Search Results (19,629)

A methodology for deriving high-resolution (5-km) surface shortwave radiative (SWR) fluxes over the Arctic was applied to observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) during the spring and summer melt season (March–September) of 2007, when the Arctic experienced a historically significant and well-documented decline in sea ice extent. The derived SWR fluxes were used to estimate solar heat input into the Arctic Ocean during the melt season, a task that had not previously been undertaken at such high spatial resolution. According to the National Snow and Ice Data Center (NSIDC), Arctic sea ice extent reached a record minimum of 4.13 million km² on 16 September 2007, approximately 38% below the 1979–2000 climatological mean and 24% below the previous record minimum in 2005. This extreme reduction in sea ice resulted in several weeks of ice-free opening along portions of the ‘Northwest Passage’. Availability of high spatial resolution SWR fluxes in the Arctic is particularly important for improving estimates of solar heat input into the Arctic Ocean, especially within the highly heterogeneous marginal ice zone. To facilitate comparison with sea ice concentration products from NSIDC, the MODIS-derived 5-km SWR fluxes were aggregated to 0.25° equal-area grid cells (approximately 25 km resolution). Our results show that the abrupt increase in the open water fraction produced anomalies in solar heating to the upper ocean exceeding 300%, hereby enhancing the ice–albedo feedback mechanism and promoting further sea ice melt. The estimated monthly cumulative solar heat input to the ocean for a nominal 1° grid cell was 164.9 MJ m⁻² in May. In contrast, the corresponding four 0.25° sub-grid cells, resolved using the high-resolution MODIS data, exhibited cumulative heat inputs of 58.0, 93.0, 189.3, and 296.4 MJ m⁻², respectively. Although the average heat input for the 1° grid cell (165 MJ m⁻² was similar to the average value obtained from the four 0.25° grid cells (159 MJ m⁻² the substantial sub-grid variability is important because the oceanic and sea-ice responses to solar heating are highly nonlinear. Consequently, unresolved spatial variability can significantly affect the magnitude of derived quantities and associated feedback processes. These findings demonstrate the importance of high-spatial-resolution radiative flux information for accurately quantifying ocean heating and ice–ocean interactions in the Arctic. Full article

The digital documentation of existing buildings is particularly important when original construction drawings or reliable as-built records are unavailable. This study evaluates the feasibility and selected dimensional consistency of a locally registered Scan-to-BIM workflow integrating unmanned aerial vehicle (UAV) photogrammetry for exterior documentation and smartphone LiDAR for interior data capture. A two-storey reinforced-concrete building with unavailable original project documentation was selected as a single case study. Exterior images were acquired using a DJI Mavic 3E (DJI, Shenzhen, China), while interior spaces were scanned using an iPhone 16 Pro Max (Apple Inc., Cupertino, CA, USA) and Polycam v5.1.5 in LiDAR mode. The UAV images were processed in Agisoft Metashape Professional 2.2.0 to generate the exterior photogrammetric point cloud, and the smartphone LiDAR data were organised with this dataset in Autodesk ReCap Pro 2025. Both point clouds were then used as geometric references for creating a geometry-oriented as-is BIM model in Autodesk Revit 2025. To evaluate selected dimensional consistency, 32 independent field measurements collected using a steel tape measure and a laser distance meter were compared with corresponding BIM-derived dimensions. The dimensional comparison yielded a mean absolute error (MAE) of 29.56 mm, a root mean square error (RMSE) of 31.21 mm, a maximum absolute error (MaxAE) of 46.00 mm, and a mean signed error (MSE) of +29.56 mm. These results indicate centimetre-level dimensional consistency for the selected validation dimensions, with a small systematic positive offset in the BIM-derived dimensions. The workflow can support preliminary geometric documentation and general as-is BIM for a small existing building, but it does not demonstrate survey-grade georeferencing, full registration accuracy, modelling reproducibility, or general applicability without further testing. Full article

Artificial-source frequency-domain electromagnetic methods are important tools for deep mineral exploration and concealed geological structure detection. Apparent resistivity is a key parameter linking measured electromagnetic fields to the interpretation of subsurface electrical structures, and its calculation method directly affects geological interpretation and engineering applicability. Although substantial efforts have been devoted to the theoretical development, data processing, and practical application of different apparent resistivity formulations, most previous studies have focused on the analysis and improvement of a single method. Systematic comparisons of the main apparent resistivity formulations under unified conditions remain limited, particularly in terms of deep basement characterization, anti-interference performance, and engineering applicability. To fill this gap, this study systematically compares the $E--E_x$ wide-field apparent resistivity, the $E--E_x$ far-zone apparent resistivity, and the $E--Z_{xy}$ Cagniard apparent resistivity. Through theoretical derivation, forward modeling of typical one-dimensional models, and field verification, the differences among these three formulations in geological characterization, anti-interference capability, and engineering applicability are analyzed, with the aim of clarifying their applicable boundaries and selection principles for artificial-source frequency-domain electromagnetic exploration. Full article

Objectives: Three stability-indicating analytical methods featuring outstanding sensitivity, selectivity, and precision were set up for the quantification of salicylamide (SAD) and ascorbic acid (ASC) in the presence of salicylic acid (SAL), which represents a possible impurity and degradation product of SAD. The aim was to develop sensitive, selective, precise, and eco-friendly assays appropriate for routine quality control of pharmaceuticals. Methods: Method (A) was a spectrophotometric technique of a successive derivative of ratio spectra built upon a two-step derivatization of ratio spectra utilizing double-distilled water as a solvent. SAD was quantified at 247.2 nm and 257.0 nm, and ASC at 251.8 and 259.8 nm, while SAL was quantified at 305.6 nm. Technique (B) relied on ratio spectra for the mean centering analytical process applied via two sequential stages, where the amplitudes derived after the second ratio spectra of the mean centering have been recorded on 291.0, 266.0, and 241.0 nm for SAD, ASC, and SAL, in that order. Method (C) involved TLC densitometric analysis, in which the separation was carried out upon plates of silica gel with chloroform–hexane–methanol–acetone–formic acid (5:3:2:1:0.2, in volumes) as a mobile phase, monitored by densitometric detection at 240 nm. The linear relationships were observed over concentration ranges of (0.2–2 µg/band) for SAD with ASC and (0.1–1 µg/band) for SAL. Validation of the presented techniques was performed in accordance with ICH strategies. Results: These developed techniques have been effectively analyzed for SAD with ASC in pharmaceutical dosage forms with non-interfering ingredients. A statistical comparison with the previously used HPLC technique revealed no considerable difference in terms of accuracy and precision. Greenness assessment using the AGREE platform produced scores of 0.72 for the spectrophotometric approach (benefiting from aqueous solvent) and 0.62 for HPTLC (limited by chloroform). Practical applicability (BAGI = 80 for both spectrophotometry and HPTLC) and overall quality indices (CACI = 83 for spectrophotometry; 80 for HPTLC) supported routine QC suitability. Conclusions: The three developed stability-indicating methods are accurate, precise, and selective for simultaneous assay of SAD and ASC in the presence of SAL and are suitable for quality control use. The spectrophotometric procedures combine high analytical performance with an improved environmental profile, while HPTLC offers comparable analytical reliability with slightly lower greenness due to organic solvent use. Full article

Software quality assessment in agile projects remains fragmented. Technical, process-related, and team-related indicators are often evaluated through separate models, tools, and reports. This fragmentation limits cross-project comparability and weakens evidence-based decisions for software quality improvement. To address this problem, this study proposes the Overall Software Quality Index (OSQI), a multidimensional decision-support framework for software quality assessment in agile projects. OSQI integrates code quality, process quality, and team quality into a single project-level assessment model. The framework was initially grounded in ISO/IEC 25010:2011 and is discussed in relation to the ISO/IEC 25010:2023 revision, particularly its explicit inclusion of Safety as a product quality characteristic. Since the industrial datasets used in this study were not collected from safety-critical systems, Safety was not modeled as a separate OSQI dimension in the current version; instead, it is addressed as a scope limitation and future extension. The measurement structure was defined using the Goal–Question–Metric (GQM) approach. An initial set of 49 candidate metrics was reduced to 15 core indicators. This reduction was performed using dimension-specific strategies: Random Forest-based feature importance for code quality, Delphi and Analytic Hierarchy Process (AHP) for process quality, and thematic consolidation for team quality. The selected indicators were normalized and integrated through entropy-based weighting. This process generates an interpretable composite quality score. The main contribution of OSQI is not the isolated use of these methods, but their integration into a reproducible and tool-supported framework. The framework converts heterogeneous software engineering signals into a unified decision-support index. OSQI was evaluated using industrial agile project data. The data included static code analysis outputs, issue-tracking records, team assessment results, and product outcome indicators. In an exploratory validation across five industrial projects, OSQI showed a strong positive association with Net Promoter Score ($r=0.97, p=0.0076$) and a strong negative association with churn rate ($r=-0.97, p=0.0061$). A supporting software tool was also developed to automate data integration, score calculation, visualization, and project-level comparison. The findings suggest that OSQI can support quality monitoring, project benchmarking, and evidence-based improvement decisions in agile software engineering contexts. Full article

Egon Steinmann’s industrial architecture represents a significant yet insufficiently researched contribution to the development of post-war industrial architecture in Croatia. This paper examines his industrial projects designed between 1947 and 1965 within the context of post-war industrialization and modernization in socialist Yugoslavia. Based on archival documents, historical photographs, field observations, and comparative analysis, the paper first identifies Steinmann’s broader industrial work and then examines six selected industrial complexes in Zagreb. The case studies are compared in terms of their urban context, spatial organization, structural systems, production logistics, daylighting strategies, and architectural expression, highlighting differences between heavy industrial facilities and food-processing plants. A comparison of historical and contemporary orthophotos is further used to evaluate the long-term spatial transformation and adaptability of these industrial sites. The findings demonstrate that Steinmann’s designs were characterized by rational planning, large-span and flexible structures, integration of technological and transport requirements, and the capacity for phased expansion. The continued industrial use and preservation of many of these complexes confirm the lasting value of his architectural and planning concepts, contributing to a broader understanding of Croatian industrial architecture and socialist industrial modernism of the 1950s and 1960s. Full article

Millimeter-wave radar enables non-contact monitoring of cardiac activity and therefore has the potential to reconstruct electrocardiogram signals without surface electrodes. However, existing radar-based electrocardiogram reconstruction methods still suffer from incomplete extraction of heartbeat-related information and insufficient modeling of electrocardiogram-related features, which limits reconstruction accuracy. To address these issues, this study proposes a millimeter-wave radar-based electrocardiogram reconstruction method that integrates a respiratory-harmonic-suppressed multi-channel signal-processing frontend with the proposed CA-WTBNet deep reconstruction network. First, based on maximal overlap discrete wavelet transform-based multi-resolution analysis, respiratory harmonics mixed into heartbeat-related components are suppressed by combining respiratory harmonic detection with a heart-rate frequency protection strategy, while cardiac-related information is preserved as much as possible. A multi-channel input representation is then constructed. Meanwhile, the proposed deep reconstruction network is developed to jointly model complementary channel-wise features, local waveform morphology, and temporal dependencies by integrating channel-attention mechanisms, convolutional residual modules, window-based Transformer blocks, and bidirectional long short-term memory. Experiments conducted on the public dataset show that our method achieves an average Pearson correlation coefficient of 0.9641, a mean normalized root mean square error of 0.0458, an average R-peak F1 score of 0.9956, and an average R-peak timing error of 3.13 ms on the test set. In comparison with related studies on the same public Resting dataset, the proposed method achieves the best overall performance among the compared methods, with a 0.53% improvement in Pearson correlation coefficient and a 10.20% reduction in normalized root mean square error over the best-performing compared method. Full article

Background: Parental burnout results from chronic stress related to the parental role and reflects a persistent imbalance between parenting demands and available psychological resources, negatively affecting parental well-being and parent–child relationships. This study examined the associations between parental burnout, parenting attitudes, and psychological resilience within the parental adaptation of the job demands–resources model, with particular attention to the potential mediating role of parenting styles in the relationship between resilience and parental burnout, while controlling for sociodemographic factors. Methods: A cross-sectional design was applied with 447 Hungarian parents who completed an anonymous online questionnaire including the Parental Burnout Assessment, the Parenting Styles and Dimensions Questionnaire, and the 10-item Connor–Davidson Resilience Scale. Data were analyzed using nonparametric correlations, group comparisons, multiple linear regression models with bootstrap estimation, and mediation analyses. Results: Resilience showed negative associations with all dimensions of parental burnout. Authoritarian and permissive parenting styles were positively associated with burnout, whereas authoritative parenting style showed negative associations. In multivariate analyses, authoritative parenting attitudes and fulfillment of the ideal parental role emerged as protective factors, while authoritarian parenting style functioned as a significant risk factor. Mediation analyses further indicated that the association between resilience and parental burnout may partly operate through parenting styles, particularly across the dimensions of emotional exhaustion, contrast, and emotional distancing. Conclusions: Parental burnout appears to be a dynamic psychological process shaped by the interaction of internal resources and parenting functioning, underscoring the importance of resource-oriented approaches in prevention and intervention. Full article

Background/Objectives: Health-related quality of life perception (HRQoL) reflects the impact of individuals’ health conditions on their physical, psychological, and social well-being, and can be compromised after an accident The general aim of this study was to analyze the effect of occupational accident (OA) outcomes on injured workers’ HRQoL. Methods: We conducted a cross-sectional study, using a convenience sample of 101 participants at the end of their recovery and professional reintegration (PR) process. They were submitted to a personal injury assessment (PIA) conducted by medico-legal specialists, and data related to injury severity (IS), permanent professional disability (PD), and PR were collected from the respective forensic reports. Subsequently, they underwent a psychological interview and filled out self-report questionnaires to measure HRQoL (SF-36) and resilience (RSA). For each variable, two groups were defined. Analyses included descriptive statistics, correlations, group comparisons, and multiple linear regression analyses. Results: Injured workers reported lower HRQoL than Portuguese norms across most domains. HRQoL scores were positively associated with resilience and PR, and negatively associated with IS and PD. In multivariable models, IS, and RSA emerged as significant independent associated variables of the physical–social HRQoL component. Conclusions: These findings highlight the importance of a biopsychosocial and multidisciplinary approach to OA victims’ professional reintegration, integrating physical treatment and psychological support with resilience-building and work rehabilitation, before medical discharge and PIA. Full article

It is essential to exercise control over the environmental impact of deep excavation construction in soft soil areas from the perspective of deformation in order to ensure engineering safety. A three-dimensional finite element model of the foundation pit was developed, thereby creating a comparison between the results of the numerical simulation and the actual on-site monitoring data. This process served to validate the precision of the simulations. The focal point of the study pertained to the three-dimensional effects of support structure deformation and ground settlement during excavation. A comprehensive analysis of the spatial distribution and evolutionary patterns of underground diaphragm wall deformation and ground settlement behind the wall at varying excavation depths was conducted. The results demonstrated that both support structure deformation and ground settlement behind the excavated structure exhibited substantial spatial effects. In particular, larger deformations were observed near the symmetrical plane of the excavation centre. Conversely, greatly smaller deformations were observed in the corners of the excavation. The research findings aim to provide useful references for practical engineering projects. Full article

This study examined acute pre- to post-match changes in perceived mental fatigue, subjective workload, and psychomotor performance in professional male soccer players, and whether cognitive changes were associated with GPS-derived external-load metrics, match outcome, and playing position. The dataset comprised 101 player–match measurements from 40 elite players, with paired pre–post psychomotor assessments yielding n = 202 total measurements. Pre–post comparisons were analysed using repeated-measures ANOVA, supplemented by linear mixed-effects models with a random intercept for player. Soccer matches produced large increases in perceived exertion, mental fatigue, mental demand, physical demand, and effort (all p < 0.001), and significant deteriorations in reaction time, accuracy, processing speed, and response variability (all p ≤ 0.005), confirmed in the mixed-effects analyses (all p ≤ 0.014). In the initial player–match-level analyses, high-intensity accelerations (>3 m·s⁻²) were weakly associated with greater Δreaction-time slowing (r = 0.203), increased response variability (r = 0.276), and reduced Rate Correct Score (r = −0.242), while high metabolic load distance was weakly associated with post-match perceived mental fatigue but not with psychomotor-performance changes. One-way ANOVAs indicated greater post-match psychomotor decrements following losses than draws. Once within-player dependence was modelled, the effects of match outcome, playing position, and most external-load metrics were attenuated, except for a residual match-outcome effect on accuracy and a high-intensity deceleration effect on accuracy. These findings indicate that competitive soccer match play is followed by acute psychomotor-performance decrements and increased perceived mental fatigue, whereas the contributions of mechanical load, match outcome, and playing position appear modest and partly reflect stable between-player differences. Full article

Predicting landslide displacement is important for geological-hazard early warning. In reservoir areas, displacement evolution is affected by rainfall, reservoir water level, vegetation variation, and the intrinsic non-stationarity of the displacement sequence, which makes accurate prediction difficult for conventional single-sequence models. To address this problem, this study proposes a residual-increment-oriented landslide displacement prediction framework that fuses multi-source monitoring variables. The displacement sequence is first processed into trend and periodic-related fluctuation representations, and the residual increment is used as the prediction target. Rainfall, reservoir water level, and the normalized difference vegetation index (NDVI) are incorporated as external monitoring variables. A cross-branch attention mechanism models interactions among heterogeneous feature branches, and a sparse MoE-based fusion module is introduced to adaptively adjust branch contributions under different deformation conditions. The model predicts the displacement residual increment, from which the final displacement is reconstructed. A case study using the Baishui River (Baishuihe) landslide monitoring dataset was conducted, together with additional validation on the related Bazimen Z110 landslide monitoring dataset and comparisons against conventional recurrent, convolutional, statistical, and Transformer-based baselines. The results show that the proposed model achieves lower RMSE and MAE than the compared methods on the tested datasets. These findings suggest that residual-increment modeling, multi-source monitoring variables, and condition-dependent branch fusion can improve short-term displacement prediction for the tested reservoir-area landslide cases. Full article

Transparent soil technology provides a non-invasive experimental approach for visualizing internal processes in geotechnical infrastructure systems, where soil deformation, seepage, erosion, and failure evolution are often difficult to observe using conventional model tests. This review examines the material systems and applicability of transparent soil with emphasis on infrastructure-related applications, including foundation engineering, underground construction, seepage and grouting, internal erosion, slope failure, disaster mitigation, and thermal monitoring. The discussion focuses on transparent sand and transparent clay, comparing their engineering relevance, typical application scenarios, and main limitations rather than treating transparency as the sole criterion for material selection. Based on the reviewed studies, a four-dimensional applicability framework is proposed, consisting of mechanical similarity, optical measurability, system compatibility, and scenario matching. This framework is used to clarify how transparent soil can support mechanism interpretation, model calibration, and scheme comparison in infrastructure-related geotechnical experiments. The review indicates that transparent soil is particularly useful for revealing displacement fields, flow paths, localized deformation, and progressive failure processes in foundations, tunnels, slopes, and other geotechnical systems. However, direct extrapolation of model test results to engineering design parameters remains constrained by material equivalence, optical measurement conditions, model scale, and similarity calibration. Overall, the proposed framework and synthesis provide a systematic reference for transparent soil material selection, infrastructure-oriented scenario matching, and the assessment of applicability boundaries in transparent soil model tests. Full article

Background/Objectives: Latent fingerprints are routinely recovered from conventional porous and non-porous substrates; however, biologically active surfaces such as plant leaves are generally regarded as unsuitable for dactyloscopic evidence. Because vegetation is frequently present at crime scenes, this study aimed to systematically evaluate whether plant leaves can retain usable friction ridge detail and to determine the durability and forensic value of such traces under laboratory and outdoor conditions. Methods: Latent fingerprints were deposited on leaves of multiple plant species (maple, ash, dandelion, bird cherry, chestnut, climbing ivy, and five-leaved ivy) under dry and hydrated conditions and at defined time intervals after deposition. Visualization was performed using several powders, with SupraNano Fluorescent Green magnetic powder providing the best performance. Developed impressions were photographed using controlled illumination and evaluated using automated quality assessment (NFIQ 2.0) and comparison software (Innovatrics IDkit 9.1.7.1004). Additional experiments examined living, growing leaves exposed to natural weather conditions for extended periods. Results: Usable ridge detail was successfully visualized on all tested species. Bottom leaf surfaces and hydrated samples generally provided better preservation and contrast. Identifiable traces persisted for up to 20 h on detached leaves and for up to 35 days on living leaves despite growth-related deformation. Under outdoor exposure, fingerprints on ivy remained visible and comparable for up to 60 days. Although overall automated quality scores were reduced by background venation, selected impressions achieved measurable comparison scores and successful matches. Conclusions: Plant leaves can serve as unconventional yet viable carriers of latent fingerprints. Magnetic fluorescent powder development combined with careful documentation enables recovery of forensically useful ridge detail even after prolonged environmental exposure. These findings expand the range of substrates that should be considered during crime scene processing and provide practical guidance for evidence collection on vegetation. Full article

Accurate waveform classification in noisy environments is an important task in modern communications, radar signal analysis, biomedical signal interpretation, industrial monitoring and other signal processing systems. This paper investigates the performance of three neural network architectures: Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) for multiclass waveform classification in the presence of Additive White Gaussian Noise (AWGN). A time series dataset consisting of multiple waveform classes is generated and corrupted with AWGN across a wide range of signal-to-noise ratio (SNR) levels to simulate noisy signal distortion conditions. The three models are trained and evaluated under identical conditions to ensure a fair comparison. Their classification performance is evaluated in terms of accuracy, Confusion Matrix (CM), Receiver Operating Characteristic (ROC) curve and the Area Under the ROC curve (AUC) across varying SNR values. Simulation results demonstrate that the 1D-CNN effectively captures local temporal patterns and achieves superior robustness in classification at moderate and high SNR levels. The LSTM model demonstrates the ability to capture temporal dependencies in sequential waveform data but exhibits sensitivity to waveform variations due to amplitude, phase and frequency changes and noise at lower SNR values. The MLP, although computationally simpler, shows comparatively limited performance in low-SNR conditions due to its lack of temporal feature extraction capability. For the case of multiclass deterministic waveforms, the accuracy of classification for the 1D-CNN and LSTM is nearly 100% at SNR = 5 dB showing their robustness in classification, whereas the accuracy of MLP is approximately 70% that shows poor classification in noisy conditions. When there is random amplitude, frequency and phase variations in the waveforms, the accuracy of the 1D-CNN and MLP increases with SNR, and 1D-CNN superior to MLP. However, the LSTM accuracy fails to improve with SNR, resulting in poor classification performance in such a scenario. The results provide an insight into the suitability of different neural architectures for waveform classification tasks in noisy communication or other time series applications and highlight the advantages of convolutional feature extraction for robust signal recognition. Full article