Search Results (5,412)

In the ecosystem, birds are important indicators that can sensitively reflect changes in the ecological environment and its health. However, bird monitoring has challenges due to species diversity, variable behaviors, and distinct morphological characteristics. Therefore, we propose a parallel dual-branch hybrid CNN–Transformer architecture for feature extraction that simultaneously captures local and global image features to address the “local feature similarity” issue in dual tasks of bird species and behaviors. The dual-task framework comprises three main components: the Token Re-segmentation Module (TRM), the Multi-scale Adaptive Module (MAM), and the Feature Interleaving Structure (FIS). The designed MAM fuses hybrid attention to address the problem of different-scale birds. MAM models the interdependencies between spatial and channel dimensions of features from different scales. It enables the model to adaptively choose scale-specific feature representations, accommodating inputs of different scales. In addition, we designed an efficient feature-sharing mechanism, called FIS, between parallel CNN branches. FIS interleaving delivers and fuses CNN feature maps across parallel layers, combining them with the features of the corresponding Transformer layer to share local and global information at different depths and promote deep feature fusion across parallel networks. Finally, we designed the TRM to address the challenge of visually similar but distinct bird species and of similar poses with distinct behaviors. TRM adopts a two-step approach: first, it locates discriminative regions, and then performs fine segmentation on them. This module enables the network to allocate relatively more attention to key areas while merging non-essential information and reducing interference from irrelevant details. Experiments on the self-made dataset demonstrate that, compared with state-of-the-art classification networks, the proposed network achieves the best performance, achieving 79.70% accuracy in bird species recognition, 76.21% in behavior recognition, and the best performance in dual-task recognition. Full article

Effective asset management is crucial for improving the reliability, resilience, and cost efficiency of distribution networks throughout the asset life cycle. Distribution transformers are among the most critical components, as their failures can cause extensive service interruptions and substantial economic impacts. Therefore, robust and transparent maintenance prioritization strategies are essential, particularly for utilities managing several transformers. Traditional time-based maintenance, while simple to implement, often results in inefficient resource allocation. Condition-based maintenance provides a more effective alternative; however, its performance depends strongly on the reliability of indicator selection and weighting. This study proposes a systematic weighting framework for distribution transformer maintenance prioritization using a multi-criteria decision-making (MCDM) approach. Each transformer is evaluated across two dimensions, including health condition and operational impact, based on indicators identified from the literature and expert judgment. To address uncertainty and judgmental inconsistency, particularly when the consistency ratio (CR) exceeds the conventional threshold of 0.10, the Fuzzy Analytic Hierarchy Process (FAHP) is employed. Seven condition parameters characterize transformer health, while impact is quantified using five indicators reflecting failure consequences. The proposed framework offers a transparent, repeatable, and defensible decision-support tool, enabling utilities to prioritize maintenance actions, optimize resource allocation, and mitigate operational risks in distribution networks. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

Background/Objectives: Cognitive decline and compromised attention control serve as early indicators of neurodysfunction that manifest as broader psychopathological symptoms, yet conventional mental health assessment relies predominantly on subjective self-report measures lacking objectivity and temporal granularity. We propose Neurosense, an AI-driven brain health assessment framework using electroencephalography (EEG) to non-invasively capture neural dynamics. Methods: Our Dual-path Spatio-Temporal Adaptive Gated Encoder (D-STAGE) architecture processes temporal and spatial EEG features in parallel through Transformer-based and graph convolutional pathways, integrating them via adaptive gating mechanisms. We introduce a two-stage paradigm: first training on cognitive task EEG for reaction time prediction to acquire cognitive performance-related representations, then featuring parameter-efficient adapter-based transfer learning to estimate p-factor—a transdiagnostic psychopathology dimension. The adapter-based transfer achieves competitive performance using only 1.7% of parameters required for full fine-tuning. Results: The model achieves effective reaction time prediction from EEG signals. Transfer learning from cognitive tasks to mental health assessment demonstrates that cognitive efficiency representations can be adapted for p-factor prediction, outperforming direct training approaches while maintaining parameter efficiency. Conclusions: The Neurosense framework reveals hierarchical relationships between neural dynamics, cognitive efficiency, and mental health dimensions, establishing foundations for a promising computational framework for mental health assessment applications. Full article

Background: In a context of profound transformation within Public Administration, the growing generational diversity of the workforce poses critical challenges to organisational well-being. While ageism is a known risk, the intersectionality of age and gender—manifesting as gendered ageism—remains an under-explored area that can significantly undermine job satisfaction and employee health. Objective: This study aimed to explore the subjective work experience of public sector employees, specifically focusing on intergenerational relations and the impact of gendered ageism. Methods: A qualitative study was conducted involving 30 employees of the Italian Public Administration, recruited via purposive sampling. Data were collected through semi-structured interviews lasting approximately 38 min and analysed using a thematic analysis of elementary contexts via T-Lab software. Results: The analysis revealed four distinct thematic clusters positioned along two main factor axes (Individual/Organisation and Difficulties/Potential). The results show a dichotomy: while positive relationships with colleagues (Cluster 1) and the drive for change (Cluster 4) act as potential resources, the experience is marred by significant difficulties. These include organisational imbalances (Cluster 3) and, crucially, specific experiences of gendered ageism (Cluster 2), manifesting as stereotypes, pressure on women’s physical appearance, and exclusionary dynamics. Conclusions: The findings highlight that gendered ageism is a distinct stressor impacting workforce sustainability. Combating intersectional discrimination represents a strategic priority to safeguard well-being, retain skills, and build a healthy, resilient, and productive working environment. Full article

The detection of titanium dioxide (${\mathrm{TiO}}_2$) nanoparticles is a significant challenge due to their extensive industrial use and potential health and environmental impacts, which demand accurate, label-free approaches. This work presents an automatic detection system based on spectroscopy with optical frequency combs (OFC) in dual-coupled microresonators. The OFC generation was modeled through the Lugiato-Lefever equation, while propagation in distilled water containing ${\mathrm{TiO}}_2$ was simulated using the finite element method (FEM). The water–${TiO}_2$ mixture was described with the Yamaguchi model in a 5 × 5 mesh to represent non-uniform concentrations. From the norm of the electric field at a probe, a database of 11 classes (0–100%) with controlled Gaussian noise was constructed. A Transformer-based classifier was trained and compared with 1D-CNN and SVM under Monte Carlo validation (100 random 70/30 splits). The Transformer achieved 99.84 ± 0.01% accuracy with an inference time of 0.793 ± 0.05 s, while the 1D-CNN reached 99.64 ± 0.09% and the SVM 84.73 ± 1.48%. A repeatability test with 200 iterations confirmed deterministic DKS trajectories. The results demonstrate that combining dual-coupled microresonators, FEM, and Transformer architectures enables precise and efficient detection of ${\mathrm{TiO}}_2$ nanoparticles in aqueous solutions. Full article

Amid escalating ecological degradation, social fragmentation, and rising mental health challenges—especially in rural and agricultural communities—there is an urgent need to reimagine systems that support both planetary and human flourishing. This viewpoint examines an emerging paradigm in agriculture that emphasizes the role of farmers’ inner development in fostering practices that enhance ecological health, community well-being, and a resilient food system. A key goal is to draw more academic attention to growing community calls for more holistic, relational, and spiritually grounded approaches to food systems as an important focus for ongoing research. Drawing on diverse case studies from Japan, India, and Europe, we examine how small-scale and natural farming initiatives are integrating inner development, universal human values, and ecological consciousness. These case studies were developed and/or refined through a program led by the Conscious Food Systems Alliance (CoFSA), an initiative of the United Nations Development Programme (UNDP) that seeks to integrate inner transformation with sustainable food systems change. The initiatives are intended as illustrative examples of how agriculture can transcend its conventional, anthropocentric role as a food production system to become a site for cultivating deeper self-awareness, spiritual connection, and regenerative relationships with nature. Participants in these cases reported significant shifts in mindset—from materialistic and extractive worldviews to more relational and value-driven orientations rooted in care, cooperation, and sustainability. Core practices such as mindfulness, experiential learning, and spiritual ecology helped reframe farming as a holistic process that nurtures both land and life. These exploratory case studies suggest that when farmers are supported in aligning with inner values and natural systems, they become empowered as agents of systemic change. By linking personal growth with planetary stewardship, these models offer pathways toward more integrated, life-affirming approaches to agriculture and future academic research. Full article

Cervical cancer remains a significant global health burden, disproportionately affecting women in low- and middle-income countries despite being preventable. Since 2018, rapid advances in molecular profiling, immunotherapy, refinement of minimally invasive surgery, and targeted therapeutics have transformed diagnostic and therapeutic paradigms. This narrative review synthesizes clinical and translational progress across the continuum of care from 2018 to 2025. We summarize the evolving landscape of precision screening—including HPV genotyping, DNA methylation assays, liquid biopsy, and AI-assisted cytology—and discuss their implications for global elimination goals. Surgical management has shifted toward evidence-based de-escalation with data from SHAPE, ConCerv, and ongoing RACC informing fertility preservation and minimally invasive approaches. For locally advanced disease, KEYNOTE-A18 establishes pembrolizumab plus chemoradiation as a new curative standard, while INTERLACE underscores the benefit of induction chemotherapy. In the metastatic setting, survival outcomes have improved with the integration of checkpoint inhibitors (KEYNOTE-826, BEATcc, EMPOWER-Cervical 1), vascular-targeted therapies, and antibody–drug conjugates, including tisotumab vedotin and emerging HER2 and TROP-2–directed agents. We further highlight emerging biomarkers—PD-L1, TMB, MSI status, HPV integration patterns, APOBEC signatures, methylation classifiers, ctHPV-DNA—and their evolving role in treatment selection and surveillance. Future directions include neoadjuvant checkpoint inhibition, PARP-IO combinations, HER3-directed ADCs, DDR-targeted radiosensitizers, HPV-specific cellular therapies, and AI-integrated precision medicine. Collectively, these advances are reshaping cervical cancer care toward biologically individualized, globally implementable strategies capable of accelerating WHO elimination targets. Full article

This paper introduces flourishing circularity as a transformative approach to resource assessment that transcends both traditional Resource-Based View (RBV) theory and conventional circular economy concepts. We demonstrate RBV’s fundamental limitations in addressing the polycrisis of breached planetary boundaries and social inequities. Similarly, while the circular economy focuses on resource reuse and recycling, it often merely delays environmental degradation rather than reversing it. Flourishing circularity addresses these shortcomings by reconceptualizing natural and social capital not as externalities but as foundational sources of all value creation. We develop a comprehensive framework for assessing resources within an open systems perspective, where competitive advantage increasingly derives from a firm’s ability to regenerate the systems upon which all business depends. The paper introduces novel assessment tools that capture the dynamic interplay between organizational activities and coevolving social and ecological systems. We outline the core competencies required for flourishing circularity: regenerative approaches to social and natural capital, and systems thinking with cross-boundary collaboration capabilities. These competencies translate into competitive advantage as stakeholders increasingly favor organizations that enhance system health. The framework provides practical guidance for transforming resource assessment from extraction to regeneration, enabling business models that create value through system enhancement rather than depletion. Full article

Background/Objectives: Rapid and accurate diagnosis of acute appendicitis is crucial for patient health and management, and the diagnostic process can be prolonged due to varying clinical symptoms and limitations of diagnostic tools. This study aims to shorten the timeframe for these vital processes and increase accuracy by developing a quantum-inspired hybrid model to identify appendicitis types. Methods: The developed model initially selects the two most performing architectures using four convolutional neural networks (CNNs) and two Transformers (ViTs). Feature extraction is then performed from these architectures. Phase-based trigonometric embedding, low-order interactions, and norm-preserving principles are used to generate a Quantum Feature Map (QFM) from these extracted features. The generated feature map is then passed to the Multiple Head Attention (MHA) layer after undergoing Hadamard fusion. At the end of this stage, classification is performed using a multilayer perceptron (MLP) with a ReLU activation function, which allows for the identification of acute appendicitis types. The developed quantum-inspired hybrid model is also compared with six different CNN and ViT architectures recognized in the literature. Results: The proposed quantum-inspired hybrid model outperformed the other models used in the study for acute appendicitis detection. The accuracy achieved in the proposed model was 97.96%. Conclusions: While the performance metrics obtained from the quantum-inspired model will form the basis of deep learning architectures for quantum technologies in the future, it is thought that if 6G technology is used in medical remote interventions, it will form the basis for real-time medical interventions by taking advantage of quantum speed. Full article

The Potocki family of the Pilawa coat of arms was among the most powerful noble lineages of the former Polish–Lithuanian Commonwealth, and its history is closely intertwined with that of Poland, Lithuania, Belarus, and Ukraine. In the late seventeenth century, Feliks Kazimierz Potocki (1630–1702) founded the town of Krystynopol (now Chervonohrad), named in honor of his wife, Krystyna Lubomirska. The residence, passed down through successive generations of the Potocki family, was transformed in the mid-eighteenth century into an impressive Baroque palace-and-garden complex designed by Pierre Ricaudde Tirregaille, becoming a model example of the magnate cultural landscape on the border of present-day Poland and Ukraine. In the centuries that followed, the estate changed owners multiple times, suffered devastation during the world wars, and in the Soviet period housed the Museum of Atheism. Today, the partially restored palace accommodates a small regional museum. Although in the eighteenth century the palace was surrounded by an extensive Italian-French style garden with water canals, ponds, and fountains, the area has since been built over with public-utility buildings. This study presents a concept for the development of the surviving elements of the historical palace park. The project is based on historical analyses, field research, site inspections, interviews with museum staff and town residents, as well as a detailed dendrological inventory including an assessment of tree health. The study area covers 4.71 ha, and the current tree stand is composed mainly of Salix alba, Populus nigra, Populus alba, Betula pendula, Quercus robur, Fraxinus excelsior, Ulmus laevis, Acer negundo, and Acer pseudoplatanus. Archival sources allowed for the reconstruction of the original layout of the palace-park complex. The aim of the project is therefore to introduce new representative, educational, recreational, social, ecological, and touristic functions to the currently neglected area while respecting its historical heritage. Full article

Human immunodeficiency virus type 1 (HIV-1) continues to be a major global health burden. Combination antiretroviral therapy (cART) effectively abrogates HIV-1 replication and has transformed HIV-1 infection from a fatal to chronic disease. While ART can suppress viremia to undetectable levels in people living with HIV-1 (PWH), a small reservoir of cells infected with replication-competent HIV-1 persists and can lead to viral rebound upon ART interruption. This persistent HIV-1 reservoir can be quantified and characterized by measuring replication of infectious HIV-1 using a quantitative viral outgrowth assay (qVOA), or by measuring HIV-1 DNA, RNA, or protein levels as a proxy for the reservoir. Tools to quantify the reservoir in these distinct molecular compartments have been developed for HIV-1 subtype B, which is predominant in the Global North. However, non-B subtypes constitute the majority of HIV-1 infections worldwide. Here, we discuss the wide range of reservoir quantitation and characterization tools, explore their limitations, and, where applicable, their adaptations to non-B subtypes. We conclude that standardized tools should be used to characterize reservoir dynamics of HIV-1 B and non-B subtypes. These tests should be well-validated and accessible to all laboratories world-wide to be able to draw conclusions about subtype-specific reservoir dynamics. Full article

Infectious diseases remain a persistent global health threat, intensified by the rapid emergence of antibiotic-resistant pathogens. Despite the transformative impact of antibiotics, the escalating resistance crisis underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides (AMPs) have emerged as promising candidates due to their broad-spectrum antimicrobial and immunomodulatory activities. The present study investigated 82 honey bee antimicrobial peptides (BAMPs) representing seven families: abaecin, apamin, apisimin, apidaecin, defensin, hymenoptaecin, and melittin among eight honey bee species. Immunoinformatics analyses identified five peptides (P15450, A0A2A3EK62, Q86BU7, C7AHW3, and I3RJI9A) with high antigenicity and non-allergenic profiles. Structural modeling, molecular docking with TLR3 and TLR4-MD2, and molecular dynamics simulations revealed stable receptor-peptide interactions and favorable binding energetics, further supported by silico immune simulations. Overall, these findings suggest that the selected BAMPs exhibit strong immunogenic potential and may serve as effective adjuvants or carrier molecules in subunit vaccine design against drug-resistant pathogens; however, further experimental validation is essential to confirm their safety and immunological efficacy. Full article

The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10–20 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of reshaping the entire pharmaceutical research and development (R&D) pipeline. The purpose of this narrative review is to examine the role of AI in drug discovery and development, highlighting its contributions, challenges, and future implications for pharmaceutical sciences and global public health. A comprehensive review of the scientific literature was conducted, focusing on published studies, reviews, and reports addressing the application of AI across the stages of drug discovery, preclinical development, clinical trials, and post-marketing surveillance. Key themes were identified, including AI-driven target identification, molecular screening, de novo drug design, predictive toxicity modelling, and clinical monitoring. The reviewed evidence indicates that AI has significantly accelerated drug discovery and development by reducing timeframes, costs, and failure rates. AI-based approaches have enhanced the efficiency of target identification, optimized lead compound selection, improved safety predictions, and supported adaptive clinical trial designs. Collectively, these advances position AI as a catalyst for innovation, particularly in promoting accessible, efficient, and sustainable healthcare solutions. However, substantial challenges remain, including reliance on high-quality and representative biomedical data, limited algorithmic transparency, high implementation costs, regulatory uncertainty, and ethical and legal concerns related to data privacy, bias, and equitable access. In conclusion, AI represents a paradigm shift in pharmaceutical research and drug development, offering unprecedented opportunities to improve efficiency and innovation. Addressing its technical, ethical, and regulatory limitations will be essential to fully realize its potential as a sustainable and globally impactful tool for therapeutic innovation. Full article

Workers’ exposure to silica dust is a global occupational and public health concern and is particularly prevalent in Southern Africa, mainly because of inadequate dust control measures. It is worsened by the high prevalence of HIV/AIDS, which exacerbates tuberculosis and other occupational lung diseases. The prevalence of silicosis in the region ranges from 9 to 51%; however, silica dust exposure levels and controls, especially in the informal mining sector, particularly in artisanal small-scale mines (ASMs), leave much to be desired. This is important because silicosis is incurable and can only be eliminated by preventing worker exposure. Additionally, several studies have indicated inadequate occupational health and safety policies, weak inspection systems, inadequate monitoring and control technologies, and inadequate occupational health and hygiene skills. Furthermore, there is a near-absence of silica dust analysis laboratories in southern Africa, except in South Africa. This protocol aims to systematically evaluate the effectiveness of respirable dust and respirable crystalline silica dust exposure evaluation and control methodology for the mining industry. The study will entail testing the effectiveness of current dust control measures for controlling microscale particles using various exposure dose metrics, such as mass, number, and lung surface area concentrations. This will be achieved using a portable Fourier transform infrared spectroscope (FTIR) (Nanozen Industries Inc., Burnaby, BC, Canada), the Nanozen DustCount, which measures both the mass and particle size distribution. The surface area concentration will be analysed by inputting the particle size distribution (PSD) results into the Multiple-Path Particle Dosimetry Model (MPPD) to estimate the retained and cleared doses. The MPPD will help us understand the sub-micron dust deposition and the reduction rate using the controls. To the best of our knowledge, the proposed approach has never been used elsewhere or in our settings. The proposed approach will reduce dependence on highly skilled individuals, reduce the turnaround sampling and analysis time, and provide a reference for regional harmonised occupational exposure limit (OEL) guidelines as a guiding document on how to meet occupational health, safety and environment (OHSE) requirements in ASM settings. Therefore, the outcome of this study will influence policy reforms and protect hundreds of thousands of employees currently working without any form of exposure prevention or protection. Full article