Search Results (1,470)

Background: Cognitive behavioral therapy (CBT) has shown consistent efficacy in individuals with psychosis, as supported by many trials. One classical distinction is that between affective and non-affective psychosis. Few studies have specifically examined the possible moderating role of substantial affective elements. In this systematic review and meta-regression analysis, we assess how CBT response differs across the affective spectrum in psychosis. Methods: We included studies assessing various CBT modalities, including third-wave therapies, administered in people with psychosis. The study protocol is published in the Open Science Framework. Meta-regression was conducted to assess whether the proportion of participants with affective psychosis (AP), as proxied by a documented diagnosis of schizoaffective (SZA) disorder, moderated CBT efficacy across positive, negative, and depressive symptom domains. Results: The literature search identified 4457 records, of which 39 studies were included. The median proportion of SZA disorder participants was 17%, with a total of 422 AP participants represented. Meta-regression showed a trend toward lower CBT efficacy for positive symptoms with a higher SZA disorder proportion (β = +0.10 SMD per 10% increase in AP; p = 0.12), though it was not statistically significant. No significant associations were found for negative (β = +0.05; p = 0.73) or depressive symptoms (β = −0.02; p = 0.78). Heterogeneity was substantial across all models (I² ranging from 54% to 80%), and funnel plot asymmetry was observed in negative and depressive symptoms, indicating possible publication bias. Risk of bias assessment showed the anticipated inherent difficulty of psychotherapies in blinding and possibly dropout rates affecting some studies. Conclusions: Affective symptoms may reduce the effectiveness of CBT for positive symptoms in psychotic disorders, although the findings did not reach statistical significance. Other patient-level characteristics in psychosis could indicate which patients can benefit most from CBT modalities. Full article

This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommendations by their relevance to a user’s competencies, a graph neural network (GNN)-based approach is proposed, specifically utilizing and comparing the Heterogeneous Graph Transformer (HGT) architecture, Graph Sample and Aggregate network (GraphSAGE), and Heterogeneous Graph Attention Network (HAN). Experiments were conducted on a heterogeneous graph comprising various node and relation types. The models were evaluated using regression and ranking metrics. The results demonstrated the superiority of the HGT-based recommendation model as a link regression task, especially in terms of ranking metrics, confirming its suitability for generating accurate and interpretable recommendations in educational systems. The proposed approach can be useful for developing adaptive learning recommendations aligned with users’ career goals. Full article

Space-Air-Ground Integrated Network (SAGIN), which is considered a network architecture with great development potential, exhibits significant cross-domain collaboration characteristics at present. However, most of the existing works ignore the matching and adaptability of differential tasks and heterogeneous resources, resulting in significantly inefficient task execution and undesirable network performance. As a consequence, we formulate a category attribute-oriented resource allocation and task offloading optimization problem with the aim of minimizing the overall scheduling cost. We first introduce a task–resource matching matrix to facilitate optimal task offloading policies with computation resources. In addition, virtual queues are constructed to take the impacts of randomized task arrival into account. To solve the optimization objective which jointly considers bandwidth allocation, transmission power control and task offloading decision effectively, we proposed a deep reinforcement learning (DRL) algorithm framework considering type matching. Simulation experiments demonstrate the effectiveness of our proposed algorithm as well as superior performance compared to others. Full article

Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

The primary objective of this work is to develop a comprehensive theory of weighted fractional Sobolev spaces within the framework of timescales. To this end, we first introduce a novel class of weighted fractional operators and rigorously define associated weighted integrable spaces on timescales, generalising classical notions to this non-uniform temporal domain. Building upon these foundations, we systematically investigate the fundamental functional-analytic properties of the resulting Sobolev spaces. Specifically, we establish their completeness under appropriate norms, prove reflexivity under appropriate duality pairings, and demonstrate separability under mild conditions on the weight functions. As a pivotal application of our theoretical framework, we derive two robust existence theorems for solutions to the proposed model. These results not only extend classical partial differential equation theory to timescales but also provide a versatile tool for analysing dynamic systems with heterogeneous temporal domains. Full article

The Internet of Things (IoT) is transforming how devices communicate, with more than 30 billion connected units today and projections exceeding 40 billion by 2025. Despite this growth, the integration of heterogeneous systems remains a significant challenge, particularly in sensitive domains like healthcare, where proprietary standards and isolated ecosystems hinder interoperability. This paper presents an extended version of the Share design pattern, a lightweight and contract-based mechanism for dynamic service composition, tailored for resource-constrained IoT devices. Share enables decentralized, peer-to-peer integration by exchanging executable code in our examples written in the LUA programming language. This approach avoids reliance on centralized infrastructures and allows services to discover and interact with each other dynamically through pattern-matching and contract validation. To assess its suitability, we developed an emulator that directly implements the system under test in LUA, allowing us to verify both the structural and behavioral constraints of service interactions. Our results demonstrate that Share is scalable and effective, even in constrained environments, and supports formal correctness via design-by-contract principles. This makes it a promising solution for lightweight, interoperable IoT systems that require flexibility, dynamic configuration, and resilience without centralized control. Full article

As cyberattacks continue to rise in frequency and sophistication, extracting actionable Cyber Threat Intelligence (CTI) from diverse online sources has become critical for proactive threat detection and defense. However, accurately identifying complex entities from lengthy and heterogeneous threat reports remains challenging due to long-range dependencies and domain-specific terminology. To address this, we propose XLNet-CRF, a hybrid framework that combines permutation-based language modeling with structured prediction using Conditional Random Fields (CRF) to enhance Named Entity Recognition (NER) in cybersecurity contexts. XLNet-CRF directly addresses key challenges in CTI-NER by modeling bidirectional dependencies and capturing non-contiguous semantic patterns more effectively than traditional approaches. Comprehensive evaluations on two benchmark cybersecurity corpora validate the efficacy of our approach. On the CTI-Reports dataset, XLNet-CRF achieves a precision of 97.41% and an F1-score of 97.43%; on MalwareTextDB, it attains a precision of 85.33% and an F1-score of 88.65%—significantly surpassing strong BERT-based baselines in both accuracy and robustness. Full article

With the rapid advancement of space technology, satellites are playing an increasingly vital role in fields such as Earth observation, communication and navigation, space exploration, and military applications. Efficiently deploying satellite missions under multi-objective, multi-constraint, and dynamic environments has become a critical challenge in the current aerospace domain. This paper integrates the concepts of game theory and proposes a distributed collaborative task model suitable for on-orbit satellite mission planning. A two-player impulsive maneuver game model is constructed using differential game theory. Based on the ideas of Nash equilibrium and distributed collaboration, multi-agent technology is applied to the distributed collaborative task planning, achieving collaborative allocation and countermeasure strategies for multi-objective and multi-satellite scenarios. Experimental results demonstrate that the method proposed in this paper exhibits good adaptability and robustness in multiple impulse scheduling, maneuver strategy iteration, and heterogeneous resource utilization, providing a feasible technical approach for mission planning and game confrontation in satellite clusters. Full article

Background: Androgen deprivation therapy (ADT) is a primary treatment for prostate cancer (PCa) that effectively reduces androgen levels to suppress tumor progression. However, growing evidence suggests potential cognitive side effects, raising concerns about the long-term neurological consequences of this treatment. Objective: This scoping review aims to synthesize the existing evidence linking ADT to cognitive changes in men with PCa, identifying the key cognitive domains affected and outlining gaps in the existing literature. Methods: A systematic literature search was conducted according to the PRISMA-ScR guidelines in CINAHL, PubMed, Scopus, and Web of Science. Studies investigating cognitive function in ADT-treated PCa patients were included, covering randomized controlled trials (RCTs) and cohort, case–control, and cross-sectional studies. The extracted data included the study design, evaluated cognitive characteristics, measurement tools, and overall findings. Results: A total of 22 studies met the inclusion and exclusion criteria. Cognitive assessments varied across studies. While some studies reported cognitive impairments in ADT-treated patients—particularly in working, verbal, and visual memory and executive function—others found no significant effects. The variability in prostate cancer staging, epidemiological study designs, and treatment regimens; the exclusion of comorbid conditions; and the differences in assessment tools, sample sizes, and study durations hinder definitive conclusions about the cognitive effects of ADT. Conclusions: This scoping review highlights the heterogeneous and often contradictory evidence regarding ADT-associated cognitive dysfunction. While certain cognitive domains may be affected, methodological inconsistencies limit robust conclusions. Standardized cognitive assessments and longer longitudinal studies are required to clarify ADT’s role in cognitive decline. As the PCa survival rate increases with extended ADT use, integrating routine cognitive monitoring into clinical practice should be considered for PCa patients. Full article

Background/Objectives: Frailty is a multifactorial clinical syndrome characterized by diminished physiological reserves and increased vulnerability to stressors. It is increasingly recognized as a predictor of poor outcomes in cardiac rehabilitation (CR). However, how frailty is defined, assessed, and addressed across outpatient CR programmes remains unclear. This scoping review aimed to map the extent, range, and nature of research examining frailty in the context of outpatient CR, including how frailty is measured, its impact on CR participation and outcomes, and whether sex and gender considerations or participation barriers are reported. Methods: Following the PRISMA-ScR guidelines, we conducted a comprehensive search across six electronic databases (from inception to 15 May 2025). Eligible peer-reviewed studies included adult participants assessed for frailty using validated tools and enrolled in outpatient CR programmes. Two reviewers independently screened citations and extracted data. Results were synthesized descriptively and narratively across three domains: frailty assessment, sex and gender considerations, and barriers to CR participation. The protocol was registered with the Open Science Framework. Results: Thirty-nine studies met inclusion criteria, all conducted in the Americas, Western Pacific, or Europe. Frailty was assessed using 26 distinct tools, most commonly the Kihon Checklist, Fried’s Frailty Criteria, and Frailty Index. The median pre-CR frailty prevalence was 33.5%. Few studies (n = 15; 38.5%) re-assessed frailty post-CR. Sixteen studies reported sex or gender data, but none applied sex- or gender-based analysis (SGBA) frameworks. Only eight studies examined barriers to CR participation, identifying physical limitations, emotional distress, cognitive concerns, healthcare system-related factors, personal and social factors, and transportation as key barriers. Conclusions: The literature on frailty in CR remains fragmented, with heterogeneous assessment methods, limited global representation, and inconsistent attention to sex, gender, and participation barriers. Standardized frailty assessments and individualized CR programme adaptations are urgently needed to improve accessibility, adherence, and outcomes for frail individuals. Full article

Background: Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders characterized by a complex, multifactorial etiology with a strong genetic contribution. Our study aimed to evaluate the link between the burden of rare genetic variants within a specific panel of ASD and intellectual disability-associated genes and phenotypic variability in a cohort of children with autism in Slovakia. Methods: Gene burden scores were calculated based on pathogenic, likely pathogenic, and uncertain significance rare DNA variants identified by whole-exome sequencing. We then assessed the effect of three different scoring methods on the variance across 15 psycho-behavioral parameters describing the phenotypic profiles of 117 ASD probands. Results: The burden score showed a significant multivariate effect on the combination of psycho-behavioral parameters. This score was associated with the social affect of ADOS-2, as well as with the socialization domain, and total adaptive behavior scores from the Vineland Adaptive Behavior Scales-3 (VABS). While a score based solely on count of pathogenic and likely pathogenic variants did not show a multivariate effect, incorporating variants of uncertain significance revealed a multivariate effect on two adaptive behavior parameters: daily living skills and total adaptive behavior score (VABS). Conclusions: Our findings partially explain the variability in phenotypic manifestation in our ASD patient cohort, highlighting the importance of considering the cumulative effect of rare genetic variants, including those of uncertain significance, in shaping the diverse clinical presentation of ASD. Full article

The existing deep learning-based Airborne Laser Scanning (ALS) point cloud semantic segmentation methods require a large amount of labeled data for training, which is not always feasible in practice. Insufficient training data may lead to over-fitting. To address this issue, we propose a novel Multi-branch Feature Extractor (MFE) and a three-stage transfer learning strategy that conducts pre-training on multi-source ALS data and transfers the model to another dataset with few samples, thereby improving the model’s generalization ability and reducing the need for manual annotation. The proposed MFE is based on a novel multi-branch architecture integrating Neighborhood Embedding Block (NEB) and Point Transformer Block (PTB); it aims to extract heterogeneous features (e.g., geometric features, reflectance features, and internal structural features) by leveraging the parameters contained in ALS point clouds. To address model transfer, a three-stage strategy was developed: (1) A pre-training subtask was employed to pre-train the proposed MFE if the source domain consisted of multi-source ALS data, overcoming parameter differences. (2) A domain adaptation subtask was employed to align cross-domain feature distributions between source and target domains. (3) An incremental learning subtask was proposed for continuous learning of novel categories in the target domain, avoiding catastrophic forgetting. Experiments conducted on the source domain consisted of DALES and Dublin datasets and the target domain consists of ISPRS benchmark dataset. The experimental results show that the proposed method achieved the highest OA of 85.5% and an average F1 score of 74.0% using only 10% training samples, which means the proposed framework can reduce manual annotation by 90% while keeping competitive classification accuracy. Full article

Gastric cancer (GC) remains a major cause of cancer-related mortality worldwide, with human epidermal growth factor receptor 2 (HER2)-positive disease representing a clinically relevant subset. Trastuzumab combined with chemotherapy is the standard first-line treatment in advanced settings, following the landmark ToGA trial. However, resistance to trastuzumab has emerged as a significant limitation, prompting the need for more effective second-line therapies. Trastuzumab deruxtecan, a novel antibody–drug conjugate (ADC) composed of trastuzumab linked to a cytotoxic payload, has demonstrated promising efficacy in trastuzumab-refractory, HER2-positive GC, including cases with heterogeneous HER2 expression. Other HER2-targeted ADCs are also under investigation as potential alternatives. In addition, strategies to overcome resistance include HER2-specific immune-based therapies, such as peptide vaccines and chimeric antigen receptor T cell therapies, as well as antibodies targeting distinct HER2 domains or downstream signaling pathways like PI3K/AKT. These emerging approaches aim to improve efficacy in both HER2-high and HER2-low GC. As HER2-targeted treatments evolve, addressing resistance mechanisms and optimizing therapy for broader patient populations is critical. This review discusses current and emerging HER2-directed strategies in GC, focusing on trastuzumab deruxtecan and beyond, and outlines future directions to improve outcomes for patients with HER2-positive GC across all clinical settings. Full article

Depending on the feedstock type and the pyrolysis conditions, biochars exhibit different physical, chemical, and structural properties, which highly influence their performance in various applications. This study presents a comprehensive characterization of biochar materials derived from the waste wood of pine (Pinus sylvestris L.) and beech (Fagus sylvatica) after low-temperature pyrolysis at 270 °C, followed by chemical activation using zinc chloride. The resulting materials were thoroughly analyzed in terms of their chemical composition (FTIR), thermal behavior (TGA/DTG), structural morphology (SEM and XRD), elemental analysis, and particle size distribution. The successful modification of raw biomass into carbon-rich structures of increased aromaticity and thermal stability was confirmed. Particle size analysis revealed that the activated carbon of Fagus sylvatica (FSAC) exhibited a monomodal distribution, indicating high homogeneity, whereas Pinus sylvestris-activated carbon showed a distinct bimodal distribution. This heterogeneity was supported by elemental analysis, revealing a higher inorganic content in pine-activated carbon, likely contributing to its dimensional instability during activation. These findings suggest that the uniform morphology of beech-activated carbon may be advantageous in filtration and adsorption applications, while pine-activated carbon’s heterogeneous structure could be beneficial for multifunctional systems requiring variable pore architectures. Overall, this study underscored the potential of chemically activated biochar from lignocellulosic residues for customized applications in environmental and material science domains. Full article