Search Results (10,215)

Underwater images often suffer from mixed degradations, including motion blur, which reduce structural clarity and adversely affect downstream vision tasks. To address this problem, we propose a physically guided Transformer framework for underwater motion deblurring. The proposed method combines two-stage cepstrum-based blur estimation with a point spread function (PSF)-guided self-attention mechanism. Specifically, blur parameters are first robustly estimated through cepstrum analysis, ellipse fitting, and negative-peak refinement, and the resulting PSF is then embedded into the Transformer attention module to guide feature aggregation. On the real underwater benchmark datasets UIEB Challenge-60 and EUVP330, the proposed method achieves UIQM/UCIQE scores of 4.09/0.56 and 3.40/0.58, respectively, significantly outperforming UFPNet and Phaseformer, thereby demonstrating superior perceptual restoration in terms of sharpness, contrast, and color consistency. On the synthetic test set, the proposed method attains 24.23 dB PSNR and 0.918 SSIM, outperforming both recent deep models and classical non-blind deconvolution methods, which confirms its strong restoration fidelity and structural consistency. In the controlled water-tank experiments, the proposed method consistently achieves the best performance under different camera motion speeds, demonstrating excellent robustness and practical applicability. Overall, the proposed framework provides an effective and physically interpretable solution for underwater motion deblurring. Full article

The WELL Building Standard (WELL) is a certification system designed to enhance occupant health and well-being in indoor environments. Conventional building energy-saving strategies typically rely on fixed temperature setpoint adjustments, which may conflict with WELL thermal comfort requirements. However, achieving high energy efficiency remains essential. This study uses a quantitative evaluation framework with TRNSYSs to examine the effectiveness of integrating load reset control (LRC) into simultaneous heating and cooling (SHC) systems. It compares LRC with conventional fixed setpoint (SP) and predicted mean vote (PMV) control strategies, based on WELL’s thermal comfort criteria (maintaining the PMV between −0.5 and +0.5). Six simulation cases were analyzed, considering radiant (RAD) and convection (CONV) terminals. The results indicate that radiant terminals provide more stable PMV performance while consuming less energy than convection terminals, demonstrating better compliance with WELL objectives. Although PMV control achieves the highest thermal comfort, it substantially increases energy consumption. In contrast, LRC emerges as an optimal strategy, effectively balancing the energy efficiency of SP control with the comfort of PMV control. The RAD-LRC configuration delivers the best overall performance. It achieves higher thermal comfort than SP, with comparable energy consumption, making it a highly practical approach for modern building energy management. Full article

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. Full article

The rapid deterioration of road infrastructure requires accurate and efficient methods for detecting pavement distresses. Unmanned Aerial Vehicles (UAVs) have emerged as a reliable alternative to conventional inspection techniques, enabling high-resolution data acquisition and improved operational safety. This study investigates the application of the Smart Oblique Capture (SOC) technique for pavement inspection through a systematic calibration of UAV flight parameters, including Ground Sample Distance (GSD), frontal and lateral overlap, camera tilt angle, and flight pattern. A structured experimental campaign was conducted, comprising 135 parameter combinations evaluated across three independent scenarios, resulting in a total of 405 UAV flights. The analysis focused on assessing the impact of these parameters on the visual quality of two-dimensional pavement reconstructions and processing efficiency. The results show that a configuration consisting of a 0.5 cm/pixel GSD, 70% frontal overlap, 80% lateral overlap, and a 70° camera tilt angle achieves the best balance between reconstruction quality and computational cost. Furthermore, the findings indicate that Smart Oblique Capture does not provide a statistically significant improvement in reconstruction quality for linear infrastructure compared to conventional oblique configurations, despite requiring a higher number of images and longer processing times. Overall, the results demonstrate that flight parameter calibration plays a more critical role than the adoption of advanced acquisition strategies such as Smart Oblique Capture. This study provides practical and reproducible guidelines for UAV-based pavement inspection, supporting efficient data acquisition while minimizing redundant information and unnecessary computational costs in infrastructure monitoring workflows. Full article

Background/Objectives: Chronic liver diseases, including metabolic dysfunction-associated steatohepatitis (MASH) and chronic viral hepatitis (CVH), are major global health concerns due to their potential progression to cirrhosis, liver failure, and hepatocellular carcinoma. Because liver biopsy, despite meeting the diagnostic gold standard, is invasive and associated with complications, non-invasive fibrosis assessment tools have been increasingly recommended in clinical practice. This study aimed to compare the diagnostic performance of several non-invasive fibrosis markers (ARR, APRI, FI, FIB-4, API, NFS, BARD) and transient elastography in detecting advanced liver fibrosis (F4) in patients with MASH and CVH. Methods: This retrospective study included 237 adult patients (77 MASH, 160 CVH) who underwent liver biopsy between 2017 and 2025 at the University Clinical Center of Serbia. CVH included chronic hepatitis B (CHB) and C (CHC). Patients were evaluated using serum fibrosis indices and TE, and results were compared to histological staging (F0–F4). ROC analysis assessed diagnostic performance. Results: Cirrhosis (F4) was more common in CVH than MASH (p < 0.001). In MASH, NFS (AUROC 0.931), FIB-4 (0.915), BARD (0.872), and APRI (0.878) showed high diagnostic accuracy for F4. In CHC, APRI (0.931), FIB-4 (0.863), and TE (0.938) had strong performance, while in CHB, TE (0.987) outperformed FIB-4 (0.821). Sensitivity and specificity varied by test and cohort, with TE consistently yielding the best results where available. Conclusions: Non-invasive methods, particularly NFS and FIB-4 for MASH and TE for CVH, effectively identify advanced fibrosis. Their application could significantly reduce the need for biopsy, especially in high-risk groups. TE demonstrated superior accuracy, but access limitations highlight the continued relevance of serum-based scores. Full article

A k-partite graph is one whose vertices can be partitioned into k disjoint partite sets, with edges allowed between but not within these sets. In such a graph, a maximal k-partite clique is a subgraph with at least one vertex from each partite set and every allowable edge such that the subgraph cannot be enlarged by the incorporation of additional vertices. A maximum k-partite clique is of course a maximal k-partite clique of the greatest size. The results reported here describe a novel and practical modification of the best previously published algorithm for the enumeration of these special subgraphs. The relative performance of this new method relies on implicit edge addition and search tree pruning and is evaluated on graphs constructed from both pseudorandom and real-world data. Full article

Providing reliable sensory feedback is one of the most challenging aspects of transfemoral prosthetics, motivating the development of intuitive vibrotactile interfaces capable of conveying information about limb position in real-time. The aim of this study was to develop a vibrotactile feedback prototype and examine which interstimulus intervals (ISIs) and vibration waveforms might best enhance recognition of sequential tactile patterns. The results will be used to inform the development of a prototype to be tested on participants with transfemoral amputation where prosthetic feedback is provided. A forearm-mounted six-actuator feedback system, encoding eight lower-limb configurations, was used in two experiments with healthy adults. Experiment 1 assessed recognition accuracy across ISIs from 10 to 110 ms, while Experiment 2 compared sinusoidal and square waveforms under matched conditions. Recognition accuracy was high across all tested conditions, with no significant effects of ISI (p = 0.79) or waveform type (p = 0.17). These results indicate that participants were able to interpret spatially distributed vibrotactile patterns even under rapid temporal sequencing and with differing signal shapes. The system therefore offers design flexibility for real-time prosthetic feedback, suggesting that fast update rates may be achievable without a statistically detectable reduction in perceptual clarity within the tested conditions. These findings provide practical guidance for developing robust, user-friendly sensory substitution systems intended to increase proprioceptive awareness in transfemoral prosthesis users. Full article

Spectral remote sensing enables efficient acquisition of large-scale land surface information and is a key approach for monitoring soil salinity content (SSC). However, surface mulching significantly alters the spectral reflectance responses of croplands, increasing the uncertainty of SSC retrieval using remote sensing. This study aimed to systematically identify SSC-sensitive spectral features under different mulching conditions and to evaluate the effects of spatial resolution on SSC–spectral relationships. Multi-resolution datasets were constructed based on plastic mulch geometric parameters, and SSC–spectral relationships were analyzed using correlation methods and recursive feature elimination (RFE). Results indicate that under near-ground ultra-high-resolution conditions, the correlation between inter-mulch bare soil spectral features and SSC was weakly influenced by mulch type, and distinguishing mulch types provides limited improvement in inter-variable relationships. Pearson’s r exceeded 0.40 for both white- and black-mulched samples, and distinguishing mulch types provided only marginal gains in model accuracy (RFR–RFE R² = 0.9524 for white-mulched and 0.9252 without distinguishing; R² = 0.9387 for black-mulched). In contrast, under multi-resolution settings at the field scale, separating black-mulched, white-mulched, and non-mulched fields significantly enhanced the correlation between spectral indices (SIs) and SSC, with the coefficient of determination (R²) based on the recursive feature elimination (RFE) algorithm increasing by up to 0.28. The highly sensitive SIs of non-mulched farmland are generally consistent with those of white-mulched farmland but differ markedly from those of black-mulched farmland. Scale optimization analysis further indicated that the optimal spatial resolution was 1.35 m for white-mulched and non-mulched farmland. Black-mulched farmland performed best at 5.4 m, likely because stronger spectral masking by black mulch increases mixed-pixel dominance and benefits from spatial aggregation. These findings provide methodological guidance and practical approaches to accurately retrieve SSC in plastic-mulched croplands and to determine the optimal image spatial resolution. Full article

Reliable determination of radon adsorption properties in candidate adsorbents is essential for developing highly sensitive methods capable of measuring low ²²²Rn activity concentrations in air. Such measurements are increasingly important in environmental monitoring, climate research, and low-background experiments. Conventional approaches for determining the adsorption coefficient and heat of adsorption are labor- and time-intensive, limiting their suitability for comparative studies under identical conditions. Here, a recently proposed method is applied for the first time in a systematic comparative study. The approach couples solid-state nuclear track detectors (SSNTDs) with adsorbents that simultaneously act as radon collectors and alpha emitters, enabling fully parallel exposure and signal acquisition across multiple samples. Eight adsorbents—three activated carbon fabrics, two bulk activated carbons, and three synthetic zeolites—were evaluated simultaneously over a temperature range of 0–46.5 °C. Activated carbon fabrics exhibited the highest adsorption coefficients, with ACC-5092-10 reaching 11.8 ± 1.3 m³/kg at 20 °C. The heats of adsorption ranged from 24.8 ± 3.9 to 33.3 ± 5.0 kJ/mol, consistent with the literature values. For synthetic zeolites, the adsorption coefficient increased linearly with the Si:Al ratio. The influence of water content was further investigated for the five best-performing materials. The most hydrophobic material, zeolite SA-25 (Si:Al = 25), showed only a 25% reduction in adsorption coefficient under saturated humidity, whereas activated carbons exhibited strong suppression. These results demonstrate the practicality, sensitivity, and efficiency of the SSNTD–adsorbent method for comparative radon adsorption studies. Full article

The development of high-performance copper alloys requires balancing mechanical strength and electrical conductivity, properties that are often inversely correlated due to competing strengthening mechanisms. This study presents a comparative machine learning analysis of Cu-Ni-Si and Cu low alloys using a curated dataset of 1690 entries derived from the Gorsse et al. database, comprising 1507 samples with hardness measurements and 1685 samples with electrical conductivity data. Three ensemble-based regression algorithms, Random Forest, XGBoost, and Gradient Boosting, were trained to predict Vickers hardness (HV) and electrical conductivity (%IACS) from an augmented feature set encompassing alloy composition, thermomechanical processing parameters, missingness indicators, and physics-informed descriptors (valence electron concentration, atomic size mismatch, electronegativity difference, and Ni:Si atomic ratio). XGBoost achieved optimal performance for hardness prediction (R² = 0.8554, RMSE = 29.90 HV), while Gradient Boosting performed best for electrical conductivity (R² = 0.8400, RMSE = 5.96%IACS). Averaged tree-based feature-importance analysis identified valence electron concentration as the most influential predictor for hardness (39.9%), followed by aging temperature (11.2%), while Cu content dominated conductivity prediction (37.7%), followed by aging time (8.9%). Complementary SHAP analysis confirmed these trends while revealing directional relationships and nonlinear feature interaction effects. Composition-grouped cross-validation by unique alloy formula (K = 10) yielded substantially lower performance, with grouped CV R² = 0.438 for hardness and 0.293 for conductivity, indicating that generalization to unseen alloy formulations remains limited. The models were further applied for practical tasks, including property prediction for new alloy compositions, processing parameter optimization via differential evolution with metallurgical constraints (achieving hardness up to 293.9 HV or conductivity up to 45.7%IACS for the same base composition, with prediction intervals reported), and inverse design to identify alloy formulations meeting specified target properties. This work demonstrates the potential of interpretable machine learning to support copper alloy development by enabling rapid computational screening of the compositional and processing parameter space, subject to the generalization limitations identified herein. Full article

Over the past two decades, Citizen Science (CS) has experienced rapid growth, driven by technological advancements and the rise of digital platforms. This work examines the necessity for standardization in Citizen Science data management and discusses how existing data standards can enhance the impact of citizen-generated data. CS standardization ensures data quality, comparability, reusability, and interoperability, making data suitable for contributing to the Marine Strategy Framework Directive (MSFD) and the United Nations Sustainable Development Goals (SDGs). This paper examined 130 Citizen Science publications and found that most collected data referred to the MSFD Descriptor 1 (Biodiversity—44.96%) and Descriptor 10 (Marine Litter—20.93%), followed by the alien species distribution (D2—11.63%), hydrography (D7—6.20%), eutrophication (D5—6.20%), and marine pollution (D8—3.10%). Analysis of 108 publications on SDG alignment revealed that the majority (35.58%) focused on reducing marine pollution. This paper reviews the best practices for effective Citizen Science data management, including standards for data structures, content, values, and exchange. Based on this review, Darwin Core, Ecological Metadata Language (EML), and the OGC SensorThings API appear to be the most suitable standards for MSFD-relevant CS data. Therefore, policymakers could enable the formal integration of standardized CS datasets into MSFD monitoring workflows. Full article

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises. Full article

Building Information Modeling (BIM) has fundamentally changed the way interdisciplinary coordination works in construction projects; however, the theoretical mechanisms underlying open collaboration standards in this field remain insufficiently explored. This article fills this gap by presenting a systematic analysis of the BIM Collaboration Format (BCF) through the lens of reification and serialization, two fundamental concepts in information systems theory. Although the BCF format is widely used in the industry and implemented in major BIM tools for clash detection and issue tracking, the existing literature treats it primarily as an operational tool, overlooking the deeper information systems principles that govern its architecture. The analysis demonstrates that BCF achieves reification by transforming informal coordination knowledge—such as verbally communicated clashes, scattered email threads, and undocumented design decisions—into first-class objects (Topic, Comment, Viewpoint) equipped with unique identifiers, typed attributes, ownership, temporal metadata, and formalized inter-object relationships. Further analysis was conducted on BCF’s serialization mechanisms, including XML encoding for file exchange, JSON for RESTful API communication, and ZIP archiving as a distribution container, each of which was selected to balance human readability, schema validation, compression, and cross-platform portability. The complementarity of these two mechanisms was examined: reification determines what to preserve and in what structure, while serialization determines how to encode and in what format, which together enable interoperable, auditable, and automatable coordination workflows in heterogeneous software environments. The analysis was illustrated with a real-world BCF example from a major infrastructure project in Poland, demonstrating practical alignment between theoretical constructs and their implementation. The research results provide both a conceptual foundation for researchers working on openBIM standards and practical guidance for practitioners seeking to optimize issue management, the implementation of a Common Data Environment (CDE), and the specification of Exchange Information Requirements (EIR). The study contributes new knowledge in three areas: (1) To the best of the authors’ knowledge, it provides the first systematic theoretical analysis of BCF through the lens of reification and serialization, filling a gap between the format’s widespread practical use and its limited theoretical understanding. (2) It demonstrates how the formal criteria of reification (unique identity, typed attributes, ownership, temporal metadata, and inter-object relationships) map onto specific BCF entities, offering a transferable analytical framework for evaluating other openBIM standards. (3) It identifies the complementarity of reification and serialization as a design principle that can guide the development of future standards for digital twins and IoT-based facility management. Full article

Dye-sensitized solar cells (DSSCs) remain attractive as low-cost photovoltaic devices; however, their practical efficiency is still constrained by electron-transport losses, interfacial recombination, and incomplete light harvesting in conventional titanium dioxide (TiO₂) photoanodes. The effects of TiO₂ film thickness, multi-walled carbon nanotube (MWCNT) incorporation, and multilayer oxide interface engineering on DSSC performance were examined. Degussa P25-TiO₂ photoanodes were first optimized with respect to thickness, after which controlled MWCNT loadings and sequential compact sol–gel TiO₂ and tin dioxide (SnO₂) sublayers were introduced. The optimum pristine P25-TiO₂ photoanode thickness was 9.11 μm, yielding an open-circuit voltage of 0.74 ± 0.01 V, a short-circuit current density of 14.10 ± 0.40 mA/cm², a fill factor of 56.24 ± 1.00%, and a power-conversion efficiency of 5.93 ± 0.20%. The incorporation of 0.025 wt% MWCNTs increased the efficiency to 6.04 ± 0.20%, corresponding to an absolute gain of 0.11 percentage points. The best performance was obtained with the sol–gel SnO₂/sol–gel TiO₂/P25-CNT multilayer photoanode, which delivered 0.74 ± 0.02 V, 16.22 ± 0.40 mA/cm², 57.59 ± 1.00%, and 6.89 ± 0.30%, respectively. FE-SEM, EIS, XRD, Heated Ultrasonic Cleaner and UV–visible analyses indicate that the multilayer architecture preserves porosity, enhances light harvesting, and suppresses interfacial recombination, while the CNT network facilitates charge transport. Full article

The increasing use of antibiotics around the globe has contributed to an increase in antimicrobial resistance and become a major risk to both public health and sustainable development. Reliable and fast detection of antibiotic residues in clinical, agricultural, and environmental matrices is required to monitor antimicrobial resistance effectively. The conventional analytical techniques are sensitive, but they are also expensive, complex and lacking in portability. Voltammetry is a recently emerging electrochemical detection technique that is low-cost and rapid. To the best of our knowledge, for the first time, a meta-analysis was conducted on graphene-based electrochemical sensors and biosensors for antibiotic detection over the last decade. This systematic review critically examines the analytical properties of sensors and biosensors, the physicochemical properties of antibiotics, adsorption characteristics, and the use of nanoparticles to improve the selectivity and sensitivity of devices. This review critically examines the cost-effectiveness, scalability, and practicality of point-of-use devices using graphene-based sensors and biosensors. This systematic review also discusses the potential risks to human health from antibiotic contamination and the role of monitoring in contributing to achieving the UN’s Sustainable Development Goals. This systematic review identifies a gap between developing sensors in laboratories versus their deployment as field-deployable devices; it highlights challenges associated with stability, matrix effects and the complexity of manufacturing devices. Finally, it provides recommendations for future research that may help to address this gap to promote the transition of innovative devices from academic to practical applications. Full article