Search Results (16,134)

Synthetic load profiles are essential for distribution network planning, protection sizing, and demand-side management studies. However, most generation methods are validated on a single load typology, and their transferability remains unexamined. Two broad paradigms dominate the generation literature: data-driven approaches that learn the demand distribution directly from historical records (generative adversarial networks, diffusion models, Markov-chain generators) and bottom-up, physically motivated approaches that reconstruct demand from the superposition of discrete appliance ON/OFF events. Same-data, same-metric comparisons across these two families for structurally distinct load typologies remain absent from the literature, and this gap is the one this paper addresses. This paper presents a systematic comparison of three stochastic models (a first-order autoregressive (AR(1)) profile, a physically constrained ON/OFF event model, and a nonlinear-least-squares (NLS) calibrated variant) applied to two fundamentally different load typologies measured with a Class A power-quality recorder at 10-minute resolution: a single-family residential dwelling (13 days, 1860 samples) and an institutional commercial building (9 days, 1333 samples). Evaluation spans six distributional statistics (mean, standard deviation, and the percentiles $p_{50}$, $p_{90}$, $p_{95}$ and $p_{99}$), the Kolmogorov–Smirnov (KS) statistic, root mean square error (RMSE), and hourly variance profiles. In this two-site study, load typology, not model sophistication, emerges as the dominant factor shaping fit quality. The simple AR(1) reproduces all percentiles within 7% for the near-Gaussian commercial load (skewness 3.47). By contrast, no model reproduces the centre, the dispersion and the upper tail of the highly skewed residential load (skewness 6.56) simultaneously to within 10%. On the residential tail the AR(1) ensemble is the least biased ($p_{99}$: −5.8%) but by far the most dispersed across realizations (CV = 15.3%), whereas the event-based models are more stable but biased, so model choice on skewed loads is a bias–stability trade-off rather than an accuracy ranking. The NLS-calibrated model attains near-exact residential $p_{95}$ reproduction (−0.3%) and commercial upper-tail errors below 2%, at a computational cost roughly three orders of magnitude above the AR(1). The dynamic characterization of demand obtained from these models, including the magnitude and frequency of the detected events, provides elements that may be of interest for the sizing and operation of photovoltaic systems in the context considered. Based on these two cases, a preliminary recommendation matrix mapping models to engineering applications is proposed, motivating typology-specific model selection rather than universal approaches; broader validation on additional sites is identified as future work. Full article

Diabetes mellitus is associated with progressive hepatic injury driven by oxidative stress, inflammation, and apoptosis. Aucubin, a natural iridoid glycoside, possesses potent antioxidant and anti-inflammatory activities; however, its hepatoprotective mechanisms in diabetic liver injury remain unclear. This study investigated the protective effects of aucubin against alloxan-induced diabetic hepatic injury and the underlying molecular mechanisms. Male albino rats were assigned to five groups: normal control, alloxan-induced diabetic, diabetic treated with metformin (150 mg/kg), and diabetic treated with aucubin (25 or 50 mg/kg) for 28 days. We evaluated body weight, fasting blood glucose, liver function, lipid profile, oxidative stress biomarkers, inflammatory cytokines, and hepatic expression of Nrf2, HO-1, NF-κB p65, Bax, and Bcl-2, along with histopathological and immunohistochemical examinations. Alloxan induced marked hyperglycemia, weight loss, hepatic dysfunction, dyslipidemia, oxidative stress, inflammation, and apoptosis. Aucubin significantly ameliorated these alterations, with the 50 mg/kg dose generally showing greater effects than the 25 mg/kg dose. Aucubin improved liver function, ameliorated dyslipidemia, reduced lipid peroxidation, enhanced antioxidant defenses, increased Nrf2 and HO-1 expression, attenuated NF-κB p65 expression and pro-inflammatory cytokines, favorably modulated the Bax/Bcl-2 balance, preserved hepatic architecture, and increased Ki-67 immunoreactivity, indicating enhanced cellular proliferative activity. These findings indicate that aucubin is associated with improved hepatic antioxidant, inflammatory, apoptotic, and metabolic status in alloxan-induced diabetic rats. Full article

Generative artificial intelligence (GenAI) has changed the conditions under which students in higher education read, write, solve problems, and complete assessed work. The central pedagogical question is therefore no longer whether students use AI, but how AI use reshapes the relationship between intended learning outcomes, teaching–learning activities, and the evidence on which judgments of competence are based. This article presents a critical narrative review that integrates constructive alignment theory with research on cognitive offloading, scaffolding, self-regulated learning, formative feedback, and assessment validity under AI-rich conditions. The review argues that GenAI can function either as a pedagogically valuable scaffold that stimulates elaboration and reflection or as a shortcut that inflates task performance without building durable competence; which pathway dominates depends on instructional design, guardrails, and the assessment formats through which learning is inferred. The review further argues that GenAI does not invalidate constructive alignment but requires its extension: intended learning outcomes must specify the conditions of performance—with or without AI—as part of the construct, and the tool environment becomes an explicit component of alignment. Building on this analysis, the article develops a design framework organized around four decisions—whether AI should be prohibited, permitted, permitted with documentation, or required for a given task—and derives implications for course design, formative feedback, and credible certification. The central conclusion is that higher education should neither ban nor normalize GenAI indiscriminately but redesign aligned teaching and assessment systems that protect independent judgment while preparing students for responsible human–AI collaboration. Full article

How much a college education pays off varies widely from one student to the next, and that variation matters for admissions, financial aid, and mobility policy. Most estimates, however, report a single average return and treat each institution as an isolated row in a table, ignoring how schools relate to one another and how outcomes are distributed across demographic groups. We present FairEdu-GCT(Graph-Enhanced Causal Transformer), a framework that couples a heterogeneous graph encoder with a Transformer sequence model and an explicit fairness penalty. Institutions, geographic regions, and academic disciplines form a typed graph whose edges record graduate flows, spatial proximity, and disciplinary overlap. A Relational Graph Attention Network (R-GAT) turns this structure into institutional ecosystem embeddingsthat carry peer effects, regional labor-market signals, and the spread of institutional prestige, none of which survives in tabular representations. A Transformer then encodes each student’s educational history and merges it with the institutional embedding through a cross-modal attention bridge, and a counterfactual decoding head returns the full conditional earnings distribution under alternative institutional choices rather than a single point estimate. Because students are not randomly assigned to schools, we make no claim of strict causal identification; we treat CATE and PEHE strictly as estimation-quality diagnostics for a confounding-adjusted contrast, not as evidence of a proven causal effect. We instead adjust for observed confounders through a doubly robust objective and add an equalized opportunity regularizer so that accuracy does not come at the expense of protected subgroups. On linked U.S. College Scorecard, IPEDS, and NLSY97 data (6256 institutions drawn from 7312 Title IV schools; 161,043 person-institution-year records from 8984 respondents followed for ten years), FairEdu-GCT lowers RMSE by 16.2% and Precision in Estimation of Heterogeneous Effects (PEHE) by 25.1% against the strongest baseline (Causal Forest, DragonNet, TARNet, and TabTransformer), and shrinks the demographic parity gap by 46.5%. All gains are reported with 95% confidence intervals and effect sizes over ten seeds, and an extended fairness audit (calibration, predictive parity, and subgroup robustness) confirms the improvement is not confined to the two metrics we optimize. Ablations attribute a 9.0% RMSE reduction to the R-GAT encoder alone. A group-conditional SHAP analysis further shows that graph-derived proximity to regional technology clusters is an unusually strong predictor for first-generation minority students, a signal that tabular features cannot recover and one we read as a within-model association rather than a policy lever. Full article

Agrivoltaics (AV) is a concept that involves dual land use for both growing crops (or grazing) and installing solar panels for electricity production. AV should be assessed not only as a dual land use strategy, but also as a climate adaptation and resilience solution for agriculture; solar panels can shield crops and livestock from extreme weather events (EWEs). This PRISMA review synthesises evidence from Scopus- and PubMed-indexed studies (up to 2025) on AV and EWEs: 1152 (Scopus) and 1208 (PubMed) records were identified, 93 (Scopus) and 160 (PubMed) full texts were assessed, and 27 studies were included in the final review. Published data indicate that AV can reduce frost damage risk by more than 40% (simulation-based evidence), prevent crop leaf sunburn (experiment-based evidence) and hail damage (experiment-based evidence), alleviate heat (experiment-based evidence) and water stress (experiment-based evidence), and act as windbreaks (experiment-based evidence). The majority of reviewed studies were conducted in temperate climates (54%), limiting the generalisability of findings to more extreme climate types where EWE impacts may be most pronounced. The EWE effect on agriculture is complex: crop losses, interruptions of energy, fuels, fertilisers, and other supplies, which could lead to the inability to complete the agricultural work on time and cause economic losses. AV, like other distributed PV systems, has the potential to enhance the energy resilience of farms, though direct evidence of this benefit from AV-specific studies is limited. However, resilience to EWEs is explicitly addressed in less than 3% of Scopus-indexed AV studies. This review presents current evidence, key design trade-offs, and research gaps in the field of AV aiming to reduce EWE consequences. Full article

Land-use change is the primary direct driver of terrestrial biodiversity loss. Around Cerro Castillo National Park (PNCC) in Chilean Patagonia, rural land subdivision increased by 400% between 2011 and 2023, generating ecological, economic, and social pressures on the park and surrounding communities. Protected-area conservation requires integrating social and cultural dimensions that shape how territories are inhabited and transformed. This exploratory study analyzes how relational valuation contributes to understanding people–nature relationships by comparing two socio-environmental profiling models of landowners adjacent to PNCC. Based on 129 surveys, relational values and disvalues were coded from an open-ended question. Models were compared using Multiple Correspondence Analysis, Hierarchical Clustering on Principal Components, and a Chi-square test. Although exploratory and non-generalizable, both models identified three profiles, with Model B offering a complementary characterization that emphasized relational orientations and pro-environmental preferences. These findings suggest that relational valuation can complement sociodemographic and territorial characterization by revealing differentiated ways in which landowners in this sample relate to nature and territory, including identity- and stewardship-oriented relationships, as well as relationships shaped by socio-environmental tensions. These exploratory insights provide a basis for further research and participatory validation in protected-area contexts and may inform differentiated conservation and land-use management strategies under rural subdivision pressures. Full article

Foot-and-mouth disease (FMD) is a highly contagious viral disease that seriously threatens livestock health. Protective immunity induced by vaccination is primarily associated with the generation of neutralizing antibodies; however, conventional virus neutralization tests are time-consuming and require the handling of live virus, limiting their suitability for rapid and repeated evaluation in routine settings. In this study, we established a surrogate neutralization assay for the functional evaluation of neutralizing antibody activity against FMD virus (FMDV) serotype O. The assay measures changes in luciferase-based luminescent signals generated following exposure of LgBiT-expressing cells to HiBiT-tagged virus-like particle (VLP) preparations. FMDV serotype O-derived VLPs exhibited stable capsid protein expression, assembly characteristics, and morphological integrity similar to those of virus particles, as confirmed by sucrose gradient fractionation and electron microscopy. In the presence of neutralizing antibodies, a reduction in luminescent signals was observed, enabling functional discrimination of antibody activity. The surrogate assay results showed a strong correlation with those of the conventional virus neutralization test for FMDV serotype O (R² = 0.9068). This study demonstrates the feasibility of the surrogate neutralization assay for assessing the functional activity of neutralizing antibodies and its potential applicability to vaccine immunogenicity assessment and functional antibody analysis. Full article

Habitat fragmentation caused by human activities threatens plant genetic diversity, but the mechanisms shaping gene flow and spatial genetic structure in epiphytic orchids remain poorly understood. Here, we investigated the genetic structure and gene flow patterns of the epiphytic orchid Rhynchostylis gigantea in a human-modified landscape on Hainan Island, China, using SNP markers. Based on 2005 high-quality SNPs generated via double-digest restriction site-associated DNA sequencing (ddRADseq) from 275 individuals, we assessed genetic diversity, population differentiation, and fine-scale spatial genetic structure (FSGS) and further explored mating patterns through parentage analysis of 100 F1 offspring. The adult population maintained moderate genetic diversity (H_o = 0.243, F_IS = 0.082), whereas offspring cohorts showed stronger heterozygote deficiency (F_IS = 0.198), suggesting that high contemporary geitonogamous selfing may be counterbalanced by post-zygotic selective mortality during early life stages prior to adult recruitment. The two adult subpopulations separated by agricultural fields exhibited significant genetic differentiation (Φ_PT = 0.131, p = 0.001), indicating reduced gene exchange connectivity caused by landscape fragmentation. Significant FSGS was detected across the population and subpopulations, with three-dimensional spatial analyses revealing the influence of both spatial distance and host-tree distribution. Parentage analysis revealed a high rate of geitonogamy and a pattern in which single pollen donors fertilized multiple flowers on the same recipient plant. This indicates that pollinator movement is highly restricted, leading to localized mating within immediate flower clusters. Our results demonstrate that the genetic structure of R. gigantea is shaped by the combined effects of mixed mating systems, limited seed dispersal, host-tree dependence, and habitat fragmentation. Conservation of epiphytic orchids should therefore integrate the protection of orchid populations, host trees, and landscape connectivity to maintain long-term genetic diversity. Full article

Generative artificial intelligence (GenAI) has diffused through higher education faster than institutions have been able to govern it, reshaping the conditions under which universities produce knowledge, judgement, credentials, and public trust. Current responses (prohibition, detection, and accommodation) fall short of a settled governance posture, and existing frameworks, from AI ethics principles to standard Responsible Research and Innovation (RRI) models, are not calibrated to higher education’s distinctive epistemic, formative, and public-good missions. This article addresses that gap through a disciplined conceptual synthesis drawing on RRI, reflexive governance, and higher education theory. The synthesis develops a Reflexive and Responsible Innovation Paradigm (RRIP): a six-dimensional framework that re-specifies RRI’s canonical dimensions (anticipation, reflexivity, inclusion, responsiveness) for the university context and adds two higher-education-specific dimensions: epistemic stewardship and distributive justice. Epistemic stewardship, the article’s central theoretical contribution, names the institutional obligation to protect the conditions under which knowledge claims are formed, warranted, assessed, and trusted under AI mediation. RRIP is operationalized through a multi-level architecture of institutional mechanisms (deliberative AI councils, transparency registers, and reflexive assessment redesign) with a tiered implementation pathway calibrated to institutions of varying capacity. Institutional leaders, program directors, policymakers, and accreditation bodies will find in RRIP a theoretically grounded and practically applicable guide for assessment redesign, curriculum decisions, procurement governance, and sectoral coordination. Full article

The spectral line density of an optical frequency comb (OFC) generated in a microring resonator is fixed by the free spectral range (FSR), and hence by the resonator size: the dense combs required for spectroscopy, optical clocks, and high-capacity communications conventionally demand centimeter-scale cavities, in direct conflict with photonic integration. Here, we propose a route around this FSR–footprint trade-off using topological ring resonators (TRRs) built on a silicon valley photonic crystal (VPC) platform. Evanescently coupling two identical TRRs, an optical analog of quantum tunneling in a double-well potential, deterministically splits each resonance into a doublet of supermodes (Rabi splitting), doubling the spectral line density within a fixed bandwidth while the parallel two-ring layout occupies orders of magnitude less chip area than a single conventional ring of equivalent effective FSR. A coupled-mode-theory model quantitatively captures the splitting observed in full-wave 3D finite-difference time-domain (FDTD) simulations, and the topological protection of the valley edge states preserves the doublet against lattice disorder; a fabrication-tolerance analysis shows the splitting varies by only a few percent for nanometer-scale gap errors. Nonlinear simulations based on the coupled nonlinear Schrödinger equation indicate that the doubled supermode grid translates directly into a denser comb, increasing the generated line count from 48 to 122 under identical Kerr-only pumping conditions. An explicit nonlinear-loss budget, including two-photon and free-carrier absorption, bounds these results for silicon at 1550 nm and identifies mid-infrared silicon and TPA-free platforms such as silicon nitride as physically realistic implementations. This design study establishes coupled topological resonators as a compact, disorder-tolerant architecture for high-density comb generation, which can potentially be experimentally demonstrated. Full article

Momast is a patented polyphenolic complex sustainably extracted from the vegetation water generated during the processing of the Coratina olive cultivar. Although its antioxidant and anti-inflammatory properties have been previously demonstrated, its neuroprotective activity and underlying mechanisms remain largely unexplored. In this study, we investigated the neuroprotective effects of Momast using complementary cellular and biophysical approaches. SH-SY5Y human neuroblastoma cells were exposed to cadmium, mercury, or lead to reproduce distinct mechanisms of heavy metal-induced neurotoxicity, and cell viability was evaluated following co-treatment with Momast. In parallel, the interaction of Momast with biomimetic planar lipid membranes composed of phosphatidylcholine and cholesterol was investigated by electrophysiological analysis. Momast significantly attenuated heavy metal-induced cytotoxicity, although the magnitude and concentration dependence of the protective effect varied according to the metal. Electrophysiological measurements demonstrated that Momast interacts directly with lipid bilayers through a sequential adsorption–insertion process, without compromising membrane integrity, and forms stable, conductive assemblies within the membrane. Overall, the combined cellular and biophysical findings support a mechanistic model in which Momast exerts neuroprotective activity through complementary membrane-dependent and intracellular mechanisms, highlighting membrane interactions as an important determinant of the biological activity of complex olive polyphenolic mixture. Full article

Grid-forming doubly fed induction generator (DFIG) systems integrated with flywheel energy storage systems (FESSs), hereafter referred to as FESS-DFIG systems, must be designed to provide rapid frequency support while maintaining DC-link voltage stiffness and respecting rotor-speed limits under finite kinetic-energy reserves. This study presents a model-based engineering design and verification framework that allocates these coupled requirements between the grid-side converter (GSC) and the rotor-side converter (RSC). For the GSC, stator–rotor coupling terms derived from the dq model are implemented as load-current feedforward signals to reduce the transient power imbalance across the DC link. For the RSC, virtual synchronous control is combined with a sigmoid-based rotor-energy constraint whose minimum-speed limit, transition width, and shape coefficient serve as physically interpretable design parameters for balancing frequency support against mechanical protection. The resulting architecture replaces abrupt support withdrawal with a continuous transition from inertial response to rotor-speed recovery. MATLAB/Simulink verification under load disturbances shows that compared with the conventional feedback-only dual-loop PI controller under the same 0.2 p.u. step-load disturbance, the proposed load-current-feedforward design reduces the maximum DC-link voltage deviation by approximately 33%, suppresses the secondary frequency dip and power oscillations caused by hard-switching logic, and maintains the rotor speed at the prescribed safety boundary of 0.8 p.u. Tests under low and high kinetic-energy conditions further demonstrate stable operation without changing the overall control architecture. The proposed framework therefore provides a systematic design basis for integrating DC-link regulation, grid-forming response, and rotor-energy management in converter-interfaced wind-energy systems. Full article

Background/Objectives: Streptococcus suis is an important zoonotic pathogen responsible for severe infections in animals and humans, and the emergence of diverse strains has reduced the effectiveness of conventional antimicrobial therapies. Since there is no broadly protective vaccine, there is a need for new vaccination strategies that focus on conserved antigens from a variety of strains. This study aimed to design and evaluate a multi-epitope vaccine candidate against diverse S. suis strains using an integrated pangenome-guided reverse vaccinology approach. Methods: To design a multi-epitope vaccine (MEV) candidate against diverse S. suis, an integrated computational framework was employed, incorporating pangenome analysis, subtractive proteomics, reverse vaccinology, immunoinformatics, structural modeling, molecular docking, molecular dynamics simulation, immune simulation, and in silico cloning. The conserved core proteins were systematically screened for essential, non-homologous, antigenic, non-allergenic and non-toxic vaccine candidates for epitope prediction. Results: A total of 7421 gene families, including 1169 conserved core genes, were identified through pangenome analysis of 24 complete S. suis genomes. Three computationally prioritized candidate proteins were identified through sequential subtractive proteomics: sucrose phosphorylase, peptidoglycan hydrolase PcsB and an RND transporter-associated adaptor protein, annotated in the source database as an RND efflux transporter periplasmic adaptor subunit. We selected eight cytotoxic T-lymphocyte (CTL) epitopes, five helper T-lymphocyte (HTL) epitopes, and three linear B-cell epitopes with favorable predicted immunological properties to develop a 397-amino acid multi-epitope vaccine construct that contains the S. suis 50S ribosomal protein L7/L12 adjuvant with rationally designed peptide linkers. The vaccine construct exhibited favorable physicochemical properties, predicted structural stability, and high antigenicity scores. The predicted combined HLA population coverage of the selected CTL and HTL epitopes was 90.77% across the populations included in the analysis. Immune simulation predicted patterns consistent with humoral and cellular immune activation, including sustained IgG production, elevated IFN-γ and IL-2 secretion, efficient antigen clearance, and generation of immunological memory, whereas molecular docking and molecular dynamics simulations characterized the predicted interaction and conformational behavior of the MEV–TLR1/TLR2 complex. Codon optimization (CAI = 0.996) and in silico cloning into the pET-30a(+) expression vector supported the potential feasibility of recombinant expression in Escherichia coli. Conclusions: In this study, a rationally designed multi-epitope vaccine candidate against diverse S. suis strains was developed using an integrated pangenome-guided reverse vaccinology approach. Based on these computational analyses, the proposed vaccine candidate showed favorable predicted immunogenicity, predicted structural quality, predicted HLA population coverage, and expression feasibility, providing a foundation for future experimental validation and development of a vaccine against diverse S. suis. Full article

The global expansion of interconnected edge network components requires immediate strategies for securing low-power computing nodes. Cryptographic algorithms executing over binary extension fields yield considerable computational benefits because their carry-free arithmetic significantly optimizes dynamic power consumption. However, general-purpose silicon architectures lack the dedicated hardware structures to run these finite-field operations efficiently, resulting in severe processing throughput bottlenecks. This study addresses this limitation by introducing a parallelized modular multiplier framework designed to integrate smoothly with the multicore execution environments of modern embedded platforms. Our approach deploys a progressive multiplier reduction (PMR) protocol that segments dense mathematical workloads into distributed structural thread groups. This architectural alignment allows multiplication matrices and spatial field reductions to take place concurrently, balancing localized workloads while decreasing intermediate data buffering demands. We present two distinct topological styles based on column division and row division techniques, deriving comprehensive analytical formulations to capture precise silicon area footprints, critical path delays, and total operational cycle counts. The resulting hardware metrics demonstrate that the parallel PMR design achieves a highly competitive area–delay product alongside optimized dynamic consumption characteristics. This structural paradigm delivers a scalable and robust security alternative for general edge hardware, ensuring system runtime stability while meeting tight environmental power constraints, protecting vital industrial assets, and sustaining emerging macroeconomic infrastructure. Full article

Understanding how dams restructure fish diversity is critical for biodiversity conservation and reservoir management. Here, we integrated species, taxonomic, and functional diversity within a unified framework to assess fish community responses to the Datengxia Water Conservancy Project impoundment on the Hongshui River (Pearl River basin). Based on surveys before (2013) and after (2020–2022) impoundment across upstream, downstream, and tributary reaches, we recorded 162 species, including four nationally protected, four endangered, two critically endangered, and 19 exotic taxa. Our three-dimensional assessment uncovered three key patterns: (1) a marked community shift toward limnophilic and slow-flowing species, with three new dominants (Ptychidio jordani, Culter alburnus, and Oreochromis mossambicus) emerging, while rheophilic and migratory forms contracted; (2) trait-selective diversity losses—locomotion-related functional richness (FRic) declined substantially (up to 42.1% at Wuxuan), whereas feeding-related FRic increased markedly (up to 55.1% at Laibin), and reproduction-related traits exhibited stable compositional profiles (spring-dominated spawning, demersal-adhesive eggs) with heterogeneous index responses across sections; taxonomic diversity revealed closer taxonomic relatedness and structural imbalance; (3) a consistent spatial gradient (tributary > downstream > upstream), with downstream assemblages exhibited lower spatial variation in diversity indices. These community shifts were observed alongside habitat modification (lotic habitat shrinkage and fragmentation) and coincided with exotic invasions and over-dominance of resident species. These patterns should be interpreted as descriptive findings given the observational design. This three-dimensional assessment framework provides complementary insights for evaluating dam-related ecological impacts, generating testable hypotheses for future research and conservation planning. Full article