Search Results (12,655)

Heavy metal pollution in forest and agroforestry soils represents a persistent environmental challenge with direct implications for ecosystem functioning, food security, and human health. In tropical and subtropical regions, intense weathering, rapid organic matter turnover, and dynamic redox conditions strongly modulate metal mobility, bioavailability, and long-term soil vulnerability. This review synthesizes current knowledge on the sources, biogeochemical mechanisms, ecological impacts, monitoring approaches, and restoration strategies associated with heavy metal contamination in forest and agroforestry systems, with particular emphasis on tropical landscapes. We examine natural and anthropogenic metal inputs, highlighting how atmospheric deposition, legacy contamination, land-use practices, and soil management interact with mineralogy, organic matter, and hydrology to control metal fate. Key processes governing metal behavior include sorption and complexation, Fe–Mn redox cycling, pH-dependent solubility, microbial mediation, and rhizosphere dynamics. The ecological consequences of contamination are discussed in terms of soil health degradation, plant physiological stress, disruption of ecosystem services, and risks of metal transfer to food chains in managed systems. The review also evaluates integrated monitoring frameworks that combine field-based soil analyses, biomonitoring, and geospatial technologies, while acknowledging methodological limitations and scale-dependent uncertainties. Finally, restoration and remediation strategies—ranging from phytotechnologies and soil amendments to engineered Technosols—are assessed in relation to their effectiveness, scalability, and relevance for long-term functional recovery. By linking mechanistic understanding with management and policy considerations, this review provides a process-oriented framework to support sustainable management and restoration of contaminated forest and agroforestry soils in tropical and subtropical regions. Full article

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

Mental health conditions have become an increasing concern among university students, particularly those pursuing health science disciplines such as pharmacy. Rigorous academic demands, high workloads, and sustained psychological pressure place pharmacy students at a high risk of mental health disorders, including depression, anxiety, stress, and suicidal ideation. This study aimed to systematically review and quantitatively synthesize existing evidence on the prevalence of mental health conditions among pharmacy students in Thailand and globally using a meta-analytic approach. A comprehensive literature search was conducted across the major academic databases, including PubMed, ScienceDirect, Scopus, and ThaiJo, using predefined search terms and stringent inclusion criteria to ensure methodological rigor and relevance. Data from eligible studies were extracted and analyzed using STATA software to ensure statistical precision and reliability of the pooled estimates. A total of 51 studies, comprising 17,717 pharmacy students across 16 countries, including the United States, Thailand, Brazil, Malaysia, Syria, Pakistan, Poland, France, Portugal, Nigeria, Saudi Arabia, Sudan, Lebanon, Egypt, the United Arab Emirates, and Vietnam, were included. The meta-analysis revealed pooled prevalence rates of 44.26% for depression (95% CI: 36.08–52.61), 52.01% for anxiety (95% CI: 42.86–61.09), 48.10% for stress (95% CI: 32.96–63.43), and 24.52% for suicidal ideation (95% CI: 14.10–36.70). These findings reflect a substantial mental health burden among pharmacy students, necessitating immediate and context-specific interventions. Considering these findings, academic institutions must develop and implement comprehensive mental health support strategies. Such initiatives should include early identification and screening programs, access to psychological counseling services, resilience-building interventions, and stress management workshops to effectively address the psychological needs of pharmacy students and enhance their academic and personal well-being. Full article

Research on muscle dysmorphia (MD), currently conceptualized as a clinical specifier for body dysmorphic disorder (BDD), is rapidly expanding. Although personality traits and disorders have been proposed as relevant risk factors for the development of BDD, their role in MD remains insufficiently understood. This scoping review aims to synthesize the existing empirical literature on the associations between MD and personality, while identifying key research gaps and clinical challenges. Following the PRISMA-ScR guidelines, a systematic search was conducted across PsycArticles, PubMed, Scopus, Web of Science, and Google Scholar between 1 October and 1 December 2024. A total of 15 studies met the inclusion criteria and were analyzed. Findings highlight the significant contribution of narcissism, neuroticism, and perfectionism to the development and severity of MD. In particular, traits associated with vulnerable narcissism consistently emerged as predictors of MD symptomatology. Sociocultural factors—such as the competitive environment of elite sports and early relational experiences—were also found to interact with personality-based vulnerabilities in shaping the onset and clinical expression of MD. However, most available studies relied on self-report measures, cross-sectional designs, and convenience samples predominantly composed of men, limiting the generalizability of the results. Despite these methodological limitations, this review emphasizes the importance of identifying personality-based vulnerabilities to enhance the understanding of MD and inform the development of person-centered prevention and intervention strategies. Full article

This study presents a novel methodology that integrates Transformer-based textual analysis from professional networking platforms with traditional demographic variables within a survival analysis framework to predict turnover. Using a dataset comprising 4087 work events from Maimai (a leading professional networking platform in China) spanning 2020 to 2022, our approach combines sentiment analysis and deep learning semantic representations to enhance predictive accuracy and interpretability for HR decision-making. Methodologically, we adopt a hybrid feature-extraction strategy combining theory-driven methods (sentiment analysis and TF-IDF) with a data-driven Transformer-based technique. Survival analysis is then applied to model time-dependent turnover risks, and we compare multiple models to identify the most predictive feature sets. Results demonstrate that integrating textual and demographic features improves prediction performance, specifically increasing the C-index by 3.38% and the cumulative/dynamic AUC by 3.43%. The Transformer-based method outperformed traditional approaches in capturing nuanced employee sentiments. Survival analysis further boosts model adaptability by incorporating temporal dynamics and also provides interpretable risk factors for turnover, supporting data-driven HR strategy formulation. This research advances turnover prediction methodology by combining text analysis with survival modeling, offering small and medium-sized enterprises a practical, data-informed approach to workforce planning. The findings contribute to broader labor market insights and can inform both organizational talent retention strategies and related policy-making. Full article

Benzodichalcogenophenes represent a valuable class of organic π-conjugated systems that have been investigated in a plethora of cutting-edge applications in the field of materials chemistry. Isomeric benzodifuran (BDF), benzodithiophene (BDT) and benzodiselenophene (BDS) analogs of phenanthrene, in which the two heteroaromatic rings are ortho-fused onto a benzene ring, represent convenient frameworks as functional materials in organic electronics. The orientation of the two condensed heteroaromatic rings with respect to the central benzene ring provides diverse structural isomers, which significantly differ in degrees of curvature, electronic and electrochemical properties. Furthermore, tailored modification and functionalization strategies enable fine-tuning of their intrinsic properties, leading to unique systems. This review offers a comprehensive overview of synthetic methodologies for constructing isomeric BDF, BDT and BDS skeletons, alongside an analysis of their electrochemical properties as influenced by the nature of heteroatoms. Finally, the most relevant applications of these systems, ranging from optoelectronics, supramolecular chemistry, and emerging biological studies, are discussed, providing valuable insights for future research direction. Full article

Cardiac transplantation remains an important therapy for end-stage heart failure, although allograft rejection continues to pose significant clinical challenges. This review evaluates both established and emerging blood-based biomarkers for noninvasive monitoring of rejection in heart transplant recipients. Donor-derived cell-free DNA (ddcfDNA) and gene expression profiling (GEP) represent well-validated, commercially available molecular tools that demonstrate strong discriminative capacity for acute rejection episodes. Additionally, microRNAs (miRs) and extracellular vesicles (EVs) show considerable potential as novel biomarkers, although further validation is required. In contrast, conventional biomarkers such as B-type natriuretic peptide (BNP), cardiac troponins, and creatine kinase-MB (CK-MB) offer limited specificity in the context of rejection. This review synthesizes current evidence on the clinical utility, methodological challenges, and integration strategies of these biomarkers, highlighting a shift toward molecular-based approaches for improving post-transplant surveillance and patient outcomes. Full article

Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and validates a technical framework that incorporates multi-source remote sensing and tree species functional trait heterogeneity to systematically improve the accuracy of plantation age mapping. We constructed a processing chain—“multi-source feature fusion–species identification–heterogeneity modeling”—for a typical karst plantation landscape in southeastern Yunnan. Using the Google Earth Engine (GEE) platform, we integrated Sentinel-1/2 and Landsat time-series data, implemented a Gradient Boosting Decision Tree (GBDT) algorithm for species classification, and built age estimation models that incorporate species identity as a proxy for the growth strategy heterogeneity delineated by the Plant Economic Spectrum (PES) theory. Key results indicate: (1) Species classification reached an overall accuracy of 89.34% under spatial block cross-validation, establishing a reliable basis for subsequent modeling. (2) The operational model incorporating species information achieved an R² (coefficient of determination) of 0.84 (RMSE (Root Mean Square Error) = 6.52 years) on the test set, demonstrating a substantial improvement over the baseline model that ignored species heterogeneity (R² = 0.62). This demonstrates that species identity serves as an effective proxy for capturing the growth strategy heterogeneity described by the Plant Economic Spectrum (PES) theory, which is both distinguishable and valuable for modeling within the remote sensing feature space. (3) Error propagation analysis demonstrated strong robustness to classification uncertainties (γ = 0.23). (4) Plantation structure in the region was predominantly young-aged, with forests aged 0–20 years covering over 70% of the area. Despite inherent uncertainties in ground-reference age data, the integrated framework exhibited clear relative superiority, improving R² from 0.62 to 0.84. Both error propagation analysis (γ = 0.23) and Monte Carlo simulations affirmed the robustness of the tandem workflow and the stability of the findings, providing a reliable methodology for improved-accuracy plantation carbon sink quantification. Full article

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition. Full article

Background: Depression remains a major global mental health concern, significantly intensified during the COVID-19 pandemic. As social media usage surged during this period, it emerged as a valuable source for identifying early signs of depression. Artificial intelligence (AI) offers powerful tools to analyze large volumes of user-generated content, enabling timely and effective detection of depressive symptoms. This review aims to preliminarily explore and compare evidence on the use of AI models for detecting depression in social content across the pre-, during, and post-pandemic phases, assessing their effectiveness and limitations. Methods: A narrative literature review was conducted using PubMed and Scopus, following the SANRA guidelines to ensure methodological quality and reproducibility. The study was pre-registered in the OSF database and employed the PICOS framework for the strategy. Inclusion criteria comprised studies in English from the past 10 years that analyzed depression detection via AI, machine learning (ML), and deep learning (DL) applied to textual data, images, and social metadata. This review addresses the following four research questions: (1) whether AI models improved effectiveness in detecting depression during/after the pandemic vs. pre-pandemic; (2) whether textual, visual, or multimodal data approaches became more effective during the pandemic; (3) whether AI models better addressed technical challenges (data quality/diversity) post-pandemic; and (4) whether strategies for responsible AI implementation improved during/after the pandemic. Results: Out of 349 identified records, nine primary studies were included, as most excluded articles had a predominantly technical focus and did not meet the clinical relevance criteria. AI models demonstrated strong potential in detecting depression, particularly through text-based classification and social content analysis. Several studies reported high predictive performance, with notable improvements in accuracy and sensitivity during and after the pandemic, although evidence remains limited. Conclusions: Our preliminary analysis suggests that AI-based depression detection on social media shows potential for clinical use, highlighting interdisciplinary collaboration, ethical considerations, and patient-centered approaches. These findings require confirmation and validation through larger, well-designed systematic reviews. Full article

Background: Dynapenia refers to the age-related decline in muscle strength that occurs even when muscle mass is preserved. It has become an important issue in older adults because reduced strength is strongly linked to many negative health outcomes. When dynapenia occurs together with obesity—referred to as dynapenic obesity or dynapenic abdominal obesity—the risks, including mortality, falls, and the development of multiple chronic conditions, appear to increase even further. This umbrella review aimed to bring together and summarize existing systematic reviews and meta-analyses that examined how dynapenia and its obesity-related subtypes are associated with mortality, falls, and multimorbidity among community-dwelling older adults. Methods: Following PRISMA 2020 and JBI guidelines, six major databases and search engines (PubMed, Embase, Cochrane Library, Scopus, CINAHL, and Airiti Library) were searched from their inception to October 2025. Systematic reviews and meta-analyses involving adults aged 60 years and older and reporting quantitative results on the relationships between dynapenia-related conditions and adverse health outcomes were included. The methodological quality of each review was evaluated using AMSTAR 2, and the certainty of evidence was assessed with the GRADE approach. This umbrella review followed the PRIOR framework and was reported according to PRISMA 2020. The protocol for this review was registered in PROSPERO (ID: CRD 42023415232). Results: A total of four systematic reviews and meta-analyses were included, covering more than 73,000 community-dwelling older adults. The pooled data showed that dynapenic obesity significantly increased the risk of all-cause mortality, with hazard ratios ranging from 1.50 (95% CI 1.14–1.96) to 1.73 (95% CI 1.38–2.16). Dynapenic abdominal obesity was also strongly linked to falls, with pooled estimates ranging from HR = 1.82 (95% CI 1.04–3.17) to RR = 6.91 (95% CI 5.42–8.80). For multimorbidity, older adults with dynapenia had 1.38 times higher odds of having two or more chronic diseases than those without dynapenia (OR = 1.38, 95% CI 1.10–1.72). Based on the GRADE evaluation, the certainty of evidence was moderate for mortality and falls and low for multimorbidity. Conclusions: Overall, the findings indicate that dynapenia and its obesity-related forms meaningfully increase the risks of mortality, falls, and multimorbidity among community-dwelling older adults. Importantly, these results position dynapenia not merely as a musculoskeletal condition, but as a clinically relevant marker of aging-related vulnerability. This underscores the need for early screening of muscle strength alongside obesity-related indicators, as well as the development of integrated preventive strategies that combine strength-oriented interventions with obesity management in older populations. Full article

In nanometer integrated-circuit (IC) manufacturing, advanced technology scaling has intensified the effects of process variations on circuit reliability and performance. Random fluctuations in parameters such as threshold voltage, channel length, and oxide thickness further degrade design margins and increase the likelihood of functional failures. These variations often lead to rare circuit failure events, underscoring the importance of accurate yield estimation and robust design methodologies. Conventional Monte Carlo yield estimation is computationally infeasible as millions of simulations are required to capture failure events with extremely low probability. This paper presents a novel reliability-based circuit design optimization framework that leverages deep transfer learning to improve the efficiency of repeated yield analysis in optimization iterations. Based on pre-trained neural network models from prior design knowledge, we utilize model fine-tuning to accelerate importance sampling (IS) for yield estimation. To improve estimation accuracy, adversarial perturbations are introduced to calibrate uncertainty near the model decision boundary. Moreover, we propose a cascaded batch Bayesian optimization (CBBO) framework that incorporates a smart initialization strategy and a localized penalty mechanism, guiding the search process toward high-yield regions while satisfying nominal performance constraints. Experimental validation on SRAM circuits and amplifiers reveals that CBBO achieves a computational speedup of 2.02×–4.63× over state-of-the-art (SOTA) methods, without compromising accuracy and robustness. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

Background/Objective: This systematic review aims to evaluate ex vivo brain slice models in glioblastoma (GBM) research, with a specific focus on tumour invasion, tumour–microenvironment interactions, and therapeutic response. Methods: A systematic search looking for studies employing ex vivo organotypic brain slice models in GBM research was conducted across multiple databases (January 2010–July 2025) in accordance with PRISMA guidelines. The study was registered in PROSPERO database (CRD420251138341). Inclusion criteria encompassed patient-derived brain slices, hybrid rodent–human slice co-cultures, and microfluidic-integrated ex vivo platforms designed to assess tumour invasion, microenvironmental interactions and therapeutic responses. Exclusion criteria included reviews, abstracts, conference proceedings, in vivo-only studies, purely in vitro models without organotypic integration, and studies not focused on GBM. Results: Twenty-six studies met the inclusion criteria. Among these, 18/26 (69%) investigated GBM invasion, 18/26 (69%) evaluated therapeutic responses, and 5/26 (19%) examined tumour–microenvironment interactions, with several studies spanning multiple domains. Across platforms, organotypic slices consistently recapitulated key features of GBM biology—including perivascular and white-matter-aligned invasion, stromal–immune interactions, and patient-specific drug sensitivity—while engineered systems enhanced perfusion and exposure control. Methodological variability, particularly regarding slice preparation, oxygenation and viability assessment, limits direct comparability between studies. Conclusions: Organotypic brain slice models represent an extremely relevant tool for translational investigations of GBM biology and treatment response. However, substantial methodological heterogeneity together with limited standardisation hamper reproducibility and cross-study validation. Future work should focus on enhancing reproducibility and harmonising protocols to support the development of clinically meaningful precision oncology strategies. Full article

In many land cover classification tasks, the limited precision of individual sensors hinders the accurate separation of certain classes, largely due to the complexity of the Earth’s surface morphology. To mitigate these issues, decision fusion methodologies are employed, allowing data from multiple sensors to be synthesized into robust and more conclusive classification outcomes. This study employs fully polarimetric Synthetic Aperture Radar (PolSAR) imagery and leverages the strengths of three decomposition methods, namely Pauli’s, Krogager’s, and Cloude’s, by extracting their respective components for improved detection. From each decomposition method, three scattering components are derived, enabling the extraction of informative features that describe the scattering behavior associated with various land cover types. The extracted scattering features, treated as independent sensors, were used to train three neural network classifiers. The resulting outputs were then considered as local decisions for each land cover type and subsequently fused through a decision fusion rule to generate more complete and accurate classification results. Experimental results demonstrate that the proposed Multi-Class Bahadur–Lazarsfeld Expansion (MC-BLE) fusion significantly enhances classification performance, achieving an overall accuracy (OA) of 95.78% and a Kappa coefficient of 0.94. Compared to individual classification methods, the fusion notably improved per-class accuracy, particularly for complex land cover boundaries. The core innovation of this work is the transformation of the Bahadur–Lazarsfeld Expansion (BLE), originally designed for binary decision fusion into a multi-class framework capable of addressing multiple land cover types, resulting in a more effective and reliable decision fusion strategy. Full article