Search Results (1,219)

Urban sustainability depends on rural hinterlands, yet national-scale evaluation and AI screening often rely on urban-centric proxies, which can under-recognize remote villages where the evidence base is sparse. Using China’s national honored-village programme (N = 24,450) as a case, we examine how recognition patterns change when data availability and observability are unequal across regions, with a focus on the Qinghai–Tibetan Plateau (QTP), where 923 honored villages account for only 3.78% of the national total. We interpret urban-centric proxy reliance as the tendency for recognition patterns to correlate with urban-linked observability signals (e.g., nighttime lights). In this study, discovery lag refers to situations where villages exhibit characteristics similar to historically recognized villages but remain unrecognized under the current honor regime due to uneven data availability and observability. Methodologically, we build a scene-aware predictive framework that integrates multi-source geospatial indicators and explicitly handles extreme imbalance and environmental heterogeneity to estimate recognition likelihood under the current honor regime, treating national honor lists as administratively produced recognition outcomes rather than objective measures of village value. The model highlights four high-probability nomination belts on the QTP and reveals a pronounced DEM–NTL decoupling: the median NTL of currently honored QTP villages is 0, suggesting that NTL-based urban proxies can fail in high-altitude, data-scarce contexts. Overall, the observed under-representation is consistent with uneven observability and institutional constraints within the current honor system, and the proposed framework provides a scalable diagnostic and screening tool for identifying villages with high predicted recognition likelihood and supporting more evidence-aware rural data collection. Full article

Electroencephalogram (EEG)-based seizure classification remains challenging due to inter-subject variability and heterogeneous signal characteristics. Foundation models offer a promising alternative to dataset-specific training by leveraging pretrained priors. In this study, we evaluate a tabular foundation model, the Tabular Prior-Data Fitted Network (TabPFN), against classical ensemble baselines (gradient boosting, random forests, AdaBoost, and XGBoost) for EEG seizure segment classification. We use subject-independent GroupKFold cross-validation without out-of-fold evaluation to assess generalization to unseen individuals. Experiments on the Bangalore EEG Epilepsy Dataset (BEED) and the University of Bonn (Bonn) dataset show that TabPFN achieves higher accuracy than classical ensembles, reaching 99.7% on BEED and 99.6% on Bonn. These results suggest that pretrained tabular priors can be effective in feature-based EEG pipelines where subject-level generalization is required. Full article

The automotive sector is currently undergoing a rapid and complex transition from classic internal combustion engines to hybrid or fully electric propulsion systems, at the core of which is the battery pack. Currently, the battery packs of almost all electric vehicles on the road consist of lithium-ion cells. The thermal management of these cells represents a complex and fundamental challenge, essential not only to ensure optimal vehicle performance but also to guarantee passenger safety. Therefore, this paper examines and compares four main systems used for battery thermal management, highlighting their strengths, weaknesses, and overall effectiveness. First, a standard module comprising 21700 cylindrical cells, representative of automotive applications, is designed. Subsequently, computational fluid dynamics (CFD) thermal analyses of this module are performed to evaluate four different cooling methods: forced air cooling, bottom cold plate cooling, liquid tube cooling, and immersion cooling combined with tab cooling. Finally, an experimental validation is conducted by testing these systems on a physical module, which is subjected to the same electrical discharge profile simulated in the CFD analyses, to verify the effectiveness of the four considered methods. Full article

Composite solid propellant mixers face severe post-mixing cleaning challenges, especially for high-burn-rate propellants. Manual cleaning remains necessary due to the high viscosity and friction sensitivity of energetic ballistic modifiers (EBMs), which hinders automation and poses safety risks. This study explores the wall slip behavior of high-burn-rate propellants (non-Newtonian fluids)—a phenomenon that departs from the no-slip boundary condition in fluid mechanics (where fluid velocity at the solid surface is assumed to be zero) and occurs when the applied shear stress exceeds a critical value—and its application in mixer cleaning. We performed rheological tests using HAAKE Viscotester IQ (Couette system) (Thermo Fisher Scientific, located in Karlsruhe, Germany) and TA/ARES-G2 rheometer (parallel plate system) (TA Instruments, located in New Castle, DE, USA) to analyze the shear stress, viscosity, and wall slip characteristics of the propellants and inert materials. Tests on three inert materials (A, B, C) showed that A and B exhibit wall slip with shear stress exceeding 2313.6 Pa, achieving complete or near-complete residue removal. In contrast, C does not exhibit wall slip and has insufficient stress, resulting in poor cleaning performance. This work verifies that leveraging the wall slip behavior of high-burn-rate propellants with inert materials can achieve manual-free mixer cleaning, laying a foundation for future unmanned, automated cleaning of high-burn-rate propellant mixers. Full article

Most components fail under complex states of stress and for this reason the study of materials failure under these conditions is an important topic. The article presents the experimental study of the failure of a CFRP material, with a 0/90° cross-ply configuration, subjected to both simple loading conditions (tension, compression, and shear) and combined loading (tension–shear), using a modified Arcan testing method. The Arcan device and specimen geometry were redesigned to reduce experimental errors and the dispersion of results. It was found that there are significant differences between the strength values obtained for simple loads performed by the standardized methods and by the Arcan method, respectively. For this reason, it is recommended to use the Arcan method only for mixed loading modes. Specimens with steel tabs were used to reduce both hole ovality during testing and the number of clamping screws to only four. It was found that the experimental results under complex stress states are well described by the Tsai–Hill failure criterion and the failure envelope for the material studied was plotted. Recommendations are provided regarding the appropriate use of the Arcan method in order to obtain precise results for CFRP composites under multiaxial loading. Full article

Hampea rovirosae Standl. is traditionally used by local communities to treat infections, pain-related conditions, and to reduce blood sugar levels. In this investigation, we produced aqueous, ethanolic, and hydroethanolic extracts of H. rovirosae and assessed their antioxidant, antibacterial, and antihyperglycemic properties in addition to their phytochemical profiles and contents. The phytochemical characterization was performed through a targeted chromatographic and mass spectrometric analysis of phenolic compounds and the quantitation of total phenolic content (TPC), total flavonoid content (TFC), and total tannin content (TTC) by spectrometric assays. The antioxidant capacity was assessed using the DPPH, ABTS, and FRAP assays, and the antibacterial activity was determined by disk diffusion (DD) and minimum inhibitory concentration (MIC) methods. In addition, antihyperglycemic activity was evaluated by inhibiting α-amylase and α-glucosidase. Phytochemical analysis was performed using high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS), employing a targeted analysis approach based on comparing retention times and fragmentation patterns with standards and databases. This analysis revealed a phytochemical profile dominated by phenolic compounds, with quercetin-3-glucoside (155,930.2), caffeic acid (134,399.1), catechin (98,408.8), procyanidin B2 (85,661.7), protocatechuic acid (83,824.3), and epicatechin (53,704.1) being the major metabolites. The hydroethanolic extract exhibited the highest phenolic (426.70 mg GAE/g), flavonoids (119.17 mg CE/g), and tannin (324.46 mg GAE/g) contents, as well as the strongest antioxidant capacity in the DPPH and FRAP assays. Regarding the antibacterial effects, the aqueous extract inhibited Salmonella typhimurium and Escherichia coli, while the hydroethanolic extract was active against S. aureus, B. cereus, and B. subtilis. In enzyme inhibition assays, the hydroethanolic extract showed strong α-glucosidase inhibition and moderate α-amylase inhibition. The findings provide preliminary scientific evidence of the antioxidant and biological activities of Hampea rovirosae in vitro, supporting its traditional use, which should be validated through vivo trials. Full article

Background/Objectives: The rise of artificial intelligence is injecting intelligence into the healthcare sector, including surgery. Vision-based intelligent systems that assist surgical procedures can significantly increase productivity, safety, and effectiveness during surgery. Surgical instruments are central components of any surgical intervention, yet detecting and locating them during live surgeries remains challenging due to adverse imaging conditions such as blood occlusion, smoke, blur, glare, low-contrast, instrument scale variation, and other artifacts. Methods: To address these challenges, we developed an advanced segmentation architecture termed the frozen-filters-based morphology-aware segmentation network (FFMS-Net). Accurate surgical instrument segmentation strongly depends on edge and morphology information; however, in conventional neural networks, this spatial information is progressively degraded during spatial processing. FFMS-Net introduces a frozen and learnable feature pipeline (FLFP) that simultaneously exploits frozen edge representations and learnable features. Within FLFP, Sobel and Laplacian filters are frozen to preserve edge and orientation information, which is subsequently fused with learnable initial spatial features. Moreover, a tri-atrous blending (TAB) block is employed at the end of the encoder to fuse multi-receptive-field-based contextual information, preserving instrument morphology and improving robustness under challenging conditions such as blur, blood occlusion, and smoke. Datasets focused on surgical instruments often suffer from severe class imbalance and poor instrument visibility. To mitigate these issues, FFMS-Net incorporates a progressively structure-preserving decoder (PSPD) that aggregates dilated and standard spatial information after each upsampling stage to maintain class structure. Multi-scale spatial features from different encoder levels are further fused using light skip paths (LSPs) to project channels with task-relevant patterns. Results/Conclusions: FFMS-Net is extensively evaluated on three challenging datasets: UW-Sinus-surgery-live, UW-Sinus-cadaveric, and CholecSeg8k. The proposed method demonstrates promising performance compared with state-of-the-art approaches while requiring only 1.5 million trainable parameters. In addition, an open-source large language model is integrated for non-clinical summarization of the surgical scene based on the predicted mask and deterministic descriptors derived from it. Full article

Given the challenges in treating metastatic melanomas, there is a growing need for novel and effective therapeutic strategies. This study aimed to understand molecular mechanisms underlying synergistic effects of a Tempol and ML210 combination in B16F10 murine melanoma cells and to evaluate its therapeutic potential. We hypothesized that this combination would synergistically induce cell death by increasing oxidative stress and triggering ER stress. B16F10 melanoma cells were treated with Tempol and ML210 alone or in combination for 48 h. Cell viability was determined using MTT assay. Oxidative stress was evaluated by measuring Total Antioxidant Status (TAS), Total Oxidant Status (TOS), and intracellular H₂O₂ levels. Apoptotic markers (caspase-3, Bax, Bcl-2) and ER stress proteins (GRP78, GADD153, IRE1α, ATF6) were quantified by ELISA. Combination treatment significantly inhibited cell proliferation compared to monotherapies. Molecular analyses revealed that combination caused depletion of TAS and increase in TOS and intracellular H₂O₂ levels. Furthermore, combination treatment synergistically upregulated ER stress markers and pro-apoptotic proteins while significantly suppressing anti-apoptotic Bcl-2 expression. In conclusion, the combination of Tempol and ML210 synergistically induces cell death in B16F10 melanoma cells by disrupting redox balance and activating ER stress-mediated apoptosis. These findings suggest a potential strategy for melanoma treatment that warrants further in vivo investigation. Full article

The colour matching of ceramic restorations is sensitive to ceramic thickness, ceramic optical properties, the tooth region, the tooth/substrate basis colour, and the shade of the bonding agent. This in vitro study evaluates the influence of substrate darkening, resin cement shade and zirconia thickness on the final colour of monolithic Prettau^®2 zirconia restorations. An in vitro factorial design was used combining four resin substrates simulating increasing darkening (ND6–ND9), three shades of dual-cure resin cement (universal, transparent, white opaque) and three zirconia thicknesses (0.5, 1.0, 1.5 mm) of Prettau^®2 zirconia. Standardized photographs were taken under controlled conditions, and CIELAB coordinates (L*, a*, b*) were obtained in Adobe Photoshop. Colour differences relative to the Prettau^®2 A1 shade tab were calculated as ΔL*, Δa*, Δb* and ΔE*. An additive linear model on ΔE* and a main-effect MANOVA on ΔL*, Δa* and Δb* were fitted to assess the impact of each factor. The mean ΔE* was 6.67 ± 2.66, and all but two specimens showed a clinically perceptible colour difference (ΔE* > 2.7) from the A1 shade tab. Substrate shade accounted for 38.4% of the explained variance in ΔE*, cement for 27.6% and zirconia thickness for 6.7%. MANOVA confirmed significant multivariate effects of substrate and cement, but not of zirconia thickness. Translucent monolithic zirconia showed limited ability to reproduce the A1 reference shade over darkened substrates. Substrate shade was the main determinant of colour mismatch, followed by resin cement, whereas zirconia thickness within 0.5–1.5 mm played a minor role. White opaque cement reduced ΔE* and brought the final shade closer to A1, but residual mismatches often remained clinically relevant. These findings highlight the need to control and, when possible, modify the underlying substrate and to select high-opacity cements when shade matching is critical. Full article

The environmental persistence of post-consumer plastics remains a critical challenge due to their chemical stability, the presence of additives, and prior environmental weathering. This study evaluates the partial biodegradation and chemical transformation of post-consumer low-density polyethylene (LDPE) and expanded polystyrene (EPS) by Tenebrio molitor larvae under uncontrolled environmental conditions. Four diets were tested, including LDPE+S and EPS+S (polymers supplemented with wheat bran), to assess the influence of a co-substrate on larval performance and polymer transformation. Fourier-transform infrared spectroscopy (FTIR) revealed the emergence of oxygen-containing functional groups (–OH and C=O) in the frass, which were absent or negligible in pristine materials, indicating oxidative modification of the polymer matrix. Gel permeation chromatography (GPC) revealed pronounced reductions in number-average molecular weight (Mn) and increased polydispersity for EPS-derived fractions, consistent with heterogeneous chain scission and partial depolymerization. For LDPE, GPC evidenced the formation of THF-soluble, low-molecular-weight polymer-derived fragments, indicating fragmentation despite the inability to quantify pristine LDPE due to its insolubility in the mobile phase. Gas chromatography–mass spectrometry (GC–MS) identified aromatic hydrocarbons, phthalate esters, organosiloxanes, and fatty acid derivatives, reflecting both degradation intermediates and migrated additives from post-consumer plastics. Together, these results provide integrated evidence that Tenebrio molitor can induce chemical transformation of post-consumer LDPE and EPS under non-controlled environmental conditions, offering mechanistic insight into a biologically mediated degradation pathway that is directly relevant to realistic plastic waste scenarios. Full article

The rapid growth of Internet-of-Things (IoT) deployments has substantially expanded the attack surface of modern cyber–physical systems, making accurate and computationally feasible malware detection essential for enterprise and industrial environments. This study presents a large-scale, systematic comparison of 27 machine learning (ML) and 18 deep learning (DL) models for IoT malware detection across eight major malware categories: Trojan, Botnet, Ransomware, Rootkit, Worm, Spyware, Keylogger, and Virus. A realistic dataset was constructed using 50,000 executable samples collected from the Any.Run platform, including 8000 malware instances (1000 per class) and 42,000 benign samples. Each sample was executed in a sandbox to extract detailed static and behavioral telemetry. A targeted feature-selection pipeline reduced the feature space to 47 diagnostic features spanning static properties, behavioral indicators, process/file/registry activity, debug signals, and network telemetry, yielding a compact representation suitable for malware detection in IoT settings. Experimental results demonstrate that ensemble tree-based ML models consistently dominate performance on the engineered tabular feature set as 7 of the top 10 models are ML, with CatBoost and LightGBM achieving near-ceiling accuracy and low false-positive rates. Per-malware analysis further shows that optimal model choice depends on malware behavior. CatBoost is best for Trojan/Spyware, LightGBM for Botnet, XGBoost for Worm, Extra Trees for Rootkit, and Random Forest for Keylogger, while DL models are competitive only for specific categories, with TabNet performing best for Ransomware and FT-Transformer for Virus. In addition, an end-to-end computational time analysis across all 45 models reveals a clear efficiency advantage for boosted tree ensembles relative to most DL architectures, supporting deployment feasibility on commodity CPU hardware. Overall, the study provides actionable guidance for designing adaptive IoT malware detection frameworks, recommending gradient-boosted ensemble ML models as the primary deployment choice, with selective DL models only when category-specific gains justify additional computational cost. Full article

The primary objective of this study was to evaluate the relative contributions of the MMSE and nWBV in three-class cognitive stage classification, with a secondary objective of conducting a subgroup analysis to investigate latent risk within the MMSE-normal population. To achieve this, we proposed an explainable deep-learning-based analytical framework integrating the MMSE with nWBV, a structural brain atrophy indicator, and systematically assessed the relative contributions of each variable in cognitive impairment stage classification and potential risk screening. Although the MMSE is widely used in clinical practice as a cognitive screening tool, it has limited sensitivity to early or subtle cognitive decline and may not adequately reflect structural brain changes due to the ceiling effect. To address this limitation, we compared four tabular deep learning models—MLP, Tab ResNet, Tab Transformer, and FT Transformer—under identical fivefold cross-validation conditions. Age and sex were fixed as covariates, and feature ablation analysis was conducted to examine the independent and combined effects of the MMSE and nWBV. The results showed no statistically significant differences in classification performance among model architectures, indicating that predictive performance was primarily determined by the informational content of the input variables rather than model complexity. In the feature ablation analysis, the MMSE alone demonstrated strong discriminative power, whereas nWBV alone showed relatively limited performance; however, when combined with the MMSE, nWBV consistently improved classification performance. Furthermore, for interpretability analysis, both Integrated Gradients (IG) and SHAP were applied to validate variable contributions from complementary perspectives. Across both methods, the MMSE and nWBV were repeatedly identified as key contributing features, and interpretability stability was maintained throughout cross-validation folds, supporting the robustness and reliability of the explanatory results. Beyond simple model performance comparisons, this study provides evidence supporting the complementary integration of structural brain atrophy information into MMSE-centered traditional cognitive assessment by jointly considering variable contribution and interpretability stability. This approach is expected to contribute to precision risk screening and clinical decision support in the early stages of cognitive decline. Although the MMSE exhibited strong discriminative performance, nWBV provided complementary structural risk signals within the MMSE-normal subgroup, suggesting that integrating cognitive assessment with structural biomarkers may enhance early risk identification. Full article

A multiple strong coupling system comprising monolayer MoS₂ and Ag nanodisk (Ag-ND) arrays is investigated using transient absorption (TA) spectroscopy. By tuning the diameter and period of the Ag-NDs arrays, the surface plasmon polariton (SPP) resonances are made to simultaneously overlap with the A (~660 nm) and B (~608 nm) excitons of monolayer MoS₂. As a result, three distinct negative ground-state bleaching (GSB) peaks, corresponding to the upper (UP), middle (MP), and lower (LP) hybrid polariton states, were observed in the TA spectra. This confirms that a multiple strong coupling regime was achieved with both the A and B excitons of monolayer MoS₂ and SPPs modes, which was also highlighted by the anti-crossing behavior across varied Ag-NDs arrays parameters. Finally, by adding an insulating spacer layer of Al₂O₃ film, the coupling strength can be modulated from a strong coupling regime to a weak coupling regime. These results reveal a multi-exciton–plasmon strong coupling system and establish a versatile platform for ultrathin polaritonic devices, including polariton lasers and all-optical switches. Full article

Large flange shafts are the core load-bearing and connecting components of high-end equipment, and their welding multi-physical fields directly affect the quality and service safety of the components. Traditional experiments and finite element methods suffer from long cycles and low efficiency, which can hardly meet the demand for rapid prediction. Aiming at the fast and accurate prediction of welding temperature, deformation and residual stress, this study combines thermal–mechanical coupled finite element simulation with machine learning to construct and compare a variety of prediction models. A dataset is built based on simulation data from 100 groups of process parameters. Overfitting is reduced through strategies including early stopping and dropout, and models such as MLP, RF, RBF-SVR, TabNet, XGBoost, and FT-Transformer are established and verified through 10-fold cross-validation. The results show that the MLP model performs best in the prediction of temperature, deformation and residual stress, and is in good agreement with the simulation values. The prediction errors of the peak temperature of the weld and base metal are below 5%, and the errors of deformation and residual stress are controlled within 10%. The average error of peak residual stress is about 6 MPa, and the deviation of most samples is less than 5 MPa. The RF model ranks second in accuracy, with an average error of about 6.5 MPa for peak residual stress, showing a satisfactory interpretability and engineering applicability. RBF-SVR and TabNet can meet basic prediction requirements. Under the small-sample condition in this work, XGBoost and FT-Transformer present relatively large errors and a weak generalization ability, making it difficult to achieve high-precision prediction. The MLP model established in this paper can effectively reproduce the evolution of welding multi-physical fields and supports the rapid prediction and process optimization of large flange shaft welding. The generalization ability and practical performance of the model can be further improved by expanding the dataset and experimental verification in the future. Full article

The discovery of bidirectional microRNA transfer between two organisms during plant–microbe interactions and the ability of some fungal pathogens to absorb double-stranded RNA (dsRNA) or short interfering RNA (siRNA) from the environment provided an impetus for exploiting this mechanism in plant defense against pathogens. In this study, we investigated the role of conserved wheat microRNAs (miRNAs), miRNA408 and miRNA159, in inducing plant defense responses and suppressing the virulence of the phytopathogenic ascomycete fungus Parastagonospora nodorum, mediated by necrotrophic effectors (NEs) encoded by SnTox genes regulated by fungal transcription factors (TFs). The foliar spraying with in vitro synthesized siRNA408 and siRNA159 duplexes before inoculation with SnTox3-producing P. nodorum isolate increased wheat plant resistance to the SnB isolate and suppressed the pathogen growth and development. Most likely, silencing of the miRNA408 target genes TaCAT-2A, TaCAT-2B, and TaCLP1, and the miRNA159 target gene TaMYB65, led to the induction of a defense response of wheat plants against P. nodorum. This defense response was characterized by a decrease in the catalase activity, accumulation of hydrogen peroxide, activation of the expression of salicylic acid signaling pathway genes (TaWRKY13, TaPR1), and suppression of the expression of ethylene signaling pathway genes (TaEIN3, TaPR3). We demonstrated for the first time the ability of siRNA159 and siRNA408 to penetrate the mycelium of the pathogen P. nodorum and be involved in the cross-kingdom regulation of fungal genes to suppress the expression of some genes of NE (SnToxA, SnTox3) and fungal TFs (SnStuA). We predicted potential targets for wheat miRNA408 and miRNA159 in the P. nodorum transcriptome, making spray-induced gene silencing (SIGS) promising for use against this pathogen. These results provide valuable insights for studying the cross-kingdom transfer of plant miRNAs. Full article