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This study tackles the feasibility of energy harvesting from wearable life jackets to assist in search and rescue operations through modeling and analyzing the motions of drowning individuals and the associated power (energy) distributions. Computer vision analysis of underwater footage and motion capture lab experiments are used to collect data for modeling and simulation to predict the movements and potential power generation of the life jackets. The proposed model enables the identification of optimal design parameters for energy harvesting with wearable life jackets, providing an insight into the potential of wearable energy harvesting technologies for enhancing the safety of individuals in aquatic environments. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM), commonly referred to as 3D printing, has undergone significant advancements, particularly in the realm of stimuli-responsive 3D printable and programmable materials. This progress has led to the emergence of 4D printing, a fabrication technique that integrates AM capabilities with intelligent materials, introducing dynamic functionality as the fourth dimension. Among the stimuli-responsive materials, shape memory polymers have gained prominence, notably for their crucial applications in stress-absorbing components. However, the exact 3D shape morphing of 4D printed products is affected by both the 3D printing conditions as well as the stimuli activation. Hence it has been hard to precisely control the 3D shape morphing accuracy. To model and optimize the dynamic 3D evolution of the 4D printed parts, we conducted both simulation studies and real-world experiments and introduced a novel machine-learning approach extending the concept of normalizing flows. This method not only enables the process optimization of the dynamic 3D profile evolution by optimizing the process conditions during 3D printing and stimuli activation but also provides interpretability for the intermediate shape morphing process. This research contributes to a deeper understanding of the nuanced interplay between process parameters and the dynamic 3D transformation process in 4D printing. [ABSTRACT FROM AUTHOR]

Dimensional tolerances for high-speed-machined aluminum products continue to tighten due to the demand for automated assembly of complex monolithic parts in aerospace and other industries. Understanding the contribution of inherent residual stress in wrought Al 7050-T7451 plate, common in aircraft manufacture, to distortion of high-aspect-ratio machined parts is critical but remains problematic due to the alloy's low residual stress magnitude over large geometries. Prior investigations into residual stress effects on machined part distortion suffer inadequate characterizations of the wrought material stress field, either because of low fidelity due to "slitting" methods, confounding effects in machined-layer removal methods, or small sample size when using neutron diffraction (ND). In this work, inherent residual stress is measured via ND at 860 locations in a 90.5 mm thick Al 7050-T7451 plate having dimensions 399 mm in the rolling direction and 335 mm in the transverse direction. Unlike prior studies, the ND residual stress is reconstructed using an iterative algorithm to ensure fully compatible, equilibrated 3D field prior to examining its effect on distortion. The findings from simulations and experiments show that inherent residual stress alone could distort a high-aspect-ratio part beyond aerospace industry requirements, that slitting measurements may not sufficiently characterize residual stress for predicted distortion, and that parts machined from different plate thickness locations could exhibit reversed distortion patterns. Thus, research into distortion prediction that considers machining should carefully characterize and reconstruct inherent residual stress so that the coupled machining effects are accurately modeled. [ABSTRACT FROM AUTHOR]

Obtaining the desired strip profile is of paramount importance in cold rolling. The final strip profile depends on several parameters of the plants including crown and taper profiles and segmented backup roll positions. This paper aims to present a newly developed combined finite element-analytical model of the rolling mill able to model not only the roll cluster but also the mill housing and backup bearings deformations. This makes the proposed model especially suitable to simulate cluster-type rolling mills. The model exploits a modified elastic foundation formulation for a precise computation of the contact forces between rolls, taking into account rolls' crown and taper. A reduced stiffness matrix approach is implemented for accurate mill housing modeling. A full 3D finite element model of the rolling mill and experimental data are used for the validation of the presented model. By exploiting the validated model, the paper shows that the deformation of supporting bearings and mill housing plays a crucial role in the strip profile calculation and should be included in any cluster-type roll mill model. [ABSTRACT FROM AUTHOR]

Multicomponent injection molding industry is experiencing a growth due to its ability to reduce production costs and streamline processes. However, compared to single injection, multicomponent injection molding introduces interface regions where multiple engineering polymers meet. Consequently, it is essential to comprehend and enhance the adhesive bonding strength properties of these polymers. This study investigates the adhesive bond strength of polymer-polymer multimaterial molding using two-shot bi-injection and overmolding techniques. The research also emphasizes the influence of injection molding process parameters of mold temperature and melt temperature on the adhesive bond strength of polycarbonate (PC), polycarbonate-acrylonitrile butadiene styrene (PC-ABS), acrylonitrile butadiene styrene (ABS), and styrene ethylene butadiene styrene (SEBS). Tensile strength results revealed that the bi-injection method yields the highest interface strength, approximately 10 MPa lower than the reference value for single-material hard-hard plastics. Results from overmolded samples for both injection sequences are presented, indicating that material with low melting temperature was found to be the first injected part for better adhesion strength. Empirical equations for estimating adhesion strength were derived as a function of interface temperature obtained from CAE numerical simulations and polymer glass transition temperatures. The proposed equation achieved R2 values greater than 0.96. This empirically derived equation will serve as a guide for multi-injection manufacturing processes. [ABSTRACT FROM AUTHOR]

Dopamine D2 receptor occupancy (D2RO) significantly influences the clinical effectiveness and safety of many antipsychotic drugs. Maintaining a D2RO range of 65%–80% provides the best antipsychotic effects while minimizing adverse reactions. Data from a Phase III trial were used to establish an exposure–response relationship for monthly intramuscular Risperidone ISM (75 and 100 mg) or placebo administered to adults with schizophrenia. Pharmacodynamic analysis was based on an Emax model for Positive and Negative Syndrome Scale (PANSS) developed in NONMEM. Plasma concentrations of the active moiety were derived using a previously developed population pharmacokinetic model, which was used for D2RO simulations in conjunction with a published Emax model. The optimal D2RO range (65%–80%) was reached for the median within hours following the first injection of both Risperidone ISM doses. At steady state, median D2RO for both doses remained above 65% throughout the 28‐day dosing period and demonstrated lower variability than oral risperidone. PANSS response did not differ significantly between dose groups, most likely because active moiety concentrations had already reached the plateau of the concentration–response relationship. The pharmacokinetic/pharmacodynamic analysis showed a profound placebo effect (−11.7%), and an additional maximal drug effect (−6.6%) resulting in a total PANSS improvement over time of −18.3%. Pharmacokinetic/pharmacodynamic modeling quantified a PANSS improvement over time after Risperidone ISM administration. The response was not significantly different in either dose group, likely because D2RO was already above the proposed efficacy threshold (65%) within 1 h after the first Risperidone ISM injection and remained above this level following repeated administrations. [ABSTRACT FROM AUTHOR]

Deutenzalutamide is a new molecular entity androgen receptor antagonist. The primary aim of this study was to develop a population pharmacokinetic model of deutenzalutamide and evaluate effects of intrinsic and extrinsic factors on pharmacokinetics. A nonlinear mixed‐effects modeling approach was performed to develop the population pharmacokinetic of deutenzalutamide using data from 1 Phase I trial of deutenzalutamide. Goodness‐of‐fit plots, prediction‐corrected visual predictive check, and bootstrap analysis were carried out to evaluate the final model. Simulation for the developed model was used to evaluate the covariate effects on the pharmacokinetics of deutenzalutamide. A 2‐compartment model with first‐order absorption and elimination from the central compartment was established for deutenzalutamide. The final covariate included body weight on peripheral compartment volume. This is the first research developing the population pharmacokinetic model of deutenzalutamide in patients with metastatic castration‐resistant prostate cancer, and it is expected to support the future clinical administration of deutenzalutamide. [ABSTRACT FROM AUTHOR]

Over the past 20 years, there have been high-profile examples of Department of the Navy (DON) unmanned undersea vehicle (UUV) programs with expensive cost overruns and late deliveries. While best practices exist to account for cost and schedule risk, this article presents a practical approach to assessing the likelihood of success through model-based conceptual design (MBCD) within a real-world problem context. The author applies model-based systems engineering (MBSE) to quantify the impact MBCD has on the success of a critical UUV software update when applied prior to expensive design, test, and delivery. First, the scope is framed with a realistic narrative, then the existing UUV development architecture is modeled to set the existing DON process in context. Next, a formal time and cost model is simulated to set baseline values for the modeled scenario, including user-focused MBCD. Finally, simulation and analysis of four scenarios capturing varying levels of rework demonstrate a significant reduction in time and cost. While narrowly focused on a UUV development scenario, future work can generalize the approach and quantify the impact of these methods to quantify, track, and mitigate program risk across DON acquisition programs. [ABSTRACT FROM AUTHOR]

This paper presents a root cause analysis case study of defective Hall effect sensor devices. The study identified a complex failure mode caused by chip–package interaction, which has a similar signature to discharging defects such as ESDFOS (electrostatic discharge from outside to surface). However, the study revealed that the defect was induced by local mechanical force applied to IC structures due to the presence of large irregular shaped filler particles within the mold compound. Extensive failure analysis work was conducted to identify the failure mode, including the development of a new backside analysis strategy to preserve the mold compound during IC defect localization and screening. A combination of different failure analysis techniques was used, including CMP delayering, PFIB trenching, SEM PVC imaging and large area FIB cross-sectioning. The study found that the mold compound of the package caused thermo-mechanical strain onto the silica filler particle due to epoxy shrinkage during the molding process. Additionally, extra-large, irregularly shaped filler particles (called twin particles), located on top of the chip surface, can cause locally high compression stresses onto the IC layers, initiating cracks in the isolation layers under certain conditions forming a leakage path over the time. Thermo-mechanical finite element analysis was applied to verify the mechanical load condition for these large irregular shaped filler particles. As a result, an additional polyimide layer was introduced onto the IC to mitigate the mechanical stress of mold compound particles to avoid this failure mode. [ABSTRACT FROM AUTHOR]

We present a mesoscale model and the simulation results of a system composed of polyacrylonitrile (PAN), carbon nanotubes (CNTs), and a mixed solvent of dimethylsulfoxide (DMSO) and water. The model describes a fragment of a nascent PAN/CNT composite fiber during coagulation. This process represents one of the stages in the production of PAN composite fibers, which are considered as precursors for carbon fibers with improved properties. All calculations are based on dynamic density functional theory. The results obtained show that the greatest structural heterogeneity of the system is observed when water dominates in the composition of the mixed solvent, which is identified with the conditions of a non-solvent coagulation bath. The model also predicts that the introduction of CNTs can lead to an increase in structural heterogeneity in the polymer matrix with increasing water content in the system. In addition, it is shown that the presence of a surface modifier on the CNT surface, which increases the affinity of the filler to the polymer, can sufficiently reduce the inhomogeneity of the nascent fiber structure. [ABSTRACT FROM AUTHOR]

With the development of Model-Based Systems Engineering (MBSE), model reuse technology is an important method to improve the modeling efficiency and credibility, and the research difficulties and hotspots in the field of complex system simulation. However, model reuse still faces challenges due to the existence of text structure bias and feature mismatch. To this end, this paper presents a structure-based model similarity quantization algorithm to obtain the similarities among models. In the first stage, the properties of the structure between the models are obtained by the structural vectorization method, and different module data are accounted. In the second stage, weights are assigned to the module data using the AHP–CRITIC assignment method. The experiment is based on the circuit amplifier model as a case study, and the experimental results verify the feasibility of structure-based model similarity quantification. [ABSTRACT FROM AUTHOR]

This study presents a finite element model (FEM) simulation of optoelectronic stimulation in the rat brain, designed to investigate the effects of this emerging neural stimulation technique. A detailed 3D model of the rat brain and an equivalent circuit representation of the organic semiconductor-based stimulation device was used to simulate the subdural application of optoelectronic stimuli to the rat brain. The model is based on realistic electrode geometries and material properties. The stimulated brain volume is approximately 38 mm3 with a maximal stimulation depth of 2.6 mm. Our results indicate that optoelectronic stimulation can achieve targeted activation of cortical neurons. Further studies will focus on optimizing device parameters, exploring long-term effects, and expand this approach for specific neural bioelectronics to fully exploit its potential in clinical applications. [ABSTRACT FROM AUTHOR]

Photoacoustic detection has shown excellent performance in measuring thickness and detecting defects in metal nanofilms. However, existing research on ultrafast lasers mainly focuses on using picosecond or nanosecond lasers for large-scale material processing and measurement. The theoretical study of femtosecond laser sources for photoacoustic nondestructive testing (NDT) in nanoscale thin film materials receives much less emphasis, leading to a lack of a complete physical model that covers the entire process from excitation to measurement. In this study, we developed a comprehensive physical model that combines the two-temperature model with the acoustic wave generation and detection model. On the basis of the physical model, we established a simulation model to visualize the ultrafast lasermaterial interaction process. The damage threshold of the laser source is determined, and the effect of key parameters (laser fluence, pulse duration, and wavelength) for AlCu nanofilms on the femtosecond photoacoustic NDT process is discussed using numerical results from the finite element model. The numerical results under certain parameters show good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

A three-dimensional model of cephalosporin powder particles was constructed, and the interaction between the particles and the surface of the transport tool was simulated. In this paper, the microstructure of the tool surface was prepared by femtosecond laser technology and the effect of different tilt angles on the particle transport characteristics was investigated. The experiments show that the interaction force between the particles and the surface of the transport tool increases with increasing particle size, and the rate of change is 0.00854 μN/μm. The resistance required for particle shedding decreases and then increases as the angle of inclination of the machine increases. At a tilt angle of 30°, the interaction force between the particles and the machine is minimal. At less than 30°, the rate of change of resistance reduction is − 0.06021 μN/°. Above 30°, the rate of change of resistance increase is 0.0414 μN/°. The shedding rate of powder particles was higher on the transversely and longitudinally etched tool surfaces than on the unetched surfaces. This work demonstrates that femtosecond laser microstructures on metal tool surfaces can effectively prevent powder deposition on the tool surface. It provides an experimental basis for improving the metrology accuracy of ultrafine powder materials. [ABSTRACT FROM AUTHOR]

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]

In this study, we examine the adsorption of sulfur-containing pollutant gases, specifically H2S, SO2, and CS2, on a pentagonal BCP nanosheet (referred to as penta-BCP) using periodic density functional theory. The findings demonstrate that the presence of adsorbed H2S, SO2, and CS2 gases on a penta-BCP sheet leads to a decrease in the band gap by 24.39, 26.79, and 33.98% respectively. The adsorption energy values for the most stable complexes of H2S/penta-BCP, SO2/penta-BCP, and CS2/penta-BCP are − 0.722, − 1.073, and − 0.619 eV respectively. Additionally, the calculated recovery time at 300 K for the relevant complexes without radiation is 1.42 s for H2S/penta-BCP and 0.026 s for CS2/penta-BCP. Furthermore, the impact of sulfur-containing gases on the transmission characteristics of the penta-BCP nanosheet has been investigated through current–voltage analyses. These analyses provide conclusive evidence supporting the potential use of penta-BCP nanosheet as a substrate for adsorbing and sensing sulfur-containing gases. [ABSTRACT FROM AUTHOR]

In this study, a computational analysis based on density functional theory is conducted to study the elastic, mechanical, vibrational, and thermodynamic properties of novel chalcogens, Ag2BeSnX4 (X = S, Se, and Te). We used the generalized gradient approximation (GGA) within the framework of density functional theory (DFT). The mesh parameter values (a and c) were calculated using the X-ray diffraction method. The calculated elastic constants indicate that the bond strength along the [1 0 0] directions is stronger than that along the direction [0 0 1]; according to the Born-Huang stability criterion, we can see that they are mechanically stable. A high value of the ratio (B/G) is associated with ductility for Ag2BeSnX4 (X = S, Se, and Te) materials. Additionally, the Raman shifts of all samples are calculated. Between 10 and 1000 K in temperature, the vibrational mode shifts were calculated for three chalcoginides. The thermal behavior of these movements shows that these structures can undergo deformation with increasing temperature. These results suggest a first contribution to the understanding of the effect of temperature on the vibrational modes of three kesterite structures Ag2BeSnX4 (X = S, Se, and Te) and consequently on their structures. The heat capacity (C V) , free energy (F) , entropy (S) , and enthalpy (H) are also computed. The kesterite phase of the Ag2BeSnX4 (X = S, Se, and Te) structures aligns with theoretical findings in elastic properties, exhibiting superior elastic properties. These attributes are valuable for the design of optoelectronic devices. [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]

Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm's results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization. [ABSTRACT FROM AUTHOR]

For predicting casting deformation by FEM (finite element method) thermal stress analysis, this paper experimentally constructed and validated the Drucker–Prager (DP) model and its revised model (RDP) for the condensation zone of bentonite-bonded sand molds. The condensation zone has been known as a remarkably low-strength region in the mold, and its mechanical response should be dominant to the casting deformation. To construct the models through uniaxial and triaxial compression tests, the test pieces reproducing the condensation zone were prepared by permeating water vapor through a test piece of bentonite-bonded sand. The constructed models were then applied to the FEM stress analysis of the triaxial test to validate them by comparing the analytical stress–strain curves with the experiment. The experimental stress–strain curve after permeating water did not show any distinct yielding point; however, the analytical curve with the original DP model clearly exhibited a yielding point due to the yield criterion of the model. The distinct yielding criterion should be essentially fatal to reproduce the stress–strain relationship of the condensation zone. Contrarily, the RDP model reproduced the experimental curve with less than 10 % error and was found to be advantageous for modeling the smooth stress–strain relationship of the condensation zone. [ABSTRACT FROM AUTHOR]

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.

Modeling and Simulation (M&S) tools have become indispensable for the comprehensive design, operations, and maintenance of products in the space industry. An example is the European Space Agency (ESA), which relies heavily on M&S throughout the entire lifecycle of a spacecraft. However, their use in operational settings poses significant challenges, mainly attributable to (i) the harsh, uncontrollable, and often unforeseen environmental conditions; (ii) the dramatic changes in operating conditions throughout a spacecraft's lifespan, often beyond the intended designed-for lifetime; and (iii) the presence of epistemic and aleatoric uncertainty. This results in unavoidable discrepancies between the numerical simulations and real measurements, limiting their use for delicate operational tasks. To address those challenges, we present a Bayesian framework for simultaneous calibration of M&S tools, reduction of the model discrepancy, and quantification of the process and model uncertainties. The approach leverages the Kennedy and O'Hagan (KOH) calibration, tailored for a multi-objective problem. Its effectiveness is shown by its application to flying Earth observation spacecraft data and the operational simulation models. [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.

This paper proposes a possible methodology for detecting and mitigating traffic congestion. This method is carried out using a custom-designed traffic scenario model. The model is fully developed in lieu of abundant data support from actual traffic events, which is applicable to localized traffic surveillance conditions, where massive data collection from surveilling devices is infeasible or unviable. This approach includes two parts: model construction and re-routing strategy. The model construction part focuses on the development of a traffic driving scenario, which takes various criteria such as traffic volume and traffic signal into consideration. The goal of this setup is to create a realistic-possible environment, where the proposed methods can be tested. The re-routing strategy is implemented based on the model simulation result of a medium-scale drive-able road map. The idea of the adaptive vehicle re-routing strategy is inspired by the k-shortest path algorithm, adapted with the dynamic congestion re-routing strategy. It will be shown that the model is able to automatically identify congestion patterns that are happening on any road segments, and then initiates a proper re-routing strategy to alleviate such congestion in a timely manner. Although the methodology is realized and validated within a simulated model, the concept is transparent to any transportation system under study without extra complexity. In addition, the proposed modeling and simulation technique can be used for real-time implementation in intelligent transportation management systems.

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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]

Silicon is considered one of the most promising anode materials owing to its high theoretical energy density, however, the volume expansion/contraction during electrochemical lithiation/delithiation cycles leads to instability of the solid electrolyte interphase (SEI), which ultimately results in capacity degradation. Herein, the local stress and deformation evolution status of an SEI layer on an anode particle are investigated through a quantitative electrochemical-mechanical model. The impacts of structural uniformity, mechanical strength, and operating conditions on the stability of the SEI layer are investigated in detail. The simulation results demonstrate that when the silicon particle radius decreases from 800 nm to 600 and 400 nm, the failure time increases by 29% and 65%, respectively, of the original failure time; When the structural defect depth ratio is reduced from 0.6 to 0.4 and 0.2, the failure time increases by 72% and 132%, respectively; For the discharge rate, the condition at 0.1 C has 34% and 139% longer time to failure than that at 0.2 C and 0.3 C, respectively. This work provides insight into the rational design of stable SEI layers and sheds light on possible methods for constructing silicon-based lithium-ion batteries with longer cycling lives. [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]

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]

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]