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Background: Triazolam is a typical drug commonly used in the elderly; however, there have been concerns about its adverse events resulting from age-related changes in physiological function and drug interactions with concomitant drugs. Thus, updated information contributing to the appropriate use based on the latest pharmacokinetic and post-marketing surveillance methods is needed. In this study, we evaluated the appropriate use of triazolam in the elderly by integrating real-world data with a modeling and simulation approach. Methods: The occurrence risk of adverse events in the elderly was evaluated using the spontaneous adverse event reporting regulatory databases from Japan and the United States. Information on drug concentrations and reactions was extracted from previous publications to estimate the threshold for plasma triazolam concentrations that cause adverse events. The pharmacokinetic/pharmacodynamic (PK/PD) model was then constructed, and the dose and administration were evaluated in various situations anticipated in medical practice. Results: Among all prescriptions, 25.4% were prescribed to individuals aged 80 years or above, and 51.8% were for those aged 70 years or above. A majority of cases involved CYP3A-metabolized drug combinations, accounting for 85.6%. Elderly individuals were at a higher risk of developing delirium and fall-fracture. Based on the constructed PK/PD model, the risk of adverse events increased when the plasma concentration of triazolam exceeded the calculated threshold of 0.44 ng/mL at approximately 6 h after administration. Administering 0.125 mg of triazolam, is half the approved dose for the elderly in Japan was deemed appropriate. Moreover, there was a substantial risk of adverse events even at a dosage of 0.0625 mg in combination with a moderate or strong inhibitor of cytochrome P450 3 A. Conclusion: Analyzing large-scale databases and existing research publications on PK/PD can practically contribute to optimizing triazolam drug therapy for the elderly in the daily clinical setting. [ABSTRACT FROM AUTHOR]

Obicetrapib is a selective inhibitor of cholesteryl ester transfer protein that is currently in phase 3 of development for the treatment of dyslipidemia as adjunct therapy. The purpose of this study was to comprehensively characterize the pharmacokinetic (PK) and pharmacodynamic (PD) disposition of obicetrapib. Data from 7 clinical trials conducted in healthy adults and those with varying degrees of dyslipidemia were included for model development. The structural model that best described obicetrapib PK was a 3‐compartment model with 4‐compartment transit absorption and first‐order elimination. Body weight was the only covariate found to significantly explain observed variability and was therefore included using allometric scaling on all disposition parameters. For a typical patient weighing 75 kg, the estimated apparent total body clearance and apparent volume of distribution of the central compartment was 0.81 L/h and 36.1 L, respectively. The final PK model parameters were estimated with good precision and were ultimately leveraged to sequentially inform 2 turnover models that describe obicetrapib's effect on low‐density lipoprotein cholesterol (LDL‐C) and high‐density lipoprotein cholesterol (HDL‐C) concentrations. The maximum stimulatory effect of obicetrapib on LDL‐C loss was estimated to be 1.046, while the maximum inhibitory effect of obicetrapib on HDL‐C loss was 0.691. This corresponds to a predicted typical maximum percent change from baseline LDL‐C and HDL‐C of 51.1% and 224%, respectively. The final sequential model described obicetrapib PKPD well and was ultimately able to both demonstrate evidence of internal consistency and support decision‐making throughout the development lifecycle. [ABSTRACT FROM AUTHOR]

Currently, numerous studies have applied gear skiving processes to produce face gear. However, there remains a significant challenge in achieving a flexible computing model for manufacturing a precise tooth surface for face gear. This study proposes a novel mathematical model that combines the cutter modification method and computer numerical control (CNC)-axis motion modification methods within a unified "closed-loop optimization." This approach aims to enhance the tooth surface accuracy of skived helical face gears by determining optimal coefficients. Applying the Levenberg-Marquardt algorithm and sensitivity matrix enables the calculation of new polynomial coefficients, ensuring the attainment of gear surfaces with an accuracy grade of B6 (according to the ANSI/AGMA 2009-B01 standard) for each target surface. The proposed methodology involves the generation of a helical skiving cutter using a corrected rack. Subsequently, the cutting path on the CNC machine is optimized by incorporating additional motions expressed in polynomials. A comprehensive skiving simulation is conducted to achieve the desired face-gear surface, which is corrected by specified polynomial coefficients. The proposed model is validated through numerical and machining simulations using vericut software. The results affirm the practicality and efficacy of our approach in achieving the desired accuracy in producing helical face gears through power skiving processes. [ABSTRACT FROM AUTHOR]

Residual stress (RS) significantly impacts the mechanical performance of components. Measurement of RS often provides incomplete data in terms of components of stress and spatial density. Employing such fields in finite element simulations results in significant modification of the field to achieve equilibrium and compatibility among strains. To overcome this, an iterative stress reconstruction algorithm (ISRA) is developed to estimate 3D RS fields that satisfy equilibrium, are stress component-wise complete, and represent the characterized data sampled. An Al 7075-T651 plate and an additively manufactured (AM) A36 steel wall are considered for RS reconstruction using measurement data from the literature. A maximum variation of ~2.5 MPa in the Al plate, and ~10 MPa in the steel wall are observed between the reconstructed and measured stresses. Furthermore, unknown stress components emerge and reach significant magnitudes (upto ~2.3 MPa in the Al plate and ~45 MPa in the AM wall) during ISRA. Indeed, it is found that minor errors in measurement or data processing are eliminated through the physical requirements during ISRA. Employing a reconstructed RS field is hence not just more accurate given its compatibility, but it additionally corrects for minor errors in measurement. Furthermore, it is found that spatially dense measurement data result in convergence with fewer iterations. Finally, although ISRA yields a nonunique solution dependent on boundary conditions, measurement errors, fitting errors, and mesh density, it accommodates for uncertainties and inaccuracies in measurement, as opposed to failing to reach a physically realistic converged solution. [ABSTRACT FROM AUTHOR]

High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. In addition, many models report a low mean-square error (MSE) across the entire domain of a part. However, in each time-step, most areas of the domain do not experience significant changes in temperature, except for the regions near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated. This article presents a data-driven model that uses the Fourier neural operator to capture the local temperature evolution during the AM process. Besides MSE, the model is also evaluated using the R² metric, which places great weight on the regions where the temperature changes significantly than MSE. The model was trained and tested on numerical simulations based on the discontinuous Galerkin finite element method for the direct energy deposition AM process. The results shows that the model maintains 0.983 - 0.999 R² over geometries not included in the training data, which is higher than convolutional neural networks and graph convolutional neural networks we implemented, the two widely used architectures in data-driven predictive modeling. [ABSTRACT FROM AUTHOR]

AbstractUsing cosmic muons allows for a noninvasive imaging approach to examine nuclear fuel in sealed dry storage casks. By assessing muons both before and after passing through the cask, one can infer details about the cask’s interior by analyzing scattering angle data. The effective scattering angles of muons depend on the characteristics of the interacting material, such as the atomic number (Z). This allows for the deduction of the material and geometric composition of the cask’s inventory. When employing simulations to forecast muon paths within the cask, it is essential to scrutinize the impact of modeling assumptions and simplifications on the scattering angle distribution.In this study, we examine the influence of modeling assumptions and simplifications on the effective scattering angle. Additionally, the significance of the number of particles used is shown. We evaluate four GEANT4 cask models of a CASTOR® V/19, each incorporating varying degrees of simplification, and analyze their impact on the projected muon scattering angle. These simplifications include both the simplification of individual geometric components of the cask and the complete exclusion of specific components. We assess and prioritize the various model simplifications in terms of their effect on the observed scattering angle. We recognize the importance of thoughtfully considering the degree of simplification used in the model to ensure accurate and reliable results for the scattering angle distribution. [ABSTRACT FROM AUTHOR]

Intra-Target Microdosing (ITM), integral to Phase 0 clinical studies, offers a novel approach in drug development, effectively bridging the gap between preclinical and clinical phases. This methodology is especially relevant in streamlining early drug development stages. Our research utilized a Physiologically Based Pharmacokinetic (PBPK) model and Monte Carlo simulations to examine factors influencing the effectiveness of ITM in achieving target engagement. The study revealed that ITM is capable of engaging targets at levels akin to systemically administered therapeutic doses for specific compounds. However, we also observed a notable decrease in the probability of success when the predicted therapeutic dose exceeds 10 mg. Additionally, our findings identified several critical factors affecting the success of ITM. These encompass both lower dissociation constants, higher systemic clearance and an optimum abundance of receptors in the target organ. Target tissues characterized by relatively low blood flow rates and high drug clearance capacities were deemed more conducive to successful ITM. These insights emphasize the necessity of taking into account each drug's unique pharmacokinetic and pharmacodynamic properties, along with the physiological characteristics of the target tissue, in determining the suitability of ITM. [ABSTRACT FROM AUTHOR]

In order to address the issues of significant energy and resource waste, low-energy management efficiency, and high building-maintenance costs in hot-summer and cold-winter regions of China, a research project was conducted on an office building located in Nantong. In this study, a data-driven golden jackal optimization (GJO)-based Long Short-Term Memory (LSTM) short-term energy-consumption prediction and optimization system is proposed. The system creates an equivalent model of the office building and employs the genetic algorithm tool Wallacei to automatically optimize and control the building's air conditioning system, thereby achieving the objective of reducing energy consumption. To validate the authenticity of the optimization scheme, unoptimized building energy consumption was predicted using a data-driven short-term energy consumption-prediction model. The actual comparison data confirmed that the reduction in energy consumption resulted from implementing the air conditioning-optimization scheme rather than external factors. The optimized building can achieve an hourly energy saving rate of 6% to 9%, with an average daily energy-saving rate reaching 8%. The entire system, therefore, enables decision-makers to swiftly assess and validate the efficacy of energy consumption-optimization programs, thereby furnishing a scientific foundation for energy management and optimization in real-world buildings. [ABSTRACT FROM AUTHOR]

The primary goal of the paper is to monitor the health of the spindle in machine tools to ensure optimal performance and reduce costly downtimes. Spindle health monitoring is essential to detect wear and cracks in spindle bearings, which can be challenging due to their gradual development and hidden locations. The proposed approach combines physics-based modeling and data-driven techniques to monitor spindle health effectively. In Part I and Part II of the paper, mathematical models of bearing faults and spindle imbalance are integrated into the digital model of the spindle. This allows for simulating the operation of the spindle both with and without faults. The integration of fault models enables the generation of vibrations at sensor locations along the spindle shaft. The generated vibration data from the physics-based model are used to train a recurrent neural network-based (RNN) fault detection algorithm. The RNN learns from the labeled vibration spectra to identify different fault conditions. Bayesian optimization is used to automatically tune the hyperparameters governing the accuracy and efficiency of the learning models during the training process. The RNN classifiers are further fine-tuned using a small set of experimentally collected data for the generalization of the model on real-world data. Once the RNN classifier is trained, it can distinguish between different types of damage and identify their specific locations on the spindle assembly. The proposed algorithms achieved an accuracy of 98.43% on experimental data sets that were not used in training the network. [ABSTRACT FROM AUTHOR]

This study models the temperature evolution during additive friction stir deposition (AFSD) using machine learning. AFSD is a solid-state additive manufacturing technology that deposits metal using plastic flow without melting. However, the ability to predict its performance using the underlying physics is in the early stage. A physics-informed machine learning approach, AFSD-Nets, is presented here to predict temperature profiles based on the combined effects of heat generation and heat transfer. The proposed AFSD-Nets includes a set of customized neural network approximators, which are used to model the coupled temperature evolution for the tool and build during multi-layer material deposition. Experiments are designed and performed using 7075 aluminum feedstock deposited on a substrate of the same material for 30 layers. A comparison of predictions and measurements shows that the proposed AFSD-Nets approach can accurately describe and predict the temperature evolution during the AFSD process. [ABSTRACT FROM AUTHOR]

Mathematical modeling of bearing faults, worn tool holder taper contact interface, and unbalance are presented and integrated into a digital dynamic model of spindles in Part I of this paper. These faults lead to changes in preload and dynamic stiffness over time, consequently resulting in observable vibrations. This paper predicts the vibrations of a spindle at a particular measurement location by simulating the presence of a specific fault or multiple faults during spindle rotation. The vibration spectra generated by the digital spindle model at the spindle speed and its harmonics, the changes in the natural frequencies, and dynamic stiffnesses are correlated to faults with experimental validations. The simulated vibration spectrums are later used in training an artificial neural network for fault condition monitoring presented in Part III of the paper. [ABSTRACT FROM AUTHOR]

The spindle determines the performance of machine tools; hence, monitoring its health is essential to maintain machining productivity and avoid costly downtimes. The magnitudes and locations of wear and cracks in the bearing balls and races gradually develop which are difficult to detect. This article presents a physics-based digital model of the spindle with bearing faults, worn contact interface between the shaft and tool holder, and spindle imbalance. The wear of races and balls is considered in the bearing model. The worn taper contact interface and the spindle imbalance are included in the digital model. The spindle's dynamic model is used to simulate the vibrations at any location in the spindle assembly where sensors can be mounted for online monitoring. The wear type and bearing location are correlated with the frequency spectrum of vibrations at operating speeds. The proposed fault models are used to analyze the critical signal features and experimentally validated by the frequency extracted from a damaged spindle in Part II. The physics-based digital model is used to train data analytic models to detect spindle faults in Part III. [ABSTRACT FROM AUTHOR]

This paper employed a scaling analysis to represent the processing parameters, affecting the melting process in the dimensionless numbers, identify the relationships of these dimensionless numbers, and develop semi-empirical correlations to predict the width and depth of the melt pool. To develop the correlations, Ti-6Al-4V powder was used to print 38 tracks at various processing conditions. The correlations were then fit into this experimental data using python code to find the constants of the correlations. The correlations were then used to predict the depth and width of the melt pools. It was found that the mean discrepancy between the predicted melt pool dimensions and the experiment is 7%. To evaluate the accuracy of the correlation in predicting the melt pool dimensions of the materials never used during the development of the correlations, the melt pool depth of some tracks made out of stainless steel 316L printed at various conditions was predicted using the model, and it was found that the mean discrepancy between the predicted melt pool depth and experiment is 11%. [ABSTRACT FROM AUTHOR]

The accessibility of healthcare system is vulnerable to various types of hazards, where the failure of one system component may lead to a diffusion of the pressure and result in cascading failures. This study proposes a network-based simulation framework for robustness assessment of access to healthcare through integrating cascading failure mechanism. Weighted complex networks are constructed to model the accessible patient transfer under both general and elderly healthcare scenarios. The cascade failure mechanism is incorporated into the constructed networks, and several attack strategies (including random, initial degree (ID), initial betweenness (IB), recalculated degree (RD), and recalculated betweenness (RB) attack) are adopted to simulate the process of system robustness assessment. Results indicate that the proposed framework enables to discover the vulnerable nodes in the constructed healthcare accessibility networks, where the robustness metric combining network efficiency and relative size of the largest component acts as a benchmark; all the intentional attack strategies outperform the random attack strategy, which indicates the effectiveness of the detection of vulnerable healthcare facilities by the developed model; and the metrics of node degree and betweenness centrality make progress on identifying the vulnerable healthcare facility nodes, which should be taken heed of to optimize the management and operation of healthcare systems. [ABSTRACT FROM AUTHOR]

Stepwise covariate modeling (SCM) has a high computational burden and can select the wrong covariates. Machine learning (ML) has been proposed as a screening tool to improve the efficiency of covariate selection, but little is known about how to apply ML on actual clinical data. First, we simulated datasets based on clinical data to compare the performance of various ML and traditional pharmacometrics (PMX) techniques with and without accounting for highly-correlated covariates. This simulation step identified the ML algorithm and the number of top covariates to select when using the actual clinical data. A previously developed desipramine population-pharmacokinetic model was used to simulate virtual subjects. Fifteen covariates were considered with four having an effect included. Based on the F1 score (an accuracy measure), ridge regression was the most accurate ML technique on 200 simulated datasets (F1 score = 0.475 ± 0.231), a performance which almost doubled when highly-correlated covariates were accounted for (F1 score = 0.860 ± 0.158). These performances were better than forwards selection with SCM (F1 score = 0.251 ± 0.274 and 0.499 ± 0.381 without/with correlations respectively). In terms of computational cost, ridge regression (0.42 ± 0.07 seconds/simulated dataset, 1 thread) was ~20,000 times faster than SCM (2.30 ± 2.29 hours, 15 threads). On the clinical dataset, prescreening with the selected ML algorithm reduced SCM runtime by 42.86% (from 1.75 to 1.00 days) and produced the same final model as SCM only. In conclusion, we have demonstrated that accounting for highly-correlated covariates improves ML prescreening accuracy. The choice of ML method and the proportion of important covariates (unknown a priori) can be guided by simulations. [ABSTRACT FROM AUTHOR]

This article presents the design, fabrication, and analysis of a textile-based band-stop frequency selective surface (FSS), in GSM, Wi-Fi, LTE, and WiMAX bands where the electromagnetic (EM) pollution is intense. The unit cell of the proposed FSS has been designed and simulated via a full-wave EM solver; CST Microwave Studio at the frequency of interest. In this study, embroidered textile-based FSSs were designed as an alternative to conventional FSSs. In the study, high textile detailed (from thread to embroidery direction) modeling was performed and the results of the modeling and experiments were compared. The results of this study showed that the embroidery direction has a dramatic effect on the electromagnetic behavior of the FSS. Both experimental and high textile detailed modeling results indicated that vertical or horizontal embroideries are not sufficient for band-stopping FSS. To overcome this problem, vertical-over-horizontal embroidery was used. The highly textile detailed model and its experimental results have proven that horizontal-over-vertical embroidery is successful in band-stopping FSS. [ABSTRACT FROM AUTHOR]

Cold spray additively manufactured (CSAM) aluminum 6061 components are characterized by heterogeneous compressive residual stresses induced during manufacturing. This heterogeneity is further compounded by spatial variations in microstructures and mechanical properties, leading to poor inter-particle (intersplat) bonding and significant marring of overall component performance. Thermal post-processing is a keenly researched method for recovering mechanical toughness by enhancing intersplat bonding and altering highly concentrated residual stress distributions. The current work incorporates a modified microscale–mesoscale material model into a viscoplastic self-consistent simulation framework to capture material response in the as-sprayed and post-processed states. The updated model incorporates physically informed parameters emphasizing residual stresses measured experimentally through X-ray diffraction. The model calibrated using experimental tests and published literature was able to predict the stress–strain response of CSAM parts at post-heat-treated conditions. Results of the parametric study showed the significance of intersplat boundary effects on the overall yield and strain hardening of the CSAM parts. Without any information on the processing conditions of CSAM parts, the modified plasticity model predicted the deformation response using information gathered from microstructure characterization. [ABSTRACT FROM AUTHOR]

The electrical power distribution system (EPDS) is an essential component of the power system. Electricity and its services have risen enormously in the recent period, and the primary task of EPDS is to distribute uninterrupted electricity to the consumer. The EPDS comprises numerous intricate, unpredictable, and interlinked components that are frequently susceptible to disruption or malfunction. Faults on EPDS are intended to be appropriately recognized and categorized before being eliminated as quickly as feasible. An efficient fault recognition mechanism makes relaying operations realistic, fast, safe, and dependable. In this article, we introduce a MATLAB simulation model based on the Electrical Fault Detection and Classification (EFDC) algorithm for shunt fault analysis in the EPDS and distinguish among heterogeneous types of shunt fault based on current and voltage waveform throughout the fault. The recommended fault scrutiny and isolation framework might aid in segregating malfunctioning sections from healthy EPDS. Numerous electrical shunt faults are modeled and simulated to detect such deficient behaviors. The suggested EFDC algorithm’s efficacy, including Digital Comparative Fault Classifier (DCFC), is examined by simulating different kinds of shunt faults throughout ring-main EPDS’s source and load regions to evaluate the proposed technique’s adaptability, as well as the outcomes are promising. [ABSTRACT FROM AUTHOR]

This paper explores the optimization of critical parameters in the Czochralski (CZ) crystal growth process, a key technique in semiconductor device manufacturing. The CZ system involves the melting of high-purity polycrystalline silicon and controlled growth of a single-crystal ingot. This process relies on key variables such as the thermal shield thickness, shield gap size, cooling jacket length, crucible dimensions, and rotation speed. A computational model was applied to simulate heat transfer, fluid flow, and radiation heat exchange within the CZ system. The model was validated using experimental data, which demonstrated its precision in predicting crystal-front deflection and heater power. The results highlight the substantial impact of increasing the thermal shield thickness on the crystal-pulling speed and uniformity. Greater cooling jacket lengths increased the cooling rate, which increased the pulling speed, although more heater power was required. The shield gap size had negligible effects, and variations in the heater power ratio showed minimal impact. The optimal combination of parameters led to significantly improved crystal-pulling speed and reduced power consumption, making it a compelling choice for enhanced CZ crystal growth in semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC. [ABSTRACT FROM AUTHOR]

This research introduces the NeuRaiSya (Neural Railway System Application), an innovative railway signaling system integrating deep learning for passenger analysis. The objectives of this research are to simulate the NeuRaiSya and evaluate its effectiveness using the GreatSPN tool (graphical editor for Petri nets). GreatSPN facilitates evaluations of system behavior, ensuring safety and efficiency. Five models were designed and simulated using the Petri nets model, including the Dynamics of Train Departure model, Train Operations with Passenger Counting model, Timestamp Data Collection model, Train Speed and Location model, and Train Related-Issues model. Through simulations and modeling using Petri nets, the study demonstrates the feasibility of the proposed NeuRaiSya system. The results highlight its potential in enhancing railway operations, ensuring passenger safety, and maintaining service quality amidst the evolving railway landscape in the Philippines. [ABSTRACT FROM AUTHOR]

In the face of a changing climate and a rising number of "food deserts" in both rural and urban areas, there is a demand to supply fresh produce year-round to communities at the end of the traditional agriculture supply chain. Vertical indoor farming is a promising mode of next-generation agriculture that boasts reduced water and pesticide usage, improved yields, more consistent quality, year-round cultivation, and cheaper transportation and harvesting costs. Indoor farms can rival industrial greenhouses in size, but small-scale "pod farms" can be deployed to smaller communities and areas where large swaths of land are either unavailable or too costly. These pods are often the size of shipping containers with their temperature, humidity, and plant nutrient supply carefully controlled. Plants inside the pods are grown hydroponically with light supplied by panels of LEDs and, thus, this mode of farming is fundamentally different from greenhouse farming. Many indoor farming pods have recently become commercially available claiming high energy efficiency, but little analysis and optimization work has been done to prove these claims. To drive innovation in the design of these physical systems, we have developed a digital-twin and genomic optimization framework for the optical design of vertical indoor farming pods. We model a completely enclosed indoor farming pod with plants in the three mutually-orthogonal planes and illuminated by LED "walls." We employ ray-tracing methods and a genetic algorithm to determine the LED source tube area size, beam aperture spread, and power requirements for maximal power absorption by the plants. [ABSTRACT FROM AUTHOR]

The automotive industry is entering a digital revolution, driven by the need to develop new products in less time that are high-quality and environmentally friendly. A proper manufacturing process influences the performance of the door grommet during its lifetime. In this work, uniaxial tensile tests based on molecular dynamics simulations have been performed on an ethylene–propylene–diene monomer (EPDM) material to investigate the effect of the crosslink density and its variation with temperature. The Mooney–Rivlin (MR) model is used to fit the results of molecular dynamics (MD) simulations in this paper and an exponential-type model is proposed to calculate the parameters C 1 (T) and C 2 T . The experimental results, confirmed by hardness tests of the cured part according to ASTM 1415-88, show that the free volume fraction and the crosslink density have a significant effect on the stiffness of the EPDM material in a deformed state. The results of molecular dynamics superposition on the MR model agree reasonably well with the macroscopically observed mechanical behavior and tensile stress of the EPDM at the molecular level. This work allows the accurate characterization of the stress–strain behavior of rubber-like materials subjected to deformation and can provide valuable information for their widespread application in the injection molding industry. [ABSTRACT FROM AUTHOR]

In this study, the structural, electronic, and optical properties of pristine graphene and graphene doped/co-doped with (N, Si) atoms are examined using a first-principles investigation. However, pristine graphene is characterized by a unique electronic structure known as the zero band gap (Eg = 0 eV), and this gap was opened up after the addition of N and Si substitutions, where it became 0.2, 0.21, and 1.38 eV for graphene doped with nitrogen, silicon and double doped with both (N, Si), respectively. For the band gap and the density of states, many parameters have been studied such as the complex dielectric function, conductivity, absorption spectra, loss function, and refractive index. The absorption curve shows two sharp peaks for all structures, where their intensities become lower and shift slightly towards lower energy after doping graphene with N, Si, and N-Si, indicating that this doping introduces additional energy states in the graphene band structure, making the transition between states easier to achieve. [ABSTRACT FROM AUTHOR]

Additive and subtractive (Add/Sub) manufacturing processes are increasingly being combined to produce complex parts with unique geometries and properties. However, the design of such combined processes is often challenging as it requires a deep understanding of the interaction between the different processes. On the other hand, digital twin (DT) technology has become a powerful tool in recent years for optimizing manufacturing processes. This article explores the use of the digital twin technology for a holistic process planning of combined additive and subtractive processes. The article describes the integration of laser metal deposition (LMD) and micro-milling prediction models of resulting geometry (width and height), hardness, and surface roughness into the digital twin. This is then used for combined process planning to achieve different target values regarding resulting geometry and surface roughness. For the planning of this combined process chain, further criteria such as tool life, energy, and process time are considered in the optimization, showing the potential for sustainable and efficient production. Sensorless cutting force estimation is also used to detect small cutting forces, with the potential to use this as a soft sensor for roughness estimation. The measured width, height, and roughness as a result of the process parameters suggested by the optimization algorithms showed a mean absolute percentage error (MAPE) of 4, 17, and 16%, respectively. [ABSTRACT FROM AUTHOR]

Warm forming holds significant potential in shaping challenging materials due to its comprehensive advantages compared to cold and hot forming. Effective heating is crucial for precise warm forming processes. This study explored an innovative warm air bending technique employing an online local contact heating method. Heat transfer during the preheating stage and the air bending of AZ31B sheets were investigated through experiments and finite element analysis at temperatures ranging from room temperature (24 °C) to 250 °C, considering the thermomechanical coupling effect. The results indicate a significant increase in the bending center angle, advancing from 69.8 deg to more than 119 deg as the heating temperature rises from room temperature to 250 °C. Fractures were effectively eliminated when the sheet heating temperatures exceeded 150 °C. Simultaneously, as the localized heating temperature increases, a corresponding decrease in the rebound angle is observed, reducing from 10.6 deg to 2.3 deg. The findings validate the feasibility of warm air bending using online local contact heating in industrial applications, with regard to forming quality and production efficiency. [ABSTRACT FROM AUTHOR]

We examine the potential of prompting a large language model-based chatbot, ChatGPT, to generate functional simulation model code from a prose-based narrative. The simple narrative describes how the mode of transportation for elementary school students changed due to the COVID-19 pandemic and related factors, including a lack of available bus drivers, lack of mask enforcement on buses, and inclement weather. We document the process of providing this narrative to ChatGPT and further prompting the chatbot to generate model code to represent the narrative and to make it executable. We test ChatGPT's ability to use prose descriptions of a phenomenon to generate simulation models using three paradigms: discrete event system, system dynamics, and agent-based modeling. Our findings reveal that ChatGPT could not produce concise or executable models, facing higher difficulty when asked to do so in programming languages it was less familiar with. This analysis underscores the strengths and limitations of the current state of this technology for modeling and simulation. Furthermore, we propose how future advancements in Large Language Models may aid the simulation modeling process, increasing transparency and participation in multidisciplinary team efforts. [ABSTRACT FROM AUTHOR]

As digitalization is permeating all sectors of society toward the concept of "smart everything," and virtual technologies and data are gaining a dominant place in the engineering and control of intelligent systems, the Digital Twin (DT) concept has surfaced as one of the top technologies to adopt. This paper discusses the DT concept from the viewpoint of Modeling and Simulation (M&S) experts. It both provides literature review elements and adopts a commentary-driven approach. We first examine the DT from a historical perspective, tracing the historical development of M&S from its roots in computational experiments to its applications in various fields and the birth of DT-related and allied concepts. We then approach DTs as an evolution of M&S, acknowledging the overlap in these different concepts. We also look at the M&S workflow and its evolution toward a DT workflow from a software engineering perspective, highlighting significant changes. Finally, we look at new challenges and requirements DTs entail, potentially leading to a revolutionary shift in M&S practices. In this way, we hope to foster the discussion on DTs and provide the M&S expert with innovative perspectives. [ABSTRACT FROM AUTHOR]

The growing demand for efficient, resilient, and sustainable electricity infrastructure has led to the emergence of smart grids as cyber‐physical systems of systems. Co‐simulation has proven an effective tool for their analysis and validation by coordinating independent subsystem simulations. However, the reuse and integration of diverse models in co‐simulation poses challenges, requiring compatibility and integration efforts. In response, this paper proposes a model taxonomy with the purpose of facilitating co‐simulation; it comprises three layers: concrete‐instance models, abstract‐instance models, and type models. The taxonomy contributes to the creation of independently developed models that can be seamlessly integrated into a coupled co‐simulation. Furthermore, it reflects the emergence of digital twins in smart grid engineering by the explicit distinction of abstract and concrete instances. The three‐layer taxonomy was derived and validated through a case study on co‐simulation of electric‐vehicle charging infrastructure. The research further analyzes and formalizes three modeling‐and‐simulation challenges framed through the lens of the taxonomy: the integration of models across all three layers, the merging of layers, and the consolidation of instance models to craft joint co‐simulation scenarios. Finally, three concrete recommendations for research and industrial practice are given. Thereby, the study contributes to the efficient and effective model‐based validation of cyber‐physical systems of systems using co‐simulation. [ABSTRACT FROM AUTHOR]

Resilience is the ability to avoid, withstand, and recover from adversity. In this paper, we examine organizational resilience using a case study of an organization that suffers from a series of scandals that lead to problems with its reputation as an inclusive organization, which results in difficulties in recruiting and retaining employees. We suggest some policies involving leadership efforts to change the culture in the organization and thereby restore its reputation. Based on the results of a System Dynamics Model that mixes quantitative and qualitative measures, we find that changing organizational culture is difficult. There can be inertia and long delays before the leadership sees results in improved recruiting and retention statistics. [ABSTRACT FROM AUTHOR]

Over the past decade, technology has experienced a rapid and widespread evolution, resulting in new capabilities across the defense sector. While these capabilities often bring about beneficial changes, they can also beget unforeseen consequences. As a result, it is becoming increasingly important for the military to understand how this changing technological landscape can impact its missions. To address this issue, we have developed an Integrated Mission Simulation (IMS) to assess the potential impact that different technologies may have on a given mission. The Integrated Mission Simulation combines a mission context model with several technology performance models, which enables users to assess the results of different 'what‐if' scenarios on key performance parameters for a given mission. To illustrate its utility, we present two publicly available scenarios and simulate how known technological advances could have altered the outcomes of those scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we explore the concept of operational resilience of a network or system of computer systems, focusing on the processes of a cybersecurity team within the multi‐disciplinary network security operations center. The computer system under examination has faced a cyber‐attack that has reduced its capability. The organization's reputation is damaged temporarily but can be restored if the network security operations center can quickly restore the organization's ability to produce desired results. After a cyber‐attack, we examine the processes for restoring the system's capability to its original level. These processes will happen sequentially and require close coordination of the cybersecurity team members. We examine a balanced and adaptive assignment policy within the cybersecurity organization to the various processes, showing how these policies can impact the speed with which the system's capability can be restored. Our findings reveal that the adaptive assignment policy among the team members can increase the system restoration rate even though recovering the complete capability of the system may be the same. [ABSTRACT FROM AUTHOR]

This paper investigates the model performance of an industrial box used to remove ammonia from the air using a doped activated carbon. To this end, process models are developed and experimental measurements are carried out. The models are based on momentum and mass balance equations along with thermodynamic, hydrodynamic and adsorption kinetic relations. The measurements consist of pressure drops in the air purification box and breakthrough curves of ammonia on activated carbon. The models are implemented and solved using COMSOL Multiphysics®. Adsorption is investigated by means of a Fickian multi‐scale model, which demonstrated to efficiently predict the experimental data. The optimal values of the most estimable unknown parameters involved in each model, as well as their confidence intervals are determined from experimental measurements. The performance of the industrial air purification model is rigorously assessed using statistical criteria, including mean‐square error (RMSE = 0.0013) and Pearson correlation coefficient (r = 0.999). [ABSTRACT FROM AUTHOR]

Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux's completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper. [ABSTRACT FROM AUTHOR]

The development of reliable joining techniques for ceramics and metals is crucial for energy applications, such as fuel cells, nuclear reactors, and high-temperature sensors, most especially for the sealing of hydrothermal sensors to study multiphase flows. However, during one-step active laser brazing it is a serious problem that a high thermal stress concentration can occur at the joint interfaces or on the ceramic side of the joint due to mismatches between the CTEs (coefficients of thermal expansion) and/or elastic constants. The uncontrolled thermal residual stress can lead to cracks and defects in the brazement. In the present work, an elastoplastic finite element method/numerical model was formulated to study the thermal residual stresses developed in the brazement between ceramics and austenitic stainless steel during cooling in active laser brazing. Calculations and comparison experiments were conducted to validate the simulated stress distribution in un-patterned ceramics. Stress analyses were conducted for planar and cylindrical specimen geometries (lab joints) relevant for miniaturized energy sensors. Laser interface patterning was employed to create micro-scale features on ceramic interfaces that reduce thermal stress concentrations. The optimization of the interface designing parameters including hatch size, structure width, pattern depth and metal/ceramic thickness ratio was performed using the Taguchi method with orthogonal arrays. The study suggests that laser interface structuring can modify thermal residual stresses in ceramic-to-metal brazements, thereby increasing the reliability of active brazing joints. [ABSTRACT FROM AUTHOR]

The quenching and partitioning (Q&P) process has been shown to be quite promising in the development of a third generation of Advanced High-Strength Steels. As in other metallurgical processes, the austenitic grain size (AGS) is crucial to obtain a microstructure that provides a great mechanical behavior. Hence, this work evaluated the effect of the austenitizing temperature on the AGS and, consequently, on the constituents formed under continuous cooling at different rates. Then, a Q&P modeling for a commercial CMnSi steel, predicting the microstructural evolution and the final phase fractions as a function of the AGS and other parameters, was performed. The AGS increased exponentially with the austenitizing temperature, in the way that the smaller the AGS the finer the martensitic structure formed after cooling. The decrease in the continuous cooling rate leaded to the formation of diffusional constituents prior to the martensitic transformation, which promoted the increase in Ms temperature for larger AGS. In conclusion it is possible to state that an austenitizing condition that provides the smallest possible AGS, followed by an optimized Q&P cycle, has the potential to generate an advanced steel with a final microstructure assisted by the TRIP effect due to the retained austenite content. [ABSTRACT FROM AUTHOR]

With the development of lithium-ion batteries and fuel cells, the application of hybrid power systems is becoming more and more widespread. To better optimize the energy management problem of fuel cell hybrid systems, the accuracy of system modeling and simulation is very important. The hybrid system is formed by connecting the battery to the fuel cell through an active topology. Digital twin technology is applicable to the mapping of physical entities to each other with high interactivity and fast optimization iterations. In this paper, a relevant model based on mathematical logic is established by collecting actual operational data; subsequently, the accuracy of the model is verified by combining relevant operating conditions and simulating the model. Subsequently, a three-dimensional visualization model of a hybrid power system-based sightseeing vehicle and its operating environment was established using digital twin technology to improve the model simulation of the fuel cell hybrid power system. At low speeds, the simulation results of the hybrid power system-based sightseeing vehicle have a small error compared with the actual running state, and the accuracy of the data related to each internal subcomponent is high. In the simple interaction between the model display vehicle and the environment, the communication state can meet the basic requirements of the digital twin model because the amount of data to be transferred is small. This study makes a preliminary attempt at digital parallelism by combining mathematical logic with visualization models and can be used as a basis for the subsequent development of more mature digital twin models. [ABSTRACT FROM AUTHOR]

This study describes the design of a parallel spatial manipulator with four degrees of freedom actuated with shape memory alloy (SMA) wire to validate the use of SMA in complicated mechatronics systems. The manipulator has a closed kinematic structure, which includes a fixed base and a moving square platform (end effector). The four arms of the manipulator are SMA wires fastened between the fixed base and the end effector. SMA wire-based actuators replace bulky conventional revolute actuators. This work spotlights the development of an actuator model, dimensional analysis, and design of cascade control strategies of various PID and sliding mode controller in their integral and fractional order configurations. Experimental evaluation of the actuator is performed through trajectory tracking to quantify the different controller configurations. The experimental results indicate that the parallel manipulator associated with SMA wire actuators is the best alternative to conventional motion stages for highly precise micro-positioning and tracking applications in the fields of 3D printing, intricate surgical operations, the medical and pharmaceutical industries, and flight and gaming simulators. [ABSTRACT FROM AUTHOR]

Accurately characterizing the substrate used in anaerobic digestion is crucial for predicting the biogas plant's performance. This issue makes particularly challenging the application of modeling in codigestion plants. In this work, a novel methodology called substrate prediction module (SPM) has been developed and tested, using virtual codigestion data. The SPM aims to estimate the inlet properties of the substrate based on the reverse application of the anaerobic digestion model n1 (ADM1). The results show that, while the SPM can estimate some properties of the substrate based on certain output parameters, there are limitations in accurately determining all required variables. HIGHLIGHTS Extensive substrate characterization is challenging, especially for codigestion.; A reverse modeling approach is proposed to estimate unknown substrate properties.; The substrate prediction module processes measured AD data based on the ADM1.; Two data processing strategies are assessed: 7-day moving block and daily data.; The SPM could estimate some substrate properties but there are limitations.;

Abstract This article introduces and formalizes a novel stochastic method that combines inverse simulation with the theory of generalized Polynomial Chaos (gPC) to solve and study inverse problems under uncertainty in energy system design applications. The method is particularly relevant to design tasks where only a deterministic forward model of a physical system is available, in which a target design quantity is an input to the model that cannot be obtained directly, but can be quantified reversely via the outputs of the model. In this scenario, the proposed method offers an analytical and direct approach to invert such system models. The method puts emphasis on user-friendliness, as it enables its users to conduct the inverse simulation under uncertainty directly in the gPC domain by redefining basic algebra operations for computations. Moreover, the method incorporates an optimization-based approach to integrate supplementary constraints on stochastic quantities. This feature enables the solution of inverse problems bounding the statistical moments of stochastic system variables. The authors exemplify the application of the proposed method with proof-of-concept tests in energy system design, specifically performing uncertainty quantification and sensitivity analysis for a Multi-Energy System (MES). The findings demonstrate the high accuracy of the method as well as clear advantages over conventional sampling-based methods when dealing with a small number of stochastic variables in a system or model. However, the case studies also highlight the current limitations of the proposed method such as slow execution speed due to the optimization-based approach and the challenges associated with, for example, the curse of dimensionality in gPC.

ABSTRACTBackground Next generation, higher valency pneumococcal conjugate vaccines (PCVs) are assessed and licensed by comparing the immune response across serotypes shared with the PCVs that are standard of care for prevention of pneumococcal disease.Methods Using a previously qualified method we predicted the serotype-specific vaccine effectiveness (VE) against invasive pneumococcal disease of V114 and PCV20 for the serotypes shared with PCV13 in an EU, Russian, and Australian pediatric population that is recommended to receive a 2 + 1 dosing regimen.Results The estimated protective antibody concentrations ranged from 0.03 (serotype 23F) to 1.49 µg/mL (serotype 19F). Predicted VE values for V114 ranged from 79% (serotype 5) to 100% (serotype 23F). V114 had comparable effectiveness to PCV13 for all but one of shared serotypes, with predicted higher effectiveness (in V114) against serotype 3 (93% vs. 65%). Predicted VE values for PCV20 ranged from 47% (serotype 3) to 91% (serotype 14). PCV20 predicted VE was lower than PCV13’s for serotypes 4, 19F, 23F, 1, 3, 5, 6A, 7F, and 19A.Conclusions Predicted serotype-specific VE values suggest that, with a 2 + 1 dosing regimen, V114 will have greater effectiveness than PCV20 against PCV13 serotypes, particularly for the still-prevalent serotype 3. Real-world VE studies will ultimately provide clarity on the effectiveness of novel PCVs and support further confidence in and/or improvements to modeling efforts.

ABSTRACTBackground Next-generation, higher-valency pneumococcal conjugate vaccines (PCVs), 15-valent PCV V114 and 20-valent PCV (PCV20), have been assessed by comparing their immune responses across serotypes shared with the 13-valent PCV (PCV13). Without efficacy or real-world vaccine effectiveness (VE) it becomes important to relate IgG titers to VE to aid in the interpretation of the immune response elicited by V114 and PCV20.Methods We estimated the protective antibody concentrations for each serotype in 7-valent PCV (PCV7) and PCV13 which were then used to predict the serotype-specific VE for each PCV7 and PCV13 non PCV7 serotype present in V114 and PCV20.Results The predicted effectiveness of V114 was comparable to PCV7 and PCV13 for 11 of the 13 shared serotypes (1, 4, 5, 6B, 7F, 9 V, 14, 18C, 19A, 19F, and 23F), with improved effectiveness against serotype 3 and decreased effectiveness against serotype 6A. PCV20 had predicted effectiveness comparable to PCV7 and PCV13 for 7 of the 13 shared serotypes (5, 6A, 7F, 9 V, 18C, 19F, and 23F), with decreased effectiveness against the remaining serotypes (1, 3, 4, 6B, 14, and 19A).Conclusions Prediction of serotype-specific VE values suggests that V114 retains greater effectiveness than PCV20 toward most serotypes present in PCV7 and PCV13.