Search Results (8,318)

Extreme precipitation events severely impact agriculture, reducing yields and land use efficiency. The spatiotemporal distribution of Total Column Water Vapor (TCWV), the primary gaseous form of water, directly influences sustainable agricultural management. This study, through multi-source data fusion, employs methods including the Mann–Kendall test, sliding change-point detection, wavelet transform, pixel-scale trend estimation, and linear regression to analyze the spatiotemporal dynamics of global TCWV from 1959 to 2023 and its impacts on agricultural systems, surpassing the limitations of single-method approaches. Results reveal a global TCWV increase of 0.0168 kg/m²/year from 1959–2023, with a pivotal shift in 2002 amplifying changes, notably in tropical regions (e.g., Amazon, Congo Basins, Southeast Asia) where cumulative increases exceeded 2 kg/m² since 2000, while mid-to-high latitudes remained stable and polar regions showed minimal content. These dynamics escalate weather risks, impacting sustainable agricultural management with irrigation and crop adaptation. To enhance prediction accuracy, we propose a novel hybrid model combining wavelet transform with LSTM, TCN, and GRU deep learning models, substantially improving multidimensional feature extraction and nonstationary trend capture. Comparative analysis shows that WT-TCN performs the best (MAE = 0.170, R² = 0.953), demonstrating its potential for addressing climate change uncertainties. These findings provide valuable applications for precision agriculture, sustainable water resource management, and disaster early warning. Full article

The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient in areas such as data privacy, authentication, access control, and scalable protection. Moreover, centralized security systems face increasing fragility due to single points of failure, various AI-based attacks, including adversarial learning, model poisoning, and deepfakes, and the rising threat of quantum computers to encryption protocols. This study systematically examines the individual and integrated solution potentials of technologies such as Blockchain, Edge Computing, Artificial Intelligence, and Quantum-Resilient Cryptography within the scope of IoE security. Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. In addition to the proposed multi-layered architecture, the study also structures solution methods and technology integrations specific to IoE environments. Classifications, architectural proposals, and the balance between performance and security are addressed from both theoretical and practical perspectives. Furthermore, a future vision is presented regarding federated learning-based privacy-preserving AI solutions, post-quantum digital signatures, and lightweight consensus algorithms. In this context, the study reveals existing vulnerabilities through an interdisciplinary approach and proposes a holistic framework for sustainable, scalable, and quantum-compatible IoE security. Full article

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

Addressing global challenges such as climate change and inequality requires convergence competencies that enable learners to devise sustainable solutions. Such competencies have been emphasized in Science, Technology, Engineering, Mathematics (STEM) fields, but empirical research and assessment tools tailored to Humanities, Arts, and Social Sciences (HASS) remain scarce. This study aimed to develop and validate a self-assessment tool to measure convergence competencies among HASS learners. A three-round Delphi survey with domain experts was conducted to evaluate and refine an initial pool of items. Items with insufficient content validity were revised or deleted, and all retained items achieved a Content Validity Ratio (CVR) of ≥0.800, with most scoring 1.000. The validated instrument was administered to 455 undergraduates participating in a convergence education program. Exploratory factor analysis identified five key dimensions: Convergent Commitment, Future Problem Awareness, Future Efficacy, Convergent Learning, and Multidisciplinary Inclusiveness, explaining 69.72% of the variance. Confirmatory factor analysis supported the model’s goodness-of-fit (χ² (160) = 378.786, RMSEA = 0.054, CFI = 0.952), and the instrument demonstrated high internal consistency (Cronbach’s α = 0.919). The results confirm that the tool is both reliable and valid for diagnosing convergence competencies in HASS contexts, providing a practical framework for interdisciplinary learning and reflective engagement toward sustainable futures. Full article

This study explores the perceptions of future educators, specifically Early Childhood Education students at the Universities of Cádiz and Gdansk, regarding the interconnections between critical thinking and sustainability. The work aims to provide valuable insights into general teacher training, examining how these students’ experiences are contextualized within their respective educational systems and cultural contexts. To achieve this, eleven group interviews (three in Cádiz, eight in Gdansk) were conducted using a structured and expert-validated script. The transcribed data were qualitatively analyzed using QDA MINER v.6 software. Key findings reveal divergent perceptions of critical thinking among pre-service teachers: while Spanish students leaned towards a subjective understanding, Polish students emphasized an objective, data-driven approach. This distinction has significant implications for the conceptualization and teaching of critical thinking in educator training. Despite these differences, both groups of participants highlighted the necessity of implementing active methodologies in higher education (such as cooperative learning, problem-solving, and debates) to foster critical thinking, both for their own development and for preparing for their future practice with young children. This study also identified an excessive emphasis on theoretical aspects of sustainability in these future teachers’ training and a limited understanding of their practical application in the classroom. Furthermore, explicit connections between critical thinking and sustainability were scarce in student responses, highlighting a gap in current educator training in these areas. Collectively, the results suggest significant weaknesses in current teacher training efforts regarding the development of critical thinking and its effective integration with sustainability competencies. Full article

Groundwater plays a crucial role in sustaining agriculture and livelihoods in the arid Middle Draa Valley (MDV) of southeastern Morocco. However, increasing groundwater extraction, declining rainfall, and the absence of effective floodwater harvesting systems have led to severe aquifer depletion. This study applies and compares six machine learning (ML) algorithms—decision trees (CART), ensemble methods (random forest, LightGBM, XGBoost), distance-based learning (k-nearest neighbors), and support vector machines—integrating GIS, satellite data, and field observations to delineate zones suitable for groundwater recharge. The results indicate that ensemble tree-based methods yielded the highest predictive accuracy, with LightGBM outperforming the others by achieving an overall accuracy of 0.90. Random forest and XGBoost also demonstrated strong performance, effectively identifying priority areas for artificial recharge, particularly near ephemeral streams. A feature importance analysis revealed that soil permeability, elevation, and stream proximity were the most influential variables in recharge zone delineation. The generated maps provide valuable support for irrigation planning, aquifer conservation, and floodwater management. Overall, the proposed machine learning–geospatial framework offers a robust and transferable approach for mapping groundwater recharge zones (GWRZ) in arid and semi-arid regions, contributing to the achievement of Sustainable Development Goals (SDGs))—notably SDG 6 (Clean Water and Sanitation), by enhancing water-use efficiency and groundwater recharge (Target 6.4), and SDG 13 (Climate Action), by supporting climate-resilient aquifer management. Full article

The integration of artificial intelligence (AI) in education offers novel opportunities to enhance critical thinking while also posing challenges to independent cognitive development. In particular, Human-Centered Artificial Intelligence (HCAI) in education aims to enhance human experience by providing a supportive and collaborative learning environment. Rather than replacing the educator, HCAI serves as a tool that empowers both students and teachers, fostering critical thinking and autonomy in learning. This study investigates the potential for AI to become a collaborative partner that assists learning and enriches academic engagement. The research was conducted during the 2024–2025 winter semester within the Pedagogical and Teaching Sufficiency Program offered by the Audio and Visual Arts Department, Ionian University, Corfu, Greece. The research employs a hybrid ethnographic methodology that blends digital interactions—where students use AI tools to create artistic representations—with physical classroom engagement. Data was collected through student projects, reflective journals, and questionnaires, revealing that structured dialog with AI not only facilitates deeper critical inquiry and analytical reasoning but also induces a state of flow, characterized by intense focus and heightened creativity. The findings highlight a dialectic between individual agency and collaborative co-agency, demonstrating that while automated AI responses may diminish active cognitive engagement, meaningful interactions can transform AI into an intellectual partner that enriches the learning experience. These insights suggest promising directions for future pedagogical strategies that balance digital innovation with traditional teaching methods, ultimately enhancing the overall quality of education. Furthermore, the study underscores the importance of integrating reflective practices and adaptive frameworks to support evolving student needs, ensuring a sustainable model. Full article

This systematic literature review investigates how the concept of time is taught and learned in early mathematics education. While young children are commonly expected to learn how to tell time, this review explores what additional aspects should be emphasised to foster a deeper and more sustainable understanding of time. Using the EBSCO database, 36 relevant articles published up to December 2024 were identified. To cover different aspects related to the teaching and learning of time, peer-reviewed scientific articles as well as practice-based reports were included in the search. A majority of the articles focused on clock reading as an aspect of time. The aspects duration, sequencing, and measurement of time also frequently appeared whereas expressions of time, or cross-disciplinary aspects were seldom mentioned. Drawing on the findings, this review proposes a comprehensive framework outlining key aspects that should be included in early mathematics education to support the teaching and learning of time. Full article

Motivation remains a central concern in second language (L2) and English as a foreign language (EFL) education, yet its underlying mechanisms are insufficiently understood. This study employs the theory of Directed Motivational Currents (DMCs) to explore periods of intense, sustained L2 motivation among Chinese adolescent EFL learners across secondary and tertiary levels. Through in-depth interviews with ten participants, this research identified the conditions (e.g., collaborative peer dynamics, vivid goal visualization) that triggered their DMC experiences. The data also highlighted how facilitative elements—such as clear starting points, personalized goal alignment, behavioral routines, and timely feedback—played a crucial role in initiating and sustaining these motivational currents. These findings contribute to DMC theory by revealing how intrinsic and extrinsic factors jointly foster and maintain high levels of motivation over time, offering valuable insights for designing targeted interventions to enhance EFL motivation and learning among Chinese adolescents. Full article

The sustainable development of teaching expertise in research-intensive universities remains a critical global challenge. This study investigates the distinctive characteristics of expert teachers—exemplary faculty in research universities—addressing their developmental trajectories and motivational mechanisms within prevailing incentive systems that prioritize research productivity over pedagogical excellence. Employing grounded theory methodology, we conducted iterative coding of 20,000-word interview transcripts from 13 teaching-awarded professors at Chinese “Double First-Class” universities. Key findings reveal the following: (1) Compared to the original K-12 expert teacher model, university-level teaching experts exhibit distinctive disciplinary mastery—characterized by systematic knowledge structuring and cross-disciplinary integration capabilities. (2) Their developmental trajectory transcends linear expertise acquisition, instead manifesting as a problem-solving continuum across four nonlinear phases: career initiation, dilemma adaptation, theoretical consciousness, and leadership expansion. (3) Sustainable teaching excellence relies fundamentally on teachers’ professional passion, sustained through a virtuous cycle of high-quality instructional engagement and external validation (including positive student feedback, institutional recognition, and peer collaboration). Universities must establish comprehensive support systems—including (a) fostering a supportive and flexible learning atmosphere, (b) reforming evaluation mechanisms, and (c) facilitating interdisciplinary collaboration through teaching development communities—to institutionalize this developmental ecosystem. Full article

Urban air pollution poses a significant challenge to public health and sustainable development, particularly in mid-sized cities with limited monitoring capabilities. This study investigates the impact of Nature-Based Solutions (NBS) on air quality and Land Surface Temperature (LST) in Guimarães, Portugal. The first phase involves mapping pollutants and assessing European guidelines, traditional monitoring methods, and emerging tools such as sensors and satellite data. The findings indicate gaps in spatial coverage, emphasizing the importance of integrating data from Sentinel-3, Sentinel-5P, local sensors, and drones. These insights establish a foundation for the next phase, which involves predictive modeling of NBS, LST, and pollutants using machine learning techniques to support data-driven policy-making. Full article

Paper-based colorimetric biosensors represent a promising class of low-cost diagnostic tools that do not require external instrumentation. However, their broader applicability is limited by the environmental concerns associated with conventional metal-based nanomaterials and the subjectivity of visual interpretation. To address these challenges, this study introduces a proof-of-concept platform—using CA19-9 as a model biomarker—that integrates naturally derived melanin nanoparticles (MNPs) with machine learning-based image analysis to enable environmentally sustainable and analytically robust colorimetric quantification. Upon target binding, MNPs induce a concentration-dependent color transition from yellow to brown. This visual signal was quantified using a machine learning pipeline incorporating automated region segmentation and regression modeling. Sensor areas were segmented using three different algorithms, with the U-Net model achieving the highest accuracy (average IoU: 0.9025 ± 0.0392). Features extracted from segmented regions were used to train seven regression models, among which XGBoost performed best, yielding a Mean Absolute Percentage Error (MAPE) of 17%. Although reduced sensitivity was observed at higher analyte concentrations due to sensor saturation, the model showed strong predictive accuracy at lower concentrations, which are especially challenging for visual interpretation. This approach enables accurate, reproducible, and objective quantification of colorimetric signals, thereby offering a sustainable and scalable alternative for point-of-care diagnostic applications. Full article

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $49\%$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

Nuclear power is a low-emission and economically competitive energy source, yet the effective disposal and management of its associated radioactive waste can be challenging. Radioactive waste can be categorised as high-level waste (HLW), intermediate-level waste (ILW), and low-level waste (LLW). LLW primarily comprises materials contaminated during routine clean-up, such as mop heads, paper towels, and floor sweepings. While LLW is less radioactive compared to HLW and ILW, the management of LLW poses significant challenges due to the large volume that requires processing and disposal. The volume of LLW can be significantly reduced through sorting, which is typically performed manually in a labour-intensive way. Smart management techniques, such as computer vision (CV) and machine learning (ML), have great potential to help reduce the workload and human errors during LLW sorting. This paper provides a comprehensive review of previous research related to LLW sorting and a summative review of existing applications of CV in solid waste management. It also discusses state-of-the-art CV and ML algorithms and their potential for automating LLW sorting. This review lays a foundation for and helps facilitate the applications of CV and ML techniques in LLW sorting, paving the way for automated LLW sorting and sustainable LLW management. Full article

This research aims to develop an educational board game, “Sustainable Home: Energy Challenge,” based on Monopoly by integrating sustainable development goals and renewable energy to use ChatGPT in human–computer collaboration. ChatGPT was used for game conceptualization, rule development, board creation, card design, and simulation in an iterative design. The developed board game demonstrated ChatGPT’s efficiency in educational game design and the benefits of human–computer collaboration. Game simulations validated the board game’s potential as a simulation tool to enhance diversity, cooperation, and strategic depth. The game effectively promoted SDG engagement and sustainable development education in gamified learning. Full article