Search Results (1,210)

Background/Objectives: Emotion processing has increasingly been conceptualized as a transdiagnostic mechanism underlying psychological adaptation and psychopathology. From an attachment perspective, individual differences in emotion perception may be rooted in internal working models shaped by early relationships. This systematic review synthesized the literature on the relationship between adult attachment representations and intrapersonal emotion perception (Impathy) and interpersonal emotion perception (Empathy). Methods: The review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search was conducted on 9 February 2026, in PsycINFO, PsycArticles, and PubMed. Studies were included if they investigated adolescents or adults, assessed attachment representations using narrative-based measures (Adult Attachment Interview (AAI) or Adult Attachment Projective Picture System (AAP)), and examined intrapersonal and/or interpersonal emotion perception. Findings were synthesized narratively, and a random-effects meta-analysis examined the association between attachment security and reflective functioning. Results: Thirty-eight studies, including 2736 participants, met the inclusion criteria. Across studies, attachment representations were systematically associated with intrapersonal and interpersonal emotion perception. The strongest evidence emerged for reflective functioning, the Impathy dimensions Perceiving and Understanding, and cognitive-empathic processes. Secure attachment representations were consistently associated with higher reflective functioning and more adaptive emotion perception, whereas insecure and especially unresolved attachment representations were linked to impairments in emotional self-awareness, alexithymia-related processes, differentiated emotional understanding, and cognitive-empathic processing. The meta-analysis showed a large positive association between secure attachment representations and reflective functioning (k = 8; r = 0.64, 95% CI [0.50, 0.74]). Conclusions: Attachment representations appear systematically associated with the perceptual foundations of emotion processing. Intrapersonal and interpersonal emotion perception may therefore represent attachment-sensitive processes relevant to psychological adaptation, psychopathology, caregiving, and therapeutic change. Full article

Feature selection is crucial for high-dimensional sensor and biomedical data because it reduces redundancy, improves generalization, and supports interpretable biomarker discovery. In this study, we propose a Binary Chaos-Enhanced Newton–Raphson-Based Optimizer (BCNRBO) for wrapper-based feature selection. The method integrates chaotic search dynamics, a Hamming-distance-based Dynamic Potential mechanism, and a new binary transfer function to enhance exploration and prevent premature convergence. BCNRBO was evaluated on 26 benchmark datasets using a variety of classifiers, including K-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM) classifiers. The proposed method consistently achieved competitive or superior classification performance while selecting fewer features than competing binary metaheuristic methods. In particular, BCNRBO consistently achieved the best feature reduction performance across all classifiers and secured the top Friedman rankings for DT, NB, and SVM, demonstrating its overall effectiveness. Statistical tests confirmed significant improvements over competing methods in most pairwise comparisons. These results suggest that BCNRBO is a promising feature selection strategy for sensor-derived biomedical and neurorehabilitation data, where compact and reliable digital biomarkers are needed. Full article

Cyber-physical systems (CPSs) based on the Internet of Things (IoT) form the backbone of modern smart infrastructures, including smart cities, healthcare monitoring, industrial automation, and intelligent transportation. However, connecting many resource-limited IoT devices makes them more vulnerable to cyber threats, particularly quantum attacks. This review comprehensively examines quantum-secure communication (QSC) frameworks for IoT-enabled CPS, focusing on Quantum Key Distribution (QKD), post-quantum cryptographic (PQC) algorithms, and hybrid quantum–classical security models suitable for constrained devices. A PRISMA-guided search of the Scopus and Google Scholar database was conducted in January 2026 using three keyword groups related to hybrid security, artificial intelligence, and cyber-physical systems. Based on the evaluation, 6008 publications have been identified between 2001 and 2026. The first-round screening was performed for 4948 articles, after excluding duplicates. During the screening stage, 348 articles were selected for abstract scrutiny, 115 records were excluded due to no direct focus on CPS/IoT applications, 52 studies were excluded because these papers relied on traditional security models, 25 studies were excluded due to insufficient relevance to the review objectives, and 15 additional non-English studies were removed. Following the screening stage, 141 studies were selected for full-text eligibility. Out of those, 86 studies were removed due to a lack of specific evaluation metrics or not being published in a peer-reviewed venue. Furthermore, the publications are classified as QKD-based secure CPS and QSC for industrial IoT, AI-Assisted Secure Communication for CPS Networks, and hybrid PQC-QKD models for CPS/IoT devices. This article investigates recent advancements in secure data transmission, verified protocols, and AI-driven anomaly detection customized to CPS/IoT environments. In addition, operational hurdles, interaction with open innovations, real-time deployment, and secure edge-cloud integration are highlighted. By analyzing recent developments and identifying research gaps, this review provides a structured roadmap for designing secure, scalable, and quantum-safe IoT-based CPS frameworks capable of withstanding next-generation cyber threats. This systematic review was performed and reported according to the PRISMA 2020 guidelines. Full article

Introduction: Remote education is increasingly used in ostomy care, but its components, timing, governance, and evaluation remain inconsistently defined. This study aimed to develop practice-oriented recommendations for implementing remote patient education for people living with an ostomy. Methods: An Italian expert consensus using a modified Delphi method and reported according to the ACCORD guidelines was conducted. An expert panel (n = 11), recruited nationally, included stomatherapists (n = 6) and people living with an ostomy (n = 5). Round 1 comprised a remotely conducted focus group to generate and refine statements informed by a targeted literature search. Rounds 2 and 3 were anonymous online surveys in which panelists rated statements on a four-point Likert scale and could provide comments or propose additional items. Consensus was predefined as ≥75% agreement. Results: Response rates were 100% across the three rounds (October–November 2025). The panel achieved consensus on 8 definitions and 14 statements, organized into six domains: (1) model of care and eligibility; (2) privacy and data protection; (3) program structure, outcomes, and evaluation; (4) educational content and teaching strategies; (5) timing, intensity, follow-up, and caregiver involvement; and (6) dignity, relational quality, and professional and organizational requirements. Recommendations supported a hybrid-by-default model with eligibility criteria, privacy-by-design using secure platforms and traceable documentation, structured programs with tailored multimodal content, staged pathways lasting 2–6 months after an initial in-person foundation, dignity-preserving options during remote encounters, professional training in communication and digital empathy, and integration into clinical planning and records. Conclusions: This consensus provides the first ostomy-specific, implementation-focused recommendations for standardizing remote patient education in Italy, with an emphasis on equity, privacy, dignity, evaluation, and workforce competencies. Full article

Monitoring inland surface water quality is essential for water security, ecosystem conservation, public health, and sustainable water resource management. Although in situ measurements remain indispensable, they are often limited by high costs, restricted spatial coverage, low temporal frequency, and discontinuous monitoring networks. This study presents a systematic literature review, guided by the PRISMA 2020 framework, of empirical studies published between 2021 and 2025 on the integration of artificial intelligence (AI) and remote sensing (RS) for inland surface water quality monitoring. Searches were conducted in the Web of Science database, resulting in a final corpus of 367 peer-reviewed articles. Preliminary bibliometric characterization and qualitative content analysis were performed to identify sensors, platforms, AI paradigms, algorithms, estimated parameters, validation strategies, limitations, challenges, trends, and research gaps. The results show rapid growth in the field, with Sentinel-2 and Landsat-8 as the most recurrent sensors and multispectral data as the dominant spectral source. Machine learning approaches, especially Random Forest, Artificial Neural Networks, XGBoost, and Support Vector Machine, predominated, while deep learning, multi-source integration, hybrid models, and Explainable AI emerged as relevant trends. AI–RS integration shows strong potential to complement conventional monitoring, but persistent challenges remain regarding in situ data dependence, limited external and temporal validation, model transferability, generalization, uncertainty reporting, validation robustness, and interpretability. Full article

As micro, small and medium-sized enterprises (MSMEs) compete with limited resources, lightweight systems are needed to secure their digital assets. Fuzzy vaults (FVs) are useful for protecting secrets and, when applied to biometric systems, provide error-tolerance and privacy to enrolled biometric features. Combining multiple biometric traits also improves performance against attacks like spoofing in multimodal (MM) authentication systems. However, the design of the FV and the biometric-fusion method applied can limit the system’s effectiveness. This study systematically evaluates recent studies on FVs and MM systems and presents an up-to-date review to identify gaps, give directions for future studies, and, ultimately, improve the design of these systems. The research targeting MSMEs was carried out in two parts, with the first search focused on MM systems and the second on FVs, following the PRISMA guidelines. The main findings include the need to optimise the resource intensity of FV systems for the authentication of large numbers of individuals. It also found the need to make the model compatible with other biometric modalities as greater focus is on minutiae features. By reviewing these systems, we aim to foster the development of lightweight MM FV models to provide privacy and security in MSMEs. Full article

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT. Full article

In the global transition toward low-carbon power systems with high renewable energy penetration, pumped storage has emerged as a strategic cornerstone for modern power grids. However, the collaborative planning of pumped storage and backbone-grids faces critical challenges, including the lack of explicit quantification of the resilience value of pumped storage and the coarse treatment of N-1 connectivity constraints. This paper proposes a bi-objective resilient backbone-grid planning approach that integrates the pumped-storage hub effect, aiming to minimize total life-cycle costs and the system resilience mismatch index. The proposed framework incorporates network connectivity, N-1 connectivity (edge connectivity ≥ 2), and dual-scenario power flow security as rigid constraints. Furthermore, a three-stage constrained evolutionary algorithm TER-NSGA-II is developed. During the N-1 connectivity reinforcement phase, the max-flow min-cut theorem is employed to achieve precise validation and guidance for edge-connectivity enhancement. Case studies on the IEEE 118-bus system, together with extended validation on the IEEE 300-bus system, show that the proposed method can explicitly quantify the resilience value of pumped storage, obtain Pareto solutions that balance economy and resilience under strict edge-connectivity constraints, and demonstrate competitive overall performance in terms of solution-set quality, feasible-domain search stability, and scalability compared with NSGA-II and the more recent NSGA-III/NG benchmark. Full article

Egg production in Gallus gallus domesticus represents a complex, economically critical trait shaped by multiple interrelated phenotypes, including age at first egg, total egg number, egg weight, and clutch characteristics. These traits are governed by polygenic inheritance and modulated by environmental factors, making the dissection of their genetic architecture essential for improving breeding efficiency, particularly under the emerging “long-life layers” production model. This systematic review aimed to integrate current knowledge on the genetic and molecular basis of egg production traits through analysis of genome-wide association studies and related genomic approaches. A structured literature search identified 27 eligible studies, which were evaluated following PRISMA guidelines. Data extraction and meta-analysis were conducted using standardized genome annotations and computational pipelines. The synthesis of available evidence demonstrates moderate to high heritability for key reproductive traits and highlights consistent genomic signals across multiple chromosomes. Importantly, the findings reveal a shift toward a systems-level understanding of egg production, involving conserved biological pathways related to neuroendocrine regulation, folliculogenesis, and energy metabolism. The integration of diverse genomic approaches enables the development of more precise, breed-specific selection strategies. Overall, these advances support a transition from traditional selection toward molecularly informed breeding frameworks, with significant implications for productivity, sustainability, and global food security. Full article

Digital resilience has become an increasingly important concept in information systems research due to growing dependence on digital infrastructures, escalating cyber threats, and the emergence of regulatory frameworks that formalize resilience obligations. This study provides a systematic literature review of how digital resilience is conceptualized, operationalized, and aligned with emerging European Union (EU) regulatory frameworks. Following PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, and IEEE Xplore databases. Fifty-three peer-reviewed studies published between 2006 and 2026 were analyzed using a structured analytical coding framework capturing conceptual clarity, dimensional structure, methodological maturity, and regulatory alignment. The results reveal significant conceptual fragmentation across the literature. While governance, ICT risk management, incident response, and third-party risk management emerge as recurring resilience dimensions, definitional and structural convergence remains limited. Measurement approaches are dominated by maturity models and qualitative assessment frameworks, with relatively few studies proposing validated indicator-based models. Regulatory alignment with EU frameworks such as the Digital Operational Resilience Act (DORA) and the Network and Information Security Directive (NIS2) remains partial and inconsistent. The study identifies a structural alignment gap between regulatory resilience requirements, conceptual resilience models, and operational measurement approaches, providing a foundation for developing regulator-compatible digital resilience assessment frameworks. Full article

Objectives: Food insecurity (FI) among college students is an emerging global public health concern. While the burden is international in scope, this systematic review evaluates the prevalence of FI in college populations in the United States (U.S.) and examines the suitability of commonly used FI assessment tools for this population. Methods: A systematic search of PubMed, Scopus, and Web of Science was conducted (up to April 2026) in accordance with the PRISMA 2020 Abstracts checklist. Eligible studies were peer-reviewed research articles published between 2005 and 2026, conducted in the U.S., written in English, and including college or university students with sample sizes ≥ 30. Studies were required to use validated FI assessment tools developed by the United States Department of Agriculture (USDA) or Health Watch. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal tools and only studies rated as moderate or high quality were included. Results were synthesized by grouping studies according to the FI assessment tools used. Results: Thirty studies met the inclusion criteria (total n = 213,624 students surveyed). FI prevalence among U.S. college students ranged from 14% to 72.9%. Variability in estimates was influenced by the assessment tool used, demographic characteristics, institutional settings, and regional socioeconomic differences. Shorter screening instruments, including the USDA six-Item Household Food Security Survey Module (HFSSM) Short Form and Hunger Vital Sign, demonstrated greater variability in reported FI prevalence (47% and 41%, respectively) compared with longer assessment measures. Higher FI prevalence was also more frequently reported among students of color, those from lower socioeconomic backgrounds, and female students. Conclusions: Findings demonstrate FI is prevalent among college students. Limitations of the current study include restriction to three databases, exclusion of pre-2005 studies, and inclusion of only U.S.-based studies. Variability in assessment methods, as well as consideration of confounding variables (e.g., socioeconomics, demographics and institutional settings), underscores the need for context-specific tools tailored to this population to inform effective interventions and policies globally. Full article

Under the dual impact of new urbanization and rural population mobility, divorced rural women in China face severe challenges in obtaining collective membership qualification for land expropriation compensation. The newly enacted Rural Collective Economic Organization Law (RCEOL) contains ambiguous provisions, hindering effective implementation. This study asks: How can collective membership qualification for divorced rural women be determined based on pre-enactment court judgments to refine the law’s ambiguities? Adopting a qualitative design, data were collected from China Judgments Online. Through systematic keyword search, 238 court judgments were retrieved and analyzed using a three-level coding procedure (open, axial, selective). The theoretical framework draws on Amartya Sen’s capability approach. Three main findings are briefly summarized. First, a concrete determination scheme is proposed: the “stable rights-obligations relationship” is operationalized via collective medical insurance purchase and non-abandonment of contracted land; “basic livelihood security” emphasizes land’s security function without requiring primary income reliance; the “stable production-living relationship” criterion should be discarded. Second, the household registration (hukou) condition is becoming ambiguous, but such ambiguity reflects governance adaptation to complexity, moving toward “de-hukouization.” Third, legal ambiguity, while challenging, creates a flexible space for adaptive rural governance. This study contributes by introducing Sen’s capability approach into the analysis of divorced rural women’s membership qualification and providing empirical grounds for clarifying Article 11 of the RCEOL. Future research may observe changes in case volume and litigant testimonies after the law’s implementation to evaluate its real effects, further enriching the discussion on institution—agency interaction. Full article

As cloud computing proliferates, outsourced data faces severe security threats, yet existing searchable encryption (SE) schemes rely on classical hardness assumptions, centralized trust authorities, and static access control, leaving critical gaps in quantum resistance, single-point-of-failure prevention, and dynamic permission management. To address these limitations, we propose BL-ABSE, a blockchain-enhanced, lattice-based attribute-based searchable encryption framework. BL-ABSE employs the Ring Learning With Errors (RLWE) problem as its security foundation and applies the Number Theoretic Transform (NTT) to reduce polynomial multiplication from $O\left(n^2\right)$ to $O(nlogn)$. To eliminate single-point trust risks, the framework further integrates a $(t,n)$ threshold key protocol across an edge-node consortium governed by Practical Byzantine Fault Tolerance (PBFT) consensus. A smart-contract-maintained on-chain revocation list enables permission withdrawal via a single blockchain transaction without re-encryption. Experimental evaluation demonstrates that commitment generation requires approximately 23 ms at $n=1024$, search latency scales linearly at roughly 29 µs per record, and revocation completes in approximately 2 s regardless of system scale. Formal security proofs under the quantum polynomial-time (QPT) adversary model reduce six security properties—index indistinguishability, query privacy, threshold key security, Byzantine fault tolerance, audit immutability, and revocation immediacy—to the hardness of RLWE and the Short Integer Solution (SIS) problems. To the best of our knowledge, BL-ABSE is the first framework to simultaneously achieve post-quantum security, attribute-based access control, decentralized key management, instant revocation, and immutable auditing within a single unified framework. We further conduct threshold parameter verification, end-to-end revocation latency decomposition, blockchain throughput stress testing, search-pattern leakage quantification, and communication/storage overhead analysis, providing a comprehensive evaluation of both performance and security trade-offs. We explicitly characterize the search-pattern leakage inherent in the deterministic commitment design as a correctness–privacy trade-off and discuss mitigation directions. Full article

Fall armyworm, Spodoptera frugiperda (J.E. Smith), has emerged as one of the most damaging invasive pests affecting maize production and household food security across sub-Saharan Africa since its first detection in 2016. This systematic review synthesizes empirical evidence published between 2016 and 2025 to assess the agronomic, livelihood, and food security impacts of FAW on smallholder farming systems across Eastern, Southern, Western, and Central Africa. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Population, Intervention, Comparison, Outcome, Time, and Setting (PICOTS) framework, 20 studies (17 empirical and 3 contextual) were identified through comprehensive searches of academic databases and institutional repositories and were included in the final synthesis after methodological screening. The evidence indicates that FAW invasion causes substantial maize yield losses ranging from approximately 20% to 50%, with the greatest reductions reported in rain-fed systems with limited access to pest management technologies. Infestation rates frequently exceeded 50%, particularly during early invasion phases. Beyond agronomic losses, several studies reported reduced household income, constrained food availability, and livelihood disruptions, including increased labor requirements, higher production costs, and reliance on short-term coping strategies. Only a small proportion of studies (n = 4) directly assessed nutrition-related indicators, but the available evidence indicates declines in dietary diversity in severely affected communities. Overall, the agronomic impacts of FAW are consistently documented across regions, whereas the socioeconomic and nutrition outcomes remain comparatively underreported, indicating a significant evidence gap. These findings highlight FAW as both an agronomic and livelihood challenge, underscoring the need for integrated pest management strategies, strengthened extension services, and coordinated policy responses to safeguard food and income security among smallholder farmers in Africa. Full article

Rice is one of the world’s most important food crops, feeding over half of the global population and being crucial for food security. Accurate, timely mapping of rice fields is essential for precision agriculture, yet conventional methods relying on static samples fail to capture dynamic farmers’ planting decisions. To address this, we propose the Multi-Source Dynamic Sample Generation and Phenology-Guided Feature Selection Framework for In-Season Rice Identification (MSDF-RiceID) using multi-source remote sensing imagery. It incorporates two key innovations: (i) a rule-based sample updating mechanism based on historical rice maps and a dynamic threshold algorithm, and (ii) phenology-guided feature optimization through exponential weighting. Developed specifically to handle complex cropping patterns and high cloud cover in Hunan Province, MSDF-RiceID integrates these innovations within a grid-search-optimized Random Forest classifier to produce reliable monthly rice distribution maps. In-season samples corresponding to transplanting dates in April (DOY 100, 120), June (DOY 160), and July (DOY 184), differentiated as early-, middle-, and late-rice crops. The optimal feature set combined Sentinel-1 (PRI, VH, VH_VV), Sentinel-2 (NDYI, PSRI, NDBI, NDWI), and MODIS (NDVI, EVI, NDBI, LSWI) indices. Accuracy increased seasonally, with F1-score rising from 0.82 in May to 0.97 at harvest. Cross-region validation in Taishan (Guangdong) and Panjin (Liaoning) showed that the earliest identifiable stage (F1-score > 0.9) occurred earlier than in Hunan due to Hunan’s more complex triple-cropping phenology, highlighting the model’s strong transferability. Furthermore, MSDF-RiceID outperformed existing products (TWDTW-Rice and EARice10), increasing overall accuracy by 0.12–0.18, Kappa by 0.23–0.35, and F1-score by 0.09–0.15. These results demonstrate its effectiveness for in-season, large-scale, and dynamic rice mapping under persistent cloud cover, thereby providing direct support for precision agricultural management in heterogeneous cropping systems. Full article