Search Results (316)

Fixed external louvers are widely used to improve the environmental performance of glass curtain wall office buildings, yet existing studies more often report preferred solutions than transferable decision ranges for early-stage design. This study develops interval-based design rules for a standard-floor prototype of a point-supported glass curtain wall office building in Tianjin, a representative cold-climate city in China. A seven-variable design space integrating spatial-scale and shading variables was evaluated for 3000 Latin hypercube samples in a Rhino–Grasshopper–Honeybee workflow linked to Radiance and EnergyPlus, using Tianjin’s typical meteorological year data and GB 55015—2021-based office schedules, including an occupant density of 10 m²/person and occupied heating/cooling setpoints of 20/26 °C. Raw-sample statistics, Bootstrap-based stability testing, and surrogate-model-assisted continuous-response analysis were used to identify dominant variables, single-objective preferred intervals, and a neutral equal-weight baseline compromise zone. Under a neutral equal-weight baseline adopted for early-stage comparison, the compromise interval is concentrated around 20–25°, with 15–30° as a practical starting range, while alternative weighting scenarios show directional shifts toward the prioritized objective. Full article

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R² values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings. Full article

The optimal design of long-span structures is hindered by the combination of prohibitively high computational costs and the limited physical consistency of purely data-driven surrogates. To address this challenge, this study proposes a multi-stage automated design framework that shifts the workflow from repeated per-task solving to reusable digital asset creation. First, a large-scale surrogate-optimized dataset containing 100,000 design samples is generated by embedding a high-speed MLP emulator into a Genetic Algorithm (GA). The core innovation lies in training a physics-regularized neural design generator. By incorporating a reduced-order total potential energy term derived from the principle of minimum potential energy as a regularization constraint, the network learns the mapping from external design conditions to validated near-optimal internal parameter combinations while suppressing mechanically unfavorable configurations associated with low stiffness. This mechanism improves mechanical admissibility, particularly in data-sparse regions. Validation results show that the generator achieves millisecond-level candidate generation and reduces the prediction error to 31% of that of conventional models under sparse-data conditions. In a like-for-like case study with identical external input parameters, the generated candidate design achieves a 21.1% reduction in total steel consumption. The proposed framework is therefore best understood as a rapid preliminary design tool for producing weight-efficient and mechanically admissible candidate schemes, which can then be subjected to subsequent high-fidelity analysis and code-based verification. Full article

Bidirectional power converters generate significant heat losses during high-frequency operation, posing a severe challenge to the performance of heat dissipation systems. Traditional thermal design methods often struggle to balance multiple objectives, such as cooling efficiency, cost, weight, and size, thereby limiting the reliability and safety of the system. To address these challenges, this paper proposes a novel Multi-Objective Exponential Distribution Optimizer algorithm based on the Exponential Distribution Optimizer. Subsequently, key design variables of the heat dissipation system are selected. Next, the Optimal Latin Hypercube Sampling method is employed to generate sample points, and a second-order response surface surrogate model for the heat pipe radiator’s volume and temperature is developed. Lastly, by integrating elite non-dominated sorting, crowding distance mechanisms, and an information feedback mechanism, the multi-objective challenge is decomposed into subproblems, thereby enhancing optimization efficiency. Through comparative simulation experiments on benchmark functions, the Wilcoxon signed-rank test results for the MOEDO algorithm on the majority of the three metrics are denoted as ‘+’, indicating statistically significant advantages over the compared algorithms, thereby demonstrating its superior performance in addressing multi-objective optimization problems. The study further conducts simulation verification of the heat pipe heat dissipation system before and after optimization using ANSYS Icepak. The simulation results demonstrate that, compared with the conventional design, the maximum Insulated Gate Bipolar Transistor (IGBT) temperature is reduced by 17.12% and the heat sink volume is reduced by 14.61%. Full article

Background/Objectives: Vasomotor symptoms (hot flashes) affect 70–80% of menopausal women, significantly impairing quality of life. Current treatments include hormone therapy, which is contraindicated for many patients, and non-hormonal alternatives with limited efficacy or adverse effects. Cannabidiol (CBD), a non-psychoactive phytocannabinoid, has emerged as a potential therapeutic candidate due to its interaction with the endocannabinoid system. This study aimed to investigate whether a standardized Cannabis sativa extract containing isolated CBD attenuates heat dissipation in ovariectomized rats, a preclinical model of estrogen deficiency. Methods: Female Wistar rats were randomly assigned to sham-operated vehicle-treated (SHAM-V), ovariectomized vehicle-treated (OVX-V), or ovariectomized CBD-treated (OVX-CBD; 10 mg/kg/day, oral gavage) groups. Treatment began on postoperative day 2 and continued for 21 days. Tail-skin temperature, a surrogate marker of heat dissipation, was assessed by infrared thermography on day 14. Energy metabolism was evaluated by indirect calorimetry on day 21. Uterine weight was measured as a biomarker of estrogen depletion. Results: Ovariectomy significantly increased tail temperature compared to SHAM-V. CBD treatment completely prevented this effect, with OVX-CBD animals exhibiting thermographic profiles similar to SHAM-V. Uterine atrophy was not reversed by CBD. No differences in the calorimetry parameter were observed among groups. Conclusions: This study provides novel preclinical evidence that cannabidiol attenuates ovariectomy-induced heat dissipation in rats, without detectable effects on uterine weight or metabolic parameters. These findings suggest that CBD may represent a potential non-hormonal approach for the management of menopausal vasomotor symptoms; however, further studies are required to elucidate the underlying mechanisms and to determine its translational and clinical relevance. Full article

Spiking Neural Networks (SNNs) executed on neuromorphic hardware promise energyefficient, low-latency inference well-suited to edge deployment in size, weight, and powerconstrained environments such as autonomous vehicles, wearable devices, and unmanned aerial platforms. However, a coherent research pathway to deployment of neuromorphic devices remains elusive. This paper presents a structured review and position on the state of SNN-based vision across four interconnected dimensions: network architectures, training methodologies, event-based datasets and simulation techniques, and neuromorphic computing hardware. We survey the evolution from shallow convolutional SNNs to spiking Transformers and hybrid designs which leverage the advantages of SNNs and conventional artificial neural networks. We also examine surrogate gradient training and ANN-to-SNN conversion approaches, catalogue real-world and simulated event-based datasets, and assess the landscape of neuromorphic platforms ranging from rigid mixed-signal architectures to fully-configurable digital systems. Our analysis reveals that while each area has matured considerably in isolation, critical integration challenges persist. In particular, event-based datasets remain scarce and lack standardisation, training methodologies introduce systematic gaps relative to deployment hardware, and access to neuromorphic platforms is restricted by proprietary toolchains and limited development kit availability. We conclude that bridging these integration gaps, rather than advancing individual components alone, represents the most important and least addressed work required to realise the potential of SNN-based vision at the edge. Full article

Accurate prediction of subsurface temperature distributions is essential for geothermal reservoir assessment, thermal performance evaluation, and decision support in reservoir management. However, repeated high-resolution numerical simulations are computationally expensive, particularly when multiple scenarios, heterogeneous petrophysical fields, and varying grid resolutions must be analyzed. This study presents a U-Net-based surrogate modeling framework for fast geothermal temperature field prediction on structured grids, coupled with interpolation strategies for handling unseen grid resolutions and intermediate time instances. Training and evaluation data are generated using the MATLAB Reservoir Simulation Toolbox (MRST) (24.1.0.2578822 (R2024a) Update 2) under multiple porosity–permeability realizations and at several grid resolutions (130 × 73, 67 × 37, 36 × 19, and 20 × 11) on a 2D grid. Data preprocessing and reshaping techniques are used to preserve spatial correspondence across resolutions. For fixed trained grids, the surrogate directly predicts temperature fields from porosity, permeability, and time inputs. For unseen grids, a grid interpolation strategy combines predictions from neighboring trained resolutions using weighted blending based on target grid cell count, followed by spatial resizing to the requested resolution. In addition, time interpolation is used to estimate temperature maps at intermediate time steps between predicted/simulated snapshots. The proposed framework enables rapid generation of temperature maps while maintaining spatial structure, making it suitable for efficient geothermal screening and multiscale scenario analysis. Full article

Decarbonizing downstream steel logistics remains underexplored in sustainable supply chain management. This study proposes a machine learning-enhanced tri-objective optimization framework for the ship stowage planning problem (SSPP). The framework handles heterogeneous finished steel products, including coils, plates, ingots, tubes, and sections. The model simultaneously maximizes deadweight utilization and minimizes a novel Adaptive Weighted Moment Balance (AWMB) index. It also minimizes voyage carbon emissions through a trim-and-heel resistance penalty. A spatial-to-sequential discretization strategy transforms the NP-hard placement problem into a tractable permutation optimization. A deep neural network (DNN) surrogate achieves a 3.57-fold speedup with only 1.52% hypervolume degradation. An improved NSGA-III algorithm with adaptive operators ensures Pareto front exploration. Embedded step-wise moment verification guarantees dynamic stability throughout loading and unloading. Validated on real data from a Chinese steel enterprise, the framework achieves 99.88% deadweight utilization, reduces transverse and longitudinal imbalance by 48.27% and 90.54%, and cuts CO₂ emissions by 95.5% per voyage. SOLAS constraints, load line limits, and CII/FuelEU targets are addressed through embedded stability and capacity constraints. Multi-route and weather-dependent validation remains necessary before fleet-scale deployment. Full article

Injection molding process optimization has predominantly relied on simulation-generated data, which cannot capture machine-specific variability and stochastic process noise inherent in real manufacturing environments. This paper presents an experiment-driven machine learning framework for multi-objective optimization of injection molding process parameters targeting volumetric shrinkage, warpage, cycle time, and part weight. Physical experiments were conducted on an industrial injection molding machine using high-density polyethylene with a face-centered central composite design. Systematic benchmarking of four machine learning algorithms under identical cross-validation protocols identified Gaussian process regression as the best-performing surrogate model for the majority of quality metrics, while warpage prediction remained challenging across all algorithms due to its complex thermo-mechanical origins. Permutation-based feature importance analysis established a clear parameter hierarchy, identifying holding time as the dominant factor governing multiple quality responses. Constrained Bayesian optimization with progressive constraint tightening was employed to identify optimal parameter sets and fundamental process capability boundaries. The resulting parameter configurations were validated against a held-out test set. This work demonstrates that rigorous, data-driven optimization using exclusively experimental data provides a viable and practically achievable alternative to simulation-based approaches, contributing to experiment-centric smart manufacturing in polymer processing. Full article

Real biodiesel contains numerous components with complex molecular structures, and thus a representative surrogate fuel is required to develop tractable chemical kinetic mechanisms for biodiesel combustion. In this study, how physicochemical properties and molecular–structural descriptors interact when used as target parameters in biodiesel surrogate fuel formulation was clarified. Specifically, waste cooking oil biodiesel was selected as the target fuel, and methyl decanoate, methyl 3-hexenoate, n-hexadecane, and 1,4-hexadiene were selected as candidate components. A multi-objective genetic algorithm was employed to optimize component mass fractions under three target schemes, including physicochemical properties only, molecular–structural descriptors only, and a coupled scheme. Weight-perturbation calculations were then performed to quantify weight-dependent changes in target-matching errors and component fractions and to analyze the coupling mechanism among target parameters. The result shows that the coupled scheme provides the most balanced matching performance across physicochemical properties and molecular–structural descriptors while retaining all four components. Density, molar mass, the O/C ratio, viscosity, cetane number, and key molecular–structural descriptors exhibit the strongest coupling and dominate composition redistribution. The observed coupling among target parameters is realized through coordinated changes in component fractions, providing a basis for key-parameter screening in biodiesel surrogate fuel formulation. Full article

Background and Objectives: Overt perioperative stroke remains a feared complication of adult cardiac surgery. Diffusion-weighted magnetic resonance imaging (DWI-MRI) has revealed a more prevalent form of cerebral injury, termed silent stroke or silent brain injury (SBI). Covert ischemic lesions occur without focal neurological deficits but are increasingly associated with postoperative delirium, cognitive decline, and elevated long-term cerebrovascular risk. Despite growing recognition, the true burden, mechanisms, and clinical relevance of SBI remain incompletely integrated into perioperative practice. Materials and Methods: We performed a narrative review of the literature published between January 2000 and December 2025, identified through PubMed/MEDLINE and Scopus. Eligible studies included prospective and retrospective cohorts, randomized trials, systematic reviews, and meta-analyses involving adult patients undergoing coronary artery bypass grafting, valve surgery, or minimally invasive cardiac procedures, with or without cardiopulmonary bypass, and reporting MRI-detected ischemic lesions or validated surrogate markers of cerebral injury. Pediatric studies, transcatheter interventions, case reports, and non-English publications were excluded. Sixty studies met the inclusion criteria. Results: Silent stroke occurred more frequently than clinically apparent stroke, with new DWI-MRI lesions detected in approximately 20–60% of patients following cardiac surgery. Lesions were typically small, multifocal, and embolic in distribution, predominantly affecting cortical and watershed regions. Cardiopulmonary bypass-related factors, including aortic manipulation, cerebral microembolization, hemodilution, hypoperfusion, and impaired oxygen delivery, emerged as key contributors. Several studies demonstrated associations between SBI burden and postoperative delirium, early cognitive dysfunction, and functional decline. Perfusion-based neuroprotective strategies showed mechanistic benefit, although no single intervention conclusively prevented SBI. Conclusions: Silent stroke represents the most frequent form of neurological injury in adult cardiac surgery. Evidence suggests that these covert lesions reflect clinically meaningful cerebral injury, with potential short- and long-term consequences. Recognition of silent stroke as a relevant neurological endpoint supports a shift toward multimodal, perfusion-driven neuroprotective strategies and the routine incorporation of MRI-based outcomes in future cardiac surgical research. Full article

Background/Objectives: Type II superior labrum anterior–posterior (SLAP) lesions of the long head of the biceps (LHB) tendon are associated with excessive tendon loading and are commonly treated surgically using SLAP repair, tenotomy, or tenodesis. These procedures alter musculotendinous length and loading and may affect functional outcomes, including forearm supination strength. Appropriate restoration of tendon tension is critical for favorable muscle adaptation and recovery. Shear wave elastography (SWE) is a non-invasive imaging technique capable of quantifying tissue stiffness as a surrogate for in vivo musculotendinous tension. This study aimed to characterize LHB tendon tension across forearm positions and loading conditions to improve the understanding of functional tendon loading relevant to postoperative activation and rehabilitation. Methods: In this controlled laboratory study, thirteen healthy female volunteers without shoulder pathology were assessed using SWE with the elbow positioned at 90° flexion. LHB tendon tension was measured in forearm pronation and supination under passive, active (unresisted), and weighted conditions. Paired t-tests were used to compare forearm positions within each loading condition. Results: LHB tendon tension was significantly greater during active and weighted conditions compared with passive loading in the pronated position (p < 0.05). During active contraction, tendon tension was significantly lower in supination than pronation (p < 0.05), whereas no positional differences were observed under passive or weighted conditions. Relative to passive loading, tendon tension increased by approximately 18.2% and 89.2% in supination, and 67.0% and 97.9% in pronation during active and weighted conditions, respectively. Conclusions: Forearm position selectively influences LHB tendon tension during active, unresisted contraction. Forearm orientation affected LHB tendon stiffness primarily during active, unweighted contraction, where pronation resulted in higher stiffness than supination. On the other hand, stiffness outcomes measured during passive and weighted positions were comparable between forearm orientations, indicating that positional effects are most evident when tendon loading is primarily muscle-driven. These findings highlight the relevance of forearm positioning during early postoperative activation and provide normative in vivo reference data to inform personalized rehabilitation strategies and future investigations of postoperative tendon loading following SLAP lesion treatment. Full article

Background: Insulin resistance (IR) is a hallmark of metabolic disorders including type 2 diabetes mellitus (T2DM), metabolic syndrome, metabolic dysfunction-associated steatotic liver disease (MASLD), obesity, polycystic ovary syndrome (PCOS), and cardiovascular diseases. It arises from impaired insulin signaling in muscle, liver, and adipose tissue, driven by ectopic lipid accumulation, chronic inflammation, oxidative stress, and gut microbiota dysbiosis. Methods: This narrative review synthesizes IR mechanisms and the evidence on specific dietary patterns. A structured search of PubMed/MEDLINE and Embase (up to January 2026) prioritized RCTs, systematic reviews, meta-analyses, and clinical guidelines. Results: IR assessment relies on the hyperinsulinemic–euglycemic clamp as gold standard, with HOMA-IR and QUICKI as validated clinical surrogates. The Mediterranean diet is the most evidence-supported strategy, with consistent HOMA-IR reductions, a 31% decrease in T2DM incidence (PREDIMED-Plus), and demonstrated efficacy across T2DM, MASLD, and PCOS. Low-GI and DASH diets improve postprandial insulin dynamics and are particularly effective in PCOS. Low-carbohydrate and ketogenic diets produce the largest short-term reductions in fasting glucose and insulin secretion, though long-term sustainability requires further study. Plant-based diets and intermittent fasting improve IR primarily via weight loss and gut microbiota modulation. Most studies rely on surrogate IR indices and are short-term (≤26 weeks). Conclusions: Dietary pattern selection should be individualized according to metabolic phenotype, comorbidities, and adherence potential. Larger, longer, head-to-head trials measuring hard clinical outcomes are needed. Full article

The detection of micro-defects within cemented carbides necessitates a high-frequency, high-sensitivity ultrasonic non-destructive testing transducer (UNDTT), whose performance is highly sensitive to geometric structural parameters. Conventional design approaches rely heavily on empirical trial-and-error, resulting in low efficiency and difficulty in achieving globally optimal solutions. To address this limitation, an intelligent multi-objective optimization method is proposed for transducer structural parameters—namely, radius, matching layer thickness, and backing layer thickness—to simultaneously maximize sensitivity ($V_{pp}$), center frequency ($f_c$), and bandwidth (BW). By investigating the relationship between structural parameters and performance metrics, a dataset was constructed and used to develop a convolutional neural network (CNN) surrogate model that captures their nonlinear mapping. The CNN was integrated with the NSGA-III multi-objective optimization algorithm to iteratively generate a Pareto-optimal solution set, from which the best design was selected using the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Finite element analysis (FEA) validation confirmed prediction errors below 7.0%. Compared to conventional designs, the proposed approach delivers a 46.1% higher sensitivity and a 7.7% broader bandwidth while maintaining a thinner matching layer. These results confirm the effectiveness and practical advantage of the proposed framework. This data-driven approach offers an efficient alternative for designing a high-performance UNDTT. Full article

To reduce the time and computational cost of vibro-acoustic simulations in gear reducer noise evaluation, this study develops a simulation-driven surrogate modeling framework for a three-stage helical gear reducer. A high-fidelity “vibration–acoustic radiation” simulation chain is established, where the housing vibration responses computed in Romax Designer are mapped into ACTRAN to obtain the radiated noise. Using Optimal Latin Hypercube Sampling, 300 designs are generated by varying the first-stage pinion micro-modification parameters (tooth drum, tooth slope, and tooth profile), and the average RMS sound pressure level over six field points is adopted as the noise metric. A BP neural network (BPNN) surrogate is then constructed, in which Bayesian Optimization (BOA) is used to tune hidden layer nodes and learning rate, and an improved Sparrow Search Algorithm (ISSA) is employed to optimize the initial weights and biases, forming the proposed BOA-ISSA-BPNN model. On the test set, the proposed model achieves R² = 0.97499, RMSE = 0.91385, and MAE = 0.6547, with an average prediction time of 32.35s. Meanwhile, comparisons with SVM, BPNN, BOA-BPNN, SSA-BPNN, and ISSA-BPNN demonstrate superior prediction accuracy; moreover, relative to the hour-level computational cost of high-fidelity simulations, the proposed surrogate enables rapid noise evaluation on the order of tens of seconds, enabling fast micro-modification design iteration and practical engineering decision-making. Full article