weak in the performance.

[Assistant]
The primary  is the proposed ACO_B optimization method for continuous-domain function optimization. The experimental evaluation on standard benchmark functions is used to assess the method's accuracy and efficiency against existing optimization approaches.

Methodology

Explanation: The primary  is the proposed ACO_B optimization method for continuous-domain function optimization. The experimental evaluation on standard benchmark functions is used to assess the method's accuracy and efficiency against existing optimization approaches.


Handling Catastrophic Forgetting: Online Continual Learning for Next Activity Prediction

Predictive business process monitoring focuses on predicting future process trajectories, including next-activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume a static environment, overlooking dynamic characteristics and concept drifts. This results in catastrophic forgetting, where training while focusing merely on new data distribution negatively impacts the performance on previously learned data distributions. Continual learning addresses, among others, the challenges related to mitigating catastrophic forgetting. This paper proposes a novel approach called Continual Next Activity Prediction with Prompts (CNAPwP) which adapts the DualPrompt algorithm for next-activity prediction to improve accuracy and adaptability while mitigating catastrophic forgetting. New datasets with recurring concept drifts are introduced, alongside a task-specific forgetting metric that measures the prediction accuracy gap between initial and subsequent task encounters. Extensive testing on both synthetic and real-world datasets shows that this approach outperforms five competing methods, demonstrating its potential applicability in real-world scenarios. An open source implementation of our method together with datasets and results are available under: https://github.com/TamaraVerbeek/CNAPwP.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Handling Catastrophic Forgetting: Online Continual Learning for Next Activity Prediction
Abstract: Predictive business process monitoring focuses on predicting future process trajectories, including next-activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume a static environment, overlooking dynamic characteristics and concept drifts. This results in catastrophic forgetting, where training while focusing merely on new data distribution negatively impacts the performance on previously learned data distributions. Continual learning addresses, among others, the challenges related to mitigating catastrophic forgetting. This paper proposes a novel approach called Continual Next Activity Prediction with Prompts (CNAPwP) which adapts the DualPrompt algorithm for next-activity prediction to improve accuracy and adaptability while mitigating catastrophic forgetting. New datasets with recurring concept drifts are introduced, alongside a task-specific forgetting metric that measures the prediction accuracy gap between initial and subsequent task encounters. Extensive testing on both synthetic and real-world datasets shows that this approach outperforms five competing methods, demonstrating its potential applicability in real-world scenarios. An open source implementation of our method together with datasets and results are available under: https://github.com/TamaraVerbeek/CNAPwP.

[Assistant] Methodology


Universality, Phase Transitions and Statistical Mechanics

This survey will describe some mathematical results and conjectures related to phase transitions and statistical mechanics. In particular, we will focus on the principle of universality which asserts that singularities associated with second order phase transitions are universal. This means that they do not depend on the details of the model. Hence, although the temperature at which a phase transition occurs is typically model dependent, the macroscopic or long distance behavior at the transition is believed to depend on only a few general features such as the dimension of space and the symmetry of the model. Universality explains why relatively simple mathematical models can give quantitatively accurate information about transitions for a wide class of physical and mathematical systems. After a brief digression about random walks and self-avoiding walks we shall focus on the Ising model for interacting spins. The phase transition of the Ising model is believed to describe liquid-gas transitions, coupled chains of quantum anharmonic oscillators, certain quantum field theories and models of probabilistic cellular automata. The last section is devoted to a review of some problems in random Schrodinger operators and GOE and GUE matrix ensembles. The universality of eigenvalue correlations is explored.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Universality, Phase Transitions and Statistical Mechanics
Abstract: This survey will describe some mathematical results and conjectures related to phase transitions and statistical mechanics. In particular, we will focus on the principle of universality which asserts that singularities associated with second order phase transitions are universal. This means that they do not depend on the details of the model. Hence, although the temperature at which a phase transition occurs is typically model dependent, the macroscopic or long distance behavior at the transition is believed to depend on only a few general features such as the dimension of space and the symmetry of the model. Universality explains why relatively simple mathematical models can give quantitatively accurate information about transitions for a wide class of physical and mathematical systems. After a brief digression about random walks and self-avoiding walks we shall focus on the Ising model for interacting spins. The phase transition of the Ising model is believed to describe liquid-gas transitions, coupled chains of quantum anharmonic oscillators, certain quantum field theories and models of probabilistic cellular automata. The last section is devoted to a review of some problems in random Schrodinger operators and GOE and GUE matrix ensembles. The universality of eigenvalue correlations is explored.

[Assistant] Review


Genome-Scale Brain Metabolic Networks as Scaffolds for the Systems Biology of Neurodegenerative Diseases: Mapping Metabolic Alterations

Systems-based investigation of diseases requires integrated analysis of cellular networks and high-throughput data of gene products. The use of genome-scale metabolic networks for such integration has led to the elucidation of cellular mechanisms for several cell types from microorganisms to plants. It has become easier and cheaper to generate high-throughput data over years in the form of transcriptome, proteome and metabolome. This has tremendously improved the quality and quantity of information extracted from such data enabling the documentation of active pathways and reactions in cell metabolism. A number of omics-based datasets for several neurodegenerative diseases are now available in public repositories. This increases the potential of using genome-scale brain metabolic networks as a scaffold for this type of data to map metabolic alterations for the purpose of elucidating disease mechanisms and for the diagnosis and treatment of such disorders. This chapter first reviews omics data collected for neurodegenerative diseases to map their effect on metabolism. Later, the potential for genome-scale metabolic modeling of such data is reviewed and discussed in light of recently reconstructed brain metabolic networks at genome-scale.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Genome-Scale Brain Metabolic Networks as Scaffolds for the Systems Biology of Neurodegenerative Diseases: Mapping Metabolic Alterations
Abstract: Systems-based investigation of diseases requires integrated analysis of cellular networks and high-throughput data of gene products. The use of genome-scale metabolic networks for such integration has led to the elucidation of cellular mechanisms for several cell types from microorganisms to plants. It has become easier and cheaper to generate high-throughput data over years in the form of transcriptome, proteome and metabolome. This has tremendously improved the quality and quantity of information extracted from such data enabling the documentation of active pathways and reactions in cell metabolism. A number of omics-based datasets for several neurodegenerative diseases are now available in public repositories. This increases the potential of using genome-scale brain metabolic networks as a scaffold for this type of data to map metabolic alterations for the purpose of elucidating disease mechanisms and for the diagnosis and treatment of such disorders. This chapter first reviews omics data collected for neurodegenerative diseases to map their effect on metabolism. Later, the potential for genome-scale metabolic modeling of such data is reviewed and discussed in light of recently reconstructed brain metabolic networks at genome-scale.

[Assistant] Review


Optimal experimental designs for dose–response studies with continuous endpoints

In most areas of clinical and preclinical research, the required sample size determines the costs and effort for any project, and thus, optimizing sample size is of primary importance. An experimental design of dose-response studies is determined by the number and choice of dose levels as well as the allocation of sample size to each level. The experimental design of toxicological studies tends to be motivated by convention. Statistical optimal design theory, however, allows the setting of experimental conditions (dose levels, measurement times, etc.) in a way which minimizes the number of required measurements and subjects to obtain the desired precision of the results. While the general theory is well established, the mathematical complexity of the problem so far prevents widespread use of these techniques in practical studies. The paper explains the concepts of statistical optimal design theory with a minimum of mathematical terminology and uses these concepts to generate concrete usable D-optimal experimental designs for dose-response studies on the basis of three common dose-response functions in toxicology: log-logistic, log-normal and Weibull functions with four parameters each. The resulting designs usually require control plus only three dose levels and are quite intuitively plausible. The optimal designs are compared to traditional designs such as the typical setup of cytotoxicity studies for 96-well plates. As the optimal design depends on prior estimates of the dose-response function parameters, it is shown what loss of efficiency occurs if the parameters for design determination are misspecified, and how Bayes optimal designs can improve the situation.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Optimal experimental designs for dose–response studies with continuous endpoints
Abstract: In most areas of clinical and preclinical research, the required sample size determines the costs and effort for any project, and thus, optimizing sample size is of primary importance. An experimental design of dose-response studies is determined by the number and choice of dose levels as well as the allocation of sample size to each level. The experimental design of toxicological studies tends to be motivated by convention. Statistical optimal design theory, however, allows the setting of experimental conditions (dose levels, measurement times, etc.) in a way which minimizes the number of required measurements and subjects to obtain the desired precision of the results. While the general theory is well established, the mathematical complexity of the problem so far prevents widespread use of these techniques in practical studies. The paper explains the concepts of statistical optimal design theory with a minimum of mathematical terminology and uses these concepts to generate concrete usable D-optimal experimental designs for dose-response studies on the basis of three common dose-response functions in toxicology: log-logistic, log-normal and Weibull functions with four parameters each. The resulting designs usually require control plus only three dose levels and are quite intuitively plausible. The optimal designs are compared to traditional designs such as the typical setup of cytotoxicity studies for 96-well plates. As the optimal design depends on prior estimates of the dose-response function parameters, it is shown what loss of efficiency occurs if the parameters for design determination are misspecified, and how Bayes optimal designs can improve the situation.

[Assistant]
The primary contribution is the development of concrete D-optimal experimental designs for dose-response studies based on statistical optimal design theory. The proposed designs are evaluated against traditional experimental designs and further assessed for parameter misspecification and Bayesian optimization.

Methodology

Explanation: The primary contribution is the development of concrete D-optimal experimental designs for dose-response studies based on statistical optimal design theory. The proposed designs are evaluated against traditional experimental designs and further assessed for parameter misspecification and Bayesian optimization.


Systems biology of the metabolic network regulated by the Akt pathway

Cancer has been proposed as an example of systems biology disease or network disease. Accordingly, tumor cells differ from their normal counterparts more in terms of intracellular network dynamics than single markers. Here we shall focus on a recently recognized hallmark of cancer, the deregulation of cellular energetics. The constitutive activation of the phosphatidylinositol 3-kinase (PI3K)/Akt pathway has been confirmed as an essential step toward cell transformation. We will consider how the effects of Akt activation are connected with cell metabolism; more precisely, we will review existing metabolic models and discuss the current knowledge available to construct a kinetic model of the most relevant metabolic processes regulated by the PI3K/Akt pathway. The model will enable a systems biology approach to predict the metabolic targets that may inhibit cell growth under hyper activation of Akt.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Systems biology of the metabolic network regulated by the Akt pathway
Abstract: Cancer has been proposed as an example of systems biology disease or network disease. Accordingly, tumor cells differ from their normal counterparts more in terms of intracellular network dynamics than single markers. Here we shall focus on a recently recognized hallmark of cancer, the deregulation of cellular energetics. The constitutive activation of the phosphatidylinositol 3-kinase (PI3K)/Akt pathway has been confirmed as an essential step toward cell transformation. We will consider how the effects of Akt activation are connected with cell metabolism; more precisely, we will review existing metabolic models and discuss the current knowledge available to construct a kinetic model of the most relevant metabolic processes regulated by the PI3K/Akt pathway. The model will enable a systems biology approach to predict the metabolic targets that may inhibit cell growth under hyper activation of Akt.

[Assistant] Review


A survey on security and privacy of federated learning

Abstract   Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon decentralized data and training that brings learning to the edge or directly on-device. FL is a new research area often referred to as a new dawn in AI, is in its infancy, and has not yet gained much trust in the community, mainly because of its (unknown) security and privacy implications. To advance the state of the research in this area and to realize extensive utilization of the FL approach and its mass adoption, its security and privacy concerns must be first identified, evaluated, and documented. FL is preferred in use-cases where security and privacy are the key concerns and having a clear view and understanding of risk factors enable an implementer/adopter of FL to successfully build a secure environment and gives researchers a clear vision on possible research areas. This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible. We present an illustrative description of approaches and various implementation styles with an examination of the current challenges in FL and establish a detailed review of security and privacy concerns that need to be considered in a thorough and clear context. Findings from our study suggest that overall there are fewer privacy-specific threats associated with FL compared to security threats. The most specific security threats currently are communication bottlenecks, poisoning, and backdoor attacks while inference-based attacks are the most critical to the privacy of FL. We conclude the paper with much needed future research directions to make FL adaptable in realistic scenarios.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: A survey on security and privacy of federated learning
Abstract: Abstract   Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon decentralized data and training that brings learning to the edge or directly on-device. FL is a new research area often referred to as a new dawn in AI, is in its infancy, and has not yet gained much trust in the community, mainly because of its (unknown) security and privacy implications. To advance the state of the research in this area and to realize extensive utilization of the FL approach and its mass adoption, its security and privacy concerns must be first identified, evaluated, and documented. FL is preferred in use-cases where security and privacy are the key concerns and having a clear view and understanding of risk factors enable an implementer/adopter of FL to successfully build a secure environment and gives researchers a clear vision on possible research areas. This paper aims to provide a comprehensive study concerning FL’s security and privacy aspects that can help bridge the gap between the current state of federated AI and a future in which mass adoption is possible. We present an illustrative description of approaches and various implementation styles with an examination of the current challenges in FL and establish a detailed review of security and privacy concerns that need to be considered in a thorough and clear context. Findings from our study suggest that overall there are fewer privacy-specific threats associated with FL compared to security threats. The most specific security threats currently are communication bottlenecks, poisoning, and backdoor attacks while inference-based attacks are the most critical to the privacy of FL. We conclude the paper with much needed future research directions to make FL adaptable in realistic scenarios.

[Assistant] Review


Integrated Framework of Fragment-Based Method and Generative Model for Lead Drug Molecules Discovery

Generative models have proven valuable in generating novel lead molecules with drug-like properties. However, beyond generating drug-like molecules, the generative model should also be able to create drug molecules with structural properties and pharmacophores to modulate a specific disease. The molecular generation process should also address the multi-objective optimization challenge of producing molecules with the desired efficacy and minimal side effects. This may entail the generation of a diverse pool of molecules with the desired structural properties and pharmacophore, which would offer diverse options and paths to developing potential new drug candidates by prioritizing molecules that balance the desired properties that can cater to the needs of different individuals. Achieving this requires a generative model learning a large dataset of molecular instances with the desired chemical/structural properties. However, large sets of drug molecules are not readily available for many diseases as there are few known drug molecular instances for treating any disease. To address this challenge, this paper presents an in silico molecular generative framework aided by fragment-based molecules’ synthesis for generating a pool of lead molecular instances possessing structural properties and pharmacophores to treat a disease of interest. The operation of the framework is explored using Hypertension as the disease of interest and beta-blocker as the reference hypertension drug to be generated. We generated over 123 beta-blocker-like molecules and further virtual-screened them for drug-likeness, docking probability, scaffold diversity, electrostatic complementarity, and synthesis accessibility to arrive at the final lead beta-blocker-like molecules.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Integrated Framework of Fragment-Based Method and Generative Model for Lead Drug Molecules Discovery
Abstract: Generative models have proven valuable in generating novel lead molecules with drug-like properties. However, beyond generating drug-like molecules, the generative model should also be able to create drug molecules with structural properties and pharmacophores to modulate a specific disease. The molecular generation process should also address the multi-objective optimization challenge of producing molecules with the desired efficacy and minimal side effects. This may entail the generation of a diverse pool of molecules with the desired structural properties and pharmacophore, which would offer diverse options and paths to developing potential new drug candidates by prioritizing molecules that balance the desired properties that can cater to the needs of different individuals. Achieving this requires a generative model learning a large dataset of molecular instances with the desired chemical/structural properties. However, large sets of drug molecules are not readily available for many diseases as there are few known drug molecular instances for treating any disease. To address this challenge, this paper presents an in silico molecular generative framework aided by fragment-based molecules’ synthesis for generating a pool of lead molecular instances possessing structural properties and pharmacophores to treat a disease of interest. The operation of the framework is explored using Hypertension as the disease of interest and beta-blocker as the reference hypertension drug to be generated. We generated over 123 beta-blocker-like molecules and further virtual-screened them for drug-likeness, docking probability, scaffold diversity, electrostatic complementarity, and synthesis accessibility to arrive at the final lead beta-blocker-like molecules.

[Assistant] Applied


Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology

Synthetic biology (SynBio) is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation (GDA) allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time, and resources in the process. Establishing SynBio's future is dependent on GDA, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers recent advancements in GDA, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Engineering genetic circuits: advancements in genetic design automation tools and standards for synthetic biology
Abstract: Synthetic biology (SynBio) is a field at the intersection of biology and engineering. Inspired by engineering principles, researchers use defined parts to build functionally defined biological circuits. Genetic design automation (GDA) allows scientists to design, model, and analyze their genetic circuits in silico before building them in the lab, saving time, and resources in the process. Establishing SynBio's future is dependent on GDA, since the computational approach opens the field to a broad, interdisciplinary community. However, challenges with part libraries, standards, and software tools are currently stalling progress in the field. This review first covers recent advancements in GDA, followed by an assessment of the challenges ahead, and a proposed automated genetic design workflow for the future.

[Assistant] Review


NO sparks off chromatin: Tales of a multifaceted epigenetic regulator

The discovery of nitric oxide (NO) revealed its ambiguous nature, which is related to its pleiotropic activities that control the homeostasis of every organism from bacteria to mammals in several physiological and pathological situations. The wide range of action of NO basically depends on two features: 1) the variety of chemical reactions depending on NO, and 2) the differential cellular responses elicited by distinct NO concentrations. Despite the increasing body of knowledge regarding its chemistry, biology and NO-dependent signaling pathways, little information is available on the nuclear actions of NO in terms of gene expression regulation. Indeed, studies of a putative role for this diatomic compound in regulating chromatin remodeling are still in their infancy. Only recently has the role of NO in epigenetics emerged, and some of its putative epigenetic properties are still only hypothetical. In the present review, we discuss the current evidence for NO-related mechanisms of epigenetic gene expression regulation. We link some of the well known NO chemical reactions and metabolic processes (e.g., S-nitrosylation of thiols, tyrosine nitration, cGMP production) to chromatin modification and address the most recent, striking hypothesis about NO and the control of chromosomes structure.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: NO sparks off chromatin: Tales of a multifaceted epigenetic regulator
Abstract: The discovery of nitric oxide (NO) revealed its ambiguous nature, which is related to its pleiotropic activities that control the homeostasis of every organism from bacteria to mammals in several physiological and pathological situations. The wide range of action of NO basically depends on two features: 1) the variety of chemical reactions depending on NO, and 2) the differential cellular responses elicited by distinct NO concentrations. Despite the increasing body of knowledge regarding its chemistry, biology and NO-dependent signaling pathways, little information is available on the nuclear actions of NO in terms of gene expression regulation. Indeed, studies of a putative role for this diatomic compound in regulating chromatin remodeling are still in their infancy. Only recently has the role of NO in epigenetics emerged, and some of its putative epigenetic properties are still only hypothetical. In the present review, we discuss the current evidence for NO-related mechanisms of epigenetic gene expression regulation. We link some of the well known NO chemical reactions and metabolic processes (e.g., S-nitrosylation of thiols, tyrosine nitration, cGMP production) to chromatin modification and address the most recent, striking hypothesis about NO and the control of chromosomes structure.

[Assistant] Review


Modeling and Optimization of Phenylalanine Ammonia Lyase Stabilization in Recombinant Escherichia coli for the Continuous Synthesis of <scp>l</scp>-Phenylalanine on the Statistical-Based Experimental Designs

Some approaches for improving recombinant phenylalanine ammonia lyase (PAL) stability in Escherichia coli during the enzymatic methods of l-phenylalanine (l-Phe) production were developed following preliminary studies by means of statistical-based experiment designs (response surface method). The traditional non-statistical technology was used to screen four critical factors for PAL stability during the bioconversion process, viz., glycerin, sucrose, 1,4-dithiothreitol (DTT), and MgSO(4). The central composite design (CCD) was applied to optimize the combined effect of critical factors for recombinant PAL stability and understand the relationship between the factors and PAL stability. The optimum values for testing variables were 13.04 mM glycerin, 1.87 mM sucrose, 4.09 mM DTT, and 69 mM Mg(2+). A second-order model equation was suggested and then validated experimentally. The model adequacy was very satisfactory because the coefficient of determination was 0.88. The maximum PAL activity was retained as 67.73 units/g after three successive cycles of bioconversion. In comparison to initial PAL activity, the loss of PAL activity was only 22%. PAL activity was enhanced about 23% in comparison to the control (without any stabilizer additives). PAL stability was significantly improved during successive bioconversion. The results obtained here verified the effectiveness of the applied methodology and may be helpful for l-Phe production on an industrial scale.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Modeling and Optimization of Phenylalanine Ammonia Lyase Stabilization in Recombinant Escherichia coli for the Continuous Synthesis of <scp>l</scp>-Phenylalanine on the Statistical-Based Experimental Designs
Abstract: Some approaches for improving recombinant phenylalanine ammonia lyase (PAL) stability in Escherichia coli during the enzymatic methods of l-phenylalanine (l-Phe) production were developed following preliminary studies by means of statistical-based experiment designs (response surface method). The traditional non-statistical technology was used to screen four critical factors for PAL stability during the bioconversion process, viz., glycerin, sucrose, 1,4-dithiothreitol (DTT), and MgSO(4). The central composite design (CCD) was applied to optimize the combined effect of critical factors for recombinant PAL stability and understand the relationship between the factors and PAL stability. The optimum values for testing variables were 13.04 mM glycerin, 1.87 mM sucrose, 4.09 mM DTT, and 69 mM Mg(2+). A second-order model equation was suggested and then validated experimentally. The model adequacy was very satisfactory because the coefficient of determination was 0.88. The maximum PAL activity was retained as 67.73 units/g after three successive cycles of bioconversion. In comparison to initial PAL activity, the loss of PAL activity was only 22%. PAL activity was enhanced about 23% in comparison to the control (without any stabilizer additives). PAL stability was significantly improved during successive bioconversion. The results obtained here verified the effectiveness of the applied methodology and may be helpful for l-Phe production on an industrial scale.

[Assistant]
The primary contribution is the development and validation of a statistical-based experimental methodology using response surface and central composite designs to optimize PAL stability. The l-phenylalanine production context represents the application and potential industrial use of the methodology rather than the primary intent of the study.

Methodology

Explanation: The primary contribution is the development and validation of a statistical-based experimental methodology using response surface and central composite designs to optimize PAL stability. The l-phenylalanine production context represents the application and potential industrial use of the methodology rather than the primary intent of the study.


Addressing optical modulator non-linearities for photonic neural networks

Within the context of neuromorphic computing, analog photonics, especially after the advent of photonic integrated technologies, offers unparalleled computing speeds per core, and the reduction of size and power consumption compared to digital electronics. However, the functionality of analog systems is limited by noise and non-linear distortions, which degrade signal resolution. Here, a method is presented for analyzing and minimizing the effect of non-linearities associated with the optical power transfer function of a generic modulator, to inform choices of design and operation conditions. The Mach-Zehnder interferometer, micro-ring modulator, and ring-assisted Mach-Zehnder interferometer are compared using this method. The analysis is applied to compare three analog photonic processor architectures for machine learning applications, based on wavelength, space, and time division multiplexing. Our results indicate that despite the lower maximum resolution exhibited by Mach-Zehnder interferometers, they are the most balanced choice for space and time division multiplexing architectures due to stability and power consumption.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Addressing optical modulator non-linearities for photonic neural networks
Abstract: Within the context of neuromorphic computing, analog photonics, especially after the advent of photonic integrated technologies, offers unparalleled computing speeds per core, and the reduction of size and power consumption compared to digital electronics. However, the functionality of analog systems is limited by noise and non-linear distortions, which degrade signal resolution. Here, a method is presented for analyzing and minimizing the effect of non-linearities associated with the optical power transfer function of a generic modulator, to inform choices of design and operation conditions. The Mach-Zehnder interferometer, micro-ring modulator, and ring-assisted Mach-Zehnder interferometer are compared using this method. The analysis is applied to compare three analog photonic processor architectures for machine learning applications, based on wavelength, space, and time division multiplexing. Our results indicate that despite the lower maximum resolution exhibited by Mach-Zehnder interferometers, they are the most balanced choice for space and time division multiplexing architectures due to stability and power consumption.

[Assistant] Methodology


Experimental study on optimization of continuous steel box girder bridge deck

In order to accurately optimize the structural parameters of steel bridge deck(OSD), an optimal design method of continuous steel box girder bridge deck based on response surface method is proposed. Optimization design process includes full scale model test, finite element analysis, response surface function fitting and design parameter optimization. The orthotropic steel bridge deck’s deck thickness, stiffener thickness, diaphragm thickness, stiffener height, and diaphragm opening form are chosen as independent variables, and the internal forces of important structural components discovered are used as dependent variables. Design-Expert software created a five-factor, three-level response surface test. A multivariate quadratic response surface regression model between structural parameters and target response values was developed after the objective function and constraint criteria were identified. Numerical theory’s variance analysis was used to find the best combination, and Design Expert software was used to confirm it to make sure the design value was accurate. The findings indicate that optimized structural parameters are trustworthy because the difference between the predicted value and the theoretical value is less than 5%. An orthotropic steel bridge deck’s structural parameters can be optimized using the response surface approach. It serves as a reference for subsequent steel bridge deck design and construction.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Experimental study on optimization of continuous steel box girder bridge deck
Abstract: In order to accurately optimize the structural parameters of steel bridge deck(OSD), an optimal design method of continuous steel box girder bridge deck based on response surface method is proposed. Optimization design process includes full scale model test, finite element analysis, response surface function fitting and design parameter optimization. The orthotropic steel bridge deck’s deck thickness, stiffener thickness, diaphragm thickness, stiffener height, and diaphragm opening form are chosen as independent variables, and the internal forces of important structural components discovered are used as dependent variables. Design-Expert software created a five-factor, three-level response surface test. A multivariate quadratic response surface regression model between structural parameters and target response values was developed after the objective function and constraint criteria were identified. Numerical theory’s variance analysis was used to find the best combination, and Design Expert software was used to confirm it to make sure the design value was accurate. The findings indicate that optimized structural parameters are trustworthy because the difference between the predicted value and the theoretical value is less than 5%. An orthotropic steel bridge deck’s structural parameters can be optimized using the response surface approach. It serves as a reference for subsequent steel bridge deck design and construction.

[Assistant]
The primary contribution is a proposed optimization design methodology based on response surface modeling, finite element analysis, and experimental validation. The steel bridge deck serves as the application and validation context for the proposed methodology

Methodology

Explanation: The primary contribution is a proposed optimization design methodology based on response surface modeling, finite element analysis, and experimental validation. The steel bridge deck serves as the application and validation context for the proposed methodology


Chromatin and epigenetic regulation in malignant tumors: A comprehensive review

Abstract                   Epigenetics and chromatin biology have been two distinct research fields for many years. There is a close relationship between epigenetics and chromatin biochemistry, and the dynamic changes of chromatin serve as an important basis for epigenetic regulation. The relationship between epigenetics and chromatin is reflected in the dynamic changes and structural adjustments of chromatin. In this article, we focus on previously underappreciated chromatin remodeling proteins. This review summarizes the biological significance of chromatin remodeling complexes in malignancies, including the physiological role of chromatin remodeling complexes in normal cells and the dysregulation of chromatin remodeling complexes in malignant tumors with genetic, epigenetic, and gene expression alterations. It is important to explore the impact of chromatin remodeling complexes on cancer therapy, as predictors of drug response, targets of drug response, and contributors to therapeutic resistance.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Chromatin and epigenetic regulation in malignant tumors: A comprehensive review
Abstract: Abstract                   Epigenetics and chromatin biology have been two distinct research fields for many years. There is a close relationship between epigenetics and chromatin biochemistry, and the dynamic changes of chromatin serve as an important basis for epigenetic regulation. The relationship between epigenetics and chromatin is reflected in the dynamic changes and structural adjustments of chromatin. In this article, we focus on previously underappreciated chromatin remodeling proteins. This review summarizes the biological significance of chromatin remodeling complexes in malignancies, including the physiological role of chromatin remodeling complexes in normal cells and the dysregulation of chromatin remodeling complexes in malignant tumors with genetic, epigenetic, and gene expression alterations. It is important to explore the impact of chromatin remodeling complexes on cancer therapy, as predictors of drug response, targets of drug response, and contributors to therapeutic resistance.

[Assistant] Review


CIGR Handbook of Agricultural Engineering, Volume I Land and Water Engineering, Chapter 5 Irrigation and Drainage, Part 5.4 Irrigation Methods

First paragraph: Irrigation has been practiced for millennia. Until the 20th century, all irrigation depended on gravity to deliver water to the fields and to spread water across the surface of the land. Development of efficient engines, pumps, and impact sprinklers in the first half of this century allowed farmers to mimic rainfall with sprinkler irrigation. In the past 20 years, microirrigation has become more common where water is scarce, crop values are high, and the technology is practical. Surface irrigation remains the dominant system in use worldwide, although sprinkler irrigation has become widely used in some areas. Total irrigated areas in theWorld are given in Table 5.3.An estimate of the percentages of the three primary methods of irrigation in four regions is presented in Table 5.4. Data in the table cannot be extrapolated to world figures because the data are not available for most regions, particularly Asia, which represents 69% of the world’s irrigated area and where surface irrigation is predominant.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: CIGR Handbook of Agricultural Engineering, Volume I Land and Water Engineering, Chapter 5 Irrigation and Drainage, Part 5.4 Irrigation Methods
Abstract: First paragraph: Irrigation has been practiced for millennia. Until the 20th century, all irrigation depended on gravity to deliver water to the fields and to spread water across the surface of the land. Development of efficient engines, pumps, and impact sprinklers in the first half of this century allowed farmers to mimic rainfall with sprinkler irrigation. In the past 20 years, microirrigation has become more common where water is scarce, crop values are high, and the technology is practical. Surface irrigation remains the dominant system in use worldwide, although sprinkler irrigation has become widely used in some areas. Total irrigated areas in theWorld are given in Table 5.3.An estimate of the percentages of the three primary methods of irrigation in four regions is presented in Table 5.4. Data in the table cannot be extrapolated to world figures because the data are not available for most regions, particularly Asia, which represents 69% of the world’s irrigated area and where surface irrigation is predominant.

[Assistant]
The abstract primarily provides a historical and descriptive overview of irrigation methods, practices, and regional use. It does not introduce a new methodology, dataset, theoretical contribution, or a specific empirical application. Therefore, Review is the most appropriate intent label.

Review

Explanation: The abstract primarily provides a historical and descriptive overview of irrigation methods, practices, and regional use. It does not introduce a new methodology, dataset, theoretical contribution, or a specific empirical application. Therefore, Review is the most appropriate intent label.


CIGR Handbook of Agricultural Engineering, Volume I Land and Water Engineering, Chapter 5 Irrigation and Drainage, Part 5.8 Water Quality in Agriculture

First paragraph: Water that is intended for agricultural uses but in some way endangers agricultural activity can be defined as “unsuitable” water. Natural waters are defined as “polluted” when they become unsuitable following the addition of substances other than those normally present. The use of water of less than optimal quality for irrigation introduces various kinds of risk (agronomic, sanitary, and environmental) and can produce damage of direct and indirect types, depending on the form of pollution involved (see Fig. 5.76; [1–3]).

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: CIGR Handbook of Agricultural Engineering, Volume I Land and Water Engineering, Chapter 5 Irrigation and Drainage, Part 5.8 Water Quality in Agriculture
Abstract: First paragraph: Water that is intended for agricultural uses but in some way endangers agricultural activity can be defined as “unsuitable” water. Natural waters are defined as “polluted” when they become unsuitable following the addition of substances other than those normally present. The use of water of less than optimal quality for irrigation introduces various kinds of risk (agronomic, sanitary, and environmental) and can produce damage of direct and indirect types, depending on the form of pollution involved (see Fig. 5.76; [1–3]).

[Assistant]
The abstract is a handbook chapter that summarizes existing knowledge on water quality, pollution, and irrigation risks. It does not present a new methodology, dataset, experiment, or specific application. Although it addresses practical agricultural engineering issues, the content is primarily a general overview of existing knowledge. Therefore, “Review” is more appropriate than “Applied.”

Review

Explanation: The abstract is a handbook chapter that summarizes existing knowledge on water quality, pollution, and irrigation risks. It does not present a new methodology, dataset, experiment, or specific application. Although it addresses practical agricultural engineering issues, the content is primarily a general overview of existing knowledge. Therefore, “Review” is more appropriate than “Applied.”


Lean Six Sigma through ISO 9001 standard-based quality management system: an investigation for research

In the recent past, researchers and practitioners have been attempting to integrate various manufacturing managerial models. One of such endeavours has resulted in the conglomeration of Lean manufacturing and Six Sigma concepts under the title 'Lean Six Sigma'. Parallel to this endeavour, few researchers have integrated the Six Sigma concepts with ISO 9001 standard-based quality management system (QMS). However, there has been no significant effort made by the researchers to integrate Lean Six Sigma with ISO 9001 standard-based QMS. This research gap has been indicated and explored further by reporting a literature review in this paper. By conducting this literature review, the research investigations in the direction of drawing synergy out of Lean Six Sigma and ISO 9001 standard-based QMS have been drawn. A roadmap for implementing Lean Six Sigma through ISO 9001:2008 standard-based QMS has also been contributed in this paper.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Lean Six Sigma through ISO 9001 standard-based quality management system: an investigation for research
Abstract: In the recent past, researchers and practitioners have been attempting to integrate various manufacturing managerial models. One of such endeavours has resulted in the conglomeration of Lean manufacturing and Six Sigma concepts under the title 'Lean Six Sigma'. Parallel to this endeavour, few researchers have integrated the Six Sigma concepts with ISO 9001 standard-based quality management system (QMS). However, there has been no significant effort made by the researchers to integrate Lean Six Sigma with ISO 9001 standard-based QMS. This research gap has been indicated and explored further by reporting a literature review in this paper. By conducting this literature review, the research investigations in the direction of drawing synergy out of Lean Six Sigma and ISO 9001 standard-based QMS have been drawn. A roadmap for implementing Lean Six Sigma through ISO 9001:2008 standard-based QMS has also been contributed in this paper.

[Assistant] Review


LEGISLATIVE SUPPORT OF LANGUAGE RIGHTS OF NATIONAL MINORITIES IN THE FIELD OF EDUCATION AS A COMPONENT OF EUROPEAN INTEGRATION OF UKRAINE

The purpose of the study is to reveal the content of the legislative support of language rights of national minorities of Ukraine in the field of education in the context of its European integration and to clarify the internal and external factors affecting it. The political-legal method of research is used, which consists in the analysis of political-legal norms and political factors that influenced and influence their formation and functioning. The provisions of the legislation of Ukraine regarding ensuring the language rights of national minorities in the field of education of Ukraine in the period from the adoption of the Law of Ukraine “On Languages in the Ukrainian SSR” (October 1989) to the adoption of the Law of Ukraine “On National Minorities (Communities) of Ukraine” (December 2022) were analyzed ). The peculiarities of the transformation of the language legislation of Ukraine in the conditions of Russian aggression and the European integration of Ukraine are clarified. The analysis of the language legislation of Ukraine was carried out using the conclusions and recommendations of the European Commission for Democracy through Law (Venice Commission). The objective conditionality of the narrowing of the linguistic and educational rights of the national minorities of Ukraine in the conditions of Russian aggression in Crimea and Donbas and the full-scale invasion of Russian troops into the territory of Ukraine is emphasized. A conclusion was made about the need for further improvement of the legislation of Ukraine on the language rights of national minorities in the field of education in the context of the European integration of Ukraine.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: LEGISLATIVE SUPPORT OF LANGUAGE RIGHTS OF NATIONAL MINORITIES IN THE FIELD OF EDUCATION AS A COMPONENT OF EUROPEAN INTEGRATION OF UKRAINE
Abstract: The purpose of the study is to reveal the content of the legislative support of language rights of national minorities of Ukraine in the field of education in the context of its European integration and to clarify the internal and external factors affecting it. The political-legal method of research is used, which consists in the analysis of political-legal norms and political factors that influenced and influence their formation and functioning. The provisions of the legislation of Ukraine regarding ensuring the language rights of national minorities in the field of education of Ukraine in the period from the adoption of the Law of Ukraine “On Languages in the Ukrainian SSR” (October 1989) to the adoption of the Law of Ukraine “On National Minorities (Communities) of Ukraine” (December 2022) were analyzed ). The peculiarities of the transformation of the language legislation of Ukraine in the conditions of Russian aggression and the European integration of Ukraine are clarified. The analysis of the language legislation of Ukraine was carried out using the conclusions and recommendations of the European Commission for Democracy through Law (Venice Commission). The objective conditionality of the narrowing of the linguistic and educational rights of the national minorities of Ukraine in the conditions of Russian aggression in Crimea and Donbas and the full-scale invasion of Russian troops into the territory of Ukraine is emphasized. A conclusion was made about the need for further improvement of the legislation of Ukraine on the language rights of national minorities in the field of education in the context of the European integration of Ukraine.

[Assistant]
The study applies political-legal analysis to the concrete case of Ukrainian legislation on the language rights of national minorities in education from 1989 to 2022. The methodology is used to analyze a real-world legal and policy issue rather than being the primary contribution.

Applied

Explanation: The study applies political-legal analysis to the concrete case of Ukrainian legislation on the language rights of national minorities in education from 1989 to 2022. The methodology is used to analyze a real-world legal and policy issue rather than being the primary contribution.


A Knowledge-Guided Temporal Graph Neural Network for Predicting Rate of Penetration in Complex Formations

Summary                   Accurate rate of penetration (ROP) prediction is essential for optimizing drilling performance and reducing operational costs. However, the complexity and uncertainty inherent in drilling operations pose significant challenges to existing data-driven models, limiting their capacity to generalize across diverse conditions and effectively incorporate domain knowledge. In this paper, we propose a knowledge-guided hybrid temporal graph neural network (KHTGNN) for robust ROP prediction. First, explicit domain knowledge is applied to perform physical corrections on drilling parameters during feature engineering, enhancing data quality. Second, an adaptive graph construction method based on node embedding is introduced to dynamically model the evolving correlations among drilling parameters. Third, a hybrid temporal graph neural network (GNN) is designed to capture both intravariable and intervariable dependencies. Moreover, domain-specific rock-breaking mechanism equations are embedded as soft constraints within the model’s loss function to enforce physical consistency. To further enhance generalization, a multiple kernel (MK) maximum mean discrepancy (MMD) approach is integrated to achieve domain-adaptive learning across different wells. The results demonstrate that KHTGNN outperforms current models, achieving a mean absolute percentage error (MAPE) of 9.49%, a mean absolute error (MAE) of 1.64 m/h, and a coefficient of determination (R2) of 0.99. Ablation studies validate the contributions of physical data correction, knowledge constraints, and domain adaptation, emphasizing the critical role of domain knowledge integration in improving predictive performance. This work provides a robust knowledge-guided framework for sequential parameter prediction tasks in complex industrial systems.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: A Knowledge-Guided Temporal Graph Neural Network for Predicting Rate of Penetration in Complex Formations
Abstract: Summary                   Accurate rate of penetration (ROP) prediction is essential for optimizing drilling performance and reducing operational costs. However, the complexity and uncertainty inherent in drilling operations pose significant challenges to existing data-driven models, limiting their capacity to generalize across diverse conditions and effectively incorporate domain knowledge. In this paper, we propose a knowledge-guided hybrid temporal graph neural network (KHTGNN) for robust ROP prediction. First, explicit domain knowledge is applied to perform physical corrections on drilling parameters during feature engineering, enhancing data quality. Second, an adaptive graph construction method based on node embedding is introduced to dynamically model the evolving correlations among drilling parameters. Third, a hybrid temporal graph neural network (GNN) is designed to capture both intravariable and intervariable dependencies. Moreover, domain-specific rock-breaking mechanism equations are embedded as soft constraints within the model’s loss function to enforce physical consistency. To further enhance generalization, a multiple kernel (MK) maximum mean discrepancy (MMD) approach is integrated to achieve domain-adaptive learning across different wells. The results demonstrate that KHTGNN outperforms current models, achieving a mean absolute percentage error (MAPE) of 9.49%, a mean absolute error (MAE) of 1.64 m/h, and a coefficient of determination (R2) of 0.99. Ablation studies validate the contributions of physical data correction, knowledge constraints, and domain adaptation, emphasizing the critical role of domain knowledge integration in improving predictive performance. This work provides a robust knowledge-guided framework for sequential parameter prediction tasks in complex industrial systems.

[Assistant] Methodology


A Graph Neural Network Recommendation Model with Knowledge Graph and Attention Mechanism

Abstract         With the growth of e-commerce and social media platforms, recommendation systems have become crucial for businesses to increase user satisfaction and retention. Collaborative filtering has been the primary recommendation algorithm, but it faces challenges such as the cold start problem and data sparsity. Researchers have tried to introduce supplementary information to complement the data, and Graph Neural Networks (GNNs) have been useful in capturing the relevance of graphs in recommender systems. In addition, traditional GNNs only fuse the first-order neighborhood features of nodes and cannot capture the deeper structural relationships of nodes in the network. Therefore, when the data set is sparse and each node has only a very small number of neighbors, the recommendation quality of traditional GNN-based recommendation algorithms decreases significantly. In this paper, we propose a GNN recommendation model based on user-side information, which can effectively capture the potential interest of users through user relationship graph for recommendation, and fully exploit the large amount of association information among users. The traditional collaborative filtering idea is incorporated with the knowledge graph as the auxiliary information to fully utilize the potential information from both ends to improve the accuracy and diversity of the recommendation model. In addition, the interaction behaviors and opinions in the user-item graph are jointly captured. Different strengths of user-item interactions are considered, which indicate that various interactions contribute differently to user preferences (or item characteristics), and thus attention mechanisms are used to achieve this goal. The algorithm has been verified by public dataset, and it has better recommendation effect than several other classical recommendation algorithms.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: A Graph Neural Network Recommendation Model with Knowledge Graph and Attention Mechanism
Abstract: Abstract         With the growth of e-commerce and social media platforms, recommendation systems have become crucial for businesses to increase user satisfaction and retention. Collaborative filtering has been the primary recommendation algorithm, but it faces challenges such as the cold start problem and data sparsity. Researchers have tried to introduce supplementary information to complement the data, and Graph Neural Networks (GNNs) have been useful in capturing the relevance of graphs in recommender systems. In addition, traditional GNNs only fuse the first-order neighborhood features of nodes and cannot capture the deeper structural relationships of nodes in the network. Therefore, when the data set is sparse and each node has only a very small number of neighbors, the recommendation quality of traditional GNN-based recommendation algorithms decreases significantly. In this paper, we propose a GNN recommendation model based on user-side information, which can effectively capture the potential interest of users through user relationship graph for recommendation, and fully exploit the large amount of association information among users. The traditional collaborative filtering idea is incorporated with the knowledge graph as the auxiliary information to fully utilize the potential information from both ends to improve the accuracy and diversity of the recommendation model. In addition, the interaction behaviors and opinions in the user-item graph are jointly captured. Different strengths of user-item interactions are considered, which indicate that various interactions contribute differently to user preferences (or item characteristics), and thus attention mechanisms are used to achieve this goal. The algorithm has been verified by public dataset, and it has better recommendation effect than several other classical recommendation algorithms.

[Assistant] Methodology


Network Attack Knowledge Reasoning Model based on Graph Convolutional Neural Networks

Abstract         To address the challenge of analyzing large-scale penetration attacks involving complex multiple relations and multi-hop pathways, we have proposed an attack knowledge inference model based on graph convolutional neural networks. The core concept of this model involves obtaining knowledge embeddings related to CVE, CWE, and CAPEC to construct attack context feature data and relationship matrices. Subsequently, we utilize a graph convolutional neural network model for attack classification and employ the CONVE model for attack inference within the same attack category. Research results have shown that this approach outperforms other inference methods in terms of both inference accuracy and efficiency. Through improvements to the graph convolutional neural network model, we have significantly enhanced the accuracy and generalization capability of the attack classification task. Furthermore, we have introduced the use of the CONVE model for attack inference tasks for the first time, and experimental results demonstrate that the CONVE model can infer implicit relationships between CVE-CVE, CVE-CWE, and CVE-CAPEC. This method allows for a deeper learning and understanding of entity relationships within the attack knowledge graph and even aids in revealing certain hidden connections.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Network Attack Knowledge Reasoning Model based on Graph Convolutional Neural Networks
Abstract: Abstract         To address the challenge of analyzing large-scale penetration attacks involving complex multiple relations and multi-hop pathways, we have proposed an attack knowledge inference model based on graph convolutional neural networks. The core concept of this model involves obtaining knowledge embeddings related to CVE, CWE, and CAPEC to construct attack context feature data and relationship matrices. Subsequently, we utilize a graph convolutional neural network model for attack classification and employ the CONVE model for attack inference within the same attack category. Research results have shown that this approach outperforms other inference methods in terms of both inference accuracy and efficiency. Through improvements to the graph convolutional neural network model, we have significantly enhanced the accuracy and generalization capability of the attack classification task. Furthermore, we have introduced the use of the CONVE model for attack inference tasks for the first time, and experimental results demonstrate that the CONVE model can infer implicit relationships between CVE-CVE, CVE-CWE, and CVE-CAPEC. This method allows for a deeper learning and understanding of entity relationships within the attack knowledge graph and even aids in revealing certain hidden connections.

[Assistant] Methodology


Curvature measures for nonlinear regression models using continuous designs with applications to optimal experimental design

We present and illustrate the methodology to calculate curvature measures for continuous designs, and extend design criteria to incorporate continuous designs. These design algorithms include quadratic design procedures, a subset design criterion, a second-order mean-square error design criterion, and a marginal curvature design methodology. A discussion of confidence intervals is also provided for continuous designs.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Curvature measures for nonlinear regression models using continuous designs with applications to optimal experimental design
Abstract: We present and illustrate the methodology to calculate curvature measures for continuous designs, and extend design criteria to incorporate continuous designs. These design algorithms include quadratic design procedures, a subset design criterion, a second-order mean-square error design criterion, and a marginal curvature design methodology. A discussion of confidence intervals is also provided for continuous designs.

[Assistant]
introducing the methodology and several design algorithms/criteria

Methodology

Explanation: introducing the methodology and several design algorithms/criteria


Variational Quantum Circuits for Pesticide Environmental Fate Prediction: A Comparative Study with Classical Machine Learning on the EU SPIN Database

Predicting pesticide environmental fate properties—specifically soil degradation halflife (DegT50) and organic carbon sorption coefficient (Koc)—is essential for regulatory risk assessment under EC 1107/2009. Classical QSAR models achieve moderate predictive power for Koc but struggle with DegT50 due to its dependence on complex soil microbial processes. This work investigates whether variational quantum circuits (VQCs) can capture non-linear molecular descriptor relationships that elude classical regressors. We encode 21 physicochemical descriptors for 110 EU-registered pesticides into per-target quantum circuits: an 8-qubit circuit for DegT50 (17 core features) and a 12-qubit circuit for Koc (all 21 features), each with IQP-style encoding and data re-uploading. Leave-one-out crossvalidation benchmarks reveal that Random Forest (RF) achieves R2 = 0.287 for DegT50 and 0.759 for Koc, while Gradient Boosting (GB) reaches 0.283 and 0.752, respectively. An MLP neural network achieves only 0.075 and 0.497. The VQC alone underperforms all classical baselines (R2 = −0.028 for DegT50, 5-fold CV). A hybrid stacking framework with nested LOO-CV for leakage-free α optimization yields α = 0.20±0.02, but the resulting hybrid R2 (0.288 for DegT50) improves over standalone RF by only +0.001—a negligible and statistically insignificant margin. These results demonstrate that near-term VQCs on small regulatory datasets (N ≈ 110) cannot outperform properly feature-engineered classical models, and caution against premature claims of quantum advantage in chemistry applications with limited training data.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Variational Quantum Circuits for Pesticide Environmental Fate Prediction: A Comparative Study with Classical Machine Learning on the EU SPIN Database
Abstract: Predicting pesticide environmental fate properties—specifically soil degradation halflife (DegT50) and organic carbon sorption coefficient (Koc)—is essential for regulatory risk assessment under EC 1107/2009. Classical QSAR models achieve moderate predictive power for Koc but struggle with DegT50 due to its dependence on complex soil microbial processes. This work investigates whether variational quantum circuits (VQCs) can capture non-linear molecular descriptor relationships that elude classical regressors. We encode 21 physicochemical descriptors for 110 EU-registered pesticides into per-target quantum circuits: an 8-qubit circuit for DegT50 (17 core features) and a 12-qubit circuit for Koc (all 21 features), each with IQP-style encoding and data re-uploading. Leave-one-out crossvalidation benchmarks reveal that Random Forest (RF) achieves R2 = 0.287 for DegT50 and 0.759 for Koc, while Gradient Boosting (GB) reaches 0.283 and 0.752, respectively. An MLP neural network achieves only 0.075 and 0.497. The VQC alone underperforms all classical baselines (R2 = −0.028 for DegT50, 5-fold CV). A hybrid stacking framework with nested LOO-CV for leakage-free α optimization yields α = 0.20±0.02, but the resulting hybrid R2 (0.288 for DegT50) improves over standalone RF by only +0.001—a negligible and statistically insignificant margin. These results demonstrate that near-term VQCs on small regulatory datasets (N ≈ 110) cannot outperform properly feature-engineered classical models, and caution against premature claims of quantum advantage in chemistry applications with limited training data.

[Assistant] Applied


KG-FLoc: Knowledge Graph-Enhanced Fault Localization in Secondary Circuits via Relation-Aware Graph Neural Networks

This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as graph nodes and their multi-dimensional relationships (e.g., electrical connections, control logic) as edges, constructing the first comprehensive knowledge graph (KG) to structurally and operationally model secondary circuits. By reframing fault localization as a knowledge graph link prediction task, KG-FLoc identifies missing or abnormal connections (edges) as fault indicators. To address dynamic topologies and sparse fault samples, KG-FLoc integrates two core innovations: (1) a relation-aware gated unit (RGU) that dynamically regulates information flow through adaptive gating mechanisms, and (2) a hierarchical graph isomorphism network (GIN) architecture for multi-scale feature extraction. Evaluated on real-world datasets from 110 kV/220 kV substations, KG-FLoc achieves 97.2% accuracy in single-fault scenarios and 93.9% accuracy in triple-fault scenarios, surpassing SVM, RF, MLP, and standard GNN baselines by 12.4–31.6%. Beyond enhancing substation reliability, KG-FLoc establishes a knowledge-aware paradigm for fault diagnosis in industrial systems, enabling precise reasoning over complex interdependencies.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: KG-FLoc: Knowledge Graph-Enhanced Fault Localization in Secondary Circuits via Relation-Aware Graph Neural Networks
Abstract: This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as graph nodes and their multi-dimensional relationships (e.g., electrical connections, control logic) as edges, constructing the first comprehensive knowledge graph (KG) to structurally and operationally model secondary circuits. By reframing fault localization as a knowledge graph link prediction task, KG-FLoc identifies missing or abnormal connections (edges) as fault indicators. To address dynamic topologies and sparse fault samples, KG-FLoc integrates two core innovations: (1) a relation-aware gated unit (RGU) that dynamically regulates information flow through adaptive gating mechanisms, and (2) a hierarchical graph isomorphism network (GIN) architecture for multi-scale feature extraction. Evaluated on real-world datasets from 110 kV/220 kV substations, KG-FLoc achieves 97.2% accuracy in single-fault scenarios and 93.9% accuracy in triple-fault scenarios, surpassing SVM, RF, MLP, and standard GNN baselines by 12.4–31.6%. Beyond enhancing substation reliability, KG-FLoc establishes a knowledge-aware paradigm for fault diagnosis in industrial systems, enabling precise reasoning over complex interdependencies.

[Assistant] Methodology


Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules

The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computational studies. Machine learning (ML) has been shown to be able to model these interactions probabilistically, with successful applications demonstrated for classical molecular dynamics. While first instances of ML implicit solvents for quantum-mechanical (QM) calculations exist, the high computational cost of QM reference calculations hinders the development of a generally applicable ML implicit solvent model for QM calculations. Here, we present a novel way of developing such a general machine-learned QM implicit solvent model, which relies neither on QM reference calculations for training nor experimental data, by transferring knowledge obtained from classical interactions to QM. This strategy makes the obtained graph neural network (GNN) based implicit solvent model (termed QM-GNNIS) independent of the chosen functional and basis set. The performance of QM-GNNIS is validated on NMR and IR experiments, demonstrating that the approach can reproduce experimentally observed trends unattainable by state-of-the-art implicit-solvent models.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules
Abstract: The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computational studies. Machine learning (ML) has been shown to be able to model these interactions probabilistically, with successful applications demonstrated for classical molecular dynamics. While first instances of ML implicit solvents for quantum-mechanical (QM) calculations exist, the high computational cost of QM reference calculations hinders the development of a generally applicable ML implicit solvent model for QM calculations. Here, we present a novel way of developing such a general machine-learned QM implicit solvent model, which relies neither on QM reference calculations for training nor experimental data, by transferring knowledge obtained from classical interactions to QM. This strategy makes the obtained graph neural network (GNN) based implicit solvent model (termed QM-GNNIS) independent of the chosen functional and basis set. The performance of QM-GNNIS is validated on NMR and IR experiments, demonstrating that the approach can reproduce experimentally observed trends unattainable by state-of-the-art implicit-solvent models.

[Assistant] Methodology


Social Influence Analysis for Information Diffusion in Complex Commercial Network

Social influence causes ideas to diffuse across the globe and track the rate at which awareness spans through online social communities. The users with better connectivity and higher interactivity bear greater potentials of spreading as well as perceiving the social trend. This concept has been further considered in the research to identify such prospective users in social network with high probability of influencing the opinions of others to whom they are connected. For the experimentation, Twitter profiles of 1900 followers from three major e-commerce portals, including Amazon, Snapdeal, and Flipkart, have been extracted to serve as the social network for conducting research. Acquired data is further narrowed down to yield a set of influential users in terms of central and centrally active, which are parameterized by neighborhood size and frequency of Twitter activity of individuals. Diffusion analysis is further performed on the derived set of key influencers to track the rate at which information propagates in the network.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Social Influence Analysis for Information Diffusion in Complex Commercial Network
Abstract: Social influence causes ideas to diffuse across the globe and track the rate at which awareness spans through online social communities. The users with better connectivity and higher interactivity bear greater potentials of spreading as well as perceiving the social trend. This concept has been further considered in the research to identify such prospective users in social network with high probability of influencing the opinions of others to whom they are connected. For the experimentation, Twitter profiles of 1900 followers from three major e-commerce portals, including Amazon, Snapdeal, and Flipkart, have been extracted to serve as the social network for conducting research. Acquired data is further narrowed down to yield a set of influential users in terms of central and centrally active, which are parameterized by neighborhood size and frequency of Twitter activity of individuals. Diffusion analysis is further performed on the derived set of key influencers to track the rate at which information propagates in the network.

[Assistant] Applied


The Allen Telescope Array Twenty-centimeter Survey - A 690-Square-Degree, 12-Epoch Radio Dataset - I: Catalog and Long-Duration Transient Statistics

We present the Allen Telescope Array Twenty-centimeter Survey (ATATS), a multi-epoch (12 visits), 690 square degree radio image and catalog at 1.4GHz. The survey is designed to detect rare, very bright transients as well as to verify the capabilities of the ATA to form large mosaics. The combined image using data from all 12 ATATS epochs has RMS noise sigma = 3.94mJy / beam and dynamic range 180, with a circular beam of 150 arcsec FWHM. It contains 4408 sources to a limiting sensitivity of S = 20 mJy / beam. We compare the catalog generated from this 12-epoch combined image to the NRAO VLA Sky Survey (NVSS), a legacy survey at the same frequency, and find that we can measure source positions to better than ~20 arcsec. For sources above the ATATS completeness limit, the median flux density is 97% of the median value for matched NVSS sources, indicative of an accurate overall flux calibration. We examine the effects of source confusion due to the effects of differing resolution between ATATS and NVSS on our ability to compare flux densities. We detect no transients at flux densities greater than 40 mJy in comparison with NVSS, and place a 2-sigma upper limit on the transient rate for such sources of 0.004 per square degree. These results suggest that the > 1 Jy transients reported by Matsumura et al. (2009) may not be true transients, but rather variable sources at their flux density threshold.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: The Allen Telescope Array Twenty-centimeter Survey - A 690-Square-Degree, 12-Epoch Radio Dataset - I: Catalog and Long-Duration Transient Statistics
Abstract: We present the Allen Telescope Array Twenty-centimeter Survey (ATATS), a multi-epoch (12 visits), 690 square degree radio image and catalog at 1.4GHz. The survey is designed to detect rare, very bright transients as well as to verify the capabilities of the ATA to form large mosaics. The combined image using data from all 12 ATATS epochs has RMS noise sigma = 3.94mJy / beam and dynamic range 180, with a circular beam of 150 arcsec FWHM. It contains 4408 sources to a limiting sensitivity of S = 20 mJy / beam. We compare the catalog generated from this 12-epoch combined image to the NRAO VLA Sky Survey (NVSS), a legacy survey at the same frequency, and find that we can measure source positions to better than ~20 arcsec. For sources above the ATATS completeness limit, the median flux density is 97% of the median value for matched NVSS sources, indicative of an accurate overall flux calibration. We examine the effects of source confusion due to the effects of differing resolution between ATATS and NVSS on our ability to compare flux densities. We detect no transients at flux densities greater than 40 mJy in comparison with NVSS, and place a 2-sigma upper limit on the transient rate for such sources of 0.004 per square degree. These results suggest that the > 1 Jy transients reported by Matsumura et al. (2009) may not be true transients, but rather variable sources at their flux density threshold.

[Assistant] Dataset


Benchmarking Distributional Alignment of Large Language Models

Language models (LMs) are increasingly used as simulacra for people, yet their ability to match the distribution of views of a specific demographic group and be \textit{distributionally aligned} remains uncertain. This notion of distributional alignment is complex, as there is significant variation in the types of attributes that are simulated. Prior works have underexplored the role of three critical variables -- the question domain, steering method, and distribution expression method -- which motivates our contribution of a benchmark explicitly addressing these dimensions. We construct a dataset expanding beyond political values, create human baselines for this task, and evaluate the extent to which an LM can align with a particular group's opinion distribution to inform design choices of such simulation systems. Our analysis reveals open problems regarding if, and how, LMs can be used to simulate humans, and that LLMs can more accurately describe the opinion distribution than simulate such distributions.

[System] You are a multilingual research paper intent classifier. Given a paper title and abstract, classify its primary research intent as exactly one of: Methodology, Dataset, Review, Applied, Theoretical, Unclassifiable.

[User]
Classify the research intent of this paper:

Title: Benchmarking Distributional Alignment of Large Language Models
Abstract: Language models (LMs) are increasingly used as simulacra for people, yet their ability to match the distribution of views of a specific demographic group and be \textit{distributionally aligned} remains uncertain. This notion of distributional alignment is complex, as there is significant variation in the types of attributes that are simulated. Prior works have underexplored the role of three critical variables -- the question domain, steering method, and distribution expression method -- which motivates our contribution of a benchmark explicitly addressing these dimensions. We construct a dataset expanding beyond political values, create human baselines for this task, and evaluate the extent to which an LM can align with a particular group's opinion distribution to inform design choices of such simulation systems. Our analysis reveals open problems regarding if, and how, LMs can be used to simulate humans, and that LLMs can more accurately describe the opinion distribution than simulate such distributions.

[Assistant] Dataset