Search Results (26,885)

Hydrological drought poses a significant threat to water security and ecosystems globally. While remote sensing offers vast spatial data, advanced analytical methods are required to translate this data into actionable insights. This review addresses this need by systematically synthesizing the state-of-the-art in using convolutional neural networks (CNNs) and satellite-derived vegetation indices for hydrological drought detection. Following PRISMA guidelines, a systematic search of studies published between 1 January 2018 and August 2025 was conducted, resulting in 137 studies for inclusion. A narrative synthesis approach was adopted. Among the 137 studies included, 58% focused on hybrid CNN-LSTM models, with a marked increase in publications observed after 2020. The analysis reveals that hybrid spatiotemporal models are the most effective, demonstrating superior forecasting skill and in some cases achieving 10–20% higher accuracy than standalone CNNs. The most robust models employ multi-modal data fusion, integrating vegetation indices (VIs) with complementary data like Land Surface Temperature (LST). Future research should focus on enhancing model transferability and incorporating explainable AI (XAI) to strengthen the operational utility of drought early warning systems. Full article

Background: During social foraging, individuals typically adopt one of two mutually exclusive strategies: (1) producing, which involves searching for, discovering, and acquiring resources, or (2) scrounging, which entails exploiting resources previously discovered by others. The distribution of these strategies within a group is referred to as the Producer–Scrounger (P-S) Game. Although the influence of personality on the Producer–Scrounger Game has been examined in non-human species through measures of individual differences, few studies have yet explored this relationship in humans. Objective: We aimed to examine the association between social foraging strategies and personality traits in human participants, using the Big Five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism, with their higher-order metatraits measured as composite scores: stability and plasticity, and psychopathy traits measured with the Antisocial Process Screening Device (APSD): callous–unemotional, impulsivity, and narcissism. Methods: Forty-five participants completed the Guaymas Foraging Task (GFT), designed to simulate a social foraging scenario under two 4 min conditions: one in which the cost of producing was 0 s, and another in which it was 8 s. Participants also completed the Big Five Inventory and the APSD. Results: Openness (p = 0.018, R² = 0.124), agreeableness (p = 0.002, R² = 0.209), extraversion (p = 0.019, R² = 0.121), stability (p = 0.022, R² = 0.117), and plasticity (p = 0.007, R² = 0.160) traits were associated with higher producer’s indexes. However, these correlations emerged only under the low-cost condition. No correlations were found between the producer’s index and psychopathic traits; nonetheless, participants above the APSD’s cutoff score scrounged significantly more, but only in the low-cost condition. Conclusions: Individual differences such as personality seem to be correlated with different foraging strategies; nonetheless, the behavioral expression of these traits seems to diminish when the environment is not favorable for their preferred strategy. Full article

Background: Individuals with mental health conditions frequently experience stigmatization and discrimination. Among the primary objectives in the fight against stigma is to examine groups that play a crucial role in addressing it, such as healthcare professionals. Although research has examined stigma among healthcare professionals, few studies have specifically addressed how nurses perceive and contribute to the stigmatization of individuals with mental health conditions. Objective: The aim of this review was to compile and compare the scientific literature addressing nurses’ stigma toward individuals with mental health conditions. Methods: Following the methodological guidelines of the Joanna Briggs Institute and in accordance with the PRISMA 2020 guidelines, an integrative review was conducted of MEDLINE (PubMed), EMBASE, APA PsycInfo (EBSCO), and CINAHL Complete (EBSCO). Database-specific indexing terms were combined with the Boolean operators AND/OR. Studies with quantitative or qualitative methodologies, published in Spanish or English and without restrictions by year of publication, were included. Two independent reviewers selected the studies and performed the critical appraisal. Results: The search retrieved 4256 records, of which 32 articles were finally included. A content analysis of the selected studies was conducted. Most studies used validated questionnaires to assess stigma and its associations with various variables, while only a limited number employed qualitative designs. Across the 32 studies (n = 6283 nurses from 29 countries), stigma was observed across settings but tended to be lower among mental health specialists. Insufficient training and limited contact were consistently associated with higher levels of stigma, whereas specialization and positive contact were linked to lower levels. Associative stigma emerged as a recurrent theme with implications for psychiatric nursing identity. Conclusions: Nurses working in mental health settings generally demonstrate more positive attitudes toward individuals with mental health conditions compared with those in other clinical areas; however, stigma persists across all settings. Associative stigma may be influencing the development and advancement of psychiatric nursing. Specific academic training, capacity building, and specialization in mental health are essential to counteract stigma. Further qualitative research is required to achieve a deeper understanding of this phenomenon. Full article

Background/Objective: Traditional therapeutic drug monitoring is limited by manual interpretation and specific constraints like sampling at steady-state and requiring a minimum of two drug concentrations. The integration of model-informed precision dosing (MIPD) into population health informatics represents a promising approach to address drug safety and efficacy. This article explored the integration of MIPD within population health informatics and evaluated its potential to enhance precision dosing using artificial intelligence (AI), machine learning (ML), and electronic health records (EHRs). Methods: PubMed and Embase searches were conducted, and all relevant peer-reviewed studies in English published between 1958 and December 2024 were included if they pertained to MIPD and population-level health, with the use of AI/ML algorithms to predict individualized drug dosing requirements. Emphasis was placed on vulnerable populations such as critically-ill, geriatric, and pediatric groups. Results: MIPD with the Bayesian method represents a scalable innovation in precision medicine, with significant implications for population health informatics. By combining AI/ML with comprehensive electronic health records (EHRs), MIPD can offer real-time, precise dosing adjustments. This integration has the potential to improve patient safety, optimize therapeutic outcomes, and reduce healthcare costs, especially for vulnerable populations where evidence is limited. Successful implementation requires collaboration among clinicians, pharmacists, and health informatics professionals, alongside secure data management and interoperability solutions. Conclusions: Further research is needed to define, implement, and evaluate practical applications of AI/ML. This insight may help develop standards and identify drugs for MIPD to advance personalized medicine within population health informatics. Full article

Background: This research explores the complex relationship between Attention Deficit Hyperactivity Disorder (ADHD) and language skills, focusing on the impact of the disorder on children’s language development. It is designed as a systematic literature review to synthesize and evaluate existing evidence on this topic. Based on the existing literature, ADHD affects multiple dimensions of language, including phonological awareness, pragmatic comprehension, morphosyntactic structure, narrative skills, and written expression. The difficulties that children with ADHD exhibit at the language level are directly related to their deficits in working memory, attention, and organization, which make it challenging for them to acquire and use language at both educational and social levels. Methods: This study followed the PRISMA methodology, with a systematic selection process across four stages (identification, screening, eligibility, and inclusion). During the identification phase, 475 records were identified (450 from database searches and 25 through reference screening). After screening and applying inclusion criteria, 15 studies met all eligibility requirements and were included in the final synthesis. Results: The present research highlighted the important role that occupational therapists and psychologists can play in the language development of children with ADHD. Strategic interventions to alleviate the language difficulties of children with ADHD are designed to enhance phonological awareness, executive function, speech and language, the use of technological tools, and social skills training. Conclusions: The importance of early diagnosis and implementation of holistic, individualized interventions targeting the language, executive, and social difficulties manifested by children with ADHD is considered influential in addressing the barriers to improving language skills as effectively as possible. Full article

Background: Childhood cancers account for approximately 1–2% of all malignancies worldwide, with hematologic cancers representing about 35–40% of pediatric cases. Improved survival has brought increased recognition of both acute and long-term therapy-related complications, including secondary malignant neoplasms (SMNs). Survivors of pediatric hematologic malignancies face a lifelong risk of secondary malignant neoplasms (SMNs), which remain among the most severe late effects of therapy. Methods: We conducted a PRISMA 2020–aligned systematic review of cohort and registry studies evaluating SMNs after childhood hematologic cancers. Databases searched included PubMed, Embase, Web of Science, Scopus, and Cochrane Library. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Newcastle–Ottawa Scale; disagreements were resolved by a third reviewer. Results: Forty-three studies (>70,000 survivors, median follow-up 5–30+ years) were included. Standardized incidence ratios (SIRs) for secondary malignant neoplasms compared to the general population ranged from 2.0 to 6.0, with absolute excess risks (AERs) of approximately 10–40 per 10,000 person-years. Therapy-related acute myeloid leukemia occurred within 5–10 years, while solid secondary malignant neoplasms (breast, thyroid, central nervous system, sarcomas) emerged after 10–25 years. The highest risks for developing secondary malignant neoplasms were observed among female survivors of Hodgkin lymphoma treated with chest and neck radiotherapy, particularly during adolescence, and among hematopoietic stem cell transplant recipients exposed to total body irradiation or chronic graft-versus-host disease. Conclusions: SMNs are predictable late effects requiring lifelong, exposure-anchored surveillance. Precision survivorship—integrating treatment exposures, transplant conditioning, and genetic predisposition—should guide future screening strategies. Full article

Accurate reconstruction of train collision accidents is essential for understanding impact conditions, assessing crashworthiness, and supporting safety improvements. This study proposes a surrogate-based optimization framework for reconstructing structural damage in train collisions from post-accident observations. The pre-impact kinematic state, expressed by a six-dimensional vector of relative offsets, rotations, and impact velocity, is formulated as an inverse problem in which a Sum of Squared Relative Deviations (SSRD) between measured and simulated residual deformations serves as the objective function. A reduced two-vehicle finite element (FE) model is developed to capture the dominant impact dynamics, an Optimal Latin Hypercube Design is used to sample the parameter space, and a Kriging surrogate model is constructed to approximate the response. A simulated annealing algorithm is applied to search for the global minimum. The framework is demonstrated on a real high-speed rear-end collision of electric multiple units. The Kriging model achieves a coefficient of determination of about 0.85, and the optimized kinematic state yields FE-predicted residual deformations that agree with field measurements at key locations to within about 5%. The results show that the method can efficiently reconstruct physically plausible collision scenarios and provide insight into parameter sensitivity and identifiability for railway safety analysis. Full article

The Social Golfer problem (SGP) consists of scheduling v players into rounds of equally sized groups in such a way that (1) any two players are assigned to the same group in at most one round and (2) as many rounds as possible are obtained. Combinatorial properties dictate the maximum theoretical number of rounds that may or may not be achievable. Any solution with the theoretically maximum number of rounds is called a maximal solution, and solutions with the number of rounds that is the best currently known (but not necessarily maximal) are said to be optimal. Existing techniques to find optimal solutions consist of exhaustive search methods and constructions based on combinatorial structures such as mutually orthogonal Latin squares (MOLSs) and mutually orthogonal Latin rectangles (MOLRs). In this paper, we investigate other combinatorial designs that can provide optimal solutions with at least as many rounds as those published and introduce novel constructions based on transversal designs, incomplete transversal designs, and starter blocks. We also provide optimal solutions to a related problem, where group sizes may differ by one but all rounds have the same number of groups of each size (the Social Golfer problem with adjacent group sizes (SGA)). We show how optimal solutions to this problem can be derived from optimal solutions to an instance of the SGP with either more or fewer players. An algorithm is presented to find an optimal solution in general, and solutions are provided for up to 150 players. Full article

To address the challenge of predicting runoff processes in the middle reaches of the Yangtze River under the influence of complex river–lake relationships and human disturbances, this paper proposes a coupled model based on the Sparrow Search Algorithm-optimized Long Short-Term Memory neural network (SSA-LSTM) for daily runoff forecasting at the Jiujiang Hydrological Station. The input data were preprocessed through feature selection and sequence decomposition. Subsequently, the Sparrow Search Algorithm (SSA) was utilized to perform automated of key hyperparameters of the Long Short-Term Memory (LSTM) model, thereby enhancing the model’s adaptability under complex hydrological conditions. Experimental results based on multi-station hydrological and meteorological data of the middle reaches of the Yangtze River from 2009 to 2016 show that the SSA-LSTM achieves a Nash–Sutcliffe Efficiency (NSE) of 0.98 during the testing period (2016). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are reduced by 49.3% and 51.3%, respectively, compared to the standard LSTM. A comprehensive evaluation across different flow levels, utilizing Taylor diagrams and error distribution analysis, further confirms the model’s robustness. The model demonstrates robust performance across different flow regimes: compared to the standard LSTM model, SSA-LSTM improves the NSE from 0.45 to 0.88 in high-flow scenarios, exhibiting excellent capabilities in peak flow prediction and flood process characterization. In low-flow scenarios, the NSE is improved from −0.77 to 0.72, indicating more reliable prediction of baseflow mechanisms. The study demonstrates that SSA-LSTM can effectively capture hydrological nonlinear characteristics under strong river–lake backwater and human disturbances, providing a high-precision and high-efficiency data-driven method for runoff prediction in complex basins. Full article

Background/Objectives: Missed nursing care is a recognized indicator of nursing quality and safety, while caring is a foundational concept in nursing practice. Few studies have empirically examined their relationship. This scoping review aimed to map and synthesize existing evidence on the conceptualisation, measurement approaches, and empirical relationships between caring and missed nursing care. Methods: The review was conducted using JBI methodology, reported according to PRISMA-ScR guidelines, and was registered in the Open Science Framework. Literature was searched in PubMed, CINAHL Ultimate (EBSCOhost), MEDLINE (EBSCOhost), and Web of Science, with additional grey literature searches in ProQuest Dissertations & Theses and Google Scholar. The review included studies examining caring in relation to missed nursing care across any healthcare setting. All study designs were considered. Data were extracted using an extraction tool, developed based on JBI guidelines, and piloted. Data were analyzed descriptively, tabulated, and summarized narratively. Results: Five quantitative cross-sectional studies met the inclusion criteria, conducted between 2012 and 2024 in the Philippines and Slovenia. Caring was assessed using the Caring Behaviors Inventory, Caring Ability Inventory, or CARE-Q, while missed nursing care was measured using the MISSCARE Survey or the Missed Nursing Care Scale. Most studies used Watson’s Theory of Human Caring, Duffy’s Quality Caring Model, or the Missed Nursing Care Model as theoretical frameworks. Across studies, caring behaviours and caring ability were negatively associated with missed nursing care. Conclusions: Caring can function as a moral and relational ideal and as a measurable and actionable factor related to patient outcomes. However, the evidence base remains limited, with inconsistent theoretical foundations and a lack of experimental studies. Future research should adopt theory-based, experimental approaches with diverse samples to explore causal mechanisms and evaluate strategies that strengthen caring competence and caring organizational cultures. Full article

Background/Objective: Type 2 Diabetes Mellitus (T2DM) represents a major increasing burden for primary care systems worldwide. Digital health interventions (DHIs) have been proposed as scalable tools to improve glycemic control, yet uncertainty remains regarding which intervention characteristics yield the greatest benefit. To evaluate the effectiveness of DHIs on HbA1c levels in adults with T2DM and to examine whether intervention duration, engagement intensity, glucometer integration, and healthcare provider involvement modify glycemic outcomes. Data Sources: PubMed, Embase, Cochrane Library, and JMIR databases were systematically searched for relevant studies published between January 2020 and May 2025. Study Eligibility Criteria: Randomized controlled trials comparing DHIs plus usual care versus usual care alone in adults with T2DM and reporting HbA1c as the primary outcome. Methods: Data were extracted using the Jadad scale and TIDieR framework. Random-effects meta-analysis estimated pooled mean differences (MD) in HbA1c with 95% CIs. Subgroup analyses examined effects by intervention characteristics. Heterogeneity and sources of variance were assessed through Cochran’s Q, I², meta-regression, and sensitivity analyses (leave-one-out and trim-and-fill). Results: Thirteen RCTs (n ≈ 20,000) met inclusion criteria. DHIs achieved significant HbA1c reductions (range 0.01% to 1.57%; pooled MD −1.08%; 95% CI −1.18 to −0.99; p = 0.001). Short-term (≤6 months), low-intensity interventions showed the largest effect sizes (MD −1.16%, 95% CI 0.94 to 1.39). Glucometer integration and healthcare provider involvement contributed minimally to additional benefit. Meta-regression confirmed substantial heterogeneity, but no single factor explained variance across studies. Limitations: Considerable heterogeneity across interventions and variability in engagement measurement may limit the generalizability of findings. Conclusions: Short-term, low-intensity DHIs significantly improve glycemic control in primary care populations with T2DM. Advanced meta-analytic techniques confirm the robustness of these effects, providing practical guidance for selecting and implementing effective digital interventions in routine diabetes care. Full article

Background and Objectives: This narrative review evaluates the potential of Tetranectin (TN) and Paraoxonase-1 (PON1) to bridge the gap between biological pathology and clinical risk stratification by mapping the “Fibrosis-Oxidative Axis”. Materials and Methods: A targeted literature search was conducted using Scopus, PubMed, and Google Scholar to identify studies examining the diagnostic and prognostic value of TN and PON1 in heart failure (HF). Evidence was synthesized qualitatively to analyze their roles in structural fibrosis and oxidative defense. Results: Tetranectin functions as a structural indicator, where its dynamics reflect fibroblast activation, extracellular matrix (ECM) deposition, and protein sequestration during tissue remodeling. On the other hand, PON1 serves as a functional metabolic barometer; its reduced activity correlates with systemic oxidative burden, loss of endothelial protection, and pro-inflammatory signaling. These markers capture a bidirectional pathology where oxidative injury drives fibrotic remodeling, which subsequently continue metabolic dysfunction. A dual-biomarker profile is proposed to stratify disease activity: early-stage metabolic stress (reduced PON1) precedes structural changes, while progressive HF involves active fibrosis (altered TN) alongside persistent oxidative injury. Conclusions: The combined assessment of TN and PON1 offers a complementary approach to HF profiling, potentially refining risk stratification beyond hemodynamic parameters. However, clinical implementation requires large-scale validation to address standardization issues and specificity limitations regarding multimorbidity. Full article

Background/Objectives: Sodium-glucose co-transporter 2 (SGLT2) inhibitors are essential antidiabetic medications. However, their side effects warrant careful consideration. The search for novel SGLT2 inhibitors with high affinity remains an ongoing endeavor. Medicine food homology (MFH) herbs show promise for drug development due to their nutritional and medicinal value. Methods: This study aims to address the shortcomings of existing virtual screening models for SGLT2 inhibitors by optimizing feature selection and integrating multidimensional molecular fingerprints. Subsequently, an integrated virtual screening pipeline is constructed to identify potential SGLT2 inhibitors from eight selected MFH herbs. Results: The results indicate that the optimal model (LightGBM and RF) achieved an accuracy of 0.97 and an AUC of 0.98. Following rigorous filtering, a total of 44 potential SGLT2 inhibitors were identified, among which, Isoononin (from Gancao) and Ononin (from Huangqi, Gegen, and Gancao) exhibit favorable drug likeness and safety. Molecular docking demonstrate that both compounds can effectively bind to the SGLT2 active site, establishing stable hydrophobic interactions with critical residues such as Phe98 and Phe453. Furthermore, molecular dynamics simulations confirm the stability of the interactions between the two compounds and SGLT2. Conclusions: This study significantly enhances the accuracy and stability of SGLT2 inhibitor virtual screening models by addressing deficiencies in structural characterization and feature selection. It provides candidate molecules for the development of novel SGLT2 inhibitors and offers new scientific evidence for the application of MFH herbs in the prevention and treatment of chronic metabolic diseases. Full article

Multimodal multiobjective optimization problems (MMOPs) are widely encountered in real-world applications. While numerous evolutionary algorithms have been developed to locate equivalent Pareto-optimal solutions, existing Multimodal Multiobjective Evolutionary Algorithms (MMOEAs) often struggle to handle large-scale decision variables and sparse Pareto sets due to the curse of dimensionality and unknown sparsity. To address these challenges, this paper proposes a novel approach named MASR-MMEA, which stands for Large-scale Sparse Multimodal Multiobjective Optimization via Multi-stage Search and Reinforcement Learning (RL)-assisted Environmental Selection. Specifically, to enhance search efficiency, a multi-stage framework is established incorporating three key innovations. First, a dual-strategy genetic operator based on improved hybrid encoding is designed, employing sparse-sensing dynamic redistribution for binary vectors and a sparse fuzzy decision framework for real vectors. Second, an affinity-based elite strategy utilizing Mahalanobis distance is introduced to pair real vectors with compatible binary vectors, increasing the probability of generating superior offspring. Finally, an adaptive sparse environmental selection strategy assisted by Multilayer Perceptron (MLP) reinforcement learning is developed. By utilizing the MLP-generated Guiding Vector (GDV) to direct the evolutionary search toward efficient regions and employing an iteration-based adaptive mechanism to regulate genetic operators, this strategy accelerates convergence. Furthermore, it dynamically quantifies population-level sparsity and adjusts selection pressure through a modified crowding distance mechanism to filter structural redundancy, thereby effectively balancing convergence and multimodal diversity. Comparative studies against six state-of-the-art methods demonstrate that MASR-MMEA significantly outperforms existing approaches in terms of both solution quality and convergence speed on large-scale sparse MMOPs. Full article

Background/Objectives: In recent years, improvements and innovative treatments in pediatric cancer have significantly increased survival rates, but challenges in both cognitive and psychosocial development in children remain significant. This review applies a comprehensive framework to evaluate psychosocial interventions in pediatric populations, offering novel insights into intervention strategies and their effectiveness across diverse contexts. Methods: A systematic search was conducted in the PubMed, Scopus, PsycINFO, and Web of Science databases for the period 2000–2024. Controlled studies, systematic reviews, and qualitative studies examining psychosocial interventions for children and adolescents with cancer or survivors were included. Quality assessment was performed using the RoB2 tool, and data were analyzed using narrative synthesis by age group and type of intervention. Results: Studies have shown that developmentally targeted interventions, such as therapeutic play, cognitive–behavioral therapy, and school reintegration programs, improve emotional regulation, cognitive functioning, and social adjustment in children with cancer. However, the heterogeneity of the samples and the variety of measurements limit the generalizability of the results. Conclusions: Integrating a developmental perspective into the design of psychosocial interventions can enhance their effectiveness and sustainability in pediatric oncology. Future research should focus on long-term, culturally sensitive programs and their implementation in clinical practice. Full article