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We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works. [ABSTRACT FROM AUTHOR]

The interactions of electrons with molecular systems under various conditions are essential to interdisciplinary research fields extending over the fundamental and applied sciences. In particular, investigating electron-induced ionization and dissociation of molecules may shed light on the radiation damage to living cells, the physicochemical processes in interstellar environments, and reaction mechanisms occurring in combustion or plasma. We have, therefore, studied electron-induced ionization and dissociation of the gas phase 3,4-dihydro-2H-pyran (DHP), a cyclic ether appearing to be a viable moiety for developing efficient clinical pharmacokinetics and revealing the mechanisms of biofuel combustion. The mass spectra in the m/z = 10–90 mass range were measured at several different energies of the ionizing electron beam using mass spectrometry. The mass spectra of DHP at the same energies were simulated using on-the-fly semi-classical molecular dynamics (MD) within the framework of the QCxMS formalism. The MD settings were suitably adjusted until a good agreement with the experimental mass spectra intensities was achieved, thus enabling a reliable assignment of cations and unraveling the plausible fragmentation channels. Based on the measurement of the absolute total ionization cross section of DHP (18.1 ± 0.9) × 10−16 cm2 at 100 eV energy, the absolute total and partial ionization cross sections of DHP were determined in the 5–140 eV electron energy. Moreover, a machine learning algorithm that was trained with measured cross sections from 25 different molecules was used to predict the total ionization cross section for DHP. Comparison of the machine learning simulation with the measured data showed acceptable agreement, similar to that achieved in past predictions of the algorithm. [ABSTRACT FROM AUTHOR]

Consumers' dramatic demand has a pernicious effect throughout the supply chain. It exacerbates inventory distortion because of significant revenue loss caused by stock-level issues. Despite the availability of several forecasting techniques, large organisations, manufacturing firms, and e-commerce websites collectively lose around $1.8 trillion annually to inventory distortion. If this problem is solved, sales may increase by 10.3 percent. The businesses are concerned about mitigating this loss. Artificial intelligence (AI) can play a significant role in building resilient supply chains. However, developing AI models consumes time and cost. In this paper, we propose a No Code Artificial Intelligence (NCAI) enabling non-technical companies to build machine learning models based on production quantity and inventory replenishment. The development of the NCAI model is fast and inexpensive. However, little research deals with applying NCAI to operations and supply chain problems. Addressing the existing gap, we show the application of NCAI in the retail industry. [ABSTRACT FROM AUTHOR]

Modern supply chains are complex, interconnected systems that contain emergent, invisible dependencies. Lack of visibility often hinders effective risk planning and results in delayed discovery of supply chain problems, with examples ranging from product contamination, unsustainable production practices, or exposure to suppliers clustered in geographical areas prone to natural or man-made disasters. Initiatives that rely on manual collection of data often fail due to supply chain complexity and unwillingness of suppliers to share data. In this paper, we propose a neurosymbolic machine learning technique to proactively uncover hidden risks in supply chains and discover new information. Our method uses a combination of graph neural networks and knowledge graph reasoning. Unlike existing research our model is able to infer multiple types of hidden relationship risks, presenting a step change in automated supply chain surveillance. The approach has been tested on two empirical datasets from the automotive and energy industries, illustrating that it can provide inference in multiple types of links such as companies, products, production capabilities, certifications; thereby facilitating complex queries that go beyond who-supplies-whom. As such, additional risk insights can emerge from graph structure, providing practitioners with new knowledge. [ABSTRACT FROM AUTHOR]

The ability to control the behavior of fluid instabilities at material interfaces, such as the shock-driven Richtmyer–Meshkov instability, is a grand technological challenge with a broad number of applications ranging from inertial confinement fusion experiments to explosively driven shaped charges. In this work, we use a linear-geometry shaped charge as a means of studying methods for controlling material jetting that results from the Richtmyer–Meshkov instability. A shaped charge produces a high-velocity jet by focusing the energy from the detonation of high explosives. The interaction of the resulting detonation wave with a hollowed cavity lined with a thin metal layer produces the unstable jetting effect. By modifying the characteristics of the detonation wave prior to striking the lined cavity, the kinetic energy of the jet can be enhanced or reduced. Modifying the geometry of the liner material can also be used to alter jetting properties. We apply optimization methods to investigate several design parameterizations for both enhancing or suppressing the shaped-charge jet. This is accomplished using 2D and 3D hydrodynamic simulations to investigate the design space that we consider. We also apply new additive manufacturing methods for producing the shaped-charge assemblies, which allow for the experimental testing of complicated design geometries obtained through computational optimization. We present a direct comparison of our optimized designs with experimental results carried out at the High Explosives Application Facility at Lawrence Livermore National Laboratory. [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]

We propose a scheme for achieving basic quantum gates using ultracold polar molecules in pendular states. The qubits are encoded in the YbF molecules trapped in an electric field with a certain gradient and coupled by the dipole–dipole interaction. The time-dependent control sequences consisting of multiple pulses are considered to interact with the pendular qubits. To achieve high-fidelity quantum gates, we map the control problem for the coupled molecular system into a Markov decision process and deal with it using the techniques of deep reinforcement learning (DRL). By training the agents over multiple episodes, the optimal control pulse sequences for the two-qubit gates of NOT, controlled NOT, and Hadamard are discovered with high fidelities. Moreover, the population dynamics of YbF molecules driven by the discovered gate sequences are analyzed in detail. Furthermore, by combining the optimal gate sequences, we successfully simulate the quantum circuit for entanglement. Our findings could offer new insights into efficiently controlling molecular systems for practical molecule-based quantum computing using DRL. [ABSTRACT FROM AUTHOR]

A crucial aspect in the simulation of electrochemical interfaces consists in treating the distribution of electronic charge of electrode materials that are put in contact with an electrolyte solution. Recently, it has been shown how a machine-learning method that specifically targets the electronic charge density, also known as SALTED, can be used to predict the long-range response of metal electrodes in model electrochemical cells. In this work, we provide a full integration of SALTED with MetalWalls, a program for performing classical simulations of electrochemical systems. We do so by deriving a spherical harmonics extension of the Ewald summation method, which allows us to efficiently compute the electric field originated by the predicted electrode charge distribution. We show how to use this method to drive the molecular dynamics of an aqueous electrolyte solution under the quantum electric field of a gold electrode, which is matched to the accuracy of density-functional theory. Notably, we find that the resulting atomic forces present a small error of the order of 1 meV/Å, demonstrating the great effectiveness of adopting an electron-density path in predicting the electrostatics of the system. Upon running the data-driven dynamics over about 3 ns, we observe qualitative differences in the interfacial distribution of the electrolyte with respect to the results of a classical simulation. By greatly accelerating quantum-mechanics/molecular-mechanics approaches applied to electrochemical systems, our method opens the door to nanosecond timescales in the accurate atomistic description of the electrical double layer. [ABSTRACT FROM AUTHOR]

This paper studies a novel robotic warehousing system called the overhead robotic compact storage and retrieval system, which can free up the floor space occupation at a low cost. Bins, as basic storage containers, are stacked on top of each other to form a bin stack. Along overhead tracks, bin-picking robots transport bins between storage/retrieval positions and workstations with the aid of track-changing robots. Little research has been done to study operational policies and performance analysis for this new robotic compact warehousing system. We propose a nested queuing network model that considers two transportation resources and performs reinforcement learning using real data to improve the reshuffling efficiency. We find that reinforcement learning based reshuffling policy greatly reduces the reshuffling distance and saves computation time compared to existing policies. We find that the storage policy of stacks affects the optimal width/length ratio regardless of the system height. Interestingly, we obtain the number of robots that can stabilise the system to avoid an explosion of the order queue; two more robots than that number will produce relatively low throughput times. Compared to an AutoStore system, using our system reduces cost by 30% with a slight increase in throughput time. [ABSTRACT FROM AUTHOR]

We introduce a data-driven potential aimed at the investigation of pressure-dependent phase transitions in bulk germanium, including the estimate of kinetic barriers. This is achieved by suitably building a database including several configurations along minimum energy paths, as computed using the solid-state nudged elastic band method. After training the model based on density functional theory (DFT)-computed energies, forces, and stresses, we provide validation and rigorously test the potential on unexplored paths. The resulting agreement with the DFT calculations is remarkable in a wide range of pressures. The potential is exploited in large-scale isothermal-isobaric simulations, displaying local nucleation in the R8 to β-Sn pressure-induced phase transformation, taken here as an illustrative example. [ABSTRACT FROM AUTHOR]

Machine learned potentials (MLPs) have been widely employed in molecular dynamics simulations to study thermal transport. However, the literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical solids. Here, we quantitatively analyze this underestimation in the context of the neuroevolution potential (NEP), which is a representative MLP that balances efficiency and accuracy. Taking crystalline silicon, gallium arsenide, graphene, and lead telluride as examples, we reveal that the fitting errors in the machine-learned forces against the reference ones are responsible for the underestimated LTC as they constitute external perturbations to the interatomic forces. Since the force errors of a NEP model and the random forces in the Langevin thermostat both follow a Gaussian distribution, we propose an approach to correcting the LTC by intentionally introducing different levels of force noises via the Langevin thermostat and then extrapolating to the limit of zero force error. Excellent agreement with experiments is obtained by using this correction for all the prototypical materials over a wide range of temperatures. Based on spectral analyses, we find that the LTC underestimation mainly arises from increased phonon scatterings in the low-frequency region caused by the random force errors. [ABSTRACT FROM AUTHOR]

Much of the current research on sprint start has attempted to analyze the biomechanical characteristics of elite athletes to provide guidance on the training of sprint technique, with less attention paid to the effects of motor experience gained from long-term training on neuromuscular control characteristics. The present study attempted to investigate the effect of motor experience on the modular organization of the neuromuscular system during starting, based on he clarification of the characteristics of muscle synergies during starting. It was found that exercise experience did not promote an increase in the number of synergies but rather a more focused timing of the activation of each synergy, allowing athletes to quickly complete the postural transition from crouching to running during the starting. [ABSTRACT FROM AUTHOR]

Digital Supply Chain Surveillance (DSCS) is the proactive monitoring and analysis of digital data that allows firms to extract information related to a supply network, without the explicit consent of firms involved in the supply chain. AI has made DSCS to become easier and larger-scale, posing significant opportunities for automated detection of actors and dependencies involved in a supply chain, which in turn, can help firms to detect risky, unethical and environmentally unsustainable practices. Here, we define DSCS, review priority areas using a survey conducted in the UK. Visibility, sustainability, resilience are significant areas that DSCS can support, through a number of machine-learning approaches and predictive algorithms. Despite anecdotal narrative on the importance of explainability of algorithmic results, practitioners often prefer accuracy over explainability; however, there are significant differences between industrial sectors and application areas. Using a case study, we highlight a number of concerns on the unchecked use of AI in DSCS, such as bias or misinterpretation resulting in erroneous conclusions, which may lead to suboptimal decisions or relationship damage. Building on this, we develop and discuss a number of illustrative cases to highlight risks that practitioners should be aware of, proposing key areas of further research. [ABSTRACT FROM AUTHOR]

The surfaces of many minerals are covered by naturally occurring cations that become partially hydrated and can be replaced by hydronium or other cations when the surface is exposed to water or an aqueous solution. These ion exchange processes are relevant to various chemical and transport phenomena, yet elucidating their microscopic details is challenging for both experiments and simulations. In this work, we make a first step in this direction by investigating the behavior of the native K+ ions at the interface between neat water and the muscovite mica (001) surface with ab-initio-based machine learning molecular dynamics and enhanced sampling simulations. Our results show that the desorption of the surface K+ ions in pure ion-free water has a significant free energy barrier irrespective of their local surface arrangement. In contrast, facile K+ diffusion between mica's ditrigonal cavities characterized by different Al/Si orderings is observed. This behavior suggests that the K+ ions may favor a dynamic disordered surface arrangement rather than complete desorption when exposed to deionized water. [ABSTRACT FROM AUTHOR]

The advent of Neural-network Quantum States (NQS) has significantly advanced wave function ansatz research, sparking a resurgence in orbital space variational Monte Carlo (VMC) exploration. This work introduces three algorithmic enhancements to reduce computational demands of VMC optimization using NQS: an adaptive learning rate algorithm, constrained optimization, and block optimization. We evaluate the refined algorithm on complex multireference bond stretches of H2O and N2 within the cc-pVDZ basis set and calculate the ground-state energy of the strongly correlated chromium dimer (Cr2) in the Ahlrichs SV basis set. Our results achieve superior accuracy compared to coupled cluster theory at a relatively modest CPU cost. This work demonstrates how to enhance optimization efficiency and robustness using these strategies, opening a new path to optimize large-scale restricted Boltzmann machine-based NQS more effectively and marking a substantial advancement in NQS's practical quantum chemistry applications. [ABSTRACT FROM AUTHOR]

Project managers need reliable predictive analytics tools to make effective project intervention decisions throughout the project life cycle. This study uses Machine learning (ML) to enhance the reliability in project cost forecasting. A XGBoost forecasting model is developed and computational experiments are conducted using real data of 110 projects representing 1268 cost data points. The developed model performs better than some Earned value management (EVM), ML (Random forest, Support vector regression, LightGBM, and CatBoost), and non-linear growth (Gompertz and Logistic) models. The model produces more accurate estimates at the early, middle, and late stages of the project execution, allowing for early warning signals for more effective cost control. In addition, it shows more accurate estimates in most projects tested, suggesting consistency when repeatedly used in practice. Project forecasting studies mainly used ML to estimate the project duration; a few ML studies estimated the project cost at the project's conceptual stage. This study uses real data and EVM metrics, proposing an effective XGBoost model for forecasting the cost throughout the project life cycle. [ABSTRACT FROM AUTHOR]

Machine learning is a promising method to model production processes and predict product quality. It is challenging to accurately model complex systems due to data scarcity, as mass customisation leads to various high-variety low-volume products. This study conceptualised knowledge accumulation, extraction, and transfer (KAET) to exploit the knowledge embedded in similar entities to address data scarcity. A sequential cross-product KAET (SeqTrans) is proposed to conduct KAET, integrating data preparation and preprocessing, feature selection (FS), feature learning (FL), and transfer learning (TL). The FS and FL modules conduct knowledge extraction and help address various practical challenges such as changing operating conditions and unbalanced datasets. In this paper, sequential TL is introduced to production modelling to conduct knowledge transfer among multiple entities. The first case study of auxetic material performance prediction demonstrates the effectiveness of sequential TL. Compared with conventional TL, sequential TL can achieve the same test mean square errors with 300 fewer training examples when facing data scarcity. In the second case study, balancing anomaly detection models were constructed for two gas turbines in the same series using real-world production data. With SeqTrans, the F1-score of the anomaly detection model of the data-poor engine was improved from 0.769 to 0.909. [ABSTRACT FROM AUTHOR]

We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform "system calls" and enable the incorporation of long-range Ewald electrostatics into the external software's self-consistent field procedure. The infrastructure provides access to QM/MM models that may serve as the foundation for QM/MM–ΔMLP potentials, which supplement the semiempirical QM/MM model with a MLP correction trained to reproduce ab initio QM/MM energies and forces. Efficient optimization of minimum free energy pathways is enabled through a new surface-accelerated finite-temperature string method implemented in the FE-ToolKit package. Furthermore, we interfaced Sander with the i-PI software by implementing the socket communication protocol used in the i-PI client–server model. The new interface with i-PI allows for the treatment of nuclear quantum effects with semiempirical QM/MM–ΔMLP models. The modular interoperable software is demonstrated on proton transfer reactions in guanine-thymine mispairs in a B-form deoxyribonucleic acid helix. The current work represents a considerable advance in the development of modular software for performing free energy simulations of chemical reactions that are important in a wide range of applications. [ABSTRACT FROM AUTHOR]

The application of first-principles calculations for predicting lattice thermal conductivity (LTC) in crystalline materials, in conjunction with the linearized phonon Boltzmann equation, has gained increasing popularity. In this calculation, the determination of force constants through first-principles calculations is critical for accurate LTC predictions. For material exploration, performing first-principles LTC calculations in a high-throughput manner is now expected, although it requires significant computational resources. To reduce computational demands, we integrated polynomial machine learning potentials on-the-fly during the first-principles LTC calculations. This paper presents a systematic approach to first-principles LTC calculations. We designed and optimized an efficient workflow that integrates multiple modular software packages. We applied this approach to calculate LTCs for 103 compounds of wurtzite, zinc blende, and rocksalt types to evaluate the performance of the polynomial machine learning potentials in LTC calculations. We demonstrate a significant reduction in the computational resources required for the LTC predictions. [ABSTRACT FROM AUTHOR]

The train stowage planning problem (TSPP) of the steel industry aims to select steel coils and allocate them to trains cost-effectively. It is a key component in the transportation of steel products. This study focuses on a multiobjective train stowage planning problem (MoTSPP) that maximises both the loading efficiency of the crane and the loading rate of the train. The MoTSPP also considers operation constraints related to steel coils, train wagons, and stowage modes in real-life railway transportation. An integer programming model is established to mathematically describe this problem. To obtain an efficient solution, a multiobjective analytical evolutionary algorithm (MAEA) that combines evolutionary algorithm (EA) with machine learning (ML) is presented. The EA part is a multiobjective differential evolution that introduces guided evolution and parameter adaptation to produce promising individuals and parameters, respectively. ML part adopts clustering algorithm and surrogate model to accelerate the search. Extensive comparisons and insight analyses are conducted from various perspectives to demonstrate the effectiveness and efficiency of the MAEA for solving the MoTSPP. [ABSTRACT FROM AUTHOR]

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As mobile devices have become a necessity in our daily lives, mobile advertising is also prevalent. Accordingly, it is critical for practitioners to understand how consumers visually attend to mobile advertisements. One popular way of doing so is via eye-tracking methodology. However, scant eye-tracking research exists in mobile settings due to technical challenges, e.g., cumbersome data annotation. To tackle these challenges, the authors propose an object-detection machine learning (ML) algorithm—You Only Look Once (YOLO) v3—to analyze eye-tracking videos automatically. Moreover, we extend the original YOLO v3 model by developing a novel algorithm to optimize the analysis of eye-tracking data collected from mobile devices. Through a lab experiment, we investigate how two types of ad elements (i.e., textual vs. pictorial) and shopping devices (i.e., mobile vs. PC) affect consumers' visual attention. Our findings suggest that (1) textual ad elements receive more attention than pictorial ones, and such differences are more pronounced in ads on mobile devices than those on PCs; and (2) mobile ads receive less attention than PC ads. Our findings provide managerial insights into developing effective digital advertising strategies to improve consumers' visual attention in online and mobile advertisements. [ABSTRACT FROM AUTHOR]

In this contribution, we propose machine learning techniques to predict zombie firms. First, we derive the risk of failure by training and testing our algorithms on disclosed financial information and nonrandom missing values of 304,906 firms active in Italy from 2008 to 2017. We then identify the highest financial distress conditional on predictions that lie above a threshold for which a combination of the false positive rate (false prediction of firm failure) and the false negative rate (false prediction of active firms) is minimized. Therefore, we identify zombies as firms that remain in financial distress, i.e. whose forecasts fall into the risk category above the threshold for at least three consecutive years. To this end, we implement a gradient boosting algorithm (XGBoost) that exploits information about missing values. The inclusion of missing values in our prediction model is crucial because patterns of undisclosed accounts are correlated with firm failure. Finally, we show that our preferred machine learning algorithm outperforms (i) proxy models such as Z -scores and the distance-to-default, (ii) traditional econometric methods, and (iii) other widely used machine learning techniques. We provide evidence that zombies are less productive and smaller on average and that they tend to increase in times of crisis. Finally, we argue that our application can help financial institutions and public authorities design evidence-based policies—e.g. optimal bankruptcy laws and information disclosure policies. [ABSTRACT FROM AUTHOR]

Purpose: To explore how the change-point method can be used to analyze complex longitudinal data and detect when meaningful changes (change points) have occurred during rehabilitation. Method: This design is a prospective single-case observational study of a football player in a professional club who sustained an acute lower-limb muscle injury during high-speed running in training. The rehabilitation program was entirely completed in the football club under the supervision of the club's medical team. Four wellness metrics and 5 running-performance metrics were collected before the injury and until the player returned to play. Results: Data were collected over 130 days. In the univariate analysis, the change points for stress, sleep, mood, and soreness were located on days 30, 47, 50, and 50, respectively. The change points for total distance, acceleration, maximum speed, deceleration, and high-speed running were located on days 32, 34, 37, 41, and 41, respectively. The multivariate analysis resulted in a single change point for the wellness metrics and running-performance metrics, on days 50 and 67, respectively. Conclusions: The univariate approach provided information regarding the sequence and time point of the change points. The multivariate approach provided a common change point for multiple metrics, information that would benefit clinicians to have a broad overview of the changes in the rehabilitation process. Clinicians may consider the change-point method to integrate and visualize data from multiple sources to evaluate athletes' progression along the return-to-sport continuum. [ABSTRACT FROM AUTHOR]

Purpose: The relationship between external load and creatine-kinase (CK) response at the team/position or individual level using Global Positioning Systems (GPS) has been studied. This study aimed to compare GPS-derived and Football Movement Profile (FMP) –derived CK-prediction models for national-team soccer players. The second aim was to compare the performance of general and individualized CK prediction models. Methods: Four hundred forty-four national-team soccer players (under 15 [U15] to senior) were monitored during training sessions and matches using GPS. CK was measured every morning from whole blood. The players had 19.3 (18.1) individual GPS-CK pairs, resulting in a total of 8570 data points. Machine learning models were built using (1) GPS-derived or (2) FMP-based parameters or (3) the combination of the 2 to predict the following days' CK value. The performance of general and individual-specific prediction models was compared. The performance of the models was described by R2 and the root-mean-square error (RMSE, in units per liter for CK values). Results: The FMP model (R2 =.60, RMSE = 144.6 U/L) performed similarly to the GPS-based model (R2 =.62, RMSE = 141.2 U/L) and the combination of the 2 (R2 =.62, RMSE = 140.3 U/L). The prediction power of the general model was better on average (R2 =.57 vs R2 =.37) and for 73% of the players than the individualized model. Conclusions: The results suggest that FMP-based CK-prediction models perform similarly to those based on GPS-derived metrics. General machine learning models' prediction power was higher than those of the individual-specific models. These findings can be used to monitor postmatch recovery strategies and to optimize weekly training periodization. [ABSTRACT FROM AUTHOR]

To maintain high-quality semiconductor wafer production processes, it is necessary to build high-quality virtual metrology (VM) model. Based on the result of the VM, the engineer can monitor and control the specific process. In the manufacturing process, various sources are obtained from sensor equipment. It is important to consider that these source data exhibit varying characteristics during the model construction. However, when all data sources are simply aggregated into a single model, the performance of the overall model may degrade, especially if any of the individual sources contain outliers or noise. To address this issue, we develop a data-fusion model designed to incorporate diverse data sources into a unified multi-source model. In particular, we improve an Adaboost regression algorithm to make it suitable for multi-source data in the field of VM. The algorithm combines the residuals of the models derived from each individual data source and all sources to adaptively adjust the thresholding value, which, in turn, determines whether the predicted values are accurate or not for each weak learner. Extensive practical validations on real-world processing data from semiconductor manufacturers have demonstrated that the proposed method outperforms single learning algorithms and is more robust than all benchmarks. [ABSTRACT FROM AUTHOR]

This study conducts a comprehensive analysis of deep reinforcement learning (DRL) algorithms applied to supply chain inventory management (SCIM), which consists of a sequential decision-making problem focussed on determining the optimal quantity of products to produce and ship across multiple capacitated local warehouses over a specific time horizon. In detail, we formulate the problem as a Markov decision process for a divergent two-echelon inventory control system characterised by stochastic and seasonal demand, also presenting a balanced allocation rule designed to prevent backorders in the first echelon. Through numerical experiments, we evaluate the performance of state-of-the-art DRL algorithms and static inventory policies in terms of both cost minimisation and training time while varying the number of local warehouses and product types and the length of replenishment lead times. Our results reveal that the Proximal Policy Optimization algorithm consistently outperforms other algorithms across all experiments, proving to be a robust method for tackling the SCIM problem. Furthermore, the (s, Q)-policy stands as a solid alternative, offering a compromise in performance and computational efficiency. Lastly, this study presents an open-source software library that provides a customisable simulation environment for addressing the SCIM problem, utilising a wide range of DRL algorithms and static inventory policies. [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 inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. An effective response to humanitarian emergencies is often associated with excess inventories, which leads to high costs. Henceforth, using accurate demand forecasts to control the inventories of these medical items is shown to be a significant lever to reduce inventory costs while keeping high service levels. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with items characterised with intermittent demand patterns. Moreover, we propose a new hybrid deep learning method that combines the long short-term memory neural network and the support vector regression. Such combination enables to deal with complex and non-linear data. We conduct an empirical investigation by means of data related to 523 medical items managed in two warehouses of a major humanitarian organisation. The forecast accuracy and the inventory efficiency of the methods are analysed. The empirical results show the high performance of the deep learning methods. These insights are valuable not only in the context of medical items in humanitarian operations, but also for any items with intermittent demand patterns. [ABSTRACT FROM AUTHOR]

Amorphous molybdenum disulfide has shown potential as a hydrogen evolution catalyst, but the origin of its high activity is unclear, as is its atomic structure. Here, we have developed a classical inter-atomic potential using the charge equilibration neural network method, and we have employed it to generate atomic models of amorphous MoS2 by melting and quenching processes. The amorphous phase contains an abundance of molybdenum and sulfur atoms in low coordination. Besides the 6-coordinated molybdenum typical of the crystalline phases, a substantial fraction displays coordinations 4 and 5. The amorphous phase is also characterized by the appearance of direct S–S bonds. Density functional theory shows that the amorphous phase is metallic, with a considerable contribution of the 4-coordinated molybdenum to the density of states at the Fermi level. S–S bonds are related to the reduction of sulfur, with the excess electrons spread over several molybdenum atoms. Moreover, S–S bond formation is associated with a distinctive broadening of the 3s states, which could be exploited for experimental characterization of the amorphous phases. The large variety of local environments and the high density of electronic states at the Fermi level may play a positive role in increasing the electrocatalytic activity of this compound. [ABSTRACT FROM AUTHOR]

Electronic devices get smaller and smaller in every generation. In micro-/nano-electronic devices such as high electron mobility transistors, heat dissipation has become a crucial design consideration due to the ultrahigh heat flux that has a negative effect on devices' performance and their lifetime. Therefore, thermal transport performance enhancement is required to adapt to the device size reduction. β-Ga2O3 has recently gained significant scientific interest for future power devices because of its inherent material properties such as extremely wide bandgap, outstanding Baliga's figure of merit, large critical electric field, etc. This work aims to use a machine learning approach to search promising substrates or heat sinks for cooling β-Ga2O3, in terms of high interfacial thermal conductance (ITC), from large-scale potential structures taken from existing material databases. With the ITC dataset of 1633 various substrates for β-Ga2O3 calculated by full density functional theory, we trained our recently developed convolutional neural network (CNN) model that utilizes the fused orbital field matrix (OFM) and composition descriptors. Our model proved to be superior in performance to traditional machine learning algorithms such as random forest and gradient boosting. We then deployed the CNN model to predict the ITC of 32 716 structures in contact with β-Ga2O3. The CNN model predicted the top 20 cubic and noncubic substrates with ITC on the same level as density functional theory (DFT) results on β-Ga2O3/YN and β-Ga2O3/MgO interfaces, which has the highest ITC of 1224 and 1211 MW/m2K, respectively, among the DFT-ITC datasets. Phonon density of states, group velocity, and scattering effect on high heat flux transport and consequently increased ITC are also investigated. Moderate to high phonon density of states overlap, high group velocity, and low phonon scattering are required to achieve high ITC. We also found three Magpie descriptors with strong Pearson correlation with ITC, namely, mean atomic number, mean atomic weight, and mean ground state volume per atom. Calculations of such descriptors are computationally efficient, and therefore, these descriptors provide a new route for quickly screening potential substrates from large-scale material pools for high-performance interfacial thermal management of high-electron mobility transistor devices. [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]

Over 2300 years ago, the discovery of tourmaline led to the understanding of pyroelectric properties, which opened new doors to various applications of pyroelectric crystal, such as neutron and x-ray generation, energy harvesting, mass spectrometry, high-voltage sources, and more. In the last two decades, researchers have carried out extensive research and development to select components and materials and innovate the design and construction of the pyroelectric neutron generator (PNG). This manuscript investigates the process and history of the PNG's development. It explains the physics governing pyroelectric crystals and the method of producing neutrons in a comprehensive and straightforward manner. Although PNGs have a lower yield and shorter lifetime compared to other neutron generators, they are still significant for research purposes due to their lack of need for an external high-voltage power supply, lower cost, smaller size, and safety. The main objective of this manuscript is to bring more attention to the research and development of PNGs. In recent years, new methods have been introduced that reduce the amount of neutron flux required for various applications. This has raised hope for the progress of commercial and industrial use of PNGs in the near future. The manuscript mentions some research cases that represent the future perspective of PNG development. Furthermore, the challenges faced by PNGs can be handled more efficiently with the utilization of generative learning algorithms and improvements in the components/mechanisms used for PNG design. [ABSTRACT FROM AUTHOR]

In recent years, much attention has been devoted to understanding the pathways of phase transition between two equilibrium condensed phases (such as liquids and solids). However, the microscopic pathways of transition involving non-equilibrium, non-diffusive amorphous (glassy) phases still remain poorly understood. In this work, we have employed computer simulations, persistence homology (a tool rooted in topological data analysis), and machine learning to probe the microscopic pathway of pressure-induced non-equilibrium transition between the low- and high-density amorphous (LDA and HDA, respectively) ice phases of the TIP4P/2005 and ST2 water models. Using persistence homology and machine learning, we introduced a new order parameter that unambiguously identifies the LDA- and HDA-like local environments. The LDA phase transitions continuously and collectively into the corresponding HDA phase via a pre-ordered intermediate phase during the isothermal compression. The local order parameter susceptibilities show a maximum near the transition pressure (P*)—suggesting maximum structural heterogeneities near P*. The HDA-like clusters are structurally ramified and spatially delocalized inside the LDA phase near the transition pressure. We also found manifestations of the first-order low-density to high-density liquid transition in the sharpness of the order parameter change during the LDA to HDA transition. We further investigated the (geometrical) structures and topologies of the LDA and HDA ices formed via different protocols and also studied the dependence of the (microscopic) pathway of phase transition on the protocol followed to prepare the initial LDA phase. Finally, the method adopted here to study the phase transition pathways is not restricted to the system under consideration and provides a robust way of probing phase transition pathways involving any two condensed phases at both equilibrium and out-of-equilibrium conditions. [ABSTRACT FROM AUTHOR]

Thermal transport plays a pivotal role across diverse disciplines, yet the intricate relationship between amorphous network structures and thermal conductance properties remains elusive due to the absence of a reliable and comprehensive network's dataset to be investigated. In this study, we have created a dataset comprising multiple amorphous network structures of varying sizes, generated through a combination of the node disturbance method and Delaunay triangulation, to fine-tune an initially random network toward both increased and decreased thermal conductance C. The tuning process is guided by the simulated annealing algorithm. Our findings unveil that C is inversely dependent on the normalized average shortest distance L n o r m connecting heat source nodes and sink nodes, which is determined by the network topological structure. Intuitively, the amorphous network with increased C is associated with an increased number of bonds oriented along the thermal transport direction, which shortens the heat transfer distance from the source to sink node. Conversely, thermal transport encounters impedance with an augmented number of bonds oriented perpendicular to the thermal transport direction, which is demonstrated by the increased L n o r m . This relationship can be described by a power law C = L n o r m α , applicable to the diverse-sized amorphous networks we have investigated. [ABSTRACT FROM AUTHOR]

Switching Spectroscopy Piezoresponse Force Microscopy (SSPFM) stands out as a powerful method for probing ferroelectric properties within materials subjected to incremental polarization induced by an external electric field. However, the dense data processing linked to this technique is a critical factor influencing the quality of obtained results. Furthermore, meticulous exploration of various artifacts, such as electrostatics, which may considerably influence the signal, is a key factor in obtaining quantitative results. In this paper, we present a global methodology for SSPFM data processing, accessible in open-source with a user-friendly Python application called PySSPFM. A ferroelectric thin film sample of potassium sodium niobate has been probed to illustrate the different aspects of our methodology. Our approach enables the reconstruction of hysteresis nano-loops by determining the PR as a function of applied electric field. These hysteresis loops are then fitted to extract characteristic parameters that serve as measures of the ferroelectric properties of the sample. Various artifact decorrelation methods are employed to enhance measurement accuracy, and additional material properties can be assessed. Performing this procedure on a grid of points across the surface of the sample enables the creation of spatial maps. Furthermore, different techniques have been proposed to facilitate post-treatment analysis, incorporating algorithms for machine learning (K-means), phase separation, and mapping cross correlation, among others. Additionally, PySSPFM enables a more in-depth investigation of the material by studying the nanomechanical properties during poling, through the measurement of the resonance properties of the cantilever–tip–sample surface system. [ABSTRACT FROM AUTHOR]

We present new results on the underlying guest–host interactions and spectral characterization of a CO2 molecule confined in the cages of the sI clathrate hydrate. Such types of porous solids raise computational challenges, as they are of practical interest as gas storage/capture materials. Accordingly, we have directed our efforts toward addressing their modeling in a proper manner, ensuring the quality of the input data and the efficiency of the computational approaches. The computational procedure for spectral simulations, within the multi-configurational time-dependent Hartree framework, involves the development of a fully coupled Hamiltonian, including an exact kinetic energy operator and a many-body representation of the potential, along with dipole moment surfaces, both obtained through neural network machine learning techniques. The resulting models were automatically trained and tested on extensive datasets generated by PW86PBE-XDM calculations, following the outcome of previous benchmark studies. Our simulations enable us to explore various aspects of the quantized dynamics upon confinement of CO2@D/T, such as constrained rotational–translational quantum motions and the averaged position/orientation of the CO2 guest in comparison to the experimental data available. Particularly notable are the distinct energy patterns observed in the computed spectra for the confined CO2 in the D and T cages, with a considerably high rotational–translational coupling in the CO2@T case. Leveraging reliable computations has proved instrumental, highlighting the sensitivity of the spectral features to the shape and strength of the potential interactions, with the explicit description of many-body contributions being significant. [ABSTRACT FROM AUTHOR]

In this study, we introduce SAPT10K, a comprehensive dataset comprising 9982 noncovalent interaction energies and their binding energy components (electrostatics, exchange, induction, and dispersion) for diverse intermolecular complexes of 944 unique dimers. These complexes cover significant portions of the intermolecular potential energy surface and were computed using higher-order symmetry-adapted perturbation theory, SAPT2+(3)(CCD), with a large aug-cc-pVTZ basis set. The dispersion energy values in SAPT10K serve as crucial inputs for refining the ab initio dispersion potentials based on Grimme's D3 and many-body dispersion (MBD) models. Additionally, Δ machine learning (ML) models based on newly developed intermolecular features, which are derived from intermolecular histograms of distances for element/substructure pairs to simultaneously account for local environments as well as long-range correlations, are also developed to address deficiencies of the D3/MBD models, including the inflexibility of their functional forms, the absence of MBD contributions in D3, and the standard Hirshfeld partitioning scheme used in MBD. The developed dispersion models can be applied to complexes involving a wide range of elements and charged monomers, surpassing other popular ML models, which are limited to systems with only neutral monomers and specific elements. The efficient D3-ML model, with Cartesian coordinates as the sole input, demonstrates promising results on a testing set comprising 6714 dimers, outperforming another popular ML model, component-based machine-learned intermolecular force field (CLIFF), by 1.5 times. These refined D3/MBD-ML models have the capability to replace the time-consuming dispersion components in symmetry-adapted perturbation theory-based calculations and can promptly illustrate the dispersion contribution in noncovalent complexes for supramolecular assembly and chemical reactions. [ABSTRACT FROM AUTHOR]

In this work, we utilize our recently developed machine learning (ML)-corrected ab initio dispersion (aiD) potential, known as D3-ML, which is based on the comprehensive SAPT10K dataset and relies solely on Cartesian coordinates as input, to address the dispersion deficiencies in second-order Møller−Plesset perturbation theory (MP2) by replacing its problematic dispersion and exchange-dispersion terms with D3-ML. This leads to the development of a new dispersion-corrected MP2 method, MP2+aiD(CCD), which outperforms other spin-component-scaled and dispersion-corrected MP2 methods as well as popular ML models for predicting noncovalent interactions across various datasets, including S66 × 8, NAP6 (containing 6 naphthalene dimers), L7, S12L, DNA−ellipticine, the C60 dimer, and C60[6]CPPA. In addition, MP2+aiD(CCD) exhibits comparable or even superior performance compared to the contemporary ωB97M-V functional. The limited performance of pure ML models for systems outside the training set or larger than those in the training set highlights their instability and unpredictability. Conversely, the outstanding performance and transferability of the hybrid MP2+aiD(CCD) method can be attributed to the fusion of the physical electronic structure method and a data-driven ML model, combining the strengths of both sides. This investigation firmly establishes MP2+aiD(CCD) as one of the most accurate and reliable fifth-order scaling correlated wave function methods currently available for modeling noncovalent interactions, even for large complexes. MP2+aiD(CCD) is expected to be reliably applicable in investigating real-life complexes at the hundred-atom scale. [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]

Supply chains operate in a highly distuptive environment where a SC master plan should be updated in line with disruptions to ensure that a high service level is provided to customers while total cost is minimised. There is an absence of knowledge of how a SC master plan should be updated to cope with disruptions using hybrid modelling. To fill this gap, we present a hybrid modelling framework to update a SC master plan in presence of disruptions. The proposed framework, which is a precursor to a SC digital twin, integrates simulation, machine learning, and optimisation to identify the production, storage, and distribution values that maximise SC service level while minimising total cost under disruptions. This approach proves effective in a SC disrupted by demand increase and lead time extension. Results show that employing hybrid modelling leads to a noticeable improvement in service level and total cost. The outcome of the new knowledge on using hybrid modelling for managing disruptions provides essential learning for the extension of modelling through a digital twin for SC master planning. We observe that in the presence of disruptions it is more economical to keep higher inventory at downstream SC members than the upstream SC members. [ABSTRACT FROM AUTHOR]

The state-to-state (STS) inelastic energy transfer and O-atom exchange reaction between O and CO(v), as two fundamental processes in non-equilibrium air flow around spacecraft entering Mars' atmosphere, yield the same products and both make significant contributions to the O + CO(v) → O + CO(v′) collisions. The inelastic energy transfer competes with the O-atom exchange reaction. The detailed reaction mechanisms of these two elementary processes and their specific contributions to the CO relaxation process are still unclear. To address these concerns, we performed systematic investigations on the 3A′ and 3A″ potential energy surfaces (PESs) of CO2 using quasi-classical trajectory (QCT) calculations. Analysis of the collision mechanisms reveals that inelastic collisions have an apparent PES preference (i.e., they tend to occur on the 3A′ PES), while reactive collisions do not. Reactive rates decrease significantly when the total collision energy approaches dissociation energy, which differs from the inelastic process. Inelastic rates are generally lower than the reactive rates below ∼10 000 K, except for single quantum jumps, whereas the reverse is observed above ∼10 000 K. In addition, by combining QCT with convolutional neural networks, we have established neural network (NN)-STS1 (inelastic) and NN-STS2 (reactive) models to generate all possible STS cross sections. The NN-based models accurately reproduce the results calculated from QCT calculations. In this study, all calculations have been focused on analyzing collisions at the ground rotational level. [ABSTRACT FROM AUTHOR]

As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing us to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective, we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid–liquid interfaces. [ABSTRACT FROM AUTHOR]

Obtaining accurate lattice thermal conductivity data of LiF under extreme conditions not only provides important reference for performance evaluation, prediction, and control of materials, but also helps to alleviate the significant challenges of precise experimental measurements. The high-temperature phonon properties and lattice thermal conductivity (LTC) of solid LiF were calculated by combining on-the-fly machine learning force fields (MLFFs) with the Green–Kubo method. The introduction of MLFF successfully combines the accuracy of ab initio molecular dynamics with the scalability advantage of classical molecular dynamics. At high temperatures, there is a significant enhancement in the vibrational coupling between the acoustic and optical branches of LiF, as well as resonant effects between Li and F atoms, resulting in strong anharmonicity. Additionally, the main peak of the phonon density of states shows a noticeable redshift compared to the harmonic case. The enhanced coupling of TO and TA modes at high temperature leads to a significant increase in phonon scattering rate. By considering higher-order phonon anharmonicity, the predicted LTC is significantly reduced compared to the results obtained from considering only three-phonon interactions. Along the Hugoniot curve up to 100 GPa (2150 K), the predicted LTC agrees well with the experimental values. These findings demonstrate the crucial role of phonon anharmonicity in promoting phonon scattering. [ABSTRACT FROM AUTHOR]

The discovery of advanced thermal materials with exceptional phonon properties drives technological advancements, impacting innovations from electronics to superconductors. Understanding the intricate relationship between composition, structure, and phonon thermal transport properties is crucial for speeding up such discovery. Exploring innovative materials involves navigating vast design spaces and considering chemical and structural factors on multiple scales and modalities. Artificial intelligence (AI) is transforming science and engineering and poised to transform discovery and innovation. This era offers a unique opportunity to establish a new paradigm for the discovery of advanced materials by leveraging databases, simulations, and accumulated knowledge, venturing into experimental frontiers, and incorporating cutting-edge AI technologies. In this perspective, first, the general approach of density functional theory (DFT) coupled with phonon Boltzmann transport equation (BTE) for predicting comprehensive phonon properties will be reviewed. Then, to circumvent the extremely computationally demanding DFT + BTE approach, some early studies and progress of deploying AI/machine learning (ML) models to phonon thermal transport in the context of structure–phonon property relationship prediction will be presented, and their limitations will also be discussed. Finally, a summary of current challenges and an outlook of future trends will be given. Further development of incorporating AI/ML algorithms for phonon thermal transport could range from phonon database construction to universal machine learning potential training, to inverse design of materials with target phonon properties and to extend ML models beyond traditional phonons. [ABSTRACT FROM AUTHOR]

Purpose: The study had 3 purposes: (1) to develop an index using machine-learning techniques to predict the fitness status of soccer players, (2) to explore the index's validity and its relationship with a submaximal run test (SMFT), and (3) to analyze the impact of weekly training load on the index and SMFT outcomes. Methods: The study involved 50 players from an Italian professional soccer club. External and internal loads were collected during training sessions. Various machine-learning algorithms were assessed for their ability to predict heart-rate responses during the training drills based on external load data. The fitness index, calculated as the difference between actual and predicted heart rates, was correlated with SMFT outcomes. Results: Random forest regression (mean absolute error = 3.8 [0.05]) outperformed the other machine-learning algorithms (extreme gradient boosting and linear regression). Average speed, minutes from the start of the training session, and the work:rest ratio were identified as the most important features. The fitness index displayed a very large correlation (r =.70) with SMFT outcomes, with the highest result observed during possession games and physical conditioning exercises. The study revealed that heart-rate responses from SMFT and the fitness index could diverge throughout the season, suggesting different aspects of fitness. Conclusions: This study introduces an "invisible monitoring" approach to assess soccer player fitness in the training environment. The developed fitness index, in conjunction with traditional fitness tests, provides a comprehensive understanding of player readiness. This research paves the way for practical applications in soccer, enabling personalized training adjustments and injury prevention. [ABSTRACT FROM AUTHOR]

The competition among Food & Beverage companies has substantially increased in today's age of digitization. Sales forecasting is one of their main challenges. Due to space limitations, employee shortages, and rising online demand, retail sales forecasting became extremely important for Food and Beverage companies. This research analyzed the sales data of a multinational Food & Beverage Company. It proposed a framework using Gaussian Mixture Model (GMM) clustering, Hierarchical Agglomerative Clustering (HAC), and Random Forest algorithm for forecasting sales. This model analyzes the impact of the weekends, holidays, promotional activities, customer sentiments, festivals, and socio-economic situations in sales data and is able to forecast sales ranging from one to 15 months. An investigation of the suggested model's performance compared to numerous cutting-edge sales forecasting techniques is carried out to show its efficacy. Here, we demonstrate that the proposed hybrid model surpasses current predicting and computing efficiency methods. The results of this study can help retail managers to allocate resources and manage inventories in well-informed ways. The findings suggest that combining many strategies may produce the most precise forecasts. [ABSTRACT FROM AUTHOR]

Large-scale manufacturers aim to reduce the number of faulty products by finding tools or process factors that cause product faults through tool monitoring. The causes of faulty electronic components such as semiconductor chips and printed circuit boards (PCBs) include abnormalities in single tools and abnormalities caused by interactions between the tools of a specific process and a related process. Here, the tools exhibiting an interaction effect are called the fault-introducing tool group. This study presents a numerical association rule mining method for discovering the fault-introducing tool groups based on a genetic algorithm. A novel fitness function and rule pruning process are developed to identify the fault-introducing tool groups. The effectiveness of the method is verified using simulations and a case study of actual PCB production lines. The proposed method can discover fault-introducing tool groups better than machine learning algorithms. Additionally, the method can accurately identify fault-introducing tool groups in various manufacturing environments, such as those with highly skewed yield distributions or variations in yield distributions over time. In an actual PCB production line, the groups identified by the proposed method produced up to 36.5% more faulty chips than those identified by the comparison models. [ABSTRACT FROM AUTHOR]