Search Results (7,057)

Purpose: With the advent of sixth-generation (6G) communication technology, systematic mapping of its use cases to associated technical requirements has become essential for accelerating standardization, guiding R&D investment, and informing policy formulation. Methods: This study consolidated 65 use case scenarios from key academic and institutional 6G sources into 21 representative cases. A three-round Delphi-based expert assessment, employing a five-point Likert scale and interquartile-range-based consensus monitoring, was used to assign primary and secondary technical requirements across six core dimensions: immersive communication, massive communication, hyper-reliable low-latency communication, integrated sensing and communication, integrated artificial intelligence and communication (IAAC), and ubiquitous connectivity. A three-dimensional (3D) prism-based visualization framework was subsequently developed to represent the interdependencies among these requirements. Results: IAAC and massive communication emerged as the most critical requirements, each functioning as a primary or secondary driver across most use cases. The prism framework revealed hierarchical and complementary relationships among the six dimensions that conventional 2D wheel diagrams cannot adequately capture. Furthermore, a nine-criterion multidimensional classification framework, encompassing data transmission mode, decision-making mode, communication flow, interaction type, device type, deployment type, human activity innovation, user type, and personalization level, was developed, offering industry-specific guidance for service design. Collectively, the proposed framework supports user-centric design, informs strategic technology planning, and contributes to policy development while acknowledging existing limitations in quantitative mapping and economic analysis. Full article

In the late 1990s, there was much speculation on what music and music education would look like at the beginning of the 21st century. Few predicted the level of change that we have witnessed since then. In fact, developments in technologies, demographics, societies and global relations that have taken place in the world over the past 100 years would have been neigh unimaginable decade by decade, and keep coming with ever-increasing intensity. Travel, trade and technology have connected people and cultures in myriad and often wonderful ways. But inequities, divisions, and conflicts also reached new heights, with the first half of the 2020s subject to a seemingly endless stream of natural and manmade disasters and conflicts. Inevitably, all of these developments impacted on the world of music in general, and also on music education. In this essay, I try to summarise some key experiences and observations of my own first fifty years of living musical diversity (a world that started to open before me when I began learning Indian sitar in Amsterdam in 1975), and efforts across five continents that I have been involved in or researched. Juxtaposing this with key literature on the topic this provides a broad basis for presenting ideas and views on progress towards giving musical practices from across the globe an appropriate place in music education at all levels: in community settings, schools, and institutions for professional training of performers and educators. In that process, I identify three critical junctures which can simultaneously present obstacles and opportunities for positive change: (1) terminologies, social inclusion, and the politics of diversity; (2) musical dynamics, technology, and institutional change; and (3) evolutions and revolutions in music learning and teaching. These inform a challenging but clear agenda for scholars, policy makers, institutional leaders, practising musicians and music educators worldwide who strive for more inclusive, diverse, equitable and relevant practices. Full article

Background: ChatGPT is a large language model (LLM) online chatbot developed by OpenAI and launched in November 2022. Early adoption studies have shown high readiness to use this technology for health-related questions and self-diagnosis. However, the quality and clinical adequacy of health-related responses remain incompletely characterized. This study aimed to explore responses generated by ChatGPT-3.5 and ChatGPT-4.0 to common patient questions regarding scoliosis. Methods: Ten scoliosis-related frequently asked questions (FAQs) were selected from a larger pool of over 250 patient-facing questions compiled from 17 publicly available FAQ webpages and informed by a Google Trends analysis. Questions were harmonized, grouped by theme, and then reduced by rule-based expert review to a final set intended to represent common patient concerns. Results: The median ratings of ChatGPT-3.5 and ChatGPT-4.0 responses ranged from satisfactory, requiring minimal (2) to moderate clarification (3). Across the ten matched questions, no statistically detectable difference was found between models in this study setting (W = 8.0, p = 0.59; Cliff’s δ = −0.12 [95% CI −0.58, 0.40]); however, given the small question set, unblinded rating process, and poor inter-rater reliability, this should not be interpreted as evidence of equivalence, non-inferiority, or comparable model performance. The results apply only to the 10–15 April 2024, online snapshots of ChatGPT-3.5 and ChatGPT-4.0 and should not be generalized to later model iterations. Conclusions: This study should be interpreted as a clinically oriented observational report, intended to inform physician awareness and patient-physician communication rather than validate chatbot accuracy or safety. In this 10–15 April 2024, sample, both model outputs frequently required clinician clarification. Given the small FAQ set, low inter-rater reliability, unblinded design, and single-sample outputs, the findings do not establish equivalence or superiority and apply only to the specific 10–15 April 2024, model snapshots and evaluated questions. Full article

Achieving urban climate neutrality and interim mitigation targets requires rapid demand-side emission reductions, yet current user-centred interventions remain fragmented, are often concentrated on low-impact actions, and rarely provide a traceable basis for comparing outcomes, validity conditions, and equity implications across contexts. This paper reframes demand-side mitigation as a design problem of “use infrastructures”: integrated configurations of communication, product-technology, services, interaction, and governance that make low-carbon choices practicable within everyday routines. We introduce the Design Evidence Link (DEL) as a traceability device supporting ex ante configuration (selection and orchestration of levers) and ex post verification (monitoring, attribution of outcomes, and trade-off control). Through a design-led comparative analysis of nine international cases in high-impact sectors (household energy, ground mobility, food systems, and circular economy/materials), we derive and consolidate a shared extraction and coding protocol that links determinants (barriers and enablers) to design requirements and decision-grade metrics (carbon impact, adoption, continuity, and equity), explicitly qualifying uncertainty and evidence levels. Cross-case results show that effective interventions rely less on isolated information and more on coordinated action packages that reduce cognitive and economic frictions, enhance data credibility through standards and accountability, and embed follow-up mechanisms that support behavioural continuity. DEL also surfaces recurring validity conditions and failure modes (digital exclusion, trust erosion, rebound, and lock-in), translating them into operational criteria for policy and design. Compared with behaviour-change or theory-of-change framings, DEL focuses on the observable orchestration of integrated conditions of use and on the explicit grading of evidence. It should therefore be read as a structured analytical–operational framework for ex ante and ex post assessment, whose transferability remains conditional on source quality, contextual prerequisites, and the limits of the selected cases. Full article

This study examines how university type (public vs. private) and disciplinary background influence the adoption of Information and Communication Technologies (ICT) and self-perceived digital competence among university professors in Latin America. Identifying institutional and disciplinary disparities is essential in the context of accelerated digital transformation in higher education. The sample comprised 1114 professors from public and private universities, and data was collected using a validated instrument measuring ICT valuation, frequency of use, and perceived digital competence. Multivariate analyses were conducted to assess differences by institutional type and disciplinary field. The results show significant differences in ICT valuation, usage frequency, and perceived digital competence across university types and disciplines. Professors from private universities reported higher digital preparedness, while disciplinary areas displayed distinct ICT adoption patterns. Although ICT use increased across all groups during the pandemic, the digital gap between public and private institutions narrowed but was not fully eliminated. These findings support the development of targeted professional training, strategic resource allocation, and institutional policies, particularly in public universities, to enhance digital competence and promote sustainable ICT integration, contributing to educational equity and progress toward Sustainable Development Goals. Full article

Latin America faces growing challenges in the management of municipal solid waste (MSW). This is particularly evident in medium-sized and metropolitan cities where rapid urbanization, limited infrastructure, and high proportions of organic waste (40–70%) converge. This review synthesizes the most recent advances in organic waste management, valorization strategies, environmental performance, and policy frameworks in Mexico and Latin America. To provide a comprehensive overview, evidence from studies on informal recycling systems, route optimization, sustainable landfill siting, food waste valorization, life cycle assessments (LCAs), and biogas production is integrated. Techno-economic analyses of energy recovery from organic fractions are specifically reviewed. This review highlights that valorization of organic waste through composting, anaerobic digestion, food supplementation, and bioproduct generation can reduce greenhouse gas emissions by 40–70% compared to landfilling, with AD–composting hybrids achieving the highest reductions of 60–70%. Community composting achieved moderate reductions, 30–50%, but at significantly lower cost and with greater social co-benefits. These alternatives for valorizing the organic fraction extend the lifespan of both confined and open landfills. It also contributes to mitigating the public health impacts related to open dumping, disease vectors, and contaminated leachate. In short, this review also highlights shortcomings in policy coherence, financial mechanisms, source separation, and technology adoption. A strategic framework is proposed that prioritizes decentralized treatment systems, the integration of informal recyclers, tax incentives, community-based waste separation, and planning based on Life Cycle Assessment (LCA). The findings point to a viable strategy for transitioning from landfill dependency to circular waste management systems that improve the quality of life for the population of Latin America and the Caribbean. Full article

Background: The integration of artificial intelligence (AI) into lung cancer screening offers significant potential; however, public adoption of AI-assisted lung cancer screening remains inconsistent and poorly understood. A robust understanding of the psychological and social determinants underlying adoption is critical to inform evidence-based implementation strategies. Objective: This study aims to identify the key factors that influence the public’s Behavioral Intention (BI) to adopt AI-assisted lung cancer screening. We built on the Unified Theory of Acceptance and Use of Technology (UTAUT) and integrated Doctor–Patient Trust and Perceived Risk into the framework to examine the associations between these medically specific factors, together with traditional adoption variables, and the public’s BI. Methods: A cross-sectional survey was conducted among 971 residents in China from September to November 2025. Based on the extended UTAUT, a measurement instrument was developed and refined through expert consultations and pilot testing. Exploratory factor analysis (EFA) was performed to validate the questionnaire’s construct validity. Hypothesis testing was then carried out via Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement model and examine the structural relationships among latent constructs. Results: EFA results indicated a seven-factor structure (KMO = 0.897, p < 0.001). The structural model accounted for 35.0% of the variance in BI. Social Influence (β = 0.292, p < 0.001), Facilitating Conditions (β = 0.156, p < 0.001), Performance Expectancy (β = 0.101, p = 0.004), Doctor–Patient Trust (β = 0.107, p = 0.002) were positively associated with BI, while Perceived Risk (β = −0.106, p < 0.001) showed a negative association. Furthermore, Doctor–Patient Trust was significantly and negatively associated with Perceived Risk (β = −0.168, p < 0.001), suggesting a potential mediating pathway from trust to intention (Indirect Effect = 0.018, p = 0.003). Conclusions: Adoption of AI-assisted lung cancer screening appears to be associated not only with perceived utility but also with trust in medical professionals and Perceived Risk. These findings suggest the importance of integrating technological innovation with strategic public education and tailored communication strategies to foster its adoption. Public health interventions should leverage physician endorsements and promote AI awareness to support informed, trust-based engagement with AI technologies. Full article

As a key technology for wireless signal identification, modulation recognition plays an important role in the fields of unmanned aerial vehicle (UAV) communications, low-altitude spectrum management, etc. However, the accuracy of modulation recognition often cannot be guaranteed in scenarios with serious noise interference when a few samples are available. In this paper, we propose an intelligent modulation recognition method for UAV signals based on small-sample augmentation and soft threshold denoising. We first propose a new dual-driven dataset expansion method by combining the UAV air–ground channel propagation model with the received data samples. Then, we construct a background learning-based long short-term memory (BL-LSTM) model to extract the environmental background features embedded in the UAV signal, including Line-of-Sight (LoS) state, multi-scale fading parameters and Doppler shift characteristics. We integrate environmental background information into the data training model and optimize the authenticity of data distribution. As a result, the model adaptability can be enhanced. Finally, we construct a deep residual shrinkage network based on the soft threshold function (STF-DRSN). By leveraging the capability of the soft threshold that resists noise interference, we integrate it into each residual block of the deep residual shrinkage network. Simulation results show that compared with the state of the art, our method can improve the modulation recognition accuracy of UAV signals in small-sample scenarios. Full article

After major disruptive events, particularly natural and human-made disasters, community leaders face the challenge of rebuilding societal infrastructure and managing the allocation of funds, which can affect the duration of recovery periods. Decision-makers must quickly determine how to allocate financial resources while minimizing population distress. Conventional methods of assessing damage and evaluating relief requirements fall short of meeting the urgent recovery needs after a disaster, potentially leading to negative effects on communities, such as involuntary relocation and neighborhood gentrification. The study evaluates current methods and technologies to propose a new approach that leverages low-cost consumer drones and modern image analysis techniques to support initial damage assessments and track recovery progress, thereby promoting the dynamic allocation of limited resources. Using low-cost drone imagery enables rapid, cost-effective data collection and dynamic analysis through iterative reviews during the disaster response and recovery phases that can adjust baseline disaster funding allocations. The study investigates the potential of temporary blue tarp roofs (“blue roofs”) as a metric for recovery progress during the 2020 tornado in Middle Tennessee and conducts an R-squared and error analysis. The goal of this research is to evaluate an affordable and efficient data analysis method (e.g., modern image analysis; artificial intelligence; low-cost drones) that can improve post-disaster resource allocation and inform decision-making for governmental and planning officials. Full article

Artificial intelligence (AI) is increasingly embedded in banking risk management, yet academic research on this topic remains conceptually fragmented and dispersed across multiple disciplines. This study examines global publication trends and thematic structures related to AI applications in banking risk management through a bibliometric analysis of 83 peer-reviewed articles indexed in the Web of Science Core Collection for the period 2020–2024. The analysis was conducted using Bibliometrix (R-package, version 4.1), its web interface Biblioshiny (2024 release), to evaluate publication dynamics, citation performance, authorship patterns, and thematic clusters. Results show a substantial rise in scientific interest, with annual publication growth of 41.4% and international co-authorship reaching 30%. Five major thematic clusters were identified, including AI-enabled credit risk assessment, fraud detection, operational and cyber-risk mitigation, FinTech adoption, and regulatory compliance. Approximately 30% of the articles appeared in the top ten journals publishing on the topic, and the dataset recorded more than 3800 cited references. The findings indicate that AI contributes to enhanced predictive accuracy, real-time anomaly detection, and supervisory efficiency in banking risk management, while persistent challenges relate to model transparency, data quality, and regulatory adaptation. This study offers a systematic, data-driven understanding of the intellectual landscape and research evolution of AI-driven banking risk management from 2020 to 2024. Full article

Most empirical research on drinking water governance and regional transformations in Bangladesh has focused on developed and urban regions. We focus on coastal rural areas to address this gap and employ a three-step methodology for the systematic analysis of the broader and site-specific drinking water landscape, involving (i) stakeholder analysis and mapping to identify key actors and characteristics, (ii) field work to identify case study sites, meet stakeholders and identify local water technologies, and (iii) in-depth analysis, triangulating stakeholders, drinking water technologies and governance structures, to identify synergies, differences, gaps and overlaps within them. Taking four different but illustrative case study areas in the Southwestern coastal region of Bangladesh, we identify key stakeholders and capture the multiplicity and dynamism of governance models. We provide the first in-depth mapping of the drinking water landscape in coastal rural Bangladesh. Results reveal a fragmented rural water governance landscape, marked by failed or non-functional technologies due to disputes, poor maintenance, high costs, and lack of accountability after project completion, leaving communities to seek alternative water resources for themselves. We argue that informal water provision currently needs to exist beyond well-known governance models due to system inefficiencies and short legacies of implemented technologies. As the water deficit is predicted to increase, it is imperative to identify the complexities and inefficiencies in governance structures to construct collaborative, sustainable approaches to ensuring drinking water security. Full article

The construction industry in South Africa faces challenges with the current payment system used to manage progress payments. Contractors often experience delays in progress payments for completed works. These late payments stem from the improper management of progress payment procedures, namely, information, communication, and collaboration, as well as corruption. This study proposes the integration of common data environment (CDE) as it has emerged central in managing information, improving communication and collaboration in a transparent manner. However, the implementation of CDE is facing challenges in the industry. Therefore, the study aimed at developing a model based on the implementation of CDE to uphold efficiency in the management of payment systems for progress payments. A systematic review was conducted to examine the enabling factors, characteristics of CDE in managing progress payment challenges, and benefits of integrating a payment system in a CDE platform. Furthermore, the study utilised questionnaire surveys to purposively collect data from construction professionals who implemented CDE in their projects. From 201 valid questionnaire responses, a structural equation model was developed; testing for the reliability, validity, model fit, and hypotheses was conducted using AMOS and ADANCO. The findings revealed that enabling factors such as quality technology and quality assurance team are the strongest enablers, followed by training and policy. The findings further predict that CDE integration will improve the management of the payment system by 0.589. The study provides theoretical and practical guidance for researchers, policy makers, and construction professionals seeking to strengthen CDE-based payment system frameworks in South Africa. Furthermore, it is recommended to adopt the method of questionnaire surveys and SEM to validate variables and establish their influence on one another to improve generalisation. Full article

Multispectral object detection addresses the limitations of single-modal approaches by fusing complementary information from visible and infrared images, thereby improving robustness in complex environments. However, the inter-modal representations are inherently misaligned due to sensing discrepancies, and the complementary cues they provide are often imbalanced, making it difficult to exploit modality-specific information effectively. Moreover, directly merging features from different modalities can introduce noise and artifacts that deteriorate the detection performance. To this end, this paper proposes a patch-aware enhancement and fusion network for multispectral object detection (PMDet). This method employs a dual-stream backbone equipped with the patch-aware Feature Enhancer (FE) module for cross-modal features alignment and enhancement. FE not only reinforces the feature representation of key regions but also helps to suppress local noise and enhance the model’s perception of fine textures and differences. Building on these enriched features, the patch-based Feature Aggregator (FA) module allows for efficient inter-modal feature interaction and semantic fusion with noise resistance. Specifically, both FE and FA modules leverage the shifted-patch design to preserve computational efficiency while enabling long-range modeling. In this regard, PMDet couples multi-scale cross-modal semantic enhancement with deep semantic fusion to form a stable and discriminative multimodal representation pipeline. Experiments on FLIR, LLVIP, and VEDAI demonstrate that the method outperforms mainstream approaches in detection accuracy and robustness, and ablation studies further verify the effectiveness of each module. Full article

In the post-pandemic higher education context, multilingual students, particularly those from widening participation backgrounds, continue to face academic, linguistic, and socio-emotional challenges that can limit their participation and sense of belonging. This study examines the use of Reflective Video Diaries (RVDs) facilitated through Microsoft Flipgrid as an inclusive pedagogical approach to support reflective engagement, communication, and socio-emotional development among multilingual undergraduate students. Adopting a qualitative iterative action research approach, the study was conducted within a UK university module and involved three cycles of implementation, reflection, and pedagogical refinement, capturing students’ lived experiences rather than measuring causal effects. Multiple methods, including RVDs, end-of-module reflective reports, an anonymous survey, and lecturers’ field notes, were deliberately combined to provide complementary perspectives on students’ experiences, allowing triangulation of data and enhancing the validity and richness of findings. Thematic analysis of this longitudinal dataset collected across the three action-research cycles explored how students experienced RVDs as a space for reflection, peer support, and engagement with learning. Findings indicate that Flipgrid-mediated RVDs functioned as a low-anxiety, flexible, and dialogic learning environment that enabled students to articulate challenges, share progress, and develop reflective awareness, confidence, and a sense of connection with peers and lecturers. Improvements in participation and reflective depth were more evident in later cycles, suggesting that benefits emerged through iterative pedagogical adjustment rather than by video technology alone. Both positive experiences and challenges are reported, providing a balanced account of engagement with the RVDs. The study underscores the potential of inclusive digital pedagogies to inform curriculum planning and policy implementation, supporting equitable learning opportunities and socio-emotional development. By conceptualizing RVDs as relational and inclusive pedagogical practices rather than technological interventions, and by demonstrating how reflective engagement developed across successive action-research cycles, this research contributes to understanding how reflective digital practices can support multilingual learners’ academic and socio-emotional development within socially just higher education contexts. Practical implications for designing inclusive reflective learning environments are discussed. Full article

To enhance the measuring capabilities of modern Non-Destructive Evaluation (NDE) devices, it has become essential to integrate standardized digitization services and industry-compliant functionalities. This perspective paper examines approaches for improving NDE systems by incorporating key Industry 4.0 technologies, specifically digital representations such as the Asset Administration Shell (AAS) and OPC UA (Open Platform Communications Unified Architecture). We discuss requirements for interoperable, semantically rich descriptions of NDE systems, outline how OPC UA information models and AAS submodels can be combined with MQTT-based transport, and illustrate these concepts through representative prototype implementations, including predictive maintenance and chatbot assistant use cases. By leveraging these technologies, NDE devices can be transformed into interoperable, data-rich, and intelligent components within smart industrial ecosystems. Compared with previous studies, this Perspective is the first to systematically bring together the requirements, architectural patterns, and evaluation criteria for digital representations designed specifically for NDE systems. It also provides, in a practical and accessible way, NDE-focused OPC UA and AAS-based architectures that support both predictive maintenance and LLM-assisted operator guidance. The presented implementations are at an early stage and serve as illustrative examples, while systematic quantitative validation is ongoing and is outlined as future work. Full article