Search Results (3,492)

Large-span bridges are critical components of transportation networks. Environmental variability, material degradation, and cumulative fatigue continuously affect their long-term performance. Digital Twin (DT) technology has emerged as a promising paradigm for integrating sensing, modeling, and data analytics. Most existing DT implementations in civil infrastructure rely on dense sensor networks, assume near-complete observability, and primarily serve as passive visualization or diagnostic tools, limiting their scalability and practical applicability. This paper proposes a DT framework specifically designed for the monitoring and management of existing large-span bridges under sparse sensing conditions. The framework adopts an information-centric perspective in which limited physical measurements are complemented by full-field state reconstruction through the integration of physics-based modeling, data-driven learning, and uncertainty-aware inference. A synchronized reference configuration, termed State 0, is introduced as the initial basis for tracking structural changes over time, while allowing conditional re-baselining through a Dynamic State 0 (DS0) when verified reassessment justifies it. On this basis, the proposed DT is formulated as an adaptive and decision-oriented cyber–physical system that supports optimization-based recommendations for sensing, inspection, and maintenance planning. Full article

The hyporheic zone (HZ) mediates biogeochemical exchanges between rivers and aquifers, yet its spatial and temporal dynamics in large, regulated rivers remain poorly characterized due to limitations of point-based measurements. Here, we combined three time-lapse electrical resistivity tomography (T-ERT) surveys with continuous hydrological and hydrochemical monitoring along a meandering reach of the lower Yellow River, generating a two-dimensional, profile-integrated view of HZ geometry under three hydrodynamic states: low flow (1 December 2020), natural rising stage (1 March 2021), and peak stage during the Xiaolangdi (XLD) water-and-sediment regulation (1 July 2021). Absolute tomograms identified two hydrostratigraphic units: an upper sandy-silt cap (35–170 Ω·m) and an underlying sand aquifer (12–35 Ω·m). Percent-difference tomograms, relative to the low-flow baseline, revealed lateral HZ expansion from ~15 m and vertical growth of 2.5 m at the rising stage to ~36 m and 4.5 m at peak stage, with local resistivity decreases exceeding 38%. In contrast, the deeper mixing zone varied by <10% across surveys. Temperature, rainfall infiltration, and groundwater freshening could not explain the observed patterns. These results were corroborated by three independent lines of evidence: lateral conductivity excursions and in-well temperature records at floodplain well W2, and analytical Darcy–Archie calculations, all consistent with the predicted lateral extent and mixing fraction. River stage, amplified by the XLD release, emerged as the dominant control on two-dimensional HZ geometry. This study provides direct empirical evidence of hyporheic dynamics in a large regulated river and demonstrates that T-ERT, supported by sparse hydrological data, offers a minimally invasive and effective tool for characterizing hyporheic zones. Full article

Floating belt conveyor routes consisting of serially arranged belt conveyors, the end parts of which are mechanically attached to floating bodies, are designed for the continuous transport of extracted granular materials from water. This paper deals with the analytical determination of the position of the centre of gravity of the buoyancy force, the coordinates of which change depending on the longitudinal deflection of the floating body from the equilibrium state, which acts as a supporting element of individual conveyor belts. Analysis of the individual phases of deflection of the floating body, consisting of a pair of floats with a circular cross-section, shows that the complete submergence of one of the floats occurs at a higher value of the angle of inclination in the case when the floats are initially submerged under the surface to exactly half their diameter. On the realized experimental device, the buoyancy force was detected using strain gauges during the deflection of the floating body from the equilibrium position for three defined levels of immersion. The floating body of the experimental device consists of a pair of floats with a circular cross-section with a diameter of 80 mm. The output is a structured methodological procedure for determining the position of the centre of gravity of the displacement (centre of buoyancy) of a floating body when it deviates from the equilibrium position and a methodology for calculating the stability arm, which is a key parameter for assessing the buoyancy and stability of the body. On the basis of the laboratory measurements, the magnitude of the buoyancy force can be quantified as a function of the immersion depth of the floating body. It was found that the buoyancy force remains constant when the body deflects only when the immersion corresponds to half the diameter of a float with a circular cross-section. If the depth of the immersion is less than the radius of the float, the buoyancy force increases during deflection; however, if the immersion is greater than the radius of the float, the buoyancy force decreases. Full article

Engineering education is increasingly shifting toward data-driven and creativity-centred pedagogies that foster innovation, communication, ethical awareness, and teamwork. However, traditional Problem-Based Learning and Design Thinking approaches rely heavily on subjective evaluation and lack scalable mechanisms for monitoring learning progression and creativity development. These pedagogical limitations highlight the need for data-driven approaches that can support iterative learning processes, continuous feedback, and objective evaluation of creativity and performance. This study proposes a Generative Artificial Intelligence (GenAI)-driven predictive analytics framework for modelling student performance and creativity in engineering design systems. The framework integrates deep learning architectures, including Long Short-Term Memory (LSTM) networks and Transformer-based multimodal fusion, to analyze temporal and heterogeneous learning data. The novel Creativity Index (CI) is introduced to quantify design innovation by combining novelty and feasibility metrics derived from AI-assisted interactions and project milestones. The model was evaluated on a longitudinal dataset comprising 450 students across 10 semesters (~5400 time-series observations). Experimental results demonstrate strong predictive performance, achieving 89% classification accuracy and RMSE of 3.8. Comparative analysis shows significant improvements in engineering design (+15%), communication (+16%), ethical awareness (+17%), and teamwork (+16%) (p < 0.01). The proposed framework enables real-time feedback, early risk detection, and adaptive learning optimization. These findings highlight the potential of integrating generative AI and predictive analytics to develop scalable, data-driven intelligent learning systems. Full article

Rising global demand for safe, high-quality foods has accelerated the development of rapid, sensitive, and cost-effective analytical technologies for detecting harmful substances and quality markers. Electrochemical sensors have emerged as promising tools for food safety monitoring due to their high sensitivity, fast response, portability, and affordability compared with conventional laboratory methods. This review highlights recent advances in nanostructured electrochemical sensors for detecting key food analytes, including antioxidants, mycotoxins, allergens, and flavor compounds in diverse food matrices. It examines advanced nanomaterials such as metal oxides, MXenes, doped carbon nitrides, and noble metal-decorated graphene, which enhance sensor performance through improved surface area, conductivity, and electrocatalytic activity. Integrated with screen-printed or glassy carbon electrodes, these materials achieve ultra-low detection limits, wide linear ranges, and strong selectivity in complex food systems. The review also explores next-generation applications such as NFC-enabled smart packaging for continuous, non-invasive monitoring across the supply chain. Emerging trends in miniaturization, multiplex sensing, and artificial intelligence are discussed, along with key challenges in translating laboratory innovations into practical commercial solutions for global food safety. Full article

Background/Objectives: The hepatitis A virus (HAV) infection continues to represent a considerable public health issue in Eastern Europe, particularly in Bulgaria, where incidence rates exceed the EU average. This study sought to investigate the epidemiological and clinical aspects of acute hepatitis A in Southern Bulgaria between 2015 and 2023 and to assess changes during the COVID-19 pandemic period. Methods: A retrospective descriptive-analytic study was conducted among 1810 hospitalized patients with confirmed acute HAV infection at a tertiary infectious diseases center from 2015 to 2023. Demographic, clinical, laboratory, and temporal data were analyzed, comparing the pre-pandemic period (2015–2019) with the pandemic phase (2020–2023). Results: Most hospitalized cases occurred during the pre-pandemic period (88.0%), with epidemic peaks observed in 2016–2017. Individuals under 18 years comprised 69.9% of cases, with a median age of 9 years and a slight male predominance of 54.9%. A notable seasonal pattern was identified, characterized by peaks in autumn and early winter. Patients hospitalized during the pandemic period were significantly older compared with the pre-pandemic period (median age 14 vs. 8 years, p < 0.001). Adults experienced significantly longer hospitalization and higher ALT, AST, total bilirubin, and direct bilirubin levels compared with pediatric patients (all p < 0.001). The median duration of hospitalization was 7 days (IQR 6–10). Two in-hospital deaths were recorded, corresponding to a case fatality rate of 0.11%. Conclusions: Hepatitis A in Southern Bulgaria mostly impacts children but exhibits changing epidemiological trends, underscoring the necessity for focused preventative methods, such as vaccination and enhanced surveillance. Full article

The development of offshore wind energy in tropical cyclone-prone regions requires analytical frameworks that capture non-stationary climate dynamics. This study presents a multi-scale spectral approach to characterize Atlantic tropical cyclone variability and assess implications for offshore wind resilience in the Caribbean Basin. The methodology integrates Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) to resolve temporal variability in sea surface temperature, cyclone frequency, and intensity, complemented by two-dimensional kernel density estimation (KDE) and non-stationarity analysis. Using NOAA and National Hurricane Center datasets, results identify dominant periodicities at annual and ENSO (2–7 year) scales, a post-1995 spectral energy shift associated with the positive AMO phase, and a thermodynamically consistent energy corridor along 12–16° N. A statistically significant change point in 1987 (Pettitt test, p < 0.05) is detected, although spatial displacement is not significant. An integrated Wind Risk Index highlights the central-western Caribbean as a high-exposure zone overlapping offshore wind development areas. Exceedance analysis shows that 39.8% of observations surpass 25 m/s, 6.0% exceed 50 m/s, and 1.3% approach 70 m/s, indicating relevant design considerations. These findings support the need for non-stationary, multi-scale approaches in offshore wind risk assessment under tropical cyclone influence. Full article

Fluid flow in fractured-vuggy carbonate reservoirs is characterized by extreme multiscale heterogeneity, where the coexistence of tight matrix rock and macroscopic cave challenges traditional Darcy-based continuum models. This paper presents a semi-analytical solution for two-phase immiscible displacement in a one-dimensional composite porous–cavernous–porous (PCP) system. The main feature of the model is that the cave region is treated separately from the porous domains: classical Darcy flow is used in the surrounding matrix, whereas an idealized free-flow representation is introduced for open caves based on a simplified one-dimensional treatment of the cave momentum balance. To elucidate the impact of distinct flow regimes on displacement dynamics, three physical models are compared for the cave region: (1) an open-cave model represented by a simplified free-flow formulation; (2) a filled-cave non-Darcy model governed by the Forchheimer equation using the Ergun correlation; and (3) a creeping-flow model governed by Darcy’s law. A piecewise semi-analytical solution procedure is established to enforce flux continuity, characterize interfacial state remapping, and determine the downstream front under global water-balance closure. The results show that both cave geometry and internal cave-flow mechanism critically control water-front advancement. While the open-cave model exhibits piston-like displacement behavior with high local displacement efficiency but stronger preferential flow, the Forchheimer model shows that inertial resistance can modify the saturation profile and delay breakthrough relative to the Darcy prediction. The proposed framework provides an idealized theoretical reference for benchmarking numerical simulators and for interpreting waterflooding behavior in complex vuggy reservoirs under one-dimensional, incompressible, gravity-free, and capillarity-free conditions. Full article

Decarbonizing the building sector requires integrating on-site renewable generation with systematic energy management. Among the most widely adopted alternatives are photovoltaic (PV) systems in buildings; however, they are often implemented as a standalone technological intervention (size–install–estimate savings), without being formally incorporated into an Energy Management System (EnMS) aimed at continuous improvement. In this context, this research addresses this gap through an integrated methodological framework aligned with ISO 50001, in which PV is explicitly included in energy performance management through energy review, the definition of an Energy Baseline (EnB), and the monitoring of Energy Performance Indicators (EnPIs) within the PDCA cycle. The approach articulates the analytical sizing of the PV system based on electricity demand and solar resources; its validation through simulation to ensure operational consistency and a technical, economic, and environmental assessment that translates PV generation into a verifiable reduction in energy imported from the grid and, consequently, into traceable improvements in EnPI under an audit-compatible scheme. The methodology is demonstrated in a multi-family building in Chorrillos, Lima (Peru), where a 14.5 kWp rooftop PV system (25 modules of 580 Wp) is designed to maximize self-consumption during daylight hours. The results show technical performance consistent with the demand profile, economic viability under the conditions of the case, and environmental benefits from replacing grid electricity, along with offsets associated mainly with the manufacture of PV components. The residual gap between the Post-PV EnPIs and the ISO 50001 target confirms that PV integration is a necessary but not sufficient first-cycle action within a comprehensive building decarbonization strategy, with demand-side management and envelope improvements identified as subsequent PDCA cycle priorities. In summary, the central contribution is not the PV sizing itself, but its operational and traceable integration within ISO 50001, making PV a quantifiable, verifiable, and scalable energy improvement action for residential buildings in emerging economies. Full article

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article

This paper systematically reviews losartan, a hypertension pharmaceutical compound that is one of many newly identified emerging contaminants in water. Worldwide use of pharmaceuticals continues to grow, and losartan has been identified as a contaminant that frequently accumulates in aquatic systems as a result of this global increase in use. The paper presents systematic reviews on the environmental occurrence, physicochemical characteristics, analytical methods of detection, and remediation techniques associated with losartan contamination. Losartan is often detected at levels of ng L⁻¹–µg L⁻¹ in wastewater systems, surface water and marine ecosystems, very effectively demonstrating the inadequacies of existing conventional wastewater treatment facilities, which are typically capable of removing only 20–70% of the contamination, with this variability largely attributed to differences in hydraulic/solids retention times, operational conditions, influent organic load, and the limited microbial acclimatization to recalcitrant pharmaceutical compounds. Emerging remediation technologies demonstrate the potential for removal efficiencies of >90% include hybrid systems, advanced electrochemical processes, new improved adsorption systems, and novel material for adsorption. However, there are still considerable barriers to progress, including excessive energy use, high operating costs, and perhaps most concerning, potentially toxic transition products generated by partial degradation. Furthermore, the literature review identified key literature gaps: lack of specific regulations, absence of full-scale studies, and inconsistencies in by-product toxicity assessments. The conclusion of this review is that to achieve worldwide water security and sustainability of aquatic resources, effective mitigation of the environmental risks associated with losartan requires combined approaches comprising innovative technologies, comprehensive ecotoxicological investigations, and improved collaboration between scientists, policymakers, and industry. Full article

This paper develops an analytical treatment of vibro-acoustic wave propagation in a cylindrical waveguide containing two clamped elastic membranes and a central flexible-shell segment. The acoustic field obeys the time-harmonic Helmholtz equation, the shell motion is described by Donnell–Mushtari thin-shell theory under axisymmetric loading, and the membrane response is governed by classical membrane theory and incorporated through a tailored Galerkin scheme. The resulting coupled fluid–structure boundary-value problem is solved by the Mode-Matching Method: the acoustic potentials are expanded in orthogonal radial eigenfunctions within each subregion, and continuity of pressure, normal velocity, and structural displacement are enforced at every interface. The mirror symmetry of the configuration is exploited by an exact decomposition into symmetric and anti-symmetric sub-problems, each of which reduces to a truncated linear algebraic system of dimension $4N+4$ for the unknown modal amplitudes. Acoustic power-balance identities provide a quantitative consistency check on the numerical implementation and diagnose convergence with respect to the truncation order; structural damping is accommodated through complex-modulus substitutions for the shell and the membrane tension without altering the algebraic structure of the system. The numerical results demonstrate that the dual-membrane configuration delivers transmission-loss values exceeding $25\mathrm{dB}$ across the low-frequency band relevant to HVAC and automotive applications, with a representative plateau near $13\mathrm{dB}$ at the reference geometry, through resonance-driven modal coupling between the acoustic field and the compliant interfaces. Parametric studies identify the excitation frequency, the inner-membrane radius, the shell radius, and the chamber length as effective design parameters for tuning the attenuation. The formulation furnishes a unified and computationally efficient analytical tool for predicting and optimising noise attenuation in flexibly coupled cylindrical duct systems. Full article

Neck pain is increasingly associated with exposure-dependent dysfunction linked to digitally mediated behaviors, prolonged near-work, sustained postures, and reduced movement variability, whereas cervical assessment remains dominated by static imaging and brief in-clinic examination. This narrative review evaluates why current diagnostic approaches remain poorly suited to the dynamic nature of many contemporary cervical disorders and examines segmental cervical motion as a clinically relevant but insufficiently observed functional target. Evidence from static imaging, dynamic radiographic methods, laboratory motion analysis, wearable inertial sensing, markerless video, and digital measure validation frameworks is synthesized to assess both current capabilities and translational limitations. Dynamic radiographic methods can characterize intervertebral motion with high anatomical specificity, but they are not suitable for scalable longitudinal monitoring. By contrast, wearable and video-based approaches are more compatible with real-world assessment, yet they capture external head–neck kinematics rather than vertebral-level kinematics directly and remain constrained by indirect observability, soft-tissue artifact, and inference uncertainty. On this basis, the review proposes a four-layer framework for continuous non-invasive cervical functional assessment based on sensing, representation, inference, and clinical interpretation, in which segmental cervical behavior is treated as a latent segment-informed functional construct inferred from multimodal external signals and periodically anchored to sparse reference-grade imaging anchors. Segmental motion signatures are consequently positioned as candidate digital measures for longitudinal cervical monitoring, provided that their development is supported by rigorous analytical and clinical validation, explicit uncertainty reporting, and demonstrated incremental clinical value. Full article

Background: Internal quality control (IQC) data offers continuous insight into analytical performance under routine conditions. This study evaluated IQC practices and long-term analytical imprecision (CV_a) across primary healthcare laboratories to derive analyte-specific reference change values (RCVs) for non-communicable disease (NCD) monitoring. Methods: A 22-month retrospective analysis of IQC data was conducted across 29 primary healthcare laboratories using 32 analytical units (Beckman Coulter AU480) in Malaysian primary healthcare. Six analytes were assessed: glucose, creatinine, total cholesterol, triglycerides, HDL cholesterol, and ALT. CV_a was estimated using median and 90th percentile (P90) coefficients of variation across two concentration levels. RCVs were calculated at 95% probability (Z = 1.96) by integrating observed CV_a with within-subject biological variation (CV_i) from EFLM databases. Results: IQC testing was highly standardized (median: 20 measurements/month). Long-term data showed stable, concentration-dependent imprecision. Median CV_a was lowest for glucose and lipids (1.7–1.9%) but higher for ALT (3.79%) and creatinine (3.52%) at Level 1. Derived RCV ranged from 14% (glucose) to 55.1% (triglycerides), with CV_i being the dominant contributor to RCV magnitude for most analytes. Conclusions: Long-term routine IQC data provide an analytically realistic foundation for deriving RCV in primary healthcare by reflecting real-world performance. Applying these RCV supports evidence-based interpretation of serial results, enhancing NCD monitoring by distinguishing true physiological change from analytical and biological noise. Full article

As a form of living heritage, Historic Urban Landscapes (HULs) have long been limited by the static perspectives and reductionist tendencies of conventional conservation and research approaches. Although the geological and archaeological concept of “stratification” offers a methodological basis for understanding the diachronic evolution of heritage, its unidimensional temporal lens fails to capture the inherent complexity and systemic nature of historic urban landscapes. To address this gap, this study proposes a “multidimensional stratification” theoretical framework through theoretical critique and paradigm reconstruction. The framework introduces innovations at the ontological, epistemological, and methodological levels, positing that the evolution of historic urban landscapes emerges from the nonlinear interaction and dynamic interweaving of four core dimensions: time, space, society, and value. It further systematizes five intrinsic attributes of such landscapes: authenticity, integrity, continuity, adaptability, and dynamism. Building on this foundation, the paper constructs a systematic analytical pathway—elements–processes–patterns–modes–drivers–characteristics—that enables dynamic analysis from micro-level identification to macro-level generalization, offering a scalable tool for HUL conservation and regeneration. To demonstrate the framework’s applicability, the historic urban area of Shenyang—a nationally designated historical and cultural city—is selected as a case study. Its urban landscape comprises four core districts: the Shengjing City District, the South Manchuria Railway Concession District, the Commercial Port District, and the Tiexi Industrial District, representing historical strata from the Qing dynasty capital, modern colonial planning, commercial opening, to industrial heritage. Using the multidimensional stratification approach, this study elucidates the spatial complexity, temporal nonlinearity, social dynamism, and value pluralism embedded in Shenyang’s historic urban area. Corresponding conservation strategies grounded in holism, dynamism, and differentiation are proposed. The research not only advances the theoretical understanding of HUL but also provides a novel paradigm—integrating holistic, dynamic, and operational perspectives—for the conservation, renewal, and regenerative practice of historic urban landscapes worldwide. Full article