Search Results (31,585)

High-accuracy potential energy surface (PES) and rovibrational energy levels are essential for computational IR line lists used in (exo)planetary atmospheric spectroscopic analysis and modeling. We present a new ¹⁴N₂¹⁶O PES refinement achieving 0.001–0.002 cm⁻¹ statistical accuracy for E_vib ≤ 7000 cm⁻¹ and J_max = 88–100, relative to complete experiment-based rovibrational energy levels in RITZ, MARVEL, HITRAN2020, and NOSL-296 datasets. Building upon the high-quality ab initio Comp-I PES, the resulting D2n (and D2nB) PES outperform the Ames B1b PES, the UCL TYM PES, and the UCL 2025 PES series in both energy-resolved and J-resolved comparisons, exhibiting the smallest mean residuals and scatter below E_vib = 8000 cm⁻¹, as well as the highest fractions of |δ| < 0.0010 cm⁻¹ and |δ| < 0.0005 cm⁻¹. Robust analysis identified only seven outliers among the UCL-2025 reference level set; all remaining levels are retained to ensure resilient statistics. The D2n PES also shows stable IR intensities with the G10K dipole moment surface and reasonably consistent isotopologue accuracy. Analysis of J-resolved σ_rms highlights the critical role of reference-dataset accuracy and internal consistency. We discuss factors enabling (sub-)0.002 cm⁻¹ accuracy and prospects for extending similar accuracy to higher energies, additional isotopologues, and other molecules. Full article

Although Sustainable Urban Drainage Systems (SUDS) are widely recognised as essential components of resilient urban water management, the large-scale planning and evaluation of such systems remain challenging. This study assesses the hydrological and economic performance of SUDS in Madrid (Spain) under the SSP1-2.6 and SSP5-8.5 climate scenarios, applying a dual-scale methodology based on the Curve Number (CN) model. At the catchment scale, SUDS show substantial potential for irrigation reuse, with runoff-rich catchments reaching hydrological saturation earlier (plateau at r ≈ 0.4) and runoff-limited catchments stabilising at higher implementation levels (plateau at r ≈ 0.6). At the parcel scale, partial-coverage configurations (50% irrigation coverage) outperform full-coverage solutions (100% irrigation coverage), achieving maximum retention levels of 70% in SSP1-2.6 and 50% in SSP5-8.5 while requiring less surface area (10–15%). From an economic perspective, positive net present values (NPVs), acceptable internal rates of return (IRRs), and feasible payback periods occur only at very low retention levels (r < 0.05), with financial performance declining rapidly as storage capacity increases. Full article

Aesthetic medicine is shifting from symptomatic correction to biological structural restoration. Regenerative aesthetics represents a frontier in dermatology, focusing on the restoration of the skin microenvironment to enhance cellular vitality and tissue resilience. Central to this approach is the concept of “skin bed preparation”, a strategic priming phase designed to optimize the physiological terrain before the delivery of advanced aesthetic interventions. This review explores the molecular and cellular mechanisms by which skin bed preparation modulates the extracellular matrix (ECM) and the dermal niche to maximize the efficacy of subsequent treatments and promote long-term skin longevity. Evidence suggests that biostimulatory priming utilizing senolytics, senomorphics, mitochondrial, and/or epigenetic rejuvenators rehabilitates the fibroblast–collagen interactome. By reducing oxidative stress and chronic low-grade inflammation, these preparatory steps transition the skin from a catabolic to an anabolic state. This metabolic reset ensures that subsequent procedures, such as laser therapy, injectable fillers, encounter a responsive cellular environment, resulting in superior collagen induction and prolonged clinical outcomes. Optimizing the skin microenvironment via regenerative aesthetics is not merely an adjunctive step but a fundamental requirement for therapeutic success. Integrating skin bed preparation into clinical protocols provides a synergistic framework that enhances immediate procedural results while addressing the underlying hallmarks of skin aging, ultimately redefining the trajectory of skin health and longevity. Full article

Distributed economic dispatch in AC distribution systems relies heavily on communication networks and is therefore vulnerable to denial-of-service (DoS) attacks. To address this issue, this paper proposes a resilient event-triggered distributed economic dispatch control strategy. Two typical DoS attack scenarios, namely communication-link blocking and node isolation, are first modeled, and an event-triggered distributed economic dispatch controller is then developed to maintain incremental cost consensus and system power balance while reducing communication overhead. Based on Lyapunov stability theory and a linear matrix inequality approach, sufficient conditions for the asymptotic stability of the closed-loop system are derived, tolerable bounds on the frequency and duration of DoS attacks are established, and the absence of Zeno behavior is proved. Simulations on the IEEE 33-bus AC distribution system show that, under load disturbances, dispatch-command variations, and DoS attacks, the proposed strategy can maintain stable system operation, restore dispatch performance after attacks, and reduce communication overhead by 91.86% compared with a fixed-step periodic updating baseline. These results demonstrate the effectiveness and resilience of the proposed method for distributed economic dispatch in AC distribution systems under DoS attacks. Full article

Accurate short-term prediction of urban waterlogging depth is essential for real-time flood risk management in rapidly urbanizing areas under climate variability. Departures from quasi-stationary operating conditions, caused by changes in drainage efficiency, inflow patterns, or measurement quality, weaken historical rainfall–water depth relationships, making purely data-driven models prone to error accumulation. In this study, a GeoAI-based, physics-enhanced machine learning framework is proposed, which translates the water balance principle into Physical Violation Scores (PVSs) and incorporates them as additional input features. PVSs remain zero under expected rainfall–water depth behavior and become positive only under departure scenarios, providing sparse and lightweight diagnostic signals without modifying model structures or loss functions. The framework is implemented on five algorithms (Support Vector Machine, Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and XGBoost) to construct physics-enhanced models (PEMs). These are evaluated against original feature models (OFMs) across 1 h and 2 h forecasting horizons. Results show that most PEMs improve prediction performance compared with their corresponding OFMs, with more pronounced gains at the 2 h horizon. Bootstrap analysis and RMSE-based error amplification factor further indicate comparable or lower R² variability and reduced recursive error amplification for most PEMs. Interpretability analyses show that rainfall forcing and water-depth persistence remain dominant predictors, whereas PVSs act as auxiliary diagnostic signals. Overall, the proposed framework provides a lightweight, reliable, interpretable, and scalable GeoAI approach for incorporating water balance knowledge into short-term urban waterlogging prediction, supporting climate resilience and smart urban water management. Full article

The agricultural sector, particularly animal production, faces numerous unprecedented challenges driven by climate change, resource depletion, and an ever-growing global demand for quality food. These challenges are further compounded by the increasing environmental impact of livestock farming, including greenhouse gas emissions, overuse of water and land resources, and the destruction of vital ecosystems. Ensuring the sustainability of animal production systems while mitigating the negative environmental impacts of these factors is essential for future global food security. As the demand for animal-derived products continues to rise, there is a pressing need for innovations that can enhance productivity without compromising environmental integrity or animal welfare. Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize the animal production industry. AI-driven solutions offer promising avenues for optimizing production efficiency, enhancing animal health and welfare, and reducing the environmental footprint of livestock farming. Machine learning, sensor technologies, and advanced data analytics are being increasingly utilized to monitor and predict various aspects of animal farming, such as feed efficiency, disease prevention, and climate resilience. These technologies enable farmers to make data-driven decisions, fostering more sustainable and environmentally responsible practices. This review examines the integration of AI into animal production systems, emphasizing its applications in climate change mitigation, resource management, and advancing sustainability. The discussion addresses how AI technologies can be utilized to improve productivity while minimizing environmental impact and enhancing animal welfare. Additionally, the paper outlines future opportunities, challenges, and potential barriers to integrating AI technologies into livestock farming, thereby ensuring long-term sustainability amid global challenges. Full article

Protected horticulture moves fragile pots, plug trays, seedlings, harvested products, and carriers through narrow, humid, and crowded spaces. Transport robots must therefore integrate locomotion, perception, localization, handling, placement, scheduling, and human–robot interaction rather than operate as simple carts. This structured narrative review reorganizes evidence from seedling transplanting, nursery operations, harvest support, manipulation, perception, and autonomous navigation around the complete transport chain: target recognition, pickup, loading, loaded navigation, docking, unloading or placement, payload protection, and workflow feedback. The synthesis covers mobile platforms, payload support, perception and localization, motion control, gentle handling, digital support, and fleet coordination. Three barriers remain: short laboratory tests rarely provide season-long evidence; many prototypes are too specialized for variable workflows; and benchmarks seldom combine motion accuracy, handling reliability, payload quality, and resilience. Progress will require modular platforms, robust sensing, payload-safe control, standardized interfaces, and closer co-design between robotics and horticultural operations. Full article

For the first time, a regulatory network linking melanin, genes, pathways, and edible quality was constructed for 138 Auricularia cornea strains sourced domestically and internationally. This marks the inaugural study of A. cornea spanning from cellular to physical-mechanical properties. Correlation analysis between melanin and edible quality traits (hardness, springiness, cohesiveness, gumminess, chewiness, and resilience) revealed that hardness, cohesiveness, and gumminess increased with rising melanin content, while springiness correspondingly decreased. Genome-wide association analysis identified 15,597,589 SNP loci. A total of 39 genes related to food quality were annotated, including one melanin-related lacquer enzyme gene, ACW004924. Real-time quantitative PCR validation of key genes identified for melanin and edible quality traits revealed results consistent with those from correlation analysis. The lacquer enzyme genes ACW004736, ACW006232, which regulate melanin synthesis, and the tyrosinase genes ACW001451, ACW002443, and ACW001003 were also identified in edible quality traits. These genes perform similar functions in GO-enriched metabolic processes, catalytic activity, and cellular structural complexes, as well as in KEGG-enriched pathways such as carbon metabolism and polysaccharide synthesis. They catalyze melanin synthesis and promote interactions between melanin and cell wall polysaccharides, chitin, and structural proteins, thereby stabilizing the cellular scaffold structure, jointly mediating the effect of melanin on the edible quality of A. cornea. The results supplement the downstream regulatory chain of catalytic enzymes and edible quality in the γ-L-glutaminyl-3,4-dihydroxybenzene (GDHB) pigment synthesis pathway, and form an information network of melanin synthesis, cell wall structure optimization, and edible quality regulation. Full article

This paper investigates the thermal buckling behavior of a four-edge simply supported bimodular functionally graded rectangular thin plate subjected to thermal loads. Unlike existing studies, this work introduces the bimodular effect into the thermal buckling analysis of functionally graded thin plates for the first time, accounting for the influence of tension–compression modulus on the critical temperature difference. The problem is challenging due to the complexity of materials and the nonlinearity of structural thermal buckling. For the theoretical analysis, we propose a simplified mechanical model which contains the four important assumptions: there exists a neutral plane in bending; the influence of shear stresses may be neglected; the membrane effect and bending effect are considered separately; and there are two different buckling regimes: a compression-dominated pre-buckling state and a bending-dominated post-buckling state. Three types of thermal loading cases are considered, including uniform temperature rise, linear temperature gradient through the thickness, and nonlinear temperature distribution satisfying Fourier’s law of heat conduction. Within the framework of the simplified mechanical model, the pre-buckling membrane forces, equilibrium equations, and stability equations are derived, thus obtaining a closed-form analytical expression for the critical buckling temperature difference under three different temperature rise modes. The reliability of the present analytical model is validated through comparison with finite element results. Furthermore, a detailed parametric study is conducted to reveal the influences of aspect ratio, width-to-thickness ratio of plate, bimodular indices, and gradient parameters of materials on the critical temperature difference. The results provide a theoretical basis for the thermal stability design of bimodular functionally graded plates operating in high-temperature environments. Full article

This study examines how peer perceptions and school power dynamics shape the identity development of learners with disabilities in South African full-service schools. Guided by Michel Foucault’s lens, particularly ‘care of the self,’ the research explores pathways from vulnerability to empowerment. Using a qualitative, exploratory design, data were generated through semi-structured interviews with learners (n = 20; ages 12–18) and non-participant classroom observations across four full-service schools, followed by thematic analysis with double-coding to enhance reliability. Findings show that negative peer perceptions and routine categorisation practices intensify isolation, self-doubt, and internalised stigma; yet many learners actively navigate identity threats via self-advocacy, supportive relationships, and self-care practices (e.g., mindfulness, journaling, goal setting). These practices are associated with greater self-awareness, confidence, and agency, particularly where school climates promote inclusion, positive peer interaction, and arts/sport participation. The analysis highlights a persistent tension between biopower in schooling (assessment, surveillance, normalisation) and students’ self-care efforts; however, supportive environments mitigate this tension and enable identity affirmation. The study concludes that embedding structured self-care opportunities, strengthening positive peer cultures, and integrating disability perspectives across the curriculum can convert harmful perceptions into opportunities for resilient identity formation and learner empowerment. Full article

The utilization of unconventional feed resources offers a sustainable strategy to mitigate feed shortages particularly in tropical and subtropical regions where access to conventional feeds is often limited. Among these, water hyacinth (Eichhornia crassipes) is one of the world’s most aggressive aquatic weeds, which has drawn attention due to its dual role as a problematic invasive species and a potential livestock feed. This plant reduces water quality, contributes to biodiversity loss and causes economic damage in farming systems. At the same time, its high capacity for nutrient absorption makes it a viable source of protein and energy for ruminants when properly harvested and processed into forms such as hay, dried leaves, and silage. However, its utilization requires caution, as the plant can accumulate toxins and heavy metals from polluted water, which may harm animal health if unprocessed. This review focuses on the potential of water hyacinth to improve ruminant growth performance, nutrient digestibility and rumen fermentation. Including water hyacinth in ruminant diet safely can possibly improve animal productivity, contribute to sustainable weed management and also provide a practical strategy to alleviate feed shortage in dry seasons, thereby encouraging resilience and sustainable ruminant production. Full article

Background: Cardiac magnetic resonance (MRI) images often exhibit low contrast, anatomical variability, and indistinct boundaries, particularly in the myocardium (MYO) and right ventricle (RV). These challenges can reduce the reliability of both manual and automated segmentation, highlighting the need for more robust and boundary-aware approaches. Methods: In this study, an Aspect-Aware Boundary-Resilient UNet3D (ABR-UNet3D) architecture is proposed for cardiac MRI segmentation. The model incorporates an Aspect-Aware Complementary Attention (AAC) module that combines multi-planar contextual information with a complementary gating mechanism to enhance boundary representation. The method was evaluated on the ACDC dataset under consistent training conditions. In addition to Dice Similarity Coefficient (DSC) and Intersection over Union (IoU), boundary-based metrics, including the 95th percentile Hausdorff Distance (HD95), Average Surface Distance (ASD), and Surface Dice, were employed. Furthermore, a five-fold cross-validation protocol and detailed ablation studies were conducted to assess robustness and analyze the contribution of individual AAC components. Results: The proposed method achieved a mean DSC of 0.9603 in single-run experiments on the ACDC dataset and showed consistent performance in anatomically challenging regions, particularly for RV and MYO segmentation. In addition, five-fold cross-validation experiments resulted in an average DSC of 0.952 ± 0.009 and IoU of 0.908 ± 0.012, indicating stable performance across different data splits within the evaluated dataset. Boundary-based metrics also showed improved surface agreement and lower boundary errors compared with the evaluated baseline models. Ablation studies further indicated that the combined use of multi-planar contextual information and complementary gating contributes more effectively to segmentation performance than the individual components used separately. Conclusions: The results suggest that the proposed ABR-UNet3D architecture provides a stable and competitive segmentation framework for cardiac MRI images within the scope of the ACDC dataset. By jointly modeling contextual information and boundary refinement, the method improves segmentation reliability in challenging regions while maintaining competitive and consistent performance with respect to existing approaches. Full article

With growing global concern over climate risk, high-carbon enterprises are assuming an increasingly critical role in strengthening climate resilience and fostering low-carbon development. However, how climate risk disclosure shapes their carbon performance—specifically through what mechanisms and pathways—remains a pivotal yet underexplored question. To address this gap, this study constructs a panel dataset comprising Chinese listed high-carbon companies over the period 2006–2022 and employs a two-way fixed-effects econometric model to assess how climate risk disclosure affects carbon performance while investigating the underlying mediating channel. The empirical results provide robust evidence that enhanced climate risk disclosure improves the carbon performance of high-carbon enterprises. Mechanism analysis indicates that this beneficial outcome is mainly achieved through promoting green technological innovation and easing corporate financial constraints. Heterogeneity analysis further shows that the effect is stronger among smaller companies, firms operating in less concentrated industries, and those headquartered in China’s eastern region. The policy implications derived from these findings include establishing and strengthening a mandatory climate risk disclosure framework, introducing targeted incentives for green innovation and transition finance and tailoring climate risk management strategies according to firm-specific characteristics. Overall, this study underscores climate risk disclosure as a crucial factor in supporting the shift toward low-carbon operations among high-carbon enterprises. Full article

Background: Chronic critical illness (CCI) following acute brain injury involves persistent immune dysfunction, yet its genetic determinants remain unclear. We investigated whether the rate of T-cell receptor excision circle (TREC) depletion—a proposed marker of adaptive homeostatic resilience—is associated with the burden of rare damaging genetic variants. Methods: Whole-exome sequencing (WES) was performed on a cohort of 84 patients (64 with traumatic brain injury, 20 with stroke). In a longitudinal sub-cohort (n = 27), patients were stratified into quartiles (Q1–Q4) based on the slope of their TREC trajectories. ‘Qualifying variants’ (QVs) were defined using strict rarity (gnomAD allele frequency ≤ 0.001) and pathogenicity criteria. Gene-level burden (collapsing) analysis and permutation-based statistical testing (10,000 iterations) were employed to evaluate genetic enrichment in the extreme quartiles. Results: While baseline TREC levels were strictly age dependent (p < 0.0001), the rate of change (TREC slope) was age independent. Rapid TREC decline (Q1) correlated with significantly higher final SOFA scores (p = 0.001) and neutrophil-to-lymphocyte ratios (p = 0.020). Rare variant burden analysis revealed that Q1 patients were significantly more likely to harbor QVs in immune-related genes compared to the Q4 recovery group (odds ratio = 8.25; permutation p = 0.016). Patients with rapid decline were enriched for QVs in putative core “housekeeping” pathways essential for T-cell maintenance and DNA repair (e.g., ERCC3, FANCM), whereas variants in recovering patients were restricted to peripheral effector or structural pathways. Conclusions: Our findings suggest, as a conceptual framework, that an individual’s ability to maintain T-cell homeostasis during critical illness is influenced by their underlying genetic buffering capacity. We propose a hypothetical “two-hit” framework where physiological stress unmasks pre-existing fragilities in core homeostatic pathways—potentially reflecting a state of functional haploinsufficiency under extreme proliferative demand—leading to accelerated immune exhaustion. These results position the TREC slope as a dynamic, age-independent biomarker of genomic resilience in the ICU. All findings are exploratory and hypothesis generating. Full article

This paper presents a practical and communication-independent energy management system (EMS) for a DC microgrid supply within the firezone of a cruise ship. The proposed approach prioritizes operational reliability and fault tolerance under emergency conditions, where communication availability and control complexity should be minimized. The proposed DC microgrid integrates photovoltaic systems (PVs), fuel cell systems (FCs), and lithium-iron-phosphate (LFP) battery energy storage systems (BESSs), coordinated through a rule-based EMS combined with droop-controlled converters. The electrical topology considered in this study is a collaborative development of the project consortium of the publicly funded project Sustainable DC Systems (SuSy), featuring a novel configuration with two independent horizontal busbars for the Cabin Area Distribution (CAD) and Technical Area Distribution (TAD). The EMS can manage two operational scenarios: (i) regular operation, with two decentralized droop controls where power generation is distributed among all generators based on their respective capacities, and a power curtailment strategy is applied to prevent overcharging of BESSs; and (ii) irregular operation, where a fault on one of the vertical busbars triggers the use of reserved battery storage capacity on both sides of the ship and activates load-shedding to ensure continued operation of critical loads and sustain grid functionality. The effectiveness of the proposed architecture is validated through detailed MATLAB/Simulink simulations. Under regular conditions, the EMS achieves stable voltage regulation, balanced power sharing, and efficient energy curtailment. During fault conditions, the battery storage on both sides successfully supports the critical loads. The fuel cells are operated in power-controlled mode effectively up to their full rated 6$\mathrm{k}$$\mathrm{W}$ capacity while the DC bus voltage stabilization is ensured by the battery energy storage systems. These results validate the proposed EMS as a robust and low-complexity solution for maritime DC microgrids, offering stable voltage regulation, effective load prioritization, and resilient operation of critical loads. Full article