Search Results (17,792)

Mobile Edge Computing (MEC) is a key enabler of 5G/6G services, but multi-base-station deployment enlarges the attack surface and motivates edge-native intrusion detection systems (IDSs). Existing MEC-based IDSs are mainly single-node or centralized, which struggle with heterogeneous traffic across next-generation Node Bs (gNBs) and incur latency and network load due to data aggregation. To address these limitations, this paper proposes a Column-Wise Autoencoder Ensemble (CW-AE) distributed learning framework for multi-MEC environments. Each MEC node trains column-wise autoencoder encoders locally to extract compact latent features, and a master MEC trains a stacking-based meta-classifier using concatenated latent features, avoiding raw traffic transfer and parameter averaging. By preserving node-specific behavior while integrating heterogeneous features, CW-AE improves detection performance and reduces communication overhead. Using the real-world 5G-NIDD dataset collected from two physical 5G base stations, we compare local single-node, centralized, and CW-AE-based distributed learning. The results show that CW-AE achieves superior detection capability and network efficiency, making it suitable for scalable edge IDS deployments. Full article

Large-scale integration of electric vehicle charging stations into the energy distribution network introduces highly variable power demands leading to additional voltage drops, increase in power losses, and quality degradation. Conventional mitigation strategies, including reactive power control only and multi-loop dq-axis-based controllers, often suffer from high computational complexity and limited flexibility for simultaneous active and reactive power control. This study presents a delay-modulated pulse width modulation control scheme for coordinated active and reactive power control in an energy distribution network with high penetration of electric vehicle charging load that are both time-varying and site-shifting in nature. The scheme uses a unified system comprising a solar photovoltaic array, battery storage system and a distribution STATCOM system. In this scheme, the control of active and reactive power is directly incorporated in the PWM pulse generation process by adding an adjustable delay parameter that controls the phase shift between the inverter current and the grid voltage. The proposed scheme is validated using a representative distribution feeder supplying the electric vehicle charging loads. The result illustrates that the feeder receiving end bus voltage drop is about 35% lower, the active power losses are about 40% lower, and the total harmonic distortion is at about 3%, which is within the IEEE 519 limit recommendations. Thus, the proposed control scheme is seen to be effective and computationally efficient, providing a scalable solution for real-time voltage regulation and power loss reduction. Full article

Adolescents increasingly engage with digital technologies, yet understanding patterns of smartphone use and fostering reflective awareness remain challenging. Traditional assessments in clinical or school settings may limit participation and self-reflection. This study evaluated the feasibility and impact of a Computer-Assisted Web Interviewing (CAWI) approach to monitor smartphone use, provide immediate individualized feedback, and support peer-mediated dissemination in a public science engagement context. Across three editions of the European Researchers’ Night in Rome (2023–2025), 807 adolescents aged 10–19 completed the SAS-SV questionnaire via on-site tablets or personal devices using QR codes. Smartphone use was categorized into Low Involvement, At-Risk, or Problematic. Participants were encouraged to share the survey link with peers, enabling snowball-mediated recruitment. Participant acceptance was assessed through the Net Promoter Score (NPS). Snowball participation accounted for the majority of responses, highlighting the effectiveness of peer-mediated diffusion. SAS-SV categorization indicated 46% Low Involvement, 39% At-Risk, and 15% Problematic use, with minimal gender differences. NPS values ranged from +69 to +79, with snowball participants reporting slightly higher satisfaction than on-site attendees. These results underscore high engagement, perceived value, and the role of peer networks in promoting reflective digital behavior. Integrating CAWI assessment, immediate feedback, and peer-mediated diffusion created a socially situated environment supporting self-reflection and voluntary dissemination. Peer networks extended both the temporal and social reach of the initiative beyond the public event, demonstrating a scalable and non-stigmatizing model. CAWI-based monitoring combined with feedback and peer-driven diffusion is feasible and effective for adolescent digital wellbeing interventions. This approach fosters reflective digital citizenship, supports self-awareness, and leverages social networks to enhance the reach and impact of youth-centered health promotion initiatives. Full article

Rapid, state-led urbanization in China often generates socio-spatial vulnerabilities, leaving interstitial “waiting lands” in a state of regulatory and ecological limbo. This paper investigates “nomadic gardens”—spontaneous, resident-led cultivation in Jinan—as a bottom-up strategy for adaptive capacity. Using a mixed-methods approach involving site typologies and community surveys (n = 100), we identify eight distinct garden forms that function as socio-ecological buffers, mitigating the risks of social isolation and psychological distress among elderly residents. Findings reveal a significant resilience gap caused by rigid land-use policies that prioritize ornamental aesthetics over functional productivity. We propose an Adaptive Urbanism framework that utilizes modular design and transitional governance to transform these precarious spaces into managed resilience assets. By shifting the planning focus from enforcement to risk-responsive design, this research provides a scalable model for sustainable urban risk management in rapidly transforming global cities. Full article

Background: Although pneumococcal conjugate vaccines (PCVs) have substantially reduced invasive pneumococcal disease, the emergence of non-vaccine serotypes and antimicrobial-resistant strains has driven the development of higher-valency vaccines. To support functional immune evaluation of these vaccines, we developed and validated a sixplexed opsonophagocytic killing assay (OPA) covering 24 pneumococcal serotypes. Methods: Eight additional serotypes, beyond the 16 included in the conventional fourplex OPA, were generated through stepwise natural mutation under increasing concentrations of ciprofloxacin or doxycycline. Assay conditions were optimized by evaluating multiple effector-to-target (E:T) ratios and baby rabbit complement (BRC) concentrations to minimize non-specific killing (NSK). Validation assessed (1) specificity using inhibition OPA with homologous and heterologous polysaccharides, (2) accuracy by comparison with the single-serotype OPA (SOPA), and (3) precision across five independent experiments using the coefficient of variation (CV). Results: An E:T ratio of 200:1 combined with 10% BRC consistently maintained NSK below 30% across all assay sets. High serotype specificity was demonstrated by near-complete inhibition following homologous polysaccharide adsorption for all serotypes except serotypes 4 and 8, which exhibited very low opsonic indices. Results from the sixplexed OPA showed strong concordance with SOPA, and overall assay precision was acceptable, with CVs generally below 30% when serotypes with very low opsonic activity were excluded. Conclusions: The sixplexed OPA expands functional antibody assessment from 16 to 24 serotypes within four assay sets, providing an efficient and scalable platform for immunogenicity evaluation of current and next-generation high-valency pneumococcal vaccines. Full article

High-temperature carburization (HTC, 950–1050 °C) has emerged as a pivotal low-carbon, energy-efficient manufacturing technology for gear steels, accelerating carbon diffusion for reducing processing cycles by over 60% while achieving significant energy savings and emission reductions. However, the inherent contradiction between HTC efficiency and microstructural stability, specifically austenite grain coarsening, severely degrades mechanical properties (e.g., strength, toughness, fatigue resistance) and limits widespread application. This review systematically synthesizes recent advances in austenite grain size regulation during HTC of gear steels, focusing on the core scientific framework of “grain coarsening mechanism—regulation strategy—performance enhancement”. It elaborates on thermodynamic and kinetic mechanisms of austenite grain growth, ripening behavior of microalloying precipitates (Nb(C,N), Ti(C,N), AlN, etc.), and their synergistic grain-refining effects. Comprehensive coverage of regulatory strategies (microalloying design, pretreatment technologies, process optimization, and integrated regulation) and characterization techniques is provided, along with a quantitative correlation between grain size, microstructure, and surface performance (wear resistance, corrosion resistance, and fatigue life). Numerical simulation and predictive models (empirical, theoretical, multiphysics coupling, machine learning-based) are critically analyzed, and current challenges (temperature-grain stability trade-off, multifactor synergy understanding, industrial scalability) and future research directions (advanced microalloying systems, intelligent process optimization, cross-scale modeling, green technology integration) are proposed. This review aims to provide theoretical guidance and technical support for optimizing the HTC performance of gear steels, catering to the demands of high-power-density transmission systems in automotive, aerospace, and heavy machinery industries. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

In climate adaptation plans, national sustainability strategies, and agency-level resilience frameworks, planetary limits are routinely acknowledged, yet proposed responses continue to center on expansion, replication, and scalability. This paper argues that this tension is not merely political or technical but grammatical. It reflects the dominance of the grammar of scale—a patterned way of organizing, evaluating, and legitimizing sustainability action through expansion, metrics, piloting, and exit. While indispensable in many contexts, scale increasingly struggles to secure durable ecological responsibility amid irreversibility, uneven exposure, and intergenerational harm. The paper advances a framework of plural sustainability grammars to diagnose this mismatch. In addition to scale, it identifies six alternative grammars—attachment, settlement, sufficiency, inheritance, exposure, and refusal—that already circulate, often implicitly, within sustainability discourse. Each grammar foregrounds dimensions of responsibility that scalability tends to background, including permanence, restraint, cumulative consequence, and ethical limits. The paper traces these grammars through climate adaptation planning frameworks across governance levels, showing how plural grammars are prominent in problem framing and diagnosis but are progressively narrowed as plans move toward implementation, monitoring, and accountability, where scale becomes dominant. The paper concludes by reflecting on the implications of this grammatical narrowing for practitioners, policymakers, and scholars concerned with adaptation, justice, and the governance of sustainability under planetary limits. Full article

Topology optimization is a powerful methodology for designing lightweight, economical, and efficient structures. However, traditional approaches often face challenges such as numerical instabilities and high computational costs, limiting their practical applicability. Recently, radial basis function (RBF)-based and neural network-based methods have emerged as promising alternatives through the reparameterization of the density field. Despite their potential, these methods typically rely on isotropic basis functions or static feature encodings, which limit their ability to capture fine-scale structural details, particularly in high-aspect-ratio features such as slender bar-like members and in geometrically symmetric structural patterns. To address this research gap, this paper introduces a novel Geometric-enhanced Neural Network (GeNN) for topology optimization based on Anisotropic Radial Basis Functions (ARBFs). By embedding ARBFs into the neural network framework, the proposed method provides a more geometrically informed density representation and inherently suppresses checkerboard patterns without additional filtering techniques. The proposed GeNN framework is thoroughly validated on benchmark problems, including several representative symmetric structural layouts, demonstrating improved computational efficiency compared to traditional methods and other neural-network-based topology optimization methods. In addition, the proposed method demonstrates strong scalability across various optimization problems. Notably, GeNN successfully optimized a 256 m-long bridge involving millions of degrees of freedom within ten minutes on a standard personal computer. This advancement demonstrates the practical potential of the proposed method for large-scale civil engineering applications. Full article

Accurate tree species mapping is critical for forest inventory, biodiversity assessment, and ecosystem management. In mountainous regions, terrain-induced radiometric non-stationarity and limited field access often produce scarce, clustered, and environmentally biased samples, limiting model generalization. To address this issue, this study proposes a terrain-aware self-supervised representation learning framework for tree species classification under small-sample conditions. The framework integrates terrain information into representation learning and adopts a hybrid contrastive–generative self-supervised strategy to learn discriminative and terrain-robust features from large volumes of unlabeled multi-source remote sensing data. These learned representations are subsequently combined with limited field samples to produce regional-scale tree species maps. Experiments conducted across Yunnan Province, China, using Sentinel-1, Sentinel-2 and Landsat time-series data show that the proposed framework substantially improvesa class separability and classification robustness in complex mountainous environments. The framework achieves an overall accuracy of 75.8%, significantly outperforming conventional feature engineering (38.3–40.6%) and supervised deep learning models (37.3–47.8%). Species with relatively homogeneous structure and strong ecological niche dependence can be accurately mapped with limited training samples, whereas structurally complex forest communities require broader environmental sample coverage. Overall, the results highlight the potential of terrain-aware self-supervised representation learning as a scalable and data-efficient paradigm for forest mapping in mountainous and environmentally heterogeneous regions. Full article

This paper presents the concept of a 2D m × n waveguide-based power combiner (PC) that is scalable with respect to the operating frequency band and number of input ports. To our knowledge, this work reports the first planar (2D) power combiner, where the input waveguide ports are distributed in two spatial dimensions to form an array, rather than arranged along a single linear (1D) axis as in conventional corporate or cascaded waveguide combiners. The novelty of the approach relies on using H-plane rectangular waveguide T-junctions and low-loss polarization twisters in between vertically stacked T-junctions to facilitate scalability. The work is motivated by the aim to coherently combine the output power of multiple modified uni-traveling carrier (MUTC) terahertz (THz) waveguide photodiodes (PDs) in a 2D array configuration. In the manuscript, the design of a 2 × 2 planar waveguide power combiner for the WR3 band (220–320 GHz) is reported, and it is also shown that this block can be further extended to m × n input ports. Full-wave numerical analysis of the proposed 2 × 2 power combiner shows a return loss of 11 dB at the output port and an average transmission coefficient of about −6.5 dB, i.e., an overall power combining efficiency of ~90%. Furthermore, to enable 2D photodiode array integration, the manuscript presents a new slot-bow tie antenna integrated MUTC photodiode for radiating the optically generated THz power from each PD vertically into the rectangular waveguide. The simulation results of reflection loss and insertion loss for the slot bow-tie antenna are shown to be better than 10 dB and 1.4 dB over the full WR3 band, respectively. To prove scalability of the power combiner concept w.r.t. the number of input ports, a 2 × 4 power combiner is also analyzed. Results reveal a return loss better than 10 dB from 225 to 318 GHz and a transmission coefficient of approximately −9.7 dB at 300 GHz, i.e., a power combining efficiency of ~85%. Full article

Malaria remains a major global health burden, with an estimated 282 million cases and 610,000 deaths reported in 2024, disproportionately affecting children under five years of age and pregnant women in sub-Saharan Africa. Although antimalarial drugs are highly effective at clearing infections, their reliance on timely diagnosis and treatment limits their scalability as a population-wide control strategy. Vaccines therefore represent a critical tool for reducing malaria-associated morbidity and mortality, as well as interrupting parasite transmission, by inducing durable protective immunity. However, the complex lifecycle of Plasmodium parasites poses significant challenges for vaccine development, including the identification of protective antigens and optimal vaccine formulations. In this review, we summarize current vaccine strategies and discuss their key limitations. We also highlight emerging opportunities for possible avenues for future research and development. Full article

Text-based automatic compliance checking (ACC) employs natural language processing technologies to scrutinize a corporation’s business documents, ensuring adherence to related normative texts. The current methods fall into two primary categories: symbol-based and embedding-based approaches. Symbol-based methods, noted for their accuracy and transparent processing, suffer from limited versatility. Conversely, embedding-based methods operate independently of expert knowledge yet often yield challenging-to-interpret results and require substantial volumes of annotated data. While both types of methods exhibit advantages in different aspects, the current research fails to combine these advantages effectively. Therefore, the existing methods fail to balance interpretability, generalization ability, and accuracy, which are key requirements for practical compliance systems. To address this problem, we introduce a novel approach termed the Dynamic Prompt-based Logic-Aided Compliance Checker (DPLACC), which is grounded in the prompt learning framework. This method initially parses target texts, transforming the results into first-order logical expressions. It subsequently retrieves pertinent knowledge from a knowledge graph, converting the knowledge into analogous first-order logical expressions. These expressions are then encoded into a global semantic vector via a pre-trained first-order logistic encoder. Ultimately, the semantics of expressions and initial texts are amalgamated within the prompt template, facilitating the logical knowledge enhancement of model reasoning. Experiments on Chinese and English datasets demonstrate that DPLACC comprehensively outperforms existing methods based solely on symbols or embeddings in terms of accuracy, precision, recall, and F1 score and significantly surpasses current mainstream large language models. Furthermore, DPLACC exhibits enhanced interpretability and reduced data dependence, maintaining 70% checking accuracy with as few as ten training samples. This capability allows DPLACC to be rapidly deployed in data-scarce real-world scenarios with minimal annotation overhead, thus offering a practical pathway toward the scalable implementation of compliance inspection systems. Full article

Background: Parents of school-age children with neurodevelopmental and conduct difficulties face elevated stress, reduced self-efficacy and relational strain, yet evidence for scalable, school-embedded support remains limited. Drawing on mentalization theory—which emphasises parents’ capacity to understand behaviour in terms of underlying mental states—this mixed-methods study evaluated a weekly parent learning group integrating psychoeducation, mentalization-based practice and peer support, delivered within an alternative provision school. Methods: A group of twelve parents who attended at least six sessions completed retrospective pretest–posttest questionnaires assessing parental reflective functioning (PRFQ) and parenting self-efficacy (PSOC). Semi-structured interviews explored parents’ subjective experiences and perceived changes in parent–child interactions and parent–school relationships. Quantitative outcomes were analysed using paired t-tests and effect sizes; qualitative data underwent reflexive thematic analysis. Results: Quantitative analyses revealed statistically significant improvements in parental reflective functioning and self-efficacy. Pre-mentalizing scores decreased substantially (d = 1.34), indicating reductions in non-mentalizing, while interest and curiosity about children’s mental states increased markedly (d = 1.83). Parenting self-efficacy improved significantly (d = 1.61). Although a reduction in excessive certainty about mental states approached significance (d = 0.63, p = 0.053), trends suggested greater epistemic balance. Qualitative analysis identified six themes elucidating mechanisms of change, including enhanced mentalizing capacity, reduced parental stress, transformed parent–child interactions and facilitation style as a critical active ingredient. Integration of findings suggests that psychoeducational content provided conceptual grounding for understanding behaviour, facilitator modelling scaffolded reflective practice, and relational safety within the group enabled authentic engagement with challenging experiences. Conclusions: These preliminary findings indicate that a school-based parent learning group combining psychoeducation, mentalization-based practice and peer support is feasible and associated with meaningful improvements in parental reflective functioning and self-efficacy. Parent narratives of transformed relational practices and shifts from reactive to reflective engagement echo broader literature demonstrating that group-delivered mentalization-oriented programmes can enhance reflective capacities and caregiving quality in diverse family contexts. The school setting may extend the reach of such interventions to families not engaged with clinical services and support collaborative parent–school partnerships. Future research should employ larger, controlled designs, incorporate observational and child outcome measures, and explore scalability across educational contexts. Full article

Background: Automated discharge instructions are increasingly used to support post-discharge communication, patient education, and nursing follow-up, yet the current state remains unidentified. This systematic review explores the types of automated discharge instructions used and their effectiveness in enhancing patient engagement and reducing readmission, emergency department visits and reoperation rates. Methods: A systematic search was conducted on 15 April 2025, using Embase, PubMed, Scopus, Web of Science, and CINAHL, following PRISMA guidelines. Inclusion criteria required peer-reviewed original research evaluating the utilization of automated patient discharge instructions following hospital admission or surgical stay. Exclusion criteria included correspondence, reviews, educational materials, not peer-reviewed, retracted reports, not retrievable, and no English translation. Risk of bias was assessed independently using NIH, JBI, ROB-2, and ROBINS-I tools. Two investigators independently conducted the screening, extraction, and synthesis of results using Endnote and Microsoft Excel. Results: Of the 1252 records identified, 13 studies were selected for analysis. There was a total of 34,386 patients across a diverse range of healthcare settings and clinical contexts. The average sample size per study was approximately 4912, with study samples ranging from 16 to 13,188 patients. The modalities of discharge instructions included automated phone calls (23.1%) and/or text messages (53.8%), as well as printed out auto-generated summaries (15.4%). Patient engagement was generally high, with automated phone calls showing the most consistent interaction, with completion rates ranging from 44% to 56%, often prompting clinical follow-up. SMS tools demonstrated strong scalability and response rates up to 87%. Two studies reported on hospital readmission outcomes and only a single study reported on emergency department revisit rates, while none assessed reoperation outcomes. Among those reporting readmission, automated phone calls and SMS were associated with lower or proxy-reduced readmission rates. Included studies had low to moderate levels of bias. Conclusions: While evidence on clinical outcomes such as readmissions, emergency department revisits, and reoperations remains limited and inconclusive, automated discharge tools—particularly phone calls and SMS—consistently demonstrated high patient engagement. Automated discharge tools show promise for supporting transitional care, discharge education, and post-discharge monitoring, highlighting the future role of automated tools in nursing workflows to support follow-up, escalation, and continuity of care. Full article