Search Results (1,249)

Foam concrete is a lightweight material characterized by low density and moderate mechanical strength, which can be combined with aluminum alloys to form a novel type of column. Such composite members enable rapid assembly, disassembly, and functional reconfiguration in prefabricated structures. However, research on the axial compressive performance of this new column system remains limited. This study investigates the axial behavior of aluminum alloy-foam concrete short columns through a combination of numerical simulation, theoretical analysis, and machine learning prediction enhanced by the SHAP (SHapley Additive exPlanations) interpretability method. A three-dimensional finite element model was developed in ABAQUS to examine the effects of frame thickness, foam concrete strength, and section dimension on load-bearing capacity. The results indicate that the column sectional dimensions have a significant influence on the axial compressive capacity. The foam concrete strength and frame thickness have relatively smaller effects. In addition, the frame thickness can effectively restrain lateral deformation and delay buckling. Based on the confinement mechanism, polynomial fitting, Mander’s model, and a composite column formulation were proposed for axial capacity prediction. Furthermore, eleven machine learning models were trained on 64 simulation datasets 64 independent computational experiments, among which the Gradient Boosting Decision Tree (GBDT) demonstrated the best performance (R² = 0.9984, MAE = 5.97, RMSE = 7.51). SHAP analysis further revealed the relative contributions of key features, showing that section dimension is the most influential parameter, followed by foam concrete strength, while frame thickness contributes the least. These findings not only enhance the theoretical understanding of the load-transfer mechanism of columns but also provide reliable predictive models and analytical formulations for their application in lightweight prefabricated structures. Full article

In contemporary Information Technology (IT) enterprises, workforce role configuration decisions sit at the intersection of leadership strategy, corporate Artificial Intelligence (AI) transformation and sustainable operational governance. This study investigates how heterogeneous workforce role configurations affect Service Level Agreement (SLA) compliance in SLA-aware Business Process Model and Notation (BPMN) IT business processes, framing this as a people management and leadership decision problem with explicit Environmental, Social and Governance (ESG)-oriented trade-offs. A validated MATLAB Simulink simulation tool is employed to conduct structured scenario testing across combinations of human role configurations and AI maturity levels, measuring their impact on key Service Level Objectives (SLOs) and Key Performance Indicators (KPIs). To support leadership decision making, a Workforce Sustainability Index (WSI) is introduced that integrates SLA compliance with workforce cost and AI dependency risk, where AI dependency risk captures the social dimension of ESG by emphasizing workforce skill development, upskilling and reskilling pathways, human oversight and responsible AI adoption. The simulation results reveal that no universally optimal configuration exists; the best-performing workforce design depends on organizational context and leadership priorities, as captured through scenario-based weight configurations representing performance-driven, cost-driven, human-centric and balanced governance orientations. These findings provide IT leaders and organizational decision-makers with actionable, evidence-based guidance for sustainable workforce design in the context of corporate AI transformation, contributing to the broader discourse on people management, merit-based organizational performance, responsible AI governance and sustainable digital operations management. Full article

Hybrid CNN–Transformer (HCT) synthesis for automated neurodevelopmental diagnostics is an effective approach to constructing intelligent detection systems that are not merely oriented toward feature classification but primarily toward solving spatiotemporal pattern recognition problems in motor disorder assessment. In neurodevelopmental diagnostics, existing automated methods rely on fixed, single-model architectures that process spatial or temporal motion features independently, failing to adapt to the heterogeneous motor irregularities characteristic of developmental coordination disorder and degrading detection sensitivity and generalization across diverse patient populations. There is therefore a pressing need for models capable of simultaneously capturing intra-frame spatial coordination patterns and inter-frame temporal movement dependencies against interrelated diagnostic criteria including accuracy, sensitivity, and motor irregularity specificity. To address this challenge, this paper proposes HCT, a novel framework that integrates ResNet-based spatial feature extraction from optical flow maps and pose estimation skeletons with multi-head self-attention Transformer encoding for modeling long-range temporal dependencies across multi-frame motion sequences. Unlike conventional single-stream approaches, where spatial and temporal processing remain confined to independent architectures, HCT decouples spatiotemporal feature learning through a cross-modal fusion pipeline, constructing a unified discriminative architecture that captures motor coordination dependencies between motion imaging inputs and multiple diagnostic criteria simultaneously. The convolutional encoder generates diverse joint displacement features, which are consolidated through cross-modal attention fusion into a robust, unified embedding with enhanced generalization and resilience to inter-individual motor variability. Integration within neurodevelopmental assessment frameworks facilitates reliable developmental coordination disorder classification, motor irregularity prediction, and interpretable diagnostic decision support, advancing the accuracy, flexibility, and clinical validity of intelligent motor disorder diagnostic systems. Full article

Speech deepfake detection (SDD) is commonly formulated as offline utterance-level binary classification, which limits early decisions in streaming communication and provides little mechanism-level evidence for a spoofing prediction. We propose C-MCSS-Mamba, a block-causal SDD framework that processes fixed-duration audio blocks with a partially fine-tuned XLS-R frontend and carries detection states across blocks through Mamba. Its selective scan is replaced by the proposed Counterfactual Mechanism-Contrastive Selective Scan (C-MCSS), which maintains shared, text-to-speech (TTS), and voice conversion (VC) states. Frame-level TTS/VC hypotheses directly modulate state update, retention, and forgetting. A Causal Counterfactual Evidence Reliability Module (C-CERM) further suppresses transient mechanism evidence through a recurrent reliability state that adaptively regulates these dynamics. Model-internal TTS/VC evidence is obtained by counterfactually disabling the corresponding recurrent state and measuring the resulting spoof-logit decrease. The model produces prefix-level spoof decisions and contribution-based state-dependence measures; these measures only characterize the model’s internal decision process, and do not identify the physical speech generation process of an individual sample. A joint objective combines detection, early-prefix, mechanism, contrastive, and intervention supervision. Experiments demonstrate competitive detection performance, achieving $0.47\%$ EER on the ASVspoof 2019 LA evaluation set while maintaining effectiveness across four cross-dataset benchmarks. Full article

The Myval balloon-expandable transcatheter heart valve was developed for transcatheter aortic valve implantation, but its broad 20–32 mm size matrix and controlled deployment have prompted use in non-aortic landing zones. This narrative review critically synthesizes Myval-specific case reports, case series, and observational cohorts, together with relevant platform-level evidence, addressing device design, anatomical selection, imaging, procedural strategy, clinical outcomes, and evidence gaps. Reported applications include mitral valve-in-valve, valve-in-ring, and valve-in-mitral annular calcification; tricuspid valve-in-valve and valve-in-ring; and pulmonary implantation in conduits, surgical bioprostheses, and selected native or patched right ventricular outflow tracts. Outcomes appear most predictable within circular stented surgical bioprostheses, whereas non-circular rings, severe mitral annular calcification, and compliant or aneurysmal outflow tracts present greater risks of inadequate anchoring, paravalvular regurgitation, embolization, frame deformation, left ventricular outflow tract obstruction, and coronary compression. Intermediate and extra-large diameters increase the available nominal sizing options; however, no clinical evidence demonstrates that this reduces embolization, paravalvular regurgitation, residual gradients, or reintervention. The available data document procedural feasibility in anatomically selected patients but are predominantly observational, with limited independent adjudication and follow-up. These procedures are generally off-label and should remain individualized Heart Team decisions; prospective multicenter studies are required to define comparative safety, antithrombotic management, durability, and lifetime reintervention strategies. Full article

Federated, semi-supervised novelty detection is well suited for intrusion detection on resource-constrained Internet of Things (IoT) nodes: each device learns a model of benign traffic, shares only summary statistics, and does not transmit raw traffic samples. A previously published cross-layer federated detector, Baseline K-Means, showed that periodically merging worker statistics through a coordinator raises the detection rate, but it also exhibited a systematic side effect: after every merge, the precision decays, and the false-positive rate (FPR) climbs because the coordinator recomputes its threshold from streaming distances filtered by the closest observed anomaly, so tightens after every merge, flagging progressively more benign traffic; the threshold was also hand-tuned. We present AF-BKM, an Adaptive Federated Baseline K-Means that repairs the federated mechanism with two label-free, statistics-only enhancements, denoted as E1 and E2: (i) an adaptive decision threshold read from the benign Mahalanobis-distance distribution, requiring no manual percentile search and no attack labels (E1), and (ii) a robust, benignly anchored aggregation that blends worker means under quality weighting and outlier-worker filtering and recalibrates the threshold on a trusted benign anchor to a stable, anchor-referenced false-positive level, which a target-FPR rule can make operator-selectable instead of tightening it toward the nearest anomaly (E2). With MinMax scaling fit only on benign baseline data and non-IID federated streams on NSL-KDD, UNSW-NB15 and the N-BaIoT corpus of real traffic from commercial IoT devices, AF-BKM removes the merge-induced precision decay (the first-to-last-epoch precision change improves from $-0.134$ to $-0.002$ on NSL-KDD, from $-0.121$ to $-0.014$ on UNSW-NB15, and from $-0.170$ to $-0.009$ on N-BaIoT) and reduces the mean FPR by 30–$64\%$, depending on the dataset; all central improvements are significant across 10 seeds (Wilcoxon $p=0.002$, large effect sizes). AF-BKM preserves recall on NSL-KDD and N-BaIoT and, on the harder UNSW-NB15, exposes an explicit precision–recall trade-off through a benign target-FPR knob. In fp32, the deployed model serializes to $5.5$–52 KB, a packet is classified in 11–27 µs on a desktop CPU, and each merge round uploads a $d+3$-value summary (160–472 B) $94.6$–$98.3\%$ smaller than the same summary extended with the covariance upper triangle. A robustness study covering selected faulty-worker updates, contamination of the commissioning anchor, and detector-level white-box evasion reports the measured degradation patterns: fabricated threshold candidates have no direct path to the threshold, although a fabricated mean still reaches it indirectly through the blended centroid, and the anchor-referenced false-positive level remains stable under percent-level anchor contamination, while recall sensitivity is dataset-dependent and the evasion budget tracks the benign–attack margin of each dataset. We frame the contribution with a focused taxonomy that identifies merge-induced precision decay under non-IID workers as an open gap. Code is released for reproducibility. Full article

The growing environmental impact of the construction sector is driving a shift toward sustainable design practices, in which digital technologies are integrated to enable designers to make informed decisions from the early design stages. In this study, a DSS is developed that combines BIM, VPL and TPL to identify the optimal wall stratigraphy for the building envelope. The process is structured into sequential phases, in which Autodesk Revit v2026.06.24.01, Dynamo v.3.6.1 and Python v3.9 are integrated within an end-to-end workflow. In the first phase, wall stratigraphies are modelled in BIM, and parametric variations in layers are allowed alongside customisation of the material database. In the second phase, an automated workflow calculates a set of indicators covering thermal performance, environmental assessments (LCA, MRc2 LEED and mandatory national requirements), and economic evaluations (LCC). In the third phase, indicators are imported into an automated Dynamo-based MCDM, where a hybrid AHP/PROMETHEE analysis is applied and results are directly integrated into BIM, thereby supporting sustainability-focused decisions. The tool is validated on different sustainable wall stratigraphies in warm-climate contexts. The hybrid solution is ranked first, followed by rammed earth, while platform frame and X-LAM are ranked lower. Full article

Falls are a major cause of severe injury and mortality among older adults, requiring rapid state recognition and alerts in healthcare and caregiving environments. However, many existing fall-recognition approaches primarily focus on post-fall detection or assume access to cloud/GPU computing, leaving limited evidence for short-term pre-fall recognition on low-end CPU-based edge devices. This study proposes a lightweight CNN–TCN–Attention framework for recognizing fall and pre-fall states on resource-constrained edge devices. Using MediaPipe, three-dimensional coordinates and visibility scores of 33 human body landmarks are extracted from input videos. Raw RGB frames are not directly used by the classifier, thereby reducing the processing of visually identifiable information. A total of 2300 fall-related videos from the AI-Hub dataset were reorganized into three classes: normal, pre-fall, and fall. The pre-fall onset was defined as the point at which the downward vertical velocity of the nose landmark exceeded a dataset-specific threshold. The model uses a CNN to extract frame-level skeletal patterns, a temporal convolutional network (TCN) to learn temporal changes in posture, and an attention module to aggregate temporal features. It was deployed on a Raspberry Pi Zero 2 W using ONNX Runtime. The proposed model achieved 94.71% accuracy and a macro F1-score of 93.63%, with an average state-decision latency of 325.88 ms/decision. It improved accuracy by 2.83 percentage points over the lightweight CNN–LSTM–Attention baseline while maintaining comparable latency and achieved approximately 1.75× faster processing than the MobileNetV3–TCN–Attention model. Full article

Mangrove ecosystems underpin the livelihoods of coastal and rural households across the tropics, yet how their ecosystem service (ES) values are associated with livelihood-strategy choices remains unevenly understood. Following a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided protocol, this systematic review searched Web of Science and Scopus for studies published between 2016 and 2026 (database search was run on 27 May 2026); of the 353 records identified and 207 screened after removing 146 duplicates, 133 unique studies were retained; 95 of these contributed evidence to one or more of seven outcome themes, while 38 were retained as contextual or background evidence. The seven themes were graded using a hybrid Grading of Recommendations Assessment, Development and Evaluation (GRADE)/Confidence in the Evidence from Reviews of Qualitative Research (CERQual) approach (zero, high certainty; two, moderate certainty; five, low certainty). A structured assessment of meta-analytic feasibility across six candidate outcome domains found that no domain met the operational requirement of at least three independent studies reporting one outcome construct on a harmonisable metric with usable variance information; therefore, this review reports the direction, consistency, and certainty of associations rather than pooled-effect magnitudes. The corpus shows a pronounced value-epistemology asymmetry, with monetary valuation forming the largest single evidence theme (45 studies, approximately one-third of the corpus) and relational or indigenous framings remaining a smaller but substantive theme (20 studies, about 15%), meaning that household decision-making is incompletely captured. Evidence for mangrove–fishery production linkages and the coastal-protection/ecosystem-based disaster-risk-reduction function was the most consistent and was rated as moderate certainty. However, the fishery evidence provides the more direct connection to livelihood dependence, whereas most coastal-protection estimates are biophysical or aggregate and do not directly demonstrate household livelihood-strategy responses. The five remaining themes rest on low-certainty evidence, with welfare estimates spanning orders of magnitude and value-to-behaviour pathways weakly tested. Asian settings account for roughly half of the evidence (Southeast and South Asia together 45%, 60/133), while African, Latin American, and Pacific Island settings remain under-represented, limiting transferability. Evidence on bundled payments for ecosystem services, blue-carbon governance, and secure community tenure should therefore be treated as context-dependent options for prospective evaluation rather than as established prescriptions. The clearest research priority is quasi-experimental and longitudinal work linking elicited values to observed livelihood behaviour. Full article

The latest generation of 4D imaging radar demonstrates significant potential in autonomous driving environmental perception, leveraging its capability to provide target elevation data and dense point clouds. This paper introduces a complete method for constructing a multimodal 4D imaging radar dataset for three-dimensional traffic scenes. It illustrates the hardware and software configurations of the data-acquisition vehicle. Methods including multi-sensor coordination, parameter calibration, timestamp synchronization and spatial datum synchronization are proposed. And eight typical three-dimensional traffic scenarios are designed, such as rainy weather environments, dense heterogeneous targets, enclosed tunnels, high-speed cut-in of multiple vehicles, multi-layered stereoscopic structures and edge working condition reproduction. In addition, this paper puts forward a frame-by-frame processing method for high-resolution images and point cloud data collected by the high-definition camera-LiDAR-4D imaging radar collaborative system. A large model-based 3D annotation method for multiple types of targets is proposed, generating a spatio-temporal sequence-optimized four-dimensional annotation sequence, and finally constructs a complete and high-quality multimodal 4D imaging radar dataset for three-dimensional traffic scenes. The results show that the constructed dataset enables the synchronization of timestamps and spatial coordinate systems. The large model can achieve high-precision 3D annotation for the four predefined target types. The dataset contains 11,400 frames of data from high-definition cameras, LiDAR, and 4D imaging radar, with 131,642 labels. This study will provide reliable fundamental support for the training and verification of 4D imaging radar perception algorithms, vehicle decision-making and planning in complex scenarios, and multi-sensor fusion technologies. Full article

The use of deepfake media, particularly face-swap and face-shifter videos, has proliferated rapidly on social media and online news outlets. Though there are legitimate uses for this synthetic media in certain applications, such as media reporting, there is always the possibility of misleading people, creating distrust of digital information, and even posing security threats. Most existing studies have focused on deepfake detection using either image or video frames; however, these methods are computationally costly and do not perform well in real time. To address these limitations, this work proposes a pipeline for deepfake video detection using network packet analysis. A novel PCAP dataset is constructed by streaming real and manipulated video content over WebRTC and TCP (Port 8080) protocols, and machine learning models are then trained on 48 extracted network-level features to distinguish deepfake traffic from authentic streams. For implementation, four deepfake video datasets of varying quality are utilized, namely, HIDF, FaceForensics++, SDFVD-V2, and ManualFake-2022, each containing both real and manipulated samples. Videos are segmented into 3, 6, and 9-s clips and sequentially streamed over WebRTC and TCP (Port 8080) protocols to capture network traffic in PCAP format. Experiments are conducted using six classical machine learning classifiers, namely KNN, Logistic Regression, Decision Tree, Random Forest, Histogram Gradient Boosting, and Naïve Bayes, trained on 48 network-level features. The proposed pipeline achieved the highest classification accuracy of 91.2% on TCP (Port 8080) and 79.9% on the WebRTC protocol, both obtained using 6-s video captures. Full article

This article examines the European Union’s response to the United States’ challenge to two pillars of the multilateral trading system: the Most Favoured Nation principle and compulsory third-party adjudication. Drawing on Hirschman’s analysis of the “Rhetoric of Reaction”, it argues that the United States has framed its attack to the WTO disciplines through claims of perversity, futility, and jeopardy. Rather than resisting this rhetoric, the European Union has increasingly echoed that discourse. Using the “Liberation Day” tariffs and the ensuing Turnberry “deal” as a test case, the article shows how the European Union moved from its firmer reaction to the 2018 Section 232 measures to a more deferential posture in 2025, including the acceptance of discriminatory tariffs and the decision not to initiate WTO litigation. The article contends that this shift weakens the European Union’s traditional role as a defender of multilateralism, normalizes departures from non-discrimination and dispute settlement, and risks accelerating the erosion of key principles of the WTO legal order. Full article

Risky choice depends not only on individual differences but also on characteristics of the decision context, including how decision problems are framed. This study examined whether HEXACO personality dimensions were associated with risky choices in four hypothetical financial lotteries varying by outcome domain and stake size. A total of 310 Polish adults completed the HEXACO-60 inventory and made decisions in lotteries involving small gains, large gains, small losses, and large losses. Lottery type was strongly associated with choice: participants more often selected the safe option in gain conditions and the risky option in loss conditions. In contrast, only Honesty–Humility showed a significant overall association with risky choice after controlling for lottery type, whereas the remaining HEXACO dimensions did not. Interaction analyses further indicated that the associations of Honesty–Humility and Conscientiousness with risky choice varied across lottery scenarios. Higher Honesty–Humility was associated with lower odds of choosing the risky option in gain lotteries, whereas higher Conscientiousness was associated with higher odds of choosing the risky option in loss lotteries. These findings provide preliminary evidence that selected personality traits show context-dependent associations with risky choices across the four hypothetical lottery scenarios examined in this study. However, because the study used only four hypothetical scenarios, including two involving very large monetary amounts, these effects should be interpreted cautiously and require replication using broader and incentive-compatible measures of financial risk taking. Full article

Infrastructure systems play a critical role in shaping environmental performance, economic investment, and social well-being. Infrastructure systems are responsible for nearly 60% of global greenhouse gas (GHG) emissions. Pavement construction and maintenance alone consume over 350 million tons of asphalt annually and account for 30% of public infrastructure expenditure. While sustainability frameworks increasingly emphasize the integration of environmental, economic, and social dimensions commonly conceptualized as the Triple Bottom Line (TBL), their application in pavement engineering remains uneven. This study investigates the extent and nature of TBL integration in pavement sustainability research through a combined scientometric and qualitative content analysis of 529 peer-reviewed publications indexed in Scopus between 2015 and 2025. Using a PRISMA-guided review protocol, the study maps publication trends, geographic distribution, and thematic evolution, and systematically classifies each study according to its level of sustainability integration. The results reveal a pronounced imbalance across sustainability dimensions. Only 7.4% of studies achieve full TBL integration, while 45.2% address a single dimension and 29.1% remain primarily technical with limited sustainability framing. Environmental and economic dimensions are consistently operationalized through standardized methodologies such as Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), whereas social sustainability appears in less than 10% of studies and is typically represented through simplified or proxy-based indicators. The findings suggest that this imbalance is not solely a result of data or awareness limitations but is associated with prevailing methodological preferences that favour quantifiable and optimization compatible indicators. These tendencies create systematic challenges for integrating context-dependent and participatory social considerations into engineering decision-making frameworks. By highlighting these structural patterns, the study contributes to ongoing discussions on sustainability governance and calls for more inclusive, methodological plural approaches to infrastructure assessment. The paper concludes by outlining pathways for advancing TBL integration through standardized social indicators, participatory assessment methods, and alignment with emerging ESG reporting requirements. Full article

This qualitative case study examined how university taekwondo coaches and athletes perceive artificial intelligence (AI) in sport, with emphasis on the psychological dimensions of implementation readiness. Using convenience sampling, semi-structured interviews were conducted with 23 coaches and 30 athletes competing in inter-university taekwondo championships in Turkey. Data were analysed through qualitative content analysis, and inter-coder agreement was quantified using Cohen’s Kappa. Competition analysis and training support were identified as the most useful applications. Reported benefits included faster feedback, improved athlete monitoring, and more systematic performance analysis, while concerns centred on incorrect guidance, loss of human elements in coaching, and insufficient accountability in AI-supported decisions. Athletes were generally positive about AI-assisted refereeing when it was framed around fairness and visibility, whereas coaches were more cautious when AI was presented as a substitute for core coaching functions. Interpreted through self-determination theory and self-efficacy as sensitising lenses, the findings indicate that AI adoption is most feasible when systems support rather than displace human roles, operate under clear governance and human oversight, and are accompanied by targeted training. The findings are context-specific and provide a basis for further empirical work. The findings of this study contribute to UN-SDG 9 by supporting the responsible adoption of artificial intelligence in sport management. Full article