Search Results (5,891)

Although much research in environmental and sustainability education has focused on knowledge and awareness, fewer studies have examined how school-based projects can foster young learners’ capacity for action. This study investigates how emancipatory, school-based environmental projects can foster young learners’ foundational capacities for contributing to a more sustainable and caring future. Grounded in the Bildung perspective and the action competence framework, a 16-week intervention was implemented with Grade 8 students who collaboratively identified and addressed authentic environmental issues—such as waste mismanagement, sanitation concerns, and safety risks—within their school community. Using a concurrent mixed-methods design, quantitative data from the Student Action Competence Questionnaire were integrated with qualitative evidence from worksheets and reflective journals. Results show consistent improvement across all dimensions of action competence, particularly in democratic collaboration and students’ willingness to take shared responsibility for environmental well-being. Qualitative findings reveal the development of critical reflection, co-creation with school stakeholders, and a growing sense of social responsibility, as students engaged in activities ranging from redesigning waste systems to proposing improvements through official communication channels. Rather than focusing on large-scale environmental outcomes, the projects cultivated everyday practices of care, participation, and ethical awareness—key dispositions for inspiring long-term change toward a greener and more sustainable future. The study highlights how context-based, dialogic learning can empower students as emerging environmental citizens within their immediate communities. Full article

Lithium minerals are essential raw materials for new energy storage systems, and accurate instance segmentation of their microscopic images is crucial for efficient resource exploration and utilization. However, existing segmentation methods face challenges when processing lithium mineral images, including complex texture overlaps, missed detection of small particles, and deployment difficulties on edge devices, making it hard to balance segmentation accuracy with inference speed. To address these challenges, this paper proposes an efficient instance segmentation method based on YOLO11-seg, named EMS-YOLO-seg. First, we designed Multi-Scale Partial Convolution (MSPConv) and integrated it into the C3k2 module. The modified C3k2-MSP module optimizes the model’s receptive field and enhances its multi-scale feature extraction capability. We replaced the PSABlock module with the CBAM attention mechanism, introducing the C2PSA-CBAM module, which strengthens the model’s channel focus and feature extraction abilities. The redesigned Segment-LSCDMSP segmentation head reduces computational complexity and improves detection efficiency. Experimental results on our custom-built lithium mineral microscopic image dataset show that compared to the baseline YOLO11n-seg model, the EMS-YOLO-seg model achieved a 0.8% and 0.8% improvement in $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50}^{\mathrm{b}\mathrm{o}\mathrm{x}}$ and $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50:95}^{\mathrm{b}\mathrm{o}\mathrm{x}}$, respectively, and a 1% and 0.7% improvement in $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50}^{\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{k}}$ and $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50:95}^{\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{k}}$. Additionally, the model reduced the number of parameters by 52.1%, FLOPs by 18.6%, model size by 49.4%, and increased FPS by 12.7%. This study provides reliable technical support for accurate instance segmentation of lithium mineral microscopic images and demonstrates strong scene adaptability and promising potential for real-time deployment under industrial environments and resource-constrained scenarios. Full article

In recent decades, international interest has grown in the design and implementation of evolutionary reactors based on passive systems. The design of such systems requires reliable and validated numerical tools capable of simulating phenomena driven by very small forces, especially when compared to active systems. For this reason, several international research projects aim to assess the capabilities and limitations of numerical tools in modelling passive systems and their associated physical phenomena. The HERO-2 facility was designed to provide preliminary experimental data for characterizing bayonet tubes and exploring their potential application as Steam Generators (SGs) in advanced nuclear reactor designs, such as Small Modular Reactors (SMRs). Following the agreement between the Italian Ministry of Economic Development and the ENEA, multiple experimental campaigns were conducted, and a RELAP5 (R5) input deck of the facility has been developed. Considering the RELAP5 limits in simulating condensation phenomena encountered in previous studies, the primary objective of this study is to enhance the capabilities of the code in simulating condensation phenomena in horizontal pipes under natural circulation conditions with the implementation of Thome correlation and, in the second instance, to re-evaluate the numerical model of the HERO-2 facility. Moreover, a comprehensive uncertainty analysis (UA) is carried out to identify the key parameters influencing the simulations. The analysis revealed that the simulation results are strongly affected by the filling ratio uncertainties, a given initial condition that, together with the power supplied, determines the most important thermal-hydraulic (T/H) test parameters, such as the saturation pressure, the void fraction, mass flow rate, etc. Overall, the study provides a deeper understanding of the factors governing passive system performance and highlights the importance of accurately characterizing the experimental boundary and initial conditions in the verification and validation activities of a T/H code. Full article

Urban freight pickup-and-delivery services operate in road networks where travel times are highly variable due to congestion, incidents, and operational restrictions. Such variability threatens the punctuality of deliveries and complicates the design of reliable service schedules. This paper examines an urban pickup-and-delivery vehicle routing problem with soft time windows under travel-time uncertainty and provides an empirical comparison of robust and deterministic planning approaches on a real road network. The problem is formulated as a time-dependent pickup-and-delivery VRP with soft time windows, where link travel times are represented by a finite set of scenarios calibrated from observed network conditions. The objective function combines four components that are central to urban freight operations: total travel time, total distance, and penalties for earliness and lateness relative to customer time windows. This structure captures the trade-off between routing efficiency and service quality. On this basis, a robust model is constructed that optimises tour plans with respect to scenario-based worst-case or risk-aggregated costs, while a standard deterministic model minimises the same objective using nominal (average) travel times only. An empirical study on a real urban network compares the deterministic and robust solutions with respect to delivery punctuality, tour length, and time-window violations across a range of demand and variability settings. The results show that robust routing systematically reduces the frequency and magnitude of late deliveries at the expense of only moderate increases in planned distance and travel time. Although energy use and emissions are not modelled explicitly, the improved reliability and reduced need for reactive re-routing indicate a potential to support more reliable and resource-efficient urban freight operations in the context of sustainable city logistics. Full article

Customer segmentation is essential in financial services for designing targeted interventions, managing dormant portfolios, and supporting marketing re-engagement strategies. Traditional approaches such as Recency–Frequency–Monetary (RFM) analysis offer interpretability but often lack the flexibility needed to capture heterogeneous behavioral patterns. This study presents an automated segmentation framework that integrates machine learning-based clustering with RFM-based interpretability benchmarks. KMeans and Hierarchical clustering are evaluated across multiple values of k using internal validity metrics (Silhouette Coefficient, Davies–Bouldin Index) and interpretability alignment measures (Adjusted Rand Index, Normalized Mutual Information, Homogeneity, Completeness, and V-Measure). The Hungarian algorithm is used to align machine-learned clusters with RFM segments for comparability. The framework reveals behavioral subgroups not captured by RFM alone, demonstrating that machine learning can expose hidden heterogeneity within dormant customer populations. While outcome-based financial validation is not yet feasible due to the cold-start nature of the deployment environment, the study provides a reproducible, scalable pipeline for segmentation that balances analytical rigor with business interpretability. The findings highlight how data-driven clustering can refine traditional segmentation logic, supporting more nuanced portfolio monitoring and re-engagement strategies in financial services. Full article

This paper analyzes the dynamic behavior of a passenger bus running on a raised crosswalk. The main objective was to evaluate the vertical displacements and accelerations caused by the change in elevation, and to determine the potential for suspension damage. The study involved a numerical approach to the examination of a vehicle’s displacement related to the profile pavement by the implementation of a single body finite element module with suspension subjected to the effect of road unevenness. The so-obtained dynamic behavior with this model was implemented in MATLAB software, and the results were compared with the corresponding real-world accident data record and with an experimental study carried out with a bus running on a raised crosswalk at prescribed velocities. The velocity on the day of the accident was then calculated by computational simulations using the software PC-Crash^®. The results show that the vertical displacement caused by the raised crosswalk can vary according to the bus velocity and the raised crosswalk height. Moreover, the results show that reducing the height of the raised crosswalk and redesigning it for a smoother transition with the pavement can help in minimizing the negative effects from impacts on the bus body. The findings of this study provide valuable insights for engineers and transportation planners, and can be used to improve the design and placement of raised crosswalks in the future. Full article

Background: Functional imbalance within the stomatognathic system can develop long before clinical symptoms become evident. Subtle biological changes, such as low-grade inflammation or metabolic disturbance, may precede gingival inflammation, temporomandibular discomfort, or masticatory muscle sensitivity. This study introduces the BioRisk-S (Biological Risk–Stomatognathic System) algorithm, a predictive model designed to identify early systemic alterations associated with the subclinical stage of stomatognathic dysfunction. Methods: A total of 260 clinically healthy adults without apparent stomatognathic disorders were enrolled and evaluated at baseline (T0) and re-examined after six months (T1). Routine laboratory tests were performed to determine high-sensitivity C-reactive protein (hs-CRP), neutrophil-to-lymphocyte ratio (NLR), and 25-hydroxyvitamin D levels. These biomarkers were integrated into the BioRisk-S algorithm to estimate systemic biological imbalance. Follow-up examinations focused on detecting early functional changes, including gingival inflammation, signs of temporomandibular joint (TMJ) dysfunction, and masticatory muscle tenderness. Results: Participants with higher baseline BioRisk-S scores showed significantly higher hs-CRP and NLR values, as well as lower vitamin D levels, indicating a mild but persistent inflammatory profile. After six months, these individuals exhibited early gingival inflammation, muscle tenderness, or mild TMJ discomfort more frequently than those with low BioRisk-S values (p < 0.01). The predictive model demonstrated good accuracy for detecting early biological imbalance preceding clinical dysfunction, with an area under the curve (AUC) of 0.84 (95% CI: 0.78–0.89). Conclusions: The BioRisk-S algorithm represents a feasible, low-cost tool for early systemic screening of functional imbalance within the stomatognathic system. By integrating routine laboratory parameters, this method may help identify individuals at risk before the onset of visible symptoms, supporting preventive and personalized approaches in oral and systemic health management. Full article

Contemporary sustainability practices in the built environment often focus narrowly on reducing short-term impacts within the boundaries of individual buildings. This Special Issue aims to challenge that paradigm by proposing a broader, resource-centric approach grounded in long-term system balance and post-fossil logic. It argues that sustainability should not merely mitigate harm but actively support resource regeneration. Key issues include the flawed concept of non-renewable resources, the obsolescence of primary energy metrics, insufficient system boundaries, and the undervaluation of residual material impact. Drawing on historical analogies and real-world observations, the paper outlines a framework for a regenerative built environment—where buildings take responsibility for their energy and material footprints and contribute positively over time. It concludes that truly sustainable design must be based on predictable, annual resource budgets and a holistic integration of material, ecological, and human systems. It requires re-inventing the way we evaluate and organize our built environment. Full article

This study examined the associations of job training and work environment with professionalism among direct long-term care (LTC) workers in South Korea. Given the cross-sectional design, the findings reflect statistical associations rather than causal relationships. A survey of 264 LTC workers was analyzed using descriptive statistics, correlations, and hierarchical regression. Model fit improved from Model 1 to Model 3 (R² = 0.370), and regression assumptions—including normality, homoscedasticity, and multicollinearity—were verified (all VIFs < 2.5). Work environment factors showed the strongest associations with professionalism. In the fully adjusted model, work promotion was positively associated (β = 0.177, p < 0.05), whereas work hindrance was negatively associated (β = −0.201, p < 0.01). Among sociodemographic variables, education (β = 0.183, p < 0.01) and monthly income (β = 0.113, p < 0.05) were significant. Job training showed no direct association with professionalism, likely reflecting limited variability and repetitive training content across institutions. Enhancing work environments—particularly by increasing recognition and reducing work obstacles—may strengthen professionalism among LTC workers. Job training systems may require redesign to improve relevance and effectiveness. Because data were drawn from a single region (Gyeonggi-do) and rely on self-report measures, generalizability is limited. Future studies should include multi-regional or longitudinal designs to deepen the understanding of workforce professionalism in aging societies. Practically, these findings suggest that improving recognition systems, reducing workflow barriers, and modernizing standardized training curricula may help strengthen professionalism among long-term care workers. Full article

Gel treatments have been widely applied to control water production in oil and gas reservoirs. However, for water shutoff in dense gas reservoirs, most gel-based treatments focus on individual wells rather than the entire reservoir, exhibiting limited treatment depth, poor durability, and inadequate repeatability Notably, formation damage is a primary consideration in treatment design—most dense gas reservoirs have a permeability of less than 1 mD, making them highly susceptible to damage by formation water, let alone viscous polymer gels. Constrained by well completion methods, gelant can only be bullheaded into deep gas wells in most scenarios. Due to the poor gas/water selective plugging capability of conventional gels, the injected gelant tends to enter both gas and water zones, simultaneously plugging fluid flow in both. Although several techniques have been developed to re-establish gas flow paths post-treatment, treating gas-producing zones remains risky when no effective barrier exists between water and gas strata. Additionally, most water/gas selective plugging materials lack sufficient thermal stability under high-temperature and high-salinity (HTHS) gas reservoir conditions, and their injectivity and field feasibility still require further optimization. To address these challenges, treatment design should be optimized using non-selective gel materials, shifting the focus from directly preventing formation water invasion into individual wells to mitigating or slowing water invasion across the entire gas reservoir. This approach can be achieved by placing large-volume gels along major water flow paths via fully watered-out wells located at structurally lower positions. Furthermore, the drainage capacity of these wells can be preserved by displacing the gel slug to the far-wellbore region, thereby dissipating water-driven energy. This study evaluates the viability of placing gels in fully watered-out wells at structurally lower positions in an edge-water drive gas reservoir to slow water invasion into structurally higher production wells interconnected via numerous microfractures and high-permeability streaks. The gel system primarily comprises polyethyleneimine (PEI), a terpolymer, and nanofibers. Key properties of the gel system are as follows: Static gelation time: 6 h; Elastic modulus of fully crosslinked gel: 8.6 Pa; Thermal stability: Stable in formation water at 130 °C for over 3 months; Injectivity: Easily placed in a 219 mD rock matrix with an injection pressure gradient of 0.8 MPa/m at an injection rate of 1 mL/min; and Plugging performance: Excellent sealing effect on microfractures, with a water breakthrough pressure gradient of 2.25 MPa/m in 0.1 mm fractures. During field implementation, cyclic gelant injections combined with over-displacement techniques were employed to push the gel slug deep into the reservoir while maintaining well drainage capacity. The total volumes of injected fluid and gelant were 2865 m³ and 1400 m³, respectively. Production data and tracer test results from adjacent wells confirmed that the water invasion rate was successfully reduced from 59 m/d to 35 m/d. The pilot test results validate that placing gels in fully watered-out wells at structurally lower positions is a viable strategy to protect the production of gas wells at structurally higher positions. Full article

Veronicastrum sibiricum (L.) Pennell, a species within the Plantaginaceae family, has a history of traditional application in addressing conditions such as abdominal pain, common cold, sore throat, parotitis, rheumatic discomfort, and snakebite. The plant produces diverse bioactive constituents, including phenylpropanoids, essential oils, flavonoids, and terpenoids. Terpenoids, generated via terpene synthases (TPSs), are of particular interest due to their pharmacological properties. Nevertheless, TPS enzymes in V. sibiricum have not been thoroughly investigated. In this research, a transcriptomic strategy was employed to isolate and profile TPS genes from V. sibiricum. Sequencing of the transcriptome produced 107,929 unigenes, among which 42,976 were functionally annotated using public databases. KEGG pathway examination revealed 264 genes associated with terpenoid metabolism, including 12 putative VsTPS genes harboring characteristic TPS domains. From these, VsTPS1 was successfully cloned. Functional characterization established that VsTPS1 operates as a bifunctional enzyme: in vitro, it catalyzes the conversion of FPP to (E)-nerolidol and, to a lesser extent, GPP to linalool. When expressed transiently in Nicotiana benthamiana, however, only (E)-nerolidol was detected, supporting its cytosolic localization and substrate specificity toward FPP. Accordingly, this sesquiterpene synthase was redesignated VsNES1. Co-expression of VsNES1 with HMGR in N. benthamiana markedly increased (E)-nerolidol yields, illustrating an effective strategy for heterologous production. These findings deepen our understanding of the TPS family in medicinal plant V. sibiricum and enable future biotechnological exploitation of terpenoid production in heterogenous plant cells. Full article

The study assesses how effectively succession planning and talent management facilitate the establishment of organizational agility, as well as the moderating influence of organizational learning in the context of Saudi-based banking and finance sectors. Based on the Resource-Based View theory, the study indicates that learning culture and human capital are very important as primary sources of competitiveness in turbulent environments. A stratified sampling was used in the data gathering of 400 respondents and the partial least squares structural equation modeling (PLS-SEM). The result shows that there is a positive and statistically significant relationship between succession planning and organizational agility, and, therefore, a consistent stream of leadership makes an organization more adaptable and resilient. On the other hand, talent development was negatively correlated with agility, which implies that the existing training practices do not match agility needs. Representatives of organizational learning moderated the succession planning–agility, leadership readiness, and adaptability relationship in a positive manner, but moderated the talent development–agility relationship in a negative manner, which implies that the organization has a disconnection between learning and talent strategies. It highlights the necessity to redesign HR practices to make them agile, promote the development of adaptive leadership and a culture of learning, and introduce flexible talent policies. This knowledge adds to the theoretical discussion of the dual nature of organizational learning as a facilitator and constraint as well as providing practical ways to enhance competitiveness in dynamic financial markets. Full article

Background/Objectives: Intermittent hypobaric hypoxia (IHH) induces various physiological and metabolic adaptations. This study aimed to investigate the effects of a seven-day IHH exposure on nutritional status, body composition, gut microbiota, movement intensity, and energy expenditure in 10 workers. Methods: A pre–post comparative design was employed, with measurements taken at the beginning and end of the exposure period. Nutritional status, body composition, and phase angle (PhA) were assessed via bioelectrical impedance analysis (BIA). Gut microbiota composition was analyzed through fecal DNA extraction and qPCR for specific bacterial families. Movement intensity and energy expenditure were monitored using accelerometry. An initial statistical analysis was performed, which included paired t-tests and Wilcoxon signed-rank tests. Results: A significant increase in PhA (mean difference: 0.40; p = 0.0053 for t-test, p = 0.0136 for Wilcoxon) and a significant decrease in BMI (mean difference: −0.38; p = 0.0311 for t-test, p = 0.0546 for Wilcoxon). Conclusions: While the original paper reported no significant changes in nutritional status or body composition, our re-analysis suggests a significant change in BMI. The original paper also reported significant changes in specific gut bacterial families (butyrate-producing bacteria, p = 0.037; Lactobacillus species, p = 0.006). Physical activity levels remained consistently low. Full article

This paper describes a compact low-cost telemetry system featuring ready-made sensors and an acquisition unit based on the ESP32, which makes use of the LoRa/Wi-Fi wireless standard for communication, and autonomous fallback logging to guarantee data recovery during communication loss. Ensuring safe atmospheric re-entry requires reliable onboard monitoring of capsule conditions during descent. The system is intended for sub-orbital, low-cost educational capsules and experimental atmospheric descent missions rather than full orbital re-entry at hypersonic speeds, where the environmental loads and communication constraints differ significantly. The novelty of this work is the development of a fully self-contained telemetry system that ensures continuous monitoring and fallback logging without external infrastructure, bridging the gap in compact solutions for CubeSat-scale capsules. In contrast to existing approaches built around UAVs or radar, the proposed design is entirely self-contained, lightweight, and tailored to CubeSat-class and academic missions, where costs and infrastructure are limited. Ground test validation consisted of vertical drop tests, wind tunnel runs, and hardware-in-the-loop simulations. In addition, high-temperature thermal cycling tests were performed to assess system reliability under rapid temperature transitions between −20 °C and +110 °C, confirming stable operation and data integrity under thermal stress. Results showed over 95% real-time packet success with full data recovery in blackout events, while acceleration profiling confirmed resilience to peak decelerations of ~9 g. To complement telemetry, the TeleCapsNet dataset was introduced, facilitating a CNN recognition of descent states via 87% mean Average Precision, and an F1-score of 0.82, which attests to feasibility under constrained computational power. The novelty of this work is twofold: having reliable dual-path telemetry in real-time with full post-mission recovery and producing a scalable platform that explicitly addresses the lack of compact, infrastructure-independent proposals found in the existing literature. Results show an independent and cost-effective system for small re-entry capsule experimenters with reliable data integrity (without external infrastructure). Future work will explore AI systems deployment as a means to prolong the onboard autonomy, as well as to broaden the applicability of the presented approach into academic and low-resource re- entry investigations. Full article

Background/Objectives: Thymoma is a rare epithelial neoplasm originating from the thymus gland, and its accurate detection and classification using computed tomography (CT) images remain diagnostically challenging due to subtle morphological similarities with other mediastinal pathologies. This study presents a deep learning (DL)-based model designed to improve diagnostic accuracy for both thymoma detection and subtype classification (benign vs. malignant). Methods: The proposed approach integrates a pre-trained VGG16 network for efficient feature extraction—capitalizing on its capacity to capture hierarchical spatial features—and an MLP-Mixer-based feature enhancement module, which effectively models both local and global feature dependencies without relying on conventional convolutional mechanisms. Additionally, customized preprocessing and post-processing methods are employed to enhance image quality and suppress redundant data. The model’s performance was evaluated on two classification tasks: distinguishing thymoma from healthy cases and discriminating between benign and malignant thymoma. Comparative analysis was conducted against state-of-the-art DL models including ResNet50, ResNet34, SEResNeXt50, InceptionResNetV2, MobileNetV2, VGG16, InceptionV3, and DenseNet121 using metrics such as F1 score, accuracy, recall, and precision. Results: The model proposed in this study obtained its best performance in thymoma vs. healthy classification, with an accuracy of 97.15% and F1 score of 80.99%. In the benign vs. malignant task, it attained an accuracy of 79.20% and an F1 score of 78.51%, outperforming all baseline methods. Conclusions: The integration of VGG16’s robust spatial feature extraction and the MLP-Mixer’s effective feature mixing demonstrates superior and balanced performance, highlighting the model’s potential for clinical decision support in thymoma diagnosis. Full article