Search Results (3,315)

Software-Defined Networking (SDN) centralizes control logic, improving programmability but exposing the controller to volumetric and low-rate Distributed Denial of Service (DDoS) attacks. Entropy-based detectors often raise late alarms or require significant traffic distribution changes, while machine-learning approaches impose high training and inference overhead. To address these issues, this work proposes a Statistical Mean Renyi Entropy (SMRE)-based early-warning system that amplifies micro-level disturbances in flow randomness using a tunable sensitivity weight (μ). The formulation enhances responsiveness to entropy deviations without adding computational complexity, enabling O(n) single-pass execution per monitoring window. The method was implemented on a Mininet testbed (nine switches, 64 hosts, POX controller with the L3_learning module) with mixed benign traffic and hping3/Scapy-generated UDP and TCP flood attack traffic at intensities ranging from 10 to 75%. Experimental results demonstrate that SMRE detects early-stage attacks with 94.7–98.1% accuracy, 0.8–2.3% false positive rate, and 6.5–14 ms detection latency, outperforming Shannon and classical Renyi entropy detectors. ROC analysis (AUC ≈ 0.99) and paired t-tests (p < 0.01) confirm statistical significance. Resource profiling shows negligible CPU and memory overhead, supporting real-time deployment. By eliminating model training and ensuring robust early detection, SMRE offers a lightweight and practical detection mechanism for SDN environments, whose applicability to cloud, edge, and IoT deployments will be further substantiated through validation on real traffic traces and multi-controller architectures. Full article

Family-centred approaches are being increasingly recognized as integral to inclusive mental health care. Although substantial evidence-based exists for specific interventions particularly family intervention for psychosis, there is no synthesis that maps how family-centred approaches are conceptualized, implemented, and linked to inclusivity across diagnoses, service contexts, and levels of evidence. Existing reviews are largely disorder-specific or outcome-focused and do not examine family-centred care as a system-level strategy for inclusive practice. This scoping review aimed to map and synthesize the existing literature on family-centred approaches used to promote inclusive mental health care for individuals with psychiatric disorders. This review followed the Joanna Briggs Institute (JBI) methodology for scoping reviews and the PRISMA-ScR reporting guidelines. Searches were conducted in PubMed, PsycINFO, CINAHL, Scopus, Embase, and Web of Science for English-language empirical studies published between 2000 and 2025. Studies involving individuals with psychiatric disorders and any form of family-centred, family-focused, or family-inclusive approach were eligible. Data were charted and analyzed thematically to map intervention types, reported outcomes, and implementation influences; the review did not evaluate intervention effectiveness. Twenty-four studies met the inclusion criteria. Only five themes emerged from this study. Across the literature, family involvement was associated with improved engagement, enhanced communication, and increased caregiver competence. Psychoeducation featured prominently in evidence-based interventions, while newer models emphasized shared decision-making and service co-production. Family-centred approaches are conceptualized in diverse ways across mental health services and are commonly linked to more inclusive and collaborative care. However, the strength of evidence varies substantially between intervention types. This review clarified how family-centred practices are situated across levels of evidence and service contexts, highlighting the need for greater precision in distinguishing established interventions from emerging practices and for system-level strategies to support consistent, culturally responsive family inclusion. Full article

In this study, Klobuchar coefficients optimized for the Japanese region, referred to as the Klobuchar Regional (KR) model, were estimated using the International GNSS Service (IGS) Global Ionosphere Map (GIM) and evaluated against the Klobuchar GPS (KG) and Klobuchar QZSS (KQ) models. The grid points were defined based on ionospheric pierce points (IPPs) derived from Japanese GNSS stations, and the coefficients were estimated via a nonlinear least-squares approach using the vertical total electron content (VTEC) within the defined region (12.5–52.5° N, 115–170° E) under both solar minimum (2019) and solar maximum (2024) conditions. During the solar minimum, the number of distinct KG correction coefficient values was limited to fewer than 16, and intermodel correlations were found to vary with solar activity level, with the KQ–KR pair exhibiting the highest correlation for α₀ (r = 0.85). Vertical Total Electron Contents (VTEC) root mean square error (RMSE) analysis indicated that KR achieved an approximately 50% improvement in accuracy relative to both KG and KQ. Furthermore, standard point positioning (SPP) results at three Japanese stations demonstrated that KR and KQ exhibited comparable performance within 0.1 m during the solar minimum, whereas KR generally outperformed KQ during the solar maximum, with the standard deviation of KR approximately half that of KQ, confirming superior stability and reliability in positioning performance. Full article

Proactive mental health consultation–liaison (C-L) services in neonatal intensive care units (NICUs) are increasingly recognized as essential to infant health and family functioning, yet there is limited guidance on how to develop sustainable and effective programs in real-world clinical settings. This paper describes the development, implementation, and iterative refinement of a NICU mental health C-L program using a learning health system (LHS) framework. Grounded in a theory-driven model that conceptualizes the infant as the identified patient, the program was designed to target proximal mechanisms influencing infant regulation, parent–infant interaction, and medical course. Consistent with LHS principles, program elements were introduced incrementally and refined through embedded data collection, rapid feedback loops, and multidisciplinary team learning. Data sources emphasized feasibility, reach, implementation processes, and acceptability rather than hypothesis-driven outcome testing. Over six years, this approach supported the expansion of multiple integrated service components, including dyadic intervention services, parent mental health screening and referral pathways, unit-wide family engagement initiatives, and neurobehavioral care practices. Rather than presenting a fixed protocol, this paper offers a replicable framework for developing NICU mental health C-L services that balances developmental theory, implementation realities, and sustainability. The LHS approach provides a pragmatic roadmap for NICUs seeking to build or refine mental health services in contexts characterized by clinical complexity, evolving evidence, and system-level constraints. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly used in youth mental health services, including clinical decision support, risk prediction, and digital therapeutics. However, existing frameworks provide limited guidance for integrating ethical, cultural, and relational considerations into the design, governance, and implementation of AI-enabled mental health technologies. This scoping review examined how digital humanities-informed approaches have been incorporated into AI-supported mental health interventions for children and adolescents. Methods: A scoping review was conducted following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. Peer-reviewed literature published between 2015 and 2025 was searched using PubMed and supplemented by semantic searches through Elicit. Systematic reviews, scoping reviews, and meta-analyses examining AI-enabled digital mental health interventions and digital humanities perspectives were included. Data were synthesized using inductive thematic analysis. Results: Seventeen review-level studies met the inclusion criteria. Six recurring themes were identified: engagement, participatory co-design, human oversight, equity, ethical governance, and implementation. Across the included reviews, humanities-informed approaches were associated with greater attention to relational engagement, stakeholder participation, transparency, contextual adaptation, and culturally responsive implementation. Evidence supporting intervention effectiveness was strongest in systematic reviews and meta-analyses, whereas findings related to ethics, governance, equity, and implementation were derived primarily from scoping reviews and conceptual syntheses. Conclusions: This review suggests that digital humanities provides a valuable interdisciplinary perspective for informing the design, governance, and implementation of AI-enabled youth mental health interventions. Although the current evidence base remains heterogeneous, integrating humanities-informed approaches may support the development of AI systems that are more ethical, equitable, and developmentally responsive. Future research should evaluate these approaches through empirical implementation studies and emerging generative AI applications. Full article

Urban infrastructure systems increasingly depend on textual reports generated by citizens, inspection teams, maintenance units, emergency platforms, and smart city services. Accurate identification of critical risks in these reports is essential for enhancing urban resilience and enabling timely decision-making. Nevertheless, urban infrastructure risk classification is challenging due to the brevity, noise, domain specificity, and context dependence of these reports. This study introduces RAFE-XAI, a retrieval-augmented feature engineering and explainable natural language processing framework for urban infrastructure risk classification. The term retrieval-augmented is used here in a classification-oriented sense: retrieved reports are used to construct additional features and evidence, not to generate output text as in Retrieval-Augmented Generation systems. The proposed framework incorporates semantic sentence embeddings, retrieval-based evidence, neighborhood-derived label distributions, domain-specific risk indicators, infrastructure asset cues, location indicators, and evidence-based explainability. The framework does not construct an explicit graph, adjacency matrix, graph neural network, or message-passing mechanism. Instead, retrieval is used to derive neighbor label-distribution features, which are combined with semantic embeddings and interpretable keyword, asset, and location indicators. To assess the effectiveness of this approach, UIR-Text, a semi-synthetic urban infrastructure risk dataset with scenario-level group splitting to mitigate data leakage, was constructed. Experimental results on UIR-Text show that fine-tuned DistilBERT achieves the strongest predictive performance, with Macro-F1 scores of 0.8278 for category classification, 0.9120 for binary critical-risk detection, and 0.3379 for four-level severity classification. Among the explainable feature-engineering models, RAFE-XAI with Random Forest achieves the strongest category classification performance, with Accuracy 0.8400, Macro-F1 0.8043, Weighted-F1 0.8444, and MCC 0.8062. These results suggest that fine-tuned transformers provide the highest predictive performance on this benchmark, while RAFE-XAI offers a transparent retrieval-augmented alternative that exposes retrieved evidence, neighbor label distributions, and domain cues. Four-level severity classification remains challenging, even with fine-tuned DistilBERT, indicating the need for richer impact-aware variables. Full article

Sporadic cases of trichinellosis have occurred almost every year in Serbia. The success of trichinellosis control in the country depends on effective communication among medical professionals who discover a case of trichinellosis, the Public Health Service, and the Veterinary Service, in line with the One Health concept. The collaborative approach, supported by a robust reporting and monitoring system, has been essential for identifying infection sources and preventing further spread. However, in March 2023, the National Reference Laboratory for Trichinellosis (NRLT) received reports of hospitalized patients with suspected trichinellosis, signaling the possibility of two outbreaks based on their socio-epidemiological history. The investigation revealed separate trichinellosis outbreaks occurred in Grocka, Belgrade municipality, and Dolovo, Pancevo municipality, with pork identified as the source of infection. Serological tests confirmed the presence of Trichinella-specific antibodies in the patients, including their family members and friends. A total of four patients required hospitalization. In accordance with regulations, veterinary inspectors took dried meat and meat products samples and Trichinella larvae were detected using the digestion method. The entire amount of dried meat and meat products was subsequently destroyed. Multiplex PCR confirmed the identification of Trichinella britovi at the species level in both outbreaks. These findings represent the first documented cases of T. britovi infection in domestic pigs in Serbia. The outbreak data once again highlight that inadequate animal husbandry practices, certain human behaviors, and a lack of awareness about the risks continue to be major contributors to the persistence of trichinellosis in Serbia. Full article

This study aims to assess the impact of the October 2023 Gaza War on water and sanitation systems and propose actionable solutions for effective management. The study adopted a mixed-methods approach (quantitative and qualitative). Primary data were collected through semi-structured interviews, targeted questionnaires, and direct field observations. Secondary data were gathered from reports by international organizations (UNRWA, OCHA, WHO), Palestinian authorities, academic research, in addition to satellite imagery and Geographic Information System (GIS) data for damage assessment. The results showed that the institutional level of emergency preparedness before the war varied significantly. Despite the existence of pre-developed emergency plans (mean score 3.26, relative weight 65.28%, p = 0.0355), there were significant shortcomings in practical staff training (mean score 2.64, relative weight 52.83%, p = 0.006), logistical readiness, and the activation of international partnerships. The study also revealed major challenges faced in providing water and sanitation services during the war, including fuel and resource shortages, safety risks for field teams, weak coordination with security agencies, lack of materials and equipment, insufficient emergency funding, coordination gaps, staff displacement, communication breakdowns, difficulty accessing water sources, area division and blockade, and loss of personnel. Specifically, the inability to access water sources was a critical challenge (mean score 4.30, relative weight 86.04%, p < 0.0001), as was indiscriminate shelling impacting field team safety (mean score 4.19, relative weight 83.77%, p < 0.0001) and lack of materials and equipment (mean score 4.15, relative weight 83.02%, p < 0.0001). Residents reported water availability only every 10–15 days, and over 70% of water and sanitation systems were damaged. While funding proposals were being prepared (mean score 3.26, relative weight 65.28%, p = 0.02), weaknesses in reporting systems and coordination of roles were observed (mean score 2.55, relative weight 50.94%, p < 0.0001). The research contributes to guiding local and international efforts towards reconstruction, enhancing resilience, and ensuring the provision of safe drinking water and adequate sanitation for the affected population, thereby reducing the risks of waterborne diseases and improving public health. Full article

Contemporary cyber threats increasingly employ multi-stage and behaviourally adaptive strategies that challenge static intrusion detection and non-adaptive deception mechanisms. Existing approaches typically treat threat detection, deception deployment, and adversarial attribution as separate functions, limiting timely response and underusing the behavioural evidence generated during attacker interaction. This study develops and evaluates a theory-informed computational and operational framework for autonomous cyber deception. The principal research artefact is a reusable closed-loop architecture rather than a single predictive model: it specifies the interacting components, interfaces, data and control flows, decision rules, and feedback mechanisms that connect detection, deception, telemetry, and attribution. Methodologically, the study follows an engineering design-and-evaluation approach comprising problem and requirement identification from the literature, architectural synthesis, component-level mathematical modelling, prototype implementation, and controlled cyber-range evaluation. In this context, modelling refers to the distinct computational models embedded within the framework: a hybrid detection model combining supervised classification, anomaly detection, and temporal sequence analysis; a Markov Decision Process and reinforcement-learning policy model for selecting and reconfiguring deception actions under engagement, intelligence-gain, resource, and containment objectives; and similarity-based and Bayesian attribution models for estimating MITRE ATT&CK techniques from incomplete behavioural evidence. The component models were developed offline using the NSL-KDD, CICIDS2017, UNSW-NB15, and ToN-IoT datasets, while the integrated prototype was evaluated separately in a controlled enterprise-like cyber range using reconnaissance, brute-force, exploitation, and multi-stage attack scenarios. The reported classification metrics were calculated from the labelled cyber-range evaluation events, not by pooling the four benchmark datasets. On this integrated cyber-range evaluation set, the system achieved 95.4% detection accuracy, 93.6% precision, 94.7% recall, and a 94.1% F1-score, with a mean detection latency of 85 ms. It also achieved 100% honeypot deployment reliability, 92% dynamic reconfiguration success, 88% fingerprinting resistance, and attacker engagement durations of up to 280 s. The attribution component demonstrated end-to-end generation of ATT&CK-aligned technique hypotheses from deception-derived telemetry; however, the present archived evaluation does not support per-technique or baseline-comparative performance claims. These findings show that specialised models and operational services can be coordinated within a unified adaptive defence process, while also identifying the additional class-level and ablation evidence required for rigorous attribution validation. Full article

Vehicle-to-Everything (V2X) communication is an essential building block of intelligent transportation systems, supporting high-data-rate vehicle-to-infrastructure (V2I) services, and ultra-reliable low-latency vehicle-to-vehicle (V2V) communication. However, in dense urban environments, V2X services can be significantly degraded by the presence of severe blockage, fast channel variations, and high levels of interference. Intelligent Reflecting Surfaces (IRSs) can be employed to reconfigure wireless propagation environments to improve V2X communication. However, the joint optimization of transmit power, spectrum reuse, and IRS reflection coefficients is a mixed-integer non-linear problem, which is further complicated by the fast vehicular mobility and time-varying interference in V2X networks. To tackle this challenging problem, this work proposes a scalable and deployable decentralized hierarchical multi-agent deep reinforcement learning (DH-MDRL) framework. The key design principle is the separation of control timescales, whereby each V2V link functions as an autonomous agent that responds to local observations at a fast timescale and determines its transmit power and spectrum reuse decisions, while the IRS controller at the base station (BS), using global network observations, updates the IRS reflection coefficients at a slower timescale. This hierarchical architecture reduces coordination signaling associated with centralized resource allocation while enabling distributed resource allocation. The IRS-assisted V2X network is modeled as a Markov decision process, where the reward design is tailored to optimize the V2I sum data rate while guaranteeing the latency and reliability constraints associated with safety-critical V2V communication. Simulation results show that the proposed DH-MDRL framework outperforms conventional schemes without IRSs and achieves an excellent trade-off between V2V link constraints’ satisfaction probability and V2I link sum data rates compared to centralized resource allocation approaches. Full article

Knowledge graphs (KGs) organize relational facts in a form that supports machine reasoning across many domains. In decentralized Internet of Things (IoT) environments, such graphs can describe interactions among distributed devices and services. However, privacy constraints, intermittent communication, and incomplete observations can leave the resulting graphs sparse and incomplete. This sparsity weakens path availability and makes missing-fact inference difficult. Multi-hop reasoning can recover unobserved links through explicit reasoning paths, but conventional hierarchical reinforcement learning (HRL) approaches may explore inefficiently when only a few valid paths are available. This paper presents attentiON exploratIon of cluster relatioN (ONION), a framework for sparse KG reasoning. ONION introduces a cluster relation augmentation (CRA) module to add cluster relation cues to the action space, together with a relation-level attention (RLA) mechanism to emphasize relations that are relevant to the query. These components guide hierarchical exploration toward more informative paths. ONION is evaluated on four public sparse-KG benchmarks, and the results show competitive performance with the clearest gains on highly sparse datasets. Full article

During the recruitment process, it is possible for CVs to appear well-organized. However, it is not always straightforward to compare them. The same competence may be denoted by different designations, and the levels of competence are not universally employed in the same manner. Natural Language Processing (NLP) methodologies can extract these data points; however, ensuring the consistency of this data across multiple CVs remains a challenge. In a multitude of cases, the comparability of two profiles remains ambiguous. In the present study, an ontological approach is adopted to solve this issue. The concept under discussion is that of the extraction of entities from CVs and their subsequent representation in a more structured form, utilizing RDF and an ontology aligned with ESCO—the multilingual classification of European Skills, Competences, and Occupations. Subsequently, the rules of SHACL are applied to verify the semantic coherence of the data; the validated data are transmitted to a model for classification. At this stage, the dataset becomes smaller, but semantically cleaner, more traceable, and enriched with validation indicators that can be used by the classification model. The proposed system is implemented as a set of microservices. A Spring Boot component coordinates the flow, whilst the Python services, implemented using Python 3.10.12 are responsible for the primary processing stages including extraction, validation and classification. A same-corpus ablation was conducted to separate ontology-guided profile selection from the contribution of the validation-derived quality features. On the same 35,770 filtered CV–job pairs, adding these features increased external benchmark accuracy from 0.794 to 0.809, recall from 0.760 to 0.865, F1-score from 0.749 to 0.786, and ROC-AUC from 0.881 to 0.887. A p-value of 0.00540 paired with a 1.54 effect ratio from McNemar’s test showed a statistically significant paired difference between the two configurations. However, precision decreased from 0.739 to 0.720 while Average Precision compressed from 0.851 down to 0.844. Rather than scaling performance uniformly across the entire evaluation suite, the ontology layer acts as a targeted traceability and semantic refinement filter that contributes information beyond filtered-profile selection alone and produces a metric-dependent change in classifier behaviour at the validation-selected threshold. Full article

The rapid deployment of sixth-generation (6G) Internet of Things (IoT) networks demands lightweight authentication mechanisms that provide low latency, high scalability, and robust security for resource-constrained devices operating in dynamic wireless environments. Conventional authentication approaches based on Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS), blockchain-assisted architectures, and Generic Bootstrapping Architecture (GBA)-based schemes introduce significant computational complexity, communication overhead, synchronization delays, and infrastructure dependencies, limiting their suitability for large-scale edge-assisted IoT environments. This paper proposes Lightweight Timed Efficient Stream Loss-Tolerant Authentication over the Constrained Application Protocol (L-TESLA-CoAP), a lightweight and infrastructure-independent authentication framework that integrates adaptive TESLA delayed-key authentication, CoAP communication, edge-assisted synchronization, replay-aware synchronization, SHA3-HMAC-based symmetric authentication, and rotating pseudonym identities to provide continuous packet-level authentication. The proposed framework was implemented and evaluated using a Python-based simulation environment under constrained 6G IoT communication scenarios with network sizes ranging from 50 to 1000 IoT devices. The comparative evaluation against CoAP, DTLS, TLS, Blockchain-CoAP, and GBA-Hybrid TESLA shows that the proposed framework achieves low authentication latency (approximately 0.8–1.3 s) and low energy consumption (approximately 60–75 mJ) while maintaining packet-loss recovery capability, reduced communication overhead, reduced computation time, low memory consumption, and authentication throughput. Furthermore, the proposed framework provides resilience against replay, packet injection, impersonation, synchronization manipulation, and denial-of-service attacks through adaptive synchronization and delayed key disclosure. These results indicate that L-TESLA-CoAP provides an efficient, scalable, and lightweight authentication solution suitable for next-generation edge-assisted 6G IoT applications. Full article

Background: Ultrafine particles (UFPs) represent a significant occupational health concern in commercial cooking environments, yet comprehensive exposure assessment in pizzerias remains limited despite their global prevalence and unique cooking processes. Understanding UFP exposure patterns and associated health effects in this widespread food service sector is crucial for protecting worker health. Objective: To quantify occupational exposure to airborne UFPs among pizzeria workers across different oven technologies. Methods: A cross-sectional observational study was conducted in 10 pizzerias in the Cagliari metropolitan area (April 2022–March 2024), encompassing wood-fired ovens (WFO, n = 6), electric ovens (EO, n = 3), and mixed systems (BO, n = 1). Ventilation characteristics were documented at each site to evaluate their influence under real-world operating conditions. Personal UFP exposure was measured using DISCmini diffusion size classifiers (10–700 nm range), while qualitative particle characterization employed ELPI+ impaction with SEM-EDS analysis. Results: UFP concentrations varied dramatically by oven type, with median values of 3.18 × 10⁴ particles/cm³ (WFO), 1.29 × 10⁵ particles/cm³ (EO), and 2.55 × 10⁵ particles/cm³ (BO). Seven of ten pizzerias exceeded WHO precautionary limits (20,000 particles/cm³), with electric oven facilities showing concentrations comparable to high-emission industrial operations. Elemental analysis revealed predominantly carbon-based particles with significant iron, aluminum, and silicon content, indicating combined combustion and mechanical abrasion sources. Conclusions: Pizzeria workers experience substantial UFP exposure levels that frequently surpass those in other food service environments and approach levels typically observed in high-exposure industrial workplaces. Electric ovens generate significantly higher UFP levels than wood-fired systems, likely due to ventilation efficiency differences. Full article

This paper presents an evaluation of AI-assisted human–computer interaction for mathematical problem solving within a multimodal mathematical editing environment. The proposed architecture integrates a visual equation editor, voice-based interaction, REST communication services, and externally hosted generative AI large language models (LLMs) to support the creation, interpretation, and solution of mathematical expressions. The study was conducted using the Equation Wizard environment, which provides multimodal mathematical content editing based on both proprietary and standard formula representations, including MathML and LATEX. A conversational AI interaction layer enables users to communicate with selected LLMs using natural language voice commands. The main objective of the study was to determine whether contemporary LLM-based services can reliably support mathematical problem solving within an AI-enhanced equation editing environment. To address this objective, seven contemporary GenAI LLMs were evaluated using a benchmark consisting of representative high school mathematics problems covering algebra, limits, trigonometry, logarithms, inequalities, and parameterized expressions. The evaluation focused on mathematical correctness, output interpretability and visualization quality, response latency, and compliance with mathematical encoding formats within the complete interaction workflow. The study also compares representative model families with respect to correctness, syntactic compliance, and responsiveness within the complete interaction workflow. Unlike conventional LLM benchmarks that assess models in isolation, this work evaluates end-to-end AI-assisted mathematical interaction involving the editor, communication infrastructure, and language models. The study demonstrates a practical approach to assessing the usefulness of multimodal AI-enhanced mathematical problem solving in realistic usage scenarios. The results show that, within the evaluated interaction workflow, current LLMs achieve high levels of mathematical problem-solving performance, although substantial differences were observed in response latency and output-format compliance. An important finding is the discrepancy between the models’ strong semantic understanding of custom-encoded mathematical input and their weaker ability to generate syntactically compliant encoded outputs, highlighting a key challenge in the integration of LLMs with structured mathematical software environments. Full article