Search Results (8,290)

This study investigates the formation mechanism of lactic-acid-derived coatings produced by open-air atmospheric-pressure plasma polymerization. A comparison of nebulization and bubbling precursor-delivery methods using FT-IR and XPS showed that the bubbling method facilitated plasma-assisted chemical bonding, including the possible formation of copper(II) lactate-like interfacial species and the retention of carbonyl-containing functional groups. However, the present dataset does not provide direct, discriminating evidence for a specific metal-lactate interfacial species, and alternative interpretations such as adsorption, oxidation, hydroxylation, or generic oxygenated carbon deposition cannot be excluded. Time-dependent analysis revealed a transition from oxygen-rich functional layers at short plasma exposure to carbon-rich overlayers at longer exposure, suggesting a fragmentation-recombination mechanism that is consistent with the formation of a metal-lactate-like interfacial region and a carbon-rich overlayer, while alternative interpretations related to signal attenuation and non-uniform coverage remain possible. Antibacterial testing revealed that the observed bacterial responses were not attributable to an intrinsic antibacterial property of the deposited films, but were instead strongly dependent on the underlying substrate chemistry and exposure time. C1100 retained the inherent antibacterial activity of copper, SUS430 showed no activity due to the absence of film formation, and SPCC exhibited only a transient effect attributed to lactic-acid-induced local acidification. Overall, the study elucidates the plasma-assisted deposition mechanism of lactic-acid-derived coatings under open-air conditions and highlights the critical role of interface chemistry in achieving stable and substrate-independent functional properties. Full article

Photovoltaic (PV) performance in desert environments is significantly hindered by soiling and partial shading. To bridge the gap in empirical data for extreme Saharan conditions, this study presents a novel techno-economic assessment of uniform horizontal shading (UHS) specifically conducted in Mauritania. Controlled outdoor experiments were performed using a 250 W crystalline silicon PV module and a PVPM 2540C I–V curve tracer, applying progressive shading levels from 2.5% to 20%. The novelty of this work lies in the integration of high-resolution experimental I–V/P–V characterization with a localized techno-economic model for five pre-commercial PV plants. It was observed that PV modules are exceptionally sensitive to shading; specifically, a mere 10% shaded area leads to a catastrophic 90% drop in power and current, while the voltage remains remarkably stable. Thermographic analysis further validates the thermal gradients and bypass diode functionality. By quantifying the financial impacts, this research highlights that cumulative economic losses across the five real-world sites reached 87.95%, exceeding 55,000 MRU. These findings provide a strategic framework for optimizing PV systems in arid terrains and offer a robust tool for enhancing the design and operation of large-scale solar applications in desert environments. Full article

Background/Objectives: Emergency Services (ESs) are highly demanding clinical settings where Nursing Practice Environments (NPEs) play a critical role in shaping professional- and care-related outcomes. International evidence suggests that unfavorable NPEs are associated with reduced job satisfaction, compromised care quality, and increased safety risks. This study aimed to describe NPEs and selected professional and care-related outcomes among ESs nurses in Portugal in 2018 and 2022. Methods: A descriptive, cross-sectional study was conducted using data from two national surveys of ESs nurses collected in 2018 (n = 390) and 2022 (n = 434). Data were collected through an online questionnaire including the Practice Environment Scale of the Nursing Work Index (PES-NWI), measures of job satisfaction, intention to leave, perceived quality and safety of care, safety culture, incident occurrence, and missed nursing care. Descriptive statistics were used to summarize results across both samples. Results: NPEs were predominantly classified as unfavorable in both samples, with around 70% of nurses working in unfavorable environments. The most compromised dimensions were staffing and resource adequacy, nurses’ participation in hospital affairs, and nurse manager ability, leadership, and support of nurses. Job satisfaction was low in both samples, and a high proportion of nurses reported an intention to leave the organization. Differences were observed between samples in perceived quality and safety of care, incident occurrence, and missed nursing care, particularly in relational and autonomous interventions. Collegial nurse–physician relations emerged as the only favorable dimension in both samples. Conclusions: The findings indicate that NPEs in Portuguese ESs were predominantly unfavorable in both study periods, reflecting structural and organizational challenges. These findings may be associated with nurses’ professional outcomes and perceived care quality and safety, highlighting the importance of targeted organizational interventions to improve practice environments. Full article

The rapid increase in companion animal ownership has intensified the demand for innovative tools that support animal health and overall welfare. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising approach in veterinary medicine. However, its application beyond clinical diagnostics, especially in behaviour and personality assessment, remains fragmented and insufficiently integrated into routine practice. This systematic review aims to synthesise current knowledge on AI-based applications in companion animal care, with a focus on behavioural monitoring, personality prediction, and welfare-related challenges. Following PRISMA guidelines, a structured literature search was conducted in the Scopus and PubMed databases from 2020 to 2025. In addition, grey literature sources were searched to capture relevant non-peer-reviewed data. A total of 115 studies met the inclusion criteria and were included in the analysis. Eligibility criteria included studies applying AI methods (machine learning or deep learning) to companion animals (dogs, cats, and exotic pets), while studies on humans, farm animals, or without AI methods were excluded. Due to the heterogeneity of included studies, no formal risk of bias assessment was performed, and results were synthesised narratively. The findings indicate that AI applications are most advanced in diagnostic imaging and clinical decision support, where data availability and methodological maturity are highest. In contrast, AI-based approaches for behaviour and personality prediction remain limited, particularly in cats and exotic companion animals, largely due to small, heterogeneous datasets, potential bias, and a lack of external validation. Emerging technologies such as wearable sensors, computer vision, and multimodal data integration demonstrate substantial potential for continuous behavioural monitoring and early detection of welfare-related issues in real household environments. Nevertheless, significant challenges persist, including data heterogeneity, limited model explainability, ethical considerations, and the absence of regulatory frameworks specifically addressing AI-based veterinary applications. Overall, this review highlights a substantial gap between the technical potential of AI and its current readiness for widespread application in companion animal behaviour and welfare assessment. Future research should prioritise large-scale and standardised data collection, cross-species validation, and interdisciplinary collaboration to ensure that AI-driven tools effectively support veterinary decision-making, animal welfare, and the well-being of owners. Full article

The ketogenic diet (KD) is a critical, evidence-based intervention within medical nutrition therapy for managing neurological disorders. In this article, we reviewed the published research on the efficacy of the ketogenic diet and its variations in treating epilepsy, particularly for patients unresponsive to anti-epileptic drugs. The literature review was performed on PubMed between 2022 and 2025. The review of clinical studies across various age groups reveals that, while the KD is effective for both focal and generalized seizures, infants often achieve higher rates of seizure freedom compared to adults, potentially due to better dietary compliance. Despite its success, the restrictive nature of the diet presents significant challenges for individuals suffering from epilepsy. The key challenges that reduce compliance over time include side effects, such as gastrointestinal issues, potential for malnutrition, and a high risk of micronutrient deficiencies. The role of the registered dietitian is paramount in this interdisciplinary approach, ensuring personalized education by monitoring growth and adjusting nutritional plans to optimize health outcomes for children unresponsive to anti-epileptic drugs. Ultimately, integrating MNT with traditional pharmacological or surgical treatments offers the most promising path for significant seizure reduction and improved quality of life for epileptic patients. Full article

Background: Artificial intelligence in nephrology has largely focused on predictive models for outcomes such as acute kidney injury (AKI), chronic kidney disease (CKD) progression, and transplant complications. Although these models demonstrate technical performance, their real-world clinical impact has remained limited because prediction alone rarely translates into coordinated clinical action. Clinical artificial intelligence agents represent workflow-native systems that operate in real time, interact bidirectionally with clinical environments, adapt to evolving patient and workflow states, and support coordinated clinical action rather than generating isolated predictions. This review proposes clinical artificial intelligence agents as a new paradigm for integrating artificial intelligence directly into nephrology workflows. Methods: We conducted a narrative synthesis of emerging literature on artificial intelligence systems, agentic artificial intelligence architectures, clinical decision support, and digital health infrastructures relevant to kidney care. Drawing from interdisciplinary sources in medicine, health informatics, and artificial intelligence research, we developed a conceptual framework describing the architecture, governance requirements, and evaluation principles of clinical artificial intelligence agents in nephrology. Results: Clinical artificial intelligence agents represent workflow-integrated systems capable of continuously perceiving patient data, reasoning under clinical constraints, planning tasks, and supporting coordinated clinical actions over time. We describe a layered architecture consisting of perception, cognition, planning and control, action, and learning components. Potential applications span the nephrology care continuum, including CKD management, AKI monitoring, dialysis and continuous renal replacement therapy (CRRT) optimization, kidney transplantation care coordination, glomerulonephritis management, and supervised patient-facing systems. Conclusions: Clinical artificial intelligence agents shift the role of artificial intelligence from isolated prediction toward longitudinal clinical orchestration. Future evaluation should prioritize workflow integration, time-to-action, clinician oversight, safety, and patient-centered outcomes rather than relying solely on traditional model performance metrics. This roadmap provides a conceptual foundation for the responsible development and clinical integration of agentic artificial intelligence systems in nephrology. Full article

Sustainable fertilizer technologies are essential to address nutrient losses, environmental pollution, and inefficiencies associated with conventional urea application. In this study, humic acid–functionalized starch (St–HA) gel coatings were developed and optimized via a Wurster fluidized-bed system to produce controlled-release urea granules, with an additional carnauba wax outer layer to further extend nutrient release duration. The coating formulation was synthesized through in situ crosslinking of tapioca starch with humic acid using N,N′-methylenebisacrylamide and potassium persulfate, yielding a cohesive film. A central composite rotatable design (CCRD) was employed to investigate the influence of atomizing air pressure, fluidizing air flow rate, fluidized-bed temperature, and spray rate on coating performance. Comprehensive characterization; including FTIR, XRD, rheological analysis, thermogravimetric studies, water retention, biodegradability, and surface abrasion, confirmed chemical crosslinking, structural stability, and mechanical robustness of the coatings. Nitrogen release analysis in both water and soil demonstrated a substantial extension of release longevity from less than 2 days (uncoated) to 18–20 days for St–HA-coated urea, and up to 28 days with the additional wax coating. Coated granules exhibited low abrasion (8–24%), high water-retention capacity, and 68% biodegradation in 60 days, ensuring environmental compatibility. The findings establish St–HA/wax hybrid coatings as a viable, eco-friendly strategy for controlled-release fertilizers, integrating renewable feedstocks with scalable industrial processing for precision nutrient management. Full article

The transition to smart cities is accelerating distributed wind and solar deployment. However, their intermittency challenges grid operation, thereby making accurate machine-learning-based prediction of wind speed and global horizontal irradiance (GHI) crucial. This study presents a cost-effective approach that enhances prediction accuracy by extracting additional features from timestamp records for deep learning models used to forecast GHI and wind speed. Unlike conventional methods that require onsite meteorological measurements, the proposed approach uses only date and time information as inputs to multivariate deep neural networks, including recurrent neural networks, gated recurrent units, long short-term memory (LSTM), bidirectional LSTM, and convolutional neural networks. For wind speed prediction, the proposed configuration achieves R² up to 0.9987, with RMSE as low as 0.067 m/s for 3 d ahead forecasting, outperforming univariate baselines and matching models. For GHI forecasting, the time-based configuration attains R² values above 0.9994 in 12 h ahead predictions, with the RMSE reduced to approximately 4.47 W/m², representing a substantial improvement over univariate models. The proposed framework maintains strong performance, particularly under clear and sunny conditions. These results demonstrate that timestamp-engineered features can deliver forecasting accuracy comparable to conventional multivariate meteorological models while significantly reducing infrastructure requirements, making the approach well-suited for scalable smart city energy management. Full article

Medical image segmentation represents a fundamental task in medical image analysis, serving as a critical component for accurate diagnosis, treatment planning, and disease monitoring. The emergence of Denoising Diffusion Probabilistic Models (DDPMs) has revolutionized the landscape of generative modeling and recently gained significant attention in medical image analysis. This comprehensive review examines the current state of the art in diffusion models for medical image segmentation, covering theoretical foundations, methodological innovations, computational efficiency strategies, and clinical applications. We analyze recent advances in latent diffusion frameworks, transformer-based architectures, and ambiguous segmentation modeling while addressing the practical challenges of implementing these models in clinical environments. The review encompasses applications across multiple medical imaging modalities including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), ultrasound, and X-ray imaging, providing insights into performance achievements and identifying future research directions. Through systematic analysis of publications mostly from 2019 to 2025, we demonstrate that diffusion models have achieved remarkable progress in addressing fundamental challenges including data scarcity, inter-observer variability, and uncertainty quantification. Notable achievements include inference time being reduced from 91.23 s to 0.34 s for echocardiogram segmentation (LDSeg, Echo dataset), DSC scores up to 0.96 for knee cartilage MRI segmentation, and a +13.87% DSC improvement over baseline methods for breast ultrasound segmentation. This review serves as a comprehensive resource for researchers and clinicians interested in leveraging diffusion models for medical image segmentation, providing a roadmap for future research and clinical translation. Full article

We explored the application of spatial information technology in the assessment and optimization of cultural heritage resources within traditional settlements in Meizhou City, a core area of Hakka culture in China. By integrating methods such as geographic information systems and Kernel density estimation, it systematically evaluates the spatial distribution and socioeconomic conditions of these settlements. A multi-criteria evaluation model is constructed to quantify resource endowment across cultural, historical, and ecological dimensions, with particular emphasis on key factors influencing conservation effectiveness, such as infrastructure and economic vitality. Combining field investigations and literature review, we propose adaptive reuse strategies and policy recommendations to enhance settlement resilience and balance cultural preservation with regional development. Their expected outcomes include the engineering of a multidimensional geographic database for traditional settlements, the establishment of a spatial decision-support framework for heritage infrastructure conservation, and the development of systematic optimization protocols integrated with China’s rural revitalization technical policies. These results provide a computational and methodological foundation for interdisciplinary research in sustainable cultural heritage management and smart rural engineering. Full article

Apathy is a neuropsychiatric symptom that is present in various disorders, including dementia and Mild Cognitive Impairment (MCI). Patients with MCI who exhibit symptoms of apathy are at a higher risk of progressing to dementia compared to those with depressive symptoms. The aim of the present study was to investigate the psychometric properties of the clinician-rated version of the Apathy Evaluation Scale (AES-C) in a Greek sample of MCI patients and healthy older adults. The translation and adaptation of the scale were conducted using the forward–backward method. The final sample consisted of 100 participants, 14 men (n = 14) and 86 women (n = 86), with a mean age of 72 years. Participants were administered the translated and adapted version of the AES-C, as well as the Greek version of the Beck Depression Inventory. In terms of reliability, Cronbach’s alpha was found to be high (α = 0.91), indicating excellent internal consistency. Confirmatory Factor Analysis (CFA) revealed a one-factor solution with a very good model fit (RMSEA = 0.018, CFI = 0.985, TLI = 0.983, SRMR = 0.076). The AES-C can serve as an important addition to neuropsychological assessment for detecting apathy symptoms in patients with MCI, thereby contributing to the early prognosis of dementia. Full article

Computational methods have become increasingly prominent within the historical sciences, generating significant enthusiasm among some scholars. Yet their practical demands, epistemic limits, and ethical implications are less often critically examined than praised. This article explores what it means to do computational history today, arguing that it is not primarily defined by algorithms but by datasets. It is methodologically specific, resource-intensive, selective in scope, labour-heavy, and dependent on pre-digitised sources, specialised infrastructure, and interdisciplinary collaboration. These dependencies limit the scope of research questions and can produce narrow outcomes despite substantial effort, lending some validity to the concern over whether the field yields sufficient historiographical return for the labour invested. Corpus construction and data work lie at the epistemic core of computational history. These often undervalued tasks are not merely technical precursors to analysis, but interpretive and epistemic acts. Data are shaped by digitisation politics, historical bias, and institutional power. They shape the questions asked, the answers produced, and the legitimacy of findings. Recognising and valuing data work is essential, both to embed critical perspectives into computational humanities and to counteract the privileging of certain forms of labour over others. Due to the association of quantification with rigour and scholarly prowess, algorithmic work receives more credit, creating a two-tier system in this division of labour in which those who develop algorithms are elevated above those who curate data, despite their symbiotic interdependence. Computational history, when done well, requires deep engagement with our sources, be they historical or data. For computational history to stabilise as a meaningful discipline, it must prioritise building better datasets over pursuing increasingly complex algorithms on an unstable basis of data. Full article

Learning to write begins before formal schooling through everyday interactions where children construct ideas about print. Informed by socioconstructivist theory, this quasi-experimental study evaluated the impact of naturalistic, teacher-mediated cooperative invented spelling activities in Portuguese preschools. The participants were 88 five-year-olds from six classes in three schools. Classrooms were randomly assigned within schools to an intervention group (n = 43) or a comparison group (n = 45). For two months, the intervention group engaged in weekly spelling discussions integrated into regular activities, while the comparison group followed the standard curriculum. Pre- and post-intervention spelling assessments and classroom observations were conducted. The results showed a strong positive intervention effect. The intervention group demonstrated significantly greater improvement in representing sounds in their spelling than the comparison group. Observations revealed that when teachers scaffolded discussions with open questions, and children, with varying knowledge levels, helped each other by explaining their thinking. This transformed spelling into a shared problem-solving task. The study confirmed invented spelling as a valuable classroom activity that advances alphabetic understanding. It was demonstrated that early literacy can be effectively nurtured through structured dialogue and collaboration within the regular preschool day. These findings provided a practical, theory-aligned model for supporting literacy development through social interaction. Full article

In recent years, optimization algorithms have emerged as powerful computational tools for addressing complex and dynamic challenges across diverse domains. These domains include engineering, technology, management, and decision-making. Their growing importance is motivated by (a) the increasing complexity of modern systems, (b) the need for efficient resource utilization, and (c) the demand for scalable algorithmic solutions. These algorithms enable the systematic and computational exploration of large solution spaces, supporting decision-making and design under uncertainty, large-scale data, and evolving requirements. This study provides a structured review and comparative scientometric analysis of optimization algorithms, covering: (a) exact methods, (b) approximation techniques, (c) metaheuristics, and (d) emerging physics-informed frameworks. The analysis highlights algorithmic trends, performance-oriented research directions, and the increasing integration of mathematical programming, machine learning, and numerical methods. The results show a renewed focus on classical algorithmic paradigms. Moreover, rapid growth in hybrid and physics-informed optimization approaches is observed. These findings confirm the central role of optimization algorithms in modern algorithm engineering and interdisciplinary computational research. Full article

Background/Objectives: Psilocybin has re-emerged as a promising intervention for neuropsychiatric disorders including major depressive disorder, treatment-resistant depression, anxiety associated with life-threatening illness, obsessive compulsive disorder, and substance use disorders. However, conventional randomized controlled trials (RCTs)—the current gold standard in evidence-based medicine—may not adequately capture the therapeutic complexity of psilocybin, which depends not only on pharmacological action but also on contextual, psychological, and interpersonal factors. This critical narrative review aimed to evaluate the adequacy of existing clinical research frameworks for assessing psilocybin’s therapeutic potential and to explore alternative methodologies that may better reflect real-world clinical conditions. Methods: Using the Web of Science Core Collection database, we identified and analysed the ten most cited clinical studies on psilocybin published between 2015 and 2025 inclusive. Additional literature was included through reference cross-checking, systematic reviews, meta-analyses, and interdisciplinary sources covering neurobiology, history, and real-world evidence (RWE). The review synthesizes clinical outcomes, methodological constraints, and epistemic considerations relevant to psychedelic-assisted therapy. Results: Evidence from highly cited trials demonstrates rapid and sustained antidepressant and anxiolytic effects of psilocybin, with notable benefits also observed in addiction treatment. However, significant methodological limitations were identified, including selection bias, challenges in placebo design and blinding, small sample sizes, and the underrepresentation of diverse populations. Psilocybin outcomes were strongly influenced by subjective experience and contextual factors such as set and setting. Emerging RWE studies revealed heterogeneous patterns of response and provided insights unattainable through RCTs alone. Conclusions: Psilocybin shows considerable therapeutic promise, but current RCT methodologies capture only part of its clinical effects. Comprehensive evaluation will require larger and more diverse clinical trials, long-term follow-up, standardized psychotherapeutic protocols, and the integration of RWE to reflect real-world practice. Psychedelic-assisted therapy should be conceptualized as a complex intervention that combines pharmacological and psychotherapeutic components. Full article