Search Results (407)

The integration of 5G technology with cloud and edge computing is redefining the digital landscape by enabling ultra-fast connectivity, low-latency communication, and scalable solutions across diverse application domains. This paper investigates the synergistic impact of 5G on cloud-to-edge architectures, emphasizing its transformative role in revolutionizing sectors such as healthcare, smart cities, industrial automation, and autonomous systems. Key advancements in 5G—including Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communications (mMTC)—are examined for their role in enabling real-time data processing, edge intelligence, and IoT scalability. In addition to conceptual analysis, the paper presents simulation-based evaluations comparing 5G cloud-to-edge systems with traditional 4G cloud models. Quantitative results demonstrate significant improvements in latency, energy efficiency, reliability, and AI prediction accuracy. The study also explores challenges in infrastructure deployment, cybersecurity, and latency management while highlighting the growing opportunities for innovation in AI-driven automation and immersive consumer technologies. Future research directions are outlined, focusing on energy-efficient designs, advanced security mechanisms, and equitable access to 5G infrastructure. Overall, this study offers comprehensive insights and performance benchmarks that will serve as a valuable resource for researchers and practitioners working to advance next-generation digital ecosystems. Full article

In the digital age, online teacher professional development (TPD) has become a key strategy for enhancing instructional quality and ensuring equitable access to continuous learning. This research compares and analyzes Chinese online teacher professional development (TPD) with the United States over a period of ten years, from 2014 to 2024. This study uses a mixed-methods approach based on policy documents, structured surveys, and interviews to investigate how governance regimes influence TPD outcomes for fair education. Both countries experienced a massive expansion of web-based TPD access and engagement, with participation rates over 75% and effectiveness scores over 4.3 by 2024. China focused on fast scaling by way of centralized mandates and investments in infrastructure, while the United States emphasized gradual expansion through decentralized, locally appropriate models. Most indicators had converged by the end of the period, even with these different approaches. Yet, qualitative evidence reveals persisting gaps in functional access and contextual appropriateness, especially in rural settings. Equality frameworks with attention to teacher agency, policy implementation, and digital usability must supplant weak access metrics. A hybrid paradigm presents itself as an attractive means toward building equitable and productive digital TPD environments through the symbiotic integration of China’s successful scalability and the United States’ professional autonomy. Full article

The use of low-resolution analog-to-digital converters (ADCs) in receivers has emerged as an effective solution for reducing power consumption in millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) systems. However, low-resolution ADCs also pose significant challenges for channel estimation. To address this issue, we propose a joint uplink/downlink (UL/DL) channel estimation algorithm that utilizes the spatial reciprocity of frequency division duplex (FDD) to improve the estimation of quantized UL channels. Quantified UL/DL channels are concentrated at the BS for joint estimation. This estimation problem is regarded as a compressed sensing problem with finite bits, which has led to the development of expectation-maximization-based quantitative generalized approximate messaging (EM-QGAMP) algorithms. In the expected step, QGAMP is used for posterior estimation of sparse channel coefficients, and the block maximization minimization (MM) algorithm is introduced in the maximization step to improve the estimation accuracy. Finally, simulation results verified the robustness of the proposed EM-QGAMP algorithm, and the proposed algorithm’s NMSE (normalized mean squared error) outperforms traditional methods by over 90% and recent state-of-the-art techniques by 30%. Full article

Terahertz (THz) communication is regarded as a promising technology for future 6G networks because of its advances in providing a bandwidth that is orders of magnitude wider than current wireless networks. However, the large bandwidth and the large number of antennas in THz massive multiple-input multiple-output (MIMO) systems induce a pronounced beam split effect, leading to a serious array gain loss. To mitigate the beam split effect, this paper considers a delay-phase precoding (DPP) architecture in which a true-time-delay (TTD) network is introduced between radio-frequency (RF) chains and phase shifters (PSs) in the standard hybrid precoding architecture. Then, we propose a fast Riemannian conjugate gradient optimization-based alternating minimization (FRCG-AltMin) algorithm to jointly optimize the digital precoding, analog precoding, and delay matrix, aiming to maximize the spectral efficiency. Different from the existing method, which solves an approximated version of the analog precoding design problem, we adopt an FRCG method to deal with the original problem directly. Simulation results demonstrate that our proposed algorithm can improve the spectral efficiency, and achieve superior performance over the existing algorithm for wideband THz massive MIMO systems with angular spread. Full article

Aiming to address the problem of low efficiency in the traditional manual measurement of the main arch rib components of concrete-filled steel tube (CFST) arch bridges, this study proposes a digital measurement technology based on the integration of geometric parameters and computer-aided design (CAD) models. In this method, first, we perform the high-precision registration of the preprocessed scanned point cloud of the CFST arch rib components with the discretized design point cloud of the standardized CAD model. Then, in view of the fact that the fitting of point cloud geometric parameters is susceptible to the influence of sparse or uneven massive point clouds, these points are treated as outliers for elimination. We propose a method incorporating slicing to solve the interference of outliers and improve the fitting accuracy. Finally, the evaluation of quality, accuracy, and efficiency is carried out based on distance deviation analysis and geometric parameter comparison. The experimental results show that, for the experimental data, the fitting error of this method is reduced by 76.32% compared with the traditional method, which can improve the problems with measurement and fitting seen with the traditional method. At the same time, the measurement efficiency is increased by 5% compared with the traditional manual method. Full article

In recent years, cutting-edge technologies, such as artificial intelligence (AI), blockchain, and digital twin (DT), have revolutionized the healthcare sector by enhancing public health and treatment quality through precise diagnosis, preventive measures, and real-time care capabilities. Despite these advancements, the massive amount of generated biomedical data puts substantial challenges associated with information security, privacy, and scalability. Applying blockchain in healthcare-based digital twins ensures data integrity, immutability, consistency, and security, making it a critical component in addressing these challenges. Federated learning (FL) has also emerged as a promising AI technique to enhance privacy and enable decentralized data processing. This paper investigates the integration of digital twin concepts with blockchain and FL in the healthcare domain, focusing on their architecture and applications. It also explores platforms and solutions that leverage these technologies for secure and scalable medical implementations. A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. Finally, we highlight the key challenges, emerging opportunities, and future directions in advancing healthcare digital twins with blockchain and federated learning, paving the way for a more intelligent, secure, and privacy-preserving medical ecosystem. Full article

This paper presents LokAlp, a modular timber construction system invented and developed by the authors, inspired by the traditional Blockbau technique, and designed for circularity and self-construction. LokAlp utilizes standardized interlocking blocks fabricated from CLT and GLT off-cuts to optimize material reuse and minimize waste. The study explores the application of massive timber digital materials within an open modular system framework, offering an alternative to the prevailing focus on lightweight structural systems, which predominantly rely on primary engineered wood materials rather than reclaimed by-products. The research evaluates geometric adaptability, production feasibility, and on-site assembly efficiency within a computational design and digital fabrication workflow. The definition of the LokAlp system has gone through several iterations. A full-scale demonstrator constructed using the LokAlp final iteration (Mk. XII) incorporated topological enhancements, increasing connection variety and modular coherence. Comparative analyses of subtractive manufacturing via 6-axis robotic milling versus traditional CNC machining revealed a >45% reduction in cycle times with robotic methods, indicating significant potential for sustainable industrial fabrication; however, validation under operational conditions is still required. Augmented reality-assisted assembly improved accuracy and reduced cognitive load compared to traditional 2D documentation, enhancing construction speed. Overall, LokAlp demonstrates a viable circular and sustainable construction approach combining digital fabrication and modular design, warranting further research to integrate robotic workflows and structural optimization. Full article

Low achievement in mathematics across educational levels has long been a concern for researchers. Recent evidence points to equipping teachers with skills and competencies that align with the demands of the modern, technology-rich world. This systematic review explored how technology-facilitated heutagogical practices contribute to the professional development of preservice and in-service mathematics teachers. Drawing on 21 empirical studies published between 2017 and 2024, this review identified three major findings. First, technology-enhanced heutagogical practices promote teaching skills by fostering learner autonomy, self-reflection, and professional identity development. Second, tools such as mobile apps, Massive Open Online Courses (MOOCs), adaptive learning platforms, and collaborative digital environments support the integration of heutagogical principles. Third, implementation is challenged by limited digital access, institutional constraints, and the need for gradual adaptation to self-determined learning models. These findings prove the need for policy and institutional investment in digital infrastructure, blended learning models, and teacher support. Theoretically, this study affirms heutagogy as a relevant pedagogical approach for preparing mathematics teachers in dynamic learning contexts. There is also a need for more empirical studies to investigate scalable models for technology-driven heutagogy, especially in resource-constrained settings. Full article

As digital infrastructure continues to expand, networks, web services, and Internet of Things (IoT) devices become increasingly vulnerable to distributed denial of service (DDoS) attacks. Remarkably, IoT devices have become attracted to DDoS attacks due to their common deployment and limited applied security measures. Therefore, attackers take advantage of the growing number of unsecured IoT devices to reflect massive traffic that overwhelms networks and disrupts necessary services, making protection of IoT devices against DDoS attacks a major concern for organizations and administrators. In this paper, the effectiveness of supervised machine learning (ML) classification and deep learning (DL) algorithms in detecting DDoS attacks on IoT networks was investigated by conducting an extensive analysis of network traffic dataset (legitimate and malicious). The performance of the models and data quality improved when emphasizing the impact of feature selection and data pre-processing approaches. Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time. Full article

Background and Objective: Despite growing emphasis on quality and safety in healthcare, there remains a limited understanding of how Quality Improvement and Patient Safety (QI/PS) training for health workers has evolved in response to global events like the COVID-19 pandemic and the WHO Global Patient Safety Action Plan. This rapid scoping review aimed to not only identify existing curricula but also uncover trends, innovation gaps, and global inequities in QI/PS education—providing timely insights for reshaping future training strategies. Methods: We searched MEDLINE and Scopus for English-language studies published between January 2020 and April 2024, describing QI and/or PS curricula across graduate, postgraduate, and continuing education levels. All healthcare worker groups were eligible, with no geographic limitations. Two reviewers conducted independent screening and data extraction; a third verified the results. Results: Among 3290 records, 74 curricula met inclusion criteria, with a majority originating from the US (58, 78.4%) and targeting physicians—especially residents and fellows (43/46, 93.5%). Only 27% of curricula were multidisciplinary. While traditional didactic (66.2%) and interactive (73%) approaches remained prevalent, curricula launched after 2020 introduced novel formats such as Massive Open Online Courses and gamification, with long-term programs uniformly leveraging web-based platforms. Common thematic content included Root Cause Analysis, Plan-Do-Study-Act cycles, QI tools, communication skills, and incident reporting. English-language peer-reviewed published literature indicated a marked lack of structured QI/PS training in Europe, Asia, and Africa. Conclusions: This review reveals both an uneven development and fragmentation in global QI/PS training efforts, alongside emerging opportunities catalyzed by digital transformation and pandemic-era innovation. The findings highlight a critical gap: while interest in QI/PS is growing, scalable, inclusive, and evidence-based curricula remain largely concentrated in a few high-income countries. By mapping these disparities and innovations, this review provides actionable direction for advancing more equitable and modern QI/PS education worldwide, whilst showcasing the need to systematically delve into QI/PS training in underrepresented regions. Full article

This article carries out an interdisciplinary analysis of five MOOC courses developed by the University of Valladolid and offered on higher education platforms between 2020 and 2024. This research is based on the study of the lexical categories used by the informants participating in these courses, establishing a correlation with the theoretical and practical debates surrounding the definition of heritage and the frameworks of contemporary heritage education. Through a metalinguistic approach, the semantic limits of the emerging lexical categories are examined, paying attention to their ambiguity, polysemy and contexts of use, both from a formal linguistic perspective and from a hermeneutic approach. The analysis is based on natural language processing tools, complemented by qualitative techniques from applied linguistics and cultural studies. This dual approach, both scientific–statistical and humanistically nuanced, allows us to identify recurrent discursive patterns, as well as significant variations in the conceptualization of heritage according to the socio-cultural and geographical profiles of the participants. The results of the linguistic analysis are contrasted with the thematic lines investigated by our research group, focusing on cultural policy, legacy policies, narratives linked to the culture of depopulation, disputed scientific paradigms, and specific lexical categories in the Latin American context. In this sense, the article takes a critical look at discursive production in massive online learning environments, positioning language as a key indicator of the processes of cultural resignification and the construction of legacy knowledge in the Ibero-American context. The findings of my scientific article underscore the pressing need for a multiform liberation of the traditionally constrained concept of heritage, which has long been framed within rigid institutional, legal, and disciplinary boundaries. This normative framework, often centered on materiality, monumentalism, and expert-driven narratives, limits the full potential of heritage as a relational and socially embedded construct. My research reveals that diverse social agents—ranging from educators and local communities to cultural mediators and digital users—demand a more flexible, inclusive, and participatory understanding of heritage. This shift calls for redefining legacy not as a static legacy to be preserved but as a dynamic bond, deeply rooted in affective, symbolic, and intersubjective dimensions. The concept of “heritage as bond”, as developed in contemporary critical theory, provides a robust framework for this reconceptualization. Furthermore, the article highlights the need for a new vehiculation of access—one that expands heritage experience and appropriation beyond elite circles and institutionalized contexts into broader social ecosystems such as education, digital platforms, civil society, and everyday life. This approach promotes legacy democratization, fostering horizontal engagement and collective meaning-making. Ultimately, the findings advocate for a paradigm shift toward an open, polyphonic, and affective heritage model, capable of responding to contemporary socio-cultural complexities. Full article

The advent of digital mining has become a tangible reality in recent years. This digital evolution requires a predictive understanding of key elements, particularly considering the reliable communication infrastructures needed for autonomous machines. The LoRa technology and its underground propagation behavior can make an important contribution to this digitalization. Since LoRa operates with a high signal budget and long ranges in sub-GHz frequencies, its behavior is very promising for underground sensor networks. The aim of the development and series of measurements was to observe LoRa’s applicability and propagation behavior in active salt mines and to detect and identify effects arising from the special environment. The propagation of LoRa was measured via packet loss and signal strength in line-of-sight and non-line-of-sight configurations over entire mining sections. The aim was to analyze the performance of LoRa at the macroscopic level. LoRa operated at 868 MHz in the free band, and units were equipped with omni-directional antennas. The K+S Group’s active salt and potash mine Werra, Germany, was kindly opened as a distinctive experimental setting. The LoRa exhibited characteristics that were highly distinctive in this environment. The presence of the massive salt allowed the signal to bounce along drift edges with near-perfect reflection, which enabled travel over kilometers due to a waveguide-like effect. A packet loss of below $15\%$ showed that LoRa communication was possible over distances exceeding 1000 m with no line-of-sight in room-and-pillar structures. Measured differences of $\Delta 50\mathrm{dBm}$ values confirmed consistent path loss across different materials and tunnel geometries. This effect occurs due to the physical structure of the mining drifts, facilitating the containment and direction of signals, minimizing losses during propagation. Further modeling and measurements are of great interest, as they indicate that LoRa can achieve even better outcomes underground than in urban or indoor environments, as this waveguide effect has been consistently observed. Full article

The transition from 5G to 6G wireless systems introduces new challenges at the physical layer, including the need for higher frequency operations, massive MIMO deployment, advanced beamforming techniques, and sustainable energy harvesting mechanisms. A plethora of feature articles, review and white papers, and roadmaps elaborate on the perspectives and research challenges of wireless systems, in general, including both unified physical and cyber space. Hence, this paper presents a comprehensive review of the technological challenges and recent advancements in wireless communication hardware that underpin the development of next-generation networks, particularly 6G. Emphasizing the physical layer, the study explores critical enabling technologies including beamforming, massive MIMO, reconfigurable intelligent surfaces (RIS), millimeter-wave (mmWave) and terahertz (THz) communications, wireless power transfer, and energy harvesting. These technologies are analyzed in terms of their functional roles, implementation challenges, and integration into future wireless infrastructure. Beyond traditional physical layer components, the paper also discusses the role of reconfigurable RF front-ends, innovative antenna architectures, and user-end devices that contribute to the adaptability and efficiency of emerging communication systems. In addition, the inclusion of application-driven paradigms such as digital twins highlights how new use cases are shaping design requirements and pushing the boundaries of hardware capabilities. By linking foundational physical-layer technologies with evolving application demands, this work provides a holistic perspective aimed at guiding future research directions and informing the design of scalable, energy-efficient, and resilient wireless communication platforms for the Future Internet. Specifically, we first try to identify the demands and, in turn, explore existing or emerging technologies that have the potential to meet these needs. Especially, there will be an extended reference about the state-of-the-art antennas for massive MIMO terrestrial and non-terrestrial networks. Full article

The integration of artificial intelligence (AI) chatbots in online learning environments has transformed the way students engage with educational content, offering personalised learning experiences, instant feedback, and scalable support. This study investigates the role of AI-driven chatbots in the Pedagogical Information and Communication Technology (ICTPED) Massive Open Online Course (MOOC), a professional development course aimed at enhancing teachers’ Professional Digital Competence (PDC). The study pursues two connected aims: (1) to examine how chatbots support content comprehension, self-regulated learning, and engagement among pre- and in-service teachers, and (2) to explore, through a cultural-historical perspective, how chatbot use contributes to the development of students’ digital agency. Based on data from 46 students, collected through structured questionnaires and follow-up interviews, the findings show that chatbots functioned as interactive learning partners, helping students clarify complex concepts, generate learning resources, and engage in reflection—thereby supporting their PDC. At the same time, chatbot interactions mediated learners’ development of digital agency, enabling them to critically interact with digital tools and navigate online learning environments effectively. However, challenges such as over-reliance on AI-generated responses, inclusivity issues, and concerns regarding content accuracy were also identified. The results underscore the need for improved chatbot design, pedagogical scaffolding, and ethical considerations in AI-assisted learning. Future research should explore the long-term impact of chatbots on students’ learning and the implications of AI-driven tools for digital agency development in online education. Full article

The agri-food sector is going through a massive digital transformation thanks to new technologies such as the Internet of Things (IoT), big data, and Artificial Intelligence (AI). Regional disparities and implementation barriers prevent widespread uptake despite significant research advances. Drawing on bibliometric and survey data collected up to the end of 2023, this study examines global research trends and stakeholder perceptions in Andalusia (Spain) to identify challenges and opportunities in agricultural digitalization. Bibliographic analysis revealed that research has moved from early remote sensing to precision agriculture, IoT, robotics and big data, and that AI has recently taken over in predictive analytics, automation, and decision-support systems. However, our survey of Andalusian stakeholders highlighted a limited adoption of cutting-edge tools such as AI, blockchain, and predictive models due to economic constraints, technical challenges, and skepticism. Participants emphasized the importance of trust-building, as well as the use of simple tools that require minimal input and provide immediate benefits. Priorities for the responders were also improving market transparency, optimizing resource use, and system interoperability. The findings show that closing the gap between research and practice requires developing digital solutions that are user-centered, simplified, and context-adapted, especially when dealing with complex technologies like AI and predictive systems. This must be supported by targeted public policies and collaborative innovation ecosystems, all essential elements to accelerate the integration of smart agricultural technologies and align scientific innovation with real-world needs. Full article