Search Results (1,940)

The identification of gifted students with learning disabilities (GLD) remains theoretically and methodologically contested. The present study examined cognitive and reading profiles of 150 Greek students in Grades 4–6, classified as gifted with learning disabilities (GLD) (n = 36), gifted (n = 31), or dyslexic of average intellectual ability (n = 83). Gifted classification was based on National Association for Gifted Children guidelines issued in 2018, using reasoning-based WISC-VIndices (FSIQ, GAI, EGAI, NVI, VECI ≥ 120), while learning disability was determined through formal multidisciplinary diagnosis. Cognitive performance was assessed with the WISC-V and reading with the standardized DADA battery (decoding, fluency, and comprehension). One-way ANOVAs and ROC analyses were conducted. GLD students demonstrated reasoning abilities and processing speed abilities comparable to gifted peers, but working memory deficits compared to gifted peers. In reading, GLD students showed decoding deficits like dyslexic peers and fluency impairments indistinguishable from them, yet significantly stronger comprehension. These findings reveal a differentiated literacy profile in which higher-order reasoning appears to support meaning construction despite persistent efficiency-based constraints in decoding and fluency. Overall, the results indicate that twice-exceptionality reflects a structurally uneven cognitive–academic configuration, underscoring the importance of multidimensional assessment approaches that simultaneously evaluate reasoning strengths and reading-specific vulnerabilities. Full article

Endogenous safety and security (ESS), which advocates for designing systems that are inherently safe and secure by nature, has emerged as a pivotal paradigm for addressing the inherent vulnerabilities of information systems. The Dynamic Heterogeneous Redundancy (DHR) architecture serves as its typical implementation by introducing dynamic, heterogeneous, redundant executors with equivalent function (EF) into the information system. However, the functional equivalence property explicitly connects the system’s output to that of the individual executors, thereby creating potential security risks that adversaries could exploit. In addition, EF-DHR faces an inherent contradiction between functional equivalence and heterogeneous implementations (HIS), leading to high engineering costs and limited applicability. To address these problems, this paper proposes the Non-Equivalent Functional DHR (NEF-DHR) architecture, leveraging function secret sharing (FSS) theory to replace EF executors with NEF components, which fundamentally eliminates the EF-HIS contradiction. Specifically, we propose the concept of `terminal executor output information entropy loss’ to formalize the risk of output information interception by adversaries and theoretically prove that NEF-DHR improves unpredictability and resistance to attacks. Experimental results further validate that NEF-DHR exhibits lower error rates under various attack levels, with enhanced robustness and superior ESS performance. Additionally, we generalize the DHR architecture based on three core properties (indistinguishability, output recoverability, verification) and classify ESS into three types with corresponding DHR variants. This work advances the application of entropy theory in ESS and provides a novel entropy-enhanced solution for the large-scale deployment of DHR security systems. Full article

The increasing operational variability in distribution networks (e.g., abrupt load changes and distributed generation integration) increases the demands on voltage regulation devices and, in particular, on transformers with on-load tap changers (OLTCs). This paper develops and validates a discrete supervisory control scheme based on Petri nets, implemented in Stateflow and coupled to an electromagnetic model of the OLTC transformer in Simulink/Simscape. The Petri net formalizes the conditional and sequential logic of OLTC operation, enabling state- and time-dependent decisions (e.g., delays between maneuvers) to improve voltage regulation and reduce unnecessary tap operations. The evaluation is performed by simulation under transient scenarios that include sudden load variations anda phase-to-ground fault in the IEEE 13-node standard network, specifically at node 634. In the base case, the controller maintains the voltage within the tolerance band $\pm 1.875\%$ during 96% of the simulated time, with an 88% reduction in RMS error (from 1.92% to 0.23%) and 100% operational efficiency (16 effective maneuvers, with a single hunting event). Subsequently, the scheme is validated on the standard IEEE 13-node network, with four disturbances applied over 600 s (two load increments, photovoltaic injection, and a temporary line disconnection). In this case, regulation remains within a precision zone of $\pm 0.3\%$ for 96.8% of the time, with an average RMS error of 0.23% and 100% efficiency, with no hunting events. The results confirm that a Petri net-based supervisory logic can simultaneously improve the OLTC’s voltage quality and switching efficiency, providing a reproducible alternative for distribution network automation. Full article

Clothing classification by occasion is an important area in computer vision and artificial intelligence (AI). This task is particularly challenging because of the subtle visual similarities among clothing categories such as formal, party, and casual attire. Variations in color, fabric, patterns, and lighting further increase the complexity of this task. To address this challenge, we used the Fashionpedia dataset to create a balanced subset of 15,000 images. Specifically, we adopted two different methods for labeling these images: automated classification, which relies on category identifications (IDs) and components, and manual labeling performed by human annotators. We then implemented our preprocessing pipeline, which includes several steps: resizing, image normalization, background removal using segmentation masks, and class balancing. We benchmarked traditional models, including artificial neural networks (ANNs), support vector machines (SVMs), and k-nearest neighbors (KNNs), which use a histogram of oriented gradient (HOG) features, as well as deep learning models such as convolutional neural networks (CNNs), the Visual Geometry Group 16 (VGG16) model utilizing transfer learning, and the vision transformer (ViT) model, all evaluated using identical data splits and preprocessing procedures. The traditional models achieved moderate accuracy, ranging from 54% to 66%. In contrast, the ViT model achieved an accuracy of 81.78% with automated classification and 98.09% with manual labeling. This indicates that a higher label accuracy, along with the preprocessing steps used, significantly enhances the performance. Together, these factors improve the effectiveness of ViT in context-aware apparel classification and establish a reliable baseline for future research. Full article

As variable renewable energy penetration increases, resource adequacy becomes strongly seasonal, while annual accreditation can mask temporal reliability differences. This paper proposes a Sub-Annual Capacity Market and compares it with an Annual Capacity Market and an uncapped Energy-Only benchmark. Capacity credits are calculated using a marginal ELCC formulation based on Expected Energy Not Served and embedded into phase-specific clearing constraints. Using a Shanxi case study, we examine both deterministic and stochastic settings with 151 jointly perturbed load and renewable scenarios. Results show that ACM and SubACM can both approximate EO outcomes when parameters are well calibrated, but SubACM yields more stable economic performance under uncertainty, with 29% lower cost-deviation standard deviation and 67% fewer tail-risk scenarios, as confirmed by formal dispersion tests. The main benefit of sub-annual design is improved temporal alignment between capacity payments and physical reliability contribution, rather than guaranteed large average cost reductions. Full article

Background and Objectives: Artificial intelligence (AI) has transitioned to an integral part of dermatology in only few years, yet perceptions of its use vary widely, reflecting diverse hopes, concerns, and perceived clinical utility. Materials and Methods: In this study, 300 dermatologists from 13 countries, representing a range of experience levels and AI usage statuses, were surveyed regarding the characteristics and applications of AI in dermatology. Results: Among respondents, 61.33% reported having used AI tools in clinical practice. Adoption of AI was observed across all age groups, countries, and experience levels. Analysis of the types of AI tools used revealed a strong reliance on general-purpose large language models (LLMs), with chatbots being the most frequently cited category, utilized by 58.15% of users. Younger clinicians demonstrated a significant preference for chatbots (p < 0.05). Country-specific patterns in AI adoption were also noted. The most highly rated expected benefit of AI in dermatology was improved diagnostic accuracy, while the primary concern centered on regulatory and ethical limitations, suggesting that the “AI revolution” in dermatology is currently constrained less by technical barriers and more by regulation considerations. Use of consent forms when AI use takes place was more frequently reported as mandatory by dermatologists who had never used AI, reflecting heightened caution among non-users (p = 0.03). Additionally, 75% of respondents agreed that formal training in AI is necessary, highlighting a significant gap in traditional medical education regarding emerging technologies. Full article

Background: Delirium is a frequent and serious complication in intensive care units (ICUs), associated with increased mortality, prolonged mechanical ventilation, extended length of stay, and long-term cognitive impairment. This study aimed to assess ICU nurses’ knowledge and practices regarding delirium management in Cyprus and to identify predictors of knowledge. Methods: A cross-sectional study was conducted among nurses working in adult ICUs in Cyprus. Data were collected using a structured self-administered questionnaire that included demographic characteristics, sedation and analgesia practices, and an adapted Delirium Knowledge Questionnaire incorporating ICU-specific items. Results: A total of 70 ICU nurses participated, most of whom were female (60%) with a mean ICU experience of 5.1 years. Only 27.1% reported daily delirium screening, although 65.2% perceived delirium as frequent. Sedation protocols were reported by 34.3%, sedation scales were used by 44.3%, and daily sedation interruption by 61.4%. Only 15.7% had received formal delirium training, while 87.1% expressed the need for further education. Knowledge scores were moderate to high (68.5–84.0%), with higher scores among nurses with prior training and female nurses (p = 0.003). Hospital type was associated with sedation practices, with greater use of sedation scales in public ICUs (p < 0.001) and propofol more commonly used as first-line sedation compared with midazolam in private ICUs (p = 0.018). Conclusions: Although ICU nurses demonstrated moderate knowledge of delirium, systematic screening and protocolized management remain suboptimal. Structured education and standardized implementation strategies are required to strengthen patient safety in critical care settings. Full article

Background: Air pollution, antimicrobial use, and population ageing are increasingly recognised as co-occurring pressures shaping population health. This study explores their ecological association with mortality patterns in the province of Messina (Southern Italy), within a One Health-informed framework. Methods: An ecological analysis was conducted using district-by-year data (2015–2024), integrating environmental monitoring (PM₁₀, PM_2.5, NO₂, O₃), outpatient antibiotic consumption, and cause-specific mortality rates. Multivariable regression models were used to assess associations between exposures and mortality outcomes. A post-2020 indicator was included to account for COVID-19-related disruption. Results: Marked geographic variability in pollutant concentrations was observed, with higher levels in urban-industrial districts. Infectious disease mortality increased from 13.8 to 44.6 per 100,000 inhabitants between the pre-pandemic and post-pandemic periods. In Poisson regression models, particulate matter showed a small and non-significant association with respiratory mortality (RR = 1.02, 95% CI: 0.89–1.18), while antibiotic consumption was not independently associated with mortality (RR = 0.99, 95% CI: 0.94–1.05). The post-2020 period was associated with higher mortality estimates (RR = 1.15, 95% CI: 0.72–1.83), although with wide confidence intervals. Conclusions: The findings suggest the co-occurrence of environmental, demographic, and pharmaceutical pressures within the same territories, rather than demonstrating formal synergistic interaction. The observed post-pandemic increase in mortality highlights the importance of accounting for COVID-19-related disruption. These results should be interpreted as exploratory, given the ecological design and limited sample size, but support the need for integrated surveillance approaches within a One Health perspective. Full article

Background: The landscape of paediatric oncology has undergone a remarkable transformation over recent decades. Advances in both oncological and supportive therapies have dramatically improved survival in children with haematological malignancies and solid tumours, with current survival rates exceeding 80% for many childhood cancers. However, this therapeutic success has brought with it an unexpected consequence: the intensification of treatment protocols has led to a parallel increase in life-threatening complications requiring intensive care support. Current evidence indicates that up to 40% of paediatric oncology patients will require admission to a Paediatric Intensive Care Unit (PICU) at some point during their disease trajectory. Objectives: This comprehensive review synthesises current evidence to provide an updated framework for PICU admission decision-making in oncology haematology patients. We have integrated the most recently published international guidelines, including the groundbreaking Phoenix 2024 sepsis criteria and the updated PALICC-2 2023 recommendations for paediatric acute respiratory distress syndrome. Beyond establishing admission criteria, we critically analyse the efficacy of advanced support strategies and examine emerging therapeutic approaches in this uniquely vulnerable population. Methods: Our methodology encompassed a systematic review of the literature published between 2011 and 2024, complemented by a detailed analysis of current international guidelines and expert consensus statements. We included randomised controlled trials, observational studies, meta-analyses, and consensus conference proceedings specifically addressing the intensive care management of paediatric patients with oncological or haematological conditions. Main Results: Several key findings emerge from our analysis. The Phoenix 2024 criteria represent a fundamental reconceptualisation of paediatric sepsis diagnosis, validated through an unprecedented dataset encompassing more than 3 million paediatric encounters. In the realm of respiratory support, early implementation of non-invasive ventilation (NIV) or continuous positive airway pressure (CPAP) has demonstrated remarkable efficacy, reducing the need for invasive mechanical ventilation by 45% (RR 0.45, 95% CI 0.26–0.78) when applied to appropriately selected patients. Extracorporeal membrane oxygenation (ECMO), whilst increasingly utilised, shows survival to decannulation ranging from 52% to 64%, though survival to hospital discharge remains less encouraging at 36–42%. Continuous renal replacement therapy (CRRT) has proven highly effective for tumour lysis syndrome, achieving metabolic correction in 90% of severe cases. Perhaps most promisingly, emerging biomarkers—particularly interleukin-6, interleukin-10, and procalcitonin—have substantially enhanced our ability to stratify infection risk, demonstrating sensitivity exceeding 85% for bacteraemia detection. Conclusions: The evidence unequivocally supports several core principles for optimising outcomes in this population. Early identification of deterioration through validated scoring systems enables timely intervention before irreversible organ failure develops. Prompt implementation of non-invasive respiratory support, when appropriately applied, can obviate the need for mechanical ventilation with its attendant complications. Perhaps most critically, centralisation of care in centres with dedicated expertise and comprehensive support capabilities fundamentally improves survival. These findings argue compellingly for the establishment of a formal national network of reference centres, implementing standardised protocols and structured care pathways specifically designed for critically ill paediatric oncology haematology patients. Full article

Electron correlation and quantum interference are pivotal in mesoscopic transport. We theoretically study the nonequilibrium transport dynamics of a triangular triple-quantum-dot (TTQD) molecule connected to fermionic reservoirs using the exact hierarchical equations of motion (HEOM) formalism. We demonstrate a counterintuitive transport signature in which the stationary current is significantly enhanced by increasing U, a behavior distinct from the suppression typically observed in linear quantum dot arrays. By analyzing the evolution of spectral functions, we attribute this enhancement to the interplay between Coulomb-interaction-induced energy shifts and quantum interference effects specific to the triangular topology. We also explore how the circulation of chiral currents and electrode coupling strength modulate these interaction effects. Finally, we present a three-dimensional map of the transport current as a function of inter-dot tunneling (t) and Coulomb interaction (U), illustrating their combined effect on the current magnitude and its applications. Full article

This paper investigates how referentiality interacts with the syntax of Hindi–Urdu. It argues that three patterns, namely, object reduplication, association with the focus particle hī, and cross-clausal agreement, are manifestations of a single structural contrast determined by object shift. With our novel observations, we show that only objects that introduce discourse referents undergo displacement to a higher syntactic position, where they can trigger agreement or serve as associates of the focus particle hī. Reduplicated nominals, which lack referential features, must remain inside the VP and are consequently excluded from these dependencies. The analysis formalizes this correlation through a referential licensing condition that restricts movement to SpecvP to arguments bearing a referential feature [+Ref]. This condition derives the observed interactions between object shift and interpretation of the object. The resulting account integrates phenomena of agreement and focus with the semantics of specificity, offering a unified model of how referential interpretation is encoded in the clause structure of Hindi–Urdu. Full article

Prolonged grief disorder (PGD) affects approximately 10% of bereaved individuals and is now formally recognized in both the DSM-5-TR and ICD-11. Despite its prevalence, PGD often responds poorly to traditional therapeutic approaches. This manuscript outlines the protocol for an early-stage open-label feasibility trial investigating the use of psilocybin, a psychedelic compound, in treating PGD in adults, with a focus on young adults. The study will involve 20 participants diagnosed with PGD. Each participant will undergo a structured therapeutic process that includes a preparatory session, a single 25 mg dose of psilocybin, and post-session integration. Throughout the study, participants will be monitored via symptom assessments, including qualitative and quantitative data, with the main aims related to safety, feasibility and acceptability. Functional MRIs will be obtained pre- and post-dosing and collected during a standardized grief-elicitation methodology. Key outcome measures include changes in the severity of PGD and trauma symptoms, cognitive flexibility, openness to experience, meaning in life and subjective experiences during the psilocybin session. Neural activity will also be evaluated through fMRI to better understand the neurobiological effects of the treatment. This research represents one of the first clinical protocols specifically focused on the potential of psilocybin for treating PGD. The goal is to assess feasibility and safety while laying the groundwork for future randomized controlled trials. Full article

Ecological restoration—whether active or passive—includes forest development, forest rehabilitation, and a range of other activities that contribute to ecosystem services. To provide a formal framework, we hypothesized how does reforestation (through different forestry practices) affect the conservation of soil functionality? That is, how does reforestation/afforestation/forest restoration improve soil quality? And, specifically, how do they improve physical properties (such as structural stability, infiltration) and chemical properties (such as acidity, electrical conductivity)? For this purpose, we conducted a bibliometric analysis review of the peer-reviewed scientific literature and research reports of numerous articles in order to compile a large database of forest restoration studies, with an emphasis on the Mediterranean region. The final focus was to obtain conclusions about how it affects soil quality. Overall, our examination confirms that deforestation drives a decline in soil carbon and nitrogen, subsequently impairing microbial activity. Consequently, forest removal frequently leads to accelerated erosion, nutrient depletion, and compaction. In contrast, reforestation acts as a critical intervention, stabilizing soil structure, reestablishing fertility, and enhancing soil quality overall. Additionally, three case studies are synthetically presented concerning the short-, medium-, and long-term results of forest restoration projects carried out mainly in central and northern Spain. These cases corroborate the significant role of forest restoration in the control and enhancement of ecosystem services, particularly in relation to soil improvement, the enhancement of hydrological regulation processes within watersheds (runoff, infiltration, erosion), landscape amelioration, and the socio-economic aspects of rural environments. Ultimately, forest restoration is established as a necessary and essential practice in ecological restoration efforts to counteract the impacts of anthropogenic activities. Full article

Tourist congestion and load imbalance during peak periods pose critical challenges to the safe operation and experience assurance of large scenic areas. To address the limitations of traditional management approaches in capturing the dynamic and stochastic nature of tourist flows, this study develops a dynamic visitor guidance modeling and analysis framework based on a Timed Petri Net. The proposed model provides a formal representation of tourist movements, scenic spot load evolution, and guidance decision mechanisms within a scenic area. Under unified parameter settings and controlled random conditions, multiple visitor guidance strategies with different information coverage scopes are designed, and minute-level simulation experiments are conducted using the Huangshan Scenic Area as a case study. The simulation results show that, compared with unguided tourist flows, the proposed strategies significantly reduce average load levels, alleviate spatial load imbalance, and enhance TS. Using mean–standard deviation analysis, distributional analysis, and dynamic evolution analysis, differences among guidance strategies in terms of load control, visitor experience, and operational stability are systematically evaluated. Furthermore, a quantitative relationship model between tourist satisfaction and scenic area load is constructed, revealing a consistent inverted-U pattern. Robustness tests under multiple random seeds indicate that the main conclusions are not sensitive to specific stochastic realizations. Overall, the simulation results suggest that dynamic visitor guidance may improve load control, visitor experience, and system stability by optimizing the spatiotemporal distribution of tourist flows, thereby providing simulation-based quantitative insights for peak-period management in large scenic areas. Full article

Forest Rights Mortgage Loans (FRMLs) have grown quickly in China’s collective forest areas, even though the basic conditions for this type of lending remain far from ideal. In many places, forest holdings are small and scattered, property rights are complex and not fully consolidated, and channels for disposing of collateral are limited. Under these circumstances, the Fulin Loan Model (FLM) in Fujian provides a useful case for understanding how forest-rights lending can still function in practice. Drawing on fieldwork, semi-structured interviews, and process tracing, this study explores both how the model was established and how it has been sustained over time. The analysis suggests that the FLM is neither a straightforward market-based lending tool nor merely a top-down policy arrangement. Rather, it relies on a more mixed form of governance in which local government support, banking procedures, and village-level social relations are brought together through specific organizational arrangements. These arrangements help lower the costs of early institutional experimentation, distribute and manage lending risks, and translate locally rooted trust into a form of credit support that formal financial institutions can recognize. As a single-case study, the FLM points to one possible way in which rural finance can be made workable under conditions of incomplete markets and strong social embeddedness. Full article