Search Results (6,578)

The evaluation of the seismic behavior of masonry aggregates, which characterize Italian historic centres, is a challenging and widely debated topic in the field of structural engineering. These constructions, composed of several adjacent structural units, tend to exhibit both global and local damage when subjected to horizontal seismic actions—loads that were not considered at the time of their original construction. Developed over centuries of unplanned urban growth, they are based on empirical construction rules and locally sourced materials. Due to their poor thermal properties, these buildings are also affected by significant heat losses, resulting in reduced indoor comfort. In this context, the present study aims to evaluate the seismic performance of a masonry aggregate and two of its constituent structural units located in Visso, in the province of Macerata, an area severely affected by the 2016 Central Italy seismic sequence, both before and after the application of an innovative integrated retrofitting solution. The proposed strengthening system combines aluminium alloy exoskeleton with insulating sandwich panels, simultaneously addressing seismic vulnerability and energy inefficiency. The assessment is carried out through numerical analyses, including nonlinear static and dynamic approaches, to achieve a comprehensive understanding of the structural response. Moreover, a comparative analysis between the masonry aggregate and the two individual structural units, modelled as isolated buildings, is performed to investigate the influence of structural interaction among adjacent units. The results demonstrate the effectiveness of the proposed retrofitting strategy, highlighting a significant improvement in global stability. Furthermore, the comparison confirms the critical role of inter-unit interaction and underscores the necessity of modelling historic masonry aggregates rather than isolated buildings to obtain a more realistic seismic performance evaluation. Full article

Land subsidence has emerged as a critical geohazard affecting major urban centers worldwide, particularly in coastal and deltaic regions where intensive groundwater extraction and rapid urbanization are prevalent. It is estimated that subsidence threatens more than 1.6 billion people globally, with reported subsidence rates exceeding 100 mm/year in several rapidly urbanizing cities and cumulative ground lowering exceeding 10 m in extreme cases such as Mexico City. This review provides a comprehensive synthesis of the hydrogeological drivers, impacts, and sustainable mitigation pathways of land subsidence based on a systematic literature review of 167 peer-reviewed studies following the PRISMA framework and bibliometric network analysis. The findings confirm that groundwater extraction is the dominant driver, causing pore pressure decline and irreversible consolidation of compressible aquitards, while geological conditions, recharge imbalance, and climate variability strongly influence subsidence magnitude and persistence. The consequences are severe and multidimensional, including increased flood risk, infrastructure damage, groundwater storage loss, ecosystem degradation, and significant socio-economic impacts. Global case studies from major subsiding cities demonstrate that subsidence often contributes more to relative sea-level rise and urban flood vulnerability than climate-driven ocean rise alone. Mitigation strategies, including groundwater regulation, managed aquifer recharge, water-sensitive urban design, geotechnical stabilization, and satellite-based monitoring, have shown effectiveness but remain limited when implemented independently. This study proposes an integrated management framework combining continuous monitoring, hydrogeological assessment, sustainable groundwater management, engineering and nature-based solutions, and governance integration. The findings highlight that early intervention, groundwater sustainability, and coordinated policy actions are essential to reduce subsidence and enhance long-term urban resilience. These insights support the achievement of Sustainable Development Goal 11 (Sustainable Cities and Communities), particularly in strengthening disaster risk reduction and climate resilience in subsidence-prone urban areas. Full article

The uniaxial tensile test is a common and fundamental test in materials science and engineering, in which a specimen is subjected to controlled tension until failure. From this, the stress–strain curve and many property parameters of the material can be calculated, such as tensile strength, ultimate strength, maximum elongation, Young’s modulus, Poisson’s ratio, and yield strength. As fibrous materials, such as paper and paperboard, become more popular, accurately measuring their mechanical properties becomes essential for developing and applying these materials, especially in packaging. However, since they are anisotropic and inherently inhomogeneous due to the arrangement of the fibers, accurately determining their mechanical properties is not straightforward. This study investigated how several key factors influence the results of tensile tests on fiber-based materials: sample size and deformation measurement techniques using three fiber materials. This study also compared three different strain recording methods: digital image correlation (DIC), video extensometer, and conventional extensometer (Traverse). The DIC technique emphasized the effect of the inherent inhomogeneity of the paperboard on the overall mechanical properties obtained from tensile tests. The results indicated that sample size has a negligible effect on the stress–strain curve, and any apparent influence likely stems from slip at the grips during tensile testing. However, sample size does affect paperboard fracture to some extent. The study also provided recommendations for optimal specimen geometry and deformation recording methods to improve the accuracy and repeatability of tensile testing of fiber-based materials. Full article

In recent months, some media outlets have been launching artificial intelligence-based chatbots that serve as assistants to users in their search, selection and consumption of content. This research analyses two such examples: Vera, a conversational assistant launched by the Spanish newspaper El País, and the model used by the Colombian newspaper El Espectador, which operates on the WhatsApp platform. Both chatbots share the same approach: they are tools designed for users to interact with newspaper content. This interaction takes place through natural language conversations: the technology understands ‘users’ questions or requests and provides answers based on the content hosted in the newspapers. This changes the way media content is explored. We are moving from a paradigm centred on search engines and keywords to one in which conversation determines the discovery of content. The research analyses the results of these two pioneering experiences in the Spanish-language media. The aim is to understand the extent to which they are changing the relationship with content and how they are affecting the media. Full article

Hollow graphitic nanoshells (HGSs) are widely investigated as battery materials because their conductive shells and internal voids can simultaneously influence ion transport, electron percolation, and mechanical stress accommodation. Yet, the field remains largely morphology-driven, with performance often attributed generically to “hollowness” rather than to structural parameters. This review examines HGSs from a parameter-oriented perspective. It highlights key structural features, including graphitization degree, shell thickness, cavity size, pore architecture, and defect or dopant chemistry. These features collectively shape electrochemical behavior. We discuss how these features influence transport kinetics, interphase stability, volumetric efficiency, and mechanical resilience across insertion, metal anode, multivalent, solid-state, and halogen chemistries. Major synthesis approaches, including hard-templated, soft-templated, self-templated, and biomass-derived routes, are evaluated based on the structural control they provide and the influence of synthesis conditions on shell architecture, graphitic ordering, and pore structure. Special attention is given to how these structural features develop during processing and how they affect ion accessibility, conductivity, and stability. Finally, we outline a shift toward quantitative, parameter-driven engineering supported by operando diagnostics, electrode-level modeling, and standardized reporting. HGSs will only achieve practical relevance when structural optimization extends beyond particle morphology to transport uniformity, interfacial stability, network connectivity, and life-cycle responsibility. Full article

The topology identification (TI) of low-voltage distribution networks (LVDNs) is the foundation for their intelligent operation and lean management. However, the existing identification methods may produce inconsistent results under measurement noise, missing data, and heterogeneous load behaviors. Without principled multiple method fusion and meter-level confidence quantification, the reliability of the identification results is questionable in the absence of ground-truth topology. To address these challenges, a confidence-aware TI (Ca-TI) method for the LVDN based on weakly supervised learning (WSL) and Dempster–Shafer (D-S) evidence theory is proposed, aiming to infer each meter’s latent topology connectivity label and quantify the meter-level confidence without ground truth by fusing different identification methods. Specifically, within the framework of data programming (DP) in WSL, different TI methods were modeled as labeling functions (LFs), and a weakly supervised label model (WSLM) was adopted to learn each method’s error pattern and each meter’s posterior responsibility; within the framework of D-S evidence theory, an uncertainty-aware basic probability assignment (BPA) was constructed from each meter’s posterior responsibility, with posterior uncertainty allocated to ignorance, and was further discounted according to the missing data rate; subsequently, a consensus-calibrated conflict-gated (CCCG)-enhanced D-S fusion rule was proposed to aggregate the TI results of multiple methods, producing the final TI decisions with meter-level confidence. Finally, the test was carried out in both simulated and actual low-voltage distribution transformer areas (LVDTAs), and the robustness of the proposed method under various measurement noise and missing data was tested. The results indicate that the proposed method can effectively integrate the performances of various TI methods, is not adversely affected by extreme bias from any single method, and provides the meter-level confidence for targeted on-site verification. Further, an engineering deployment scheme with cloud–edge collaboration is further discussed to support scalable implementation in utility environments. Full article

Rehabilitation is a critical component in the recovery of patients with either complete or partial loss of motor movements. Repeated and slow limb movements are usually advised by practitioners. Advanced robotic systems can help to configure monotonous movements and accelerate the recovery process as an alternative to therapist-assisted motions, especially during the later phase of recovery. In this work, robotic-assisted human limb movements are engineered and augmented with a novel electromyography (EMG) signal to characterize the movements. The proposed lower- and upper-limb assistive system is designed on a wheelchair platform and is IoT-enabled. The proposed assistive system is designed for patients affected with hemiplegia, paraplegia and tetraplegia. Existing state-of-the-art (SOTA) systems are typically focused on either the upper or lower limbs, with limited degrees of freedom (DoF). The IoT framework for remote access enables the possibility of home-based rehabilitation. A prototype was successfully developed and experiments to characterize various muscle movements using the proposed system were performed. Full article

Bridge structural health monitoring (BSHM) systems are essential for assessing the operational performance and safety of long-span bridges. However, monitoring data are often affected by factors such as sensor malfunctions, environmental disturbances, or power interruptions, leading to various anomalous data. Moreover, the multiclass imbalance of the data presents a major challenge to traditional anomaly detection methods. To address this issue, a novel multiclass anomaly detection method based on an improved deep convolutional neural network is proposed. Specifically, a ResNet50 architecture integrated with the convolutional block attention module (CBAM) is developed to enhance the extraction of discriminative features. Additionally, the Focal Loss function is introduced to emphasize the loss weight of minority samples, reducing the influence of majority classes, thereby effectively overcoming the class imbalance issue in multiclass anomaly detection. The proposed method is trained and validated using measured acceleration data collected from a large-scale cable-stayed bridge. The experimental results indicate that the model achieves an overall accuracy of 98.28%, while effectively improving the classification performance of minority categories. The method further reproduces the spatiotemporal distribution of anomalies in full-month monitoring data, confirming its robustness and engineering applicability for large-scale automated anomaly diagnosis in BSHM systems. Full article

As one of the three fundamental modes of heat transfer, thermal radiation has long attracted interest due to its independence from a medium and its strong temperature dependence. In extreme environments such as deep space exploration and cryogenic engineering, thermal radiation often becomes the dominant heat transfer mechanism. Consequently, the radiative properties of materials are crucial for achieving precise thermal control, directly influencing the thermal stability and overall performance of advanced systems, including space probes, cryogenic devices, and superconducting components operating under high-vacuum and low-temperature conditions. This paper provides a systematic review of the physical mechanisms, key factors affecting emissivity, major measurement methods, and technological developments related to material radiative properties at cryogenic temperatures. Particular attention is given to experimental methods and techniques describing material radiative behavior, along with a comparative analysis of the suitability of different measurement techniques for cryogenic applications. Finally, the study highlights the significant practical value of this research for fields such as aerospace, precision electronics, and cryogenic instrumentation, aiming to offer insights for optimizing cryogenic thermal management and guiding the design of novel functional materials. Full article

Diabetes mellitus remains a leading cause of morbidity worldwide, driven in type 1 diabetes by autoimmune destruction of pancreatic β-cells and in advanced type 2 diabetes by progressive β-cell dysfunction and failure. Diabetes affects around 830 million people globally, with the vast majority residing within low- and middle-income nations. Over the last few decades, the numbers of people who have diabetes and those with untreated diabetes have consistently increased. Although current pharmacologic therapies improve glycemic control, they do not restore functional β-cell mass. Consequently, strategies aimed at protecting, regenerating, or replacing insulin-producing cells have emerged as a major focus of regenerative medicine. Stem cell-based approaches offer the potential to generate renewable sources of glucose-responsive β-like cells, but challenges remain in achieving full functional maturation, immune protection, scalable manufacturing, and durable clinical engraftment. This review examines advances in engineering stem cell-derived insulin-producing cells for islet replacement therapy, with an emphasis on differentiation strategies, immunoprotective approaches, and the translational barriers that must be addressed for durable β-cell replacement. Full article

Port-area hydrogen refueling stations face low-frequency but high-consequence events when high-pressure leaks ignite as jet fires in wind-exposed, constrained environments. This study develops a consequence-based framework coupling theoretical screening, CFD combustion analysis, and hazard zoning to support separation-distance setting and emergency planning. A jet-fire model estimates flame-impingement distances for multiple leak diameters, and a weighted multi-point radiation model predicts heat-flux fields, from which lethal and irreversible-injury zones are delineated using thresholds of 7 and 5 kW/m², respectively. To move beyond wind-free screening, steady reacting-flow CFD is conducted for a representative release under four ambient conditions, with 4.34 m/s adopted as the representative wind speed for the windy cases based on Ningbo Port conditions. Validation against a visible-flame correlation defined by T ≥ 1573 K shows a deviation of 6.99%. Results show that radiation footprints expand markedly with diameter, with lethal and injury distances scaling approximately linearly within the studied range. Under wind, near-ground hot-plume extents defined by T ≥ 388 K and T ≥ 582 K depend strongly on wind direction and station geometry, whereas visible flame length is less sensitive. Additional sensitivity analyses indicate that the quasi-steady results are weakly affected by the selected ignition snapshot, while inclined releases modify projected plume/flame extents without altering the main engineering interpretation of the baseline case. The results support theory-based preliminary screening, but wind direction should be explicitly considered in exclusion-zone definition. Full article

The future of innovation and economic growth depends on our ability to nurture the next generation of scientists. The global shortage of qualified STEM (Science, Technology, engineering, Mathematics) teachers has led many countries to expedite the transition of subject-matter experts from industry and academia into teaching roles. These second-career science student teachers typically participate in accelerated training programs designed to address urgent shortages. This study addresses a gap in the literature regarding effective pedagogical interventions for career-changing professionals in STEM fields, focusing on the experience and transformation of second-career science student teachers. This qualitative case study explores how a Challenge-Based Learning (CBL) course fosters the development of pedagogical competences via developing an instructional unit collaboratively, among five second-career science student teachers enrolled in an accelerated teacher education program. Drawing on data collected through instructors’ field notes, iterative work-in-progress lesson drafts, and reflective final papers, the study employs qualitative content analysis to trace changes in participants’ instructional approaches and professional identity. Findings reveal that engagement with the CBL framework promoted a significant shift from teacher-centered to learner-centered instruction, as participants increasingly integrated collaborative learning, inquiry-based activities, and reflective practices into their lesson planning and classroom teaching. The iterative nature of CBL, which emphasizes real-world problem-solving and structured opportunities for reflection and peer feedback, was instrumental in supporting participants’ adaptive expertise and confidence as novice teachers. Moreover, the course experience contributed to the emergence of a professional teaching identity, with participants reporting greater self-efficacy, a stronger sense of belonging to the teaching community, and increased motivation to persist in the profession. The results underscore the potential of integrating CBL and learning sciences principles into accelerated teacher preparation programs to enhance both cognitive and affective dimensions of teacher development. Full article

As an effective technical approach for ecological environment protection in mining areas and coal resource recovery under buildings, railways and water bodies, solid backfill coal mining technology has been widely applied. When gangue was used as backfill material and placed into the goaf, its compression characteristics and crushing behavior were found to directly affect the control effect of overlying strata deformation. In this study, combined with the compression characteristics of gangue solid waste backfill materials, eight kinds of gangue solid waste backfill materials with different particle size gradations were adopted as research objects. From the perspectives of stress–strain compaction characteristics, the coupling relationship between internal crushing and acoustic emission (AE), relative density in the compacted state and particle size distribution, the hierarchical crushing behavior, and the AE response characteristics of gangue solid waste backfill materials under different gradation schemes were systematically revealed, and the optimal gradation parameters for different layers were determined. The results showed that the compaction process of gangue solid waste backfill materials could be divided into three stages: initial compression, rapid compaction and plastic compaction. During the compaction process, internal crushing was mainly concentrated in the middle layer. In the initial stage of the test, the AE intensity of the middle layer was measured to be higher than 78%, and the AE intensity remained above 50% in the compacted state. When the specimen was compressed to 220 mm, all eight gradation schemes exhibited the characteristic that the proportion of locating points and energy level in the middle layer were much higher than those in the upper and lower layers. With the continuous increase in axial pressure, the intensive area of crushing events was observed to migrate in the order of middle layer → upper layer → lower layer. With the continuous increase in axial pressure, the intensive area of crushing events was observed to migrate in the order of middle layer → upper layer → lower layer. The findings obtained in this study have provided a theoretical basis and experimental support for the gradation optimization of gangue solid waste backfill materials and roof deformation control in solid backfill coal mining engineering. Full article

The long-term prestress relaxation of soil anchors embedded in saturated clay is a critical issue affecting the safety of geotechnical structures such as slopes and foundation pits. Traditional integer-order constitutive models are often unable to accurately describe the nonlinear and time-dependent relaxation behavior observed in such anchorage systems. Based on fractional calculus theory, this study establishes a constitutive model for the coupled stress relaxation behavior of soil anchors and saturated clay. The Riemann–Liouville fractional derivative and the two-parameter Mittag-Leffler function are introduced to represent the material memory effect and continuous relaxation characteristics. To achieve reliable parameter identification, a hybrid optimization strategy combining the Adaptive Hybrid Differential Evolution (AHDE) algorithm and the Levenberg–Marquardt (L-M) method is proposed. The proposed model and identification approach are validated using field monitoring data from soil anchors in a slope engineering project at the Guangxi Friendship Pass Port. The results show that the proposed model can accurately reproduce the entire stress relaxation process, with a coefficient of determination of R² = 0.9517. Parameter sensitivity analysis further clarifies the influence of key parameters, including the fractional order and viscosity coefficient. The proposed approach provides a systematic theoretical framework and practical reference for the analysis and prediction of long-term prestress relaxation in soil anchorage systems. Full article

Three-dimensional printed concrete (3DPC) has emerged as an innovative construction technology for extreme environments, offering advantages in thermal insulation, reduced labor requirements, and rapid construction. However, this layer-by-layer deposition process brings interlayer effects that affect mechanical anisotropy, permeability, and thermal performance, posing challenges for structural reliability. This review systematically examines current methods for characterizing and mitigating interlayer effects in 3DPC. Material-related factors—including admixtures, aggregates, recycled materials, fibers, and geopolymer incorporation—alongside process parameters such as printing speed, nozzle geometry, layer height, interlayer time, and environmental conditions, are analyzed for their influence on interlayer quality. State-of-the-art techniques for evaluating interlayer voids, mechanical behavior, and thermal performance are summarized. Moreover, results from micro-imaging, mechanical testing, and heat transfer assessments are also introduced. Ultimately, strategies for optimizing material composition and printing parameters to improve interlayer bonding and overall performance are highlighted. Overall, this paper provides a methodological framework to guide the design, testing, and practical implementation of 3DPC in demanding engineering applications. Full article