Search Results (15,534)

The simulation of modern wireless communication circuits remains challenging because of the coexistence of nonlinear behavior, heterogeneous subsystems, and widely separated time scales. This review presents a structured overview of advanced numerical methods for solving multitime partial differential–algebraic equations (MPDAEs) arising in circuit-level modeling of RF and microwave systems. Compared with previous survey papers, the main contribution of this work is to organize the literature according to the underlying numerical strategy, distinguishing purely time-domain, hybrid time–frequency, multidimensional frequency-domain, and circuit-block partitioning approaches. The reviewed methods show that multitime formulations can deliver substantial computational gains over conventional simulation techniques, particularly for multirate and multiscale circuits. Time-domain techniques are generally more robust for strongly nonlinear regimes, whereas frequency-domain and hybrid methods are often more efficient when the waveform can be represented with a limited number of harmonics. Circuit-block partitioning further improves efficiency by exploiting active and latent variables, but the computational complexity of MPDAE methods increases rapidly with the number of time scales, and their applicability becomes more limited for aperiodic or highly general multirate excitations. Overall, this review highlights both the strengths and the practical limitations of current MPDAE-based numerical approaches and identifies open challenges for future research. Full article

Background: Breastfeeding enhances infant and maternal health, but global breastfeeding rates remain suboptimal. Virtual reality (VR) emerges as a promising tool for breastfeeding education. The objective of this review was to assess the effectiveness of VR-based interventions on breastfeeding outcomes in pregnant and postpartum women. Methods: PubMed, Embase, Web of Science, Scopus, and CENTRAL were searched until 10 January 2026, for randomized controlled trials (RCTs) and quasi-experimental studies comparing VR-based interventions (immersive simulations, 360° videos, or head-mounted displays) with standard care or non-VR comparators in pregnant or postpartum women. Primary outcomes included breastfeeding self-efficacy, motivation, and breastfeeding technique (LATCH score). Secondary outcomes included exclusive breastfeeding rates, milk production, and maternal anxiety. Risk of bias was assessed using the RoB 2.0 and ROBINS-I tools for RCTs and non-RCTs, respectively. A random-effects meta-analysis was conducted, with results reported as mean differences (MD) or risk ratios (RR), along with 95% confidence intervals (CIs). Certainty of the evidence was assessed using the GRADE approach. Results: Five studies (4 RCTs and 1 quasi-experimental; n = 344) were included. VR improved prenatal breastfeeding self-efficacy (2 studies, MD: 13.93; 95% CI: 10.96–16.90), motivation (1 study, MD: 2.88; 95% CI: 1.66–4.10), and LATCH score (1 study, MD: 1.72; 95% CI: 1.37–2.07), and reduced time to breastfeeding initiation (1 study, MD: −22.4 min; 95% CI: −29 to −15.9), the certainty of evidence was low to very low for these outcomes. No significant effects were observed for postnatal self-efficacy, exclusive breastfeeding, or maternal anxiety. Formal assessment of publication bias could not be done. The small sample sizes for most outcomes, heterogeneity, the open-label nature of the trials, and the subjective nature of the outcomes should be considered when interpreting these results. Conclusions: VR-based interventions may improve process outcomes, such as prenatal breastfeeding self-efficacy, motivation, breastfeeding technique, and early breastfeeding initiation; the certainty of evidence is low to very low. Evidence for clinically important outcomes, including exclusive breastfeeding and maternal anxiety, remains inconsistent. Larger, well-designed RCTs are warranted before these interventions can be considered in routine practice. Full article

Metallic glasses (MGs) exhibit excellent mechanical properties, yet their poor tensile ductility greatly limits their practical applications as structural and functional materials. Shear banding is a typical localized rheological deformation behavior inherent to amorphous materials, which stems from heterogeneous atomic rearrangement and regional viscosity fluctuations in the glassy matrix, and fundamentally determines the macroscopic mechanical properties of MGs and their composites. This review discusses the relationship between typical toughening strategies and shear banding behavior, and proposes that deliberate suppression of shear band (SB) initiation or deceleration of their rapid propagation can effectively promote distributed plastic flow. In this review, nanosizing and metamaterial strategies are shown to hinder the formation of mature SBs, while metallic glass matrix composites (MGMCs), nanoglasses (NGs), notched design, and rejuvenation treatments contribute to restraining SB propagation. Current approaches have successfully regulated shear banding behavior and thereby realized appreciable tensile ductility in MGs. Novel design and fabrication techniques for amorphous alloys, which suppress SB initiation and retard SB propagation to achieve homogeneous plastic flow, open up new avenues for realizing controllable plasticity of MGs. Full article

The growing complexity of web attacks highlights the need for adaptive, intelligent defense systems that overcome the limitations of traditional rule-based web security. Thus, the architecture proposed in this paper integrates data-driven deep learning with deterministic rule-based logic to enhance real-time detection accuracy and adaptability in dynamic web threat environments. The practical integration of a deep learning-based Gated Recurrent Unit (GRU) model with ModSecurity, an open-source Web Application Firewall (WAF), is employed to improve the detection and classification of malicious HTTP requests. The model, pre-trained on a large labeled up-to-date dataset of web traffic and attack types collected post-2020, is designed to classify requests in real-time, identifying both whether a request is malicious and the corresponding attack category (e.g., SQL Injection, Cross-Site Scripting, Command Injection). We demonstrate how the trained model is incorporated into ModSecurity’s inspection pipeline, allowing it to analyze real-time web traffic alongside traditional rule-based inspection. This hybrid approach aims to significantly reduce false positives and improve adaptability to new attack patterns. Evaluation metrics such as accuracy, receiver operating characteristic (ROC), area under the curve (AUC), Principal Component Analysis (PCA), confusion matrix, and t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization are discussed, along with performance considerations and implementation architecture. The integration presents a robust framework for ML-improved intelligent web security defense. Full article

This paper examines how historic wooden-built environments and open-air museums can function as pedagogical settings for craft education. Drawing on teaching experiences from higher education in Norway, it analyses how students develop knowledge through guided engagement with tools, materials, and traditional practices in situated learning environments. Two teaching cases, spoon carving in a museum workshop and the investigation of a historic log-built structure, are presented as pedagogical designs. The analysis focuses on how learning is structured and develops through relational and responsive engagement with materials, tools, and professional guidance, rather than solely on learning outcomes. The cases demonstrate how teaching can be organised to support the development of embodied and practice-based knowledge. The paper develops a theoretical framework grounded in 4E cognition (embodied, embedded, extended, and enactive cognition) and Tim Ingold’s concepts of meshwork and wayfaring. These perspectives are applied as analytical tools to examine how learning emerges through action, feedback, and iterative engagement within specific learning environments. Historic workshops, tools, and buildings are approached as pedagogical resources that shape the conditions for learning. While such environments carry historical and material depth, the focus here is on how they structure students’ engagement and influence learning processes in practice. The paper argues that craft pedagogy involves the design of learning situations where material engagement, reflection, and professional guidance are integrated. It proposes an understanding of learning as a situated and relational practice, in which knowledge develops through participation in practice rather than through transmission alone. Full article

Conventional signature-based defenses no longer protect the heterogeneous, large-scale infrastructures that the Internet of Things (IoT) now constitutes. Large language models (LLMs) and agentic artificial intelligence (AI)—systems that autonomously perceive, reason, plan, and act—open a path to self-defending IoT ecosystems, but the integrating literature remains fragmented. Within the IEEE Xplore, ACM Digital Library, and MDPI literature, this survey is, to the best of our knowledge, among the first systematic reviews of agentic AI and LLM-driven approaches for autonomous IoT cybersecurity. Following a PRISMA 2020 protocol, we analyze 153 peer-reviewed studies published between 2020 and 2026 in IEEE Xplore, the ACM Digital Library, and MDPI journals. We organize the corpus along a four-pillar taxonomy: agent architecture (single- vs. multi-agent), reasoning strategy (chain-of-thought, ReAct, plan-and-solve, tool use), action scope (detection, response, threat hunting, vulnerability discovery, deception), and deployment topology (edge, fog, cloud). We synthesize four flagship application domains, consolidate datasets and benchmarks, and analyze open challenges including hallucination, prompt-injection robustness, explainability, privacy, latency, and governance. A 2026 research roadmap identifies federated agentic learning, verifiable autonomous reasoning, trustworthy multi-agent collaboration, and resource-hardened edge agents as high-priority directions. A companion reproducibility kit—prompt templates, reference single- and multi-agent loops, and an Edge-IIoTset-style evaluation harness, released as illustrative scaffolding rather than a validated framework—is released publicly and archived on Zenodo (DOI 10.5281/zenodo.20726552). Full article

This study investigated undergraduates’ conceptualization of systems thinking (ST). An open-ended question was administered pre- and post-course. Pre-test findings revealed limited conceptualization, with most students unable to articulate core ST attributes. Post-course responses showed reasonable improvement, with seven key attributes—interconnectedness, feedback, causality, systems boundary, mapping, emergent behaviour, and synthesis—emerging to varying extents in their responses. While nearly all students indicated interconnectedness and mapping, fewer mentioned feedback and systems boundary, indicating these as higher-order cognitive skills. A continuum was also developed to categorize students’ conceptualization from inadequate to canonical; this also indicated that only a few students demonstrated engagement with the key attributes of ST. Novel analytical approaches such as attributes prevalence tables, attributes continuum, and evolution of threshold concepts have contributed to different modes for exploring ST in the responses. Findings underscore the complexity of ST and the challenges in fostering holistic conceptualization. Overall, the study highlights a nuanced engagement with the attributes of ST from the intervention and suggests that further work is necessary to better foster these among the students. Full article

This study examines the long- and short-run effects of sustainable development, economic growth, energy consumption, urbanization, investment and trade openness on Carbon Dioxide Emissions (CO₂) in the GCC countries utilizing the PMG-ARDL approach by including the data spanning from 2000 to 2022. In the short -run, the sustainable development index demonstrates a positive and substantial impact while it exhibits adverse long-run impact on CO₂ emission. The study also indicates a U-shaped correlation between economic growth and emissions, contrasting with the conventional Environmental Kuznets Curve (EKC) where economic growth at lower income levels often leads to a reduction in emissions; however, income increases beyond around USD 29,942 per capita correlate with higher emissions. Besides, energy use is identified as the primary factor influencing emissions, reflecting global patterns that indicate greater energy usage, particularly from fossil fuels directly boosts emissions. Moreover, the urbanization intensifies this problem, resulting in higher energy demand and greater emissions. Additionally, the study finds that gross capital formation and investments in infrastructure contribute to emissions in the short run, though these effects diminish over time. Our results are robust as it similar to the outcomes obtained from dynamic panel-data System GMM. The GCC policymakers must utilize the sustainable development framework to legally mandate national planning towards low-carbon paths while balancing for short-term transition costs with significant long-run emission reductions. This necessitates the implementation of market-oriented carbon pricing to address the post-threshold U-shaped emissions rebound, the systematic elimination of fossil fuel subsidies to promote renewable energy adoption, and the enforcement of sustainable development regulations to mitigate urbanization pressures. Full article

Coordinating robot swarms requires jointly solving the interdependent Multi-Robot Task Allocation (MRTA) and Multi-Agent Path Finding (MAPF) problems under strict time and communication constraints, yet most existing methods rely on centralized planning or expose agents’ exact positions. In this study, a fully decentralized algorithm is proposed in which each agent estimates the positions and intended plans of others from broadcast bid values rather than shared coordinates, anticipating conflicts at intersections before moving and dynamically altering its movement or task assignment when it predicts it cannot reach its task in time. The method combines the Priority Inheritance with Backtracking (PIBT) algorithm for collision-free navigation with a novel Interval Type-3 Fuzzy Logic (IT3FL) mechanism for conflict resolution and congestion-aware rerouting. The approach was evaluated across seven benchmark environments against the centralized methods Enhanced Conflict-Based Search (ECBS) and ECBS with Task Allocation (ECBS-TA) and the Consensus-Based Auction Algorithm (CBAA). It reduced path cost by up to 7.10% relative to ECBS in open environments, while centralized methods remained superior in complex corridor-based maps. In the most demanding constrained scenario, it reduced solution cost by up to 47.03% and improved task completion by 35% over CBAA, demonstrating a robust, scalable decentralized alternative. Full article

This paper presents GenPluSSS, a plugin that adds the visualization of homogeneous and heterogeneous, optically thick, translucent materials on the Blender 3D modeling tool. The working principle of this plugin is based on the GenSSS method, which combines Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based subsurface scattering representation. The proposed plugin has been implemented using the Mitsuba renderer, an open-source rendering system, and has been validated on measured subsurface scattering datasets. Experimental results demonstrate that the proposed plugin visualizes homogeneous and heterogeneous subsurface scattering effects accurately with compact data representation while maintaining computational efficiency and achieving competitive rendering times compared to dipole-based and SVD-based approaches. In addition, conceptual and quantitative comparisons with recent neural subsurface scattering methods are presented in terms of rendering speed, peak memory usage, material support, and hardware dependency. The proposed framework brings measured subsurface scattering methods into practical rendering workflows within open-source content creation environments. Full article

Conventional evaluations of indoor environmental quality (IEQ) in office spaces are typically disproportionately influenced by expert experience, often overlooking the cognitive gap between decision makers (experts) and users (employees). To quantify and explain this discrepancy, this study develops a comprehensive evaluation framework including 20 IEQ indicators, grounded in Maslow’s hierarchy of needs. Using the Shenzhen Science Park as a case study, evaluation data were collected from 13 experts and 432 employees. The Analytic Hierarchy Process (AHP) and the Kano model were applied to calculate expert weights and employees’ nonlinear sensitivities, respectively, followed by the construction of an optimization matrix via Importance–Performance Analysis (IPA). The results reveal a notable cognitive gap: experts prioritize foundational physical elements regarding spatial technology, whereas employees place greater emphasis on factors such as privacy protection and flexible layouts. Both groups concur that “noise interference” and “lack of privacy” are the primary shortcomings of open-plan offices. Prospective assessments indicate that embodied AI-enabled robots currently remain in a “early adoption phase,” with employees showing no functional dependency on them. This study confirms that merely improving building physical performance does not proportionally translate to increased employee satisfaction. Spatial optimization should adopt a human-centric approach, emphasizing acoustic control and the reconfiguration of privacy boundaries to enhance the scientific allocation of resources. Full article

Globally, Trichogramma Westwood (Hymenoptera: Trichogrammatidae) is known as the most effective biological control agent due to its ability to parasitize insect pest eggs. However, identifying an appropriate host is vital for Trichogramma to prosper. Therefore, this study delves into the complex role of semiochemicals in shaping the host-seeking behavior of Trichogramma parasitoids, with a particular focus on their responses to both plant-derived and host-derived cues. The mechanism of semiochemical reception in Trichogramma wasps relies on a highly specialized, sensitive olfactory and gustatory system to locate host eggs and mates. Semiochemicals, which mediate ecological interactions, have been identified as pivotal in influencing the parasitic efficiency of Trichogramma species. Trichogramma’s host-seeking behavior is influenced not solely by ovipositional cues but also by the intrinsic physical attributes of Lepidopteran hosts, such as the scales on the wings and abdomen, which emit semiochemicals capable of eliciting positive chemotactic responses, thereby guiding parasitoids toward optimal sites for oviposition. Furthermore, the interplay between insect-derived and plant-derived chemical cues exhibits a synergistic effect, collectively enhancing the chemotactic attraction of Trichogramma, thereby fine-tuning its host-seeking behavior with greater precision and specificity. This study further underscores Trichogramma’s innate behavioral ability to discriminate between host eggs of varying developmental stages, facilitating the precise identification and selection of the most suitable host for parasitization. Age and experience both make Trichogramma more selective of hosts, but younger parasitoids may take a broader approach to host selection due to their greater life expectancy. Furthermore, the removal of these cues affects their host localization and learning abilities. Associative learning enables Trichogramma to exhibit flexible behaviors, providing them with a selective advantage; allows them to explore various hosts; and reduces environmental uncertainty. Plant structure, host density, and host age are the key factors that significantly influence the foraging and parasitism of Trichogramma. The searching speed of this parasitoid is significantly influenced by temperature. Heat stress increases VOC emissions in plants such as potato via stomatal opening, reducing herbivore attraction and enhancing parasitoid recruitment. Furthermore, air pollution, including CO₂, O₃, and NOx, impairs parasitoid efficiency by disrupting volatile-mediated host location and reducing biological control performance. Trichogramma wasps are generally effective biological control agents, but their success depends on the species used, target pest, crop, release density, and field conditions. Overall, species such as T. ostriniae, T. japonicum, and T. leucaniae show the strongest performance in several crops by increasing parasitism, reducing pest damage, and improving yield. This study highlights the successful integration of semiochemical cues in pest management programs and the effective utilization of Trichogramma in conjunction with entomopathogenic bacteria to control Lepidopteran pests. This approach contributes to the development of more effective pest management strategies, thereby promoting agricultural sustainability. Full article

The growing worldwide prevalence of Diabetes Mellitus highlights the urgent need for effective early detection methods to enable prompt intervention. This study develops a machine learning-based decision-support prototype for predicting diabetes risk using health metrics from the DiaHealth dataset, a recently published Bangladeshi open-source dataset for Type 2 diabetes prediction. Five supervised learning algorithms were evaluated: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT), and Random Forest (RF). Models were assessed across three stages: before feature scaling, after standardisation, and following hyperparameter optimisation via GridSearchCV, using accuracy, precision, recall, and F1-score as evaluation metrics. LR and SVM showed marked improvements after standardisation, consistent with their sensitivity to feature magnitude, whilst tree-based approaches such as DT and RF remained largely unchanged. KNN displayed minimal sensitivity to scaling, which is discussed in relation to the feature distributions of the dataset. Following hyperparameter tuning, RF achieved the highest accuracy of 95%, outperforming all other models. RF predictions were interpreted using Local Interpretable Model-agnostic Explanations (LIME) to promote transparency in model decision-making. The best-performing model was subsequently deployed as an interactive web-based prototype application using Streamlit, providing real-time prediction outputs. These findings demonstrate how preprocessing choices and hyperparameter tuning can differentially affect algorithm performance and illustrate the potential of combining explainable AI with practical deployment for diabetes risk assessment in a research context. Full article

Background: Patients undergoing open-heart surgery (OHS) are at risk of postoperative morbidity and mortality. Physical performance has been increasingly recognized as an important factor influencing postoperative outcomes. Therefore, the study aimed to investigate the associations and predictive value of physical performance on postoperative complications and duration of hospital stay. Methods: A prospective cohort study was conducted in 116 patients who were admitted to OHS. Preoperative assessment of physical performance, i.e., Short Physical Performance Battery (SPPB), Five Times Sit to Stand Test (5STS), gait speed (5 m walk test: 5MWT), Timed Up and Go (TUG), and handgrip strength. Duration of hospital stay and incidence of post-operative complications were recorded. Differences between participants with and without postoperative complications were analyzed using independent samples t-tests for continuous variables and chi-square tests for categorical variables. The associations between physical performance and postoperative outcomes were assessed using Spearman’s rank correlation coefficient. Hierarchical regression analysis was conducted to determine the predictive contribution of physical performance. Results: A total of 116 participants were submitted for OHS in two medical school hospitals; however, 108 individuals completed the pre-operative physical performance. The most common procedures were coronary artery bypass grafting and valve surgery. Fifty-one participants (47.22%) experienced postoperative complications, including five deaths, corresponding to 4.63% mortality. For the length of hospital stay analysis, five participants who died postoperatively were excluded, resulting in a final sample of 103 participants. Physical performance was significantly associated with the length of hospital stay (p < 0.05). Hierarchical regression analysis showed that the final prediction model explained 13.4% of the variance in length of hospital stay, with SPPB independently contributing an additional 6.0% to the model, followed by 5STS, 5MWT, handgrip strength, and TUG, which accounted for an additional 5.1%, 4.6%, 4.4%, and 3.7%, respectively. Conclusions: Preoperative physical performance was associated with length of hospital stay. While each measure explained a relatively small proportion of the variance in hospital stay, these assessments offer a simple, non-invasive, and clinically feasible approach to evaluating functional reserve before surgery. These findings highlight the importance of incorporating functional assessment into perioperative care to support risk stratification and guide rehabilitation strategies. Full article

Chronic periapical periodontitis is a persistent inflammatory disease characterized by progressive bone destruction around the tooth apex. Manual radiographic detection of these lesions is subjective and time-consuming, highlighting the need for automated diagnostic tools. This paper presents a unified deep learning framework for joint tooth segmentation and periapical lesion detection in panoramic radiographs. Our approach employs a joint process: first, a deep learning model identifies and segments individual teeth according to standard dental numbering systems, while a second one detects periapical lesions within the tooth regions obtained from the segmentation outputs in the first stage. The framework incorporates an advanced loss function (Powerful IoU v2) to improve bounding-box regression accuracy and a spatial association mechanism to map detected lesions to specific teeth based on geometric overlap analysis. Our proposed tooth segmentation model achieves an mAP@50 of 97.7% and a mean Dice coefficient of 93.5%, while the periapical lesion detector reaches an mAP@50 of 91.9%. Furthermore, our region-of-interest approach yields a 3.49× computational speedup, averaging 0.1589 s per radiograph when compared to full-image processing. Trained exclusively on open-source datasets, this reproducible framework achieves explicit tooth-to-lesion mapping, providing an efficient and practical tool for periapical lesion screening. Full article