Search Results (2,519)

Timely and accurate detection of sow oestrus is crucial for enhancing reproductive efficiency and reducing non-productive days (NPDs) in large-scale pig farms. However, traditional manual observation is labour-intensive and subjective, while cloud-based deep learning solutions face challenges such as high latency and privacy risks when applied in intensive housing environments. This study developed an edge-intelligent monitoring system that integrates deep temporal modelling with sound source localisation technology. A three-stage hierarchical screening strategy was utilised to select and deploy a lightweight Stacked-LSTM model on the resource-constrained ESP32-S3 hardware platform. This model was trained and calibrated using a high-quality acoustic dataset validated against serum reproductive hormones, specifically follicle-stimulating hormone (FSH), luteinising hormone (LH), and progesterone ($P_4$). Experimental results demonstrate that the optimised model achieved a classification accuracy of 96.17%, with an inference latency of only 41 ms, thereby fully satisfying the stringent real-time monitoring requirements while maintaining a minimal memory footprint. Furthermore, the system integrates a localisation algorithm based on Generalised Cross-Correlation with Phase Transform (GCC-PHAT). Through spatial geometric modelling, the system successfully implements the functional mapping of vocalisation events to individual gestation stalls (Stall IDs). Laboratory pressure tests validated the robustness and low-cost deployment advantages of the “edge recognition–cloud synchronization” architecture, providing a reliable technical framework for the precision management of smart livestock farming. Full article

This study addresses the growing vulnerability of societies to extreme weather events intensified by climate change and explores how Secondary Education can foster sustainability competences aligned with the European GreenComp framework. A mixed-methods design was used, combining a content analysis of 279 curricular units from educational legislation and STEM subjects in Compulsory Secondary Education and Baccalaureate, a questionnaire administered to 190 students, and the design and classroom implementation of a GreenComp-based teaching intervention. The curricular analysis revealed uneven integration of sustainability competences across STEM disciplines, with stronger presence in Biology, Geology and Technology, and limited representation in Mathematics and Physics and Chemistry. Student perceptions showed fragmented understandings of extreme weather events, their causes and consequences, and limited awareness of global frameworks such as the SDGs and COP meetings. The implemented teaching sequence improved students’ knowledge of extreme events, strengthened their recognition of links with climate change, and increased awareness of mitigation, adaptation, and the role of education and political action. Overall, the findings highlight both opportunities and gaps in current curricula and demonstrate the potential of contextualized, inquiry-based STEM approaches to develop sustainability competences and better prepare students to face extreme weather events. Full article

The transition towards circular economy is now a key strategy to address the environmental issues we are facing. Within this framework, biochar, a carbon-rich material derived from residual agricultural pyrolysis, can represent a sustainable and circular solution. This paper aims at evaluating the possibility of implementing a local biochar-production system as part of an economic and social strategy of the redevelopment of an abandoned rural site, Borgo di Perolla, in Tuscany, Italy. A cost–benefits analysis (CBA) was conducted to evaluate the economic feasibility of three different scenarios of production and strategies: Scenario 1 considers revenues solely from the production and sale of biochar and wood vinegar; Scenario 2 additionally includes potential income from the sale of voluntary carbon credits; and Scenario 3 incorporates biochar credits within the European Union Emission Trading System (EU ETS). For each scenario, three indicators were calculated: Net-Present Value (NPV), Internal Rate of Return (IRR), and Breakeven point (BEP). The most evident result that emerged is that the sale of biochar and its by-products alone is not sufficient to ensure the project’s economic sustainability, mainly due to high production costs. Only through carbon-credit-trading markets biochar becomes not only an environmentally strategic tool but also an economically rewarding one. In this sense, market infrastructures, such as the ETS, are essential for the dissemination of circular models, like biochar, that generate both environmental and economic benefits. Previous studies on biochar have largely focused on its application and associated benefits, while cost–benefit analyses have primarily examined its economic feasibility through the commercialization of biochar as a soil amendment, particularly within the United States context. The present work contributes to this literature in three main ways. First, it provides a site-specific and replicable CBA framework applied to a real territorial regeneration project (Borgo di Perolla), grounded in primary data collected through field surveys, stakeholder interviews, and expert validation. Second, the study explicitly compares multiple market-access scenarios within the same analytical framework, ranging from biochar-only sales to voluntary carbon markets, allowing for a clear identification of the economic thresholds at which biochar becomes financially sustainable. Third, and most importantly, the main contribution of this work lies in the explicit modeling of biochar integration into the EU Emissions Trading System. This paper extends the analysis to a regulated carbon market scenario, assuming the recognition of biochar-based carbon removals within the EU ETS framework. From a methodological perspective, the study quantitatively assesses how ETS price dynamics affect the profitability, internal rate of return, and break-even point of a biochar project over a long-term horizon. From a policy perspective, the analysis anticipates recent regulatory developments, such as the EU Regulation 2024/3012, on establishing a Union certification framework for permanent carbon removals, carbon farming, and carbon storage in products, by showing how biochar could function as a fully market-integrated climate technology. Full article

The spinner shark is a widely distributed coastal species that faces significant anthropogenic pressures, yet information on its ecology in the western Indian Ocean remains poorly documented. This study provides preliminary baseline observations on temporal occurrence, sex ratio, and size distribution of a bait-attracted spinner shark aggregation in Hulhumale (North Malé Atoll, Maldives) and presents the first individual-level photo-identification (photo-ID) catalogue for the species based on underwater observations. Surveys were conducted in November 2024 and November 2025 using underwater photography, video recordings, and laser photogrammetry. In total, 69 individual spinner sharks were identified using the standard photo-ID protocol which proved to be valid. On the contrary, the preliminary application of the semi-automatic Identifin software indicated possible effectiveness for individual recognition; however, its performance cannot be reliably validated in this area because of poor image quality and environmental turbidity. Six individuals were re-sighted across years, demonstrating the feasibility of non-invasive repeated, long-term monitoring through photo-ID. Although interannual variation in sex ratio of sharks observed was detected (χ² = 10.56, p = 0.0012), this pattern should be interpreted cautiously due to provisioning-related sampling bias and unequal sampling effort across years. Total length measurements (n = 28) indicated predominantly adult and subadult individuals, with no apparent interannual differences in size distributions. Overall, this study establishes a methodological baseline for spinner shark photo-ID in the Maldives and highlights the importance of multi-year and multi-season monitoring to robustly evaluate aggregation dynamics, site fidelity, and population-level patterns in this region. Full article

For communities on the margins of hostile or indifferent power structures, the political order can be experienced as a force whose acts are not motivated by reasons in accord with recognizable norms. Power, then, as a social phenomenon, is naturalized in the sense that it is dehumanized. Derrida explored some of this territory in his final seminar, the Beast and the Sovereign. Power becomes a latent animality, structuring social life as it removes itself from mechanisms of accountability. At the same time, the Black church ritual, in the United States and elsewhere, provides an experience of a self-sustaining power, whose invisibility is taken as coextensive with its omnipresence. The act of worship becomes a project of counter-habituation whereby power can be constituted as just and life-affirming. Simone Weil’s spiritual writings on the necessity of God’s love can be of some assistance here, but her concern with “decreation” is on its face a self-erasing theological enterprise, the sociopolitical implications of which would seem to put it at odds with a movement, among marginalized people, toward increased recognition. A look at the relation between Weil’s writing method—which I analyze as a kind of endophrasis—and Edmund Husserl’s transcendental understanding of the self provides a way to reorganize our understanding of the sociocultural project supported by the ritual. To grasp the counter-habituating project of the ritual, we must see it as founded in non-linguistic thinking and post-linguistic acts. These acts are, in part, improvisational, which is a key to habituating the recognition of higher-order necessity through free activity. They bring the worshiper “through” culturally determined linguistic acts to another kind of experience, in which the freedom to worship an invisible God is manifest. Full article

Face recognition is increasingly being adopted in industries such as education, security, and personalized services. This research introduces a face recognition system that leverages the embedding capabilities of the CLIP model. The model is trained on multimodal data, such as images and text and it generates high-dimensional features, which are then stored in a vector index for further queries. The system is designed to facilitate accurate real-time identification, with potential applications in areas such as attendance tracking and security screening. Specific use cases include event check-ins, implementation of advanced security systems, and more. The process involves encoding known faces into high-dimensional vectors, indexing them using a vector index FAISS, and comparing them to unknown images based on L2 (euclidean) distance. Experimental results demonstrate a high accuracy that exceeds 90% and prove efficient scalability and good performance efficiency even in datasets with a high volume of entries. Notably, the system exhibits superior computational efficiency compared to traditional deep convolutional neural networks (CNNs), significantly reducing CPU load and memory consumption while maintaining competitive inference speeds. In the first iteration of experiments, the system achieved over 90% accuracy on live video feeds where each identity had a single reference video for both training and validation; however, when tested on a more challenging dataset with many low-quality classes, accuracy dropped to approximately 73%, highlighting the impact of dataset quality and variability on performance. Full article

The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time facial recognition processing, enhancing security and ensuring safety. However, mobile robots are commonly employed in surveillance systems to handle risky tasks that are beyond human capability. In this paper, we present a prototype of a cost-effective mobile surveillance robot built on the Raspberry PI 4, designed for integration into various industrial environments. This smart robot detects intruders using IoT and face recognition technology. The proposed system is equipped with a passive infrared (PIR) sensor and a camera for capturing live-streaming video and photos, which are sent to the control room through IoT technology. Additionally, the system uses face recognition algorithms to differentiate between company staff and potential intruders. The face recognition method combines high-order statistical features and evidence theory to improve facial recognition accuracy and robustness. High-order statistical features are used to capture complex patterns in facial images, enhancing discrimination between individuals. Evidence theory is employed to integrate multiple information sources, allowing for better decision-making under uncertainty. This approach effectively addresses challenges such as variations in lighting, facial expressions, and occlusions, resulting in a more reliable and accurate face recognition system. When the system detects an unfamiliar individual, it sends out alert notifications and emails to the control room with the captured picture using IoT. A web interface has also been set up to control the robot from a distance through Wi-Fi connection. The proposed face recognition method is evaluated, and a comparative analysis with existing techniques is conducted. Experimental results with 400 test images of 40 individuals demonstrate the effectiveness of combining various attribute images in improving human face recognition performance. Experimental results indicate that the algorithm can identify human faces with an accuracy of 98.63%. Full article

Background/Objectives: Developmental prosopagnosia (DP) is a neurodevelopmental condition characterized by lifelong difficulties in face recognition. Although substantial work has examined identity-processing impairments in DP, less is known about whether these difficulties extend to other aspects of social cognition, including implicit trait judgements from faces. Prior research using Implicit Association Task (IAT) paradigms shows that neurotypical observers can automatically associate facial composites with personality traits such as extraversion. Although some studies report preserved explicit social evaluations in DP, to our knowledge, no previous work has assessed whether individuals with DP can form implicit personality trait impressions from faces. Methods: Using a cross-sectional experimental design, the present study examined whether adults with DP (N = 36) exhibit implicit extraversion trait associations, using a validated extraversion IAT online via Gorilla, following institutional ethics approval. Results: Group-level analyses showed a significant IAT effect, indicating sensitivity to congruent face–trait pairings. Single-case analyses using Crawford and Garthwaite’s modified t-test showed that no participant scored significantly below the normative neurotypical range. Conclusions: These findings indicate that implicit trait inference performance can remain within the normative range in DP despite severe identity recognition impairments, consistent with relative independence between social-evaluative and identity-related face-processing mechanisms. Full article

Background/Objectives: Decoding affective states from Electroencephalography (EEG) signals is fundamental to non-invasive Brain–Computer Interfaces. Despite recent advances, accurate recognition is impeded by the inherently non-stationary nature of physiological signals and the entanglement of spatio-temporal dynamics within high-dimensional recordings. While Transformers excel at global modeling, they often neglect the continuous dynamical properties of neural signals and suffer from quadratic complexity. Methods: In this paper, we propose the Spatio-Temporal Hybrid Mamba-Attention (STHMA), a framework designed to explicitly disentangle and model EEG dynamics via linear-complexity State Space Models. First, to incorporate domain knowledge, we introduce a Dual-Domain Physics-Aware Embedding module. This module fuses learnable temporal convolutions with explicit frequency-domain spectral features, ensuring fidelity to neurophysiological principles. Second, we propose a novel Decoupled Spatial–Temporal Scanning strategy. By dynamically reconfiguring the serialization of the data tensor, our model strictly separates the learning of instantaneous functional connectivity from the tracking of emotional state evolution, thereby preventing the structural collapse common in 1D sequence models. Results: Extensive experiments on the FACED and SEED-V datasets demonstrate that the STHMA achieves state-of-the-art performance, significantly exceeding the random chance baselines (11.11% for 9-class FACED and 20.00% for 5-class SEED-V). Conclusions: The results validate that combining Physics-Aware Embeddings with decoupled state-space modeling offers a scalable and effective paradigm for EEG emotion recognition. Full article

Accurate plant recognition in desert grasslands is essential for ecological monitoring, yet existing models face critical limitations: poor generalization in complex natural environments and excessive computational demands for mobile deployment. This study proposes YOLOv11-PKD, a lightweight model integrating structured pruning and knowledge distillation for efficient desert grassland plant identification. First, we develop YOLOv11-STC, a high-capacity teacher model incorporating the SPPCSPC module for multi-scale feature extraction, Triplet Attention for spatial refinement, and a GSConv-based Slim Neck for optimized feature fusion. This architecture achieves 88.3% mAP50 on the DGPlant48 dataset, outperforming the baseline YOLOv11n by 6.8%. To enable edge deployment, we apply channel pruning guided by BatchNorm scaling factors, compressing the model by 19.75% in PParameters and 20% in GFLOPS (YOLOv11-Pruned: 79.5% mAP50, 4.7 MB). Subsequently, L2-based knowledge distillation recovers performance, yielding YOLOv11-PKD with 87.9% mAP50—approaching teacher-level accuracy—while maintaining 5.0 MB size, 2.150 M parameters, and 5.5 GFLOPS. The model is successfully deployed via a mobile application, achieving ~1 s response times for field-based plant identification. This work demonstrates a practical balance between accuracy and efficiency for resource-constrained ecological monitoring. Full article

This systematic review examines how gender shapes exposure to and experiences of psychosocial risks in the workplace. Drawing on 89 empirical studies published between 2010 and 2024, the review synthesizes evidence from occupational health psychology, gender studies, and organizational research. Searches were conducted in PubMed, Web of Science, Scopus, CINAHL, and PsycINFO, and included empirical studies published in English and Spanish. Following PRISMA guidelines, a qualitative thematic synthesis was conducted to integrate findings across diverse sectors, populations, and methodological approaches. The evidence reveals persistent gendered patterns in psychosocial risk exposure and outcomes: women are more frequently exposed to emotionally demanding and relational forms of work and report poorer mental health outcomes; men experience performance-driven strain linked to workload, competition, and reward insecurity more often; and transgender and non-binary workers face additional psychosocial burdens associated with stigma, discrimination, and minority stress. Across the literature, structural and cultural determinants—such as occupational segregation, unequal recognition, and gendered organizational norms—emerge as central mechanisms underlying these disparities. Theoretical frameworks including effort–reward imbalance, demand–control, work–family conflict, organizational climate, and minority stress collectively contribute to explaining how gendered psychosocial risks are produced and sustained. Overall, the review underscores the need to move beyond individualistic and binary models of psychosocial risk toward gender-responsive approaches that account for structural, relational, and identity-based dimensions of work, thereby informing research and organizational strategies aimed at promoting equitable and sustainable well-being at work. Full article

Profile images from social networks are a valuable source of data for AI analytics, but they contain biometric identifiers that pose serious privacy risks. The current face anonymization techniques often destroy semantic information, and generative de-identification methods are vulnerable to re-identification attacks. In this paper, we propose a template-driven multimodal face pseudonymization framework that allows for the privacy-preserving analysis of facial image data while retaining analytically relevant attributes. Our approach uses a FaceNet-based CelebA attribute classifier to extract fine-grained facial attributes and a DeepFace model to extract high-level demographic attributes. Rather than relying on stochastic large language models, we introduce deterministic template-based attribute-to-text conversion to ensure consistency and reproducibility and prevent unintended attribute hallucination. The resulting textual description serves as the sole conditioning input for Janus-Pro, a multimodal text-to-image generation model that synthesizes realistic yet non-identifiable face images. We evaluate our method on the CelebA dataset under a strong adversarial threat model, employing state-of-the-art face recognition systems to assess re-identification and linkability attacks. Our results demonstrate a substantial reduction in identity leakage while preserving semantic attributes. Full article

Microbial imbalance and the spread of pathogenic microorganisms pose severe threats to human health and ecological security. Traditional microbial detection methods suffer from several drawbacks such as long detection time, low sensitivity, and insufficient specificity. As an emerging fluorescent probe, carbon dots (CDs) offer an innovative direction for microbial labeling and detection due to their ultra-small particle size, unique optical properties, excellent biocompatibility, and facile surface modifiability. Herein, this article reviews the research progress of CDs on microbial labeling and detection. The content covers a brief introduction of CDs and explores the main recognition strategies including non-covalent interactions and biomolecule-mediated targeted binding. It also elaborates on the application status of multi-modal sensing technologies for microbial detection, such as CDs-based fluorescent sensing, electrochemical sensing, and surface-enhanced Raman scattering (SERS) sensing. Additionally, the challenges faced in current research, such as achieving simultaneous detection of multiple pathogens and in vivo dynamic tracking, are analyzed, and the development prospects of CDs in fields like clinical diagnosis and public health monitoring are prospected. This review aims to provide comprehensive references for further research and application of CDs in the field of microbial detection. Full article

Background: During the first wave of the COVID-19 pandemic, the importance of the recognition of vulnerable workers was well-established, but the specific impact of the timing of their recognition remains less understood. Objective: This study evaluates the impact of early recognition of vulnerable healthcare workers (VHCWs) and identifies factors associated with SARS-CoV-2 infection. Methods: We performed a retrospective cohort study at the Virgen Macarena University Hospital (HUVM) in Seville and included employees classified as VHCWs between January 2020 and December 2021. All data, including demographic, occupational, and clinical data, were collected from occupational health records and the Andalusian digital health system. The incidence of COVID-19 was analyzed using descriptive, bivariate statistics, and Cox regression. Results: A total of 471 VHCWs were included. Most of the VHCWs were women (79.8%) with a median age of 50 years. The most common vulnerability criteria were pregnancy (32.9%) and age > 60 (28.7%). During the study period, 58 VHCWs (12.3%) were diagnosed with COVID-19, compared to 18.35% of the general workforce. Recognition of VHCW status after the pandemic was declared was strongly associated with higher infection risk (HR = 48.84; 95% CI: 26.21–90.99; p < 0.001). Conclusions: The timing of vulnerability recognition emerged as the most critical protective factor in this cohort. Healthcare workers whose vulnerability was not proactively identified before the pandemic onset faced a substantially higher risk of infection (HR = 44.68; 95% CI: 26.21–90.99; p < 0.001) compared to those recognized early. These findings underscore that pre-pandemic identification facilitated the immediate implementation of task adaptations and workplace restrictions, effectively mitigating high-risk exposure during the most critical early stages of the crisis. Full article

The scientific study of affect has been historically characterized by a profound lack of terminological consensus, leading to a state of conceptual fragmentation that persists in psychology, neuroscience and many other fields. This ambiguity is not merely an academic concern; it has significant consequences for the development of artificial intelligence (AI) systems designed to recognize and respond to human “emotions”. In fact, it has an influence on the entire field of affective computing. The problem is obvious. Without a distinct definition of “emotion” it is difficult to train an algorithm to recognize it. The Walla Emotion Model, also known as the ESCAPE (Emotions Convey Affective Processing Effects) model, provides a potentially helpful and neurobiologically grounded framework to resolve this impasse and to improve any discourse about it, for businesses and even lawmakers aiming at healthy societies. By establishing clear, non-overlapping definitions for affective processing, feelings, and emotions, this model offers a path toward more precise research and more ethically sound affective computing including AI-driven “emotion” recognition. It introduces a concept that allows for the detection of incongruences between internal states and external signals with a very clear terminology supporting understandable communication. This is critical for identifying feigned or socially masked inner affective states, a challenge that traditional “face-reading” AI models frequently fail to address. Even tone of voice and body postures as well as gestures can be and are often voluntarily modified. Through the separation of subcortical affective processing (evaluation of valence; neural activity) from subjective experience (feeling) and external communication (emotion), the Walla model provides a helpful framework for AI-designs meant to have the capacity to infer an internal affective state from collected signals in the wild bypassing verbal self-report. This paper is purely theoretical; it does not provide any algorithm models or other distinct suggestions to train a software package. Its main purpose is the introduction of a new emotion model, particularly a new terminology that is considered helpful in order to proceed with this endeavor. It is considered important to first enable the clearest-possible form of communication about anything related to the term emotion across all disciplines dealing with it. Only then can progress be made. Full article