Search Results (1,244)

Litchi (Litchi chinensis Sonn.) is a pillar of the tropical agricultural economy in southern China, yet its production faces increasing instability due to climate change. Traditional agronomic models often fail to capture the complex, non-linear interactions between meteorological drivers and yield formation in perennial fruit trees. To address this challenge, the study constructed a yield prediction framework using an optimized Random Forest (RF) model integrated with interpretable machine learning (SHAP), based on a comprehensive dataset from 17 major production regions in Hainan Province (2000–2022). The model demonstrated robust predictive capability at the provincial scale (R² = 0.564, RMSE = 2.1 t/ha) and high consistency across regions (R² ranging from 0.51 to 0.94). Feature importance analysis revealed that heat accumulation (specifically growing degree days above 20 °C) is the dominant driver, explaining over 85% of yield variability. Crucially, scenario simulations uncovered asymmetric climate risks across phenological stages: while moderate warming generally enhances yield by promoting vegetative growth and ripening, it acts as a stressor during the Fruit Development stage, where temperatures exceeding 26 °C trigger yield decline. Furthermore, the yield penalty for drought during Flowering (−8.09%) far outweighed the marginal benefits of surplus rainfall, identifying this window as critically sensitive to water deficits. These findings underscore the necessity of phenology-aligned adaptation strategies—specifically, securing irrigation during flowering and deploying cooling interventions during fruit development—providing a data-driven basis for climate-smart management in tropical agriculture. Full article

Background: Composite restorations are the standard of care for posterior teeth due to their aesthetic properties and conservative nature. However, the choice between direct and semi-direct techniques can influence clinical longevity and performance. Objectives: This study aimed to compare the clinical performance of two restorative approaches: a direct technique and the semi-direct onlay technique in terms of aesthetic quality, surface finish, wear resistance, marginal integrity, and overall clinical efficiency over a two-year period. Methods: A total of 348 composite restorations were placed in 192 patients. Each restoration was evaluated at four timepoints: baseline (T0), 6 months (T1), 1 year (T2), and 2 years (T3). Clinical performance was assessed using standardised 5-point rating scales across the five dimensions. Repeated-measures ANOVA assessed changes over time, while Wilcoxon signed-rank and Mann–Whitney U tests were used for intra- and inter-group comparisons. Results: Significant time effects were observed across all clinical parameters (p < 0.0001). The direct technique exhibited superior initial results in aesthetics and surface finish at T0 and T1 (p < 0.001), but differences diminished by T3. In contrast, the semi-direct technique demonstrated improved performance in wear resistance and marginal integrity at T2 and T3. Both techniques showed progressive deterioration, particularly in marginal adaptation. Conclusions: The direct technique offers enhanced short-term aesthetics and procedural efficiency, while the semi-direct approach provides superior long-term durability and marginal adaptation. Full article

Flooding is an increasing climate risk in the UK, yet schools remain marginal in resilience planning. Flood events disrupt education, heighten pupil anxiety, increase staff workload and unsettle communities, but these experiences are rarely documented in ways that inform policy. This study examines how schools in the East and West Midlands regions of the UK have experienced and adapted to flooding. Eight qualitative case studies were undertaken in flood-affected schools using semi-structured interviews with key staff, site visits and documentary evidence. Interview transcripts were thematically analysed using NVivo to explore past flood events, levels of preparedness, and readiness for measures such as Property Flood Resilience, Sustainable Drainage Systems and Climate Action Plans. Findings show wide variation in awareness, emergency procedures and engagement with local authorities. Most schools had faced flooding or near misses but lacked formal guidance or flood-specific plans, leading to improvised responses led internally by staff. Despite limited funding, inconsistent communication and exclusion from wider planning, schools demonstrated adaptive potential and willingness to support community preparedness. The study offers evidence to guide headteachers, policymakers and local authorities in strengthening school-based flood resilience and supporting the development of a resilience innovation blueprint for flood-prone schools in the UK. Full article

Background: Mechanical loading is a fundamental regulator of bone remodelling; however, the mechanotransduction mechanisms governing alveolar bone adaptation under tensile-dominant orthodontic loading remain insufficiently defined. In particular, the molecular pathways associated with tension-driven cortical modelling in the periodontal ligament (PDL)–bone complex have not been systematically interpreted in the context of advanced biomechanical simulations. Methods: A nonlinear finite element model of the alveolar bone–PDL–tooth complex was developed using patient-specific CBCT data. Three loading configurations were analysed: (i) conventional orthodontic loading, (ii) loading combined with corticotomy alone, and (iii) a translation-dominant configuration generated by the Bone Protection System (BPS). Pressure distribution, displacement vectors, and stress polarity within the PDL and cortical plate were quantified across different bone density conditions. The mechanical outputs were subsequently interpreted in relation to established mechanotransductive molecular pathways involved in osteogenesis and angiogenesis. Results: Conventional loading generated compression-dominant stress fields within the marginal PDL, frequently exceeding physiological thresholds and producing moment-driven root displacement. Corticotomy alone reduced local stiffness but did not substantially alter stress polarity. The BPS configuration redirected loads toward a tensile-favourable mechanical environment characterised by reduced peak compressive pressures and parallel (translation-dominant) displacement vectors. The predicted tensile stress distribution is compatible with activation profiles of key mechanosensitive pathways, including integrin–FAK signalling, Wnt/β-catenin–mediated osteogenic differentiation and HIF-1α/VEGF-driven angiogenic coupling, suggesting a microenvironment that may be more conducive to cortical apposition than to resorption. Conclusions: This study presents a computational–molecular framework linking finite element–derived tensile stress patterns with osteogenic and angiogenic signalling pathways relevant to alveolar bone remodelling. The findings suggestthat controlled redirection of orthodontic loading toward tensile domains may shift the mechanical environment of the PDL–bone complex toward conditions associated with osteogenic than resorptive responses providing a mechanistic basis for tension-induced cortical modelling. This mechanobiological paradigm advances the understanding of load-guided alveolar bone adaptation at both the tissue and molecular levels. Full article

To address the complex surface curvature, massive dimensions, and variable pitch angles of wind turbine blades, this paper proposes a climbing robot design based on a variable-cell mechanism. By dynamically adjusting the support span and body posture, the robot adapts to the geometric features of different blade regions, enabling stable and efficient non-destructive inspection operations. Two reconfigurable configurations—a planar quadrilateral and a regular hexagon—are proposed based on the geometric characteristics of different blade regions. The configuration switching conditions and multi-leg cooperative control mechanisms are investigated. Through static stability margin analysis, the stable gait space and maximum stride length for each configuration are determined, optimizing the robot’s motion performance on surfaces with varying curvature. Simulation and experimental results demonstrate that the proposed multi-configuration gait planning strategy exhibits excellent adaptability and climbing stability across segments of varying curvature. This provides a theoretical foundation and methodological support for the engineering application of robots in wind turbine blade maintenance. Full article

The deployment of Vision-Language Models (VLMs) in Satellite IoT scenarios is critical for real-time disaster assessment but is often hindered by the substantial memory and compute requirements of state-of-the-art models. While parameter-efficient fine-tuning (PEFT) enables adaptation, with minimal computational overhead, standard supervised methods often fail to ensure robustness and reliability on resource-constrained edge devices. To address this, we propose EdgeV-SE, a self-reflective fine-tuning framework that significantly enhances the performance of VLM without introducing any inference-time overhead. Our framework incorporates an uncertainty-aware self-reflection mechanism with asymmetric dual pathways: a generative linguistic pathway and an auxiliary discriminative visual pathway. By estimating uncertainty from the linguistic pathway using a log-likelihood margin between class verbalizers, EdgeV-SE identifies ambiguous samples and refines its decision boundaries via consistency regularization and cross-pathway mutual learning. Experimental results on hurricane damage assessment demonstrate that our approach improves image classification accuracy, enhances image–text semantic alignment, and achieves superior caption quality. Notably, our work achieves these gains while maintaining practical deployment on a commercial off-the-shelf edge device such as NVIDIA Jetson Orin Nano, preserving the inference latency and memory footprint. Overall, our work contributes a unified self-reflective fine-tuning framework that improves robustness, calibration, and deployability of VLMs on edge devices. Full article

Background and Objectives: Basal cell carcinoma (BCC) is the most common skin carcinoma, mainly occurring in older individuals. The aim of this study was to document the immunohistochemical distribution of CD34 in different histopathological types of BCC, as well as in the peritumoral and uninvolved skin of biopsy samples. Materials and Methods: Excisional biopsies of skin BCCs were routinely processed into paraffin blocks, and microtome sections were stained immunohistochemically for CD34. Results: A consistent finding in skin samples containing BCC was the absence of CD34 in the following extravascular structures: neoplastic cells, epidermis and its derivatives (except for the cells of the isthmic part of the outer hair follicle sheath), fibroblast-like cells of BCC tumor stroma, as well as in the papillary dermis in the tumor region. Fibroblast-like cells of the tumor stroma were variably CD34 immunopositive only in the nodular type of BCC. In all examined biopsies, part of the dermis adjacent to the BCC tumor mass (juxtatumoral zone) was characterized by pronounced CD34 immunopositivity. In the transitional zone of peritumoral skin and in marginal skin, CD34-positive connective tissue cells were observed in the periadnexal dermis around: sebaceous gland lobules, the secretory coils of eccrine sweat glands, the pilosebaceous canal, as well as in the perimysium of the arrector pili muscle. Fibrocytes of fibrous sheaths encasing the isthmic part of hair follicles were CD34 negative, interposed between highly positive epithelial cells of the outer hair follicle sheath and the fibroblasts of the local reticular dermis. The transitional zone and uninvolved skin contained CD34-positive fibroblast-like cells situated between secondary bundles of reticular dermis, as well as CD34-positive cell processes within these bundles. Conclusions: The observed pattern of CD34 positivity within the examined regions shows a specific distribution, providing insight into the adaptive responses of the skin to the tumoral process. Full article

Objectives: The aim of this study was to present and describe a digital workflow integrating Digital Smile Design (DSD) with computer-aided design/computer-aided manufacturing (CAD/CAM) and additive manufacturing technologies for the fabrication of dental preparation guides, focusing on workflow feasibility, design reproducibility, and clinical handling. Materials and Methods: A digital workflow was implemented using intraoral scanning and Exocad DentalCAD 3.1 Elefsina software to design dental preparation guides based on digitally planned restorations. Preparation margins, insertion paths, and minimal material thickness were defined virtually. The guides were fabricated using both subtractive (PMMA milling) and additive (stereolithographic-based 3D printing) manufacturing techniques. Post-processing included chemical cleaning, support removal, additional light curing, and manual finishing. The evaluation was qualitative and descriptive, based on visual inspection, workflow performance, and guide adaptation to printed models. Results: The proposed digital workflow was associated with consistent fabrication of preparation guides and predictable transfer of the virtual design to the manufactured guides. Digital planning facilitated clear visualization of preparation margins and insertion axes, supporting controlled and minimally invasive tooth preparation. The workflow demonstrated good reproducibility and efficient communication between clinician and dental technician. No quantitative measurements or statistical analyses were performed. Conclusions: Within the limitations of this qualitative feasibility study, the integration of DSD with CAD/CAM and 3D printing technologies represents a viable digital approach for designing and fabricating dental preparation guides. The workflow shows potential for improving predictability and communication in restorative dentistry. Full article

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) have demonstrated remarkable zero-shot forecasting capabilities across diverse domains, their application in power grid operations remains limited due to complex coupling relationships between load, price, emissions, and renewable generation. This paper proposes GridFM, a novel physics-informed foundation model specifically designed for multi-task energy forecasting in power systems. GridFM introduces four key innovations: (1) a FreqMixer adaptation layer that transforms pre-trained foundation model representations to power-grid-specific patterns through frequency domain mixing without modifying base weights; (2) a physics-informed constraint module embedding power balance equations and zonal grid topology using graph neural networks; (3) a multi-task learning framework enabling joint forecasting of load demand, locational-based marginal prices (LBMP), carbon emissions, and renewable generation with uncertainty-weighted loss functions; and (4) an explainability module utilizing SHAP values and attention visualization for interpretable predictions. We validate GridFM using over 10 years of real-time data from the New York Independent System Operator (NYISO) at 5 min resolution, comprising more than 10 million data points across 11 load zones. Comprehensive experiments demonstrate that GridFM achieves state-of-the-art performance with an 18.5% improvement in load forecasting MAPE (achieving 2.14%), a 23.2% improvement in price forecasting (achieving 7.8% MAPE), and a 21.7% improvement in emission prediction compared to existing TSFMs including Chronos, TimesFM, and Moirai-MoE. Ablation studies confirm the contribution of each proposed component. The physics-informed constraints reduce physically inconsistent predictions by 67%, while the multi-task framework improves individual task performance by exploiting inter-variable correlations. The proposed model provides interpretable predictions supporting the Climate Leadership and Community Protection Act (CLCPA) 2030/2040 compliance objectives, enabling grid operators to make informed decisions for sustainable energy transition and carbon reduction strategies. Full article

Background: Bioactive composite resins combine the esthetic and mechanical properties of resin composites with therapeutic functions such as ion release, remineralization, and caries inhibition. While in vitro studies suggest promising bioactivity, their clinical performance in permanent teeth remains uncertain. Objective: This systematic review and meta-analysis critically appraised randomized controlled trials and prospective clinical studies to determine whether bioactive composites offer superior clinical performance compared to conventional resin composites and glass ionomer-based materials. Methods: Electronic databases (PubMed/MEDLINE, Scopus, Web of Science, Google Scholar) were searched for eligible studies (2018–2025). Clinical outcomes assessed restoration survival, marginal integrity, secondary caries, postoperative sensitivity, and esthetic outcomes (color match). Data were pooled using a random-effects model, and risk of bias was assessed with Cochrane criteria. Results: Twenty-two trials met the inclusion criteria. No significant differences were found between bioactive and control restorations for survival/retention (RD = 0.01; 95% CI, –0.01 to 0.03), marginal adaptation (RD = 0.02; 95% CI, –0.02 to 0.06), secondary caries (RD = 0.01; 95% CI, –0.01 to 0.03), or postoperative sensitivity (RD = 0.01; 95% CI, –0.02 to 0.04), with negligible heterogeneity (I² = 0–4%). For color match, glass ionomer restorations showed significantly poorer outcomes (RD = –0.23; 95% CI, –0.31 to –0.14; p < 0.00001; I² = 98%), while conventional resin composites had a slight but significant advantage over bioactive composites (RD = 0.07; 95% CI, 0.02 to 0.12; p = 0.003; I² = 76%). Most studies presented moderate risk of bias and short-term follow-up (<36 months). Conclusions: Current evidence indicates that bioactive composites perform comparably, but not superior, to conventional restoratives in permanent teeth. The discrepancy between laboratory bioactivity and clinical effectiveness highlights the need for long-term, well-designed clinical trials with standardized outcome reporting. Full article

Background and Rationale: Tinospora crispa (L.) Hook.f. & Thomson (T. crispa) is a climbing medicinal plant with long-standing ethnopharmacological use, particularly in inflammatory and hepatic disorders and cancer-related conditions. There is a knowledge gap regarding how wild versus cultivated ecotypes differ in chemotype, bioactivity, and safety, and how this might support or refine traditional use. Study Objectives: This study aimed to compare wild and cultivated ecotypes of T. crispa from the Nile Delta (Egypt) in terms of quantitative and qualitative phytochemical profiles; selected in vitro biological activities (especially antioxidant and cytotoxic actions); genetic markers potentially associated with metabolic variation; and short-term oral safety in an animal model. Core Methodology: Standardized extraction of plant material from wild and cultivated ecotypes. Determination of total phenolics, total flavonoids, and major phytochemical classes (alkaloids, tannins, terpenoids). Metabolomic characterization using UHPLC-ESI-QTOF-MS, supported by NMR, to confirm key compounds such as berberine, palmatine, chlorogenic acid, rutin, and borapetoside C. In vitro bioassays including: Antioxidant activity (e.g., radical-scavenging assay with EC₅₀ determination). Cytotoxicity against human cancer cell lines, with emphasis on HepG2 hepatoma cells and calculation of IC₅₀ values. Targeted genetic analysis to detect single-nucleotide polymorphisms (SNPs) in the gen1 locus that differentiate ecotypes. A 14-day oral toxicity study in rats, assessing liver and kidney function markers and performing histopathology of liver and kidney tissues. Principal Results: The wild ecotype showed a 43–65% increase in total flavonoid and polyphenol content compared with the cultivated ecotype, as well as substantially higher levels of key alkaloids, particularly berberine (around 12.5 ± 0.8 mg/g), along with elevated chlorogenic acid and borapetoside C. UHPLC-MS and NMR analyses confirmed the identity of the main bioactive constituents and defined a distinct chemical fingerprint for the wild chemotype. Bioassays demonstrated stronger antioxidant activity of the wild extract than the cultivated one and selective cytotoxicity of the wild extract against HepG2 cells (IC₅₀ ≈ 85 µg/mL), being clearly more potent than extracts from cultivated plants. Genetic profiling detected a C → T SNP within the gen1 region that differentiates the wild ecotype and may be linked to altered biosynthetic regulation. The 14-day oral toxicity study (up to 600 mg/kg) revealed no evidence of hepatic or renal toxicity, with biochemical markers remaining within physiological limits and normal liver and kidney histology. Conclusions and Future Perspectives: The wild Nile-Delta ecotype of T. crispa appears to be a stress-adapted chemotype characterized by enriched levels of multiple bioactive metabolites, superior in vitro bioactivity, and an encouraging preliminary safety margin. These findings support further evaluation of wild T. crispa as a candidate source for standardized botanical preparations targeting oxidative stress-related and hepatic pathologies, while emphasizing the need for: More comprehensive in vivo efficacy studies. Cultivation strategies that deliberately maintain or mimic beneficial stress conditions to preserve phytochemical richness. Broader geographical and genetic sampling to assess how generalizable the present chemotypic and bioactivity patterns are across the species. Full article

Lavender is a perennial Mediterranean plant that has been cultivated throughout history for medicinal, aromatic, and cosmetic purposes. Due to its high economic and commercial value, it has become an important agricultural product worldwide. The low production cost, adaptability to environmental conditions, and demand for its versatile use in the global market make it a significant potential source of income for developing Mediterranean countries. This study aims to identify commercially suitable cultivation sites for Lavandula angustifolia Mill. using remote sensing (RS) and geographic information systems (GIS) technologies to support rural development. Within this scope, suitable cultivation habitat parameters for the species in open fields and natural conditions were determined; these parameters were weighted according to their importance using multi-criteria decision analysis (MCDA), and thematic maps were created for each parameter. The created maps were combined using weighted overlay analysis, and a final map was generated according to the suitability class. The results indicate that within the study area, 75,679.45 ha is mostly suitable, 388,832.71 ha is moderately suitable, 24,068.43 ha is marginally suitable, and 229,327.20 ha is not suitable. As a result, it has been observed that Lavandula angustifolia Mill., which is currently cultivated on approximately 4045 ha of land and contributes 429 tons of product to the regional economy, covers only a relatively small portion of the suitable cultivation sites identified in the study and is not utilized to its full potential. It is understood that the expansion of lavender cultivation in determined suitable sites has significant potential to substantially develop the region and its rural population in terms of both yield and production volume, and to involve women and youth entrepreneurs in agricultural employment. Full article

Oesophageal cancer remains a significant global health burden with poor survival outcomes despite multimodal treatment. Recent advances in magnetic resonance imaging (MRI) have opened opportunities to improve radiotherapy delivery. This review examines the role of MRI and MR-guided radiotherapy (MRgRT) in oesophageal cancer, focusing on applications in staging, treatment planning, and response assessment, with particular emphasis on magnetic resonance linear accelerator (MR-Linac)-based delivery. Compared to computed tomography (CT), MRI offers superior soft-tissue contrast, enabling more accurate tumour delineation and the potential for reduced treatment margins. Real-time MR imaging during treatment can facilitate motion management, while daily adaptive planning can accommodate anatomical changes throughout the treatment course. Functional MRI sequences, including diffusion-weighted and dynamic contrast-enhanced imaging, offer quantitative data for treatment response monitoring. Early clinical and dosimetric studies demonstrate that MRgRT can significantly reduce radiation dose to critical organs while maintaining target coverage. However, clinical evidence for MRgRT in oesophageal cancer is limited to small early-phase studies, with no phase II/III trials demonstrating improvements in survival, toxicity, or patient-reported outcomes. Long-term clinical benefits and cost-effectiveness remain unproven, highlighting the need for prospective outcome-focused studies to define the role for MRgRT within multimodality treatment pathways. Full article

The rapid advancement of Artificial Intelligence (AI) has profoundly transformed organizational systems, reshaping how employees interact with technology and adapt to changing work environments. However, the systemic mechanisms through which AI-induced technostress influences employee career growth remain insufficiently understood. Grounded in a socio-technical systems perspective, this study conceptualizes organizations as adaptive systems where technological, organizational, and human subsystems dynamically interact. We propose a dual-path framework that distinguishes between challenge-related technostressors (a resource-gain process) and hindrance-related technostressors (a resource-loss process), elucidating how AI-related pressures can simultaneously foster and hinder career development. Furthermore, employee resilience and organizational AI support are incorporated as systemic moderators that modulate the intensity of these effects within the human–AI–organization system. Using two-stage survey data from 326 matched pairs of employees and supervisors, results largely support the proposed model, with some pathways showing marginal significance. The findings reveal that AI challenge-related technostressors stimulate proactive adaptation and skill development, whereas hindrance-related technostressors generate anxiety and insecurity, thereby impeding growth. This research extends systems theory by demonstrating how technostressors function as an emergent property of human–technology interactions and provides actionable insights for designing more adaptive and resilient socio-technical work systems. Full article