Search Results (46)

This study investigates the hydrography and geochemical signature in Ilha Grande Bay (RJ, Brazil), focusing on the seasonal intrusion of South Atlantic Central Water (SACW) and its interaction with lead sources. CTD (Conductivity, Temperature, and Depth) data revealed the presence of SACW during the summer campaigns (Mangaratiba/2011 and Frade/2012), characterized by temperatures below 20 °C and salinity between 34.6 and 36. The intrusion is driven by northeasterly winds that favor coastal upwelling, establishing a classic thermohaline stratification. The winter campaigns did not detect SACW, confirming its seasonal nature. Isotopic analysis of Pb in sediments identified six Pb²⁰⁶/Pb²⁰⁷ intervals, indicating multiple sources, including natural contributions, industrial waste, and urban effluents. The Pb²⁰⁶/Pb²⁰⁷ ranges were defined based on cluster analysis and frequency histograms, which are common methods in isotopic provenance studies. An overlap between the most radiogenic isotopic signatures and the presence of SACW suggests that this water mass acts as a vector for transporting trace elements from the deep oceanic region to the coast. This study provides the first evidence that the South Atlantic Central Water (SACW) acts as a seasonal vector, importing a distinct radiogenic Pb isotopic signature onto the continental shelf of Ilha Grande Bay. By synoptically coupling physical water-mass analysis (CTD) with Pb isotopic tracers, we introduce a novel approach that successfully discriminates oceanic from anthropogenic Pb sources, offering a new framework for understanding contaminant transport in coastal areas influenced by boundary currents. It is concluded that the coastal dynamics in Ilha Grande Bay are governed by the seasonal interaction of coastal, continental, and oceanic waters, and that the integration of physical and geochemical data is crucial for understanding mixing processes and contaminant transport in this complex environment. Full article

Seabed topography exerts a profound influence on underwater acoustic propagation, and the coupling effect between bottom acoustic properties and the source–receiver geometric configuration remains insufficiently quantified, particularly in seamount shielding scenarios. To address this gap, in this study, the BELLHOP ray model was integrated with Earth topography 1 (ETOPO1) topographic data and Hybrid Coordinate Ocean Model (HYCOM) hydrological data for seamounts east of Taiwan. Transmission loss (TL) of 300 Hz sound waves was simulated across four typical bottom types (rock, coarse sand, silt, and clay) under varying source depths (50–1000 m) and receiver depths (50–500 m). The maximum sonar detection range was delineated using an 80 dB TL threshold as the criterion for effective detection. The key findings reveal that the bottom properties are the primary factors that reduce the detection range: the maximum detection range over rock bottom exceeds that over clay by more than 8-fold. Notably, a shallow source–shallow receiver configuration mitigates the acoustic shadow effect induced by seamounts, whereas deep receiver deployment (≥500 m) diminishes the discriminative impact of bottom types on the propagation behavior. Furthermore, a segmented empirical prediction formula was established, which reconciles both the physical mechanisms (e.g., bottom reflection-absorption and seamount shielding) and engineering applicability. This formula provides a robust theoretical basis for evaluating sonar performance in complex seabed topography settings, thereby facilitating optimized underwater detection strategies in seamount-dominated marine environments. Full article

Incivility and civility have been studied for more than a century across disciplines and in many areas ranging from workplaces to communication, the digital world, and everyday life. They are often used to the detriment of marginalized groups. Their negative use is seen to set the groundwork for other negative treatments, such as bullying and harassment, impacting the social climate in a negative way. Ostracism is seen to be linked to incivility. Disabled people disproportionally face negative treatments, such as bullying and harassment, and experience a negative social climate, as highlighted by the UN Convention on the Rights of People with Disabilities, suggesting that they also disproportionately experience incivility and ostracism. Climate surveys aim to expose toxic social climate in workplaces, schools, and communities caused by incivility, ostracism, bullying, and harassment. As such, how incivility, civility, ostracism, and the design of climate surveys are discussed in the literature is of importance to disabled people. We could find no review that analyzed the use of climate surveys beyond individual surveys and the concepts of incivility and ostracism in relation to disabled people. The objective of our study was to contribute to filling this gap by analyzing the academic literature present in SCOPUS, EBSCO HOST (70 databases), and Web of Science, performing keyword frequency and content analysis of abstracts and full texts. Our findings provide empirical evidence for a systemic neglect of disabled people in the topics covered: from 21,215 abstracts mentioning “civilit*” or “incivilit*”, only 14 were relevant, and of the 8358 abstracts mentioning ostracism, only 26 were relevant. Of the 3643 abstracts mentioning “climate surveys,” 12 sources covered disabled people by focusing on a given survey, but not one study performed an evaluation of the utility of climate surveys for disabled people in general. Racism is seen as a structural problem facilitating civility/incivility. Ableism, the negative judgments of a given set of abilities someone has, and disablism, the systemic discrimination based on such judgments, are structural problems experienced by disabled people, facilitating civility/incivility. However, ableism generated only 2 hits, and disablism/disableism had no hits. Most of our sources focused on workplace incivility, and authors were mostly from the USA. We found no linkage to social and policy discourses that aim to make the social environment better, such as equity, diversity, and inclusion, well-being, and science and technology governance. This is the first paper of its kind to look in depth at how the academic literature engages with the concepts of civility, incivility, and ostracism and with the instrument of social climate surveys in relation to disabled people. Our findings can be used by many different disciplines and fields to strengthen the theoretical and practical discussions on the topics in relation to disabled people and beyond. Full article

Tuber melanosporum, the black truffle, is a fungus of high economic and ecological value, but its underground detection remains a challenge due to the lack of reliable, non-invasive methods. This study presents the development and proof of concept of a portable optoelectronic nose that integrates nine optical sensors and one electrochemical sensor for the in vitro identification of T. melanosporum. The optical sensors use colorimetric and fluorogenic molecular indicators supported on UVM-7, alumina, and silica. Tests were performed with truffles at different depths and in the presence of soil and compost to evaluate the device’s multi-source response. Partial least squares discriminant analysis (PLS-DA) models showed robust discrimination between soil, compost, and truffles, with an accuracy of 0.91 under most conditions. Detection at 30 cm showed an accuracy of 0.94, confirming the system’s ability to differentiate between sample types. Performance improved in simplified scenarios based on the presence or absence of truffles. Furthermore, the artificial neural network models achieved optimal results in binary classification. Taken together, the results support the system’s potential as an accurate, non-invasive tool with possible application to the agronomic management of truffle orchards. Full article

Female participation in NCAA athletics has grown significantly since the passage of Title IX—the 1972 U.S. federal law that prohibits sex-based discrimination in educational programs and activities receiving federal funding—yet much of the existing research continues to focus on male athletes, leaving important gaps in our understanding of women’s experiences in collegiate sports. One underexamined area with important public health implications is the role of coach–athlete relationships in shaping female athletes’ mental health, access to resources, and overall collegiate experience. This exploratory study draws on in-depth interviews with 19 former female NCAA athletes to examine how their relationships with coaches influenced their athletic careers, mental health, and perceptions of support. Participants represented a range of sports and competitive levels, allowing for variation in experiences across contexts. Findings reveal that coach–athlete relationships are not only central to performance and motivation but also serve as key sources of emotional, social, and material support—or, in some cases, stress and disengagement. The quality and impact of these relationships were shaped by competitive pressures, team dynamics, and institutional expectations. This study underscores the importance of relational context in understanding the broader landscape of female NCAA athletes’ experiences and suggests that coach–athlete dynamics merit greater attention in both research and athletic program development. These findings underscore the relevance of coach–athlete dynamics as a public health concern, particularly in relation to mental health and emotional well-being in competitive sports environments. Supporting healthier relational cultures in collegiate athletics is essential for promoting positive health outcomes among female student-athletes. Full article

As a key form of deep foundation in civil engineering, the concrete pouring quality of cast-in-place piles directly determines the integrity and long-term bearing performance of the pile body. Accurate monitoring of the pouring interface is critical to preventing defects such as mud inclusion and pile breakage. To address the limitations of existing monitoring methods for concrete pouring interfaces, this paper proposes a full-pulse acoustic monitoring method for the concrete pouring interface of cast-in-place piles. Firstly, by constructing a hardware system platform consisting of “multi-level in-borehole sound sources + interface acoustic wave sensors + orifice full-pulse receivers + ground processors”, differential capture of signals propagating at different depths is achieved through multi-frequency excitation. Subsequently, a waveform data processing method is proposed to realize denoising, enhancement, and frequency discrimination of different signals, and a target feature recognition model that integrates cross-correlation functions and signal similarity analysis is established. Finally, by leveraging the differential characteristics of measurement signals at different depths, a near-field measurement mode and a far-field measurement mode are developed, thereby establishing a calculation model for the elevation position of the pouring interface under different scenarios. Meanwhile, the feasibility of the proposed method is verified through practical engineering cases. The results indicate that the proposed full pulse acoustic monitoring method can achieve non-destructive, real-time, and high-precision monitoring of the pouring interface, providing an effective technical approach for quality control in pile foundation construction and exhibiting broad application prospects. Full article

Driver distraction is a key factor contributing to traffic accidents. However, in existing computer vision-based methods for driver attention state recognition, monocular camera-based approaches often suffer from low accuracy, while multi-sensor data fusion techniques are compromised by poor real-time performance. To address these limitations, this paper proposes a Real-time Driver Attention State Recognition method (RT-DASR). RT-DASR comprises two core components: Binocular Vision Depth-Compensated Head Pose Estimation (BV-DHPE) and Multi-source Temporal Bidirectional Long Short-Term Memory (MSTBi-LSTM). BV-DHPE employs binocular cameras and YOLO11n (You Only Look Once) Pose to locate facial landmarks, calculating spatial distances via binocular disparity to compensate for monocular depth deficiency for accurate pose estimation. MSTBi-LSTM utilizes a lightweight Bidirectional Long Short-Term Memory (Bi-LSTM) network to fuse head pose angles, real-time vehicle speed, and gaze region semantics, bidirectionally extracting temporal features for continuous attention state discrimination. Evaluated under challenging conditions (e.g., illumination changes, occlusion), BV-DHPE achieved 44.7% reduction in head pose Mean Absolute Error (MAE) compared to monocular vision methods. RT-DASR achieved 90.4% attention recognition accuracy with 21.5 ms average latency when deployed on NVIDIA Jetson Orin. Real-world driving scenario tests confirm that the proposed method provides a high-precision, low-latency attention state recognition solution for enhancing the safety of mining vehicle drivers. RT-DASR can be integrated into advanced driver assistance systems to enable proactive accident prevention. Full article

Housing vacancy rate is a key indicator for evaluating urban sustainable development. Due to rapid urbanization, population outflow and insufficient industrial support, the housing vacancy problem is particularly prominent in China’s underdeveloped regions. However, the lack of official data and the limitations of traditional survey methods restrict in-depth research. This study proposes a vacancy rate estimation method based on Baidu Street View residential exterior images and deep learning technology. Taking Nanning, Guangxi as a case study, an automatic discrimination model for residential vacancy status is constructed by identifying visual clues such as window occlusion, balcony debris accumulation, and facade maintenance status. The study first uses Baidu Street View API to collect images of residential communities in Nanning. After manual annotation and field verification, a labeled dataset is constructed. A pre-trained deep learning model (ResNet50) is applied to estimate the vacancy rate of the community after fine-tuning with labeled street view images of Nanning’s residential communities. GIS spatial analysis is combined to reveal the spatial distribution pattern and influencing factors of the vacancy rate. The results show that street view images can effectively capture vacancy characteristics that are difficult to identify with traditional remote sensing and indirect indicators, providing a refined data source and method innovation for housing vacancy research in underdeveloped regions. The study further found that the residential vacancy rate in Nanning showed significant spatial differentiation, and the vacancy driving mechanism in the old urban area and the emerging area was significantly different. This study expands the application boundaries of computer vision in urban research and fills the research gap on vacancy issues in underdeveloped areas. Its results can provide a scientific basis for the government to optimize housing planning, developers to make rational investments, and residents to make housing purchase decisions, thus helping to improve urban sustainable development and governance capabilities. Full article

The present study aims to characterize complex geological structures and significant mineralization using remote sensing and aeromagnetic studies. Structural lineaments play a crucial role in the localization and concentration of mineral deposits. For the first time over the study district, a combination of aeromagnetic data, Landsat 9, ASTER, and PRISMA hyperspectral data was utilized to enhance the characterization of both lithological units and structural features. Advanced image processing techniques, including false color composites, principal component analysis (PCA), independent component analysis (ICA), and SMACC, were applied to the remote sensing datasets. These methods enabled effective discrimination between Phanerozoic rock formations and the complex basement units, which comprise the island arc assemblage, Dokhan volcanics, and late-orogenic granites. The local and deep magnetic sources were separated using Gaussian filters. The Neoproterozoic basement rocks were estimated using the radial average power spectrum technique and the Euler deconvolution technique (ED). According to the RAPS technique, the average depths to shallow and deep magnetic sources are approximately 0.4 km and 1.6 km, respectively. The obtained ED contacts range in depth from 0.081 to 1.5 km. The research area revealed massive structural lineaments, particularly in the northeast and northwest sides, where a dense concentration of these lineaments was identified. The locations with the highest densities are thought to signify more fracturization in the rocks that are thought to be connected to mineralization. According to the automatic lineament extraction methods and rose diagram, NW-SE, NNE-SSW, and N-S are the major structural directions. These trends were confirmed and visually represented through textural analysis and drainage pattern control. The lithological mapping results were validated through field observations and petrographic analysis. This integrated approach has proven highly effective, showcasing significant potential for both detailed structural analysis and accurate lithological discrimination, which may be related to further mineralization exploration. Full article

Airborne longwave-infrared (LWIR) hyperspectral imagery acquisitions were coordinated with stationary and mobile ground-based in situ measurements of atmospheric ammonia in regions surrounding California’s Salton Sea, an area of commingled intensive animal husbandry and agriculture operations that is encumbered by exceptionally high levels of persistent ammonia and PM_2.5 pollution. The goal of this study was to validate remotely sensed ammonia retrievals against ground truth measurements as part of a broader effort to elucidate the behavior of the atmospheric ammonia burden in this area of abundant diffuse and point sources. The nominal 2 m pixel size of the airborne data revealed variability in ammonia concentrations at a diversity of scales within the study area. At this pixel resolution, ammonia plumes emitted by individual facilities could be clearly discriminated and their dispersion characteristics inferred. Several factors, including thermal contrast and atmospheric boundary layer depth, contributed to the overall uncertainty of the intercomparison between airborne ammonia quantitative retrievals and the corresponding in situ measurements, for which agreement was in the 16–37% range under the most favorable conditions. Hence, while the findings attest to the viability of airborne LWIR spectral imaging for quantifying atmospheric ammonia concentrations, the accuracy of ground-level estimations depends significantly on precise knowledge of these atmospheric factors. Full article

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data joint classification has been applied in the field of ground category recognition. However, existing methods still perform poorly in extracting high-dimensional features and elevation information, resulting in insufficient data classification accuracy. To address this challenge, we propose a novel and efficient Calibration-Enhanced Multi-Awareness Network (CEMA-Net), which exploits the joint spectral–spatial–elevation features in depth to realize the accurate identification of land cover categories. Specifically, we propose a novel multi-way feature retention (MFR) module that explores deep spectral–spatial–elevation semantic information in the data through multiple paths. In addition, we propose spectral–spatial-aware enhancement (SAE) and elevation-aware enhancement (EAE) modules, which effectively enhance the awareness of ground objects that are sensitive to spectral and elevation information. Furthermore, to address the significant representation disparities and spatial misalignments between multi-source features, we propose a spectral–spatial–elevation feature calibration fusion (SFCF) module to efficiently integrate complementary characteristics from heterogeneous features. It incorporates two key advantages: (1) efficient learning of discriminative features from multi-source data, and (2) adaptive calibration of spatial differences. Comparative experimental results on the MUUFL, Trento, and Augsburg datasets demonstrate that CEMA-Net outperforms existing state-of-the-art methods, achieving superior classification accuracy with better feature map precision and minimal noise. Full article

The general increase in awareness of environmental pollutants and typical sources reflects the application of sustainability and development goals. Energy-Dispersive X-Ray Fluorescence spectroscopy analysis has been used to analyse sand samples collected from five different beaches located on the east and north-eastern coasts of Malta and Gozo during two summers and two winters. Samples were collected along linear transects perpendicular to the shoreline at three different depths. Chemometrics were used to discriminate between four latent variables, including season, location, depth, and distance from shoreline. The highest concentrations were attributed to Fe₂O₃, Al₂O₃, SrO, and SnO₂. Principal Components Analysis and Factor Analysis classified distributions of Fe₂O₃, CoO, As₂O₃, MnO, SrO, SeO₂, and CaCO₃ under Principal Component 1. However, since no loading value dominance was observed, such distributions most likely represent a combination of lithogenic and anthropogenic natures. Discrimination using Stepwise Linear Canonical Discriminant Analysis (SLC-DA) and Partial Least Squares Discriminant Analysis (PLS-DA) using Leave-One-Out-Cross-Validation with Variance Importance Plots proved highly effective in classifying data by location, followed by seasonal variability. It follows that concentrations are not affected by depth and distance from shoreline variability, proving that accumulation and anthropogenic effects from land are not concentrated in specific zones but are spatially spread out along the bays and do not increase with depth. Full article

As an effective computer-aided diagnostic tool, deep learning has been successfully applied to the classification of breast histopathological images. However, the performance of the deep model is data-driven, and it is difficult to obtain satisfied results when the number of histopathological images is small and labelling histopathological images is difficult. Moreover, in traditional deep learning methods, the representation of features is monotonous, which leads to the limitation of the classification performance of the model. This study proposes an auto-encoder reconstructed semi-supervised domain adaptation for a breast histopathological image classification algorithm. First, the model was pre-trained and transferred to extract high-level features of the sample images. Then, the encoding and decoding parts of the auto-encoder were used to reconstruct the feature representation learning and the sample feature reconstruction learning, respectively. This ensured that the useful information for the classification was purified and retained. At the same time, the domain discriminator was used to confuse the source and target domain features to enhance the learning ability of the model. Finally, the distribution difference of features at different depths of the auto-encoder was measured to minimize the discrepancy of feature distribution between domains, so as to complete the classification of histopathological images. Compared to the results of the comparative and ablation algorithms from the BreakHis to SNL datasets, the proposed method achieved the best results in terms of F1 score (93.40%), accuracy (95.24%), sensitivity (94.66%), and specificity (95.56%). The experimental results demonstrate that the proposed method achieves a remarkable classification performance. Full article

RGB-D (depth) Salient Object Detection (SOD) seeks to identify and segment the most visually compelling objects within a given scene. Depth data, known for their strong discriminative capability in spatial localization, provide an advantage in achieving accurate RGB-D SOD. However, recent research in this field has encountered significant challenges due to the poor visual qualities and disturbing cues in raw depth maps. This issue results in indistinct or ambiguous depth features, which consequently weaken the performance of RGB-D SOD. To address this problem, we propose a novel pseudo depth feature generation-based RGB-D SOD Network, named PDFNet, which can generate some new and more distinctive pseudo depth features as an extra supplement source to enhance the raw depth features. Specifically, we first introduce an RGB-guided pseudo depth feature generation subnet to synthesize more distinctive pseudo depth features for raw depth feature enhancement, since the discriminative power of depth features plays a pivotal role in providing effective contour and spatial cues. Then, we propose a cross-modal fusion mamba (CFM) to effectively merge RGB features, raw depth features, and generated pseudo depth features. We adopt a channel selection strategy within the CFM module to align the pseudo depth features with raw depth features, thereby enhancing the depth features. We test the proposed PDFNet on six commonly used RGB-D SOD benchmark datasets. Extensive experimental results validate that the proposed approach achieves superior performance. For example, compared to the previous cutting-edge method, AirSOD, our method improves the F-measure by 2%, 1.7%, 1.1%, and 2.2% on the STERE, DUTLF-D, NLPR, and NJU2K datasets, respectively. Full article

An investigation of risk factors has been identified as a crucial aspect of the routine management of rockburst. However, the identification methods for principal impact factors and the examination of the relationship between seismic energy and other source parameters have not been extensively explored to conduct dynamic risk management. This study aims to quantify impact risk factors and discriminate hazardous high-energy seismic events. The analytic hierarchy process (AHP) and entropy weight method (EWM) are utilized to ascertain the primary control factors based on geotechnical data and nearly two months of seismic data from a longwall panel. Furthermore, the distribution law and correlation relationship among seismic source parameters are systematically analyzed. Results show that the effect of coal depth, coal seam thickness, coal dip, and mining speed covers the entire mining process, while the fault is only prominent in localized areas. There are varying degrees of log-positive correlations between seismic energy and other source parameters, and this positive correlation is more pronounced for hazardous high-energy seismic events. Utilizing the linear logarithmic relationship between seismic energy and other source parameters, along with the impact weights of dynamic risks, the comprehensive energy index for evaluating high-energy seismic events is proposed. The comprehensive energy index identification method proves to be more accurate by comparing with the high-energy seismic events based on energy criteria. The limitations and improvements of this method are also synthesized to obtaining a wide range of applications. Full article