Search Results (2,397)

Euclidean plane geometry problem solving is a challenging benchmark for artificial intelligence because it requires complex diagram understanding, symbolic deduction, and multi-step reasoning. Constructing effective datasets for this task requires geometric instances that are realizable, non-degenerate, structurally diverse, and paired with human-like proofs. However, existing random or template-based generation pipelines often produce redundant, singular, or infeasible candidates, causing substantial computation to be spent before useful reasoning trajectories can be extracted. To address these limitations, we present FGeo-GCG, a hybrid geometric data synthesis framework built on the FormalGeo-V2 deductive engine. It formulates Geometric Configuration Generation as an incremental linear construction process that decomposes global constraint satisfaction into local construction steps, thereby pruning invalid branches during the generation process. To improve reliability and efficiency, FGeo-GCG combines two validation stages: a safe stochastic Jacobian-rank filter estimates whether local candidate constraints contribute independent algebraic restrictions, and progressive geometric validation checks whether the resulting partial construction remains realizable and non-degenerate. By encoding incidence-, metric-, and symmetry-related dependencies within unified constraint graphs, the framework also connects geometric data synthesis with structural symmetry analysis. Validated constraint graphs are then converted into problem instances through forward deduction, goal decomposition, and multi-dimensional complexity filtering, producing proof targets without manual annotation. Experiments show that the full validation pipeline reduces the failure rate for highly constrained instances. The resulting FGeo-GCG dataset contains more than 50,000 formally validated plane geometric configurations and provides engine-derived reasoning traces and targets for future training and evaluation of neuro-symbolic geometry problem-solving systems. Full article

Lithium-ion batteries with high energy density and long cycle life have been widely used as secondary batteries in electric vehicles and energy storage systems. With the growing demand for high energy density in lithium-ion batteries, silicon-based materials, which possess a high theoretical specific capacity (4200 mAh g⁻¹), are regarded as core candidates for anode materials. However, Si-based materials undergo severe volume expansion (up to 300%), which leads to the collapse of the electrode structure, inducing pulverization of the active material and capacity loss, thereby hindering the commercial application of silicon-based materials. To address these issues, scholars from various countries have developed many silicon-based materials with different compositions and three-dimensional structures, and have made some research progress. This review first elaborates on the lithium storage mechanisms and advantages of diverse silicon-based anode materials by taking Si, SiO_x, SiN_x, and SiP_x as representative examples with distinct characteristics. Subsequently, from the two aspects of dimensional design (0D, 1D, 2D and 3D) and architecture design (core–shell, sandwich-like and network structure), the design strategies for various silicon-based anode structures and their enhancement on electrochemical performance are analyzed. Finally, this review elucidated the challenges faced by silicon-based anodes from the perspectives of mechanism elucidation, structural customization, industrialization, and full-cell applications. It also proposed future development directions for silicon anodes by combining actual challenges and focusing on aspects such as structure optimization, machine learning, advanced characterization techniques, and mechanistic analysis. Full article

The high-rate biological contactor (HRBC) is an enhanced-primary, biosorption-based, carbon-diversion wastewater treatment process with short hydraulic retention time (HRT), short solids retention time (SRT), low dissolved oxygen (DO), and high food-to-microorganism ratio (F/M). This paper presents modifications to a commercial full-plant wastewater biodegradation model using extracellular polymeric substances (EPS) in waste activated sludge (WAS) to simulate pilot test biosorption data. Bench-scale HRBC tests found that each mg of EPS as COD (COD_EPS) biosorbed 1.02 mg sCOD contained in raw wastewater. The fraction of AS organics identified as EPS in terms of COD was 37% in a conventional AS (CAS), 33% in a trickling filter-solids contact (TF/SC), and 18% in a membrane bioreactor (MBR). The modeling process used stoichiometry equations to convert EPS from its constituent concentrations (carbohydrates, proteins, humic acids, uronic acids) into COD. The conversion did not alter the finding that the normalized total EPS showed a positive relationship with soluble chemical oxygen demand sCOD biosorption with a 0.91 coefficient of determination. The modified commercial biodegradation model gave a maximum error of −12.6% when simulating pilot-scale results, and 80% of all data points were less than ±10% error. The modified model predicted 16% sCOD biosorption by EPS using the design data for a full-scale HRBC facility currently under construction. Full article

This study investigates the relative impact of utilizing various forms of work flexibility on mothers’ retention in full-time jobs. Drawing from a model of work reentry for new mothers and Human Ecology Theory (HET), we theorize that mothers’ flexibility needs influence retention through flexibility use and that top management support strengthens this process. Using a cross-sectional Qualtrics online survey, we recruited a diverse sample of 213 women across the United States who stayed with or left their full-time jobs after childbirth or adoption. Using relative weights and path analysis, we compared six forms of flexibility. Schedule, career, and leave flexibility emerged as stronger predictors of retention than other forms of flexibility, with schedule flexibility explaining the largest proportion of variance. For most flexibility types, the need for flexibility increased usage, which, in turn, raised the likelihood of staying in a full-time position. Strong top management work–life support further strengthened the relationship between need and use for certain forms of flexibility. We extend the work reentry framework beyond early motherhood by including mothers in later career and parenting stages. Our results also extend HET by demonstrating that top management support is a critical environmental factor influencing whether flexibility need translates into actual use by working mothers. This suggests that working mothers use flexibility to shape their work environment to meet personal needs, and top management support signals that the use of flexibility effectively addresses these needs. From a practical standpoint, organizations can use these insights to design flexibility policies that more effectively support working mothers in their full-time jobs. Full article

In this study, the influence of thermal loading in a supersonic flight environment on the mechanical stiffness of elastic structures and the corresponding aeroelastic stability limits is investigated analytically. Recognizing that elevated temperatures inherently alter constituent elastic properties, a temperature-dependent continuous elasticity framework is incorporated directly into the governing differential operators of the structural domain. The macro-mechanical behavior of representative panel- and wing-type elements is modeled utilizing the Euler–Bernoulli beam formulation, while high-speed supersonic aerodynamic effects are represented through linearized first-order piston theory. The continuous spatial displacement fields are discretized by means of a modal expansion, and the coupled aeroelastic system is subsequently transformed into a finite set of dynamic state-space equations using the Ritz–Galerkin truncation method. The numerical and analytical outputs demonstrate that aerothermal softening not only induces continuous erosion in the material stiffness but also directly modulates the aeroelastic pole trajectories, thereby prematurely contracting the safe supersonic flight envelope. The primary novelty of the proposed framework lies in the derivation of explicit analytical expressions that directly map temperature-dependent stiffness variations onto supersonic aeroelastic instability boundaries. Because this approach is formulated in a generalized analytical form, it can be applied across diverse material systems, geometric profiles, and thermal conditions with reduced computational overhead compared to full fluid–structure interaction solvers, thereby providing a theoretical basis for preliminary stability assessment of supersonic aerospace configurations operating under high-temperature conditions. Full article

Background: Sleep deprivation is one of the major public health and performance risk factors, with documented effects on vigilance, executive function, emotional regulation, and safety-critical behaviour. This review examines how event-related potentials (ERPs)—which provide millisecond-level resolution of cognitive processing stages—can clarify which neural processes are most affected by sleep loss, from early sensory encoding to later evaluative and control-related stages. Materials and Methods: This study was conducted as a systematic review of human studies on sleep deprivation and ERPs. Eligible studies included human participants, focused primarily on acute/total sleep deprivation, and reported ERP outcomes (e.g., amplitude, latency, topography, or related event-locked EEG measures). Searches were performed in major biomedical/psychology databases using sleep deprivation and ERP terms, with additional forward/backward citation searching. Data was extracted in a structured format (participant characteristics, deprivation protocol, ERP methods, behavioural outcomes, ERP findings, and recovery/countermeasure effects). Due to substantial heterogeneity in paradigms, protocols, and ERP methods, findings were synthesised narratively rather than meta-analysed. Risk of bias was assessed with RoB 2 and ROBINS-I. Results: The search identified 854 records, of which 82 studies were included following deduplication, screening, full-text review, and citation chasing. Samples were typically small, highly selected, and dominated by healthy young adults, with frequent attrition related to prolonged wakefulness and EEG data-quality constraints. Across studies, sleep deprivation produced stage-specific and task-dependent ERP effects rather than a single uniform pattern. The most consistent findings involved mid-to-late components. These components typically showed prolonged latency and reduced amplitude. In some cases, amplitude increases were observed and interpreted as compensatory recruitment. Early sensory/pre-attentive components (e.g., P1/N1/MMN/P50) were often relatively preserved, but showed selective vulnerability, including latency slowing, reduced filtering/gating, or decreased phase locking. A recurring observation was a behaviour–ERP dissociation, where ERP abnormalities were detectable even when behavioural impairment was modest, indicating covert neural inefficiency or compensation. Recovery sleep, naps, and countermeasures (e.g., modafinil, caffeine) produced partial, component-specific recovery, with amplitude and latency often recovering at different rates. Conclusions: The evidence indicates that sleep deprivation primarily disrupts higher-order, late-stage, and temporally coordinated neural processing, while earlier sensory processing is often preserved but becomes slower and less stable. Among ERP markers, the P300/P3 family is the most robust and informative signature of sleep loss effects and recovery. ERPs are therefore a sensitive tool for detecting neural dysfunction and compensation under sleep deprivation, including changes that may precede overt behavioural decline. Future research must improve the generalisability and reproducibility of ERP findings by employing larger, more diverse samples, alongside more standardised methodological, recording, and reporting practices. Full article

The DMD gene is best known for its product dystrophin, a large rod-shaped protein that plays a critical role in muscular membrane strength and integrity. Mutations affecting dystrophin lead to Duchenne muscular dystrophy, a fatal X-linked disease characterized by muscular weakness and breakdown. In addition to the full-length dystrophin product that is most often associated with disease, the DMD gene also encodes multiple shorter isoforms of dystrophin with diverse functions. One isoform in particular, Dp71, has been increasingly found to play a wide variety of roles throughout the body. In this narrative review, we consolidate the numerous studies on Dp71 to provide a comprehensive foundation for future work. We outline and summarize the current state of knowledge on the role of Dp71 in the brain, the retina, and skeletal muscles, identifying current knowns and unknowns in the field. We also explore Dp71-based therapies currently being tested in the pre-clinical landscape and identify potential limitations for clinical translation. Full article

The generation of synthetic seismic accelerograms is a critical problem in earthquake engineering, where the scarcity of strong-motion records, particularly for high-magnitude and near-fault scenarios, limits the reliability of structural analyses and probabilistic seismic hazard assessments. This paper presents a proof-of-concept wavelet-decomposed conditional Generative Adversarial Network (WD-cGAN) for the synthesis of seismic accelerograms that reproduce the physical and statistical properties of real ground-motion records. Unlike prior GAN-based approaches that rely on Fourier-domain decomposition, the proposed architecture decomposes each training signal into N wavelet sub-bands (experimentally $N=7$, six detail sub-bands D1–D6 and one approximation sub-band A6) using the Daubechies-4 (db4) discrete wavelet transform (DWT), assigning each sub-band to a dedicated discriminator. A novel energy-based weighting scheme ${\alpha }_i$ modulates the relative contribution of each discriminator to the total generator loss, ensuring that physically dominant, low-frequency bands, which carry the bulk of seismic energy, receive proportionally higher training emphasis. Seismic moment magnitude $M_w$ serves as the primary conditioning variable, enabling targeted synthesis for specific hazard scenarios. The model is implemented in Python v3.9 using PyTorch v.2.10 and trained on accelerograms drawn from the Italian INGV/ITACA v4.0 archive. Preliminary evaluation on 500 synthetic accelerograms across five magnitude classes provides evidence that the proposed wavelet-domain multi-discriminator scheme reproduces the essential spectral shape and non-stationary temporal structure of real ground-motion records within the considered magnitude range; full quantitative validation on a larger and more diverse corpus, rigorous comparison with competing methods, and extended multi-parameter conditioning are identified as the principal avenues for future work. Full article

Sparse aperture optical systems employ separated subapertures to achieve equivalent large aperture high resolution imaging, but they are also more susceptible to the combined effects of cophase errors and polarization aberrations, which lead to point spread function distortion and image quality degradation. To address this problem, this paper proposes a Polarization Phase Diversity (PPD) method for cophase errors and polarization aberrations in sparse aperture optical systems. Based on the Jones pupil theory, a unified imaging model incorporating both cophase errors and polarization aberrations is established. By combining focused and defocused images acquired under different analyzer directions, a polarization multichannel observation framework is constructed to jointly retrieve piston, tilt, diattenuation, and retardance parameters. Numerical simulations are mainly carried out on a Golay3 sparse aperture optical system, and additional Gaussian-noise and Golay6 simulations are performed to evaluate the robustness and extensibility of the proposed framework. The results show that, except for the phase-equivalent ambiguity of the piston term, the proposed method can accurately recover the cophase and polarization-aberration parameters under noise-free conditions. Compared with conventional methods considering only cophase errors, the proposed method achieves better image restoration performance in terms of image sharpness, detail recovery, PSNR, and SSIM. The noise experiments further demonstrate that the full-parameter restoration maintains clear advantages under noisy observations, and the Golay6 results indicate that the framework can be extended to sparse aperture configurations with more subapertures. The proposed method provides an effective approach for error sensing and image restoration in sparse aperture optical systems. Full article

Background/Objectives: Trace elements may influence autism spectrum disorder (ASD) severity through interactions with the gut microbiota and microbial metabolic functions, but calcium-related evidence remains limited. This cross-sectional study examined associations among hair calcium, gut microbial taxa, metabolic pathways, and behavioral phenotypes in children with ASD. Methods: We analyzed 183 children with ASD who had behavioral assessments, hair calcium measurements, and fecal shotgun metagenomic sequencing data. Participants in the lowest and highest calcium quartiles were first compared to characterize group-level microbiome differences. Full-sample analyses then tested associations among continuous hair calcium, microbial taxa, metabolic pathways, and behavioral measures after covariate adjustment. Benjamini–Hochberg false discovery rate correction was applied for multiple testing. Results: Hair calcium was positively associated with CARS, ATEC-Total, ATEC-1, and ATEC-3 scores, with the strongest associations involving ATEC-1 and ATEC-3. Alpha and beta diversity did not differ significantly between calcium quartile groups, but group-based microbiome analyses identified 63 differential species and 22 differential MetaCyc pathways. Full-sample integrated analyses connected calcium-associated microbial taxa, metabolic pathways, and ASD behavioral measures. Conclusions: Hair calcium was associated with ASD behavioral severity, selected gut microbial species, and microbial metabolic pathways. These findings support an association framework connecting longer-term calcium-related mineral profiles, gut microbial functional potential, and behavioral phenotypes, providing a basis for future longitudinal and multi-omics studies. Full article

Honeybees are indispensable pollinators for agricultural ecosystems, and a colony’s stability and reproductive capacity depend critically on the presence of a healthy queen. Acoustic monitoring has emerged as a promising non-invasive, lighting-independent approach for long-term colony observation. However, existing studies have largely been confined to single-apiary datasets or merged datasets from multiple similar apiaries for model training. Moreover, model evaluation has relied primarily on overall performance metrics, with insufficient attention to cross-region generalization and the detection of queen loss, a rare but critical condition. This study systematically investigates three complementary strategies: noise-augmented data diversification, lightweight convolutional neural network (CNN) architecture optimization via comprehensive ablation experiments, and transfer learning with fine-tuning to bridge the domain gap between source and target apiaries. Under cross-apiary evaluation, the proposed approach achieves an accuracy of 92.79%, a negative-class F1-score of 0.7900, and a negative-class recall of 0.7834 when only limited target-domain training samples are available. With full target-domain training data, the same strategy further attains an accuracy of 95.05%, a negative-class F1-score of 0.8596, and a negative-class recall of 0.8733. t-distributed Stochastic Neighbor Embedding (t-SNE) visualization demonstrates that noise augmentation effectively expands sample diversity in the feature space, while Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps confirm the successful transfer of source-domain acoustic features to the target domain. This work provides a practical approach for deploying acoustic-based queen status monitoring across diverse apiaries with minimal local data collection. Full article

As plant-based diets catalyze a global shift toward sustainable consumption, Chinese plant-based food firms are experiencing rapid growth and seeking to expand their international footprint. This study investigates the mechanisms underlying the internationalization performance of these firms by integrating the Technology–Organization–Environment (TOE) framework with a configurational perspective. We operationalize nine antecedents across three dimensions: the technological dimension (technological maturity, supply chain resilience, and digital transformation), the organizational dimension (food safety certification intensity, strategic partnership intensity, and talent acquisition intensity), and the environmental dimension (market adaptability, compliance and risk management, and product line breadth). Utilizing fuzzy-set qualitative comparative analysis (fsQCA) on a sample of N = 29 publicly listed Chinese plant-based firms, this research identifies three distinct equifinal pathways to superior internationalization performance. The first is the Collaboration-Compliance configuration (Organization–Environment-driven), which is primarily characterized by the synergy between strategic partnerships and regulatory risk management. The second is the Supply Chain-Compliance-Product Diversification configuration (Technology-Environment-driven), where international success is predicated on the interplay among supply chain resilience, institutional compliance, and product variety. The third is the Full-Factor Synergy configuration (Technology-Organization-Environment jointly driven), which emphasizes a holistic coupling of technological innovation, organizational coordination, and external institutional adaptation. By uncovering these complex causal mechanisms, this study moves beyond traditional linear analysis to reveal how diverse capability configurations can lead to equivalent internationalization outcomes. The findings provide actionable strategic guidance for firms navigating the global plant-based market and offer theoretical insights for policy frameworks supporting sustainable dietary transitions. Full article

Background/Objectives: Hospital-Acquired Infections (HAI) represent one of the most frequent adverse events during care delivery, with the pediatric population (0–18 years) presenting unique vulnerabilities due to their developing immune systems, dependence on caregivers, and need for invasive devices. Despite the availability of general guidelines, existing high-level evidence is largely extrapolated from adult studies, and pediatric settings differ significantly in patient physiology and equipment size. This scoping review aims to map the key concepts, types of evidence, and research gaps related to strategies preventing HAI in pediatric patients. Methods: This scoping review will be conducted in accordance with the Joanna Briggs Institute (JBI) methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The Population, Concept, and Context (PCC) framework will be utilized. We will include any strategy, intervention, or protocol aimed at preventing HAI. A comprehensive search will be conducted across ten major electronic databases and grey literature sources. Two independent reviewers will screen titles, abstracts, and full texts, followed by data extraction using a standardized tool to categorize the interventions and key findings. Results: The findings will synthesize diverse practices into a usable format for clinical decision-makers and identify gaps where primary pediatric research is lacking. This consolidated data aims to guide resource allocation and assist hospital infection control committees in updating pediatric safety protocols. Conclusions: This scoping review will establish a comprehensive baseline of pediatric-specific HAI prevention strategies. The findings will inform evidence-based practice, identify critical research gaps, and guide future investigations in the prevention of pediatric infections in healthcare settings. Full article

Accurate viral protein function annotation is essential for genomic surveillance, yet conventional retrieval-augmented generation (RAG) pipelines often fragment biological evidence into fixed-length text chunks, disrupting relationships among ORFs, annotations, structural domains, sequence motifs, residue mappings, and model-derived attention evidence. We propose ViroBioTree, a tree-structured biological evidence retrieval framework for downstream viral protein evidence review rather than a new primary annotation classifier. Built as an evidence organization layer on ViralMultiNet-derived ORF-level predictions and annotations, ViroBioTree converts sequence, annotation, structure, and attention evidence into typed biological nodes and traceable edges, then performs deterministic multi-channel recall, evidence-aware reranking, balanced TopK selection, rule-based verification, and node-cited report generation. In a demo benchmark, ViroBioTree achieved its strongest deterministic proxy performance on structure-explanation tasks, with Precision@K = 1.0, Recall@K = 1.0, and diversity = 0.52; these values reflect expected node-type and tag agreement rather than independent biological correctness. A bounded full-scale SARS-CoV-2 index contained 39,800 ORF rows, 80,000 attention records, 199,418 nodes, and 495,886 edges. In a stratified full20k diagnostic evaluation, ViroBioTree showed task-dependent advantages over LlamaIndex vector retrieval for conflict detection, evidence retrieval, and structure explanation, while LlamaIndex remained competitive or stronger for annotation-rich function annotation. A cross-family Influenza A Virus (IAV) diagnostic audit showed that the schema can represent IAV evidence namespaces while explicitly exposing missing formal ORF inputs, missing attention evidence, and unavailable residue/PDB assertions. Supplementary robustness, external sanity-check, diversity-risk, expert-evaluation, domain-tool positioning, and cross-family audit analyses supported traceability, report quality, and conservative evidence handling, but also showed that stable Precision@K under query perturbation does not necessarily imply stable retrieved evidence sets. ViroBioTree operates offline and deterministically, but does not address raw-read assembly, base calling, primary ORF prediction, or wet-lab validation. Its results should be interpreted as proxy and expert-reviewed evidence for traceable viral protein evidence retrieval and report generation rather than as direct validation of biological function annotation. Full article

Background and Objectives: Despite global commitment to the World Health Organization triple elimination initiative, evidence on integrated antenatal service delivery models that simultaneously address human immunodeficiency virus (HIV), syphilis, and hepatitis B virus (HBV) remains fragmented, particularly across diverse health-system contexts. Eliminating vertical transmission of HIV, syphilis, and HBV is a global priority. Pregnant women are disproportionately affected by these infections, and untreated maternal disease leads to significant infant morbidity. Integrating antenatal screening and treatment provides an opportunity to address all three conditions simultaneously. Purpose: This systematic review and meta-analysis aimed to identify and synthesise evidence on integrated antenatal service delivery models addressing HIV, syphilis, and HBV simultaneously within maternal health services. It specifically examined model characteristics, screening uptake, treatment and follow-up outcomes, implementation barriers and facilitators, and evidence on cost-effectiveness. Methods: This systematic review and meta-analysis followed PRISMA 2020 guidelines and was registered in PROSPERO (CRD420261342186). We searched Scopus, PubMed, Web of Science, and Dimensions for studies published between January 2007 and January 2026. Of 423 records identified, 11 met the inclusion criteria after excluding two studies that did not provide empirical results for an integrated service model addressing all three target infections simultaneously. Data on study characteristics, service delivery, diagnostics, outcomes, and implementation factors were extracted. A random-effects meta-analysis of proportions was conducted using the DerSimonian–Laird estimator with logit transformation. Results: Eleven studies covered Asia, Africa, Europe, and Latin America, mostly in low- and lower-middle-income countries. Integration ranged from rapid test packages in community clinics to comprehensive programmes including STI treatment, malaria testing, and HBV birth-dose vaccination. Pooled triple testing uptake was 97% (95% CI 92 to 100%). Large programmes achieved over 99% coverage and reduced HIV vertical transmission to below 3%. Pilot studies showed feasibility but noted stockouts, data gaps, and weak treatment linkage. Economic analyses supported cost-effectiveness. Conclusions: Integrated antenatal services appear feasible and can achieve high testing uptake, particularly in well-supported programmes. However, evidence remains uneven regarding treatment completion, infant follow-up, HBV prophylaxis, long-term transmission outcomes, and sustainability in resource-constrained settings. Key challenges include supply constraints, workforce limitations, and follow-up gaps. Future research should evaluate the full care cascade, not screening uptake alone. Full article