Search Results (9,522)

Computer-Aided Design (CAD) and Additive Manufacturing (AM), particularly Material Extrusion (MEX) with PLA, are increasingly used in medicine for cost-effective production of sterilizable Patient-Specific Instruments (PSIs). However, dimensional stability remains challenging when large geometries undergo post-processing such as annealing prior to sterilization. This study investigates the influence of selected MEX parameters on deformation in a clinically relevant large-scale PSI. A replicated 2⁴ factorial design was used to evaluate the effect of raft thickness, infill orientation, first-layer pattern, and cooling fan speed through overall 3D and cross-sectional deformation. Infill orientation affected all deformation responses, while fan speed mainly influenced overall 3D and longitudinal deformation. Raft thickness affected the cross-section parallel to the build plane. Interactions involving fan speed were also significant for the overall 3D response. A 25% fan speed, 90° infill orientation, and 6-layer raft were associated with the lowest predicted overall deformation. These findings demonstrate that dimensional stability in large MEX-printed High-Temperature Polylactic Acid (HTPLA) PSIs depends on both individual process parameters and their interactions, with local deformation behaviour differing from the overall response. The results provide a basis for controlling deformation in large PSIs and support further investigations incorporating additional process levels, different PSIs geometries, and sterilization effects. Full article

With the rapid growth of global waterborne tourism, the passenger capacity of ships continues to increase, which means passenger evacuation safety is becoming a critical concern. Existing passenger ship evacuation prediction methods mainly rely on computationally intensive evacuation simulations, which suffer from high computational cost. This limitation hinders rapid evacuation assessment and emergency decision-making for large passenger ships. The objective of this study is to develop a rapid and interpretable surrogate prediction model for passenger ship evacuation time. To achieve this objective, Sobol sequence sampling is employed to efficiently construct representative evacuation scenarios, while Bayesian optimization is used to improve the prediction performance of the Random Forest model. The results show that the Sobol sequence sampling method efficiently designs multi-scenario evacuation cases for the passenger ship. The optimized model achieves high prediction accuracy with an R² of 0.901, effectively capturing the nonlinear relationship between evacuation factors and total evacuation time. Feature importance analysis demonstrates that stairways near the embarkation stations and passengers positioned farther away from them significantly affect total evacuation time, highlighting the spatial disparity in evacuation efficiency. This study provides a reliable data-driven approach and technical support for emergency decision-making and safety management in passenger ships. Full article

High summer temperatures, intense solar radiation, and restricted ventilation in arid desert regions often cause heat accumulation and spatially non-uniform temperature distributions in greenhouses, constraining sustainable protected agriculture. This study investigated a multi-span plastic greenhouse in Xinjiang, China, using a field-validated three-dimensional computational fluid dynamics (CFD) model. Nine combinations of side- and roof-vent openings were evaluated under sunny and cloudy conditions to compare crop-zone temperature and airflow characteristics. The results showed that side-vent opening primarily controlled outdoor-air inflow, airflow penetration, and crop-zone cooling, whereas roof-vent opening mainly affected upper-level heat removal and airflow continuity. Their performance therefore depended on coordinated opening ratios. Under sunny conditions, high-temperature zones above 45 °C developed near the roof and arch shoulders. Enlarging the side vents and appropriately matching the roof-vent opening effectively reduced heat accumulation. In the integrated crop-zone assessment, S3T3 achieved the lowest mean crop-zone temperature of 37.32 °C, 7.35% lower than that under S1T1. Its temperature standard deviation was σ = 0.012, and its cooling demand index was Y_c = 0.406, approximately 74.5% lower than that of S1T1. Optimizing side- and roof-vent combinations can therefore improve the greenhouse thermal environment without energy-intensive mechanical cooling, providing a basis for energy-efficient operation and sustainable protected crop production in extreme climates. Full article

Background: This study investigated the fit accuracy of maxillary complete denture bases (CDBs) fabricated using conventional, fully digital, and two hybrid workflows. Methods: Thirteen edentulous patients each received four maxillary CDBs produced via conventional (C), fully digital (FD), hybrid early (HE), and hybrid late (HL) workflows. Group C employed functional impressions and heat-polymerized polymethyl methacrylate (PMMA). Group FD utilized intraoral scanning (IOS), computer-aided design (CAD), and three-dimensional printing. The HE workflow combined digitally designed custom trays with conventional impressions, while the HL workflow digitized conventional master casts prior to digital fabrication. The master cast from the C workflow served as the reference. Deviations were calculated at 22 landmarks across six anatomical regions, with overall and regional analyses performed. Results: Group C exhibited the lowest deviation, followed by HE. HL and FD groups demonstrated significantly higher deviations (p < 0.001), with no significant difference between them. Regional variations were observed across most anatomical areas, except for the posterior palatal seal (p = 0.287). Conclusions: Clinical workflow significantly influences denture base fit accuracy. Conventional and hybrid early workflows provided more predictable adaptation, whereas hybrid late and fully digital workflows showed greater deviation, particularly in functional regions, potentially affecting clinical performance. Full article

Background: Delayed cerebral ischaemia remains a major cause of secondary brain injury after aneurysmal subarachnoid haemorrhage (aSAH). Although computed tomography perfusion (CTP) is widely used for vasospasm surveillance, perfusion abnormalities are commonly reported only as present or absent, potentially overlooking the prognostic relevance of their extent. We investigated whether a territory-based perfusion burden score is associated with outcome more closely than binary perfusion deficit assessment. Methods: This retrospective single-centre cohort study included 179 patients with aSAH who underwent at least one CTP examination during hospitalization. CTP was performed according to clinical indication rather than at predefined time points; 448 examinations were evaluated. Two blinded neuroradiologists assigned one point per affected vascular territory (score range 0–12), and the maximum score during hospitalization was analysed. Outcomes were functional outcome at 6 months (modified Rankin Scale 0–2 versus 3–6), new cerebral infarction at discharge, and in-hospital mortality. Multivariable logistic regression models were built by forward selection from prespecified covariates (age, Hunt and Hess grade, modified Fisher grade, intracerebral haemorrhage, external ventricular drainage, and aneurysm treatment modality). Results: Territorial perfusion burden was independently associated with poor functional outcome (adjusted odds ratio 1.17 per point, 95% confidence interval 1.02–1.33) and new cerebral infarction (adjusted odds ratio 1.36 per point, 95% confidence interval 1.19–1.55). Any perfusion deficit was associated with new infarction (odds ratio 8.88, 95% confidence interval 3.31–23.87) but not with functional outcome. Conclusions: In this selected cohort under CTP surveillance, the extent of perfusion impairment was associated with functional outcome and infarction, whereas binary deficit assessment was associated with infarction only. These preliminary findings suggest that territorial perfusion burden may refine risk stratification, but prospective validation with standardized imaging time points is required before the score can be used as a prognostic tool. Full article

Alkylsulfonic phenyl ester (ASE) is an alternate non-phthalate plasticizer used in PVC and polyurethane manufacture, such as for medical tubing and toys. Plastic-associated endocrine disrupting chemicals have been associated with increased cardiovascular disease risks and have been established as the agonists of a xenobiotic sensor, pregnane X receptor (PXR), which has atherogenic and dyslipidemic effects. However, it is unknown whether ASE activates PXR to adversely affect cardiovascular system. Our objective is to investigate how ASE impacts lipid profiles and cholesterol uptake via PXR signaling. Human hepatic and intestinal cells were used to test activation of PXR by ASE. The computational docking study and site-directed mutagenesis were used to identify key amino acid residues in PXR that interacted with ASE. The natural PXR inhibitor resveratrol was used to prohibit PXR activities. The male C57BL/6 wildtype mice were fed with ASE to evaluate if ASE could alter plasma lipid levels. Our data suggested that ASE was a specific PXR agonist and increased atherogenic cholesterol levels in mice mediated by PXR. Furthermore, ASE treatment increased cholesterol uptake by human intestinal cells through PXR signaling. Our study elucidates the role of PXR as a mediator of xenobiotic-elicited dyslipidemia and provides evidence for the future risk assessment of ASE on cardiovascular diseases. Full article

The precise identification of musical emotion holds substantial academic and practical value, with critical applications in music recommendation, therapeutic interventions, and real-time human–computer interaction. Conventional paradigms for music emotion recognition, predominantly based on manual feature engineering and conventional machine learning, exhibit major impediments, including pronounced label subjectivity and insufficient generalizability, which have collectively hampered the further development of this domain. The development of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs) has expanded the range of automated approaches available for music emotion recognition. Every approach shows some intrinsic advantages and weakness; thus, they have had some success but are not yet fully qualified for application requirements. This paper will systematically summarize the application of deep learning in music emotion recognition in recent years, including theoretical frameworks, technical implementations, challenges, and future research trends. Typically, we will systematically discuss the current research status of deep learning and machine learning in music emotion recognition via a bibliometric analysis and then reveal the working mechanism of deep learning, which is underestimated in most previous studies. This work would shape the development of this research field, especially for the applications of deep learning in music emotion recognition. Full article

Molecular dynamics (MD) simulations rely on accurate numerical integration of the equations of motion, where the choice of the timestep (Δt) critically affects stability and precision. Rather than aiming to recover an exact atomic trajectory, second-order integrators, such as those implemented in LAMMPS with the Nosé–Hoover thermostat, yield a global error scaling as O(Δt²). However, practical results deviate from this ideal behavior. In this work, we systematically analyze the effect of Δt on the accuracy of MD simulations using a polarizable force field applied to battery-relevant systems. Different errors were quantified for a range of timesteps. We evaluate whether timestep-induced numerical deviations affect physically meaningful observables, including diffusion coefficients and radial distribution functions, and examine the role of different integration schemes in solid and liquid components. The results show that decreasing Δt beyond the stability threshold leads to only marginal improvements in accuracy while significantly increasing computational cost at medium and long timescales. Conversely, excessively large Δt values produce numerical instability and unphysical behavior. These findings indicate that the expected ideal error scaling does not directly translate into practical accuracy gains and highlight the need for balanced timestep selection based on physical robustness rather than trajectory convergence alone. Full article

Firm location in manufacturing supply chain networks is shaped by regional tax policies and network equilibrium responses. This study examines the spatial price equilibrium associated with alternative locations for an entrant manufacturer and evaluates how regional tax incentives affect firm profit and location choice. A bi-level programming model is developed. The upper-level model selects the location that maximizes after-tax profit, while the lower-level model solves the corresponding spatial price equilibrium to obtain the equilibrium transaction flows, market prices, and composite unit costs. Each composite unit cost comprises a flow-dependent transaction cost and a shortest-path transportation cost determined by the selected location. The upper-level problem is solved by complete enumeration, while the lower-level spatial price equilibrium is computed using a projection method. Numerical results show that firm profit is jointly determined by transaction volumes, equilibrium prices, transportation costs and production costs, fixed investment costs, and regional tax rates. Uniform tax-rate reductions increase profit without changing the location ranking, whereas sufficiently large location-specific reductions can reverse the ranking and alter the optimal location. The effectiveness of these incentives depends on location-specific equilibrium revenues and initial profit differences. By integrating firm location, regional tax incentives, and spatial price equilibrium within a unified bi-level framework, this study provides analytical support for manufacturing location decisions and regional investment-incentive design. Full article

Accurate prediction of building energy consumption is critical for sustainable urbanization, while dynamic occupant interactions with the building envelope remain a major source of uncertainty. Prevailing cross-scale building energy models often rely on simplified or uniform behavioral assumptions that cannot adequately represent the spatial and temporal heterogeneity of occupant actions such as window opening and curtain use. This study proposes an AI-augmented framework that integrates field-observed occupant behavior, machine-learning prediction, and physics-based building energy simulation. Time-series observations of window and curtain states were collected from 1609 rooms across 12 residential buildings in a high-density neighborhood in Hong Kong. Environmental and contextual associations were first examined at the aggregated behavioral level, after which Random Forest models were used to generate dynamic behavior schedules. These schedules were subsequently integrated into EnergyPlus to evaluate how different levels of occupant behavior representation affect simulated cooling energy demand and computational cost. Compared with the Detailed Scenario, the Template Scenario produced 20.5% higher simulated cooling energy, while the Average Scenario showed an 8.7% relative difference with 20.6% less simulation time. An additional controlled analysis showed that removing surrounding buildings increased simulated cooling energy by 1.7–9.3%, demonstrating the direct physical influence of inter-building shading. Overall, the proposed framework provides a practical pathway for incorporating context-dependent occupant behavior into urban building energy modeling and for evaluating the trade-offs between behavioral modeling detail and computational efficiency. Full article

The accumulation of water in large reservoirs can affect seismic activity due to changes in stress and pore pressure in the bedrock. This paper presents the results of a study on the influence of accumulation of the Lai Chau reservoir in northern Vietnam on local seismic activity. The reservoir was filled in June 2015. It was observed that although seismicity rates and b-values differed markedly in the months immediately following impoundment, the longer-term seismicity around the Lai Chau reservoir before and after its impoundment is broadly similar. The spatial distribution of earthquakes and stress changes induced by water loading and increased pore pressure were investigated. The load-induced stress field was computed from the pre-impoundment bathymetry of the valley and resolved without assuming any orientation of the regional stress field. Pore pressure diffusion was evaluated for three end-member scenarios of fault zone permeability and classified by the actual hypocentral depth of each event, thereby allowing the triggered subset to be bracketed in the absence of in situ hydraulic data. It was found that this influence was limited to the immediate vicinity of the reservoir, where the stress level reached values that allowed for the initiation of an earthquake. The highest seismicity was observed in the southeastern part of the lake, in the area of positive changes in Coulomb stress. The results suggest that hydraulic operations can trigger local seismic events where pre-existing faults close to failure lie within a few kilometers of the shoreline and at shallow depth. However, tectonic processes remain the dominant cause of seismicity in the region. Full article

Flood disasters often disrupt transportation networks and significantly increase the complexity of emergency relief distribution, threatening community sustainability. Existing truck–drone collaborative delivery studies mainly emphasize efficiency improvement while neglecting variations in demand urgency among affected areas, limiting their ability to balance routing efficiency and emergency response priorities. To address this issue, a truck–drone collaborative emergency relief distribution model that incorporates demand priority is proposed. A demand-point priority evaluation framework is first established based on population density, infrastructure damage, and regional economic importance. The entropy weight–TOPSIS method is employed to determine the comprehensive priority score of each demand node. A priority-delay penalty mechanism is then incorporated into the routing process to coordinate demand urgency with delivery efficiency. Accordingly, a bi-objective optimization model is formulated to minimize the total delivery completion time and maximize the comprehensive priority score, and an improved non-dominated sorting genetic algorithm II (INSGA-II) with an external archive is developed to solve the proposed model. A case study based on the May 2026 flood disaster in Shangyou County, Ganzhou, China, covering 15 demand nodes, is carried out. Compared with standard NSGA-II and SPEA2, INSGA-II shortens computational runtime by 9.50% and 29.86% respectively, and generates more diversified and evenly distributed Pareto solutions. Under compromise, priority-oriented and time-oriented decision-making scenarios, INSGA-II raises comprehensive priority scores by 7.27%, 5.26%, 14.09% versus standard NSGA-II and by 5.36%, 4.05%, 1.80% versus SPEA2, while maintaining competitive delivery-time performance. The proposed method provides effective decision support for emergency resource allocation and truck–drone collaborative emergency logistics in flood disasters. Full article

Finite element model updating (FEMU) provides a physics-based approach for reducing discrepancies between numerical models and measured responses by estimating uncertain physical parameters. Its application to rotating machinery is challenging because rotor dynamics are strongly affected by rotational speed, bearing and support properties, nonlinear interactions, operating conditions, measurement limitations, and parameter correlation. This review examines FEMU methods and applications for rotating machinery, distinguishing direct FEMU studies from nonlinear, uncertainty, surrogate, and AI-based studies that primarily provide enabling techniques. Direct matrix correction, sensitivity-based, optimization-based, surrogate-assisted, Bayesian, AI-driven, and hybrid approaches are compared in terms of physical interpretability, identifiability, computational demand, uncertainty treatment, and validation. The reviewed literature indicates that FEMU is most mature for rotor–bearing calibration, bearing and support parameter identification, and operational-response-based updating, whereas experimentally validated inverse estimation of nonlinear and compound-fault parameters remains limited. Based on these findings, a lifecycle-oriented FEMU framework and research roadmap are proposed, emphasizing multi-condition identifiability, uncertainty-aware updating, computational efficiency, independent validation, and governed synchronization. Surrogate and AI-assisted estimation should remain connected to validated high-fidelity physical models and defined operating domains for credible condition assessment and digital-twin applications. Full article

Upper cervical structural disease can alter vertebral artery biomechanics, although a mechanical contribution cannot be established by anatomical proximity alone. We describe an older woman with seronegative inflammatory arthritis and chronic atlantoaxial abnormality who developed abrupt vertigo, nausea, and inability to walk despite a National Institutes of Health Stroke Scale (NIHSS) score of 0. Computed tomography angiography (CTA) demonstrated left V3 vertebral artery dissection with pseudoaneurysmal dilation at the craniovertebral junction. Magnetic resonance imaging (MRI) showed a dominant acute left posterior inferior cerebellar artery (PICA)-territory infarction. Cervical imaging demonstrated atlantoaxial deformity, pannus-like peri-odontoid tissue, and a C1 arch abnormality adjacent to the affected V3 segment. Echocardiography and inpatient telemetry did not identify an embolic source. Anatomical colocalization of the C1-C2 abnormality and V3 lesion, together with the ipsilateral PICA infarction, suggests a possible mechanical contribution to the dissection, with subsequent artery-to-artery thromboembolism to the cerebellum. Dynamic compression was not demonstrated; spontaneous dissection, intracranial atherosclerosis, and occult embolism remain alternative or additional mechanisms. The patient received dual antiplatelet therapy followed by aspirin, statin therapy, and rehabilitation. This case emphasizes the limitations of the NIHSS in posterior circulation stroke and the importance of evaluating the craniovertebral junction when a V3 lesion occurs near upper cervical structural disease. Full article

Computer models and protocols commonly used for superficial thermotherapy assume anatomically symmetric tissue geometries. However, anatomical variability, particularly the variability of the adipose tissue thickness, may have a significant impact on heat transfer and cause non-uniform thermal doses in the patient population. This paper studies the influence of anatomical asymmetry due to adipose tissue thickness on heat propagation during superficial thermotherapy based on combined computational and experimental analyses. A Finite Element Model of the lower limb was designed with anatomically representative layers and tissue thermal properties. Parametric simulations were conducted for medium (M) and extra-large (XL) models at hot-pack temperatures (38–44 °C) for 15 min. The simulation results were compared with experimental measurements on ex vivo porcine tissue. The results showed that the thickness of adipose tissue significantly affected deep-tissue heating. When the same heating condition was applied, the boundary temperature of the muscle was increased by ~1.38 °C for the 44 °C hot-pack in the M-size model and was restricted to 0.21 °C for the XL-size model. The experimental muscle temperature increased from 22.26 °C to approximately 23.3 °C after 15 min. Under the same initial temperature and nominal 38 °C heating condition, the perfused and zero-perfusion M-size simulations reached approximately 26.8 °C and 30.5 °C, respectively; these endpoint differences support a qualitative comparison rather than quantitative validation. These results show that anatomical asymmetry has a strong effect on heat transfer during superficial thermotherapy and challenge the precept that standard heating protocols provide similar thermal doses in different body morphologies. Full article