Search Results (370)

Recently, cold-formed steel (CFS) structural systems have been increasingly used in building applications due to their lightweight characteristics, ease of fabrication, and efficient construction processes. Among these systems, built-up CFS columns are widely adopted to enhance load-carrying capacity; however, their axial compression behavior and failure mechanisms have not yet been fully clarified. This study aims to investigate the axial compression performance of built-up cold-formed steel columns through a combined experimental and numerical approach. This study investigates the axial compression performance of built-up cold-formed steel columns using a combined experimental and numerical approach. Following the full-scale testing of five different configurations, finite element models were developed in ABAQUS using the obtained material properties. The experimental results were used to validate and calibrate the finite element models, which provided a detailed simulation of the nonlinear structural behavior of the columns. The experimental load–displacement responses were compared with the numerical results to evaluate the accuracy of the finite element models and to identify the axial load-carrying capacity and dominant failure modes of the built-up columns. Furthermore, the tensile pull-out behavior of 3.9 mm diameter self-drilling screws utilized in the built-up column connections was examined through expedient fastener tests to facilitate a more profound understanding of the load transfer mechanism. The results highlight the influence of built-up configuration and connection behavior on the axial compression performance of CFS columns, providing practical insights for improving the design and numerical modeling of screw-connected built-up cold-formed steel column systems. Full article

Long-term energy sustainability in the built environment depends not only on deploying renewables but also on maintaining high energy efficiency that consistently lowers demand and enables more effective use of low-carbon electricity over time. This paper presents an illustrative case study that demonstrates a low-data, EPC/audit-based screening workflow for assessing operational energy, carbon, and water-related indicators in a non-residential building. An explanatory case study is conducted for a mixed-use logistics facility in Poland (≈610 m²), combining approaches to useful/final/primary energy indicators with operational carbon and water footprints. The operational water footprint is evaluated as a screening metric (L/kWh) applied to the annual electricity balance and tested across PV self-consumption levels (25/50/75%) to reflect the role of energy management and flexibility. The results indicate that an efficiency-oriented modernization pathway supported by PV integration (≈64 kWp; ~57,350 kWh/yr) reduces the primary energy performance indicator EP from 154 to 62.5 kWh/m²·yr, corresponding to a 59% reduction in annual primary energy demand. The operational water footprint indicator decreases nearly linearly with increasing PV self-consumption, demonstrating that long-term benefits depend on sustained efficiency and on maximizing on-site renewable utilization through controls, demand shifting, and/or storage. Overall, the framework supports transparent benchmarking and the development of staged pathways for integrating renewable and low-carbon energy systems into logistics-building portfolios, while maintaining an analytical focus on operational energy, carbon, and water performances. Full article

Serious video games are digital games designed for purposes beyond entertainment, typically to support education, training, health interventions, or behaviour change. They combine game mechanics with psychological and pedagogical principles, such as feedback, repetition, goal-setting, and scaffolding, to create interactive environments that facilitate learning, skill development, and sustained engagement. In many cases, they are built to simulate realistic tasks or decision contexts, allowing users to practise skills, test strategies, and learn from consequences in a low-risk setting. Within cyberpsychology, serious video games are particularly valuable because they provide structured digital contexts for examining how technology shapes cognition, emotion, motivation, and behaviour. They enable researchers and practitioners to observe how users respond to digital rewards, challenges, social features, and immersive environments, as well as how these features influence outcomes such as self-efficacy, persistence, attention, and emotion regulation. As a result, serious video games operate at the intersection of psychological theory, human–technology interaction, and applied digital intervention design. This entry provides an overview of their development, theoretical foundations, applications, effectiveness, and associated ethical considerations. Full article

Power system blackouts represent typical manifestations of instability in complex systems, whose evolution often exhibits non-stationarity, long-range correlations, and nonlinear scaling behavior. Most reliability assessment methods widely used in engineering practice are built on the core assumptions of event independence and light-tailed distribution, which will inevitably lead to systematic underestimation of extreme tail risks when blackouts actually present long-range memory and power-law heavy-tailed characteristics. Based on long-cycle historical blackout records of China’s power grid spanning 1981–2025, this paper develops an integrated framework combining Self-Organized Criticality (SOC) theory, Hurst exponent analysis, symbolic time-series methods, and Multifractal Detrended Fluctuation Analysis (MFDFA). This study systematically characterizes the evolution law and inherent dependence structure of blackout events from four dimensions: statistical scaling, temporal correlation, nonlinear structure, and multi-scale fractal spectrum. The results show that both the load-loss magnitudes and inter-event intervals of blackouts follow strict power-law distributions, with the system exhibiting scaling behavior consistent with SOC theory. The blackout event sequence presents significant long-range positive correlation and self-similarity, confirming a persistent long-term memory effect in the system evolution. Symbolic analysis further reveals the nonlinear fluctuation patterns and burst clustering behavior of the blackout process, reflecting the intermittency and complexity of blackout risks. MFDFA results verify that the blackout sequence has a broad-spectrum multifractal structure across different temporal scales, and Monte Carlo shuffle tests demonstrate that this multifractality mainly arises from intrinsic long-range temporal correlations, rather than being driven solely by heavy-tailed distribution. This study confirms that blackouts in China’s power grid are not random independent events, but present fractal statistical characteristics consistent with the self-organized critical mechanism. The findings provide a novel fractal perspective and quantitative framework for the statistical characterization, operational security assessment, and multi-scale early-warning modeling of blackout risks in China’s large-scale power systems. Full article

In order to simplify the stator winding structure of traditional variable reluctance (VR) resolvers and enhance their performance under high-speed operating conditions, this paper proposes a design for a variable reluctance self-coupling resolver based on ultrahigh-frequency (UHF) square wave excitation. The proposed solution optimizes the traditional winding structure by eliminating the separate excitation winding and integrating both excitation and detection functions into the two-phase sine and cosine windings. By optimizing the arrangement of the sine and cosine windings, a single-layer equal-turn winding design is successfully implemented, significantly simplifying the winding layout and reducing copper usage. In terms of excitation signal, this paper innovatively replaces the traditional sinusoidal excitation with UHF square wave excitation. Compared to sinusoidal excitation, square wave excitation not only generates higher electromotive force (EMF) peaks but also simplifies engineering implementation, reducing the complexity of system hardware. To validate the feasibility and advantages of the proposed structure, a complete experimental testing platform was built, and comparative experiments were conducted under various rotational speeds. The experimental results show that the proposed self-coupling resolver can achieve high-precision rotor position detection across the entire speed range, significantly improving the detection accuracy and dynamic response of traditional methods under high-speed conditions. Ultimately, the design demonstrates strong engineering application potential and provides a new solution for high-precision, high-dynamic response rotor position detection. Full article

This paper presents a channel segmentation proofreading network for crack counting with imbalanced samples. The network is built by stacking basic blocks called channel segmentation proofreading blocks, which are composed of the Approximate Overlapping Window Transformer and the Counting Proofreading Module. The former is designed to extract sufficient high-level semantic information, enhancing the ability of the network to judge crack quantities. Guided by the calculation results of the self-attention mechanism in the classical Transformer, Approximate Overlapping Window Transformer employs distinct computation steps to obtain the same results. Confining the computation process within overlapping windows, we continuously adjust to obtain the most suitable feature extraction process and internal structure for crack counting. Furthermore, to prevent the misidentification of multiple cracks as a single crack due to incorrect connection predictions of crack regions, the Counting Proofreading Module employs channel separation techniques. Following the concept of splitting positive and negative weights, it constructs positive and negative values with different characteristics, further confirming crack regions. Through the combined action of both components, when trained and tested on the crack counting dataset, our network achieves optimal results across all metrics. Full article

Concrete structures often develop penetrating cracks due to the initiation and propagation of local cracks during service, which may lead to the fracture and failure of the entire structure. The propagation modes and laws of cracks in structural members are closely related to the safety of the overall structure. Conducting research on crack propagation and predicting crack propagation paths for cracked structures can provide technical support for the safety design and reinforcement of structures. Based on the basic framework of the extended finite element method (XFEM), this paper develops a user-defined element (UEL) for ABAQUS using the level set method, and simulates in a two-dimensional space the crack propagation in concrete beam bending tests with the self-developed UEL and the built-in XFEM module of the software. The solution results of the self-developed UEL are consistent in trend with those of the XFEM module, yet the cracks simulated by the XFEM module can only propagate along element boundaries and cannot cross elements, and the accuracy of its results is highly dependent on mesh size. The crack tip simulated by the self-developed UEL can stay inside the element, and the simulated crack propagation paths show a higher degree of agreement with the experimental results. The correctness of the UEL is verified through comparative analysis with the results of the four-point bending tests of concrete beams and the XFEM module of the software. The UEL developed in this paper can effectively predict the crack propagation paths of concrete beams and reveal the multi-crack propagation laws of concrete beams. Full article

The nature of nonlinear dynamics of financial markets results in fractal geometry and chaotic behavior that can be viewed on a variety of scales in time. This paper conducts research on the fractal characteristics of the stock network and its contribution to the price of assets based on the Fractal Market Hypothesis (FMH). A multiscale network centrality measure is built based on high-frequency return dependencies to measure the self-similar, scale-invariant nature of inter-stock dependencies. The network factor and portfolio returns are then broken down with the wavelet packet decomposition (WPD) to obtain frequency-domain profiles, which characterize the variability of risk transmission in relation to investment horizons. The profiles are consistent with scaling properties of fractal, but the decomposition does not identify causal pathways on its own. Estimation of fractal dimension by use of the box-counting technique aided by the Hurst exponent analysis reveals that the A-share of China market exhibited long-range dependence and multifractal scaling. Network factor has the largest explanatory power in mid-frequency between the D5 and D6 bands of 32 to 128 days. This intermediary frequency concentration is consistent with the hypothesis of heterogeneous markets, in which the groups of investors with varying time horizons generate scale-related price dynamics. The addition of the network factor to a 6-factor specification lowers the GRS under the 5-factor specification by 31.45 to 17.82 on the same test-asset universe, indicating better cross-sectional coverage in the sample. The estimates of the Lyapunov exponents (0.039) as well as the correlation dimension ($D_2=4.7$) confirm the presence of low-dimensional chaotic processes of the network factor series, but these values are specific to the Chinese A-share market over the 2005–2023 sample period. These results provide a frequency-disaggregated use of network-based factor modeling and suggest that it can be applicable in multiscale portfolio risk management where the investor horizon is not uniform. Full article

Infertility treatment often requires patients to self-administer hormonal injections, creating significant physical, logistical, and psychological burdens. While medical technologies have improved pharmacological efficacy and safety, design aspects addressing usability, portability, and emotional distress remain underexplored. This study presents Blloom, a compact self-injection device that integrates ergonomic, thermal, and emotional considerations designed through an interdisciplinary design-thinking framework. This study identified critical user needs related to self-injection anxiety, medication refrigeration, and treatment-related stigma through in-depth, multi-method qualitative design research. The resulting prototype is characterized by one-handed operation, concealed needle delivery, and built-in passive cooling (2–8 °C for up to 8 h). Formative evaluations with patients and clinicians confirmed its improved usability, emotional comfort, and contextual compatibility. At this prototypical stage, medication- and container-specific compatibility, as well as long-term reliability, require further bench testing and clinical validation. Process analysis further revealed how designer–engineer collaboration evolved from empathic exploration to implementation-driven convergence. The findings demonstrate how human-centered design can mitigate the multidimensional burdens of infertility treatment and provide a replicable framework for interdisciplinary innovation in self-managed healthcare devices. Full article

In the context of Industry 4.0, reliable automatic inspection of weld surface defects is critical for structural safety, yet current deep learning-based detectors struggle with the extreme scale variation and anisotropic shapes characteristic of weld flaws such as pores, cracks, and lack of fusion. Existing YOLO-family models, although effective on general-purpose datasets, often fail to robustly localize tiny defects and long, slender discontinuities while remaining lightweight enough for industrial edge deployment. A critical research gap lies in the lack of task-specific optimization for weld defects: standard attention mechanisms are isotropic and cannot capture linear defect continuity, while existing loss functions ignore scale disparity between tiny pores (area < 100 pixels²) and large incomplete fusion defects (area > 5000 pixels²), leading to unstable regression. Here, we propose a dual-optimized lightweight YOLOv11 framework tailored for weld defect detection that addresses both feature representation and bounding-box regression. Here, we propose a dual-optimized lightweight YOLOv11 framework tailored for weld defect detection that addresses both feature representation and bounding-box regression. First, we introduce WeldSimAM, an enhanced attention module that augments parameter-free SimAM with directional (horizontal/vertical) and channel-wise enhancement to better capture the directional texture of linear weld defects. Second, we develop an Enhanced Normalized Wasserstein Distance (EnNWD) loss, which incorporates scale-disparity penalties and relative-area-based weighting to mitigate sample imbalance and improve regression accuracy for tiny and large-aspect-ratio targets. Validated via 10-fold cross-validation on three datasets (self-built + two public), the method achieves 99.48% mAP@0.5 and 73.29% mAP@0.5:0.95, outperforming YOLOv11 by 0.13 and 3.76 percentage points (p < 0.01, two-tailed t-test), with 5.21 MB and 132 FPS on NVIDIA RTX 4090. It also surpasses non-YOLO SOTA methods (e.g., EfficientDet-Lite3) by 3.8–5.5 percentage points in mAP@0.5 (p < 0.05), offering a practical real-time solution for industrial inspection. Full article

Female sports science has historically relied on evidence derived largely from male cohorts, despite known menstrual-cycle-related hormonal effects on thermoregulation, metabolism, and performance in women. We developed an Android application to support female athletes in documenting menstrual health alongside self-rated sports performance, addressing an underexplored area in current mobile health tools. The app was built in the Massachusetts Institute of Technology’s App Inventor following a rapid application development process (requirements determination, user design, construction, and implementation). Implemented features include period-date recording and prediction, health and performance logging, record review, basic personalization, and phase-specific, non-personalized training and nutrition tips. Unit test results verified core functions, including date recording, period prediction, navigation, and record retrieval, and a small-sample usability assessment (n = 5) using the system usability scale indicated above-average usability. In conclusion, the application offers a practical tool for period-date and symptom tracking with integrated performance self-logging. Full article

Land use transformation directly affects the stability and sustainability of regional ecosystems. Clarification of the trade-off/synergy dynamics among ecosystem services (ESs) provides a theoretical foundation to understand the transition of ES interactions from trade-offs to synergies, thereby facilitating the achievement in ecological sustainability in the ecoregion. This study, taking Jiangxi Province, China, as an example, utilized the InVEST model, Theil–Sen estimator, Mann–Kendall test, bivariate spatial autocorrelation, ecosystem service bundles (ESBs), and Random Forest (RF) models to conduct such an ecosystem-focused integrated analysis. According to land use changes from 1980 to 2020, the time-series spatiotemporal patterns of water yield (WY), soil conservation (SC), habitat quality (HQ), and carbon storage (CS) were analyzed. Differences in ES trade-off/synergy relationships and their underlying motivating factors were examined using a 3 km spatial grid framework. Compared with previous studies that mainly focused on typical subregions and of which driver analyses often remained at the individual ES level, this study introduced an explainable RF-SHAP framework based on the cooperative game theory at the grid scale, to quantitatively characterize the relative contributions of every motivating factor to ES trade-off/synergy relationships. The results indicate that from 1980 to 2020, forests and croplands constituted the predominant land use types, taking up 88% of the studied area. Throughout this period, forests, croplands, and grasslands decreased markedly, while built-up areas expanded notably, with a rise of 2876.65 km². Over the same time span, WY increased on average by 0.50% whereas SC, HQ, and CS declined by 0.50%, 0.98%, and 1.30%, respectively. Overall, these ESs demonstrated a geographical distribution characterized by low levels in SC, HQ and CS in the central area and high levels towards the provincial boundary. At the grid scale, the four ESs demonstrated predominantly a synergistic relationship while WY&HQ and WY&SC pairs were characterized by trade-offs. The constraint effect analysis revealed U-shaped relationships for SC&HQ, WY&HQ, and WY&SC, and inverted U-shaped relationships for SC&CS and HQ&CS, with clear threshold effects among these ES pairs. Based on self-organizing maps, the study area is partitioned into six ESBs, and the trade-off/synergy linkages of ESs are affected by the interplay of natural and societal forces. Elevation, slope, and rainfall emerge as the primary driving variables accompanied by population density and proximity to urban centers. These results are anticipated to offer reference to governments for their sustainable management in environmental resources to achieve United Nations Sustainable Development Goal (SDG) 15 (Life on Land: Protect, restore and promote sustainable use of terrestrial ecosystems). The methods used in this paper provide a replicable framework for exploring ES interactions and driving mechanisms in other ecologically sensitive regions in the world. Full article

Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has disrupted hydrogeological systems, triggering ground subsidence, groundwater leakage, and subsequent reservoir desiccation, as well as threatening regional water security and ecology. Thus, monitoring reservoir coverage evolution is critical to clarify dynamics and driving mechanisms. Synthetic Aperture Radar (SAR) is ideal for water body mapping, enabling data acquisition independent of illumination and weather. However, traditional SAR-based water extraction methods are hampered by low-scatter noise and poor adaptability to hydrological fluctuations. To address this, a two-stage dual-polarization SAR clustering algorithm (TSDPS-Clus) was developed using 452 time-series Sentinel-1 images (7 February 2017–24 August 2025). Specifically, the Kolmogorov–Smirnov test via pixel-wise time-series statistics screened core water areas, built candidate regions, and mitigated noise. Subsequently, dual-polarization and positional features were fused via singular value decomposition (SVD) to generate a high-discrimination low-dimensional feature set, followed by the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering for high-precision extraction. Results demonstrate that the algorithm suits reservoir storage-desiccation dynamics; dual-polarization complementarity boosts accuracy and clarifies six reservoirs’ spatiotemporal evolution. Notably, post-2023, tunnel excavation-induced land subsidence increased drying frequency and duration, with a 24-month maximum cumulative desiccation period. Full article

With the increasing demand for perception in complex road environments in intelligent driving, rapid and accurate identification of paved-road obstacles has become a critical prerequisite for driving safety and comfort. Various types of road obstacles can significantly affect vehicle stability and ride quality. To address this challenge, a lightweight and efficient vision-based obstacle detection framework, termed GroupScale-YOLO, is proposed, in which detection accuracy and computational efficiency are jointly enhanced through the collaborative design of multiple novel modules. First, a dedicated dataset targeting common paved-road obstacles is constructed, and six data augmentation strategies are employed to mitigate the adverse effects of road surface undulations and illumination variations on visual perception. Second, to overcome the limitations of YOLOv11n in paved-road obstacle detection tasks, targeted optimizations are introduced to the backbone network, convolutional blocks, and detection head. Experimental results indicate that GroupScale-YOLO achieves a 29.95% reduction in model parameters while simultaneously increasing mAP@0.5 by 0.6% on the self-built dataset, demonstrating its suitability for deployment in resource-constrained scenarios. Furthermore, real-vehicle road tests confirm that the proposed method maintains stable and accurate obstacle detection performance under practical driving conditions, offering a reliable solution for intelligent vehicle environmental perception. Full article

We detail a procedure to transform the current empirical stage in the study of complex systems into a predictive phenomenological one. Our approach starts with the statistical-mechanical Landau-Ginzburg equation for dissipative processes, such as kinetics of phase change. Then, it imposes discrete time evolution to explicit back feeding, and adopts a power-law driving force to incorporate the onset of chaos, or, alternatively, criticality, the guiding principles of complexity. One obtains, in closed analytical form, a nonlinear renormalization-group (RG) fixed-point map descriptive of any of the three known (one-dimensional) transitions to or out of chaos. Furthermore, its Lyapunov function is shown to be the thermodynamic potential in q-statistics, because the regular or multifractal attractors at the transitions to chaos impose a severe impediment to access the system’s built-in configurations, leaving only a subset of vanishing measure available. To test the pertinence of our approach, we refer to the following complex systems issues: (i) Basic questions, such as demonstration of paradigms equivalence, illustration of self-organization, thermodynamic viewpoint of diversity, biological or other. (ii) Derivation of empirical laws, e.g., ranked data distributions (Zipf law), biological regularities (Kleiber law), river and cosmological structures (Hack law). (iii) Complex systems methods, for example, evolutionary game theory, self-similar networks, central-limit theorem questions. (iv) Condensed-matter physics complex problems (and their analogs in other disciplines), like, critical fluctuations (catastrophes), glass formation (traffic jams), localization transition (foraging, collective motion). Full article