Search Results (791)

Slope failures in open-pit mining pose significant operational and safety issues, underscoring the importance of implementing effective stability monitoring frameworks for early hazard detection to allow for timely intervention and risk mitigation. This systematic review presents a comprehensive synthesis of existing and emerging methods and technologies used for slope stability monitoring in open-pit mining, including both remote sensing and in situ methods, as well as advanced technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and Wireless Sensor Networks (WSNs). Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines, a total of 49 studies were selected from a collection of four engineering databases, and a comparative analysis was conducted to determine the underlying differences between the various methods for open-pit slope stability monitoring in terms of their performance across key attributes, such as monitoring accuracy, spatial and temporal coverage, operational complexity, and economic viability. Their juxtaposition highlighted the notion that no universally optimal slope stability monitoring system exists, due to a series of compromises that arise as a result of inherent technological limitations and site-specific constraints. Notably, remote sensing methods offer large-scale, non-intrusive monitoring, but are often limited by environmental factors and data acquisition infrequency, whereas in situ methods provide high precision, but suffer from limited spatial coverage and scalability. This review further highlights the capacity of emerging methods and technologies to address these limitations, providing suggestions for future research directions involving the integration of multiple sensing technologies for the enhancement of monitoring capabilities. This study provides a consolidated knowledge base on open-pit slope stability monitoring methods, technologies, and techniques, to guide the development of integrated, cost-effective, and scalable slope monitoring solutions that enhance mine safety and efficiency. Full article

This article addresses the technical, aesthetic, and strategic problem of the limited attention paid to design and selection of materials in photovoltaic system (PSS) support structures despite their direct impact on the efficiency, durability and economic viability of these systems. As the costs of modules and electronic components continues to decrease, the structural elements acquire greater weight in the total cost and long-term performance. Our research comprehensively analyzes the mechanical, environmental, and regulatory factors influencing material selection and structural design in PV mounting systems. The PRISMA methodology was used to perform a systematic review of 122 articles published between 2018 and 2025, which were classified along two axes: materials (mild steel, galvanized steel, aluminum, polymers, and composites) and structural design (angle, orientation, loads, support typology, and adaptation to the environment). The results show that an adequate match between design and climatic conditions improves system stability, efficiency, and service life. With the support of digital modeling and advanced simulations, we identify trends towards modular, lightweight, and adaptive solutions, particularly in architectural applications (BIPV). This work provides a robust and contextualized technical framework that facilitates informed decision-making in solar energy projects, with direct implications for the sustainability, structural resilience, and competitiveness of the PSS sector in different geographical regions. Full article

Background/Objectives: Colorectal (CRC), breast (BC), and cervical cancer (CC) pose a significant health burden, yet screening programs have been proven to reduce cancer-specific mortality and other non-lethal endpoints. Mobile health (mHealth) technologies can enhance adherence, but effectiveness varies. This scoping review aims to explore mHealth apps for cancer screening developed with community engagement, identifying research approaches and gaps. Methods: A scoping review following PRISMA-ScR guidelines analyzed studies on mHealth apps for cancer screening developed through community engagement. Community engagement was classified per WHO’s definition. Databases were searched using a PCC-based strategy; eligible studies involved app development, excluding hypothetical apps or text messaging-/social media-only interventions. Screening and data extraction were conducted independently. Results: Thirteen articles were included. Findings indicate a growing but limited body of evidence, with most studies focusing on CRC and BC and involving minority populations through mHealth apps. Key engagement phases included research design, CAB establishment, and recruitment, while priority setting was never community-led. The wMammogram, Meet ALEX, and mMammogram apps improved screening knowledge, intention, and participation, while ColorApp enhanced knowledge but not attitudes. Only CBPR-based studies included dissemination, and one involved the CAB in data analysis. Some studies acknowledged community contributions, though details on ColorApp’s engagement were limited. Conclusions: Standardized engagement frameworks combined with mHealth were associated with greater community involvement and may improve equity. No community-designed mHealth app was found for CC screening, despite its relevance. Future research should address gaps in CC programs, prioritize early community involvement, and assess the long-term impact of mHealth interventions. Full article

The process of institutional accreditation establishes crucial mechanisms that lead to better quality childcare in orphanages through the development of organizational stability and trained staff, in addition to healthcare improvements. The assessment of accreditation effects on children’s health draws from learning organizations and quality assurance frameworks in this systematic review. A systematic database review yielded 35 peer-reviewed publications that followed PRISMA analysis procedures. Research evidence shows that accredited orphanages attain better results when it comes to hygiene practices, as well as nutrition standards, healthcare access, mental healthcare support. Accreditation enables institutions to learn continuously because the process promotes service delivery improvements. The advantages of accreditation in orphanages are clear, but accreditation faces the barriers of monetary constraints, employee reluctance towards external inspections, and erratic policy execution, which reduce its widespread adoption. Accreditation efforts in orphanages require purposeful funding alongside built-up staff competencies and stronger regulatory policies to achieve their maximum potential benefit. Full article

Governance plays a critical role at the intersection of disaster risk management (DRM) and climate change (CC). As CC increases the frequency and intensity of disasters, so DRM policies must consider the potential impacts of CC and integrate climate resilience measures. Over the past decade, extreme wildfires in wildland–urban interface (WUI) areas have left devastating effects for local economies, local development, environmental protection, and the continuity of government operations worldwide, prompting all actors to work in the same direction to face its changing context. This systematic review of the literature aims to analyze the research trends on wildfire risk governance in WUI areas during 2021–2024 and to identify the key risk governance determinants, thereby offering a robust foundation to guide technical discussions and support decision-making processes in local development planning, land use regulation, and DRM. The study is based on the application of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration to allow the identification, selection, analysis, and systematization of 68 articles from the Scopus database through three bibliographic search equations, which were then categorized using the software of text mining and natural language processing NLP software (VantagePoint 15.2) to identify four key pillars that structure extreme wildfire risk governance: political management, development planning, disaster risk management, and resilience management. Within this framework, ten governance determinants are highlighted, encompassing aspects such as regulatory frameworks, institutional coordination, information systems, technical capacities, community engagement, risk perception, financial resources, accountability mechanisms, adaptive planning, and cross-sectoral integration. These findings provide a conceptual basis for strengthening governance approaches in the face of increasing wildfire risk. Full article

The current study aims to explore the early adoption of the metaverse in the hospitality and tourism industry, with a particular focus on understanding adoption drivers for tourism providers and the anticipated benefits for end-users. Addressing the need for practical insights in a rapidly evolving digital landscape, the review identifies 33 real-world early cases of metaverse and non-fungible token (NFT) adoption, spanning from the first metaverse hotel in 2006 to ongoing digital twins of tourism destinations up to 2023. This study follows the PRISMA technique and examines early metaverse adopters at both the enterprise and destination levels, categorizing them into metaverse-based, NFT-based, and complementary-to-tourism cases. The analysis applies two theoretical frameworks: the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine adoption drivers and the Technology Acceptance Model (TAM) to assess expected end-user benefits. Key findings highlight promotion, brand engagement, new revenue streams, and community building as primary motivations for adoption, while user benefits include enhanced entertainment, social interaction, improved decision-making, and immersive experiences. By bridging theory and practice, this study contributes both actionable guidance for tourism stakeholders and a theoretical foundation for future research on digital transformation in tourism. Full article

Background/Objectives: The use of rehabilitation interventions is critical in addressing health and economic outcomes, including return to work (RTW) facilitation for individuals with disabilities. However, the economic evaluation of these interventions has been found to lack consistency, with limited adherence to reporting standards and little focus on integrated approaches. This scoping review will map the existing evidence on the economic evaluations of rehabilitation interventions and their implications for return to work (RTW) programs. Methods: A systematic search of databases, such as PubMed, Scopus, and Web of Science, to identify studies that provided full economic evaluations of rehabilitation interventions related to RTW. Using the PRISMA-ScR framework, 11 studies were ultimately included. Data extraction included the model type, cost-effectiveness models, adherence to CHEERS reporting guidelines, and implications for RTW. Results: The majority of studies examined medical or psychological interventions, with little representation of vocational or integrated approaches. ICERs differed greatly between studies based on methodologies and healthcare settings. The reporting of heterogeneity, uncertainty analysis, and societal perspectives were some of the gaps identified from adherence to CHEERS guidelines. Conclusions: Economic evaluations show that rehabilitation interventions can be cost-effective for improving RTW outcomes. Future research priorities include interdisciplinary approaches, standardized methodologies, and studies based on LMICs to address global disparities. Full article

E-leadership has become particularly prominent in the public sector over the last five years. The urgent shift requires more remote work and management via information and communication technologies. In recognition of its ever-growing popularity, the objective of this article is to provide a systematic review of the existing literature on e-leadership within public sector organisations and identify key research approaches and outcomes. The analysis provides a framework of research on e-leadership in public sector entities, by focusing on various theoretical, methodological, empirical and contextual perspectives, specifically tailored to public sector organisations. The detailed framework, presented here, incorporates dimensions, approaches, clusters and findings of previous research (articles, published in the period 2013–2022), aiding a deeper understanding of the phenomenon and its practical implementation. The study complies with PRISMA 2020 requirements. As it shall be demonstrated, interest in the phenomenon surged during the pandemic, particularly within educational and management disciplines. The findings highlight a predominant focus on leadership within educational institutions, whilst areas such as healthcare and public governance remain under-researched. The most common theoretical approach adopted is associated with the transformational leadership theory and encompasses three main interpretations. Specifically, it evaluates e-leadership as a strategic approach, a leadership process and a leadership transformation. Qualitative methodology predominates in e-leadership research within the public sector, with quantitative and mixed-method approaches being less frequent. Addressing the challenges identified by previous research, such as competency and infrastructure deficiencies, is crucial for advancing knowledge of e-leadership in the public sector and improving sustainable performance. Full article

The rapid advancement of artificial intelligence (AI) and digital transformation is reshaping labor markets, emphasizing creativity as a core competency in entrepreneurship education. Large Language Models (LLMs) provide personalized learning experiences through natural language processing (NLP), enhancing real-time feedback and problem-solving skills. However, research on how LLMs foster creativity in entrepreneurship education remains limited. This study analyzed the technical characteristics and educational impact of LLMs, focusing on their applications in entrepreneurship education and their role in fostering creativity-driven learning environments. Specifically, it explores the educational effects of LLMs, their integration into entrepreneurship education, and the ways in which they enhance learners’ creative thinking. A systematic literature review using the PRISMA methodology was conducted to analyze existing studies. Findings suggest that LLMs improve self-efficacy, cognitive engagement, and creative problem-solving, supporting entrepreneurship education in areas such as business model development, market analysis, and multicultural communication. Despite these benefits, concerns remain regarding over-reliance, ethical risks, and the need for critical thinking frameworks. This study proposes a hybrid model integrating LLMs with traditional pedagogies to maximize creativity. Future research should explore long-term effects, cross-cultural applications, and ethical challenges to ensure responsible implementation. Full article

Cyberbullying is a pervasive social issue that has gained increasing attention due to its serious consequences, such as cyberbullicide, which is suicide resulting from cyberbullying. Researchers have called for various interventions and the involvement of multiple stakeholders, including psychologists and law enforcement agencies, to address the problem. Recent studies have shown that the majority of cyberbullying incidents occur on mobile devices, referred to as mobile bullying. Therefore, there is a need to focus intervention efforts on mobile bullying, which is a subset of cyberbullying. The objective of this study was to develop a framework that could guide the creation of an awareness intervention aimed at raising awareness of mobile bullying. Although several meta-analyses have been conducted over the years on intervention effectiveness. There is a gap from the lack of frameworks and requirements to guide the development of awareness interventions. As such, a PRISMA (preferred reporting items for systematic reviews and meta-analyses) systematic literature review was conducted to identify the theories that could inform the development of such an intervention. Based on the review, a theoretical framework was developed that incorporated the basic elements identified as guiding principles for the development of awareness interventions. In the same way, theory, specifically SAT was used to inform the development of an instrumental framework for designing context-sensitive awareness interventions, incorporating the basic elements that practitioners can use to develop context-sensitive awareness interventions. The findings from the study indicate that the social context, which encompasses the social issue at hand; available media content; the proximal and distal environments; and the choice of awareness tool, which is informed by the marketing mix (i.e., price, product, people, promotion, and place), affect the reach or distribution of the intervention. The theoretical framework contributes to the body of knowledge on the subject, while the instrumental framework provides a practical approach to building customizable interventions that can be tailored to specific contexts and available resources for awareness. Full article

Due to ongoing climate change, urban areas face increasing challenges associated with rising temperatures and growing energy demand. Green roofs have emerged as a sustainable, nature-based solution to enhance urban resilience. This study presents a systematic review of the thermal and energy performance of green roofs in the Mediterranean region, and was conducted following the PRISMA framework. By identifying targeted research questions formulated using the PICO(C) structure, this review systematically evaluates the potential of green roofs to promote sustainable urban environments in Mediterranean regions. The findings highlight their effectiveness in mitigating heat stress, enhancing building energy efficiency, and counteracting urban temperature fluctuations, reinforcing their role as a key climate adaptation strategy in densely populated areas. The review also identifies critical research gaps that must be addressed to facilitate the large-scale adoption of green roofs. Specifically, the lack of long-term performance monitoring, the need for standardized assessment protocols, and the necessity of optimizing green roof configurations for Mediterranean subregions emerge as key areas for future investigation. This study bridges a crucial gap in the literature by providing a systematic, PRISMA-compliant evaluation. It offers the scientific community a robust knowledge base to inform policy, urban planning, and future research directions. Full article

COVID-19 was one of the most serious global public health emergencies in recent years, and its extremely fast spreading speed had a profound negative impact on society. A comprehensive analysis and prediction of COVID-19 could lay a theoretical foundation for monitoring and early warning systems. Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and capturing complex nonlinear transmission patterns. We systematically reviewed COVID-19 ML prediction models developed under the background of the epidemic using the PRISMA method. We used the selected keywords to screen the relevant literature of COVID-19 prediction using ML technology from 2020 to 2023 in the Web of Science, Springer and Elsevier databases. Based on predetermined inclusion and exclusion criteria, 136 eligible studies were ultimately selected from 5731 preliminarily screened publications, and the datasets, data preprocessing, ML models, and evaluation metrics used in these studies were assessed. By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. In addition, we compared the performance of ML models with other models in the COVID-19 prediction task. The results showed that the propagation of COVID-19 is affected by multiple factors, including meteorological and socio-economic conditions. Compared to traditional methods, ML methods demonstrated significant advantages in COVID-19 prediction, especially hybrid modelling strategies, which showed great potential in optimizing accuracy. However, these techniques face challenges and limitations despite their strong performance. By reviewing existing research on COVID-19 prediction, this study provided systematic theoretical support for AI applications in infectious disease prediction and promoted technological innovation in public health. Full article

Accurate hyperspectral simulations are critical for the vicarious calibration of next-generation space-based sensors and for ensuring the long-term consistency of climate data records. This study presents a refined methodology to generate simulated radiometric calibration references over bright desert pseudo-invariant calibration sites, specifically designed to meet the stringent accuracy requirements of hyperspectral observations. Building on metrology principles, and in the absence of SI-traceable references, the approach leverages simulated reflectance over stable desert targets as a community-accepted calibration reference. Key advancements include improved surface reflectance modelling using the Rahman–Pinty–Verstraete model and the Combined Inversion of Surface and Aerosol (CISAR) algorithm, enhanced atmospheric property characterization from multiple state-of-the-art datasets, and the use of the Eradiate Monte Carlo-based radiative transfer model. These refinements reduce uncertainty in simulated top-of-atmosphere reflectance, achieving an accuracy within ±3% in high-transmittance spectral regions. Validation against both multispectral and hyperspectral satellite data (i.e., EMIT, EnMAP, and PRISMA) confirms the robustness of the methodology. This work establishes a reliable framework for hyperspectral sensor calibration and intercalibration, addressing the pressing need for traceable, high-fidelity reference data in Earth observation. Full article

This systematic literature review investigates the recent applications of artificial intelligence (AI) in supply chain management (SCM), particularly in the domains of resilience, process optimization, sustainability, and implementation challenges. The study is motivated by gaps identified in previous reviews, which often exclude literature published after 2020 and lack an integrated analysis of AI’s contributions across multiple supply chain phases. The review aims to provide an updated synthesis of AI technologies—such as machine learning, deep learning, and generative AI—and their practical implementation between 2021 and 2024. Following the PRISMA framework, a rigorous methodology was applied using the Scopus database, complemented by bibliometric and content analyses. A total of 66 studies were selected based on predefined inclusion criteria and evaluated for methodological quality and thematic relevance. The findings reveal a diverse classification of AI applications across strategic and operational SCM phases and highlight emerging techniques like explainable AI, neurosymbolic systems, and federated learning. The review also identifies persistent barriers such as data governance, ethical concerns, and scalability. Future research should focus on hybrid AI–human collaboration, transparency through explainable models, and integration with technologies such as IoT and blockchain. This review contributes to the literature by offering a structured synthesis of AI’s transformative impact on SCM and by outlining key research directions to guide future investigations and managerial practice. Full article

The long-term sustainability of seaports depends on various operational factors, including infrastructure efficiency, digital innovation, environmental management, and regulatory compliance, among which maritime pilotage plays a crucial role in ensuring safe navigation and minimizing environmental, economic, and social risks. This research employed the PRISMA-ScR framework to evaluate the environmental, economic, and social impacts of pilotage on the sustainability of seaports. The findings demonstrate efficient navigation and spill avoidance, which reduce emissions, safeguard marine biodiversity, and maintain water quality. Economically, it reduces delays, optimizes operational expenses, and increases port competitiveness by increasing maritime traffic. Moreover, pilotage improves navigational safety, local professional skill development, and community interactions via ecological conservation and operational efficiency. It also indicates how environmental initiatives benefit the economy, increase port competitiveness, and promote job security and community happiness. The results also emphasize the significance of pilotage in sustainable seaport operations by quantifying pollution reductions, cost savings, and safety. The result also suggests that successful pilotage enhances ports’ viability and responsibility in global shipping networks while addressing environmental, economic, and social concerns. Full article