搜索结果：Modeling and Simulation

Surface acoustic wave (SAW) devices have become key building blocks in communications, sensing, and emerging neuromorphic hardware, yet their growing complexity in materials, architectures, and operating environments exposes limitations in conventional design and signal processing approaches. Concurrently, machine learning (ML) provides powerful tools for Modeling high-dimensional systems and extracting information from noisy multiparameter data. This review surveys the rapidly developing interface between SAW technology and ML, spanning device-level Modeling, multiparameter sensing, and SAW-driven neuromorphic hardware. We first discuss how ML is used as a surrogate for traditional simulation and compact Modeling. Examples include neural network-based inverse design of SAW resonators from target performance indicators, and regression models used to extract coupling of modes (COM) parameters from simulated or measured responses, which improves the speed and reliability of resonator design. We then examine ML-enhanced SAW sensing, with emphasis on thin-film and flexible Aluminium Scandium Nitride (AlScN) -based devices operating under complex environmental and mechanical conditions. Case studies include flexible UV sensors that use ML regression to decouple bending-induced strain from the target signal, and stacking ensemble frameworks that exploit scattering parameter features to suppress cross-interference between temperature, humidity, and UV intensity. Finally, we highlight how SAWs are being explored as low-power actuators in neuromorphic and AI hardware, enabling acoustic control of phase transitions in Iron-Rhodium alloy (FeRh) and the creation of magnetic skyrmions for artificial synapses and neurons. Across these domains, we identify common patterns in data representation, model selection, and evaluation, and we outline open challenges in dataset curation, interpretability, and deployment on edge and flexible platforms. By bridging the RF design, sensing, and neuromorphic communities, this review charts a path toward ML-native SAW systems that draw on both the rich physics of acoustic waves and the adaptability of modern data-driven methods….

The production of precision workpieces from long products by shearing remains a challenging problem due to bending deformation, end-face cracking, and insufficient dimensional accuracy. Dies with differentiated clamping represent a promising solution; however, the influence of their design parameters on force transmission, energy efficiency, and deformation localization remains insufficiently understood. The aim of this study is to investigate the technological and design features of dies with differentiated clamping of long products and to establish quantitative relationships between wedge mechanism parameters, friction conditions, and process performance. A systematic classification of die designs was developed based on clamping method, force-transmission mechanism, blade kinematics, and structural configuration. Analytical models were derived to describe force transmission in wedge mechanisms and to determine the relationship between clamping force, shearing force, friction conditions, and mechanism efficiency. Finite-element simulations of the shearing process were performed using DEFORM 3D to analyze stress-strain state evolution, deformation localization, and damage development. Experimental investigations were carried out on a 2.5 MN crank press using strain-gauge measurements to validate the theoretical predictions and evaluate workpiece quality. The results demonstrate that force transmission efficiency and the clamping-to-shearing force ratio are strongly governed by wedge geometry and friction conditions. Rational ranges of force-transmission angles were identified, providing an optimal balance between force amplification and energy efficiency. Numerical simulations revealed that differentiated clamping localizes plastic deformation and damage accumulation within a narrow region adjacent to the blade clearance, suppresses workpiece bending, and promotes stable crack propagation along the intended separation plane. Experimental validation confirmed the adequacy of the developed analytical model, with the discrepancy between calculated and measured peak shearing forces not exceeding 8%. Magnetic particle and dye penetrant inspections verified the absence of end-face cracks in workpieces produced from Steel 0 and Steel 40H. The developed die design improved geometric accuracy while reducing overall dimensions and weight compared with conventional solutions. The scientific novelty of the work lies in establishing quantitative relationships between wedge geometry, friction conditions, force transmission efficiency, and deformation localization during shearing with differentiated clamping. The obtained results provide a scientific basis for controlling the stress-strain state and fracture behavior during precision separation of long products and may be used for the design and optimization of energy-efficient shearing technologies.

Norovirus is a major enteric pathogen with pandemic potential and disproportionately high mortality in low-income countries, particularly among young children. Despite its global health burden, no approved vaccine or specific antiviral therapy is currently available. In this study, we targeted two key viral proteins, viral protein 1 (VP1) and RNA-dependent RNA polymerase (RdRp). The workflow included protein modeling, structural stability assessment, molecular docking, molecular dynamics (MD) simulations, non-covalent interaction (NCI) analysis, protein contact atlas evaluation, and pharmacokinetic (ADME-Tox) profiling. Molecular docking results indicated strong binding affinities of selected phytochemicals-Zingiberol, Cardeonolide, Boeravinone B, β-Elemene, and Fisetin-toward both VP1 and RdRp, with binding energies ranging from - 7.8 to - 9.4 kcal/mol. MD simulations further demonstrated the structural stability of protein-ligand complexes, with stable RMSD values (~ 0.3 nm for RdRp and 0.3-0.5 nm for VP1) and only minor transient fluctuations observed in VP1. RMSF analysis revealed localized flexibility, while radius of gyration, hydrogen bonding patterns, and solvent-accessible surface area collectively confirmed overall conformational stability throughout the simulation period. Complementary protein contact atlas and NCI analyses supported the persistence and robustness of protein-ligand interactions, showing comparable or improved stability relative to reference antivirals ribavirin and nitazoxanide. Additionally, all selected compounds exhibited favorable drug-likeness and acceptable ADME-Tox properties. Collectively, these findings suggest that the identified phytochemicals may serve as promising antiviral candidates against norovirus, although further validation through in vitro and in vivo studies is required.

Therapeutic ultrasound offers a noninvasive approach for modulating metabolic function, yet spatially heterogeneous acoustic and thermal responses in the abdomen remain poorly understood. Here, we developed a three-dimensional, organ-resolved computational model of ultrasound propagation and heat transfer in the mouse abdomen to systematically investigate the effects of ultrasound parameters, body composition, blood perfusion, and gastrointestinal gas on acoustic energy deposition and tissue heating. Simulations were performed using ultrasound exposures with input intensities of 1-5 W/cm2, frequencies of 400-1200 kHz, duty cycles of 10%-100%, and sonication durations of 5 minutes. Increasing duty cycle or input intensity produced monotonic rises in peak temperature, while frequency and lean versus obese body composition had modest effects. Incorporation of organ-specific blood perfusion reduced maximum temperatures across all tissues, with the largest reduction in the kidney (4.2%) and smaller reductions in the pancreas (2.7%), stomach (2.2%), liver (2.0%), and spleen (1.7%). The presence of intragastric air, modeled as an air-filled stomach cavity and compared to the baseline soft-tissue stomach model, redistributed acoustic energy. Peak intensity increased in the kidney (＋0.4 W/cm2), stomach (＋2.0 W/cm2), and liver (＋1.3 W/cm2), while modest decreases were observed in the pancreas (-0.2 W/cm2) and spleen (-0.1 W/cm2). Overall, these results provide quantitative insight into organ-specific ultrasound energy deposition and heating in the mouse abdomen. The findings highlight the moderating role of blood perfusion and the potential influence of gastrointestinal gas on localized acoustic exposure, offering guidance for optimizing ultrasound parameters in preclinical studies while maintaining thermal safety.

Young people are among the most intensive users of digital and generative artificial intelligence (GenAI)-enabled mental health tools, yet they remain underrepresented in the research and design processes that shape these technologies. Although participatory approaches such as co-design and patient and public involvement are widely endorsed as best practices, youth involvement in digital youth mental health (DYMH) research is often inconsistent, superficial, or limited to late-stage consultation. This participation gap risks producing interventions that are misaligned with young people's lived experiences, priorities, and vulnerabilities, particularly in the context of rapidly evolving and scalable GenAI systems. This Viewpoint aims to reexamine the underlying drivers of the participation gap in DYMH research; clarify how participation is conceptualized and implemented across disciplines; and propose concrete, actionable recommendations to support more meaningful and consistent youth involvement across the research life cycle. We draw on interdisciplinary literature from digital mental health, human-computer interaction, child-computer interaction, and health research policy. Our Viewpoint integrates conceptual frameworks (eg, Lundy's model of participation), existing reviews of co-design practices, and emerging evidence on GenAI in mental health. We adopt a life cycle-oriented perspective to examine how youth participation is distributed across stages of research and development, including problem formulation, design, implementation, and evaluation. We identify 3 interrelated drivers of the participation gap. First, conceptual and linguistic fragmentation obscures what participation entails in practice, with terms such as co-design, participatory design, user-centered design, and patient and public involvement used inconsistently across disciplines. Second, youth involvement is uneven across the research life cycle, with participation often concentrated in early ideation or usability testing but largely absent from upstream decision-making and downstream evaluation. Third, institutional barriers-including ethics review processes, consent requirements, funding constraints, and adult-centric research norms-systematically limit meaningful youth partnership. These challenges are amplified in the context of GenAI, where opaque "black box" systems, simulated therapeutic interactions, and rapid deployment cycles introduce distinct risks if youth perspectives are not integrated. We propose a set of minimum expectations to address these gaps, including explicit specification of participatory models, life cycle mapping of youth involvement, reporting of youth influence on decisions, dedicated funding for participation, proportional ethics frameworks, and mechanisms for youth-informed governance of GenAI systems. Closing the participation gap in DYMH research is both an ethical imperative and a practical necessity. Moving beyond aspirational commitments requires embedding youth participation as a standard, resourced, and accountable component of research, design, and governance. In the context of rapidly evolving digital and GenAI technologies, failure to do so risks producing interventions that are scalable but not safe, credible, or responsive to the needs of young people.

This paper presents a numerical-experimental study of microstructure evolution during cold rolling of AA1050 aluminum. For the first time, a dislocation density-based constitutive model used to simulate microstructural evolution during the cold rolling of commercially pure aluminum. The model was implemented within the ABAQUS/Explicit environment via a VUMAT subroutine to directly couple strain hardening behavior with dislocation density evolution and grain refinement mechanisms under varying thickness reductions. Cold rolling reductions of 10%, 20%, 30%, 40%, and 80% were simulated, and the resulting fields of dislocation density, equivalent plastic strain, grain size, and stress were analyzed. The results show that increasing thickness reduction intensifies plastic deformation and produces pronounced grain refinement, especially near the surface and edges, trending toward more homogeneous refinement at higher reductions. Dislocation density exhibits a rapid initial rise followed by stabilization, with the highest stabilized values obtained at 80% reduction. Experimental validation using X-ray diffraction line profile analysis for the 20% and 40% reductions demonstrates excellent agreement with simulation predictions, with discrepancies below 2%. The calibrated implementation provides a reliable framework for predicting microstructure-thickness relationships and serves as an effective tool for designing reduction schedules to achieve desired microstructural states and improved mechanical performance. Moreover, the techno-economic assessment shows that selecting Al1050 for lithium-ion components and numerical analysis of cold rolling process generated a 10-year net present value (NPV) gain of approximately USD 2.2-2.5 million compared with conventionally processed Al1060 and Al3003.

Digital transformation is fundamentally changing the diagnosis, monitoring and treatment of multiple sclerosis. The integration of multimodal data from imaging, laboratory tests, clinical assessments, patient-reported outcomes and continuous measurements via wearables is creating high-resolution, longitudinal profiles of disease progression. Based on this data, modern analysis methods and artificial intelligence enable predictive models for disease activity, progression and therapeutic response, supporting personalised decision-making. Digital patient pathways and patient portals open up new options for participatory, standardised care, while telemedicine, telerehabilitation and digital health applications complement care regardless of location and time. In research, real-world data, federated learning and virtual, decentralised studies are accelerating patient-centred evidence generation. Concepts such as the digital twin outline the next stage of development in simulation-based precision medicine. Key challenges relate to data protection and data security, data quality, interoperability, bias, transparency and the traceability of algorithmic decisions. Overall, digitalisation offers substantial opportunities to detect disease activity earlier, optimise treatment goals and improve quality of life and care - provided that technical, regulatory and ethical requirements are consistently addressed and translated into scalable care models. Die digitale Transformation verändert Diagnostik, Monitoring und Therapie der Multiplen Sklerose grundlegend. Durch die Integration multimodaler Daten aus Bildgebung, Labor, klinischen Assessments, patientenberichteten Ergebnissen sowie kontinuierlichen Messungen via Wearables entstehen hochauflösende, longitudinale Profile des Krankheitsverlaufs. Auf dieser Datengrundlage ermöglichen moderne Analyseverfahren und Künstliche Intelligenz prädiktive Modelle zur Krankheitsaktivität, Progression und Therapieantwort und unterstützen personalisierte Entscheidungswege. Digitale Patientenpfade und Patientenportale eröffnen neue Optionen für partizipative, standardisierte Versorgung, während Telemedizin, Telerehabilitation und digitale Gesundheitsanwendungen die Betreuung orts- und zeitunabhängig ergänzen. In der Forschung beschleunigen Real-World-Daten, föderiertes Lernen und virtuelle, dezentralisierte Studien patientenzentrierte Evidenzgenerierung. Konzepte wie der digitale Zwilling skizzieren die nächste Entwicklungsstufe einer simulationsgestützten Präzisionsmedizin. Zentrale Herausforderungen betreffen Datenschutz und Datensicherheit, Datenqualität, Interoperabilität sowie Bias, Transparenz und Nachvollziehbarkeit algorithmischer Entscheidungen. Insgesamt bietet die Digitalisierung substanzielle Chancen, Krankheitsaktivität früher zu erkennen, Therapieziele zu optimieren und die Lebens- und Versorgungsqualität zu verbessen – vorausgesetzt, dass technische, regulatorische und ethische Voraussetzungen konsequent adressiert und in skalierbare Versorgungsmodelle überführt werden.

Selection for uniformity in livestock species is desirable because it is associated with welfare and robustness. To address variability, it is essential to have more than one record per animal; therefore, the number of records per individual is crucial. The guinea pig, characterised by its low litter size, is a species of notable economic and nutritional importance, especially in the Andean region of South America. The objective of this study was to evaluate, through simulation, the impact of low record numbers on the performance of heteroscedastic models used in selection for uniformity, using guinea pig birth weight as an example. Different data structures that modulated litter size (LS), number of litters (NL), number of breeding animals (BAs), and number of generations of records were evaluated. The analysis of simulated data was performed under Frequentist and Bayesian statistical approaches. In general, the model was able to adequately estimate the variance components and predict genetic values, although its performance depended on the data structure. Under the Frequentist approach, the litter variance affecting the residual variance was biased downward in LS = 2 and LS = 3 (between -69 and -96%), although the bias decreased in LS = 5 and LS = 7 (between -10 and -44%). The BAs did not influence the bias. At NL = 2, the bias ranged from -68 to -76%, and at NL = 6, from -35 to -69%. The Bayesian method failed to converge in the scenario with two generations and NL = 2. The accuracy of the genetic values for both the trait and the residual variance was identical regardless of the statistical approach used. An increase in BAs did not lead to favourable changes in accuracy, but higher LS and NL tended to improve the accuracy of the genetic values of the trait and the residual variance. We conclude that selection for uniformity under a low number of repeated records, such as the limited number of within-litter birth weight data in guinea pigs, is feasible. However, it is recommended to carefully monitor the data structure to predict the expected response and, if possible, to use populations with the highest possible litter sizes.

Enteroviruses impose a significant global health burden, causing outbreaks of Hand, Foot, and Mouth Disease (HFMD) and severe neurological complications. Currently, no specific and broadly effective antiviral therapeutics are available due to the rapid mutation and co-circulation of diverse viral strains. The viral capsid protein VP1 is critical for viral integrity and host cell entry, making it an attractive target for drug development. Myricetin (MC) is a highly safe, naturally occurring flavonol widely distributed in medicinal plants (e.g., Myrica rubra) with well-documented ethnopharmacological applications and antiviral properties. However, its specific interaction with non-enveloped enteroviruses remains elusive. This study aimed to comprehensively investigate the broad-spectrum antiviral efficacy of MC against diverse, highly pathogenic enteroviruses, structurally and functionally elucidate its molecular mechanism targeting the VP1 capsid protein, and evaluate its preclinical therapeutic potential in vivo. The antiviral activity of MC was evaluated in vitro using cytopathic effect (CPE) reduction assays, plaque viral reduction assays, and RT-qPCR against a diverse panel of viral strains (EV-A71, CV-A16, EV-D68, CV-B3, CV-A6, and human coronavirus OC43 [HCoV-OC43]). The mechanism of action was investigated utilizing time-of-addition and temperature-shift attachment assays. Target engagement was mapped through continuous drug-resistance selection, whole-genome sequencing, and reverse genetics (via a mutant reporter virus construction). Structural interactions were modeled via molecular docking, 100-ns molecular dynamics (MD) simulations, and the binding affinity was quantitatively validated using Biolayer Interferometry (BLI). In vivo efficacy was assessed in a lethal EV-A71-infected neonatal ICR mouse model. MC exhibited robust, broad-spectrum antiviral activity against all tested enteroviruses (IC50 in the low micromolar range) with a favorable, dose-dependent safety margin (high CC50). Time-of-addition and temperature-shift assays revealed that MC functions as a viral entry inhibitor, directly blocking the initial attachment of the virus to host cells. Crucially, viral passaging identified a non-synonymous E98K escape mutation on the EV-A71 VP1 capsid protein, which was proven via reverse genetics to completely abrogate the antiviral efficacy of MC. BLI analysis confirmed a direct, strong interaction (Kd = 16.66 µM) between MC and purified VP1. Computational analyses elucidated this broad-spectrum capability, revealing that MC specifically binds to a conserved surface-exposed region formed by the flexible BC, DE, and HI loops within the VP1 protein, distinct from classical deep-pocket binders. In vivo, systemic administration of MC (100 mg/kg) successfully rescued mice from lethal EV-A71 infection, alleviating extreme weight loss and severe hind-limb paralysis, while significantly clearing viral RNA loads from the brain, lungs, and muscle tissues. MC acts as a novel, naturally derived, broad-spectrum enterovirus capsid binder that physically intercepts viral entry by targeting evolutionarily conserved surface-exposed loops on the VP1 protein. Supported by clear ethnopharmacological relevance, definitive mechanistic clarity, and robust translational efficacy in vitro and in vivo, MC represents a highly valuable natural antiviral lead for the management of current and emerging enterovirus outbreaks.

Falco biarmicus feldeggii has experienced increasing anthropogenic pressures over recent decades, resulting in regional population declines. Despite its critical conservation status, the species remains poorly characterized from a genetic standpoint. In this study, we assessed genetic differentiation between captive and wild Italian specimens of F. b. feldeggii to evaluate the genetic consequences of captive breeding. We also simulated alternative population reinforcement scenarios and developed a predictive model integrating demographic trends, population viability, and genetic outcomes based on actual genotypes and alternative mating systems. Our results showed a close genetic similarity between captive and wild specimens, supporting the use of the captive dataset as input for the simulation model. Simulation of various reinforcement scenarios highlighted that juvenile mortality had a stronger influence on the establishment of a long-term self-sustaining population. Furthermore, our model showed that while demographic parameters strongly shaped the trajectories of genetic diversity, selected mating had only a limited and short-term impact in our simulations providing a small contribution to stabilizing early-generation genetic diversity.

Large language models (LLMs) are rapidly entering clinical and consumer use, yet their probabilistic outputs have delivered a variety of unsafe user responses. Difficulties in quantifying and mitigating risks posed by LLMs threaten to stall regulatory evaluation and clinical deployment of LLM-based software as a medical device (LLM-SaMD). Practical approaches are needed to extend existing medical-device regulations to LLM-SaMDs. To demonstrate how simulation can extend existing medical-device risk management frameworks for addressing LLM-SaMD-specific risks. We implement a simulation-based methodology for estimating LLM-SaMD risk. Fourteen open-source models were evaluated on three safety-classification tasks: suicidal-ideation, therapy-request and therapy-like interaction detection. Synthetic datasets were generated by Gemini 2.5 Pro and evaluated by psychiatrists. Model false-negative rates informed estimates of P1, the likelihood that a hazard progresses to a hazardous situation, and P2, the likelihood that that situation results in harm. LLM success at generating synthetic datasets varied by task, with strong performance for neutral and non-therapeutic content but frequent errors in suicidal-ideation and therapy-like interactions. Performance generally improved with model size. Estimated P1 values ranged from 1.1×10⁻⁸ to 1.6×10⁻⁴ and P2 from 4.9×10⁻⁵ to 5.1×10⁻³, spanning four orders of magnitude. By linking model failure modes to structured pathways to harm, simulation can extend existing medical-device risk frameworks to help address the probabilistic and context-dependent risks of LLM-SaMDs. Simulation-based risk estimation offers a practical way to characterise the risk landscape for specific LLM-SaMD, patient population and clinical context combinations.

Vascular remodeling, the adaptive reshaping of the vascular network in response to changing demands of the tissue, plays a critical role in embryo development and various pathologies. Biochemical signals present in the extravascular region, such as vascular endothelial growth factor (VEGF), are key regulators in the vascular remodeling process. Although the role of these signals has been well studied in vitro, in vivo quantification of VEGF transport during vascular remodeling remains challenging since it requires computing VEGF transport on a growing, topologically changing geometry. In this work, we propose a computational method to compute the VEGF concentration inside the extraembryonic tissue of a growing quail embryo during early vascular development. The evolving vascular geometry is obtained from time-lapse images of a growing quail embryo and represented implicitly via a phase field formulation, which tracks complex topological changes without mesh regeneration, enabling accurate simulation of VEGF transport in the growing extraembryonic tissue. Our simulations demonstrate that tissue growth can significantly influence VEGF distribution, which in turn affects the spatial cues driving vascular remodeling. This effect arises because the timescale of VEGF production and binding mechanics is comparable to the timescale of the impact of tissue growth on VEGF.

Preventive strategies for type 2 diabetes (T2D) include interventions aimed at modifying population-level risk factors and interventions targeting individuals at elevated risk. However, the combined national impact of these approaches has not been quantified. This study used a simulation model to estimate the number of incident T2D cases and diabetes-related complications that could be prevented over 10 years by implementing evidence-based, combined preventive strategies. These included population-level sugar-sweetened beverage (SSB) reduction interventions and individual lifestyle intervention programs for adults with elevated glycemic risk (HbA1c 5.7%-6.5%). Two scenarios were evaluated: (a) a moderate strategy involving a 13% reduction in SSB consumption and an online lifestyle intervention; and (b) an intensive strategy involving a 55% reduction in SSB consumption and an in-person lifestyle intervention. A nationally representative cohort of U.S. adults without diagnosed diabetes was constructed using data from the 2013-2018 National Health and Nutrition Examination Survey. The moderate strategy was projected to prevent 3.7% (562,546) of new T2D cases over 10 years, while the intensive strategy would prevent 12.8% (1,958,164). The number of diabetes-related complications prevented was generally aligned with the reduction in incident T2D cases. Across different scenarios, of the total cases prevented, 64%∼98% were attributable to the SSB reduction. These findings demonstrate the potential national impact of integrating population-level strategies to reduce SSB consumption and a targeted Diabetes Prevention Program intervention to reduce the incidence of T2D and its complications.

Air quality prediction is an important environmental task that supports pollution monitoring, public health protection, and sustainable urban decision-making. This study proposes a hybrid BiGRU-MLP prediction framework enhanced using Binary Ant Colony Optimization (BACO) for feature selection and the Firefly Algorithm (FA) for hyperparameter tuning. The proposed model was evaluated using the public Air Quality dataset from Kaggle, which contains 9,357 records and multiple pollutant and meteorological attributes, including CO(GT), NOx(GT), NO2(GT), temperature, relative humidity, and sensor-based air quality indicators. The dataset was divided into 70% training, 20% validation, and 10% testing subsets to ensure reliable model development and unbiased performance evaluation. Several baseline models were implemented for comparison, including BiGRU, MLP, Neural Network (NN), LSTM, and BiLSTM. The experimental results demonstrate that the proposed BiGRU-MLP model achieved the best predictive performance, obtaining an MSE of 0.0001, RMSE of 0.0101, MAE of 0.0081, MedAE of 0.0070, MAPE of 0.0008, and R² of 99.99%. In comparison, the standalone BiGRU achieved an R² of 96.87%, while MLP, NN, LSTM, and BiLSTM achieved R² values of 95.17%, 92.71%, 90.88%, and 89.29%, respectively. These results confirm that combining BiGRU's temporal learning capability with MLP's nonlinear feature representation significantly improves prediction accuracy. The integration of BACO and FA further enhances the model by selecting informative features and optimizing model parameters. Therefore, the proposed framework provides an accurate and efficient solution for air quality prediction and can support intelligent environmental monitoring systems.

The association between near-death experiences (NDEs) and physiological indicators remains an unsolved mystery, which hinders in-depth understanding of the essence of consciousness and life processes. Traditional single-indicator analysis methods have limitations, and a comprehensive multi-modal research approach is urgently needed to advance relevant explorations in the fields of medicine, neuroscience, and philosophy. Multi-modal physiological monitoring data, including electroencephalogram (EEG) and electrocardiogram (ECG), from critical care settings (ICU, emergency department) were integrated. Signal analysis was conducted by drawing analogies from the concepts of in-mold electronics (IME) and injection molding node displacement. Latin Hypercube Sampling (LHS) was used to collect injection molding parameters, and the Multi-Strategy Differential Evolution (MSDE) algorithm (incorporating elite-sharing, perturbation-backtracking, and adaptive-tuning strategies) was combined to optimize the injection molding process of the brain-computer interface (BCI). A node displacement prediction model was constructed through Moldex3D simulation and Kriging interpolation. During the out-of-body sensation phase of NDEs, EEG showed an increase in gamma waves and a decrease in alpha waves, while ECG exhibited arrhythmia, confirming the coordinated changes between the brain and the heart. In BCI manufacturing, the MSDE algorithm reduced the average node displacement from 0.289 mm to 0.021 mm (with an optimization rate of 92.73%), the volume shrinkage rate from 10.162% to 6.39%, and the optimized voltage difference from 5.78 V to 0.42 V, which was consistent with the improvement in displacement. Multi-dimensional analysis is crucial for decoding the mechanism of NDEs. The optimized BCI hardware enables accurate collection of NDE-related physiological signals, providing scientific support for end-of-life care, optimization of resuscitation protocols, and consciousness research, while also building a cross-disciplinary bridge between engineering and life sciences.

Timely hospital admission is a prerequisite for effective acute stroke management, yet a substantial proportion of patients fail to reach medical facilities within the optimal therapeutic window. Existing prediction models often lack temporal robustness and clinical interpretability, limiting their utility in real-world, evolving health care systems. This study aimed to develop and temporally validate machine learning and deep learning models using multicenter clinical data to predict early hospital admission (≤24 h) after acute stroke. In this multicenter retrospective study, we analyzed routinely collected electronic medical record data from 1327 patients across 6 hospitals in China. We developed and compared 6 predictive models: logistic regression, support vector machine, random forest, multilayer perceptron (MLP), convolutional neural network, and long short-term memory, for early admission (≤24 h from symptom onset). Model training was performed on a train set (2019-2022), followed by independent temporal validation on a testing set (2023-2025). Model prediction performance was evaluated using discrimination metrics, sensitivity, and robustness under temporal distribution shift. Model interpretability was assessed using Shapley additive explanations. A total of 1327 patients were included, of whom 821 were assigned to the train set and 506 to the independent temporal testing set. Among the 6 candidate models, the MLP showed the best overall performance in the independent temporal testing set, achieving an area under the receiver operating characteristic curve of 0.9020 (95% CI 0.8718-0.9283), sensitivity of 91.5%, specificity of 75.6%, and F1-score of 0.9033. Formal statistical comparisons showed that the MLP achieved significantly higher area under the receiver operating characteristic curve values than logistic regression, support vector machine, random forest, and one-dimensional convolutional neural network after false discovery rate correction, with a smaller but still statistically significant improvement over the long short-term memory. Calibration analysis further showed that the MLP had the most favorable overall calibration profile among the candidate models. In this multicenter Chinese cohort, the MLP showed favorable temporal performance for predicting early hospital admission after stroke. The model may support future risk stratification and targeted public health interventions, although further external validation and calibration refinement are needed before deployment-oriented use.

Lignocellulose is a promising renewable resource for anaerobic biochemical production, but its microbial conversion remains challenging. To elucidate metabolic networks in lignocellulose-degrading consortia, inocula of various origins were enriched on cellulose or xylan. Community composition and metabolic functions were revealed by amplicon sequencing, metagenomics, genome-scale metabolic modelling, and metabolic simulations. In cellulose-enriched communities, Fibrobacter and Lacrimispora consistently dominated as primary cellulose degraders, whereas Bacteroides likely functioned as secondary degraders. Acetic acid (up to 1.3 g l-1) and CO2 were the main fermentation products. Xylan enrichments produced C2-C6 fatty acids (up to 3.9 g l-1), lactic acid (up to 1.2 g l-1), ethanol (up to 1.2 g l-1), CO2, and H2. Clostridium dominated one xylan community and produced mainly butyric acid, while Bifidobacterium dominated another and produced mainly lactic acid. Caproic acid production was experimentally observed in one xylan enrichment. Metagenomic annotations and metabolic simulations suggest that Lacrimispora amygdalina degraded xylan and Robinsoniella peoriensis consumed xylobiose as a secondary consumer, both likely producing ethanol and lactic acid that supported caproic and butyric acid production by Caproicibacter fermentans. Integrated analysis identified functional guilds and clarified the roles of degraders and non-degraders, providing a blueprint for engineering synthetic consortia for sustainable biochemical production.

pH-responsive materials that could target cariogenic environment have demonstrated great potential to inhibit dental caries. The objective is to investigate the anti-caries property of the self-assembled, pH-responsive antibacterial dodecylmethylaminoethyl methacrylate nanoparticles (DMAEM NPs) and explore the antibacterial mechanism. DMAEM was analyzed by computational simulation. Then it was synthesized and characterized. The in-vitro cytotoxicity and pH-responsive effect were tested in various pH values. Then, the effect of DMAEM NPs on the S. mutans biofilms was investigated with a treatment mode like daily oral care (10 min, twice per day). The RNA-seq and transcriptome analysis was conducted. The biocompatibility and anti-caries effect were also investigated in a rat model. . DMAEM self-assembled into nanoparticles and the morphologies varied in different pHs. The NPs demonstrated good in-vitro biosafety and acid-activated inhibiting effect on S.mutans biofilms. With repeated short-time treatment by DMAEM NPs， the biofilm activity, lactic acid production and EPS formation were inhibited. SEM and TEM images showed the bacterial cell envelops were ruptured. On the transcriptomic level, DMAEM NPs down regulated the gene expression such as gtfBC, gbpC and lrgAB. The COG, GO and KEGG analysis showed the function of ribosome and macromolecule metabolism were mainly down regulated. In the rat model, DMAEM NPs showed no side effect on oral mucosa and reduced the caries scoring. The self-assembled, pH-responsive DMAEM NPs could inhibit the cariogenic biofilm formation and the development of dental caries. This provides an interesting strategy to combat dental caries, which is promising for future application.

Dual-energy computed tomography (DECT) enables improved volumetric bone mineral density (vBMD) assessment by accounting for marrow alterations associated with aging, disease, and injury. However, DECT reconstruction kernels and monochromatic energy pair combinations may influence vBMD measurements and finite element model (FEM)-estimated bone stiffness. This study investigated the effects of reconstruction kernel and DECT energy pair combinations on proximal humeral vBMD and FEM-estimated stiffness in cadaveric specimens. Fourteen cadaveric shoulders from seven specimens were scanned bilaterally using DECT with a K2HPO4 calibration phantom. Images were reconstructed using standard (STD) and bone-sharpening (BONE) kernels. Simulated monochromatic images at 40, 90, and 140 keV were combined into 40/90, 40/140, and 90/140 keV energy pairs. Volumetric BMD was extracted from the humeral shaft diaphysis and anatomic neck using custom Python scripts and 3D Slicer software. Image-based FEMs were generated to estimate bone stiffness. Results were analyzed using repeated-measures analysis of variance (RM-ANOVA). In the cortical-dense humeral diaphysis, energy pair combinations had the greatest variation. Mean diaphyseal vBMD increased from 332.08 ± 102.54 mgK2HPO4/cc (40/90 keV BONE) to 406.84 ± 130.15 mgK2HPO4/cc (90/140 keV BONE), while FEM stiffness increased from 180.30 ± 47.07 kN/mm to 223.30 ± 63.91 kN/mm. Significant differences were observed across reconstruction conditions and energy pair combinations. In contrast, trabecular-rich anatomic neck regions demonstrated minimal variation in vBMD and FEM stiffness across energy pairs and kernels. These findings indicate that DECT energy pairs and reconstruction kernel substantially influence cortical bone assessments, particularly when using the 90/140 keV energy pair, while trabecular-rich regions remain comparatively unaffected.