Search Results (979)

Background: Workplace violence (WPV) is a pervasive occupational hazard in healthcare that undermines staff safety and quality of care. In Bulgaria, WPV remains widespread and underreported, despite recent legislative initiatives. This study assessed healthcare professionals’ perceptions of the effectiveness of WPV prevention strategies and examined how prior exposure shapes these perceptions. Methods: A nationwide cross-sectional online survey was conducted in December 2024 with 944 healthcare professionals from multiple sectors. Participants rated the perceived effectiveness of 11 prevention strategies, including environmental/security measures, organizational, and national-level interventions, on a three-point scale. Friedman ANOVA with Kendall’s W assessed overall strategy rankings, while Mann–Whitney U tests with rank-biserial correlations compared specific effectiveness ratings between subgroups defined by WPV exposure (experienced or witnessed vs. not exposed in the previous 12 months). Results: In the previous 12 months, 34.7% of respondents reported direct WPV, and 43.4% had either experienced or witnessed incidents. Friedman ANOVA indicated significant differences in perceived effectiveness across strategies (Kendall’s W = 0.13), with stronger differentiation among violence-exposed respondents (W = 0.37) than among non-exposed respondents (W = 0.09). National-level interventions and security/response measures were consistently ranked the highest. Mann–Whitney tests showed significantly higher endorsement of most strategies among violence-exposed professionals, with large effect sizes for security measures and enforcement of sanctions. Conclusions: Bulgarian healthcare professionals view WPV prevention as requiring a multicomponent approach that integrates robust national policy with organizational and environmental measures. Direct exposure to violence is associated with stronger support for security-focused and national interventions. These findings inform context-specific, evidence-based WPV prevention programs for Bulgarian healthcare facilities. Full article

Dairy cow body temperature is a key physiological indicator that reflects metabolic level, immune status, and environmental stress responses, and it has been widely used for early disease recognition. Infrared thermography (IRT), as a non-contact imaging technique capable of remotely acquiring the surface radiation temperature distribution of animals, is regarded as a powerful alternative to traditional temperature measurement methods. Under practical cowshed conditions, IRT images of dairy cows are easily affected by complex background interference and generally suffer from low resolution, poor contrast, indistinct boundaries, weak structural perception, and insufficient texture information, which lead to significant degradation in target detection and temperature extraction performance. To address these issues, a lightweight detection model named YOLOv11n-CGSD is proposed for dairy cow IRT images, aiming to improve the accuracy and robustness of region of interest (ROI) detection and body temperature extraction under complex background conditions. At the architectural level, a C3Ghost lightweight module based on the Ghost concept is first constructed to reduce redundant feature extraction while lowering computational cost and enhancing the network capability for preserving fine-grained features during feature propagation. Subsequently, a space-to-depth convolution module is introduced to perform spatial rearrangement of feature maps and achieve channel compression via non-strided convolution, thereby improving the sensitivity of the model to local temperature variations and structural details. Finally, a dynamic sampling mechanism is embedded in the neck of the network, where the upsampling and scale alignment processes are adaptively driven by feature content, enhancing the model response to boundary temperature changes and weak-texture regions. Experimental results indicate that the YOLOv11n-CGSD model can effectively shift attention from irrelevant background regions to ROI contour boundaries and increase attention coverage within the ROI. Under complex IRT conditions, the model achieves P, R, and mAP50 values of 89.11%, 86.80%, and 91.94%, which represent improvements of 3.11%, 5.14%, and 4.08%, respectively, compared with the baseline model. Using T_max as the temperature extraction parameter, the maximum error (Max. Error) and mean error (MAE. Error) in the lower udder region are reduced by 33.3% and 25.7%, respectively, while in the around the anus region, the Max. Error and MAE. Error are reduced by 87.5% and 95.0%, respectively. These findings demonstrate that, under complex backgrounds and low-quality IRT imaging conditions, the proposed model achieves lightweight and high-performance detection for both lower udder (LU) and around the anus (AA) regions and provides a methodological reference and technical support for non-contact body temperature measurement of dairy cows in practical cowshed production environments. Full article

In unstructured environments, terrain perception is essential for stability and environmental awareness of Quadruped robot locomotion. Existing approaches primarily rely on visual or proprioceptive signals, but their effectiveness is limited under conditions of visual occlusion or ambiguous terrain features. To address this, this study proposes a multimodal terrain perception method that integrates acoustic features with proprioceptive signals. This terrain perception method collects environmental acoustic information through an externally mounted sound sensor, and combines the sound signal with proprioceptive sensor data from IMU and joint encoder of the quadruped robot. The method was deployed on the quadruped robot Lite2 platform developed by Deep Robotics, and experiments were conducted on four representative terrain types: concrete, gravel, sand, and carpet. Mel-spectrogram features are extracted from the acoustic signals and concatenated with the IMU and joint encoder to form feature vectors, which are subsequently fed into a support vector machine for terrain classification. For each terrain type, 400 s of data were collected. Experimental results show that the terrain classification accuracy reaches 78.28% without using acoustic signals, while increasing to 82.52% when acoustic features are incorporated. To further enhance the classification performance, this study performs a combined exploration of the SVM hyperparameters $C$ and $\gamma$ as well as the time-window length $w_{in}$. The final results demonstrate that the classification accuracy can be improved to as high as 99.53% across all four terrains. Full article

Communication in mammals constitutes a complex, multimodal system that integrates visual, acoustic, tactile, and chemical signals whose functions extend beyond simple information transfer to include the regulation of social relationships, coordination of behaviour, and expression of emotional states. This article examines the fundamental mechanisms of communication from biological, neuroethological, and behavioural perspectives, with particular emphasis on domesticated and farmed species. Analysis of sensory signals demonstrates that their perception and interpretation are closely linked to the physiology of sensory organs as well as to social experience and environmental context. In companion animals such as dogs and cats, domestication has significantly modified communicative repertoires ranging from the development of specialised facial musculature in dogs to adaptive diversification of vocalisations in cats. The neurobiological foundations of communication, including the activity of the amygdala, limbic structures, and mirror-neuron systems, provide evidence for homologous mechanisms of emotion recognition across species. The article also highlights the role of communication in shaping social structures and the influence of husbandry conditions on the behaviour of farm animals. In intensive production environments, acoustic, visual, and chemical signals are often shaped or distorted by crowding, noise, and chronic stress, with direct consequences for welfare. Furthermore, the growing importance of multimodal technologies such as Precision Livestock Farming (PLF) and Animal–Computer Interaction (ACI) is discussed, particularly their role in enabling objective monitoring of emotional states and behaviour and supporting individualised care. Overall, the analysis underscores that communication forms the foundation of social functioning in mammals, and that understanding this complexity is essential for ethology, animal welfare, training practices, and the design of modern technologies facilitating human–animal interaction. Full article

In the context of the global pursuit of the ‘carbon neutrality’ objective, Chinese enterprises are proactively advancing green development and low-carbon transformation. Among these efforts, climate transition risks have emerged as a crucial factor affecting strategic enterprise decisions and long-term competitiveness. This study utilizes a sample comprising Chinese A-share listed enterprises over the period from 2012 to 2024 to construct an enterprise climate transition risk index using text analysis methods. It empirically investigates this index’s impact on enterprise green innovation by adopting panel data analysis method to construct a fixed effects model and further examines the moderating roles of institutional investors’ shareholding and enterprise environmental uncertainties in response to climate transition risks. The research findings indicate the following: First, climate transition risks significantly enhance enterprise green innovation. The validity of this conclusion persists following a series of robustness and endogeneity tests, including replacing the explained variable, lagging the explanatory variable, controlling for city-level fixed effects, and applying instrumental variable methods. Second, both institutional investors’ shareholding and enterprise environmental uncertainties exert a significant positive regulatory effect on the relationship between climate transition risk and green innovation, indicating that external monitoring and heightened risk perception jointly enhance enterprises’ responsiveness in driving green innovation. Thirdly, heterogeneity analysis indicates that the positive impact of climate transition risks on green innovation is notably amplified within non-state-owned enterprises and manufacturing enterprises. By examining the dual regulatory mechanisms of ‘external monitoring’ and ‘risk perception’, this study broadens the study framework on the relationship between climate risks and enterprise green innovation, offering new empirical evidence supporting the applicability of the ‘Porter Hypothesis’ within the context of climate-related challenges. Furthermore, it provides valuable implications for policymakers in refining climate information disclosure policies and assists enterprises in developing forward-looking green innovation strategies. Full article

Air-conditioning systems are vital for indoor environmental quality. However, noise can offset its benefits, making acoustic monitoring important. Recent research revealed that sound quality perceptions can be described by three psychological dimensions: Evaluation, Potency, and Activity (EPA). This is the first study to develop psychoacoustic heatmap machine learning models (PHMLM) for predicting sound quality and the negative noise impacts (O1: Discomfortable, O2: Annoying, O3: Stressful, and O4: Unacceptable) of air conditioning sounds using a 227 × 227-pixel psychoacoustic heatmap as input for machine learning. A total of 1208 jury listening tests were conducted with 101 participants on 30 s soundtracks from air-conditioned environments. Psychoacoustic heatmaps were generated by converting time-varying psychoacoustic metrics (N, S, R, and FS) into intensity maps containing 51,529 pixels of multidimensional acoustic information. The PHMLMs achieved predictive performance with correlation coefficients of 0.79, 0.80, and 0.62 for E-, P-, and A-scores, respectively. Compared to traditional regression models (TRM), PHMLM-EPA demonstrated significantly better performance with 31% lower mean absolute error (4.4 vs. 6.4) and higher regression slope (0.798 vs. 0.587). Moreover, PHMLM-EPA demonstrated a higher goodness-of-fit than TRM (+55% to +95%) and traditional acoustic metric L_Aeq (+87% to +95%). The approach offers an advanced acoustic monitoring method for sustainable building designs. Full article

Growing interest in food sustainability education aims to increase awareness of food distribution systems, environmental degradation, and the connectivity of sustainable and ethical food practices. However, recent scholarship has questioned whether such pedagogical efforts are meaningfully internalized by students or lead to sustained behavioral change. Prior studies document persistent gaps in students’ understanding of sustainability impacts and the limited effectiveness of existing instructional approaches in promoting transformative engagement. To address these concerns, the Food Ethics, Sustainability and Alternatives (FESA) course was implemented with 21 undergraduate and graduate students at Montclair State University (Montclair, NJ, USA). Course outcomes were evaluated using a mixed-methods design integrating qualitative analysis with quantitative measures informed by the Theory of Planned Behavior, to identify influences on students’ attitudes, and a Transtheoretical Model (TTM) panel survey to address progression from awareness to action, administered pre- and post-semester. Qualitative findings revealed five central themes: increased self-awareness of food system contexts, heightened attention to animal ethics, the importance of structured classroom dialogue, greater recognition of food waste, and increased openness to alternative food sources. TTM results indicated significant reductions in contemplation and preparation stages, suggesting greater readiness for change, though no significant gains were observed in action or maintenance scores. Overall, the findings suggest that while food sustainability education can positively shape student attitudes, the conversion of attitudinal shifts into sustained behavioral change remains limited by external constraints, including time pressures, economic factors, culturally embedded dietary practices, structural tensions within contemporary food systems, and perceptions of limited individual efficacy. Full article

Agrivoltaic systems (AVs) combine agricultural production with photovoltaic energy generation, enabling the dual use of land resources. This approach has gained increasing attention as a promising strategy to address pressing social, environmental, and energy challenges. Although the global expansion of AVs is accelerating, empirical research remains limited—particularly regarding farmers’ perspectives on adopting such systems. This study investigates Greek farmers’ perceptions and attitudes toward the adoption of photovoltaic technologies in agricultural practices. For this purpose, a questionnaire-based survey was conducted on a sample of 287 participants selected using purposive convenience sampling, based on predefined inclusion criteria relevant to the objectives of the study. The data were analyzed using a binary logistic regression model to identify factors positively associated with farmers’ willingness to adopt AVs. The findings reveal that 46.3% of farmers expressed willingness to adopt AVs, indicating a moderate level of acceptance. The logistic regression results indicated that higher education levels (OR = 3.53, p = 0.007), membership in farmers’ organizations (OR = 2.00, p = 0.001), and familiarity with agro-energy concepts (OR = 3.49, p = 0.016) significantly increased farmers’ motivation to engage as renewable energy producers. The model demonstrates a moderate level of explanatory power (Nagelkerke R² = 0.37). The study’s findings provide valuable insights into the key factors influencing farmers’ willingness to adopt AVs, contributing to a deeper understanding of the decision-making processes involved. Based on these findings, it is recommended that agricultural policies and community-based renewable energy initiatives focus on targeted education and extension services, the strengthening of farmers’ organizations to facilitate collective decision-making, and the implementation of focused agro-energy information campaigns. Full article

This study develops an immersive marine Information and Communication Technology (ICT) convergence framework designed to enhance coastal climate resilience by improving accessibility, visualization, and communication of scientific research on Dokdo (Dok Island) in the East Sea. High-resolution spatial datasets, multi-source marine observations, underwater imagery, and validated research outputs were integrated into an interactive virtual-reality (VR) and web-based three-dimensional (3D) platform that translates complex geophysical and ecological information into intuitive experiential formats. A geospatially accurate 3D virtual model of Dokdo was constructed from maritime and underwater spatial data and coupled with immersive VR scenarios depicting sea-level variability, coastal morphology, wave exposure, and ecological characteristics. To evaluate practical usability and pro environmental public engagement, a three-phase field survey (n = 174) and a System Usability Scale (SUS) assessment (n = 42) were conducted. The results indicate high satisfaction (88.5%), strong willingness to re-engage (97.1%), and excellent usability (mean SUS score = 80.18), demonstrating the effectiveness of immersive content for environmental education and science communication crucial for achieving Sustainable Development Goal 14 targets. The proposed platform supports stakeholder engagement, affective learning, early climate risk perception, conservation planning, and multidisciplinary science–policy dialogue. In addition, it establishes a foundation for a digital twin system capable of integrating real-time ecological sensor data for environmental monitoring and scenario-based simulation. Overall, this integrated ICT-driven framework provides a transferable model for visualizing marine research outputs, enhancing public understanding of coastal change, and supporting sustainable and adaptive decision-making in small island and coastal regions. Full article

The energy sector is counted among the environmentally unfriendly branches in many global economies, including in Poland. However, it has been pivoting towards alternatives to traditional, high-emission energy generation from non-renewable sources for years. Renewable energy sources, or renewables, are a responsible response to today’s expectations concerning country-level sustainable development, driving the global green energy transition. However, the success of increasing the share of renewables in energy mixes hinges to a large extent on the public perceptions of the changes. In the broadest perspective, research today focuses on global energy transition policy and its funding, problems with the availability of energy carriers, and the adequacy of specific energy production and transfer systems from a technical and technological point of view. Academics tend to concentrate slightly less on investigating the public opinion regarding the challenges of energy transition. This aligns with a relevant research gap for Poland, particularly in rural areas. Therefore, the present article aims to analyse public opinion on environmental protection challenges and the ensuing need to improve energy sourcing to promote the growth of renewable energy in rural Poland, with a case study of five districts in Małopolskie Voivodeship, to contribute to the body of knowledge on these issues. The goal was pursued through a survey of 300 randomly selected inhabitants of the five districts in Malopolska, conducted using Computer-Assisted Personal Interviewing (CAPI) in 2024. The results were analysed with quantitative techniques and qualitative instruments. The detailed investigation involved descriptive statistics and tests proposed by Fisher, Shapiro–Wilk, and Kruskal–Wallis, using IBM SPSS v.25. The use of the indicated methodological approach to achieve the adopted goal distinguishes the study from the approach of other authors. The primary findings reveal acceptance of the ongoing transition processes among the rural population. It is relatively well aware of the role of renewables, but there is still room for improvement, therefore it is necessary to disseminate knowledge in this area and monitor changes in sustainable awareness. We have also established that, overall, educational background is not a significant discriminative feature in rural perceptions of the energy transition. The conclusions can inform policy models to promote green transformation processes, enabling their adaptation to the current challenges and needs of rural residents. Full article

Rapid climate change and its anticipated impacts trigger significant worry and distress among vulnerable groups, including young adults. Little is known about how Dutch young adults experience and cope with climate change within their specific social and environmental context. This study examines Dutch young people’s emotional responses to climate change, their perceived emotional and psychological needs arising from these experiences, and the barriers they encounter in engaging in pro-environmental behaviour, with the aim of informing public health strategies to better support and empower this vulnerable group. Data were drawn from a large online survey among a representative sample of 1006 Dutch young adults (16–35 years; 51% women). The questionnaire included fixed-answer sections assessing emotional responses to climate change, as well as two open-ended questions exploring participants’ perceptions of their emotional and psychological needs related to climate change and the barriers they perceive to pro-environmental behaviour. Descriptive statistics were used for the fixed-response items, and thematic analysis was applied to the open-ended responses. Many Dutch young adults reported worry and sadness about climate change and its impacts, with approximately one third experiencing feelings of powerlessness. A large percentage of respondents attributed responsibility to large companies, and nearly half indicated that they still had hope for the future. One third (31%) felt that nothing could make them feel better about climate change, and another third (36%) reported to experience no climate-related emotions. Key emotional needs included more action at personal, community, and governmental levels, and more motivating positive news. Almost half (46%) of young adults said they already lived sustainably, while perceived barriers to pro-environmental behaviour were mainly financial (21%), knowledge-related (8%), and time-related (7%). This exploratory study highlights key practical and emotional barriers to pro-environmental behaviour reported by Dutch young adults 16–35, who expressed diverse emotional needs while coping with climate change. The findings underscore the need for a multi-level public health response to climate-related emotions, that simultaneously addresses emotional needs, structural barriers, and opportunities for meaningful engagement. Lowering barriers to pro-environmental behaviour and fostering supportive environments that enable sustainable action among young adults may enhance wellbeing and strengthen their sense of agency. Public health supports this by reducing barriers to pro-environmental behaviour in young adults, through targeted support, clear information, and enabling social and structural conditions that promote wellbeing and sustained engagement. Full article

In indoor environments where Global Navigation Satellite System (GNSS) signals are entirely blocked, wireless signals such as 5G and Ultra-Wideband (UWB) have become primary means for high-precision positioning. However, complex indoor structures lead to significant multipath effects, which severely constrain the improvement of positioning accuracy. Existing indoor positioning methods rarely link environmental semantic information (e.g., wall, column) to multipath error estimation, leading to inaccurate multipath correction—especially in complex scenes with multiple reflective objects. To address this issue, this paper proposes a LiDAR-assisted multipath estimation and positioning method. This method constructs a tightly coupled perception-positioning framework: first, a semantic-feature-based neural network for reflective surface detection is designed to accurately extract the geometric parameters of potential reflectors from LiDAR point clouds; subsequently, a unified factor graph model is established to multidimensionally associate and jointly infer terminal states, virtual anchor (VA) states, wireless signal measurements, and LiDAR-perceived reflector information, enabling dynamic discrimination and utilization of both line-of-sight (LOS) and non-line-of-sight (NLOS) paths. Experimental results demonstrate that the root mean square error (RMSE) of the proposed method is improved by 32.1% compared to traditional multipath compensation approaches. This research provides an effective solution for high-precision and robust positioning in complex indoor environments. Full article

Robust scene perception in adverse environmental conditions, particularly under dense fog, presents a persistent and fundamental challenge to the reliability of object detection systems. To address this critical challenge, we propose Fog-UAVNet, a novel lightweight deep-learning architecture designed to enhance unmanned aerial vehicle (UAV) object detection performance in foggy environments. Fog-UAVNet incorporates three key innovations: the Spatial-Edge Feature Fusion Module (SEFFM), which enhances feature extraction by effectively integrating edge and spatial information, the Frequency-Adaptive Dilated Convolution (FADC), which dynamically adjusts to fog density variations and further enhances feature representation under adverse conditions, and the Dynamic Task-Aligned Head (DTAH), which dynamically aligns localization and classification tasks and thus improves overall model performance. To evaluate the effectiveness of our approach, we independently constructed a real-world foggy dataset and synthesized the VisDrone-fog dataset using an atmospheric scattering model. Extensive experiments on multiple challenging datasets demonstrate that Fog-UAVNet consistently outperforms state-of-the-art methods in both detection accuracy and computational efficiency, highlighting its potential for enhancing robust visual perception under adverse weather. Full article

This study aims to understand the motivations, barriers, and preferences of northern Australian beef producers to adopt genetic tools through the views of different stakeholders. Using qualitative research with a single-case study approach, 15 semi-structured interviews were conducted to collect data which were then thematically analysed. Motivating factors to adopt genetic tools were the usefulness of genetic information, the productivity gains, and the profit of the beef enterprise. Barriers to adopting genetic tools included individual factors such as the lack of understanding, limited education, and the attitude of producers or contextual factors such as geographical location and size of production systems. This knowledge will support the development of future extension interventions to promote the use of genetic tools. To effectively promote the use of genetic tools in northern Australian beef production, extension programs should consider both the environmental and geographical context as well as the attitudes and beliefs of local beef producers. This study could present limitations related to sample bias. Future research should include more representative samples and mixed-methods approaches, supplemented by analyses of case studies to validate the reported perceptions. Full article

Odour emissions from wastewater infrastructure represent a significant environmental and social challenge in urban areas. This study evaluates the odour impact of a municipal wastewater pumping station using an integrated field-based approach that combines sensory observations, chemical measurements and meteorological data. Field olfactometry and on-site gas monitoring were applied over a two-year campaign covering different operational and seasonal conditions. The results indicate that odour perception is strongly influenced by hydrogen sulphide concentration, air temperature and wind speed, with short-term high-intensity episodes playing a disproportionate role in odour nuisance. To support integrated interpretation, a Synthetic Odour Index (SOI) was developed to consolidate chemical, sensory and microclimatic information into a single numerical indicator, extending existing odour indices by explicitly integrating field-based sensory and meteorological data. The SOI showed a moderate but statistically significant association with odour intensity (r ≈ 0.3) and effectively differentiated low- and high-nuisance conditions. The proposed methodology demonstrates the value of combining field measurements with integrated data analysis for assessing and managing odour emissions from urban wastewater pumping stations and provides a practical basis for operational monitoring and odour mitigation strategies. Full article