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Search Results (2,252)

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Keywords = sustainable learning practices

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31 pages, 944 KB  
Article
How and When Entrepreneurial Leadership Drives Sustainable Bank Performance: Unpacking the Roles of Employee Creativity and Innovation-Oriented Climate
by Rajia Ageli, Ahmad Bassam Alzubi, Hasan Yousef Aljuhmani and Kolawole Iyiola
Sustainability 2025, 17(20), 9259; https://doi.org/10.3390/su17209259 (registering DOI) - 18 Oct 2025
Abstract
The banking sector faces increasing pressure to balance financial performance with sustainability goals amid ongoing digital transformation, regulatory reform, and societal expectations for ethical responsibility. Entrepreneurial leadership has emerged as a pivotal approach for addressing these challenges; however, the behavioral and contextual mechanisms [...] Read more.
The banking sector faces increasing pressure to balance financial performance with sustainability goals amid ongoing digital transformation, regulatory reform, and societal expectations for ethical responsibility. Entrepreneurial leadership has emerged as a pivotal approach for addressing these challenges; however, the behavioral and contextual mechanisms through which it shapes sustainability remain insufficiently understood. Drawing on Social Learning Theory (SLT), this study investigates how and when entrepreneurial leadership enhances sustainable bank performance through the mediating role of employee creativity and the moderating influence of an innovation-oriented climate. A two-wave multi-source survey was conducted among 459 employees and managers from Turkish banks, and the hypothesized model was tested using structural equation modeling to ensure robust empirical validation. The results indicate that entrepreneurial leadership significantly fosters employee creativity, which serves as a critical behavioral mechanism linking leadership behaviors to sustainability-oriented outcomes. Moreover, an innovation-oriented climate strengthens both the direct effect of entrepreneurial leadership on creativity and its indirect effect on sustainable bank performance, emphasizing the contextual importance of supportive organizational environments. Theoretically, this study extends the leadership and sustainability literature by illustrating how learning and behavioral modeling processes translate leadership vision into sustainable performance. Practically, it offers actionable guidance for bank executives to develop innovation-oriented climates, empower employees’ creative engagement, and design incentive systems that align leadership behavior with sustainability imperatives, thereby enhancing resilience and long-term competitiveness. Full article
(This article belongs to the Special Issue Sustainable Organization Management and Entrepreneurial Leadership)
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37 pages, 8530 KB  
Article
AI-Driven Optimization of Plastic-Based Mortars Incorporating Industrial Waste for Modern Construction
by Aïssa Rezzoug
Buildings 2025, 15(20), 3751; https://doi.org/10.3390/buildings15203751 (registering DOI) - 17 Oct 2025
Abstract
Cementitious composites with recycled plastic often suffer from reduced strength. This study explores the partial substitution of cement with industrial by-products in plastic-based mortar mixes (PBMs) to enhance performance while reducing environmental impact. To achieve this, five hybrid machine learning (ML) models CNN-LSTM, [...] Read more.
Cementitious composites with recycled plastic often suffer from reduced strength. This study explores the partial substitution of cement with industrial by-products in plastic-based mortar mixes (PBMs) to enhance performance while reducing environmental impact. To achieve this, five hybrid machine learning (ML) models CNN-LSTM, XGBoost-PSO, SVM + K-Means, SVM-PSO, and XGBoost + K-Means were developed to predict flexural strength, production cost, and CO2 emissions using a large dataset compiled from peer-reviewed sources. The CNN-LSTM model consistently outperformed the other approaches, showing high predictive capability for both mechanical and sustainability-related outputs. Sensitivity analysis revealed that water content and superplasticizer dosage are the most influential factors in improving flexural strength, while excessive cement and plastic waste were found to negatively impact performance. The proposed ML framework was also successful in estimating production cost and CO2 emissions, demonstrating strong alignment between predicted and actual values. Beyond mechanical and environmental predictions, the framework was extended through the RA-PSO model to estimate compressive and tensile strengths with high reliability. To support practical adoption, the study proposes a graphical user interface (GUI) that allows engineers and researchers to efficiently evaluate durability, cost, and environmental indicators. In addition, the establishment of an open access data-sharing platform is recommended to encourage broader utilization of PBMs in the production of paving blocks and non-structural masonry units. Overall, this work highlights the potential of hybrid ML approaches to optimize sustainable cementitious composites, bridging the gap between performance requirements and environmental responsibility. Full article
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33 pages, 607 KB  
Article
Assessing the Drivers of Financial Vulnerability and Fraud in Brazil: The Critical Role of Financial Planning over Literacy
by Benjamin Miranda Tabak, Débora H. Cardoso and Cristiano C. Silva
Sustainability 2025, 17(20), 9219; https://doi.org/10.3390/su17209219 - 17 Oct 2025
Abstract
This paper introduces and validates a comprehensive instrument designed to measure financial literacy, its underlying determinants, and to assess how factors such as planning affect financial vulnerability and fraud in Brazil. This work represents a crucial step toward achieving several Sustainable Development Goals [...] Read more.
This paper introduces and validates a comprehensive instrument designed to measure financial literacy, its underlying determinants, and to assess how factors such as planning affect financial vulnerability and fraud in Brazil. This work represents a crucial step toward achieving several Sustainable Development Goals (SDGs). The study utilizes a two-fold methodology. First, Confirmatory Factor Analysis (CFA) is used to validate a six-component model consisting of Financial Literacy, Vulnerability, Fraud, Cognitive Reflection, Crypto Literacy, and Planning. This analysis is followed by the development and interpretation of a Random Forest model, which was identified as the best-performing predictor in a comparison of seven machine learning algorithms. The CFA results showed that Financial Planning has a stronger negative correlation with Financial Vulnerability (−0.642) and Fraud (−0.375) than Financial Literacy does. This evidence was further supported by the machine learning analysis; analyses using both SHAP and LIME identified Financial Planning as the strongest predictor of financial vulnerability and fraud. The analysis further showed significant social inequalities in the developed models and identified the gender variable (female) as an important predictor of enhanced financial vulnerability. Converging evidence from both CFA and machine learning confirms that sound planning practices are more important than financial knowledge in reducing financial distress. Our findings provide a solid foundation for the development of inclusive public policy that promotes behavioral change, aiming to reduce systemic inequalities (SDG 10) and achieve sustainable economic stability (SDG 8), thereby supporting social goals and the Sustainable Development Goals. Full article
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21 pages, 1362 KB  
Article
Teaching Equity and Perceived Learning Effect in Dual-Teacher Classroom Under Education for Sustainable Development: A Comparative Study of Student Engagement Mechanisms Through the Opportunity-to-Learn Framework
by Guangwei Hu, Yonghai Zhu, Di Liu and Ziling Liu
Sustainability 2025, 17(20), 9216; https://doi.org/10.3390/su17209216 - 17 Oct 2025
Viewed by 15
Abstract
Ensuring equitable access to quality education is essential for Education for Sustainable Development. However, the efficacy of innovative Education for Sustainable Development models like the Dual-Teacher Classroom in promoting teaching equity has not been sufficiently studied. Grounded in the Opportunity to Learn (OTL) [...] Read more.
Ensuring equitable access to quality education is essential for Education for Sustainable Development. However, the efficacy of innovative Education for Sustainable Development models like the Dual-Teacher Classroom in promoting teaching equity has not been sufficiently studied. Grounded in the Opportunity to Learn (OTL) theory—a theoretical framework that provides observational constructs for Education for Sustainable Development (ESD), which are operationalized as empirical measures in this study—this study investigates the mechanisms through which teaching equity influences perceived learning effect, with learning engagement as a mediator, among elementary students in Beijing, China. Data were collected from 278 participants using a validated questionnaire. The results reveal significant disparities between urban and rural students, with urban students demonstrating higher levels of teaching equity, learning engagement, and perceived learning effect. Mediation analysis indicated that learning engagement acts as a partial mediator for urban students, whereas it serves as a full mediator for rural students. These findings highlight the importance of context-sensitive instructional strategies to enhance equity and engagement in the Dual-Teacher Classroom. The study contributes to the discourse on educational sustainability by emphasizing the role of equitable teaching practices in fostering inclusive and effective learning environments. Practical implications for teacher collaboration and real-time feedback mechanisms are discussed. Full article
(This article belongs to the Section Sustainable Education and Approaches)
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14 pages, 539 KB  
Article
Contribution to Sustainable Education: Co-Creation Citizen Science Project About Monitoring Species Distribution and Abundance on Rocky Shores
by Ana Teresa Neves, Diana Boaventura and Cecília Galvão
Sustainability 2025, 17(20), 9198; https://doi.org/10.3390/su17209198 - 16 Oct 2025
Viewed by 104
Abstract
Citizen science is not only a participatory means of contributing to scientific knowledge but also an effective approach to addressing a wide range of societal challenges. Integrating citizen science with sustainability entails leveraging public engagement in scientific research to promote sustainable practices and [...] Read more.
Citizen science is not only a participatory means of contributing to scientific knowledge but also an effective approach to addressing a wide range of societal challenges. Integrating citizen science with sustainability entails leveraging public engagement in scientific research to promote sustainable practices and advance the United Nations 2030 Agenda for Sustainable Development Goals (SDGs). The degree of public participation can influence the learning outcomes achieved. This study investigated the benefits and limitations of a co-creation citizen science approach implemented in a school context for monitoring species distribution on rocky shores, aligned with SDGs 4, 13, and 14. A mixed-methods design was applied, combining questionnaires administered to students (n = 100); participant observations of students, teachers, and researchers; and the analysis of observations submitted by one class (C2) to the iNaturalist platform. Students recorded 21 valid observations representing 13 different taxa, and developed skills such as critical thinking, problem-solving, collaboration, and interpersonal communication. They also recognised the potential of co-creation as a means of addressing scientific questions. However, teachers reported constraints in implementing the project, notably the breadth of the school curriculum and the lack of local support. This study reinforces the potential of co-creation citizen science projects to foster sustainable education. Full article
(This article belongs to the Special Issue Challenges and Future Trends of Sustainable Environmental Education)
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73 pages, 2702 KB  
Review
Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects
by Chijioke Leonard Nkwocha and Abhilash Kumar Chandel
Computers 2025, 14(10), 443; https://doi.org/10.3390/computers14100443 - 16 Oct 2025
Viewed by 90
Abstract
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing [...] Read more.
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article
13 pages, 652 KB  
Article
Sustainable Disaster Nursing Education Through Functional Exercises and Simulation: Effects on Knowledge, Problem-Solving, and Learning Outcomes
by Myongsun Cho and Miyoung Kwon
Sustainability 2025, 17(20), 9165; https://doi.org/10.3390/su17209165 - 16 Oct 2025
Viewed by 96
Abstract
The present study developed and evaluated an integrated disaster nursing education program combining functional training and simulator-based learning to address limitations of traditional, theory-driven approaches. Overall, 49 senior nursing students completed the program using a four-stage repeated-measures design. The findings indicated a substantial [...] Read more.
The present study developed and evaluated an integrated disaster nursing education program combining functional training and simulator-based learning to address limitations of traditional, theory-driven approaches. Overall, 49 senior nursing students completed the program using a four-stage repeated-measures design. The findings indicated a substantial enhancement in disaster nursing knowledge over time. However, problem-solving ability, learning self-efficacy, and motivation exhibited improvement only in post hoc comparisons. This contradictory yet fundamental finding suggests that knowledge acquisition occurs more directly, whereas problem-solving and motivational competencies require cumulative practice, feedback, and contextual immersion. Educator reflections and student debriefings further underscored the significance of teamwork, communication, and scenario relevance in facilitating learning transfer. Despite its limitations, including a single-site, female-dominated sample, reliance on self-reported measures, and a brief follow-up period, this study makes a significant contribution to the field of disaster nursing education by presenting a sustainable and adaptable model. Incorporation of multi-institutional and longitudinal designs, as well as qualitative analyses of learning processes will be crucial in future studies. This will ensure the study’s generalizability and long-term impact. Full article
(This article belongs to the Special Issue Sustainable Disaster Risk Management and Urban Resilience)
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23 pages, 507 KB  
Article
Sustainability in Education: Exploring Teachers’ Confidence in Establishing an Out-of-School Learning Environment
by Fatma Coştu and Neslihan Karakuş
Sustainability 2025, 17(20), 9160; https://doi.org/10.3390/su17209160 - 16 Oct 2025
Viewed by 172
Abstract
Outdoor learning offers dynamic, real-world educational opportunities that extend beyond traditional classrooms and foster sustainability awareness. This quantitative study endeavors to assess teachers’ competency in facilitating outdoor learning, aiming for a more engaging and impactful introduction. Employing a relational survey design in the [...] Read more.
Outdoor learning offers dynamic, real-world educational opportunities that extend beyond traditional classrooms and foster sustainability awareness. This quantitative study endeavors to assess teachers’ competency in facilitating outdoor learning, aiming for a more engaging and impactful introduction. Employing a relational survey design in the form of a multi-survey model, the research engaged 586 teachers representing diverse academic disciplines across public and private elementary and secondary schools. Central to the investigation was the utilization of the “Outdoor Learning Regulation Scale [OLRS]” as the primary data collection instrument. The evaluation of teachers’ aptitude in regulating outdoor learning encompassed various variables, including gender, subject specialization, prior online or in-person training in outdoor learning, use of non-school environments for teaching, childhood environment, and teaching location. To analyze the collected data, a nuanced approach to statistical analysis was undertaken, aiming to provide a clearer and more specific explanation of the data analysis methods employed. The findings of the study unveiled no significant disparities in teachers’ outdoor learning regulation capabilities based on gender, subject specialization, childhood environment, or teaching location. However, discernible differences surfaced in their proficiency in outdoor learning regulation concerning previous online or in-person training in outdoor learning and their utilization of outdoor environments for teaching, thus providing deeper insights into the factors shaping teachers’ efficacy in facilitating outdoor learning experiences. Additionally, the study emphasizes the link between outdoor learning and sustainability education. By equipping teachers with the skills to regulate outdoor learning, this research supports the integration of sustainability into educational practices, promoting students’ ecological awareness and sustainable thinking. These results highlight the importance of professional development and targeted training in outdoor education, with direct implications for strengthening sustainability-oriented teaching practices. Full article
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41 pages, 4704 KB  
Review
Integrative Genomics and Precision Breeding for Stress-Resilient Cotton: Recent Advances and Prospects
by Zahra Ghorbanzadeh, Bahman Panahi, Leila Purhang, Zhila Hossein Panahi, Mehrshad Zeinalabedini, Mohsen Mardi, Rasmieh Hamid and Mohammad Reza Ghaffari
Agronomy 2025, 15(10), 2393; https://doi.org/10.3390/agronomy15102393 - 15 Oct 2025
Viewed by 363
Abstract
Developing climate-resilient and high-quality cotton cultivars remains an urgent challenge, as the key target traits yield, fibre properties, and stress tolerance are highly polygenic and strongly influenced by genotype–environment interactions. Recent advances in chromosome-scale genome assemblies, pan-genomics, and haplotype-resolved resequencing have greatly enhanced [...] Read more.
Developing climate-resilient and high-quality cotton cultivars remains an urgent challenge, as the key target traits yield, fibre properties, and stress tolerance are highly polygenic and strongly influenced by genotype–environment interactions. Recent advances in chromosome-scale genome assemblies, pan-genomics, and haplotype-resolved resequencing have greatly enhanced the capacity to identify causal variants and recover non-reference alleles linked to fibre development and environmental adaptation. Parallel progress in functional genomics and precision genome editing, particularly CRISPR/Cas, base editing, and prime editing, now enables rapid, heritable modification of candidate loci across the complex tetraploid cotton genome. When integrated with high-throughput phenotyping, genomic selection, and machine learning, these approaches support predictive ideotype design rather than empirical, trial-and-error breeding. Emerging digital agriculture tools, such as digital twins that combine genomic, phenomic, and environmental data layers, allow simulation of ideotype performance and optimisation of trait combinations in silico before field validation. Speed breeding and phenomic selection further shorten generation time and increase selection intensity, bridging the gap between laboratory discovery and field deployment. However, the large-scale implementation of these technologies faces several practical constraints, including high infrastructural costs, limited accessibility for resource-constrained breeding programmes in developing regions, and uneven regulatory acceptance of genome-edited crops. However, reliance on highly targeted genome editing may inadvertently narrow allelic diversity, underscoring the need to integrate these tools with broad germplasm resources and pangenomic insights to sustain long-term adaptability. To realise these opportunities at scale, standardised data frameworks, interoperable phenotyping systems, robust multi-omic integration, and globally harmonised, science-based regulatory pathways are essential. This review synthesises recent progress, highlights case studies in fibre, oil, and stress-resilience engineering, and outlines a roadmap for translating integrative genomics into climate-smart, high-yield cotton breeding programmes. Full article
(This article belongs to the Special Issue Crop Genomics and Omics for Future Food Security)
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15 pages, 428 KB  
Article
Framework for a Smart Breeding 4.0 Curriculum: Insights from China and Global Implications
by Zhizhong Zhang
World 2025, 6(4), 139; https://doi.org/10.3390/world6040139 - 14 Oct 2025
Viewed by 299
Abstract
This study proposes a novel curriculum framework for Smart Breeding 4.0 to address the interdisciplinary talent gap in sustainable agriculture. Responding to the limitations of traditional agricultural education, the curriculum was developed through an analysis of emerging technological trends and industry needs. It [...] Read more.
This study proposes a novel curriculum framework for Smart Breeding 4.0 to address the interdisciplinary talent gap in sustainable agriculture. Responding to the limitations of traditional agricultural education, the curriculum was developed through an analysis of emerging technological trends and industry needs. It is structured around four integrated modules: (1) Foundational Theory, tracing the evolution to data-driven breeding; (2) Technology Integration, combining AI and blockchain for precision breeding; (3) Practical Innovation, using real-world platforms for simulation projects; (4) Ethics and Policy, cultivating responsibility through case studies. Teaching emphasizes project-based learning with open-source tools, while assessment combines exams, data analysis, and innovation proposals. Explicitly aligned with key UN Sustainable Development Goals (SDGs), this conceptual framework provides a foundational model for agricultural universities worldwide. The primary contribution of this paper lies in its systematic design; future research will focus on empirical validation through pilot implementation. Full article
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22 pages, 1786 KB  
Article
University Students’ Perceptions on Climate Change Awareness and Sustainable Environments Through an Unsupervised Clustering Approach
by Deniz Karaelmas, Mükerrem Bahar Başkır, Kübra Tekdamar, Canan Cengiz and Bülent Cengiz
Sustainability 2025, 17(20), 9057; https://doi.org/10.3390/su17209057 - 13 Oct 2025
Viewed by 304
Abstract
The main objective of this study is to determine the knowledge and awareness levels of climate change among preparatory class students at Zonguldak Bülent Ecevit University in the Western Black Sea Region of Türkiye using an unsupervised clustering approach. Within this scope, a [...] Read more.
The main objective of this study is to determine the knowledge and awareness levels of climate change among preparatory class students at Zonguldak Bülent Ecevit University in the Western Black Sea Region of Türkiye using an unsupervised clustering approach. Within this scope, a survey was administered to university students (n = 280). Participant scores for the survey sections containing five-point Likert-type questions on climate change awareness were calculated using min–max normalization. The normalized data was then processed using the k-means algorithm, a well-known technique in unsupervised machine learning. This resulted in a classification (clustering) related to climate change awareness. The number of clusters was determined using the Silhouette index. Three clusters identified using k-means and Silhouette index (S0.55) revealed the knowledge and application levels of student groups regarding climate change awareness. As a result of clustering, it was determined that Cluster-3 students (n = 134, 47.9%), defined as having a high level of knowledge and application, had a higher impact value in their overall assessments of green space-focused issues related to climate change awareness compared to the overall assessments of students in other clusters. Some notable findings concerning the attitudes of Cluster-3 students highlight climate change awareness-related practices. These include minimizing water consumption to levels necessary for ecosystem water management (mean = 95.7, std. deviation = 10.9) and exercising controlled, sustainable daily energy use to alleviate pressure on green spaces (mean = 94.4, std. deviation = 12.5). This study offers practical insights for policymakers, educators, and institutions, emphasizing the need to enhance climate education and to promote the active involvement of younger generations in shaping sustainable environments. Full article
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29 pages, 631 KB  
Article
Techno-Economic Evaluation of Sustainability Innovations in a Tourism SME: A Process-Tracing Study
by Natalia Chatzifoti, Alexandra Alexandropoulou, Andreas E. Fousteris, Maria D. Karvounidi and Panos T. Chountalas
Tour. Hosp. 2025, 6(4), 209; https://doi.org/10.3390/tourhosp6040209 - 13 Oct 2025
Viewed by 343
Abstract
In response to growing pressures for sustainability in tourism, this paper examines the techno-economic evaluation of green innovations in small and medium-sized tourism enterprises (SMEs). Focusing on a single case study of a hotel in Greece, the research investigates how and why specific [...] Read more.
In response to growing pressures for sustainability in tourism, this paper examines the techno-economic evaluation of green innovations in small and medium-sized tourism enterprises (SMEs). Focusing on a single case study of a hotel in Greece, the research investigates how and why specific sustainability interventions were implemented and assesses their operational and economic impacts. The study adopts an interpretivist approach, combining process tracing with thematic analysis. The analysis is guided by innovation diffusion theory, supported by organizational learning perspectives, to explain the stepwise adoption of sustainability practices and the internal adaptation processes that enabled them. The techno-economic evaluation draws on quantitative indicators and qualitative assessments of perceived benefits and implementation challenges, offering a broader view of value beyond purely financial metrics. Data were collected through semi-structured interviews, on-site observations, and internal documentation. The findings reveal a gradual, non-linear path to innovation, shaped by adoption dynamics and organizational learning, reinforced by leadership commitment, contextual adaptation, supply chain decisions, and external incentives. Key interventions, including solar energy adoption, composting, and the formation of zero-waste partnerships, resulted in measurable reductions in energy use and landfill waste, along with improvements in guest satisfaction, operational efficiency, and local collaboration. Although it is subject to limitations typical of single-case designs, the study demonstrates how even modest sustainability efforts, when integrated into daily operations, can generate multiple types of outcomes (economic, environmental, and operational). The paper offers practical implications for tourism SMEs and policymakers and formulates propositions for future testing on sustainable innovation in the tourism sector. Full article
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18 pages, 460 KB  
Article
Exploring Pre-Service Teachers’ Self-Efficacy: The Impact of Community of Practice and Lesson Study
by Kanyarat Sonsupap, Kanyarat Cojorn, Bovornpot Choompunuch, Chanat Intakanok and Chaweewan Seesom
Educ. Sci. 2025, 15(10), 1357; https://doi.org/10.3390/educsci15101357 - 13 Oct 2025
Viewed by 367
Abstract
Self-efficacy (SE) enables pre-service teachers to manage learning tasks and solve problems with confidence, which is essential for professional development and for addressing real-world teaching challenges. This study aimed to enhance SE through an integrated approach combining Lesson Study and Community of Practice [...] Read more.
Self-efficacy (SE) enables pre-service teachers to manage learning tasks and solve problems with confidence, which is essential for professional development and for addressing real-world teaching challenges. This study aimed to enhance SE through an integrated approach combining Lesson Study and Community of Practice (CoP plus LS) to better prepare pre-service teachers for classroom practice. Thirteen pre-service teachers in a teaching practicum were assigned to either an experimental group (CoP plus LS, n = 7) or a control group receiving conventional training (n = 6). A mixed-methods design was employed: SE was measured quantitatively using validated questionnaires at three time points (pre-test, post-test, and 8-week follow-up), and qualitative data were collected through semi-structured group interviews. Quantitative results showed that the CoP plus LS group demonstrated significantly greater improvement in SE compared to the control group. Within the CoP plus LS group, SE increased significantly from pre-test to post-test, with scores at follow-up remaining higher than baseline despite a slight decline. Qualitative findings revealed four themes: (1) enhanced classroom management and instructional design, (2) stronger professional identity and commitment, (3) recognition of real-world teaching challenges, and (4) growth through collaborative reflection and learning. Overall, the findings indicate that CoP plus LS effectively strengthens SE among pre-service teachers. Incorporating this approach into teacher education is recommended to enhance psychological readiness and foster sustainable professional growth. Full article
(This article belongs to the Special Issue Education for Early Career Teachers)
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31 pages, 670 KB  
Article
A Traffic Forecasting Framework for Cellular Networks Based on a Dynamic Component Management Mechanism
by Xiangyu Liu, Yuxuan Li, Shibing Zhu, Qi Su, Jianmei Dai, Changqing Li, Jiao Zhu and Jingyu Zhang
Electronics 2025, 14(20), 4003; https://doi.org/10.3390/electronics14204003 - 13 Oct 2025
Viewed by 232
Abstract
Accurate forecasting of cellular traffic in non-stationary environments remains a formidable challenge, as real-world traffic patterns dynamically evolve, emerge, and vanish over time. To tackle this, we propose a novel meta-learning framework, GMM-SCM-DCM, which features a Dynamic Component Management (DCM) mechanism. This framework [...] Read more.
Accurate forecasting of cellular traffic in non-stationary environments remains a formidable challenge, as real-world traffic patterns dynamically evolve, emerge, and vanish over time. To tackle this, we propose a novel meta-learning framework, GMM-SCM-DCM, which features a Dynamic Component Management (DCM) mechanism. This framework employs a Gaussian Mixture Model (GMM) for probabilistic meta-feature representation. The core innovation, the DCM mechanism, enables online structural evolution of the meta-learner by dynamically splitting, merging, or pruning Gaussian components based on a bimodal similarity metric, ensuring sustained alignment with shifting data distributions. A Single-Component Mechanism (SCM) is utilized for precise base learner initialisation. To ensure a rigorous and realistic validation, we reconstructed the Telecom Italia Milan dataset by applying unsupervised clustering and meta-feature engineering to identify and label four distinct functional zones: residential, commercial, mixed use, and crucially, non-stationary areas. This curated dataset provides a critical testbed for non-stationary forecasting. Comprehensive experiments demonstrate that our model significantly outperforms traditional methods and meta-learning baselines, achieving a 9.3% reduction in MAE and approximately 70% faster convergence. The model’s superiority is further confirmed through extensive ablation studies, robustness tests across base learners and data scales, and successful cross-dataset validation on the Shanghai Telecom dataset, showcasing its exceptional generalization capability and practical utility for real-world network management. Full article
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23 pages, 1149 KB  
Article
Demand Segmentation for Sustainable Adventure Destination Management: A Study of Santa Elena, Ecuador
by Miguel Orden-Mejía, Mauricio Carvache-Franco, Paola Palomino-Flores, Orly Carvache-Franco, Mónica Torres-Naranjo, Wilmer Carvache-Franco and María Alejandro-Lindao
Sustainability 2025, 17(20), 9039; https://doi.org/10.3390/su17209039 - 13 Oct 2025
Viewed by 192
Abstract
Adventure tourism has established itself as a growing sector that integrates physical activity, interaction with nature, and cultural exchange. Understanding the heterogeneity of demand is crucial for designing effective and sustainable destination management strategies. Despite the global growth of adventure tourism, there is [...] Read more.
Adventure tourism has established itself as a growing sector that integrates physical activity, interaction with nature, and cultural exchange. Understanding the heterogeneity of demand is crucial for designing effective and sustainable destination management strategies. Despite the global growth of adventure tourism, there is a scarcity of empirical studies analyzing the motivations, segmentation, and loyalty of tourists in emerging coastal destinations. This study contributes to filling this gap by providing evidence from the case of Santa Elena, Ecuador. This study examines the motivations, market segmentation, and loyalty of adventure tourists in Santa Elena, an emerging coastal destination in Ecuador. Based on a survey of 318 visitors and using exploratory factor analysis (EFA) and k-means cluster segmentation, five motivational dimensions were identified: learning, social, biosecurity, relaxation, and competence-mastery. The results revealed two distinct segments: (i) Relaxation seekers, primarily motivated by rest and stress reduction, and (ii) multi-motivation tourists, with high levels of motivation across all dimensions. This latter group showed greater loyalty, evidenced by the intention to return, recommend, and spread a positive image of the destination. The study contributes to academic knowledge by proposing a motivation-based segmentation model that integrates emerging dimensions such as biosecurity and offers practical implications for the sustainable management of adventure destinations. It recommends designing differentiated tourism products that cater to dominant motivations, thereby strengthening competitiveness and contributing to the sustainability of tourism in emerging contexts. Full article
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