Search Results (690)

Stress in pigs poses significant challenges to animal welfare and productivity in modern pig farming, contributing to increased antimicrobial use and the rise of antimicrobial resistance (AMR). This study involves stress classification in pregnant sows by exploring five deep learning models: ConvNeXt, EfficientNet_V2, MobileNet_V3, RegNet, and Vision Transformer (ViT). These models are used for stress detection from facial images, leveraging an expanded dataset. A facial image dataset of sows was collected at Scotland’s Rural College (SRUC) and the images were categorized into primiparous Low-Stressed (LS) and High-Stress (HS) groups based on expert behavioural assessments and cortisol level analysis. The selected deep learning models were then trained on this enriched dataset and their performance was evaluated using cross-validation on unseen data. The Vision Transformer (ViT) model outperformed the others across the dataset of annotated facial images, achieving an average accuracy of 0.75, an F1 score of 0.78 for high-stress detection, and consistent batch-level performance (up to 0.88 F1 score). These findings highlight the efficacy of transformer-based models for automated stress detection in sows, supporting early intervention strategies to enhance welfare, optimize productivity, and mitigate AMR risks in livestock production. Full article

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article

Mast cell tumours (MCT) are the most common cutaneous neoplasms in dogs, with variable behaviours and patient survival time. Both indolent and aggressive forms have been described, but much remains to be explored regarding prognosis and therapy. Evidence has highlighted the influence of microbiota on multiple health and disease processes, including certain types of cancer in humans. However, knowledge remains scarce regarding microbiota biology and its interactions in both humans and canine cancer patients. This study aimed to characterise the faecal microbiota of dogs with MCT and compare it with that of healthy individuals. Twenty-eight dogs diagnosed with MCT and twenty-eight healthy dogs were enrolled in the study. Faecal samples were collected and analysed by Illumina sequencing of 16S rRNA genes. Alpha diversity was significantly lower in dogs with cancer, and the species diversity InvSimpson Indexwas reduced (p = 0.019). Principal coordinate analysis showed significant differences in the bacterial profile of the two groups: there was a significant lower abundance of the genera Alloprevotella, Holdemanella, Erysipelotrichaceae_UCG-003, and Anaerobiospirillum and, conversely, a significant increase in the genera Escherichia-Shigella and Clostridium sensu stricto 1 in diseased dogs. At the phylum level, Bacteroidota was significantly reduced in diseased dogs (25% in controls vs. 19% in MCT dogs). In conclusion, sequencing analysis provided an overview of the bacterial profile and showed statistical differences in the microbial communities of dogs with MCT compared with healthy dogs, suggesting a link between the gut microbiota and MCT in this species. Full article

Introduction: Dental caries and molar–incisor hypomineralisation (MIH) are prevalent conditions affecting children’s oral health, with functional, aesthetic, and psychosocial implications. In Spain, previous studies have highlighted geographic and sociodemographic disparities in their distribution, particularly among rural and migrant populations. Objective: To characterise oral health status, in terms of caries and MIH, among 6–7-year-old children from the towns of Palos de la Frontera, Mazagón, and San Bartolomé. Methods: A cross-sectional study was conducted involving 229 children recruited from public primary schools. Sociodemographic, anthropometric, and behavioural data were collected through clinical examination and interview. Statistical analysis included univariate and multivariate logistic regression. The study protocol was approved by the Ethics Committee of Huelva. Results: The prevalence of caries (DMFT ≥ 1) was 53.3%, with mean DMFT and dft indices of 1.78 and 0.31, respectively. MIH affected 32.8% of the cohort, with a predominance in the first permanent molars (teeth 36 and 26). Multivariate analysis identified independent predictors of caries: African (OR = 7.47; 95% CI: 2.84–23.8) and European (OR = 4.56; 95% CI: 1.26–22.3) parental origin, poor oral hygiene (OR = 3.07; 95% CI: 1.60–6.03), and the presence of MIH (OR = 3.20; 95% CI: 1.64–6.42). The municipality of San Bartolomé was associated with a higher risk of MIH (OR = 2.90; 95% CI: 1.21–7.45). Conclusions: The high prevalence of caries and MIH in the Condado-Campiña district, exceeding national averages, reflects oral health inequities linked to social determinants (migrant origin, locality) and clinical factors (MIH, oral hygiene). Targeted preventive interventions are urgently needed in high-risk populations, including culturally tailored education and policies ensuring equitable access to dental care services. Full article

Background/Objective: Fentanyl plays a pivotal role in the opioid epidemic, defined by four waves of overdose deaths. To analyse fentanyl research trends, examining its links to mental health, pharmaceutical development, healthcare, diseases, and pathophysiology within the broader social and health context of the time. Methods: To understand the evolution of scientific publications on fentanyl and its relationship to the opioid crisis, a search using Web of Science Core Collection and PubMed was conducted. A total of 53,670 documents were retrieved related to opioid scientific production, among which 1423 articles (3%) focused specifically on fentanyl. The 21,546 MeSH terms identified in these documents were analysed by publication year and specific fields: Psychiatry and Psychology, Chemicals and Drugs, Healthcare, Diseases, and Phenomena and Processes. R-statistical/FactoMineR libraries were used for the correspondence analysis. Results: In the first overdose death wave, research focused on improving therapies and reducing side effects. The second wave emphasised detoxification methods with naltrexone, methadone, and behavioural therapies. The third wave addressed psychological treatments and HIV-syringe-sharing prevention. The fourth wave prioritised less addictive analogues and understanding consumer profiles to combat the epidemic. Conclusions: Fentanyl research has evolved alongside real-world challenges, reinforcing the connection between patients’ needs, healthcare professionals’ roles, illicit users, policymakers, and the research community’s contributions to addressing both therapeutic use and its broader societal impact. These findings highlight the necessity for an interdisciplinary approach to scientific research integrating prevention, treatment, education, legal reform, and social support, emphasising the need for public health policies and collaborative research to mitigate its impact. Full article

This study investigates the cybersecurity landscape of Aotearoa-New Zealand through a culturally grounded lens, focusing on the integration of Indigenous Māori values into cybersecurity frameworks. In response to escalating cyber threats, the research adopts a mixed-methods and interdisciplinary approach—combining surveys, focus groups, and case studies—to explore how cultural principles such as whanaungatanga (collective responsibility) and manaakitanga (care and respect) influence digital safety practices. The findings demonstrate that culturally informed strategies enhance trust, resilience, and community engagement, particularly in rural and underserved Māori communities. Quantitative analysis revealed that 63% of urban participants correctly identified phishing attempts compared to 38% of rural participants, highlighting a significant urban–rural awareness gap. Additionally, over 72% of Māori respondents indicated that cybersecurity messaging was more effective when delivered through familiar cultural channels, such as marae networks or iwi-led training programmes. Focus groups reinforced this, with participants noting stronger retention and behavioural change when cyber risks were communicated using Māori metaphors, language, or values-based analogies. The study also confirms that culturally grounded interventions—such as incorporating Māori motifs (e.g., koru, poutama) into secure interface design and using iwi structures to disseminate best practices—can align with international standards like NIST CSF and ISO 27001. This compatibility enhances stakeholder buy-in and demonstrates universal applicability in multicultural contexts. Key challenges identified include a cybersecurity talent shortage in remote areas, difficulties integrating Indigenous perspectives into mainstream policy, and persistent barriers from the digital divide. The research advocates for cross-sector collaboration among government, private industry, and Indigenous communities to co-develop inclusive, resilient cybersecurity ecosystems. Based on the UTAUT and New Zealand’s cybersecurity vision “Secure Together—Tō Tātou Korowai Manaaki 2023–2028,” this study provides a model for small nations and multicultural societies to create robust, inclusive cybersecurity frameworks. Full article

In the present era, achieving sustainability requires the development of planning strategies to develop a safer urban infrastructure. This study examines the realistic aspects of cyclist safety by analysing cyclists’ fields of view, using Geographic Information Systems (GIS) and spatial data analysis. The research introduces novel geoprocessing tools-based GIS techniques that mathematically simulate cyclists’ angles of view and the distances to nearby environmental features. It provides precise insights into some potential hazards and infrastructure challenges encountered while cycling. This research focuses on managing and analysing the data collected, utilising OpenStreetMap (OSM) as vector-based supporting data. It integrates cyclists’ behavioural data with the urban environmental features encountered, such as intersections, road design, and traffic controls. The analysis is categorised into specific classes to evaluate the impacts of these aspects of the environment on cyclists’ behaviours. The current investigation highlights the importance of integrating the objective environmental elements surrounding the route with subjective perceptions and then determining the influence of these environmental elements on cyclists’ behaviours. Unlike previous studies that ignore cyclists’ visual perspectives in the context of real-world data, this work integrates objective GIS data with cyclists’ field of view-based modelling to identify high-risk areas and highlight the need for enhanced safety measures. The proposed approach equips urban planners and designers with data-informed strategies for creating safer cycling infrastructure, fostering sustainable mobility, and mitigating urban congestion. Full article

One of the important parameters of the sheet metal forming process is the coefficient of friction (CoF). Therefore, monitoring the friction coefficient value is essential to ensure product quality, increase productivity, reduce environmental impact, and avoid product defects. Conventional CoF monitoring techniques pose a number of problems, including the difficulty in identifying the features of force signals that are sensitive to the variation in the coefficient of friction. To overcome these difficulties, this paper proposes a new approach to apply unsupervised artificial intelligence techniques with unbalanced data to classify the CoF of DP780 (HCT780X acc. to EN 10346:2015 standard) steel sheets in strip-drawing tests. During sheet metal forming (SMF), the CoF changes owing to the evolution of the contact conditions at the tool–sheet metal interface. The surface topography, the contact loads, and the material behaviour affect the phenomena in the contact zone. Therefore, classification is required to identify possible disturbances in the friction process causing the change in the CoF, based on the analysis of the friction process parameters and the change in the sheet metal’s surface roughness. The Kohonen self-organising map (SOM) was created based on the surface topography parameters collected and used for CoF classification. The CoF determinations were performed in the strip-drawing test under different lubrication conditions, contact pressures, and sliding speeds. The results showed that it is possible to classify the CoF using an SOM for unbalanced data, using only the surface roughness parameter Sq and selected friction test parameters, with a classification accuracy of up to 98%. Full article

Plastic pollution poses a massive problem to the environment, particularly seas and oceans. Microplastics (MPs) ingestion by marine species can generate many adverse effects, including causing oxidative stress. This study evaluated the effects of anthropic activity-related MP presence in two coastal fish species—Serranus scriba (more related to rocky bottoms) and Lithognathus mormyrus (more related to sandy bottoms)—in two areas of Mallorca Island (Western Mediterranean) with varying anthropic pressures with similar mixed rocky/sandy bottoms. A total of eight fish samples per species and per area (total n = 32), as well as three water samples (500 mL each) and three sediment samples per area, were collected and analyzed. The results showed that despite plastic presence in both areas, the area with higher tourism affluence was also the most polluted. Fourier transform infrared spectroscopy analysis confirmed that the majority of recovered polymers were polyethylene and polypropylene. The pattern of MPs presence was reflected in the biomarker analysis, which showed higher values of antioxidants, namely catalase (CAT) and superoxide dismutase (SOD); detoxification, namely glutathione s-transferase (GST); and inflammation, namely myeloperoxidase (MPO)—enzymes in the gastrointestinal tract of fish from the more polluted area. However, no statistical differences were found for malondialdehyde (MDA) as a marker of lipid peroxidation. As for differences between species, S. scriba presented a higher presence of MPs and measured biomarkers than in L. Mormyrus, suggesting higher exposure. In conclusion, these results showed that increased anthropic activity is associated with a higher presence of MPs which, in turn, induces an adaptative response in exposed fish. Moreover, species living in the same area could be differentially affected by MPs, which is probably associated with different behavioural and feeding habits. Full article

Walking and cycling, as active modes of transportation, play a vital role in advancing sustainable urban mobility by reducing emissions and improving public health. However, widespread adoption faces challenges such as inadequate infrastructure, safety concerns, socio-cultural barriers, and policy limitations. This study systematically reviewed 56 peer-reviewed articles from 2004 to 2024, across 30 countries across five continents, employing the Capability, Opportunity and Motivation-Behaviour (COM-B) framework to identify the main drivers of walking and cycling behaviours. Findings highlight that the lack of dedicated infrastructure, inadequate enforcement of road safety measures, personal and traffic safety concerns, and social stigmas collectively hinder active mobility. Strategic interventions such as developing integrated cycling networks, financial incentives, urban planning initiatives, and behavioural change programs have promoted increased engagement in walking and cycling. Enhancing urban mobility further requires investment in pedestrian and cycling infrastructure, improved integration with public transportation, the implementation of traffic-calming measures, and public education campaigns. Post-pandemic initiatives to establish new pedestrian and cycling spaces offer a unique opportunity to establish enduring changes that support active transportation. The study suggests expanding protected cycling lanes and integrating pedestrian pathways with public transit systems to strengthen safety and accessibility. Additionally, leveraging digital tools can enhance mobility planning and coordination. Future research is needed to explore the potential of artificial intelligence in enhancing mobility analysis, supporting the development of climate-resilient infrastructure, and informing transport policies that integrate gender perspectives to better understand long-term behavioural changes. Coordinated policy efforts and targeted investments can lead to more equitable transportation access, support sustainability goals, and alleviate urban traffic congestion. Full article

Injury or fear of injury can cause stress for everyone. This is especially true for dancers, whose careers can be ruined by a serious injury. Stress or various psychological problems can play a role in the development of injury. Our research aims to explore the psychosocial patterns associated with injuries among Hungarian professional folk dancers. A cross-sectional study was carried out with 96 professional dancers (47.9% male, 52.1% female, mean age 29.9 years). Data was collected through an online questionnaire survey. Among psychological factors, perceived stress (using the Perceived Stress Scale), burnout (using the Athletic Burnout Questionnaire), coping skills (using the Athletic Coping Skills Inventory), relationship with the leader (using the Coaching Behaviour Questionnaire) and perceived social support (using the Multidimensional Scale of Perceived Social Support) were examined among injured and non-injured dancers. The two groups were compared along psychological subscales using Multivariate Analysis of Variance (MANOVA) followed by a post hoc ANOVA and Mann–Whitney test regarding social support. Our results showed a significant correlation between psychosocial factors and injuries sustained during the study period. Positive correlation was found between injuries and perceived stress (p < 0.001) and burnout (reduced sense of accomplishment p = 0.021; dance devaluation p < 0.001). Factors reflecting dancer’s behavior and coping skills also correlated with injuries, such as a decrease in coachability (p = 0.007), less concern (p = 0.029), and negative reactions to the leader’s behavior (p = 0.019). In addition, perceived social support from family also negatively correlates with injury (p = 0.019). Our findings suggest a multidirectional relationship between physical injuries and the mental state of dancers. Further investigation of the causal relationships is recommended, with the aim of using psychosocial support tools during the prevention and treatment of injuries by the professionals dealing with dance artists. It is also recommended to investigate whether individual psychological factors are directly related to injuries or interact with each other. It would also be useful to introduce prevention programs that help dancers manage their emotions related to injuries. Full article

Citizen science now relies heavily on digital platforms to engage the public in environmental data collection. Yet, many projects face declining participation over time. This study examines the effect of three elements of gamification—points, daily streaks, and real-time leaderboards—on student engagement, achievement, and immersion during a five-day campus-wide intervention utilising the GAME and a spatial crowdsourcing app. Employing a convergent mixed-methods design, we combined behavioural log analysis, validated psychometric scales (GAMEFULQUEST), and post-experiment interviews to triangulate both quantitative and qualitative dimensions of engagement. Results reveal that gamified elements enhanced students’ sense of accomplishment and early-stage motivation, which is reflected in significantly higher average scores for goal-directed engagement and recurring qualitative themes related to competence and recognition. However, deeper immersion and sustained “flow” were less robust with repetitive task design. While the intervention achieved only moderate long-term participation rates, it demonstrates that thoughtfully implemented game mechanics can meaningfully enhance engagement without undermining data quality. These findings provide actionable guidance for designing more adaptive, motivating, and inclusive citizen science solutions, underscoring the importance of mixed-methods evaluation in understanding complex engagement processes. While the sample size limits the statistical generalizability, this study serves as an exploratory field trial offering valuable design insights and methodological guidance for future large-scale, controlled citizen science interventions. Full article

Considering the controversial findings in the existing literature, this study aimed to deepen the knowledge about Hair Cortisol Concentration (HCC) in dogs by evaluating the influence on HCC of some factors related to dogs and guardians. Hair was collected from two groups: 128 Healthy Dogs (HD) and 25 dogs with a primary Chronic Gastroenteric Disease (CGD). Guardians of HD filled in a questionnaire, including dogs and respondents’ demographic information, and the perceived welfare and behaviour of the dog. HCC were measured with an enzyme immunoassay kit. A Wilcoxon rank-sum test was conducted to compare HCC in HD and CGD. For HD, a multiple linear regression and an ordinary logistic regression were performed with the dependent variable being HCC and the independent variables being dog and guardian characteristics and the questionnaire evaluations. HCC was statistically lower in CGD (medians: 4.79 versus 6.41 pg cortisol/mg hair; W = 961, p < 0.001). A positive association between HCC and guardian’s age was found (β: 0.012; t-value = 3.205; p < 0.01). Previous literature has shown that several factors can affect HCC in dogs; however, given the controversial results, a large sample and a multiparametric analysis, as in this study, can advance knowledge and highlight newly investigated factors. This study revealed the importance of also considering factors related to the guardian and the possibility that multiple factors interact and collectively influence HCC. Full article

In today’s digital era, social media platforms like Instagram have become deeply embedded in daily life, generating billions of content items each day. This vast stream of publicly accessible data presents a unique opportunity for researchers to gain insights into human behaviour and personality. However, leveraging such unstructured and highly variable data for psychological analysis introduces significant challenges, including data sparsity, noise, and ethical considerations around privacy. This study addresses these challenges by exploring the potential of machine learning to infer personality traits from Instagram content. Motivated by the growing demand for scalable, non-intrusive methods of psychological assessment, we developed a personality prediction system combining convolutional neural networks (CNNs) and random forest (RF) algorithms. Our model is grounded in the Big Five Personality framework, which includes Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Using data collected with informed consent from 941 participants, we extracted visual features from their Instagram images using two pretrained CNNs, which were then used to train five RF models, each targeting a specific trait. The proposed system achieved an average mean absolute error of 0.1867 across all traits. Compared to the PAN-2015 benchmark, our method demonstrated competitive performance. These results highlight that using social media data for personality prediction offers potential applications in personalized content delivery, mental health monitoring, and human–computer interactions. Full article

Water scarcity is a critical issue exacerbated by climate change, urbanization, and population growth, particularly in regions with insufficient water infrastructure. Rainwater harvesting (RWH) systems offer a sustainable solution to mitigate water shortages by collecting and storing rainwater for non-potable uses. This study focuses on the design, efficiency, and reliability of RWH systems in residential environments, with an emphasis on optimizing the sizing of storage volumes and collection areas. Using a behavioural simulation model, we generate nomograms that facilitate the design of RWH systems by analyzing the interactions among storage capacity, collection area, rainfall patterns, and water demand. Specifically, this paper evaluates the effectiveness of RWH systems through efficiency and reliability metrics such as water savings, mains reliance, overflow discharge, and system reliability. The proposed procedure integrates stochastic rainfall and water demand data, including a detailed analysis of toilet usage, in order to simulate the performance of RWH systems across different time scales. Case studies in Italy and Denmark are used to assess the influence of climatic differences on system performance. The findings provide a comprehensive methodology for RWH system design, and offer valuable insights into improving a sustainable water management strategy. Full article