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Demand for perishable food is sensitive to product prices and is affected by the prices of similar or alternative products. While brand loyalty influences the demand for products, determining a reasonable price requires a precise pricing strategy. In this paper, a pricing model for perishable food is presented in which the brand value of the product and the price of other manufacturers as competitors are considered. To this end, this study first predicts the price of competitors using a combination of optimized Neural Networks and presents an optimized model using a Genetic Algorithm. This algorithm combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a Genetic Algorithm (GA). The proposed model is then used to merge with a game-theory model for the pricing of perishable foods. In this game-theory model, pricing approaches are developed based on identified prices of competitors. In the coordination contract game-theory model, Multi Retailer- one Supplier and Price-sensitive demand of Perishable product are developed with and without quantity discount contract. Obtained results indicate that independent procurement provides retailers with higher profit, while lower profit will be presented when coordination is not considered. Also, with coordination, the ordering cycle increases, and the ordering frequency decrease. [ABSTRACT FROM AUTHOR]

Some of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT‐R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance‐based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision‐making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self‐efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human–machine intelligence. [ABSTRACT FROM AUTHOR]

Two trajectories underway transform human systems. Processes of growth/complexification have accelerated as stakeholders seek advantage from advances in connectivity/autonomy/sensing. Surprising empirical patterns also arise—puzzling collapses of critical valued services occur against a background of growth. In parallel, new scientific foundations have arisen from diverse directions explaining the observed anomalies and breakdowns, highlighting basic weaknesses of automata regardless of technology. Conceptual growth provides laws, theorems, and comprehensive theories that encompass the interplay of autonomy/people and complexity/adaptation across scales. One danger for synchronizing the trajectories is conceptual lag as researchers remain stuck in stale frames unable to keep pace with transformative change. Any approach that does not either build on the new conceptual advances—or provide alternative foundations—is no longer credible to match the scale and stakes of modern distributed layered systems and overcome the limits of automata. The paper examines longstanding challenges by contrasting progress then as the trajectories gathered steam, to situation now as change has accelerated. [ABSTRACT FROM AUTHOR]

The cutting characteristics of Inconel 718 alloy are high hardness and surface hardening, causing fast tool wear, severe chipping, and inadequate machining accuracy. In order to overcome the existing challenges, this study proposes a method to enhance the cutting performance by injecting fullerene C60 nanoparticle cutting fluid with minimum quantity lubrication (MQL) into the cutting zone. Leveraging the Johnson–Cook constitutive model and the imaginary heat source method, this study simulates the cooling effect and friction reduction characteristics of the cutting contact interface under minimum quantity lubrication conditions, and assessment of cutting energy consumption using predicted and measured specific cutting energy (SCE). Through friction wear tests, the friction coefficient changes under various lubrication conditions are measured. The impact of lubrication conditions on friction and wear mechanism is analyzed. The cutting test results demonstrate that variations in cutting parameters significantly influence energy efficiency, with specific cutting energy exhibiting a downward trend as the material removal rate (MRR) increases. Notably, C60 nanoparticle minimum quantity lubrication (NMQL) stands out excellent friction reduction and cooling effects among other lubrication methods. Experimental data indicate that NMQL compared with dry cutting, flood cutting, and pure MQL, the specific cutting energy is reduced by 31.3%, 19.13%, and 17.37%, respectively, and that the cutting energy performance is significantly improved. The maximum error of the SCE prediction model is 17.5%, and the prediction results align well with the experimental findings. Moreover, this study provides novel insights for advancing machining theory and exploring sustainable green machining of nickel-based alloys. [ABSTRACT FROM AUTHOR]

This perspective article presents the density functional theory and traces its evolution. With the advancement in density functional theory-based computations and the efforts to collate the data generated through density functional theory, the field now has a good repository/database of materials and their properties. This repository, though not as substantial as generally used for machine learning, has nonetheless made it possible to combine density functional theory and machine learning. This article highlights current research challenges and presents an optimistic outlook for the future of "Density Functional Theory with Machine Learning" by discussing some specific examples. [ABSTRACT FROM AUTHOR]

The complex compositional space of high entropy alloys (HEAs) has shown a great potential to reduce the cost and further increase the catalytic activity for hydrogen evolution reaction (HER) by compositional optimization. Without uncovering the specifics of the HER mechanism on a given HEA surface, it is unfeasible to apply compositional modifications to enhance the performance and save costs. In this work, a combination of density functional theory and Bayesian machine learning is used to demonstrate the unique catalytic mechanism of IrPdPtRhRu HEA catalysts for HER. At high coverage of underpotential‐deposited hydrogen, a d‐band investigation of the active sites of the HEA surface is conducted to elucidate the superior catalytic performance through electronic interactions between elements. At low coverage, a novel Bayesian learning with oversampling approach is then outlined to optimize the HEA composition for performance improvement and cost reduction. This approach proves more efficacious and efficient and yields higher‐quality structures with less training set bias compared with neural‐network optimization. The proposed HEA optimization theoretically outperforms benchmark Pt catalysts’ overpotential by ≈40% at a 15% reduced synthesis cost comparing to the equiatomic ratio HEA. [ABSTRACT FROM AUTHOR]

Macrophages induce the expression of hundreds of genes in response to immune threats. However, current technology limits our ability to capture single-cell inducible gene expression dynamics. Here, we generated high-resolution time series single-cell RNA sequencing (scRNA-seq) data from mouse macrophages responding to six stimuli, and imputed ensembles of real-time single-cell gene expression trajectories (scGETs). We found that dynamic information contained in scGETs substantially contributes to macrophage stimulus-response specificity (SRS). Dynamic information also identified correlations between immune response genes, indicating biological coordination. Furthermore, we showed that the microenvironmental context of polarizing cytokines profoundly affects scGETs, such that measuring response dynamics offered clearer discrimination of the polarization state of individual macrophage cells than single time-point measurements. Our findings highlight the important contribution of dynamic information contained in the single-cell expression responses of immune genes in characterizing the SRS and functional states of macrophages. [Display omitted] • Imputation of stimulus-induced single-cell gene expression trajectories (scGETs) • Trajectory features reveal unexpected stimulus specificity in macrophage responses • Expression integrals identify cell-cell correlations and co-regulated gene sets • Stimulus-response expression trajectories readily characterize cell functional states Because RNA measurements are cell destructive, it is unclear how variable stimulus-induced dynamic gene expression trajectories (GETs) are. Sheu et al. developed a method to assess single-cell gene expression trajectories (scGETs) in macrophages responding to stimuli and found scGETs to be much more informative than any single time-point measurement. [ABSTRACT FROM AUTHOR]

ABSTRACT Machine learning methods were shown to improve the prediction accuracies of genomic prediction of resistance scores compared to methods like RR‐BLUP, which were originally designed for metric rather than ordinal response values. We conducted a cross‐validation study with 361 wheat genotypes evaluated for five fungal diseases. Our objective was to compare the prediction accuracy and the ability to identify the most resistant genotypes of 19 genomic prediction approaches. Each approach consisted of a different combination of prediction method (RR‐BLUP, an alternative method with heterogeneous marker variances, Bayesian generalized linear regression with an ordinal response, support vector machine, gradient boosting machine and random forest), predictor (single SNP markers, LD‐based haplotype blocks, 250 variables generated with an autoencoder and SNPs identified with incremental feature selection) and response value (untransformed and logit‐transformed resistance scores). In our dataset, RR‐BLUP was consistently among the methods with the largest prediction accuracies and the best abilities to identify resistant genotypes in four of five investigated traits. However, in P. triticina, using gradient boosting machine and random forest instead of RR‐BLUP increased the prediction accuracy from 0.64 to 0.71, indicating that machine learning methods may have an advantage over linear models in genomic prediction. We also found that even though there was a positive correlation between the prediction accuracy and Cohen's κ$$ \kappa $$, a measure to judge how well the most resistant genotypes can be identified, the correlation is not perfect and a large value for the prediction accuracy does not necessarily translate into an equally large κ$$ \kappa $$ value. [ABSTRACT FROM AUTHOR]

Background: Title-abstract screening in the preparation of a systematic review is a time-consuming task. Modern techniques of natural language processing and machine learning might allow partly automatization of title-abstract screening. In particular, clear guidance on how to proceed with these techniques in practice is of high relevance. Methods: This paper presents an entire pipeline how to use natural language processing techniques to make the titles and abstracts usable for machine learning and how to apply machine learning algorithms to adequately predict whether or not a publication should be forwarded to full text screening. Guidance for the practical use of the methodology is given. Results: The appealing performance of the approach is demonstrated by means of two real-world systematic reviews with meta analysis. Conclusions: Natural language processing and machine learning can help to semi-automatize title-abstract screening. Different project-specific considerations have to be made for applying them in practice. [ABSTRACT FROM AUTHOR]

The animation-effect of the movement-image constitutes not only a form of life but also a machinic analogue for Husserl's and Kant's accounts of temporality, engendering what Bernard Stiegler calls 'a cinematography of consciousness'. If autonomous movement is the hallmark of the living being as well as the inanimate mechanism, the movement-image likewise subtends scenes of human and artificial intelligence. Pairing a cybernetic reading of Lacan's mirror stage essay with reflections on the self-recognition dance of Machina Speculatrix (an early example of a biomimetic robot), this essay considers how exosomatic feedback loops enable a kind of ipseity for machinic life as well as the human being as technical being. Despite adopting Derrida's ideas of originary prostheticity and automaticity, rendering living beings as always-already machinic life, Stiegler sets arche-cinema against arche-writing. [ABSTRACT FROM AUTHOR]

In the future ten years, the mining depths of more than 30% metal mines will exceed 1000 m. Deep metal mining is confronted with complex geological conditions such as high in-situ stress, high ground temperature, highly-changeable grounds and strong mining disturbance, which bring severe challenges to roadway construction. Due to the shortcomings of noncontinuous operation, low construction rate and frequent disaster occurrence, traditional drilling-and-blasting method cannot meet the construction requirement, therefore the use of tunnel boring machine (TBM)for continuous mechanical tunnelling becomes the future development direction of deep metal mine roadway construction. However, due to the distinctive engineering geological features and roadway construction requirements, the application of TBM tunnelling technology in deep metal mine roadway construction still suffers from some technological problems and challenges. First, this paper summarizes the three main application difficulties for TBM tunnelling technology in deep metal mine roadway construction; 1) the lack of adaptative TBM type selection and machine design theory; 2) the lack of high efficient TBM tunnelling technology; 3) the lack of prewarning, prevention and control technology for engineering disasters. Then, this paper introduces the current research progresses for TBM tunnelling technology in deep metal mine roadway construction from six aspects, i. e., efficient rock breakage theory for cutterhead and cutters, adaptative TBM type selection and machine design technology, accurate tunnelling performance prediction and evaluation methodology, tunnelling technology for small turning radius or highly-inclined roadways, intelligent prewarning, prevention and control technology for engineering disasters, intelligent decision-making and assisted-driving technology. The above research progresses primarily indicate the development direction for the extensive application of TBM tunnelling technology for rapid roadway construction in deep metal mines. [ABSTRACT FROM AUTHOR]

ABSTRACT Machine vision has had a substantial impact on human‐computer interaction and psychological research. The ability of robots to generate realistic facial expressions has been enhanced by advancements in artificial intelligence, neural networks, and deep learning, thereby nurturing more profound emotive connections between humans and machines. This paper offers an in‐depth look of the most recent developments in emotion classification technologies, with a specific focus on the period from 2019 to 2023. This research addresses the integration of facial and body gesture analysis into emotion detection, emphasising the development of methods such as partial transfer learning, sign‐based measurement systems, and lightweight convolutional neural networks. By analysing the most recent research, the paper not only provides a comprehensive overview of the most advanced methods, but also emphasises their superiority over previous approaches in terms of efficiency, and applicability. The integration of gestures into emotion analysis is underscored as a critical area for further investigation, providing novel opportunities for comprehending and interpreting the intricate layers of nonverbal communication. The objective of this survey is to provide a resource for researchers and practitioners who are interested in advancing the field of emotion classification by utilising innovative methodologies. [ABSTRACT FROM AUTHOR]

\nHIGHLIGHTSActivity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data. Presents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation techniquePresents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation technique [ABSTRACT FROM AUTHOR]

This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research. [ABSTRACT FROM AUTHOR]

The step-down DC/DC converter is characterized by the parametric uncertainty of its model and its high sensitivity to external disturbances identified by variations in the input voltage, load and output reference voltage. To overcome these limitations, this paper proposes the experimental evaluation of a new adaptive synergetic control law based on the M5P model tree for efficient output voltage control of the step-down type DC/DC converter. A combination of the synergetic technique, the fuzzy logic theory and the machine learning has been used for the first time to develop this advanced control law. The synergetic strategy is exploited to overcome the chattering problem arising from the use of the sliding mode approach which has been applied in previous research. Furthermore, the novel machine learning M5P tree-based approximator was designed and applied to approximate the unknown and imprecise dynamics of the studied converter. The learning data comes from the smart approximator, which is based on fuzzy logic, with the aim of extracting its own advantages and offering them to the new tree-based approximator M5P. This action will make it possible to improve the operation of the fuzzy approximator, which has already been studied in some research. Finally, the developed control strategy was successfully evaluated in the laboratory using the dSPACE 1104 board and compared with the fuzzy adaptive synergetic approach under different conditions of load variations and imposed references. The practical results show that the proposed tree adaptive synergetic approach is competitive with the fuzzy adaptive strategy in terms of ease of practical implementation, fast control execution and better output voltage regulation quality. [ABSTRACT FROM AUTHOR]

Lung cancer is the most diagnosed malignant neoplasm worldwide and it is associated with great mortality. Currently, developing antineoplastic agents is a challenging, time-consuming, and costly process. Computational methods can speed up the early discovery of anti-lung-cancer chemicals. Here, we report a perturbation theory machine learning model based on a multilayer perceptron (PTML-MLP) model for phenotypic early antineoplastic drug discovery, enabling the rational design and prediction of new molecules as virtual versatile inhibitors of multiple lung cancer cell lines. The PTML-MLP model achieved an accuracy above 80%. We applied the fragment-based topological design (FBTD) approach to physicochemically and structurally interpret the PTML-MLP model. This enabled the extraction of suitable fragments with a positive influence on anti-lung-cancer activity against the different lung cancer cell lines. By following the aforementioned interpretations, we could assemble several suitable fragments to design four novel molecules, which were predicted by the PTML-MLP model as versatile anti-lung-cancer agents. Such predictions of potent multi-cellular anticancer activity against diverse lung cancer cell lines were rigorously confirmed by a well-established virtual screening tool reported in the literature. The present work envisages new opportunities for the application of PTML models to accelerate early antineoplastic discovery from phenotypic assays. [ABSTRACT FROM AUTHOR]

We study the instantaneous inference of an unbounded planar flow from sparse noisy pressure measurements. The true flow field comprises one or more regularized point vortices of various strength and size. We interpret the true flow's measurements with a vortex estimator, also consisting of regularized vortices, and attempt to infer the positions and strengths of this estimator assuming little prior knowledge. The problem often has several possible solutions, many due to a variety of symmetries. To deal with this ill posedness and to quantify the uncertainty, we develop the vortex estimator in a Bayesian setting. We use Markov-chain Monte Carlo and a Gaussian mixture model to sample and categorize the probable vortex states in the posterior distribution, tailoring the prior to avoid spurious solutions. Through experiments with one or more true vortices, we reveal many aspects of the vortex inference problem. With fewer sensors than states, the estimator infers a manifold of equally possible states. Using one more sensor than states ensures that no cases of rank deficiency arise. Uncertainty grows rapidly with distance when a vortex lies outside of the vicinity of the sensors. Vortex size cannot be reliably inferred, but the position and strength of a larger vortex can be estimated with a much smaller one. In estimates of multiple vortices their individual signs are discernible because of the nonlinear coupling in the pressure. When the true vortex state is inferred from an estimator of fewer vortices, the estimate approximately aggregates the true vortices where possible. [ABSTRACT FROM AUTHOR]

This paper investigates the benefits and challenges of the multi‐MW direct‐drive offshore wind Vernier generators. It is worth noting that the comparison of generator topologies presented in ref. [1] was for the same power level and therefore a reduced machine volume for the surface‐mounted permanent magnet Vernier (SPM‐V) generators. This would mainly impact the capital expenditure (CAPEX) cost between the different investigated machines. Whereas in this paper, the performance is compared for the same machine volume that allows the Vernier generators to produce a much higher energy yield than a conventional SPM generator. As the extra energy yield would be beneficial over the lifetime of the turbine, this will have a bigger impact than the CAPEX cost savings with a reduced machine volume. In addition, a novel Vernier machine with magnets on both the stator and rotor has been proposed to further improve the energy yield. In addition to the basic electromagnetic performance, the levelised cost of energy (LCOE) of the three generator topologies, that is, the conventional SPM, SPM‐V and the proposed Vernier machines, has been compared. The direct‐drive powertrain systems with SPM‐V and the proposed Vernier generators can achieve LCOE of 12.3% and 24% lower than that of the conventional SPM generators, indicating their huge potential as an alternative to reduce the overall cost of energy. [ABSTRACT FROM AUTHOR]

Astrophysics to physical oceanography. String theory to machine learning. High energy theory to biophysics. Such leaps in research focus can be challenging—and rewarding. [ABSTRACT FROM AUTHOR]

Purpose: By combining the notion of prospect theory with advanced machine learning algorithms, this study aims to predict whether financial institutions (FIs) adopt a reactive stance when they perceive climate change as a risk, consequently leading to the adoption of environmental, social and governance (ESG) practices to avoid this risk. Prospect theory assumes that decision-makers react quickly when decisions are framed as a risk or threat rather than as an opportunity. Design/methodology/approach: We used a sample of 168 FIs across 27 countries and seven regions over the period 2003–2020. To conduct our empirical investigation, we compared the prediction accuracy of various machine learning algorithms. Findings: Our findings suggest that out of 12 machine learning algorithms, AdaBoost, Gradient Boosting and XGBoost have the most precision in predicting whether FIs react to climate change risk in adopting ESG practices. This study also tested the overall climate change risk and risks associated with physical, opportunity and regulatory shocks of climate change. We observed that risks associated with physical and regulatory shocks significantly impact the adoption of ESG practices, supporting prospect theory predictions. Practical implications: The insights of this study provide important implications for policymakers. Specifically, policymakers must take into account the risk posed by climate change in the corporate decision-making process, as it directly influences a firm's adoption of corporate actions (ESG practices). Originality/value: To the best of our knowledge, this is the first study to investigate the firm-level climate change risk and adoption of ESG practices from a prospect theory perspective using novel machine learning algorithms. [ABSTRACT FROM AUTHOR]

This paper presents four state-of-art methods for the finite-state automaton inference based on a sample of labeled strings. The first algorithm is Exbar, and the next three are mathematical models based on ASP, SAT and SMT theories. The potentiality of using multiprocessor computers in the context of automata inference was our research's primary goal. In a series of experiments, we showed that our parallelization of the exbar algorithm is the best choice when a multiprocessor system is available. Furthermore, we obtained a superlinear speedup for some of the prepared datasets, achieving almost a 5-fold speedup on the median, using 12 and 24 processes. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the benefits and challenges of the multi‐MW direct‐drive offshore wind Vernier generators. It is worth noting that the comparison of generator topologies presented in ref. [1] was for the same power level and therefore a reduced machine volume for the surface‐mounted permanent magnet Vernier (SPM‐V) generators. This would mainly impact the capital expenditure (CAPEX) cost between the different investigated machines. Whereas in this paper, the performance is compared for the same machine volume that allows the Vernier generators to produce a much higher energy yield than a conventional SPM generator. As the extra energy yield would be beneficial over the lifetime of the turbine, this will have a bigger impact than the CAPEX cost savings with a reduced machine volume. In addition, a novel Vernier machine with magnets on both the stator and rotor has been proposed to further improve the energy yield. In addition to the basic electromagnetic performance, the levelised cost of energy (LCOE) of the three generator topologies, that is, the conventional SPM, SPM‐V and the proposed Vernier machines, has been compared. The direct‐drive powertrain systems with SPM‐V and the proposed Vernier generators can achieve LCOE of 12.3% and 24% lower than that of the conventional SPM generators, indicating their huge potential as an alternative to reduce the overall cost of energy.

Due to enormous applications of large-area graphene with high quality, the epitaxial growth strategies have drawn a plethora of attention. However, the bottleneck in the production of graphene has caused delayed development in recent years, which is owing to the poor understanding of interaction mechanisms between graphene and the underlying metallic and non-metallic substrate. To understand the thermodynamics of graphene–substrate interface and growth kinetics, accurate density functional theory (DFT) calculations have been proved as an effective way, in terms of cost and time, compared with traditional experimental methods, which can calculate the interaction between graphene and substrates, helping us to better understand the practical phenomena. Here, we show the use of DFT methods to evaluate both van der Waals interaction and covalent bonding. Many of computational results fit well with the experimental observations. To address the relative low accuracy and small computation capacity (number of atoms) of common DFT models, we suggest that the machine learning (ML) methods will be a fresh impetus for epitaxial growth strategy of graphene, which put forward effective interpretations for complicated interconnections and correlations among the properties, thereby enabling ML a promising strategy for understanding, design, and synthesis of graphene over other 2D materials. [ABSTRACT FROM AUTHOR]

[Display omitted] Electrochemical carbon dioxide reduction reaction (CO 2 RR) is a promising technology to establish an artificial carbon cycle. Two-dimensional conjugated metal–organic frameworks (2D c-MOFs) with high electrical conductivity have great potential as catalysts. Herein, we designed a range of 2D c-MOFs with different transition metal atoms and organic ligands, TMN x O 4-x -HDQ (TM = Cr∼Cu, Mo, Ru∼Ag, W∼Au; x = 0, 2, 4; HDQ = hexadipyrazinoquinoxaline), and systematically studied their catalytic performance using density functional theory (DFT). Calculation results indicated that all of TMN x O 4-x -HDQ structures possess good thermodynamic and electrochemical stability. Notably, among the examined 37 MOFs, 6 catalysts outperformed the Cu(2 1 1) surface in terms of catalytic activity and product selectivity. Specifically, NiN 4 -HDQ emerged as an exceptional electrocatalyst for CO production in CO 2 RR, yielding a remarkable low limiting potential (U L) of −0.04 V. CuN 4 -HDQ, NiN 2 O 2 -HDQ, and PtN 2 O 2 -HDQ also exhibited high activity for HCOOH production, with U L values of −0.27, −0.29, and −0.27 V, respectively, while MnN 4 -HDQ, and NiO 4 -HDQ mainly produced CH 4 with U L values of −0.58 and −0.24 V, respectively. Furthermore, these 6 catalysts efficiently suppressed the competitive hydrogen evolution reaction. Machine learning (ML) analysis revealed that the key intrinsic factors influencing CO 2 RR performance of these 2D c-MOFs include electron affinity (E A), electronegativity (χ), the first ionization energy (I e), p-band center of the coordinated N/O atom (ε p), the radius of metal atom (r), and d-band center (ε d). Our findings may provide valuable insights for the exploration of highly active and selective CO 2 RR electrocatalysts. [ABSTRACT FROM AUTHOR]

The growing urgency for low-carbon lifestyles necessitates developing effective strategies to promote sustainable consumer choices. This study investigates key dimensions of information quality that shape consumer behavior within digital marketing to achieve this goal. Employing a mixed-methods approach that integrates grounded theory and machine learning, this study identifies three core dimensions of low-carbon information quality: matching quality, presentation quality, and interpretability quality. These dimensions underscore the importance of aligning information with consumer needs, ensuring clear and accurate presentation, and fostering transparency for trustworthiness. A Random Forest algorithm-based evaluation model is constructed to assess low-carbon information quality, demonstrating its effectiveness in identifying high-quality, sustainable content. This research provides a practical tool for digital marketers to enhance their strategies, raise consumer awareness of sustainable options, and ultimately contribute to the growth of the low-carbon consumption market. [ABSTRACT FROM AUTHOR]

Surface integrity is a critical factor that affects the fatigue resistance of materials. A surface mechanical rolling treatment (SMRT) process can effectively improve the surface integrity of the material, thus enhancing the fatigue property. In this paper, an analysis of variance (ANOVA) and signal-to-noise ratio (SNR) are performed by orthogonal experimental design with SMRT parameters as variables and surface integrity indicators as optimization objectives, and the support vector machine-active learning (SVM-AL) model is proposed based on machine learning theory. The entire model includes three rounds of AL processes. In each round of the AL process, the SMRT parameters with relative average deviation and high output values from cross-validation are selected for the additional experimental supplement. The results show that the prediction accuracy and generalization ability of the SVM-AL model are significantly improved compared to the support vector machine (SVM) model. A fatigue test was also carried out, and the fatigue property of the SMRT specimens predicted by the SVM-AL model is also higher than that of the other specimens. [ABSTRACT FROM AUTHOR]

Knowing the rate at which particle radiation releases energy in a material, the "stopping power," is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to hours on a supercomputer and provide valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the "Bragg Peak," varies depending on the incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical simulations. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model makes our approach appealing for applications in the age of materials data science. [ABSTRACT FROM AUTHOR]

Online health communities (OHCs) are able to facilitate social support exchange among people with similar health concerns, and the relationship between self-disclosure and social support exchange has been widely discussed. Build upon the previous studies, we extend the seminal theory of reasoned action (TRA) in this research, through the adoption of an salient but understudied construct, user role, which captures user inclination toward providing or seeking social support, and further explore how it impacts OHC users' self-disclosure intention. We adopt a mixed methods approach including manual coding, machine learning algorithms, and econometric analyses, and investigate over 4 million posts over 17 years from a well-developed breast cancer OHC to uncover both direct and indirect effects of user role on OHC users' intention of future self-disclosure intention. The results reveal both the main and moderating effects of user roles regarding users' self-disclosure intention. First, user with a more clear intention of social support seeking or providing are more likely to disclose personal information. Second, the relative tendency of support provision magnifies the positive effects of attitudinal and normative factors toward future disclosure. We also discuss the findings and implications of the extended TRA framework to generate some actionable insights of OHC design. [ABSTRACT FROM AUTHOR]

In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency, performance, and energy consumption in cloud services. We then discuss various strategies employed in auto-scaling, ranging from threshold-based rules and queuing theory to sophisticated machine learning and time series analysis approaches. After that, we explore the critical issues in auto-scaling practices and review several studies that demonstrate how these challenges can be addressed. We then conclude by offering insights into several promising research directions, emphasizing the development of predictive scaling mechanisms and the integration of advanced machine learning techniques to achieve more effective and efficient auto-scaling solutions. [ABSTRACT FROM AUTHOR]

Background: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. Objective: The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). Methods: Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. Results: A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. Conclusions: These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD. [ABSTRACT FROM AUTHOR]

This paper presents a novel numerical method for solving the control system synthesis problem through the application of machine learning techniques, with a particular focus on symbolic regression. Symbolic regression is used to automate the development of control systems by constructing mathematical expressions that describe control functions based on system data. Unlike traditional methods, which often require manual programming and tuning, this approach leverages machine learning to discover optimal control solutions. The paper introduces a general framework for machine learning in control system design, with an emphasis on the use of evolutionary algorithms to optimize the generated control functions. The key contribution of this research lies in the development of an algorithm based on the principle of small variations in the baseline solution. This approach significantly enhances the efficiency of discovering optimal control functions by systematically exploring the solution space with minimal adjustments. The method allows for the automatic generation of control laws, reducing the need for manual coding, which is especially beneficial in the context of complex control systems, such as robotics. To demonstrate the applicability of the method, the research applies symbolic regression to the control synthesis of a mobile robot The results of this case study show that symbolic regression can effectively automate the process of generating control functions, significantly reducing development time while improving accuracy. However, the paper also acknowledges certain limitations, including the computational demands required for symbolic regression and the challenges associated with real-time implementation in highly dynamic environments. These issues represent important areas for future research, where further optimization and hybrid approaches may enhance the method’s practicality and scalability in real-world applications. [ABSTRACT FROM AUTHOR]

This study investigated a precise algorithm combining X-ray Fluorescence and Neural Network (XRF-NN) for predicting ash content. The 261 sets of XRF tests show that the 34 elements in the chosen Guqiao coal can be categorized as major, secondary, and tiny elements, whose cumulative ratios were ~95%, 4-5%, and <1%, respectively. Referring to the machine learning theory, the construction strategy of the Element-Ash dataset was determined viz. value determination → standardization → division → optimization of generalization ability. Then, the hyperparameters optimization displays that the XRF-NN model with 34 inputs and 1 output was suitable to predict coal ash content, where the activation function, loss function, and optimizer were ReLU, MAE, and Momentum, respectively. After iterative training, the new XRF-NN model provides the precise prediction of coal ash content with absolute errors between -2.0% and 2%. Moreover, the prediction accuracy rose from 57.69% to 100%, as the expected relative error increased from 1% to 5%. Furthermore, the comparisons between different prediction methods reveal that the minimum MRE of 1.22% can be obtained by XRF-NN with total elements, which was only half of those given by the conventional Multiple Linear Regression and Partial Least Squares Regression. Besides, the XRF-NN model presents the root mean squared error of 0.797%, a mean absolute error of 0.625%, and the coefficient of determination of 0.999, which were significantly superior to those calculated by Dual-Energy Gammaray Transmission, Ploy, RFR, XGBoost, and DNN model. The results of this study suggest the excellent performance of the new XRF-NN model in predicting ash content. [ABSTRACT FROM AUTHOR]

To automate surgical (sub-)tasks in robotic surgery, the knowledge of the exact pose of the instrument is mandatory. The application of Optical Coherence Tomography (OCT) to the problem of pose measurement appears promising due to its advantages of 3D imaging and micron-scale resolution. To investigate this, 175 image sequences of the EndoWrist Round Tip Scissor Tool were acquired with an OCT system. The images differ in the opening angles of the scissor blades and the rotation angles of the entire instrument about its central axis. These image sequences were further processed through computer vision methods of the individual images followed by point cloud generation. For pose estimation, an Iterative Closest Point algorithm was implemented to register the acquired point clouds to reference point clouds created from the instrument CAD file. The implemented algorithm was able to determine the opening angle with an overall error of 2∘ ± 1.3∘ and the rotation angle with a standard deviation between several runs of 0.6∘ ±2.8∘. However, the overall processing time of (39 ± 17)s on a standard PC leaves room for further investigations. [ABSTRACT FROM AUTHOR]

Cognitive scientists and neuroscientists typically understand the brain as a complex communication/information-processing system. A limitation of this framework is that it requires cognitive systems to have prior knowledge about their environment to successfully perform some of their basic functions, such as perceiving. It is unclear how the source of such knowledge can be explained from within this framework. Drawing on Dennett (1981), we refer to this as the loans of intelligence problem. Recent advances in machine learning have resulted in the development of a family of algorithms, including the class known as autoencoders, that seem to provide a way for the information-processing framework to avoid this problem: cognitive systems do not require loans of intelligence, but instead acquire the knowledge necessary for perception through a process of unsupervised learning. This paper argues that although autoencoders do avoid the loans of intelligence problem, how they do so should not be understood from within the information-processing framework. Instead, their success should be interpreted as a proof of concept of how neural networks can attune to Gibsonian information. We thus propose that autoencoders belong to a class of algorithms for modeling the brain that have recently been dubbed direct fit algorithms. [ABSTRACT FROM AUTHOR]

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making. [ABSTRACT FROM AUTHOR]

With the advancement of language technology, artificial intelligence translators are also constantly developing. As an intelligent translator, the emergence of DeepL provides new opportunities for the development of human translation industry. Based on the theory of machine translation quality control, this article analyzed the translation quality control strategy of DeepL. By analyzing the performance of the designed source and target languages in the text input stage, the impact of part of speech tagging and syntactic analysis on translation quality in the text processing stage was evaluated. The machine translation quality evaluation method based on semantic similarity calculation was adopted to evaluate the translation quality of DeepL, and DeepL was compared and analyzed with other translators. This research found that DeepL translation performed well in terms of translation accuracy, fluency, and naturalness. The overall score of DeepL was 100, reaching 94.13. [ABSTRACT FROM AUTHOR]

In educational institutions, it is now more important than ever to deliver high-quality academic instruction, and educational data mining is essential for resolving problems that arise from challenging unstructured data in this field. Using machine learning (ML) approaches, the performance of students and traits related to academia, a crucial indicator of higher education, is examined. In the proposed study, the educational dataset is subjected to feature ranking algorithms, including MRMR, ReliefF, Chi-Square, ANOVA, and Kruskal–Wallis, followed by important feature selection using Shapley. The dataset has 16 attributes of integer, categorical type, and after feature ranking approaches, the features with the most important information are chosen and ML techniques are applied to them. It takes two phases to complete the work. The results are obtained after the first phase, in which all features are taken into account for ML training. The second phase of ML training takes into account selective features that are derived using ranking approaches. ML models with only selective attributes are compared to models with all features in order to determine which is more precise. In comparison, the results of the ML models with selective attributes outperformed the models with all attributes. Overall, the ensemblers, i.e., bagged tree and AdaBoost, outperformed other ML techniques such as decision trees, neural networks, naive Bayes, K-nearest neighbor, and support vector machines presented in the proposed study. Bagged trees achieved an accuracy of 81.0 percent, while AdaBoost achieved an accuracy of 74.2 percent. [ABSTRACT FROM AUTHOR]

In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale. [ABSTRACT FROM AUTHOR]

The oxidative coupling of methane (OCM) is a reaction with a promise to provide a gainful means of utilizing an abundant greenhouse gas, methane, to produce ethylene; one of the world's most important chemicals is challenged by the co-production of carbon dioxide, another greenhouse gas. The need to find efficient means of enhancing the reaction with a yield of the desirable C2 product and the reduction in the co-production of COx product continues to be the focus of increased research over the past two decades. The advent of modern computational techniques, including Density Functional Theory (DFT), and data analytical techniques, such as Machine Learning (ML), have inspired new ways of generating data and drawing intuition on the ways to improve the efficacy of the OCM reaction. This study focuses on highlighting the innovations carried out in the study of the OCM reaction over the last 22 years: the reaction mechanism, kinetics, and catalytic design. Despite the concerted efforts to model and design new catalysts, the development of improved catalysts that are selective for C2 yields higher than 30% at low temperatures continues to be a bottleneck in the process. The application of ML and DFT in OCM is poised to provide a means to predict, design, and develop new catalysts that will enhance the effectiveness of the reaction and the quality of the products. Both techniques provide opportunities to improve and ameliorate challenges bedeviling the OCM reaction, including the high activation energy, low C2 yield, and catalyst instability/deactivation. [ABSTRACT FROM AUTHOR]

This article introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize–winning Shapley value framework in game theory by conceptualizing location as a player in a model prediction game, which enables the quantification of the importance of location and the synergies between location and other features in a model. GeoShapley is a model-agnostic approach and can be applied to statistical or black-box machine learning models in various structures. The interpretation of GeoShapley is directly linked with spatially varying coefficient models for explaining spatial effects and additive models for explaining non-spatial effects. Using simulated data, GeoShapley values are validated against known data-generating processes and are used for cross-comparison of seven statistical and machine learning models. An empirical example of house price modeling is used to illustrate GeoShapley's utility and interpretation with real-world data. The method is available as an open source Python package named geoshapley. [ABSTRACT FROM AUTHOR]

Herein, a comprehensive review of the evolution of intelligent communication anti‐jamming techniques is provided. First, a clear definition of the concept and elaboration on the inherent connotations and capability characteristics of intelligent communication anti‐jamming is provided. Additionally, the initial construction of an intelligent communication anti‐jamming system architecture is outlined. Subsequently, the development of intelligent communication anti‐jamming is delved, tracing its progression from early‐stage adaptive anti‐jamming techniques to the more recent advancements in intelligent anti‐jamming, which are primarily based on game theory and machine learning. Moreover, the latest research findings in this domain are thoroughly examined and the existing challenges and bottlenecks that hinder current research progress are highlighted. Finally, several viable research directions for future studies in the field of intelligent anti‐jamming are proposed. The objective is to facilitate readers in gaining a comprehensive understanding of the concept, characteristics, and system architecture of intelligent communication anti‐jamming. Additionally, it is aimed to provide a comprehensive overview of the developmental process and current state of research, thereby furnishing conceptual and theoretical support for the design of more effective and practical intelligent communication anti‐jamming systems. [ABSTRACT FROM AUTHOR]

Currently, the medical field is witnessing an increase in the use of machine learning techniques. Supervised learning methods adopted in classification, prediction, and segmentation tasks for medical images always experience decreased performance when the training and testing datasets do not follow the independent and identically distributed assumption. These distribution shift situations seriously influence machine learning applications' robustness, fairness, and trustworthiness in the medical domain. Hence, in this article, we adopt the CycleGAN (generative adversarial network) method to cycle train the computed tomography data from different scanners/manufacturers. It aims to eliminate the distribution shift from diverse data terminals based on our previous work [14]. However, due to the model collapse problem and generative mechanisms of the GAN-based model, the images we generated contained serious artifacts. To remove the boundary marks and artifacts, we adopt score-based diffusion generative models to refine the images voxel-wisely. This innovative combination of two generative models enhances the quality of data providers while maintaining significant features. Meanwhile, we use five paired patients' medical images to deal with the evaluation experiments with structural similarity index measure metrics and the segmentation model's performance comparison. We conclude that CycleGAN can be utilized as an efficient data augmentation technique rather than a distribution-shift-eliminating method. In contrast, the denoising diffusion the denoising diffusion model is more suitable for dealing with the distribution shift problem aroused by the different terminal modules. The limitation of generative methods applied in medical images is the difficulty in obtaining large and diverse datasets that accurately capture the complexity of biological structure and variability. In our following research, we plan to assess the initial and generated datasets to explore more possibilities to overcome the above limitation. We will also incorporate the generative methods into the federated learning architecture, which can maintain their advantages and resolve the distribution shift issue on a larger scale. [ABSTRACT FROM AUTHOR]

The Adhoc On-Demand Distance Vector (AODV) protocol faces challenges in selecting the best relay nodes, which requires optimization to improve performance in Vehicular ad-hoc networks (VANETs). This study aims to enhance Vehicle-to-Vehicle (V2V) communication in VANETs by implementing the Learning Automata-Driven Ad-hoc On-Demand Distance Vector (LA-AODV) routing protocol. LA-AODV is designed to achieve higher packet delivery ratios and optimize data transfer rates, even under congested network conditions, by dynamically adjusting to changing network scenarios. The performance evaluation includes six key metrics analyzed under varying node densities and time intervals, comparing LA-AODV against the standard AODV protocol. Results indicate that LA-AODV consistently outperforms AODV, demonstrating improved efficiency in flood identifier management, reduced data loss, higher packet delivery ratios, better throughput, and reduced end-to-end delay and jitter. Specifically, under a 20-node scenario, LA-AODV exhibits lower flood ID scores (54 vs. 88), reduced packet loss (11% vs. 12%), higher PDR (88.0% vs. 87.0%), and superior throughput (85.34 Kbps vs. 47.26 Kbps). Additionally, LA-AODV achieves lower end-to-end delay (6.84E+09 ns vs. 3.76E+10 ns) and jitter (2.52E+09 ns vs. 2.15E+10 ns). These findings suggest that LA-AODV significantly enhances Quality of Service (QoS) in vehicular adhoc networks, positioning it as a promising solution for optimizing V2V communication performance. [ABSTRACT FROM AUTHOR]