Search Results (222)

Forest management planning depends on accurately collecting information on available resources, gathered by forest inventories. However, due to the extent of the planted areas in the world, collecting information traditionally has become challenging. Terrestrial light detection and ranging (LiDAR) has emerged as a promising tool to enhance forest inventory. However, selecting the optimal 3D point cloud density for accurately estimating tree attributes remains an open question. The objective of this study was to evaluate the accuracy of different point densities (points per square meter) in point clouds obtained through portable laser scanning combined with simultaneous localization and mapping (PLS-SLAM). The study aimed to identify tree positions and estimate the diameter at breast height (DBH) and total height (H) of 71 trees in a eucalyptus plantation in Brazil. We also tested a semi-automatic method for estimating total height. Point clouds with densities greater than 100 points/m² enabled the detection of over 88.7% of individual trees. The root mean square error (RMSE) of the best DBH measurement was 1.6 cm (RMSE = 5.9%) and the best H measurement (semi-automatic method) was 1.2 m (RMSE = 4.2%) for the point cloud with 36,000 points/m². When measuring the total heights of the largest trees (H > 31.4 m) using LiDAR, the values were always underestimated considering a reference value, and their measurements were significantly different (p-value < 0.05 by the t-test). For point clouds with a density of 36,000 points/m², the automated DBH and total tree height estimations yielded RMSEs of 5.9% and 14.4%, with biases of 4.8% and −1.4%, respectively. When using point clouds of 10 points/m², RMSE values increased to 18.8% for DBH and 28.4% for total tree height, while the bias was 6.2% and 18.4%, respectively. Additionally, total tree height estimations obtained via a semi-automatic method resulted in a lower RMSE of 4.2% and a bias of 1.5%. These findings indicate that point clouds acquired through PLS-SLAM with densities exceeding 100 points/m² are suitable for automated DBH estimation in the studied plantation. Despite the increased processing time required, the semi-automatic method is recommended for total tree height estimation due to its superior accuracy. Full article

The junction temperature (T_j) is a key parameter reflecting the thermal behavior of Silicon carbide (SiC) MOSFETs and is essential for condition monitoring and reliability assessment in power electronic systems. However, the limited temperature sensitivity of switching characteristics makes it difficult for traditional single temperature-sensitive electrical parameters (TSEPs) to achieve accurate estimation. To address this challenge and enable practical thermal sensing applications, this study proposes an accurate, application-oriented T_j estimation method based on multivariate linear regression (MLR) using turn-off current fall time (t_fi) and fall loss (E_fi) as complementary TSEPs. First, the feasibility of using current fall time and current fall energy loss as TSEPs is demonstrated. Then, a coupled junction temperature prediction model is developed based on multivariate linear regression using t_fi and E_fi. The proposed method is experimentally validated through comparative analysis. Experimental results demonstrate that the proposed method achieves high prediction accuracy, highlighting its effectiveness and superiority in MLR approach based on the current fall phase characteristics of SiC MOSFETs. This method offers promising prospects for enhancing the condition monitoring, reliability assessment, and intelligent sensing capabilities of power electronics systems. Full article

Concrete mix design plays a pivotal role in ensuring the mechanical performance, durability, and sustainability of construction projects. However, the nonlinear interactions among the mix components challenge traditional approaches in predicting compressive strength and optimizing proportions. This study presents a two-stage hybrid framework that integrates deep learning with reinforcement learning to overcome these limitations. First, a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model was developed to capture spatial–temporal patterns from a dataset of 1030 historical concrete samples. The extracted features were enhanced using an eXtreme Gradient Boosting (XGBoost) meta-model to improve generalizability and noise resistance. Then, a Dueling Double Deep Q-Network (Dueling DDQN) agent was used to iteratively identify optimal mix ratios that maximize the predicted compressive strength. The proposed framework outperformed ten benchmark models, achieving an MAE of 2.97, RMSE of 4.08, and R² of 0.94. Feature attribution methods—including SHapley Additive exPlanations (SHAP), Elasticity-Based Feature Importance (EFI), and Permutation Feature Importance (PFI)—highlighted the dominant influence of cement content and curing age, as well as revealing non-intuitive effects such as the compensatory role of superplasticizers in low-water mixtures. These findings demonstrate the potential of the proposed approach to support intelligent concrete mix design and real-time optimization in smart construction environments. Full article

A circular tri-trophic system integrating aquaponics, i.e., combined cultivation of crops and fish, with insect rearing is presented for lettuce cultivation. The nutrition cycle among crops, insects and fish turns waste into resource, thereby increasing the sustainability of this food production system. A comprehensive evaluation of the system’s efficiency was performed, including the growth, functional and resource use efficiency traits of lettuce, the dynamics of which were followed in a pilot-scale aquaponics greenhouse, under three treatments: conventional hydroponics (HP) as the control, coupled aquaponics (CAP) with crops irrigated with fish-derived water, and decoupled aquaponics (DCAP), where fish-derived water was amended with fertilizers to reach the HP target. The main findings indicate comparable physiological performance between DCAP and HP, despite the slightly lower yield observed in the former. The CAP treatment exhibited a significant decrease in biomass accumulation and functional impairments, which were attributed to reduced nutrient levels in lettuce leaves. The DCAP treatment exhibited a 180% increase in fertilizer use efficiency compared to the HP treatment. We conclude that the tri-trophic cropping system with the implementation of DCAP variant is an effective system that enables the combined production of crops and fish, the latter being fed with sustainably derived insect protein. The tri-trophic system improves the environmental impact and sustainability of lettuce production, while making circularity feasible. Full article

Objectives: To identify clinical, functional, laboratory, and patient-reported parameters associated with medium-term risk of hospitalization or death among older adults attending a multidisciplinary outpatient clinic, and to assess the predictive performance of these measures for individual risk stratification. Methods: In this cohort study, 350 adults aged ≥65 years were assessed at baseline and followed for an average of 8 months. The primary outcome was a composite of hospitalization or all-cause mortality. Parameters assessed included frailty and comorbidity measures, functional parameters, such as gait speed and grip strength, laboratory biomarkers, and patient-reported measures, such as quality of life (QoL, assessed on a Likert scale) and the presence of depressive symptoms. Predictive performance was evaluated using univariable logistic regression and multivariable modeling. Discriminative ability was assessed via area under the ROC curve (AUC), and selected models were internally validated using repeated k-fold cross-validation. Results: Overall, 40 participants (11.4%) experienced hospitalization or death. Traditional clinical risk indicators, including frailty and comorbidity scores, were significantly associated with the outcome. Patient-reported QoL (AUC = 0.74) and Geriatric Depression Scale (GDS) scores (AUC = 0.67) demonstrated useful overall discriminatory ability, with high specificities at optimal cut-offs, suggesting they could act as “red flags” for adverse outcomes. However, the limited sensitivities of individual predictors underscore the need for more comprehensive screening instruments with improved ability to identify at-risk individuals earlier. A multivariable model that incorporated several predictors did not outperform QoL alone (AUC = 0.79), with cross-validation confirming comparable discriminative performance. Conclusions: Patient-reported measures—particularly quality of life and depressive symptoms—are valuable predictors of hospitalization or death and may enhance traditional frailty and comorbidity assessments in outpatient geriatric care. Future work should focus on developing or integrating screening tools with greater sensitivity to optimize early risk detection and guide preventive interventions. Full article

This study evaluates design effectiveness in Digital Game-Based Learning (DGBL) for primary health education through systematic teacher assessment of the iLearn4Health platform. Rather than measuring educational transformation, the research investigates how DGBL design principles influence user engagement patterns and platform usability as evaluated by education professionals. The study contributes to design optimization frameworks for primary school digital health education applications by examining the distinction between DGBL and superficial gamification approaches in creating engaging educational interfaces. The iLearn4Health platform underwent comprehensive design evaluation by 337 teachers across 24 schools in five European countries (Greece, Cyprus, Romania, Poland, and Spain). Teachers served as design evaluators rather than end-users, assessing platform engagement mechanisms through systematic interaction analysis. The study employed multiple statistical approaches—descriptive analysis, correlation analysis, ANOVA, regression modeling, and cluster analysis—to identify design engagement patterns and their predictors, tracking completion rates, progress trajectories, and interaction time as indicators of design effectiveness. Design evaluation revealed a distinctive bimodal engagement distribution, with 52.8% of teacher–evaluators showing limited platform exploration (progress ratio 0.0–0.2) and 35.3% demonstrating comprehensive design assessment (progress ratio 0.8–1.0). A strong positive correlation (r = 0.95, p < 0.001) between time spent and steps completed indicated that design elements successfully sustained evaluator engagement. Multiple regression analysis identified initial design experience as the strongest predictor of continued engagement (β = 0.479, p < 0.001), followed by country-specific implementation factors (Romania vs. Cyprus, β = 0.183, p = 0.001) and evaluator age (β = 0.108, p = 0.049). Cluster analysis revealed three distinct evaluator profiles: comprehensive design assessors (35.3%), early design explorers (52.8%), and selective feature evaluators (11.9%). Cross-national analysis showed significant variations in design engagement, with Romania demonstrating 53% higher average progress ratios than Cyprus (0.460 vs. 0.301, p < 0.01). Teacher evaluation validates effective design implementation in the iLearn4Health platform for creating engaging primary health education experiences. The platform successfully demonstrates DGBL design principles that integrate health concepts into age-appropriate interactive environments, distinct from gamification approaches that merely overlay game elements onto existing content. Identifying initial engagement as the strongest predictor of sustained interaction highlights the critical importance of onboarding design in determining user experience outcomes. While this study establishes design engagement effectiveness through educator assessment, actual educational transformation and student learning outcomes require future implementation studies with primary school populations. The design validation approach provides essential groundwork for subsequent educational effectiveness research while contributing evidence-based design principles for engagement optimization in digital health education contexts. Full article

The ecological assessment of rivers under the Water Framework Directive (WFD) requires the use of biological quality elements (BQEs) across defined abiotic types of rivers. However, limited evidence exists on how well biological indices perform across multiple typological classes, particularly under the influence of complex, overlapping stressors. This study evaluated the diagnostic performance of four biological indices (IO—diatoms, MIR—macrophytes, MMI_PL—benthic macroinvertebrates, and EFI + PL—fish) in 16 river sites in southern Poland. These were classified into four abiotic types (5, 6, 12, and 17) and subjected to varying levels of human pressure. Biological, physical and chemical, and hydromorphological data were collected along environmental gradients including conductivity, nutrient enrichment, and habitat modification. Statistical analyses were used to evaluate patterns in community composition and index responsiveness. The IO and MMI_PL indices were the most consistent and sensitive in distinguishing between reference and degraded river conditions. MIR and EFI + PL were more variable, especially in lowland rivers, and showed stronger associations with habitat structure and oxygen levels. Conductivity emerged as a key driver of biological responses across all BQEs, with clear taxonomical shifts observed. The results support the need to consider both typological context and local environmental variation in ecological classification. The findings underscore the need for typology-aware, pressure-specific biomonitoring strategies that combine multiple organism groups and integrate continuous environmental variables. Such approaches can enhance the ecological realism and diagnostic accuracy of river assessment systems, supporting more effective water resource management across diverse hydroecological contexts. Full article

Urban forests (UFs) play a crucial role in mitigating climate change, but their management presents complex trade-offs between environmental, economic, and social aspects. We developed a Decision Support Tool (DST) to simulate 27-year UF dynamics under six different management strategies, aiming to maximize socio-economic and environmental benefits while considering costs. Business as Usual (BaU), Yielding Scenario (YS), High Management (HM), Forest Development (FD), Social Boost (SB), and Cover Maximizing (CM) strategies were tested with the DST in the Vazzieri district of Campobasso, central Italy. The DST integrates CO₂ removal, management expenditures and revenues, and the social usability of UFs. The findings show that while all the strategies contribute to climate change mitigation, FD and SB offer the best balance between the environmental and social sides. FD demonstrates significant CO₂ removal with moderate expenditures, whereas SB maximizes CO₂ removal despite its high management expenditures. Otherwise, YS and BaU show limited environmental benefits with beneficial economic outcomes. While achieving the highest environmental and social benefits, CM incurs the greatest economic costs. This study highlights the need for long-term, integrated UF strategies to harmonize climate change mitigation with economic viability and social inclusivity. The DST provides a valuable framework for urban planners and policymakers to optimize sustainable UF management. Full article

The necessity of optimizing the nutrient and water efficiency in conventional hydroponics and enhancing their sustainability has given rise to the concept of cascade cropping systems. These achieve high water and resource use efficiencies, together with a lower environmental footprint, which is especially important for Mediterranean areas. However, scientific questions about the mechanisms that drive productivity in this system remain to be answered. This study aimed at a comprehensive evaluation of crop performance in cascade systems in terms of morphoanatomical and functional responses, also including product quality parameters, which influence the marketability of the fruit. In a three-month experiment, the dynamics of melon’s photosynthetic light use efficiency, pigment contents, growth parameters, and leaf compactness were assessed in a cascade system using drainage of tomato cultivation in comparison to classic hydroponic melon. The fruits’ chroma, hardness, total soluble solids, and pH were also measured. Comparable plant functional responses in the control and cascade melon plants resulted in similar growth and morphoanatomical traits. The fruit quality attributes were also found to be almost identical. It is proposed that the cascade system is both effective and sustainable in regions facing climatic and water scarcity pressures, such as those that are prevalent around the Mediterranean basin. Full article

Rapidly evolving surveying and monitoring methods are leading the transition toward more efficient, data-driven forest management practices. Recent research highlights the potential of advanced remote sensing platforms to support “smart” forestry, enabling precise, timely, and cost-effective assessments which inform multi-function management methods and specialized silvicultural practices for each forest type, composition, and structure. We created a digital replica of a marteloscope, which is a forestry tool to practice silvicultural simulations for technicians and students. The selected stand is an official marteloscope included in the Integrate+ Network project coordinated by the European Forest Institute (EFI). We established a framework for data collection and processing to achieve an accurate digital replica, using a mobile laser scanner (MLS) in a European beech (Fagus sylvatica L.) forest stand. We extracted the main structural forest parameters (diameter at breast height (DBH) and total height (TH)), using the 3DFin software and we graphically returned the obtained digital replica with the CloudCompare software. We compared the MLS-derived values of DBH (1087 trees) and TH (50 trees) with those from a traditional field survey and obtained a root mean square deviation (RMSD) of 2.38 cm for DBH and 2.42 m for TH. The digital marteloscope can help to visualize and assess the effects of selective thinning options on forest structure. The implementation of these virtual reality or augmented reality applications is a useful step toward smarter forestry and could be further improved. Full article

Several materials widely used in scientific research and industrial applications, including nano-filters and neuromorphic circuits, consist of fiber structures. Despite the fundamental structural similarity, the key feature that should be considered depends on the specific application. In the case of membranes and filters, the main concern has been on the pores among fibers, whereas in neuromorphic networks the main functionality is performed through the junctions of nanowires simulating neuron synapses for information dissemination. Precise metrological characterization of these structural features, along with methods for their effective control and replication, is essential for optimizing performance across various applications. This paper presents a comprehensive metrological framework for characterizing the spatial point patterns formed by pores or junctions within fibrous materials. The aim is to probe the influence of fiber randomness on both the point patterns of intersections (ppi) and pores (ppp). Our findings indicate a strong tendency of ppi toward aggregation, contrasting with a tendency of ppp toward periodicity and consequent pore uniformity. Both patterns are characterized by peculiarities related to collinearity effects on neighboring points that cannot be captured by the conventional anisotropy analysis of point patterns. To characterize local collinearity, we develop a method that counts the number of collinear triplets of nearest neighbor points in a pattern and designs an appropriate parameter to quantify them, also applied to scanning electron microscopy (SEM) images of membranes, demonstrating consistency with simulated data. Full article

Ricin, a highly toxic glycoprotein derived from the seeds of Ricinus communis, poses significant risks in bioterrorism and toxicology due to its rapid absorption and ease of dissemination. Rapid, ultra-sensitive detection is crucial for timely medical intervention and implementing security measures. However, existing methods often lack sufficient sensitivity or require lengthy processing, limiting their utility for trigger-to-treat scenarios. Here, we present an optical modulation biosensing (OMB)-based ricin assay capable of detecting low concentrations of ricin in buffer, plasma, and biological samples. The assay combines magnetic-bead-based target capture with fluorescent signal enhancement, achieving a limit of detection (LoD) of 15 pg/mL in buffer and 62 pg/mL in plasma, with a 4-log dynamic range. Optimized protocols reduced the assay time to 60 min, maintaining an LoD of 114 pg/mL in plasma while preserving accuracy and reproducibility. The assay successfully detected ricin in bronchoalveolar lavage fluid and serum from mice that were intranasally exposed to ricin, with signals persisting up to 48 h post exposure. Its rapid, high-throughput capabilities and simplified workflow make the OMB-based assay a powerful tool for toxicology, forensic analysis, and counter-bioterrorism. This study highlights the OMB platform’s potential as a sensitive and robust diagnostic tool for detecting hazardous biological agents. Full article

How can the shape of biodiversity inform us about cities’ ecoclimatic fitness and guide their development? Can we use species as the harbingers of climatic extremes? Eco-climatically sensitive species carry information about hydroclimatic change in their distribution, fitness, and preferential gradients of habitat suitability. Conversely, environmental features outside of the species’ fitness convey information on potential ecological anomalies in response to extremes to adapt or mitigate, such as through urban parks. Here, to quantify ecosystems’ fitness, we propose a novel computational model to extract multivariate functional ecological networks and their basins, which carry the distributed signature of the compounding hydroclimatic pressures on sentinel species. Specifically, we consider butterflies and their habitat suitability (HS) to infer maximum suitability gradients that are meaningful of potential species networks and flows, with the smallest hydroclimatic resistance across urban landscapes. These flows are compared to the distribution of urban parks to identify parks’ ecological attractiveness, actual and potential connectivity, and park potential to reduce hydroclimatic impacts. The ecosystem fitness index (EFI) is novelly introduced by combining HS and the divergence of the relative species abundance (RSA) from the optimal log-normal Preston plot. In Shenzhen, as a case study, eco-flow networks are found to be spatially very extended, scale-free, and clustering for low HS gradient and EFI areas, where large water bodies act as sources of ecological corridors draining into urban parks. Conversely, parks with higher HS, HS gradients, and EFIs have small-world connectivity non-overlapping with hydrological networks. Diverging patterns of abundance and richness are inferred as increasing and decreasing with HS. HS is largely determined by temperature and precipitation of the coldest quarter and seasonality, which are critical hydrologic variables. Interestingly, a U-shape pattern is found between abundance and diversity, similar to the one in natural ecosystems. Additionally, both abundance and richness are mildly associated with park area according to a power function, unrelated to longitude but linked to the degree of urbanization or park centrality, counterintuitively. The Preston plot’s richness–abundance and abundance-rank patterns were verified to reflect the stationarity or ecological meta-equilibrium with the environment, where both are a reflection of community connectivity. Ecological fitness is grounded on the ecohydrological structure and flows where maximum HS gradients are indicative of the largest eco-changes like climate-driven species flows. These flows, as distributed stress-response functions, inform about the collective eco-fitness of communities, like parks in cities. Flow-based networks can serve as blueprints for designing ecotones that regulate key ecosystem functions, such as temperature and evapotranspiration, while generating cascading ecological benefits across scales. The proposed model, novelly infers HS eco-networks and calculates the EFI, is adaptable to diverse sensitive species and environmental layers, offering a robust tool for precise ecosystem assessment and design. Full article

This study examined the effect of a Corporate Social Responsibility (CSR) environmental intervention program on members’ perceptions of economic, social, and environmental responsibility, as well as trust and loyalty, in a tennis club. It further explored whether membership duration influenced these perceptions, with a focus on environmental initiatives. A three-month intervention focusing on environmental initiatives was carried out, involving 250 tennis club members who completed a questionnaire on social, environmental, and economic dimensions before and after the intervention. Data were analyzed using non-parametric tests and repeated measures ANOVA. The findings indicated a significant improvement in perceptions of environmental responsibility, highlighting the effectiveness of targeted CSR environmental intervention. However, perceptions of economic and social responsibility, as well as trust, remained unchanged. Loyalty was negatively affected. Contrary to the literature, membership duration did not significantly influence CSR perceptions. These results emphasized the importance of designing and effectively communicating CSR initiatives that resonate with member priorities. Sports organizations can leverage such environmental intervention programs to improve their image and align with societal values. However, to foster trust and loyalty, CSR efforts across multiple dimensions are necessary. This study contributes to the literature on CSR in participatory sports by demonstrating the measurable impact of environmental interventions and providing a framework for future CSR program development and evaluation in similar settings. Full article

Thornthwaite’s water balance approach serves as a fundamental tool for assessing hydrological dynamics, particularly in regions vulnerable to aridity and water stress. This study evaluates the performance of gridded datasets in estimating Thornthwaite’s water balance attributes in Greece, leveraging climatic averages of the period 1960–1997. Ground station data from 91 meteorological sites and gridded data from the Climate Research Unit (CRU) of the University of East Anglia were utilized to assess key water balance components. The results indicate that while gridded datasets offer an alternative for regions with limited ground data, local calibration is required due to notable discrepancies. More specifically, it was found that gridded data tended to underestimate precipitation, with estimates approximately 25% lower compared to ground station data. The potential evapotranspiration (PET) estimates using gridded data were more accurate, with underestimation on the order of 10%. Moreover, the gridded data produced overestimations for all of the water balance key components including soil moisture (St), monthly changes in soil moisture (ΔSt), and actual evapotranspiration (AE) compared to the ground station data. The water surplus (S) estimates showed a significant dispersion of values when using the gridded data, particularly in regions characterized by more arid conditions. In addition, the application of gridded data led to a great increase in the aridity index (AI) values, altering the desertification classification of sites from semi-arid to sub-humid or humid categories. These findings underscore the importance of careful consideration when utilizing gridded datasets for hydrological and bioclimatic assessments, particularly in Mediterranean climate regions characterized by a complex topography and temporal climatic variability. Full article