Search Results (16,466)

Copernicus, the Earth Observation component of the European Union Space Programme, plays a key role in monitoring planetary health and informing global sustainability agendas. Enhancing its uptake offers a strategic opportunity to translate climate information into actionable knowledge for sustainable institutional governance. This study examines how data visualization, translating complex climate information into context-relevant formats, can strengthen the uptake of Copernicus Climate Change and Atmosphere Monitoring Service by national institutions. Using the Italian initiative for the National Collaboration Programme of the Copernicus Climate Change Service as an empirical setting, we adopt a mixed-method design to bridge expert visualization practices with institutional stakeholders tasked with sustainability transitions. The findings show that users widely recognize the value of Copernicus. Nonetheless, uptake depends largely on how easily visual outputs can be integrated into workflows and decision procedures. By linking uptake to visualization practices, the study reveals a previously underexplored user–expert gap between production and use contexts. We introduce “adaptive translation” as a framework to align scientific integrity with usability through progressive disclosure, defensibility-oriented design, and iterative feedback loops. The results provide context-sensitive guidance for designing “workflow-ready” visual products in similar national institutional settings, enhancing the capacity of institutional actors to design the climate-resilient actions that are essential for a sustainable future. Full article

The concept of awareness in the visual arts has become an ethical, professional, and social imperative. Adopting a sustainable approach to creative practice is no longer a trend but an established and necessary field of inquiry. Within this context, awareness has been expressed not only through eco-branding and design campaigns but also through artworks and contemporary artistic practices that embody sustainable values both aesthetically and philosophically. Visual arts thus function as a reflective and critical tool, capable of reassessing past and present paradigms, encouraging more responsible uses of resources, promoting environmental sustainability, and shaping public attitudes through conscious and critical forms of expression. This study adopts a qualitative approach to examine transformations in contemporary art practices within the Saudi Arabian art scene. Selected artworks are analysed to explore historical and conceptual narratives shaping artistic production. The research is based on a bibliographic and documentary review that includes academic literature, exhibition catalogues and press sources related to the Saudi cultural context. Data are gathered through observing artworks and, where possible, through interviews with artists. A comparative analysis was developed, with the study framed by art practices, their concepts, and their ecological contributions, leading to a sustainable awareness and their potential role in encouraging social change. The comparative study among artists provides an innovative research framework and initiates a broader dialogue on sustainable creative practices rooted in Saudi cultural contexts. The findings highlight how visual arts contribute to ecological awareness and climate activism through art installations, recycled materials, and digital practices, reinforcing sustainability as a core value within contemporary Saudi society. Full article

This study proposes an intelligent decision-support framework for alternative selection in complex construction projects, where evaluation processes are affected by uncertainty, multiple decision-makers, and interdependent criteria. The framework integrates the fuzzy group best–worst method with fuzzy TOPSIS into a unified structure that explicitly captures cross-criterion influence effects. First, triangular fuzzy judgments from multiple experts are used to derive criterion weights, while interdependencies among criteria are represented through a fuzzy influence-intensity matrix and incorporated into fuzzy nonlinear optimization models. This process enables the systematic estimation of both independent and interdependency-adjusted criterion weights. Second, the resulting weights are used in a fuzzy ranking procedure to evaluate alternatives according to their relative closeness to fuzzy ideal solutions. To enhance transparency, reproducibility, and practical usability, the proposed method is implemented in Python as an automated computational workflow for decision analysis. Its applicability is demonstrated through a real-world case study on access platform system selection for mechanical, electrical, and plumbing installation in an airport terminal subject to safety, productivity, workspace, and elevation-related constraints. The results show that explicitly modeling criterion interdependencies provides a more realistic evaluation structure and enhances the robustness and reliability of alternative selection in complex construction management contexts. Full article

Tropical forests of the Amazon support exceptional biodiversity while sustaining the livelihoods, cultures, and food systems of Indigenous communities. In Loreto, Peru, hunting remains central to both subsistence and market economies, yet its sustainability depends on ecological dynamics and sociocultural systems that shape harvest behavior. Here, we evaluate the potential for sustainable wildlife management in the Maijuna–Kichwa Regional Conservation Area (MKRCA) by integrating a spatially explicit biodemographic model of hunting with a targeted review of Maijuna hunting practices, governance, and economic context. Using participatory mapping data from 19 hunters in the community of Sucusari, we parameterized a model to estimate species-specific depletion under current and projected hunting scenarios. Model results suggest that current harvest rates are largely sustainable, with localized depletion near settlements but relatively intact populations across the broader landscape, supported by access to remote hunting areas and nearby source populations. The literature review reveals that Maijuna sociocultural systems, including territorial hunting norms, seasonal mobility, food-sharing practices, and species-specific taboos, may function as informal management institutions that distribute hunting pressure and limit overexploitation. Together, these findings suggest that both ecological conditions and sociocultural institutions in Sucusari are conducive to sustainable wildlife management if supported by adaptive co-management approaches. However, external pressures, particularly a proposed highway, may fragment existing source–sink dynamics and pose a significant risk to long-term sustainability. Full article

Rising ambient temperatures and the increasing frequency of training and competition in hot climates have made heat stress a major challenge in football. Under such conditions, players experience greater cardiovascular and thermoregulatory strain, faster glycogen use, higher perceived exertion, and progressive impairment in repeated high-intensity actions and decision-making. These responses have intensified interest in nutritional strategies that might complement heat acclimation, hydration/electrolyte planning, cooling practices, and recovery management. This narrative review critically synthesizes current evidence on nutritional interventions that may be relevant to football performed in the heat, with emphasis on hydration and electrolyte replacement, carbohydrate–protein strategies, taurine, branched-chain amino acids (BCAAs), creatine, menthol, antioxidant- and nitrate-related approaches, and selected multi-ingredient products. Across the available literature, hydration/electrolyte planning and carbohydrate–protein feeding remain the practical foundation, menthol appears most consistently useful for perceptual cooling, creatine seems safe and potentially helpful for repeated-sprint support, and taurine is promising but still supported by relatively few trials. By contrast, evidence for BCAAs, antioxidants, nitrates, and caffeine as stand-alone heat strategies, as well as for many compound supplements, remains inconsistent, context-specific, or too indirect for strong football-specific endorsement. Overall, the evidence base remains heterogeneous in study quality, protocol design, exercise mode, and sport specificity. A substantial proportion of the available data is derived from cycling, endurance, or laboratory heat-exercise models rather than football-specific trials. Accordingly, any practical recommendation should be interpreted cautiously and embedded within broader heat-management strategies. Future work should prioritize ecologically valid randomized controlled trials in football or football-like intermittent protocols, with transparent reporting of dose, timing, perceptual outcomes, and match-relevant performance measures. Full article

As food demand increases, agricultural practices have evolved, prompting increased exploration of sustainable ecological techniques and utilization of plant-associated microorganisms. In this context, plant fitness has been enhanced by plant growth-promoting microorganisms (PGPM), which stimulate growth through direct mechanisms, such as improved nutrient availability and phytohormone production, as well as indirect mechanisms, including protection against phytopathogens and suppression of soil-borne diseases. However, these innate capabilities of PGPM can be further improved through genomic modification or editing. This article reviews advances in the genomic engineering of plant-beneficial microorganisms as tools to enhance their positive effects on crop performance and environmental remediation. The genetic modification strategies analyzed here include random mutagenesis, targeted genome editing (such as CRISPR-Cas), gene over-expression, genome shuffling, RNA interference, metabolic pathway engineering, and synthetic biology approaches. These tools have enabled the optimization of functions, such as nitrogen fixation, phosphate solubilization, secondary metabolite production, biocontrol, stress tolerance, and bioremediation. However, we propose expanding the discussion of their regulation and use in various countries. Additionally, these modifications must be efficient and safe for the beneficial microbiota associated with the target crop, as well as for humans, animals, and the environment, all of which depend on sustainable agricultural practices. Full article

This article examines the ethnopragmatic significance of religiously based personal names among the Agwagune people of Biase in Cross River State, Nigeria. Insights from a socio-onomastic framework are used to account for the situational, socioreligious and sociocultural contexts of Agwagune naming practices that reinforce the people’s belief systems and worldview. Using participant observation and semi-structured interviews, data were generated during a nine-month fieldwork session involving 30 participants who were knowledgeable in the traditional religious socio-onomastic tradition. Our findings show that Agwagune people draw from their symbolic linguistic resources to bestow personal names that become messaging instruments that express traditional religious affiliations, sociocultural practices and indigenous belief systems. The personal names bear ethnopragmatic relevance that manifests in the veneration of deities and traditional worship; significations in rituals and religious festivals; mysteries of death, reincarnation and commemoration; traditional familial hierarchies; and the sociocultural connection between the people and their physical and spiritual universe. Aside from contributing to the global discourses on socio-onomastics from the perspectives of a micro-minority ethnolinguistic group, the study is also relevant because it serves as documentary material for an endangered and transitioning socio-onomastic practice that characterizes the people’s cosmology, belief systems and lived experiences that are gradually being replaced by Christian orientations. Full article

This research synthesizes a more than a decade of empirical and conceptual research on Industry 4.0 technologies with circular economy ecosystem in the European context. The shifting from linear to circular economy requires adoption of I4.0 technologies in particular Artificial Intelligence (AI), Internet of Things (IoT), and Virtual Reality (VR). Yet current scholarship on circular economy ecosystems (CEE) remains theoretically fragmented. To address this gap, we conducted a systematic literature review (SLR) of 94 peer-reviewed journal articles (2010–2025) using the Web of Science (WoS) database following the PRISMA protocol by deploying theories, contexts, methods (TCM) framework and thematic analysis. We developed a comprehensive framework based on addressing key barriers e.g., diverse expectations of stakeholders, resistance to change, sustainable leadership challenges, lack of digitally enabled-capabilities and institutional pressure with the help of important enablers such as AI capabilities, collaboration with stakeholders, frugal innovation and supportive government policies. Our findings contribute to the emerging discourse on how combining digital technologies with circular economy practices can support the development of low emission manufacturing systems, in line with current zero-emission policy goals in the European Union. This review contributes fragmented literature by highlighting theoretical, contextual and methodological gaps as previously disparate perspectives to help align and move research forward. This research contributes to SDG 9- “Industry, innovation and infrastructure” and SDG 12 “Responsible Consumption and Production”. Full article

Background: RET fusion is an actionable tumor-agnostic biomarker, but its observed frequency in routine comprehensive genomic profiling (CGP) may vary across testing platforms and clinical contexts. We conducted a nationwide descriptive analysis to benchmark observed RET fusion frequency in Japanese routine practice. Methods: This retrospective descriptive study used anonymized aggregated data from the Center for Cancer Genomics and Advanced Therapeutics (C-CAT), including CGP-tested cases through 31 March 2025. Observed RET fusion frequency was summarized overall, across five standardized CGP platforms, across 12 prespecified organ groups, and in pooled tissue-based versus liquid-based comparisons. Exact binomial 95% confidence intervals were calculated to provide descriptive precision for low-frequency estimates. Results: Among 97,343 cases, 257 were RET-positive, corresponding to an overall observed RET fusion frequency of 0.26%. Platform-specific frequencies were 0.29% (192/66,992) for FoundationOne CDx, 0.28% (42/14,878) for FoundationOne Liquid CDx, 0.14% (6/4235) for GenMineTOP, 0.16% (15/9196) for NCC oncopanel, and 0.10% (2/2042) for Guardant360. Thoracic tumors showed the highest observed frequency (1.39%, 94/6740), followed by head and neck/thyroid tumors (1.04%, 42/4030). In a crude pooled comparison not adjusted for organ mix or clinical context, tissue-based and liquid-based CGP yielded numerically similar crude pooled frequencies of 0.265% (213/80,423) and 0.260% (44/16,920), respectively. Conclusions: This nationwide analysis benchmarks how RET-positive findings are surfaced to clinicians across heterogeneous routine CGP implementations in Japan. The data support platform-aware interpretation of RET results in practice, but should not be construed as biologic prevalence estimates or comparative assay performance. Full article

This article presents a comprehensive analytical, numerical, and experimental study of the amplification and detection of weak signals in magnetically coupled electromagnetic systems, using an architecture consisting of three magnetically coupled coils. A rigorous mathematical model of the system is developed, which includes the formulation of the mutual inductance matrix and a state-space representation that captures the dynamic interaction between the coils. It is important to note that the electromagnetic subsystem is linear and that the stochastic resonance effect is achieved by incorporating an external nonlinear bistable element. In this configuration, a weak periodic signal below a threshold is applied to the primary coil, while a controlled source of Gaussian white noise is injected into a secondary coil. A third coil functions as a sensing element, capturing the superimposed magnetic response resulting from coupling effects. The voltage induced in the sensor coil is subsequently processed by a bistable nonlinear element implemented via a Schmitt trigger, which provides the nonlinearity and bistability necessary to enable stochastic resonance and the detection of the weak periodic signal. The conditions of the SR are analyzed in terms of noise intensity, coupling coefficients, and system parameters, highlighting the existence of an optimal noise level that maximizes the signal-to-noise ratio (SNR) at the output. A detailed simulation framework has been developed in MATLAB/Simulink, enabling a systematic exploration of the parameter space and the validation of theoretical predictions. The simulation results are further supported by experimental measurements obtained from a physical prototype, which show agreement with the proposed model. The main contribution of this work lies in demonstrating that magnetically coupled electromagnetic structures can effectively interact with nonlinear bistable elements to exploit stochastic resonance in the detection of weak signals, even when the electromagnetic domain itself remains linear. The results demonstrate that magnetic coupling is an effective mechanism for mediating constructive interactions between noise and weak signals, thereby improving the detection of the latter. These results extend the applicability of stochastic resonance to hybrid electromagnetic systems and demonstrate its relevance in practical applications. Potential applications include ultra-sensitive magnetic detection, low-power signal detection, magnetic transducers, and robust signal recovery in noisy electromagnetic environments, particularly in contexts where conventional linear amplification fails. Full article

Digital Product Passports (DPPs) are being introduced in the European Union to support circular economy strategies, improve product transparency, and enable lifecycle-based compliance and decision-making. Despite growing interest, research on DPPs remains fragmented, and there is limited consensus on how to design and validate DPP frameworks in real-world contexts. This paper presents a systematic literature review of peer-reviewed studies that explicitly define, structure, or assess DPP-related frameworks. Using a transparent search strategy based on Scopus and IEEE Xplore, combined with structured screening, the review assesses framework design elaboration and validation maturity across included studies and interprets recurring framework archetypes across application sectors. The results show that most studies emphasise conceptual or architectural designs. These commonly adopt data-centric, layered, technology-anchored, or ecosystem-oriented structures and frequently refer to enabling technologies such as digital twins, blockchain, data spaces, and knowledge graphs. However, explicit validation remains limited and is primarily restricted to illustrative case studies, stakeholder-informed assessments, or prototypes, with few studies evaluating scalability, interoperability, or lifecycle-spanning operation in real-world contexts. By consolidating design principles and validation practices across sectors in this targeted corpus, the review clarifies the current state of the art and highlights critical research gaps. The findings indicate that DPP research is characterised by a strong emphasis on framework design, with comparatively limited empirical validation. Furthermore, critical research gaps include the lack of rigorous empirical validation, cross-organisational testing, lifecycle-spanning evaluation, clearly defined data governance responsibilities, convergence towards shared reference architectures, and sector-specific adaptation. Full article

Despite growing interest in destination branding, empirical evidence is still limited in explaining how brand personality influences tourist behavior mediated by integrated aspects. This study addresses this research gap by proposing and testing a structural model that considers the mediating role of tourist experience, authenticity, and trust in the relationship between brand personality and tourist behavior. The methodology used was quantitative and causal–correlational, using structural equation modeling and a sample of 514 Peruvian tourists selected through non-probabilistic convenience sampling. The results show that brand personality significantly influences tourist experience and destination authenticity, while its direct effect on trust is weak. In addition, experience positively influences trust and authenticity. Significantly, authenticity and experience show direct and positive effects on tourist behavior, while trust has a negative effect. These findings contribute to the advancement of the literature by integrating a single explanatory model with experiential, cognitive, and relational variables, broadening the understanding of the indirect role of brand personality in the tourism context. From a practical standpoint, the results suggest that destination managers should focus on enhancing brand personality and authenticity. Full article

Hypertrophic Cardiomyopathy (HCM) is one of the most common inherited cardiomyopathies and remains an important cause of ventricular arrhythmias and sudden cardiac death (SCD), particularly in younger individuals. Although the annual incidence of arrhythmic death is relatively low in contemporary cohorts, identifying those patients who may benefit from primary prevention with an implantable cardioverter-defibrillator (ICD) remains a major clinical challenge. Current risk stratification strategies rely on two principal paradigms. The European approach is centered on the HCM Risk-SCD score, whereas the American approach is mainly based on major clinical risk markers. Both strategies have important strengths and limitations, reflecting the persistent difficulty of accurately predicting arrhythmic events in such a heterogeneous disease. The HCM Risk-SCD score has demonstrated robust external validation and high specificity for identifying patients at higher risk, but it may fail to recognize some vulnerable individuals who remain below conventional treatment thresholds. For this reason, several additional risk modifiers have gained increasing relevance in contemporary practice. Among them, extensive late gadolinium enhancement, left ventricular systolic dysfunction, apical aneurysm, and clinically meaningful genetic findings may provide important incremental prognostic information beyond traditional models. Emerging disease-modifying therapies, in particular Mavacamten, may also influence future risk assessment. However, whether these improvements translate into a true reduction in SCD risk remains uncertain. Importantly, the decision to implant an ICD should not depend on numerical risk alone. It should arise from a process of shared decision-making integrating estimated risk, treatment burden, competing comorbidities, age, lifestyle, and patient values. In this context, the concept of an individualized threshold of “acceptable risk” becomes central. In conclusion, prevention of SCD in HCM is moving beyond conventional scores toward a personalized and dynamic framework in which predictive tools, advanced phenotyping, evolving therapies, clinical expertise, and patient preferences are combined to guide individualized care. Full article

In predicting tubing corrosion rates under multi-factor coupling, traditional methods often struggle to effectively analyze the nonlinear interactions among variables such as temperature, pressure, CO₂ partial pressure, and H₂S partial pressure, and they also lack interpretability in the prediction process. To address this, this study first establishes a corrosion dataset covering three typical steels (2205DSS, CT80, N80) through high-temperature and high-pressure weight-loss experiments. A machine learning framework is then proposed, integrating feature coupling analysis with a SHAP-Sobol-based interpretability framework. By incorporating the Context-Aware Sparse Attention (CASA) mechanism into the XGBoost ensemble, a CASA-XGBoost prediction model is constructed to systematically analyze interactions among multiple features and convert them into effective predictive information. Bayesian optimization enables adaptive hyperparameter tuning, while five-fold cross-validation tailored to different materials enhances model generalization and stability. Furthermore, the SHAP-Sobol weighting method systematically evaluates feature contributions and interaction effects across global sensitivity analysis and local sample interpretation, enabling feature coupling reconstruction. Experimental results demonstrate that the proposed framework outperforms benchmark models (Random Forest and Gaussian Process Regression) on three steel corrosion datasets, achieving test set R² values up to 0.98 with a low MAE and RMSE. The SHAP-Sobol-based interpretability framework also reveals material-specific sensitivities: 2205DSS is highly influenced by CO₂-H₂S interaction, CT80 by temperature–pressure coupling, and N80 shows reduced performance at high corrosion rates due to localized mechanisms. This study provides a reference for corrosion prevention and control by delivering high-accuracy and interpretable corrosion rate prediction for tubing under multi-factor coupling conditions, offering practical value for industrial modeling and decision-making. Full article

Urban drought pressure is increasing the operational risk and cost of maintaining municipal green infrastructure. Irrigation is still widely managed through fixed routines and fragmented information. To address this challenge, the study develops an integrated operational analysis by combining water consumption records, maintenance data and a GIS inventory for twenty municipal green spaces. System characterisation and performance screening were carried out using hourly meter readings to distinguish typical scheduled irrigation peaks from non-standard consumption patterns. To move from monitoring to control, irrigation needs were estimated using evapotranspiration (ET₀) and a garden-coefficient logic adapted to urban planting conditions and compared with measured consumption. The comparison indicates a potential reduction of 29–61% through improved scheduling and system adjustment. Based on the diagnosis, technical intervention scenarios were defined and assessed using techno-economic metrics, including ground-cover redesign and Mediterranean-adapted planting strategies. To support implementation, options were organised into intervention priorities using a multicriteria tool that balances water savings, costs and feasibility under municipal operations. Coimbra, Portugal is used as a case study, and a pilot application in a city garden, supported by 797 user surveys, clarifies practical constraints for scaling beyond isolated pilots. Turf-free scenarios indicate a 53.4% reduction in water use and a 60.5% reduction in operational costs, with a payback period below three years. The results highlight the potential of data-driven irrigation management to support more resilient, cost-effective and water-efficient municipal green infrastructure across diverse urban contexts. Full article