Search Results (7,466)

This article develops the analytical concept of post-liturgical female rituality to examine informal religious practices created by Western Ukrainian female labor migrants in Israel. Drawing on approaches that conceptualize ritual as flexible, embodied, and processual, it focuses on women’s ritual activities that take place in close temporal and symbolic proximity to official church liturgy while remaining outside canonical frameworks. Rather than directly challenging institutional religion, these practices extend and reinterpret patriarchal liturgy through gendered forms of ritual engagement. The analysis is based on qualitative research among Ukrainian Greek Catholic women in Israel, including 27 in-depth interviews, participant observation, and digital ethnography. The findings highlight three interconnected dimensions: collective gatherings following church services; post-liturgical practices involving food, singing, and embodied performance; and national-religious rituals expressing emotional belonging to Ukraine in the context of war. The article argues that post-liturgical female rituals constitute a distinct form of women’s religious agency that operates within institutional Christianity while reworking its meanings, contributing to feminist scholarship on ritual, migration, and war. Full article

To enhance the adaptability of current underwater acoustic target recognition technology in complex marine environments and improve the performance of human–machine collaborative operations, this study proposes a human–machine collaborative underwater acoustic target recognition technology based on brain–computer interface technology. This method combines synchronized underwater acoustic neural features between acoustic signals and human brains to propose an inter-domain global attention fusion module to explore the fusion relationship of features at different depths, and to enhance the joint feature expression ability by combining potential complementary information between modalities. The experimental results show that the proposed network model can enhance the feature discrimination ability and obtain a more stable recognition model. Compared to a single feature, the human–machine collaborative fusion-feature model exhibits stronger classification performance, with an average classification accuracy of 96.4444%. This method can alleviate the limitations of single-mode underwater acoustic target recognition technology, combine the complementary advantages of humans and machines to achieve effective human–machine cooperation, and provide new insights for future underwater recognition technology and marine research. Full article

This study employs a constructivist grounded theory approach based on 69 in-depth interviews conducted between March 2022 and December 2023 to examine socio-cognitive dynamics in Korea’s bottled water and household water purifier markets. The study addresses a gap in prior research by explaining how product meanings and stakeholder strategies co-evolve across adjacent “safe-water” markets under regulatory and sustainability pressures. Drawing on qualitative data from 69 stakeholders, including producers (n = 30), consumers (n = 19), and institutional experts (n = 20), we analyze how distrust, risk perception, and health consciousness reshape conceptual systems and market strategies. These shifts drive innovation across markets, including new technologies, service models, and branding strategies. The findings show that socio-cognitive stabilization arises through iterative interactions among institutional shocks, producer reinterpretation, and consumer adaptation. In the bottled water market, the meanings of “natural purity” became materially embedded in packaging, mineral labeling, and brand narratives. In the purifier sector, “technological reliability” was institutionalized through service-based maintenance systems and visible quality control technologies. These processes developed within asymmetric communicative environments shaped by corporate branding capacity and media amplification. This study refines socio-cognitive market theory by specifying boundary conditions under institutional distrust in developed economies. Although Republic of Korea possesses advanced drinking water infrastructure comparable to that of other developed economies, public confidence in tap water has periodically weakened following highly salient contamination incidents and regulatory transitions. This paradox provides a theoretically informative context for examining how product meanings and stakeholder behaviors mutually adapt over time. Although environmental impact metrics were not directly measured, the findings suggest that sustainability policies must address socio-cognitive trust dynamics alongside regulatory instruments such as plastic levies, certification schemes, and transparent risk communication. Full article

Small-scale prosumer installations are playing an increasingly important role in the Polish electricity sector. These primarily include photovoltaic systems and heat pumps installed for internal use. Noticeable losses for individual investors, generated by the power flow mechanism during peak production hours (connection to the grid) and peak demand (drawback from the grid), as well as the issue of fluctuating grid capacity and the observed redispatch procedures for photovoltaic installations, are driving increased interest in equipping home energy installations with energy storage systems, strengthening the aspect of sustainable energy development in this dimension. The impact of energy storage on investment motivation and the actual effects of incorporating it into home energy installations have not yet been sufficiently researched, particularly in Poland. Therefore, the aim of the study was to assess the use of energy storage in home installations as a socio-technical direction of power development at the micro level, in light of the constantly increasing energy demand observed worldwide in line with the challenges of sustainable development. The results of a survey of 206 individual users of power installations equipped with energy storage systems in Poland were used for this study. The research was qualitative and quantitative in nature, with descriptive statistics and a logistic regression model used in the in-depth section, and the findings were supported by PQStat software. The research revealed that the selection of energy storage systems in home power grids is related to the potential for prosumer optimization. On the other hand, they are seen as a path towards increasing energy security at the household level. Supporting this direction of installation development at the micro level is a justified concept for the development of green energy in Poland, socially and environmentally beneficial as well as economically justified, i.e., in line with the trend of sustainable development. The information campaign, combined with financial support for this type of investment, should be continued and strengthened in Poland. Full article

Wildfires have become more frequent and intense worldwide due to climate change and anthropogenic activities, which is why accurate and timely burned area mapping is essential for estimating damage and effective post-fire recovery planning. Synthetic Aperture Radar (SAR) data, which operates under all weather conditions and day-night cycles, offers a reliable source for burned area mapping. In this context, several studies have explored the use of dual-polarization SAR imagery and machine learning, yet the influence of multi-date, dual-orbit pass data and texture features remained unexplored. Therefore, this study aims to assess the Sentinel-1 acquisition configurations, varying in temporal depth and orbital direction, for wildfire burned area mapping, considering the recent Palisades wildfire event as a study area. A comparative study was conducted across different scenarios to evaluate the effectiveness of using single-date versus multi-date SAR imagery, the integration of ascending and descending orbit passes, and the contribution of Grey-Level Co-occurrence Matrix texture features. The performance of Random Forest (RF) and Extreme Gradient Boosting classifiers was analyzed through the scenarios mentioned above. The single-date configuration using RF achieved an accuracy of 82.34%, F1-score of 81.43%, precision of 83.07%, recall of 80.84%, and ROC-AUC of 90.88%, whereas the multi-date approach reached 85.78%, 85.15%, 86.45%, 84.56%, and 93.28%, respectively. Our study highlights the importance of acquisition configuration and texture information for reliable SAR-based wildfire burned area assessment. Full article

Infection prevention and control remains an essential component of effective healthcare delivery and disease prevention. This study aimed to explore healthcare workers’ perspectives on factors influencing compliance with infection prevention and control practices in Katavi Regional Referral Hospital, Tanzania. With a qualitative approach, we aimed to enable a broader narrative, gain a more detailed understanding of IPC practices, and identify experiences that may be overlooked in a forced-choice questionnaire. A cross-sectional design using a phenomenological approach was employed. An interview guide was used to collect data from 19 participants (five doctors, four nurses, four laboratory practitioners, and six from administration positions; ward in-charges, quality improvement officers and administrative officers) between 24 July 2025, and 23 August 2025. Among participants, nine were the key informants, and 10 were involved in in-depth interviews. Thematic analysis revealed that the availability of IPC supplies, desire for personal and patient protection, high patient volume, awareness of IPC protocols, institutional support, supportive supervision, and HCWs’ attitudes towards IPC activities were factors influencing IPC compliance. Strengthening structured supervision, ensuring a constant supply of IPC materials, and investing in continuous IPC capacity building may be an important approach in enhancing compliance with IPC practices and reducing hospital-associated infection risk in Katavi Regional Referral Hospital and similar resource-limited healthcare settings. Full article

This paper proposes a Q-Learning-Based Distributed Energy-Efficient Routing (QDER) protocol for underwater acoustic sensor networks (UASNs). The routing problem is formulated as a Markov Decision Process (MDP) and a distributed Q-learning approach is proposed. Each sensor node is treated as an agent that independently selects its next-hop node based on a Q-table. The rewards function is designed that jointly considers node residual energy and depth information, enabling each node to learn an effective routing policy through distributed decision-making. Unlike centralized routing approaches that rely on extensive global information exchange, the proposed scheme allows nodes to make local decisions, thereby reducing communication overhead and energy consumption while maintaining efficient routing paths. In addition, link quality is designed in the reward to account for channel conditions, which improves the robustness of the routing strategy under noisy underwater acoustic environments. Simulation results demonstrate that the QDER achieves better system performance compared with Depth-Based Routing (DBR) and Deep Q-Network-Based Intelligent Routing (DQIR). Considering channel attenuation and noise, the proposed method with the link quality metric achieves improved network lifetime and energy efficiency. It also shows good robustness and adaptability under different signal-to-noise ratio (SNR) conditions. Full article

Bird’s-eye-view (BEV) perception has emerged as a key representation for unified scene understanding in autonomous driving. However, current BEV methods relying solely on monocular cameras suffer from severe degradation under adverse weather and dynamic scenes due to limited depth cues and illumination dependency. To address these challenges, we propose a robust multi-modal BEV perception framework that integrates dual-source 4D millimeter-wave radar and multi-view camera images. The proposed architecture systematically exploits Doppler velocity and temporal information from 4D radar to model dynamic object motion, while introducing a deformable fusion strategy in the BEV space for accurate semantic alignment across modalities. Our design includes four key modules: a Doppler-Aware Radar Encoder (DARE) that enhances motion-sensitive features via velocity-guided attention; a Fog-Aware Feature Denoising Module (FADM) that suppresses modality inconsistency in low-visibility conditions through cross-modal attention and residual enhancement; a Multi-Modal Temporal Fusion Module (TFM) that encodes radar temporal sequences using a Transformer encoder for motion continuity modeling; and a confidence-aware multi-task loss that jointly supervises semantic segmentation, motion estimation, and object detection. Extensive experiments on the DualRadar dataset and adverse-weather simulations demonstrate that our method achieves significant gains over state-of-the-art baselines in BEV segmentation accuracy, detection robustness, and motion stability. The proposed framework offers a scalable and resilient solution for real-world autonomous perception, especially under challenging environmental conditions. Full article

This study presents a multidisciplinary investigation of the archeological site of Iuvanum (Abruzzo, central Italy), integrating geophysics, geomatics, architectural analysis and archeology with the purpose of exploring the relationship between surface remains and buried structures of archeological value. This research focuses on the area covering part of the forum and part of the basilica, where ground-penetrating radar (GPR) surveys were conducted to detect subsurface anomalies potentially associated with unexcavated architectural features. GPR line scans were acquired under complex topographic conditions, processed, and assembled into a three-dimensional representation, from which volumes of interest (VOIs) were extracted. These geophysical results were integrated into a comprehensive three-dimensional framework together with high-resolution UAV photogrammetry, digital elevation models, orthophotos and a virtual architectural model (VAM) of the site. The integrated visualization environment greatly facilitates the recognition of spatial relations between the detected anomalies and the hypothesized architectural elements. The observed GPR anomalies confirmed wall remains that were initially speculated or located along their geometrical continuation. Pavement levels, as well as some structures asymmetrical with respect to the purely geometric reconstruction, were also identified. This study demonstrates how integrating GPR with geomatic and archeological approaches improves the reliability and interpretative depth of non-invasive archeological prospecting. The proposed workflow provides a reproducible methodological framework propedeutical to excavation planning and suitable for the integration of information from multi-data sensors. Full article

Underwater images suffer from quality degradation such as haze, detail blurring, color distortion, and low contrast due to factors like light scattering and wavelength-dependent attenuation in water. This severely hinders the high-quality completion of target detection tasks for Autonomous Underwater Vehicles (AUV) relying on image information. Although deep learning-based methods have gained widespread attention, existing approaches still face challenges such as insufficient feature extraction and limited generalization in complex real-world scenes. Methods based on physical models, on the other hand, heavily rely on depth information which is difficult to obtain accurately. To address these issues, this paper proposes a novel underwater image restoration method that integrates depth estimation with the Akkaynak-Treibitz physical imaging model. In the depth estimation stage, efficient and robust feature extraction is achieved through a lightweight encoder–decoder architecture combined with a channel–spatial hybrid attention mechanism. To overcome the inherent scale ambiguity problem in monocular depth estimation, which prevents direct output of absolute depth consistent with the real scene, sparse depth priors are introduced. Subsequently, adaptive depth binning and depth map optimization are realized via m-Vision Transformer and convolutional regression. In the image restoration stage, the acquired high-quality depth map is combined with the Akkaynak-Treibitz physical imaging model for inverse solving, achieving high-quality restoration from degraded to clear images. Experimental results demonstrate that the proposed method outperforms mainstream depth estimation methods (LapDepth, UDepth, etc.) and mainstream image restoration methods (CLAHE, FUnIE-GAN, etc.) in terms of evaluation metrics and visual perceptual quality. When processing the extremely degraded UIEB-S dataset, the proposed method achieves evaluation metrics of SSIM = 0.8954, UCIQE = 0.6107, and PSNR = 23.35 dB. Compared to the CLAHE and FUnIE-GAN methods, SSIM improved by 2.8% and 16.7%, UCIQE improved by 9.6% and 14.3%, and PSNR improved by 22.5% and 13.9%, respectively. Comprehensive subjective and objective evaluation results validate the effectiveness of the proposed method in addressing image quality degradation, particularly demonstrating outstanding capability in severe color cast correction and detail recovery. Full article

In the field of forensic document examination, accurately determining the chronological sequence of intersecting lines between seal ink and handwriting is a crucial technical step for verifying document authenticity, identifying contract tampering, and detecting forged signatures. This technique analyzes the physical superimposition relationship formed by the deposition of the two media on the paper substrate to provide objective scientific evidence for judicial practice. Although traditional methods such as microscopic imaging and mass spectrometry analysis have achieved some progress, they still suffer from common limitations including high equipment costs, complex operation, and potential damage to samples. This study proposes and validates an innovative non-destructive determination method that integrates structured light 3D reconstruction technology with deep learning algorithms. The research captures the microscopic 3D morphological features of the ink intersection area using a high-precision structured light scanning system and effectively eliminates noise interference caused by paper substrate undulation through Gaussian flattening technology. Subsequently, a multimodal fusion strategy combines 2D texture images with 3D depth information to construct a dataset rich in features. On this basis, a deep learning model based on an improved Residual Neural Network (ResNet) is designed, incorporating the ELU activation function and an EMA mechanism to enhance the model’s feature extraction capability and convergence stability. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 94.39% on the test set, fully validating its effectiveness and application potential in the non-destructive determination of ink stroke sequencing. Full article

Following bushfires (also known as wildfires), impacted free-living wildlife with welfare or conservation concerns are captured and presented for veterinary assessment where possible. This study represents an in-depth retrospective analysis of the veterinary records of 259 animals in Victoria, Australia, impacted by bushfire in 2019–2020. In total, 35 different species were assessed, including 196 koalas. Multivariable analyses of 126 koalas with complete medical records identified several clinical prognostic factors affecting 6-month survival outcomes. Increased odds of negative outcomes (death or euthanasia) were associated with increasing age (tooth wear class; odds ratio 2.70 for one unit increase), lower body condition score (one-unit decrease OR 7.27), and the earlier animals were presented after the fire event (OR 0.94 for each passing day). In 83 koalas with burn injuries, negative outcomes were also associated with burns more severe than minor (85% survival for minor burns only, compared to 31% survival with moderate or severe burns), and burns to more than 10 digits (12% survival). In burnt koalas, the combination of burn severity and digital involvement appear to be important prognostic factors for long-term outcomes. These findings can support veterinarians to more accurately evaluate prognosis for bushfire-affected koalas during initial assessment and will facilitate the strategic allocation of limited treatment and rehabilitation resources to the animals most likely to recover. The scope of this study was limited to the consideration of health outcomes, with the recognition of health as just one of many factors that must inform decisions about rehabilitating injured wildlife. Full article

The organization of safe industrial waste management is an integral part of the global sustainable development strategy. This study provides a preliminary assessment of the processing and recycling potential of strongly alkaline (pH 11–12) sediments accumulated in an abandoned sludge pond (Berezniki, Perm Krai, Russia), based on the initial characterization of their material composition. Sediment samples from the sludge pond were collected, layer-by-layer, over the entire depths of four sediment cores. The collected samples have the following characteristics: sediment particles are composed of up to 80% fine particles < 0.05 mm, with up to 20% fine particles < 0.002 mm. XRD data showed that the sediment consisted of calcite (67.7 wt.%), halite (11.5 wt.%), and other hydrogenic and terrigenous minerals. XRF data also found that the primary constituents in the sediment are CaO (up to 40%), Cl (up to 13%), and LOI (up to 35%). The results of the material composition study indicate a high degree of similarity between the accumulated sediments and solid waste from soda ash production, known as ammonia–soda residue (ASR). Based on experience with calcium-containing waste, this study recommends options for the secondary use of sludge, identifying two main possibilities: environmental protection and construction. We have developed an algorithm for the recycling and reuse of sludge that identifies risks, limitations, and recommended next steps. However, significant knowledge gaps regarding the environmental, toxicological, and the physical–mechanical properties of sludge prevent us from recommending a specific disposal option. The results of this review will serve as guidelines to help develop a roadmap for the disposal process. They will also inform decision-makers about sustainability issues related to industrial waste disposal. Full article

The ability to reliably press physical buttons is a common requirement in robotics. Conventional vision-based methods often suffer from positional errors during execution if the end-effector’s approach is not perpendicular to the target surface. This paper proposes a novel dual-quaternion-based visual servoing method that enables robots to reach desired poses and enhances accuracy in robotic button-pressing. Our method acquires target pose information (position, depth and surface normal direction) from the RGB-D camera and converts it into dual quaternion representation to construct the visual servoing control system. The image Jacobian matrix for the dual quaternion pose is then computed. The dual-quaternion-based visual servoing ensures that the pressing direction and the optical axis of the coaxially mounted camera remain perpendicular throughout the pressing motion, thereby eliminating misalignment between the actual contact point and the visually identified target. By representing spatial displacements in SE(3) with dual quaternions, our method enables more compact, concise, and efficient pose representation and computation throughout the visual servoing process. Experimental results demonstrate that, compared to conventional methods, our technique achieves more efficient visual servoing control, significantly improving both positioning accuracy and computational efficiency. Full article

Background/Objectives: Irritable bowel syndrome (IBS) is a prevalent gastrointestinal disorder with implications for individual quality of life and society. Patients with IBS suffer a variety of symptoms but have few treatment options. The level of satisfaction with IBS treatment is low, stressing the need to expand the IBS treatment toolbox. The aim of this study is to describe drivers and barriers to the implementation of time-restricted eating (TRE) as a treatment alternative for patients with IBS. Methods: A convenience sample of 14 informants was drawn from a pool of 97 successful participants in an eight-week 16:8 TRE intervention. The informants partook in audio-recorded semi-structured in-depth interviews. Recordings were processed by a computer language model and interview transcripts were generated automatically. The transcripts were proofread, structured and analysed with a reflexive inductive thematic analysis approach. Results: The analysis generated six main themes consisting of 18 sub-themes in total. One main theme describes drivers of implementation concerning domains such as motivation, supporting factors, mentality, behaviour and determinants of sustainability. The results from this study are largely coherent with the findings from earlier feasibility studies conducted on other populations, but several key differences related to population characteristics emerged. Conclusions: Overall, the analysis suggests that TRE can be a feasible treatment option for IBS, but successful implementation is dependent on individual ability, external support and symptom relief. Full article