Search Results (722)

The increasing concern over heavy metal contamination in export crops has intensified research on the application of computer vision systems (CVS) and advanced sensing technologies within multi-level agricultural monitoring frameworks spanning soil contamination assessment, crop spectral diagnostics, and in situ elemental sensing. This study conducts a systematic literature review following Kitchenham’s methodology, from which 68 studies were finally included after screening and eligibility assessment. The review focuses on the use of hyperspectral imaging (HSI) and XRF-IoT sensors (X-ray fluorescence units enhanced with IoT connectivity) for detecting heavy metals in export crops, considering publications from the last seven years indexed in Web of Science Core Collection, Scopus, IEEE Xplore, EBSCOhost, and Springer Nature Link. The findings indicate that research is concentrated in highly digitalized countries, which limits its global applicability; moreover, a substantial proportion of studies is published in Q1 journals, although the methodologies are not always fully objective. Likewise, the most developed research lines are oriented toward image-based diagnostics and crop analysis. These results reveal a gap between technological advances in computer vision and their integration into agricultural decision-making aimed at improving the quality of export crops. It is recommended to foster research with greater geographical diversity, grounded in solid theoretical frameworks and an ethical perspective. Full article

Background: Embodied artificial intelligence (EAI), integrating advanced AI algorithms with robotic platforms capable of sensing, planning, and acting, has emerged as a transformative approach in healthcare delivery. This systematic review synthesizes evidence on robotic perception, decision-making, and clinical impact of EAI systems in healthcare settings. Methods: Following PRISMA 2020 guidelines, we searched PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library for studies published between January 2020 and August 2025. Seventeen studies met eligibility criteria, spanning four domains: surgical assistance, rehabilitation, hospital logistics, and telepresence. The protocol was prospectively registered in PROSPERO under ID: CRD420261285936. Results: Perception architectures predominantly employed multimodal sensor fusion, combining vision with force/torque, depth, and physiological signals. Decision-making approaches included imitation learning, reinforcement learning, and hybrid symbolic-neural control. Key findings indicate that surgical robots demonstrated consistency advantages in specific experimental tasks, rehabilitation robotics produced statistically significant improvements (SMD = 0.29) across 396 randomized controlled trials, and both logistics and telepresence systems achieved very high operational success levels. Nonetheless, important barriers remain, including limited external validation, small sample sizes, and insufficient cost-effectiveness data. Conclusions: Future research should prioritize standardized benchmarks, prospective multicenter trials, and patient-centered outcome measures to facilitate clinical translation of EAI technologies. Full article

Background/Objectives: We conducted a scoping review to map peer-reviewed evidence on occupational stress, burnout, and quality of life among radiographers and radiologic technologists and to identify measurement tools and reported consequences. Methods: Searches were conducted in Web of Science, Scopus, and PubMed. Eligible studies enrolled radiographers/radiologic technologists who were healthy adults; assessed at least one target construct (occupational stress, burnout, or quality of life) using validated instruments; and used cross-sectional, experimental, quasi-experimental, longitudinal, or mixed-methods designs. Articles published from 1995 onward in English, French, Spanish, or Portuguese were considered. Two reviewers independently screened, extracted data, and appraised methodological quality using Quality Assessment with Diverse Studies (QuADS). The synthesis was narrative only. Results: Of 2701 records, 10 studies from nine countries met inclusion. Most were cross-sectional, and two used mixed methods. Sample sizes ranged from 38 to 864. Frequently used instruments included MBI-HSS, OSI-R, HSE Indicator Tool, and SOC-13. Across studies, radiographers reported high stress and burnout—particularly emotional exhaustion and depersonalization—alongside reduced quality of life in multiple domains. Recurrent stressors involved workload and staffing pressures, role demands, anxiety about radiation exposure, and limited recognition. These factors were associated with intention to leave and a lower sense of coherence. Conclusions: The evidence base is largely cross-sectional, uses heterogeneous measures, and often relies on modest samples, with overall methodological quality mostly moderate. Findings indicate a persistent psychosocial risk profile in radiography and underscore the need for organizational and managerial actions—such as workplace physical activity programs—to reduce stress and burnout and protect the quality of life in this workforce. Full article

Gary Snyder’s vision of the ‘great earth sangha’ articulates a philosophy of ecological awakening in which spiritual, ethical, and affective relationships connect all forms of life into a cohesive and sacred web of interbeing. The concept of the ‘great earth sangha’ embodies a profound sense of ‘oneness,’ in which the dichotomy between the self and the other dissolves, leading to a realisation of the Earth as a sentient, experiential, and pulsating entity. Inspired by the holistic perspectives of Buddhism and the resonances of Indigenous cosmologies, Snyder’s idea of the ‘great earth sangha’ represents a heightened consciousness and an “emotional intelligence” that fosters compassion, love, care and empathy for all beings in the world. For Snyder, the great earth sangha is a practice—a way of living in mindful ecological engagement. It is embedded with the principles of sila (morality), which foregrounds visions of harmonious coexistence and ecological kinship. This article argues that Snyder’s idea of the ‘great earth sangha’ offers a counter-anthropocentric perspective that subverts entrenched human-centred hierarchies by situating human identity within a communal web of existence. The article discusses how Snyder redefines the notion of ‘community’ as an inclusive, interdependent network that transcends human boundaries and embraces all planetary beings. Finally, the article explores how Snyder’s holistic vision propounds a restorative path that centres on ideas of ethics, affect, justice, responsibility and stewardship. Full article

Eye tracking has become a central method in human–computer interaction (HCI), supported by advances in sensing technologies and AI-based gaze analysis. Despite this rapid growth, a comprehensive and up-to-date overview of eye-tracking research across the broader HCI landscape remains lacking. This study combines records from Web of Science (WoS) and Scopus to analyse 1033 publications on eye tracking in HCI published between 2020 and 2025. After merging and deduplicating the datasets, we conducted bibliometric network analyses (keyword co-occurrence, co-citation, co-authorship, and source mapping) using VOSviewer and performed a qualitative content analysis of the 50 most-cited papers. The literature is dominated by journal articles and conference papers produced by small- to medium-sized research teams (mean: 3.9 authors per paper; h-index: 29). Keyword and overlay visualisations reveal four principal research axes: deep-learning-based gaze estimation; XR-related interaction paradigms within HCI; cognitive load and human factors; and usability- and accessibility-oriented interface design. The most-cited studies focus on gaze interaction in immersive environments, deep learning for gaze estimation, multimodal interaction, and physiological approaches to assessing cognitive load. Overall, the findings indicate that eye tracking in HCI is evolving from a measurement-oriented technique into a core enabling technology that supports interaction design, cognitive assessment, accessibility, and ethical considerations such as privacy. This review identifies research gaps and outlines future directions for benchmarking practices, real-world deployments, and privacy-preserving gaze analytics in HCI. Full article

The rapid evolution of Earth observation satellite missions and computational methods made satellite remote sensing a foundation of state-of-the-art crop yield prediction. Therefore, the aim of this review is to analyze dominant drivers of crop yield prediction research based on satellite remote sensing, including dominant sensor types, satellite missions, crops, and specific research topics, as well as to identify present issues and research gaps. This review summarizes the bibliometric analysis of satellite-based crop yield prediction publications during 2000–2025, including 1174 articles that were indexed in the Web of Science Core Collection. Annual publication and citation trends, geographic patterns of research publications, prevalent satellite missions and sensor types, predominant crops used in research and trends in research themes were analyzed in the study. Findings show that there has been a consistent expansion of the study topic regarding publication count, with multispectral data, especially that of Sentinel-2, Landsat, and MODIS missions, being utilized in most of the literature in the field, while radar-based approaches are becoming increasingly important, providing complementary data to multispectral imagery. The review indicates a methodological shift in the models of simple regressions to machine learning, deep learning, and multi-sensor data fusion frameworks that use dense satellite imagery time series. Full article

This study aims to systematically review the existing literature on the use of data derived from remote sensing products to estimate groundwater recharge. The terms “recharge”, “remote sensing product data”, “remote sensing data”, “groundwater”, and “recharge estimation” were used as keywords in the Web of Science and Scopus databases. A total of 27 articles were analyzed, highlighting the use of different precipitation and evapotranspiration products for estimating potential recharge. This review emphasizes the potential of products such as CHIRPS and TRMM for precipitation and MODIS for evapotranspiration, as well as other remote sensing datasets that have shown good performance in their applications. The studies demonstrate the high feasibility of applying remote sensing to estimate groundwater recharge and indicate how its use can enhance the quality and reliability of the results obtained. Full article

This bibliometric analysis synthesizes 39 years (1987–2025) worth of highly cited publications on mangrove sustainability, using 2465 publications from the Web of Science and Scopus. This study offers researchers and policymakers a clear map of the core knowledge and emerging areas in this field. It highlights influential publications and traces the field’s development through bibliometric analyses of keywords, citations, co-authorships, and geographic collaborations. Research on this area crystallizes around threat assessment, management, ecosystem services, and operationalizing blue carbon, strongly supported by remote sensing and aligned with SDGs 13, 14, and 15. Catherine E. Lovelock is the most prolific author, and Science of the Total Environment is the leading journal. Geographically, the Global North (USA, Australia, and Europe) remains dominant, while China asserts institutional and country-level leadership as a hybrid collaborator in the Global South. The collaboration network reveals a hub-and-spoke structure and a research capacity gap in mangrove-rich nations across Africa and South America. Global events, environmental issues, and modern technologies are driving the development of new theories, concepts, and techniques regarding mangrove sustainability. This study reveals key research imbalances and concludes that achieving mangrove sustainability requires robust South–South collaboration and autonomous research capacity in climate-vulnerable regions. Full article

This paper provides insight into the development of Earth Observation (EO) research within geographic and environmental sciences from 1978 to 2024, using a spatially explicit bibliometric approach. The research is based on 28,871 publications indexed in the Web of Science database, which includes four EO-related subject categories: remote sensing, environmental science, geography physical, and geography. Two main phases of the de velopment of EO research are identified. The first period (1978–2011) is marked by fundamental research on early satellite imagery, while the second period (2012–2024) represents a strong growth spurred by open data policies, the Sentinel missions and the development of cloud computing platforms. The results indicate marked geographical asymmetries. Research activities are concentrated in the United States, China, Canada and Western Europe, while many countries of the Global South remain underrepresented and rely more heavily on international collaboration. These spatial disparities reflect the uneven global distribution of scientific and technological capacity. Thematic and network analyses show a shift in focus from sensor- and data-driven research towards the application of machine learning, time-series analysis, land use and land cover change studies and Sentinel-based applications. The results provide a contextual framework for understanding how the development of environmental observation research capacity and technological change are shaping contemporary environmental research and its ability to respond to global environmental change. Full article

The transition from cycling to electric micro-mobility, such as e-scooters, introduces distinct safety risks. While physiological sensing is established for monitoring cyclist exertion, its transferability to high-vibration e-scooter environments remains unclear. This study systematically reviews wearable sensors used to detect stress, fatigue, and exertion in cycling and micro-mobility to identify gaps preventing active safety systems. A PRISMA-guided search of IEEE Xplore, Web of Science, PubMed, Scopus, and ScienceDirect was performed on 2 October 2025 for studies published in 2015–2025. From 273 records, 11 publications representing nine unique studies met the inclusion criteria. Laboratory studies ($n=4$) utilizing deep learning (CNN-LSTM) achieved high exertion prediction accuracy (F1 86.3–91.7%) but relied on a single redundant dataset ($N=27$), lacking independent validation. Field studies ($n=7$) relied on statistical associations between heart rate variability and environmental stress but lacked real-time predictive capabilities. Notably, evidence for automated physiological safety classification in e-scooters is critically underdeveloped. Current models are overfitted to cycling biomechanics and fail to account for e-scooter constraints, such as whole-body vibration. Future research must shift toward Unsupervised Domain Adaptation (UDA) and noise-resilient edge AI architectures to bridge the technological lag in micro-mobility safety. Full article

Climate change is transforming industrial systems globally, both by exposing them to increasing environmental risks and by positioning them as key players in worldwide mitigation and adaptation efforts. This study offers a comprehensive review of how research at the climate–industry interface has developed over the past thirty years. Using a dual-method approach that combines a Systematic Literature Review (SLR) with bibliometric analysis, we examine 2458 publications from Scopus and Web of Science and visualize the field’s conceptual structure using the Thematic–Conceptual–Map (TCM) framework. Our results identify five main research themes: (1) integration of adaptation and mitigation; (2) spatial technologies and remote sensing; (3) urban heat and industrial resilience; (4) fundamental adaptation and climate resilience; and (5) connecting vulnerability with adaptive capacity. While mitigation and energy transition are predominant in industry-focused climate research, significantly fewer studies explore how industrial transformation relates to socio-ecological resilience and biodiversity conservation. This gap highlights the need for frameworks that connect decarbonization efforts with ecological preservation. By synthesizing these thematic trends, our study places industrial research at the forefront of shaping low-carbon, climate-resilient futures and offers a valuable knowledge base for scholars, practitioners, and policymakers working to integrate technology, governance, and sustainability within industrial systems. Full article

Pastures represent one of the most significant ecological components of Mediterranean landscapes, occupying large surfaces and guaranteeing ecosystem functions of primary importance. In Mediterranean silvo-pastoral systems, the coexistence of trees, shrubs, and herbaceous layers creates a complex ecological mosaic in which grazing activity plays a decisive role. In this framework, understanding the ongoing transformations affecting Mediterranean pastures becomes essential for identifying the main degradation processes and their ecological implications. Remote sensing (RS) technologies are robust and cost-effective tools for quantifying vegetation dynamics, identifying degradation patterns, and supporting sustainable management decisions. This review aims to summarize the most recent scientific evidence on the role of Mediterranean pastures as elements of ecological regulation and fire risk mitigation, while highlighting the potential of RS as a monitoring and decision-support tool. The analysis was performed considering papers from January 2000 to October 2025, by querying the Scopus and Web of Science databases. The analysis allowed the selection of 83 pertinent papers. The selected papers were analyzed, allowing exploration of the literature on RS applied to Mediterranean pastures from multiple angles, highlighting the historical progression of publications, the main geographical locations of study areas, and the evolution and intertwining of recurring themes. Full article

Background: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into sport and exercise through wearable biosensing systems that enable continuous monitoring and data-driven training adaptation. However, their practical value for coaching depends on the validity of biosensor data, the robustness of analytical models, and the conditions under which these systems have been empirically evaluated. Methods: A structured narrative review was conducted using Scopus, PubMed, Web of Science, and Google Scholar (2010–2026), synthesising empirical and applied evidence on wearable biosensing, signal processing, and ML-based adaptive training systems. To enhance transparency, an evidence map of core empirical studies was constructed, summarising sensing modalities, cohort sizes, experimental settings (laboratory vs. field), model types, evaluation protocols, and key outcomes. Results: Evidence from field and laboratory studies indicates that wearable biosensors can reliably capture physiological (e.g., heart rate variability), biomechanical (e.g., inertial and electromyographic signals), and biochemical (e.g., sweat lactate and electrolytes) markers relevant to training load, fatigue, and recovery, provided that signal quality control and calibration procedures are applied. ML models trained on these data can support training adaptation and recovery estimation, with improved performance over traditional workload metrics in endurance, strength, and team-sport contexts when evaluated using athlete-wise or longitudinal validation schemes. Nevertheless, the evidence map also highlights recurring limitations, including sensitivity to motion artefacts, inter-session variability, distribution shift between laboratory and field settings, and overconfident predictions when contextual or psychosocial inputs are absent. Conclusions: Current empirical evidence supports the use of AI-driven biosensor systems as decision-support tools for monitoring and adaptive training, but not as autonomous coaching agents. Their effectiveness is bounded by sensor reliability, appropriate validation protocols, and human oversight. The most defensible model emerging from the evidence is human–AI collaboration, in which ML enhances precision and consistency in data interpretation, while coaches retain responsibility for contextual judgement, ethical decision-making, and athlete-centred care. Full article

Ongoing global disruptions, including pandemics, geopolitical tensions, and climate-driven events, have exposed vulnerabilities in healthcare supply chains (HSCs). This study examines how artificial intelligence (AI) is reshaping HSCs to improve agility, resilience, and sustainable performance. Using a systematic literature review with PRISMA-style screening across Scopus and Web of Science, the study is complemented by bibliometric analysis and latent Dirichlet allocation topic modeling to analyze peer-reviewed articles. The results indicate an exponential increase in AI-enabled HSC research, concentrated in a small number of journals and spanning a globally diverse author community. Three dominant thematic clusters emerged: (1) sustainability-oriented supply chain design, (2) disruption and resilience management, and (3) healthcare-focused digital transformation. Across these themes, AI, digital twins, Internet of Things, and simulation are evolving from efficiency tools to strategic enablers of decision intelligence, supporting real-time sensing, scenario analysis, and proactive risk mitigation. The study highlights a convergence of “triple transformation” in which digitalization, resilience, and sustainability are increasingly co-dependent capabilities in HSCs. However, persistent barriers exist, including data quality issues, legacy systems, workforce skill gaps, limited model interpretability, and incomplete governance frameworks, which constrain large-scale adoption. The findings indicate a need for longitudinal and multi-method studies on human–AI collaboration, trust calibration, and leadership in AI-enabled HSCs. This study provides practical guidance for healthcare organizations looking to leverage AI in developing agile, resilient, and sustainable supply chain ecosystems. Full article

Dissolved oxygen (DO) is a cornerstone of aquatic ecosystem vitality, yet conventional in situ monitoring methods, reliant on field probes, buoys, and lab analyses, struggle to capture the spatiotemporal variability of DO at regional or global scales. Satellite remote sensing has revolutionized water quality assessment by enabling systematic, high-frequency, and spatially continuous monitoring of surface waters, transcending the logistical and financial constraints of traditional approaches. This systematic review critically evaluates satellite-based methodologies for estimating DO concentrations, emphasizing their capacity to address global environmental challenges such as eutrophication, hypoxia, and climate-driven deoxygenation. Following the PRISMA 2020 guidelines, large bibliographic databases (Scopus, Web of Science, and Google Scholar) identified that studies on satellite-derived DO concentrations are focused on both spectral and thermal foundations of DO retrieval, including empirical relationships with proxy variables (e.g., Chlorophyll-a, sea surface temperature, and turbidity) as well as direct optical signatures linked to oxygen absorption in the red and near-infrared spectra. The 77 results included in this review (accessed on 27 November 2025) indicate that the reported advances in sensor technologies (e.g., Sentinel-2,3’s OLCI and MODIS) have greatly expanded the ability to monitor DO levels across different types of water bodies, and that there has been a significant paradigm shift towards more complex and sophisticated machine learning and deep learning architectures. Recent work demonstrates that advanced machine learning and deep learning models can effectively estimate DO from remote sensing proxies, achieving high predictive performance when validated against in situ observations. Overall, this review indicates that their effectiveness depends heavily on high-quality training data, rigorous validation, and careful recalibration. Global case studies illustrate applications showcasing the scalability of remote sensing solutions. An OSF project was created to enhance transparency, while the review protocol was not prospectively registered, which is consistent with the PRISMA 2020 guidelines for non-registered reviews. Full article