Search Results (7,583)

Aluminum–vanadium (Al–V) master alloy is a key raw material for manufacturing high-end alloys, but the internal temperature transient field during its self-propagating high-temperature synthesis (SHS) is nearly impossible to measure in situ. This work develops a numerical simulation framework for Al–V master alloy SHS, featuring a novel temperature–time dual-criteria adaptive moving heat source and a gas–liquid–solid three-phase heat transfer model coupled with temperature-dependent thermophysical properties. The model, implemented in ANSYS Fluent via a customized user-defined function (UDF), is experimentally validated with a maximum temperature error below 7%. Results reveal that higher compact relative density accelerates combustion wave propagation, while increased slagging agent content exerts an inhibitory effect. This study provides a theoretical and quantitative tool for mechanism analysis and industrial process optimization of Al–V master alloy SHS production. Full article

DNA mixture interpretation remains one of the most technically demanding challenges in forensic genetics. While probabilistic genotyping (PG) systems have substantially advanced likelihood ratio (LR) evaluation, comparatively less attention has been devoted to the systematic reconstruction of contributor genotypes, particularly in no-suspect and database-search contexts. This review synthesizes recent developments in DNA mixture deconvolution through a four-strategy framework: (i) physical and biological separation, (ii) high-information genetic markers, (iii) continuous probabilistic algorithms, and (iv) integration with database searching infrastructures. Upstream approaches, including single-cell isolation and sequencing, reduce mixture complexity at the molecular level. Marker innovations such as microhaplotypes, MiniHaps and DIP-STRs increase per-locus information content and enhance resistance to degradation. Downstream probabilistic models—extended from STRs to SNPs and microhaplotypes—leverage quantitative signal data to infer contributor genotypes, with recent advances in Hamiltonian Monte Carlo, variational inference, and deep learning improving inferential stability and reconstruction accuracy. Importantly, genotype deconvolution and LR evaluation represent mathematically distinct objectives, requiring different validation metrics and potentially separate architectural optimization. The convergence of molecular innovation, algorithmic refinement, and LR-based database searching is progressively transforming mixture interpretation from a purely evidential assessment into an integrated investigative framework. Future progress will depend on standardized marker panels, deconvolution-specific performance metrics, and scalable LR-enabled database infrastructures. Full article

Pyropia haitanensis, an economically significant cultivated seaweed in China, exhibits substantial geographical variations in nutritional and sensory qualities that influence its market value. The nutritional quality of the samples, including total sugar, total protein, and amino acid content, as well as color quality, assessed through phycobiliprotein and chlorophyll content, and sensory quality evaluated using an electronic nose and electronic tongue, were determined. To elucidate these quality variations, this study employed an integrated metabolomics and chemometrics approach to analyze samples from five major cultivation regions. Principal component analysis (PCA) effectively differentiated the samples; orthogonal partial least squares discriminant analysis (OPLS-DA) validated this classification with robust model parameters (R²X = 0.791, R²Y = 0.995, Q² = 0.984) and identified key discriminatory metabolites. Weighted gene co-expression network analysis (WGCNA) identified origin-specific metabolic modules correlated with quality traits, revealing that pathways such as cysteine and methionine metabolism underpin the observed differences in flavor profiles across cultivation regions. Furthermore, mediation analysis quantitatively confirmed that inorganic nitrogen primarily influences key flavor attributes by regulating sulfur-containing amino acid and nucleotide metabolism. This study systematically elucidates the metabolic mechanisms governing quality formation in P. haitanensis, providing a scientific foundation for quality control and geographical origin traceability. Full article

This study presents an integrated approach for producing antioxidant-rich polar fractions from Inula salicina L. via pressurised ethanol/water extraction (PLE-EtOH/H₂O), optimised by coupling a central composite design and response surface methodology (CCD-RSM) with Aspen Plus^® simulation. The effects of PLE temperature, extraction time, and EtOH/H₂O ratio for yield, total phenolic (TPC) and flavonoid (TFC) content, and Trolox equivalent antioxidant capacity (TEAC) measured in ABTS^•+-scavenging, cupric ion reducing antioxidant (CUPRAC) and oxygen radical absorbance (ORAC) assays were assessed via a multi-response optimisation approach. Optimal conditions were set at 82 °C, 27 min, and 60% EtOH (v/v), yielding ~29 g extract per 100 g plant material, characterised by high TPC (227 mg GAE/g), TFC (34 mg QE/g), and TEAC values in the CUPRAC (1473 mg TE/g), ABTS (869 mg TE/g), and ORAC assays (1165 mg TE/g). The TPC and TEAC values of the post-extraction residue were >92% lower than those of unextracted I. salicina, confirming efficient recovery of the major portion of antioxidant-active constituents by PLE-EtOH/H₂O. The high in vitro radical scavenging capacity, reducing power, and photoprotective potential (sun protection factor ~50 at 0.5 mg/mL) of the I. salicina extract are consistent with its phenolic-rich composition, with chlorogenic acid (~97 mg/g extract) and its derivatives being the major constituents. The validated Aspen Plus^® model closely aligned with the CCD-RSM predictions, supporting process scale-up and energy feasibility and demonstrating an industry-relevant, green-solvent PLE process for producing higher value-added I. salicina fractions with potential applications in the food, pharmaceutical, nutraceutical, and cosmetic sectors. Full article

To address the limited drainage capacity of conventional porous asphalt pavements under high-intensity rainfall, this study proposes the use of epoxy-modified bitumen to develop a large-void porous asphalt concrete (LV-PAC) with a target air void content of 25%. This approach represents a novel application of epoxy-modified bitumen to enhance permeability in porous pavement systems. The LV-PAC exhibited improved high-temperature stability, permeability, and clogging recovery capability compared with a conventional high-viscosity porous asphalt concrete (HV-PAC), though its low-temperature deformation capacity was relatively lower. All evaluated performance indicators met the required specifications, highlighting the potential of epoxy-modified bitumen for use in large-void porous pavements pending further field validation. Full article

The rapid proliferation of text-to-image generative systems has transformed visual content production, yet the structural characteristics embedded in their compositional outputs remain insufficiently understood. Rather than approaching human–AI differentiation as a purely classification problem, this study investigates whether a controlled set of AI-generated and human-designed posters exhibits measurable structural divergence under thematically matched conditions. A dataset of jazz festival posters was analyzed using interpretable geometric and information-theoretic descriptors, including spatial density (padding ratio), edge density, chromatic dispersion, and entropy-based measures. Instead of relying on deep neural detection architectures, we employed a transparent machine-learning framework to examine intrinsic structural separability within feature space. Results demonstrated highly stable group separation (ROC-AUC = 0.99; 95% CI: 0.978–0.999) under cross-validated evaluation. Distributional analysis further revealed a pronounced divergence in spatial density allocation (Kolmogorov–Smirnov statistic = 0.76, p < 10⁻²⁸), accompanied by a very large effect size (Cohen’s d = 1.365). While padding ratio emerged as the dominant discriminative factor, additional entropy- and chromatic-based descriptors contributed to group separation even when spatial density was excluded (AUC = 0.903). These findings indicate that AI-generated and human-designed posters can diverge in negative space allocation and chromatic organization under controlled thematic and platform-specific conditions. The study contributes to the explainable analysis of generative visual systems by reframing human–AI differentiation as a structural divergence problem grounded in interpretable image statistics rather than as a model-specific artifact detection task. Full article

The flowers of Sophora japonica L. (SJF) and Robinia pseudoacacia L. (RPF) are edible and similar in appearance. The chemical constituents of SJF and RPF were compared by UPLC-Q-TOF-MS/MS and HPLC analysis in this study. A total of 29 and 19 constituents were identified in SJF and RPF, respectively. Flavonoid glycosides were the main constituents found in both flowers. The main aglycon moieties found in SJF were quercetin, kaempferol and isorhamnetin, whereas acacetin and kaempferol were the main ones found in RPF. Additionally, the content of flavonoids in SJF was significantly higher than that in RPF, as determined by HPLC. Rutin was the most dominant flavonoid in SJF with a content range of 72.31~88.15 mg/g, followed by quercetin (13.05~20.30 mg/g). Kaempferol-di(rhamnoside)-hexoside was the most dominant flavonoid in RPF with a content range of 25.94~30.00 mg/g. The distinct flavonoid profiles indicated the chemical non-equivalence of SJF and RPF. Therefore, RPF should not be considered a direct substitute for SJF in herbal medicine without further pharmacological and clinical validation. Full article

Background: Ankle foot orthosis–footwear combination (AFO–FC) tuning involves structured adjustment of the AFO relative to footwear to optimise shank alignment and ground reaction force (GRF) positioning during stance. Although established biomechanical frameworks and clinical algorithms are available, variability in clinical implementation persists. Previous investigations have primarily relied on self-reported practice within single healthcare settings and have not, to our knowledge, systematically examined how orthotists articulate and apply tuning principles within structured clinical reasoning across diverse educational and practice environments. Objectives: This study aimed to determine the level of awareness and framework-based proficiency in AFO–FC tuning among practising orthotists in a geographically diverse convenience sample, to examine the extent to which AFO–FC tuning is integrated into routine clinical practice, and to explore associations between framework-based proficiency level and selected professional characteristics. Methods: A cross-sectional study was conducted using an online survey of practising orthotists (n = 245). Awareness of AFO–FC tuning and self-reported routine implementation were assessed. Framework-based proficiency was evaluated among respondents reporting awareness (n = 212) using structured content analysis of open-text responses within a predefined exploratory five-domain biomechanical framework, and classified as limited (0–1 domains), partial (2–3 domains), or full (4–5 domains). Associations between framework-based proficiency level and professional characteristics were examined using chi-square tests. Binary logistic regression was performed to assess the association between framework-based proficiency level and self-reported routine implementation. Results: Self-reported awareness of AFO–FC tuning was high (86.5%), whereas 53.5% reported routine implementation. Based on the framework scoring, 59.0% demonstrated limited framework-based proficiency, 31.6% partial framework-based proficiency, and 9.4% full framework-based proficiency. No statistically significant associations were observed in this sample between framework-based proficiency level and educational qualification, years of clinical experience, or annual AFO case volume (p > 0.05). Full framework-based proficiency was associated with higher odds of self-reported routine implementation (OR = 4.03, 95% CI 1.44–11.25, p = 0.008). Conclusions: Despite high self-reported awareness, framework-based proficiency in AFO–FC tuning was limited within this sample. Self-reported routine implementation was more frequently reported among respondents with higher framework-based proficiency, whereas no statistically significant associations were observed with educational level, clinical experience, or annual AFO case volume. These hypothesis-generating findings should be interpreted cautiously given the cross-sectional design and framework-based (non-validated) classification. Full article

While Generative Artificial Intelligence technology empowers content production on user-generated content platforms, it also gives rise to novel risks of disinformation dissemination. The effective governance of these risks is critical to ensuring the cybersecurity of the online ecosystem and maintaining long-term social stability. To address the collaborative governance dilemma, this study constructs a tripartite “platform-user-government” evolutionary game model based on prospect theory. It explores the evolutionarily stable strategies and stability conditions of each actor, supplemented by numerical simulations and practical case validation. The results indicate that: (1) under specific conditions, the system can converge to an ideal equilibrium {active platform governance, engaged user participation, stringent government supervision}; (2) the government’s reward–penalty mechanisms can drive the system towards this ideal equilibrium; (3) users’ digital literacy is a key variable influencing the system’s evolutionary path; (4) both the risk preference coefficient (β) and loss aversion coefficient (λ) from prospect theory have a significant moderating effect on the system’s evolution. Finally, targeted recommendations are proposed for the three aforementioned stakeholders to accelerate the improvement of China’s collaborative governance of the content ecosystem. Full article

Allium longistylum is a relatively understudied species whose phytochemical composition and biological activities remain largely unexplored. In this study, the first true leaf (FTL) and the second true leaf (STL) of A. longistylum were compared with respect to phenolic composition, antioxidant capacity, antimicrobial activity, and quorum-sensing (QS) inhibition. Total phenolic content (TPC) and total flavonoid content (TFC) were determined spectrophotometrically, while antioxidant activity was evaluated using ABTS and DPPH radical scavenging assays. Antimicrobial and anti-QS activities were assessed against Staphylococcus aureus, Acinetobacter baumannii, and Chromobacterium violaceum. STL exhibited significantly higher TPC and TFC than FTL, consistent with its stronger radical scavenging activity. Both extracts showed moderate antimicrobial activity and reduced violacein production in C. violaceum, indicating interference with QS. UPLC-Q-Orbitrap-ESI-MS/MS profiling tentatively identified several phenolic acids and flavonoid derivatives. HPLC analysis confirmed the presence of selected phenolic compounds, although several prominent peaks in the chromatograms remained unidentified. Many of the compounds detected by UPLC-Q-Orbitrap-ESI-MS/MS and HPLC have previously been reported to exhibit antioxidant, antimicrobial, and anti-QS activities; their presence may therefore contribute to the bioactivities observed in both extracts. However, their contribution to the observed effects remains speculative and requires further validation through targeted isolation and bioactivity testing. The results suggest that A. longistylum is a promising source of phenolic compounds with antioxidant and antimicrobial properties. Full article

To address the challenge of depleting traditional feed resources, this study aimed to biovalorize sweet potato waste (SPW), a major byproduct of the Japanese shochu industry, into a high-value functional animal feed. An innovative two-stage solid-state fermentation (SSF) was employed, featuring an initial aerobic stage with Aspergillus oryzae for substrate degradation, followed by an anaerobic stage with Lactobacillus plantarum for nutritional enhancement. To optimize this complex, multi-variable process, the predictive performance of Artificial Neural Network (ANN) and Random Forest (RF) machine learning models was compared based on an augmented experimental dataset (N = 80). To ensure statistical robustness and prevent data leakage, a repeated k-fold cross-validation strategy was implemented. The RF model demonstrated significantly superior accuracy and reliability than the ANN model, particularly in predicting the primary metric, crude protein (R² = 0.61 ± 0.04 vs. R² = 0.12 ± 0.15). Subsequently, the validated RF model was integrated with a Constrained Differential Evolution (CDE) algorithm for global parameter optimization. The optimized process was predicted to yield a final product with a crude protein content of 25.0%, alongside significant increases of 114.1% in total amino acids and 123.9% in essential amino acids. These projections were experimentally validated in vitro, confirming the model’s accuracy with a relative error of less than 5%. Furthermore, comprehensive biochemical assays demonstrated a massive degradation of anti-nutritional factors and significant enhancements in total phenolic content and antioxidant activity. This study provides a scientifically validated, data-driven framework for the valorization of SPW. It confirms the superior efficacy of ensemble learning methods for optimizing complex bioprocesses with limited data, offering a contribution to the development of a circular bioeconomy and sustainable feed resources. Full article

This study examines how transnational online continuing professional development (CPD) supports language instructors’ technological pedagogical content knowledge (TPACK) in transnational higher education (TNHE). To assess this development, an existing TPACK self-report instrument was adapted to reflect cross-border online delivery, platform-mediated assessment and feedback, and collaborative course preparation. Survey data were collected from instructors at University of Southampton partner institutions in China (n = 431). Using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM), and paired-samples t-tests, the study examined the instrument’s measurement properties, the structural relations among knowledge domains, and changes over time. Results supported a stable four-factor structure—technological knowledge, content knowledge, pedagogical knowledge, and TPACK—with good model fit and acceptable reliability and validity. SEM showed that pedagogical knowledge and technological knowledge significantly predicted TPACK, whereas content knowledge did not directly predict it. Longitudinal analyses of matched pre–post responses (n = 172) indicated significant increases in technological knowledge, pedagogical knowledge, and TPACK after CPD participation, while content knowledge remained statistically stable. These findings suggest that routine online CPD is most responsive in strengthening instructors’ technology-related and pedagogical capacities, which in turn support integrative teaching competence in TNHE language teaching. Full article

Brewer’s spent grain (BSG) is one of the major by-products of the brewing industry and an abundant lignocellulosic stream with potential for energy recovery and broader biorefinery use. This review evaluates the main BSG-to-energy pathways, including anaerobic digestion (AD), combustion/co-combustion, pyrolysis, gasification, and hydrothermal processes (HTC/HTL), with emphasis on technical performance, environmental aspects, implementation constraints, and integration into brewery systems. Particular attention is given to the effect of BSG heterogeneity, high moisture content, protein and ash composition, and storage instability on process selection and operability. In addition to summarizing pathway-specific evidence, the manuscript proposes a harmonized comparative framework and an integrated technical–economic–environmental interpretation of BSG valorization options. The analysis shows that wet-feed-compatible pathways, especially AD and hydrothermal processing, are generally better aligned with the intrinsic properties of fresh BSG, whereas thermochemical routes usually require more intensive feedstock conditioning and tighter control of ash-related and gas cleaning risks. The review also highlights that long-term operational reliability, scale-up constraints, and utility integration are as important as nominal conversion efficiency when assessing practical deployment. Current evidence suggests that the most realistic implementation strategies are context-dependent and should be selected according to brewery scale, energy demand profile, available heat integration, and acceptable operational risk. Future research should prioritize harmonized reporting, long-term industrial validation, and the development of robust hybrid systems and brewery-integrated biorefinery configurations. Full article

This work experimentally evaluates the geometry-driven structural efficiency and normative performance of sandwich-type composite roofing tiles composed of a miriti wood core and fiberglass-reinforced polymer faces. Trapezoidal-profile tiles were manufactured by hand lay-up and assessed according to ABNT NBR 16753, including visual inspection, fiber content, water absorption, apparent flexural behavior, deformation resistance, and impact resistance. The miriti core exhibited an extremely low mean density of 0.091 ± 0.008 g/cm³ (CV ≈ 8.8%), enabling lightweight sandwich configurations with an average overall thickness of approximately 8 mm. Fiberglass contents ranged from 27.5% to 32.1% by mass. Sealed sandwich specimens showed median water uptake values of approximately 2.5% after 2 h and 6.0% after 24 h immersion. Deformation resistance tests indicated admissible deflections of 15.0–15.75 mm (L/40), supported by applied masses between 39.6 and 104.3 kg (≈388–1023 N) without rupture or permanent damage. Apparent flexural stresses ranged from 6.7 to 9.3 MPa, with apparent moduli between 0.7 and 1.9 GPa. All tiles achieved 100% approval in deformation, impact (2–8 J), and visual criteria. The results demonstrate that geometric effects dominate structural performance, validating miriti wood as an efficient and sustainable core for normatively compliant composite roofing systems. Full article

Background/Objectives: The significant negative correlation between protein and oil content in soybean seeds is a long-standing bottleneck for conventional breeding. Its root cause lies in insufficient understanding of related molecular regulatory processes. Methods: We selected the CSSL_R19, a chromosome segment substitution line, to thoroughly investigate the intrinsic effects of the substituted segment on the high seed storage protein (SSP) and low fatty acid (FA) phenotype. Transcriptomic, proteomic, and metabolomic analyses were performed on the recurrent parent and R19. Results: A total of 1821 differentially expressed genes (DEGs), 12 differentially expressed proteins (DEPs), and 10 differentially accumulated metabolites (DEMs) were detected. Subsequently, an integrative examination of the data demonstrated that 28 DEGs, 5 DEPs, and 4 DEMs participated in biological processes such as carbohydrate metabolism, lipid degradation, as well as protein synthesis and transport. Mechanistically, down-regulation of PGM reduces the carbon source supply for FA synthesis; up-regulation of LOX, LACS, ACX, and KAT promotes FA degradation. SRP, SAR1, and HSP70 are involved in the synthesis and transport of SSP. Crucially, qRT-PCR validation performed on all 28 core DEGs showed that their expression trends were highly consistent with the transcriptome data, confirming the reliability of the findings. Conclusions: In conclusion, we propose a potential regulatory network that enhances SSP accumulation and reduces FA content. Altogether, these findings advance our understanding of storage compound accumulation in soybeans and guide future breeding strategies. Full article