Search Results (9,651)

The management and efficient use of water resources are crucial to the sustainable development of agriculture in arid regions. The Manas River Basin faces severe water shortages due to its arid climate and heavy reliance on irrigation water. Therefore, based on water footprint theory, this study comprehensively utilized the CROPWAT model, pathway analysis, and CMIP6 data to construct an integrated “assessment–driving–prediction” framework for crop water footprints, with the aim of revealing the evolution patterns and driving mechanisms of water footprints in river basins. The results showed that the cultivated area of crops in the Manas River Basin exhibited a nonlinear expansion trend from 1990 to 2020, with a total increase of 143.56% over the 30-year period. Among all crops, cotton occupied the largest cultivated area, accounting for 60.34% of the total. During the study period, the crop water footprint, crop blue water footprint, and crop green water footprint in the Manas River Basin showed overall upward trends, increasing by 1.07 × 10⁹ m³, 1.04 × 10⁹ m³, and 3.0 × 10⁷ m³, respectively. Total agricultural machinery power and per capita grain production are the main factors influencing changes in crop water footprint. Under future climate scenarios, the crop water footprint in the Manas River Basin is projected to follow the order SSP2-4.5 > SSP5-8.5 > SSP1-2.6. By 2100, the crop water footprint under the SSP2-4.5 scenario is expected to increase by 37.01% relative to 2020, posing substantial challenges to agricultural water resource management in the basin. In contrast, the crop water footprint under the SSP1-2.6 scenario remains relatively stable, indicating a more sustainable development pathway. Full article

To address the safety assessment challenges associated with repeated and continuous excavation-induced vibrations in high-rise residential projects founded on hard rock, this study proposes a novel multi-scale dual-control evaluation framework bridging macroscopic ground peak particle velocity (PPV) with microscopic structural principal tensile stress. A geotechnical–structural decoupling approach was adopted. First, a two-dimensional dynamic finite element model of the ground was developed to simulate the propagation and attenuation characteristics of elastic waves under continuous excavation impact loading. Subsequently, the acceleration time histories extracted from the soil–structure interface were applied as base excitations in a three-dimensional structural finite element model to systematically investigate the dynamic response characteristics. The scientific novelty of this research lies in quantitatively decoupling the dynamic sensitivities between primary load-bearing shear walls and secondary non-load-bearing walls, thereby overcoming the inherent blind spots of conventional PPV-only macroscopic assessments. The results indicate that in hard-rock foundations, vibration energy is dominated by vertical attenuation characteristics. Compared with primary load-bearing structures mainly governed by static self-weight effects, secondary non-load-bearing components with lower stiffness exhibit significantly higher dynamic sensitivity and localized vulnerability to continuous impact loading. Parametric analysis further reveals that while the number of operating equipment shows a linear positive correlation with vibration amplitude, the impact frequency dictates the system’s dynamic response. Notably, when the excitation frequency approaches the dynamically sensitive frequency range of the foundation system, a pronounced divergence in response characteristics is observed—manifested as a sharp increase in local structural stress despite a reduction in ground macroscopic vibration intensity. These findings provide a rigorous theoretical foundation and a refined methodology for vibration impact assessment in urban hard-rock foundation construction. Full article

Urban centers today face unprecedented challenges in energy management, emissions, waste disposal, and public services, as nearly 70% of the global population is projected to live in cities by 2050. The complexity and rapid evolution of urban systems underscore the pressing need for stability, innovation, and adaptability, particularly regarding sustainability and digital transformation. AI-powered simulation models have emerged as transformative tools, capable of simplifying, predicting, and managing highly intricate urban systems while offering policymakers valuable insights for strategic planning. However, the integration of AI in smart and sustainable urban development presents critical legal, ethical, and regulatory concerns. This study examines these questions by evaluating existing and emerging frameworks addressing algorithmic transparency, data protection, stakeholder engagement, and sustainable development in prominent urban models including Amsterdam, Copenhagen, Singapore, Tokyo, Bangalore, and Nairobi. Comparative analysis is conducted through a doctrinal desk review, focusing on statutory provisions, international policies (EU, UN-Habitat), ISO Smart City standards, and local governance charters. Key issues addressed include the risk that AI models, if unregulated, become opaque “black boxes” that obscure both decision-making logic and accountability. Without robust standards, there is no guarantee of interoperability, revision, or representation of public interest. Equitable management, access, and inclusive participation are vital to responsible AI frameworks in urban planning. This article advances a comprehensive legal and policy framework for ensuring accountability, transparency, and stability in AI-driven city governance, bridging gaps between technological innovation, urban studies, and regulatory oversight. The proposed governance structure empowers cities to adopt multi-level, authority-driven mechanisms that safeguard the common good while leveraging AI’s potential in sustainable urban transformation. Full article

Accurate building energy prediction is essential for climate-resilient design, retrofit planning, and long-term energy management. However, most machine-learning models are developed using historical weather data, implicitly assuming that future climatic conditions will remain similar to the past. This assumption is increasingly challenged by climate change, which is altering temperature patterns, solar exposure, humidity levels, and the frequency of extreme weather events. This study presents a climate-scenario-aware artificial intelligence framework that integrates future climate conditions into simulation-driven machine-learning development and validation. Using a UK hotel case study based on the Hilton Watford context, future weather scenarios were derived from CIBSE datasets informed by UKCP18 and CMIP6 climate projections. EnergyPlus version 23.2.0 simulations were performed under baseline, moderate-warming, high-warming, and heatwave stress-test scenarios to generate hourly building energy data. Random Forest, XGBoost 2.1.1, Multiple Linear Regression, and Multi-Layer Perceptron models were trained and evaluated using both Historical-Only and Climate-Scenario-Aware training approaches. Results show that models trained exclusively on historical conditions maintain high present-day accuracy but experience notable performance degradation under future climate scenarios, particularly for cooling demand and peak-load prediction. In contrast, Climate-Scenario-Aware models demonstrated improved robustness, reduced prediction errors, and greater physical consistency during extreme heatwave conditions while maintaining comparable performance under current climatic conditions. The proposed framework provides a reproducible methodology for developing climate-resilient AI models for building energy prediction and highlights the importance of incorporating future climate scenarios into model training and validation. The findings suggest that climate stress-testing should become a standard component of AI-based building energy analytics, digital twins, and long-term energy planning tools. Full article

This study conducts a comprehensive investigation into prospective policy alternatives within Pakistan’s power sector using the PAK-TIMES model, targeting the critical challenges of energy scarcity and environmental degradation. Focused on the period from 2022 to 2050, the research evaluates the impact of various policies on energy consumption, supplies, carbon emissions, and expenditures in alignment with Pakistan’s Nationally Determined Contributions (NDC) directed at combatting climate change. The study explores three distinct scenarios: a business-as-usual (BAU) scenario, along with five policy (5% Eff, 10% Eff, 15% REN, 30% REN, 50% REN) scenarios categorized into energy efficiency and renewable integration. The first scenario concentrates on the deployment of energy-efficient devices, while the second scenario delves into diverse levels of renewable energy integration. Key results reveal that energy demand is projected to surge substantially under the BAU scenario, increasing significantly from 3459 PJ in 2022 to 7912 PJ by 2050. In contrast, scenarios prioritizing energy efficiency can potentially curb the total energy supply by 2.3%, while renewable energy integration can expand up to 1.3% compared to business-as-usual by 2050. These alternative scenarios also exhibit the potential to slash greenhouse gas (GHG) emissions from the power sector by up to 15%. Notably, the PAK-TIMES model emerges as a valuable decision support tool for the Pakistani government to facilitate the execution of energy efficiency and renewable energy policies aimed at fulfilling its NDCs, while also contributing to the fulfillment of Sustainable Development Goals (SDGs) 7 (affordable and clean energy) and 13 (climate action). The study underscores the pivotal role of policy interventions in simultaneously mitigating energy challenges and combatting climate change for sustainable development. Full article

For rural adolescent girls in the Bolivian Andes, adolescence is not a “protected” transitional life stage but a gendered laboring condition. This article explores the lived experiences of adolescent girls in a rural Quechua community in Cochabamba, drawing on the initial phase of a longitudinal Community-Based Participatory Research (CBPR) project (2023–2024). By integrating Silvia Federici’s theory of social reproduction with Axel Honneth’s recognition theory, the study conceptualizes a “laboring subjectivity” defined by a ch’ixi reality where two dimensions of labor exist in constant, dynamic interaction. The findings reveal these dimensions of labor: “Working to Do,” which encompasses the invisible, naturalized reproductive and agricultural work and unremunerated affective work required to sustain family life as a form of cultural pedagogy; and “Working to Be,” which refers to the subjective labor girls perform to negotiate recognition. Through this structural arrangement, Honneth’s spheres of love, rights, and social esteem are systematically compromised, creating a distinct recognition deficit as girls carry adult responsibilities without structural protection. Ultimately, this article argues that seasonal migration to regions such as El Trópico functions as an existential terrain where girls seek the symbolic and economic recognition denied within the local rural order. By centering adolescent girls as active laboring subjects, the research challenges Western developmental biases in youth studies and offers a nuanced reframing of the nexus between labor, mobility, and identity formation in the Global South. Full article

Mechanized tunneling with Tunnel Boring Machines (TBMs) is essential for rapid urbanization, yet traditional thick concrete segmental linings incur high material costs and geometric challenges in deep projects. This study investigates an innovative hybrid reinforcement strategy to optimize structural efficiency by reducing segment thickness through steel rings, a concept inspired by the proactive support principles of the New Austrian Tunnelling Method (NATM). Three finite element numerical models were developed using PLAXIS 3D to evaluate this design under approximately 69 m overburden in claystone. The results demonstrate that 25 cm concrete segments reinforced with structural steel rings achieve mechanical performance comparable to traditional 40 cm unreinforced segments, despite a 37.5% reduction in lining thickness. This structural optimization facilitates a 43.7% reduction in concrete consumption, reduces the outer excavation diameter, and simplifies manufacturing and construction logistics while keeping structural forces and displacements safely within engineering thresholds. The findings confirm that the proactive integration of steel rings provides a lining configuration demonstrating potential material optimization advantages for modern TBM tunnel designs in demanding ground conditions. Full article

Digital terrain models (DTMs) are essential elevation datasets that represent the morphology of the Earth’s surface and play a critical role in applications, such as urban planning, civil engineering, infrastructure design, and environmental assessment. However, the excessive cost remains the major challenge in obtaining accurate terrain models. Recent advancements in low-cost inertial navigation and motion-sensing technologies offer significant potential to enhance the cost-effectiveness of surveying projects. This study investigates the vertical accuracy and operational usability of a handheld inertial measurement unit (IMU) device (Moasure 2) for DTM generation in urban environments through the comparison with traditional total station and digital levels procedures. It also assesses the device compliance with The American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards. For this purpose, a comprehensive field survey was conducted in a small urban area characterized by varied terrain morphology. The vertical accuracy of the Moasure 2 was acceptable for many urban mapping applications based on a rigorous analysis of checkpoint data and error patterns, which were quantitatively assessed relative to reference surfaces. Profile-based validation showed that the elevation differences between similar terrain types were mainly within ±25 cm, with minimal bias and symmetric error distributions. The findings indicate that Moasure 2 can be a viable alternative tool for fast DTM generation in low-cost urban projects. It offers significant advantages in terms of portability, ease of use, and reduced fieldwork time compared to conventional methodologies. Furthermore, this study addresses the critical gap in the validation of the new IMU-based surveying technology and provides evidence for choosing appropriate equipment for urban terrain modeling. Full article

Blockchain is a fast-changing field that is highly useful in such areas as finance, supply chain management, voting systems, and healthcare. As a consequence, software developers are increasingly creating Blockchain-Based Applications (BBAs) and Smart Contracts (SCs). However, the development of BBAs has been associated with various problems, especially in the process of updating and debugging such systems with a high degree of reliability. This is due to the immutability of deployed SCs. In this paper, we conduct an in-depth analysis of 61 published BBA articles between 2017 and 2025 to identify some causes of these challenges. Our results indicate that there is inadequate adaptation of the Software Development Life Cycle (SDLC) for BBAs. In particular, few BBA projects—only 32% of the reviewed projects—address the analysis phase, and only 29% deal with the design phase, frequently ignoring formal modeling methods. Based on these observations, we propose a new, context-adaptive methodology that facilitates BBA developers passing through the requirements, analysis, design, and implementation processes. Formal modeling techniques—such as Use Case Maps (UCMs), Finite State Machines (FSMs), and extended Unified Modeling Language (UML) class and sequence diagrams—are used within the methodology to document BBA structural and behavioral features and maintain complete traceability between requirements and implementation. In order to overcome the blockchain-specific drawbacks of traditional UML, we present formal stereotype extensions of UML class diagrams, where a four-compartment structure is introduced to differentiate state variables, functions, events, and access modifiers on SCs. We also provide analogous extensions to UML sequence diagrams using differentiated arrow notations to distinguish between function calls and event emissions to support accurate modeling of decentralized transaction flows. These extensions are described with a rationale and are formally defined and justified by mapping rules. Our methodology is justified by two case studies that prove its applicability in different fields of blockchain. The initial case study thus designs and executes a system of a halal chicken meat supply chain on Ethereum, showing the complete traceability of requirements that are based on UCM-based requirements and FSM-generated algorithms to implement SCs. The second case study applies the methodology to a decentralized Electronic Health Record (EHR) management system, and it shows coverage and completeness modeling. The methodology was evaluated through two case studies using a structured questionnaire and quantitative metrics, including traceability accuracy, reduction-in-error indicators, SC defect and gas-analysis results, modeling overhead measurements, and static security analysis with Slither. It is also evaluated based on a group of seven literature-based qualitative evaluation criteria that include workflow expressiveness, reusability, technical concept coverage, intelligibility, completeness, tool support, and blockchain limitation modeling. Full article

The HEROn magazine was created as an innovation project by the S-TEAM team for the 2024/2025 SUBMERGED season of the FIRST^® LEGO^® League Challenge category. The primary aim of the HEROn project was to implement game-based learning methods to enhance environmental awareness, particularly concerning the protection of our water resources. This initiative is designed to engage individuals from ages 9 to 99 in a creative and enjoyable manner. At the core of the HEROn project is a well-known game that challenges players to find the differences between two photos. This activity not only provides entertainment but also educates participants about the importance of protecting and preserving the aquatic environment. By discovering subtle differences between images, players become more attuned to environmental issues, which promotes a deeper understanding and appreciation of water conservation. The chapters of the HEROn magazine are thoughtfully organised into themes, each focusing on various aspects of water’s importance, its protection, and sustainable usage. Additionally, a random sample of participants was surveyed to gather opinions and feedback on HEROn magazine as part of the project and this research. This feedback is invaluable for assessing the magazine’s impact and for improving future editions to better serve the goals of raising environmental consciousness. The online HEROn questionnaire consisted of 10 items and employed a 5-point Likert scale for responses. Data were collected over a three-month period (28 January–28 April 2025), with 630 Hungarian respondents participating in the survey. The HEROn magazine was generally well received, with mean scores ranging from 4.2 to 4.6. Age-group differences were examined using nonparametric Kruskal–Wallis tests, with Dunn–Bonferroni post hoc comparisons. These analyses show statistically significant differences between adults (30–89) and the younger cohorts for aggregated awareness, design/engagement, and branding measures, while teenagers (9–15) and young adults (16–29) did not differ significantly from each other. The Find-the-Difference game showed the greatest variability across groups, with young adults giving the lowest mean. Full article

Early clinical exposure (ECE) has been associated with increased confidence, professionalism, and career exploration in undergraduate medical education. Student-run free clinics (SRFCs), such as the Health Outreach Project (HOP) at Drexel University College of Medicine, provide opportunities for preclinical students to engage in patient care and community outreach. This qualitative study explored medical students’ perceptions of participation in HOP. Fourteen third- and fourth-year medical students with prior HOP experience participated in four semi-structured focus groups conducted virtually over Zoom. Data were analyzed using an inductive thematic analysis approach. Four major themes emerged: (1) early clinical exposure and clinical skills development, (2) community engagement and patient-centered perspectives, (3) professional identity formation and career exploration, and (4) opportunities, limitations, and emotional challenges of outreach work. Participants described HOP as an important source of authentic clinical exposure that increased confidence in patient interactions and broadened awareness of social determinants of health and underserved populations. Students also reflected on the influence of HOP on professional identity formation, career interests, and perspectives on patient-centered care, while acknowledging frustrations related to systemic barriers and limited resources. These findings suggest that students perceive SRFCs as valuable experiential learning environments that support clinical preparedness and professional development early in medical training. Full article

This study aims to identify math students’ argumentation processes and how the design of interactive materials may serve such processes. Despite the increasing use of such materials in mathematics education, their role in shaping students’ argumentation processes remains underexplored. The case study to be reported here is part of a project aimed at examining the potential of interactive materials to support students’ argumentation processes. Three 13- and 14-year-old students were challenged by a task presented as a guiding interactive diagram centered on an animation of multi-process motion and parallel time-position graphs. The study was based on a framework for analyzing the pedagogical functionality of interactive diagrams and the Toulmin model for analyzing the argument structure. The argumentation processes evolved from claiming a discrepancy between the two hot-linked representations in the diagram. The students used backing to generate mathematical ideas and rebuttals to strengthen them by analyzing exceptional cases. The study provides evidence of the contribution of the guiding interactive diagram design to the emergence of rich argumentation processes. The students engaged in deeper argumentation and used rebuttal as a way to address the semiotic conflict prompted by the design into their broader argument structure. Full article

The proliferation of large-scale interaction datasets, from scientific collaboration networks and legislative records to online communication platforms, has made the analysis of group-based, time-varying systems one of the central challenges of modern data analytics. Hypergraphs provide a natural formalism for such systems, where interactions involve arbitrary groups of agents rather than isolated pairs, and temporal hypergraphs extend this to sequential data by capturing how group interactions evolve over time. Yet quantifying how complex, predictable, or volatile this evolution is remains an open problem: existing entropy-based measures either operate on pairwise projections and thus discard multi-way dependencies or are not naturally defined for varying hyperedge sizes. In this paper, we propose an information–theoretic framework for characterizing how the diversity of interaction orders in a temporal hypergraph evolves over time. We introduce the hyperedge-size distribution entropy of a snapshot and, building on the theory of entropy rates for stochastic processes, we define the temporal hypergraph entropy rate as a principled, dataset-agnostic measure of the average diversity of interaction orders exhibited by the snapshot sequence over time. We further equip the framework with a bias-corrected sliding-window estimator and a lightweight change-point detector, assembling a complete pipeline that runs in time linear in the total number of hyperedges and requires no node alignment across datasets or snapshots. We prove that the measure collapses to zero under clique expansion, demonstrating that it captures interaction-order information that is discarded by the standard size-blind pairwise projection. Experiments on six small and large publicly available benchmark datasets show that the entropy rate spans 1.60 bits across domains, detects unsupervised structural change points, and discriminates between structurally distinct interaction cultures even within the same domain. Our framework is computationally lightweight and applicable to any dataset that can be represented as a temporal sequence of hypergraphs, paving the way for practical, scalable, interaction-order-aware analysis of large-scale higher-order temporal data. Full article

Background/Objectives: Electroencephalography (EEG) is widely applied in emotion recognition. Integrating diverse frequency and spatial features to improve performance remains a major challenge. Methods: This paper proposes two preprocessing methods to map EEG signals into image-style representations. These methods preserve the spatial topology and enable effective feature extraction using convolutional neural networks. The first method is a spatial concatenation method (SCM). It projects three feature types onto color channels, providing a structural prior that encourages the network to learn the three feature types within local spatial windows. It differs from traditional spectral mixing, which maps frequency bands to color channels. The second method is a band-wise stacking method (BSM). It treats frequency bands as independent depth frames to form a three-dimensional tensor. This structure is designed to facilitate the learning of inter-band relationships while preserving band-specific information. Dedicated convolutional neural network architectures are designed for these tensor structures, aligned with the spatial and spectral organization of the proposed SCM and BSM. Results: Experiments on the DEAP and DREAMER datasets for binary Arousal and Valence classification show that both representations achieve competitive results. The BSM achieves higher accuracy than the SCM on the DREAMER dataset, while both methods perform comparably on the DEAP dataset. Conclusions: The proposed strategies offer efficient convolutional neural network approaches for EEG emotion recognition systems. Full article

Agri-food systems are increasingly exposed to environmental, economic, and social challenges, including climate change, biodiversity loss, resource depletion, and growing territorial inequalities. In this context, agroecology is increasingly recognised as a transformative paradigm integrating ecological, economic, social, cultural, and political dimensions within broader processes of food-system transition. Within the PRIMA AgrEcoMed project, 24 Italian agroecological initiatives led by women and young farmers were analysed to explore their contribution to agroecological transition processes in Mediterranean rural areas. The study adopts a qualitative multiple-case study approach and evaluates the selected initiatives through the framework of the 13 Principles of Agroecology proposed by the High-Level Panel of Experts on Food Security and Nutrition, organised into three operational axes: improving resource efficiency, strengthening resilience, and ensuring social responsibility and fairness. The results show that the analysed initiatives combine ecological farming practices with processes of multifunctionality, territorial networking, knowledge co-creation, short supply chains, and community engagement. The findings suggest that several initiatives move beyond input-reduction strategies associated with “weak agroecology” and display characteristics consistent with stronger agroecological pathways based on territorial embeddedness, collective learning, and the reorganisation of relationships between production, consumption, and local communities. The paper highlights the relevance of agroecology not only as an environmentally sustainable farming approach, but also as a broader socio-ecological and territorial transition process, as well as the importance of policy frameworks to support territorial agroecological systems. Full article