Search

Deep neural network-based deep potentials (DP), developed by Tuo et al., have been used to compute the thermodynamic properties of free aluminum clusters with accuracy close to that of density functional theory. Although Jarrold and collaborators have reported extensive experimental measurements on the melting temperatures and heat capacities of free aluminum clusters, no reports exist for finite-temperature ab initio simulations on larger clusters (N > 55 atoms). We report the heat capacities and melting temperatures for 32 clusters in the size range of 48–342 atoms, computed using the multiple histogram technique. Extensive molecular dynamics (MD) simulations at twenty four temperatures have been performed for all the clusters. Our results are in very good agreement with the experimental melting temperatures for 19 clusters. Except for a few sizes, the interesting features in the heat capacities have been reproduced. To gain insight into the striking features reported in the experiments, we used structural and dynamical descriptors such as temperature-dependent mean squared displacements and the Lindemann index. Bimodal features observed in Al116 and the weak shoulder seen in Al52 are attributed to solid–solid structural transitions. In confirmation of the earlier reports, we observe that the behavior of the heat capacities is significantly influenced by the nature of the ground state geometries. Our findings show that the sharp drop in the melting temperature of the 56-atom cluster is a consequence of the change in the geometry of Al55. Mulliken population analysis of Al55 reveals that the charge-induced local electric field is responsible for the strong bonding between core and surface atoms, leading to the higher melting temperature. Our calculations do not support the lower melting temperature observed in experimental studies of Al69. Our results indicate that Al48 is in a liquid state above 600 K and does not support the high melting temperature reported in the experiment. It turns out that the accuracy of the DP model by Tuo et al. is not reliable for MD simulations beyond 750 K. We also report low-lying equilibrium geometries and thermodynamics of 11 larger clusters (N = 147–342) that have not been previously reported, and the melting temperatures of these clusters are in good agreement with the experimental ones. [ABSTRACT FROM AUTHOR]

In this Communication, we demonstrate that a deep artificial neural network based on a transformer architecture with self-attention layers can predict the long-time population dynamics of a quantum system coupled to a dissipative environment provided that the short-time population dynamics of the system is known. The transformer neural network model developed in this work predicts the long-time dynamics of spin-boson model efficiently and very accurately across different regimes, from weak system–bath coupling to strong coupling non-Markovian regimes. Our model is more accurate than classical forecasting models, such as recurrent neural networks, and is comparable to the state-of-the-art models for simulating the dynamics of quantum dissipative systems based on kernel ridge regression. [ABSTRACT FROM AUTHOR]

This article presents an augmented deep factor model that generates latent factors for cross-sectional asset pricing. The conventional security sorting on firm characteristics for constructing long–short factor portfolio weights is nonlinear modeling, while factors are treated as inputs in linear models. We provide a structural deep-learning framework to generalize the complete mechanism for fitting cross-sectional returns by firm characteristics through generating risk factors (hidden layers). Our model has an economic-guided objective function that minimizes aggregated realized pricing errors. Empirical results on high-dimensional characteristics demonstrate robust asset pricing performance and strong investment improvements by identifying important raw characteristic sources. [ABSTRACT FROM AUTHOR]

Purpose: The popularity of fencing and intense sports competition has burdened adolescents with excessive training, harming their immature bodies. Traditional training methods fail to provide timely movement corrections and personalized plans, leading to ineffective exercises. This paper aims to use artificial intelligence technology to reduce ineffective exercises and alleviate the training burden. Methods: We propose an action recognition algorithm based on the characteristics of adolescent athletes. This algorithm uses multimodal input data to comprehensively extract action information. Each modality is processed by the same network structure, utilizing attention mechanisms and adaptive graph structures. A multibranch feature fusion method is used to determine the final action category. Results: We gathered the fencing footwork data set 2.0. Our model achieved 93.3% accuracy, with the highest precision at 95.8% and the highest F1-Score at 94.5% across all categories. It effectively recognized actions of adolescents with different heights and speeds, outperforming traditional methods. Conclusion: Our artificial intelligence-based training solution improves training efficiency and reduces the training burden on adolescents. [ABSTRACT FROM AUTHOR]

In insurance, proposing an accurate premium that is adjusted to the insured risk profile allows companies to better manage their portfolios and to be more competitive. Machine learning methods have recently been adopted for various improvements in insurance ratemaking, especially in the automobile industry. These models are specifically used to mine potential data information and to build a predictive model for a variable of interest using explanatory variables. In this paper, we aim to provide a pricing method for ratemaking individual healthcare insurance contracts using machine learning algorithms that are applied to a Tunisian healthcare insurance portfolio. We start with a simple Classification and Regression Tree, and we work toward more advanced methods that are Random Forest, Extreme gradient boosting, Support Vector Regression, and Artificial Neural network regression model. The predictive performance of these non-parametric methods is compared with the standard generalized linear model. Our results showed the applicability of machine learning in the healthcare insurance market and that the XGBoost algorithm outperforms the predictive capacity of the classical generalized linear model. [ABSTRACT FROM AUTHOR]

This study presents a proof-of-principle for using optical emission spectroscopy and artificial neural networks for real-time monitoring and control of the operational parameters of a Hall effect thruster: the anode voltage, the anode xenon injection, the discharge current, and the coil current. In that regard, we build an optical database of 26 spectral lines across 6469 operating conditions to train and test the neural network. We then reduced the learning lines from 26 to 15 based on their statistical correlation with the target parameters. After tuning the hyperparameters of the network, the network predicted the thruster's parameters with notable accuracies: 95% for the anode voltage, 84% for the coil current, and 99% for both the anode flow rate and the discharge current. The estimated uncertainty of predictions, at 3 σ , is ± 51 V for voltage, ± 1 A for coil current, ± 0.15 A for discharge current, and ± 0.15 mg s − 1 for anode flow rate. The prediction calculations were within milliseconds and enabled real-time monitoring of the thruster parameters. Therefore, a proportional-integrator-derivative controller (PID) controller was implemented to regulate the anode voltage and flow rate based on the optical emission of the plume. The PID showcased short settling times from 0.1 to 0.4 s and overshoot levels up to 3% of the target value for the voltage and 10% of the target value for the flow rate. These results were for a fixed coil current at 4 A. The study showed that changing the coil current may necessitate more sophisticated prediction models and control strategies. Future work will expand the model's generalizability to different thruster types, propellants, and magnetic field configurations. [ABSTRACT FROM AUTHOR]

In this study, a novel optical super-resolution imaging technique, grating and deep neural network assisted super-resolution microscopy, is proposed. The technique utilizes a sub-wavelength grating, placed between the sample and the microscope objective, to convert the evanescent waves of a sample surface into propagating waves, allowing more high spatial-frequency information of the sample to be detected in the far field. Then, the far-field image of the sample is captured and trained end-to-end with a customized deep neural network model to heuristically reconstruct a clear image of the sample with structural features smaller than λ/3. Compared with the existing super-resolution imaging techniques, the proposed technique has the advantages of label-free, large field of view, one-time direct imaging, and white light illumination and observation in an atmospheric environment. Moreover, it has the flexibility to replace raster and network rendering components according to specific inspection requirements to meet diverse application scenarios. [ABSTRACT FROM AUTHOR]

Due to their high similarity to biological ion channels, low power consumption, small footprint, and the fact that they do not require reset circuits, threshold switching memristors have been intensively studied for simulating neurons in neuromorphic chips. Switching speed is one of the key challenges which limit the application of threshold switching memristors in chips. In this study, Ta2O5 threshold switching memristors with high switching speeds were prepared by doping with silver. The results show that 14 wt. % Ag doped Ta2O5 threshold switching memristors exhibit excellent bi-directional threshold switching performance, featuring fast switching speeds (<20 ns, <18 ns), low leakage currents (<10 pA), and high switching ratio (>107). According to the field nucleation theory, the rapid switching speed can be attributed to the low nucleation energy (0.26 eV) of silver within the Ta2O5 matrix, which is achieved by incorporating 14 wt. % Ag during the doping process. Based on Pspice, a LIF (leaky integrate-and-fire) neuron based on the silver nanoparticles doped Ta2O5 threshold switching memristors is built, and its firing function has been simulated. The results show that the LIF neuron with a short switching time is able to excite pulse spiking with high frequencies. These results demonstrated that the silver nanoparticles doped Ta2O5-based threshold switching memristors hold significant potential for constructing high-speed artificial neural networks. [ABSTRACT FROM AUTHOR]

As the temperature decreases, the dynamics of supercooled liquids significantly slow down and become increasingly heterogeneous in space. Many previous studies have found that static structures also become heterogeneous and are spatially correlated with the dynamical heterogeneity. However, there are still debates on whether the dynamical heterogeneity is controlled by the structures, and which structural order parameters should be used to describe the structural heterogeneities (if exist) in amorphous systems. The appropriate order parameter depends on the specific details of the system and needs to be determined for each system. To address this difficulty, here, we use a machine-learning-based method that was trained solely by the static structures. This method combines convolutional neural networks and gradient-weighted class activation mapping, providing interpretable characteristic structures, which can quantify the degrees of liquid-like and solid-like structures in every local part of the system. We apply this method to a canonical glass-forming system and demonstrate that particles in the liquid-like structures are mobile, while those in the solid-like structures are immobile. The present work develops a novel approach to accurately characterize amorphous structures, which will be particularly useful for systems where appropriate structural order parameters have not yet been identified. [ABSTRACT FROM AUTHOR]

The continuous effort in making artificial neural networks more alike to human brain calls for the hardware elements to implement biological synapse-like functionalities. The recent experimental demonstration of ferroelectric-like FETs promises low-power operation as compared to the conventional ferroelectric switching devices. This work presents an in-house numerical tool, which self-consistently solves the electrostatics and time-dependent electronic and ionic transport. The tool is exploited to analyze the effect that various physical parameters such as mobility and ion concentration could have on the design of the ferroelectric-like FETs. Their suitability in emulating different functions of the biological synapses is also demonstrated. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to positively impact library and archives collections and services--enhancing reference, instruction, metadata creation, recommendations, and more. However, AI also has ethical implications. This paper presents an extensive literature and review analysis that examines AI projects implemented in library and archives settings, asking the following research questions: RQ1: How is artificial intelligence being used in libraries and archives practice? RQ2: What ethical concerns are being identified and addressed during AI implementation in libraries and archives? The results of this literature review show that AI implementation is growing in libraries and archives and that practitioners are using AI for increasingly varied purposes. We found that AI implementation was most common in large, academic libraries. Materials used in AI projects usually involved digitized and born digital text and images, though materials also ranged to include web archives, electronic theses and dissertations (ETDs), and maps. AI was most often used for metadata extraction and reference and research services. Just over half of the papers included in the literature review mentioned ethics or values related issues in their discussions of AI implementation in libraries and archives, and only one-third of all resources discussed ethical issues beyond technical issues of accuracy and human-in-the-loop. Case studies relating to AI in libraries and archives are on the rise, and we expect subsequent discussions of relevant ethics and values to follow suit, particularly growing in the areas of cost considerations, transparency, reliability, policy and guidelines, bias, social justice, user communities, privacy, consent, accessibility, and access. As AI comes into more common usage, it will benefit the library and archives professions to not only consider ethics when implementing local projects, but to publicly discuss these ethical considerations in shared documentation and publications. [ABSTRACT FROM AUTHOR]

For the past 70 years, researchers and experimental musicians have been working with computersynthesized music, forming a collaborative relationship with generative artificial intelligences known as human--AI co-creation. The last several years have shown that musical artists are quickly adopting AI tools to produce music for AI music competitions and for commercial production of songs and albums. The United States Copyright Office, in response to this trend, has released its latest policy revisions to clearly define what is eligible for copyright registration. Soon after, the Program for Cooperative Cataloging (PCC) also released new guidelines, providing recommendations for how library catalogers should address AI-generated materials. In both cases, they reject the notion of considering AI as a contributor. The language in each of these policies, however, is self-contradicting, showing that they are ill equipped to address generative AI. This study leverages critical textual analysis and qualitative content analysis and uses case examples to probe the manner in which these policies regard generative AI. Recommendations are made for addressing shortcomings in the PCC's policies, and moral philosophical frameworks such as virtue ethics and consequentialism support arguments for supplementing catalog item records with information from authoritative external sources, deviating from this policy for the sake of truth-seeking. [ABSTRACT FROM AUTHOR]

The drilling quality of carbon fibre reinforced polymer (CFRP) components is a key factor affecting the service life of the components, while energy saving and emission reduction in industrial production are crucial. In this study, drilling experiments were conducted on T300 plywood using a 55° coated tungsten steel drill bit, and CNN-LSTM neural network models were used to construct mapping relationships between process parameters (spindle speed, feed rate, and fibre lay-up sequence) and delamination factor and machine energy consumption. A new method of predicting the delamination factor by process parameters is proposed, and explored the optimal process parameter combinations that reduce the energy consumption of machine tools and minimise the delamination factor at the same time. The research results show that within the parameter settings, a spindle speed of 7000 r/min, a feed rate of 40 mm/min, and a lay-up sequence of [0°, 0°, −45°, 90°]6s ensure both low power consumption in the drilling process and the highest possible hole quality. This paper clearly demonstrates the feasibility of achieving low-power, high-quality drilling of CFRP through parameter optimisation, providing guidance to the manufacturing industry to improve the quality of CFRP hole-making while easing the pressure on carbon emissions. [ABSTRACT FROM AUTHOR]

The Journal of Chemical Physics has released a special issue focused on software for atomistic machine learning. This issue aims to address the lack of journals dedicated to publishing scientific software papers. The collection of papers in this issue provides insight into the tools and goals of software implementations in the field of atomistic machine learning. The articles cover a range of topics, including machine-learning interatomic potentials, sampling, dataset repositories, workflows, and auxiliary tooling and analysis. The article concludes by emphasizing the importance of software implementations in the field and encourages further submissions on relevant topics. [Extracted from the article]

This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to dynamically react to volatile environments, and alleviate potentially costly decision-makings for small-medium enterprises (SMEs). Building on a resource-based view, this work examines the impact of AI on SC risk management for SMEs. A structural model comprising of AI-risk management capabilities, SC re-engineering capabilities and supply chain agility (SCA) was developed and tested based on data collected from executives, managers and senior managers of SMEs The main methodological approach used in this study is partial least squares-based structural equation modelling (PLS-SEM) and artificial neural network (ANN). The results identified the use of AI for risk management influences SC re-engineering capabilities and agility. Re-engineering capabilities further affect and mediate agility. PLS-SEM and ANN were compared and the results revealed consistency for models A and B. Current levels of demand uncertainties in the SC challenges managers in making complex trade-off decisions that require huge management resources in very limited time. With AI, it is possible to model various scenarios to answer crucial questions that archaic infrastructures are not able to. This study combines a multi-construct agility concept and identified non-linear relationships in the model. [ABSTRACT FROM AUTHOR]

Machine learning with artificial neural networks has recently transformed many scientific fields by introducing new data analysis and information processing techniques. Despite these advancements, efficient implementation of machine learning on conventional computers remains challenging due to speed and power constraints. Optical computing schemes have quickly emerged as the leading candidate for replacing their electronic counterparts as the backbone for artificial neural networks. Some early integrated photonic neural network (IPNN) techniques have already been fast-tracked to industrial technologies. This review article focuses on the next generation of optical neural networks (ONNs), which can perform machine learning algorithms directly in free space. We have aptly named this class of neural network model the free space optical neural network (FSONN). We systematically compare FSONNs, IPNNs, and the traditional machine learning models with regard to their fundamental principles, forward propagation model, and training process. We survey several broad classes of FSONNs and categorize them based on the technology used in their hidden layers. These technologies include 3D printed layers, dielectric and plasmonic metasurface layers, and spatial light modulators. Finally, we summarize the current state of FSONN research and provide a roadmap for its future development. [ABSTRACT FROM AUTHOR]

Automatic determination of manufacturing process sequences for the physical production of given part designs is key to facilitate on-demand cyber manufacturing. In this work, we propose an integrated framework that (i) identifies manufacturing features from 3D part designs using a Graph Neural Network (GNN), (ii) identifies the manufacturing processes necessary to produce all features in the part using a Convolutional Neural Network (CNN) that considers shape, material properties, and quality information, and (iii) outputs an ordered manufacturing sequence that can produce the designed part with the help of sequence mining. Using these methods, the knowledge required to enable automated manufacturing process selection is easily scalable and updatable without requiring manual population of ad-hoc or rule-based descriptions. We present exemplar implementations of the proposed framework by suggesting manufacturing sequences for discrete parts with multiple features. The suggested manufacturing sequences demonstrate the potential of the proposed framework for use in future on-demand cyber manufacturing applications. [ABSTRACT FROM AUTHOR]

Conductive-bridge random access memory can be used as a physical reservoir for temporal learning in reservoir computing owing to its volatile nature. Herein, a scaled Cu/HfOx/n+-Si memristor was fabricated and characterized for reservoir computing. The scaled, silicon nanofin bottom electrode formation is verified by scanning electron and transmission electron microscopy. The scaled device shows better cycle-to-cycle switching variability characteristics compared with those of large-sized cells. In addition, synaptic characteristics such as conductance changes due to pulses, paired-pulse facilitation, and excitatory postsynaptic currents are confirmed in the scaled memristor. High-pattern accuracy is demonstrated by deep neural networks applied in neuromorphic systems in conjunction with the use of the Modified National Institute of Standards and Technology database. Furthermore, a reservoir computing system is introduced with six different states attained by adjusting the amplitude of the input pulse. Finally, high-performance and efficient volatile reservoir computing in the scaled device is demonstrated by conductance control and system-level reservoir computing simulations. [ABSTRACT FROM AUTHOR]

Defect detection is crucial in ensuring the quality of steel products. This paper proposes a novel deep neural network, cascaded adaptive global location network (CAGLNet), for detecting steel surface defects. The main objective of this study is to address the challenges associated with the irregular shape and dense spatial distribution of defects on steel. To achieve this goal, CAGLNet integrates a feature extraction network that combines residual and feature pyramid networks, a cascade adaptive tree-structure region proposal network (CAT-RPN) that eliminates the need for prior knowledge, and a global localisation regression for steel defect detection. This paper evaluates the effectiveness of CAGLNet on the NEU-DET dataset and demonstrates that the proposed model achieves an average accuracy of 85.40% with a fast frames per second of 10.06, outperforming those state-of-the-art methods. These results suggest that CAGLNet has the potential to significantly improve the effectiveness of defect detection in industrial production processes, leading to increased production yield and cost savings. Abbreviations: AT-RPN, adaptive tree-structure region proposal network; CAGLNet, cascaded adaptive global location network; CAT-RPN, cascade adaptive tree-structure region proposal network; CNN, convolutional neural network; DNN, deep neural network; EPNet, edge proposal network; FPN, feature pyramid network; FCOS, fully convolutional one-stage detector; FPS, frames per second; GMM, Gaussian mixture model; IoU, intersection-over-union; ROIAlign, region of interest align; RPN, region proposal network; ResNet, residual network; ResNet50_FPN, residual network and feature pyramid network; SABL, side aware boundary localisation; SSD, single-shot multiBox detector; TPE, Tree-structured Parzen estimator [ABSTRACT FROM AUTHOR]

This article presents an approach for predicting Lockout/Tagout (LOTO) procedure sheets, which are commonly used in the manufacturing industry to prevent premature equipment restart during maintenance. The prediction problem of energetic devices to lock from machine names is regarded as a multi-task classification problem. The dataset was obtained by processing LOTO sheets in Portable Document Format (PDF). The K-Nearest Neighbours (KNN), Random Forest (RF), and Deep Neural Network (DNN) algorithms were compared for this problem. The best prediction performance was achieved with the DNN method, with top-1 accuracies exceeding 63% and top-2 accuracies exceeding 90% for all devices. The sensitivity analysis conducted on the results indicates that the approach is robust and reliable, regardless of the industrial sector considered. In other words, the approach is not significantly affected by variations in the industry or its specific characteristics. These results suggest that the proposed approach can be used to assist workers in drafting LOTO sheets, and offers strong potential for concrete applications in safety management in the era of smart manufacturing. [ABSTRACT FROM AUTHOR]

Memristive devices are promising candidates for synaptic memories in neuromorphic computing systems, but the insufficient reliability of the analog behavior has been a challenge. Lateral oxide scaling with bottom-up technologies such as thermal oxidation, coupled with new device architectures, can improve the reliability. However, the effect of thermal oxide growth on analog device behavior remains unclear. In this study, we fabricate a variety of resistive switching thermal oxides on tantalum electrode surfaces and develop a method for rapid full factorial electrical analysis. The analysis uses 2500 unique control parameter combinations of current limits and RESET-stop-voltages to compare device behavior across millions of modulated current–voltage sweeps. We clarify the mechanisms that shift the device behavior from abrupt towards gradual SET transitions, a desirable characteristic for emulating analog plasticity. We found that a mildly negative differential resistance and an increased internal series resistance at the metal–oxide interface contribute to the stabilization of the gradual SET transitions. These findings highlight the importance of fine-tuning the bottom-up oxide growth for improving switching performance. [ABSTRACT FROM AUTHOR]

Electronic–photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency of optics, especially for real-time, low-energy deep neural network inference tasks on resource-restricted edge platforms. However, current optical neural accelerators based on foundry-available devices and conventional system architecture still encounter a performance gap compared to highly customized electronic counterparts. To bridge the performance gap due to lack of domain specialization, we present a time-multiplexed dynamic photonic tensor accelerator, dubbed TeMPO , with cross-layer device/circuit/architecture customization. At the device level, we present foundry-compatible, customized photonic devices, including a slow-light electro-optic modulator with experimental demonstration, optical splitters, and phase shifters that significantly reduce the footprint and power in input encoding and dot-product calculation. At the circuit level, partial products are hierarchically accumulated via parallel photocurrent aggregation, lightweight capacitive temporal integration, and sequential digital summation, considerably relieving the analog-to-digital conversion bottleneck. We also employ a multi-tile, multi-core architecture to maximize hardware sharing for higher efficiency. Across diverse edge AI workloads, TeMPO delivers digital-comparable task accuracy with superior quantization/noise tolerance. We achieve a 368.6 TOPS peak performance, 22.3 TOPS/W energy efficiency, and 1.2 TOPS/mm2 compute density, pushing the Pareto frontier in edge AI hardware. This work signifies the power of cross-layer co-design and domain-specific customization, paving the way for future electronic–photonic accelerators with even greater performance and efficiency. [ABSTRACT FROM AUTHOR]

High-entropy alloys (HEAs) have attracted widespread attention from scholars as a new type of material employed in extreme environments. However, as a main kind of HEAs, many face-centered cubic single-phase HEAs are restricted in industrial applications due to their lower yield strength and high cost when containing expensive elements such as Co. In this study, dispersion strengthening by heat treatment was introduced in low-cost Co-free Fe40Mn20Cr20Ni20 HEA to improve its strength, and its high-temperature tensile behavior and constitutive model were studied to explore its potential application at high temperatures. It is found that when subjected to quasi-static room-temperature stretching, the heat-treated sample exhibits a yield strength of 534 MPa and a tensile plasticity of 26.8%. In addition, the tensile behavior of samples after heat treatment was investigated at high temperature (573–873 K) and low strain rate (10−3–10−1 s−1). The results suggest that the yield strength decreases with increasing temperature and decreasing strain rate. Moreover, at 873 K and 10−3 s−1, the electron backscatter diffraction system and x-ray diffraction results of the deformed sample indicate that the softening curve is driven from the recovery of materials. Finally, the flow stress was predicted using the Arrhenius equation and Artificial Neural Network model (ANN), and the two models were assessed using the average absolute relative error and coefficient of correlation (R). The results showed that the ANN had higher accuracy. [ABSTRACT FROM AUTHOR]

Understanding the mechanical and thermodynamic properties of transition-metal dichalcogenides (TMDs) and their heterostructures is pivotal for advancing the development of flexible semiconductor devices, and molecular dynamics (MD) simulation is widely applied to study these properties. However, current uncertainties persist regarding the efficacy of empirical potentials in MD simulations to accurately describe the intricate performance of complex interfaces within heterostructures. This study addresses these challenges by developing an interatomic potential based on deep neural networks and first-principles calculations. Specifically focusing on MoS2/WS2 heterostructures, our approach aims to predict Young's modulus and thermal conductivities. The potential's effectiveness is demonstrated through the validation of structural features, mechanical properties, and thermodynamic characteristics, revealing close alignment with values derived from first-principles calculations. A noteworthy finding is the substantial influence of the load direction on Young's modulus of heterostructures. Furthermore, our results highlight that the interfacial thermal conductance of the MoS2/WS2 heterostructures is considerably larger than that of graphene-based interfaces. The potential developed in this work facilitates large-scale material simulations, bridging the gap with first-principles calculations. Notably, it outperforms empirical potentials under interface conditions, establishing its significant competitiveness in simulation computations. Our approach not only contributes to a deeper understanding of TMDs and heterostructures but also presents a robust tool for the simulation of their mechanical and thermal behaviors, paving the way for advancements in flexible semiconductor device manufacturing. [ABSTRACT FROM AUTHOR]

A production plan gives the quantity of products to release on the shop floor in each period, where a period may represent a week or a month. The plan is the basis for negotiating order acceptance and delivery dates with customers and suppliers. The production plan must respect the available capacity on the shop floor, as underloading the shop floor leads to a loss of opportunity, and thus, a loss of competitiveness for the company. To properly manage the production capacity while negotiating with suppliers and customers, the production planners need a tool to accurately estimate the capacity consumption in each period. The computation of capacity consumption requires creating a detailed production schedule which is a complex task. Algorithms that find close to optimal schedules in a complex manufacturing environment are often time-consuming, which is impractical in a negotiation context. We investigate machine learning models to predict capacity consumption. We consider a flexible job-shop as commonly encountered in practice, and proposed several machine learning models. Namely, several variants of linear regression, decision trees, and artificial neural networks. Numerical experiments showed that our models outperform those found by both an exact approach and dispatching rules when computation time is short. [ABSTRACT FROM AUTHOR]

The enterprises of the make-to-order production mode are required to make accurate judgments and reasonable decisions when dealing with the disturbance factors in the manufacturing process to ensure the timely completion of orders. Order remaining completion time (ORCT) prediction can quantify the production process and is an important basis to ensure the delivery date of orders. However, an accurate prediction model of the ORCT is challenging because of the spatial and temporal (ST) characteristics of the manufacturing process. An ST features-based prediction method is proposed to solve this problem. Firstly, the ST data sets are established on the basis of analysing ST characteristics of the manufacturing process. Secondly, the spatial feature extraction network and temporal feature extraction network are built to make a more comprehensive analysis. Finally, the prediction model of the ORCT is proposed by fusing the spatial and temporal features and validated in a practical workshop. Experimental results show that considering the spatial and temporal characteristics of the manufacturing process can significantly improve the prediction model's performance, and the proposed prediction model is superior to graph convolutional neural network, gated recurrent unit network, deep auto-encoder, deep neural network, and back propagation neural network. [ABSTRACT FROM AUTHOR]

Accurate prediction of the entrained liquid droplet fraction in an annular two-phase flow regime plays a crucial role for estimating the dryout type critical heat flux to identify the optimized flow characteristics in the thermal systems across different industries. Existing studies have provided different correlations based on the limited experimental data. However, these correlations are applicable to certain operating conditions. Therefore, the present study aims at applying a deep learning method, specifically an artificial neural network (ANN), to enhance the prediction of the entrained liquid droplet fraction. Experimental data from various works on annular flow, covering a wide spectrum of pressure and flow conditions, are utilized for training the ANN model. Eight input variables, viz, superficial gas velocity (J S G ), superficial liquid velocity (J S L ), gas viscosity (μ G), liquid viscosity (μ L), gas density (ρ G), liquid density (ρ L), pipe diameter (d) and liquid surface tension (σ L V ) are considered as input features. The entrained liquid droplet fraction is the single output feature. The present model employs the Bayesian regularization backpropagation algorithm for training. The present ANN model is compared against the performance of linear regression, decision tree and support vector machine algorithms, and found that the performance of the present Bayesian regularization neural network (BRNN) model is superior within ∼ 7.5 % deviation. Further, the BRNN model is coupled with the film mass flow rate model to obtain the axial variation of the liquid film mass flow rate and good agreement is noticed when compared against the experimental data. [ABSTRACT FROM AUTHOR]

Integrating both electrical and light-modulated multi-type neuromorphic functions in a single synaptic memristive device holds the most potential for realizing next-generation neuromorphic systems, but is still challenging yet achievable. Herein, a simple bi-terminal optoelectronic synaptic memristor is newly proposed based on kesterite Cu2ZnSnS4, exhibiting stable nonvolatile resistive switching with excellent spatial uniformity and unique optoelectronic synaptic behaviors. The device demonstrates not only low switching voltage (−0.39 ± 0.08 V), concentrated Set/Reset voltage distribution (<0.08/0.15 V), and long retention time (>104 s) but also continuously modulable conductance by both electric (different width/interval/amplitude) and light (470–808 nm with different intensity) stimulus. These advantages make the device good electrically and optically simulated synaptic functions, including excitatory and inhibitory, paired-pulsed facilitation, short-/long-term plasticity, spike-timing-dependent plasticity, and "memory-forgetting" behavior. Significantly, decimal arithmetic calculation (addition, subtraction, and commutative law) is realized based on the linear conductance regulation, and high precision pattern recognition (>88%) is well achieved with an artificial neural network constructed by 5 × 5 × 4 memristor array. Predictably, such kesterite-based optoelectronic memristors can greatly open the possibility of realizing multi-functional neuromorphic systems. [ABSTRACT FROM AUTHOR]

We present a detailed assessment of deep neural network potentials developed within the Deep Potential Molecular Dynamics (DeePMD) framework and trained on the MB-pol data-driven many-body potential energy function. Specific focus is directed at the ability of DeePMD-based potentials to correctly reproduce the accuracy of MB-pol across various water systems. Analyses of bulk and interfacial properties as well as many-body interactions characteristic of water elucidate inherent limitations in the transferability and predictive accuracy of DeePMD-based potentials. These limitations can be traced back to an incomplete implementation of the "nearsightedness of electronic matter" principle, which may be common throughout machine learning potentials that do not include a proper representation of self-consistently determined long-range electric fields. These findings provide further support for the "short-blanket dilemma" faced by DeePMD-based potentials, highlighting the challenges in achieving a balance between computational efficiency and a rigorous, physics-based representation of the properties of water. Finally, we believe that our study contributes to the ongoing discourse on the development and application of machine learning models in simulating water systems, offering insights that could guide future improvements in the field. [ABSTRACT FROM AUTHOR]

Molecular-level nucleation has not been clearly understood due to the complexity of multi-body potentials and the stochastic, rare nature of the process. This work utilizes molecular dynamics (MD) simulations, incorporating a first-principles-based deep neural network (DNN) potential model, to investigate homogeneous water vapor condensation. The nucleation rates and critical nucleus sizes predicted by the DNN model are compared against commonly used semi-empirical models, namely extended simple point charge (SPC/E), TIP4P, and OPC, in addition to classical nucleation theory (CNT). The nucleation rates from the DNN model are comparable with those from the OPC model yet surpass the rates from the SPC/E and TIP4P models, a discrepancy that could mainly arise from the overestimated bulk free energy by SPC/E and TIP4P. The surface free energy predicted by CNT is lower than that in MD simulations, while its bulk free energy is higher than that in MD simulations, irrespective of the potential model used. Further analysis of cluster properties with the DNN model unveils pronounced variations of O–H bond length and H–O–H bond angle, along with averaged bond lengths and angles that are enlarged during embryonic cluster formation. Properties such as cluster surface free energy and liquid-to-vapor density transition profiles exhibit significant deviations from CNT assumptions. [ABSTRACT FROM AUTHOR]

The empirical valence bond technique allows classical force fields to model reactive processes. However, parametrization from experimental data or quantum mechanical calculations is required for each reaction present in the simulation. We show that the parameters present in the empirical valence bond method can be predicted using a neural network model and the SMILES strings describing a reaction. This removes the need for quantum calculations in the parametrization of the empirical valence bond technique. In doing so, we have taken the first steps toward defining a new procedure for enabling reactive atomistic simulations. This procedure would allow researchers to use existing classical force fields for reactive simulations, without performing additional quantum mechanical calculations. [ABSTRACT FROM AUTHOR]

The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance. [ABSTRACT FROM AUTHOR]

For two-dimensional (2D) materials, the exact thickness of the material often dictates its physical and chemical properties. The 2D quantum material WTe2 possesses properties that vary significantly from a single layer to multiple layers, yet it has a complicated crystal structure that makes it difficult to differentiate thicknesses in atomic-resolution images. Furthermore, its air sensitivity and susceptibility to electron beam-induced damage heighten the need for direct ways to determine the thickness and atomic structure without acquiring multiple measurements or transferring samples in ambient atmosphere. Here, we demonstrate a new method to identify the thickness up to ten van der Waals layers in Td-WTe2 using atomic-resolution high-angle annular dark-field scanning transmission electron microscopy image simulation. Our approach is based on analyzing the intensity line profiles of overlapping atomic columns and building a standard neural network model from the line profile features. We observe that it is possible to clearly distinguish between even and odd thicknesses (up to seven layers), without using machine learning, by comparing the deconvoluted peak intensity ratios or the area ratios. The standard neural network model trained on the line profile features allows thicknesses to be distinguished up to ten layers and exhibits an accuracy of up to 94% in the presence of Gaussian and Poisson noise. This method efficiently quantifies thicknesses in Td-WTe2, can be extended to related 2D materials, and provides a pathway to characterize precise atomic structures, including local thickness variations and atomic defects, for few-layer 2D materials with overlapping atomic column positions. [ABSTRACT FROM AUTHOR]

The article discusses the potential benefits of using artificial intelligence (AI) and machine learning (ML) tools and algorithms in physical activity and sedentary behavior research. While AI and ML have been widely utilized in healthcare and medicine, their application in studying physical activity and sedentary behavior is still underutilized. The article highlights the capabilities of AI and ML in generating novel hypotheses, improving measurement of behaviors, predicting health indicators, and advancing precision medicine. However, there are challenges in implementing these techniques, including ethical concerns and limitations in identifying cause-effect relationships. Overall, integrating AI and ML tools can contribute to a deeper understanding of physical activity and sedentary behavior. [Extracted from the article]