Search Results (5,590)

The sixth-generation (6G) mobile communication systems are anticipated to be operational by 2030, prompting extensive research efforts by governments and private entities. Designed to meet societal, economic, and technological demands unaddressed by fifth-generation (5G) networks, 6G integrates scalability, security, and reliability with ubiquity and resource-intensive artificial intelligence. Envisaged as multi-band, decentralized, autonomous, flexible, and user-centric, 6G networks incorporate innovative technologies, including cell-free (CF), three-dimensional heterogeneous networks (3D HetNet), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), as well as artificial intelligence/machine learning (ML). In 6G 3D HetNets, the densification of access points (APs) continues, accommodating increased connections and traffic volumes, alongside the use of higher frequency bands. Although 6G networks are not fully standardized, they target demanding Quality of Service (QoS) standards, such as a peak data rate of 1.0 Tbps and latency of 0.1 ms. This paper conducts a comprehensive literature review on radio resource management (RRM) in 6G cell-free and 3D HetNet systems, emphasizing challenges such as interference mitigation. It presents a taxonomy of RRM approaches, systematically studying, categorizing, and qualitatively analyzing recent techniques, outlining the current state, and indicating future trends, technologies, and challenges shaping 6G systems. Full article

Quality by design (QbD) is a systematic approach focused on achieving consistent, predictable quality based on predefined objectives. Unlike traditional methods, QbD prioritizes risk assessment and management, which significantly enhances the robustness of the analytical method. In this study, we initiated factor screening using a three-factor, two-level design to evaluate three independent variables: flow rate, pH, and mobile phase composition. To further investigate the interaction of these variables, we employed Central Composite Design (CCD). This allows us to apply response surface methodology to the Critical Analytical Attributes (CAAs), specifically retention time, peak area, and symmetry factor, by conforming to the method’s robustness. The combination of pregabalin and duloxetine hydrochloride (HCl) dosage form was determined using a straightforward, exact, specific, and accurate reverse-phase HPLC approach. The results showed retention times of 3.265 min and 4.318 min for duloxetine HCl and pregabalin, respectively. Pregabalin demonstrated linearity from 100 to 200 μg/mL (R² = 0.998), whilst duloxetine HCl demonstrated linearity between 20 and 120 μg/mL (R² = 0.997). Lower LOD values of 0.925 µg/mL and 0.853 μg/mL and LOQ values of 2.809 μg/mL and 2.587 μg/mL of pregabalin and duloxetine HCl, respectively, suggest good sensitivity for the technique. The drug content of the commercial formulation may thus be determined using the recommended method. This technique can be used for standard quality control studies to simultaneously estimate pregabalin and duloxetine HCl. The novelty of the present studies lies in the development of a robust RP-HPLC method for simultaneous estimation of pregabalin and duloxetine HCl using a systematic AQbD approach, enhancing robustness, reproducibility, and reliability, making it highly suitable for routine quality control and regulatory applications. Full article

Background: Migraine in older adults represents an increasingly relevant yet underrecognized clinical challenge in aging societies, where multimorbidity, frailty, and polypharmacy complicate both diagnosis and management. Although traditionally considered a disorder of younger individuals, migraine frequently persists or presents after the age of 60 with atypical features, contributing to diagnostic uncertainty. Methods: This narrative review, conducted in accordance with the SANRA principles, aims to provide a comprehensive overview of the epidemiology, clinical presentation, pathophysiology, and management of migraine in older adults, with particular emphasis on age-related complexities, therapeutic challenges, and unmet clinical needs. Results: Migraine in this population often presents with atypical or misleading features, such as aura without headache, vestibular symptoms, or overlap with cerebrovascular conditions, leading to delayed or incorrect diagnoses. The burden of disease is substantial, affecting physical function, mobility, cognition, emotional well-being, and social participation, and is further amplified by comorbid conditions including cardiovascular and metabolic disorders, mood disturbances, and chronic pain syndromes. Aging-related neurobiological changes, such as impaired pain modulation, endothelial dysfunction, and neuroinflammation, may influence disease expression and treatment response. Therapeutic management is challenged by contraindications, increased susceptibility to adverse drug effects, and the complexity of polypharmacy, highlighting the importance of individualized and non-pharmacological approaches. Conclusions: Migraine in older adults is a significant but often overlooked contributor to disability and reduced quality of life. Improved recognition of its unique clinical features and age-specific vulnerabilities is essential to optimize patient-centered care. Future research should prioritize the inclusion of older populations and the development of tailored, safe, and effective management strategies. Full article

Modern power systems increasingly integrate renewable energy sources (RESs), electric mobility, and dynamic market participation. Dynamic electricity pricing, reflecting real-time market conditions, is increasingly important for prosumers worldwide, enabling flexible and efficient energy management. The growing adoption of electric vehicles (EVs) and bidirectional charging technologies (V2G, V2H) allows EVs to act as mobile battery energy storage systems (mBESSs). This study presents a Python 3.11-based application for simulating and analyzing energy flows in residential systems with photovoltaic (PV) installations, EVs acting as mBESS, and optional stationary battery energy storage systems (BESSs), using real 2024 data on consumption, PV production, and market prices. The energy management system (EMS) employs a rule-based algorithm to optimize energy use and economic benefits, adjusting dispatch between PV systems, the grid, mBESSs, and BESSs based on price coefficients $\alpha$ and $\beta$. Simulation scenarios were developed based on two EV availability patterns: Profile 1, representing users unavailable during standard working hours, and Profile 2, representing users with intermittent availability for brief excursions. The results demonstrate substantial electricity cost reductions: For a Nissan Leaf e+ with Profile 1, annual costs decrease by approximately 20% compared to a system without EVs. With PV generation and Profile 2, costs drop by 57% relative to the baseline, while adding a stationary BESS further reduces costs by nearly 95%. It should be noted that the results were obtained assuming zero energy costs for propulsion. Therefore, the economic benefits reported here represent an upper-bound estimate and would be lower under real-world driving conditions. These findings highlight that coordinated EMS operation with EVs as mBESSs, supported by optional BESSs, can maximize economic performance and provide prosumers with a practical framework for flexible and efficient energy management. Full article

This paper examines how emerging economies, with a focus on India, transition from being primarily recipients of capital to becoming outward investors. It investigates whether domestic institutional governance, rather than rapid liberalization or extensive investment treaty networks, accounts for the sustained growth of both inward FDI and outward ODI. The study combines a detailed timeline of institutional developments with structural break tests, vector autoregression (VAR), and dynamic panel GMM analysis. This approach tracks the timing, spread, and longevity of reforms like the shift from FERA to FEMA and the digitalization of administration, examining their effect on capital flow patterns. Results show that major turning points in India’s FDI and ODI movements correspond with key governance reforms, such as replacing the Foreign Exchange Regulation Act with the Foreign Exchange Management Act, unifying investment policies, digitizing administration, and renegotiating treaties post-2016. Improvements in governance have a more significant and enduring impact on FDI than macroeconomic factors, while clearer regulation and stronger institutions are vital for boosting ODI. Once domestic institutional capacity is taken into account, the number of investment treaties does not significantly influence capital movements. The paper introduces a “transferability matrix” that highlights effective, low-cost reforms, such as civil penalty systems and digital governance, which other emerging economies can implement. It stresses that integrating into global capital markets depends more on developing solid domestic regulations than on rapid deregulation. The study also advances previous research by (1) combining FDI and ODI within a single institutional framework explaining both flows; (2) moving beyond static, perception-based measures to develop a comprehensive timeline showing how regulatory credibility is built over three decades; and (3) providing empirical proof that credible domestic institutions can replace large treaty networks in ensuring capital mobility. Full article

This study investigates Downlink/Uplink Decoupling (DUDe) in 5G networks, a framework that allows user equipment to select its uplink serving cell independently of the downlink anchor. This approach is designed to alleviate the “macro bias” and pathloss issues that typically degrade performance for Internet of Things (IoT) traffic. We propose a framework managed by Mobile Edge Computing (MEC) that operates on a per-Transmission Time Interval (TTI) basis, incorporating stability mechanisms such as hysteresis and Time to Trigger to prevent frequent, unnecessary handovers. The performance is evaluated using a system-level simulator across two scenarios: a high-density urban IoT deployment and an Industry 4.0 smart factory environment. Our results demonstrate that the proposed framework significantly improves uplink throughput and reduces tail latency compared to traditional coupled association methods. Furthermore, an ablation study confirms that these performance gains are derived from the structural decoupling of links, providing a scalable path for improving connectivity in 5G and beyond. Full article

Monitoring oil pipelines is crucial for effective infrastructure management and maintenance, as it helps prevent threats such as vandalism and leaks that can lead to catastrophic events. Pipeline leaks pose significant environmental and economic risks; however, existing detection methods are often expensive, slow, or unreliable, limiting their effectiveness for real-time applications. This study proposes a lightweight thermal-imaging-based intelligent leak detection system that integrates Convolutional Neural Networks (CNN), Autoencoder (AE), and Knowledge Distillation (KD), suitable for deployment on edge devices. The proposed system addresses challenges associated with existing pipeline detection techniques, including large model sizes, high transmission latency, and excessive energy consumption. Thermal images of pipelines are captured and compressed using an autoencoder before being processed by a CNN model optimized through knowledge distillation. The model was trained and tested on a locally collected thermal image dataset and designed for deployment on edge devices such as Raspberry Pi to simulate edge computing scenarios. Experimental results demonstrate that the proposed CNN + KD + AE model achieved 98% accuracy, 98% precision, 98% recall, and an F1-score of 98%, outperforming baseline models such as MobileNetV2 (91%), InceptionV3 (84%), EfficientNet-Lite (81%), and ResNet (74%). Furthermore, the number of trainable parameters was significantly reduced to 1.18 million, with a compact model size of 4.51 MB. These findings confirm the system’s suitability for real-time leak detection in remote and resource-constrained environments, contributing to the development of cost-effective, scalable, and energy-efficient solutions for intelligent pipeline monitoring. Full article

Background/Objectives: Wearable activity monitors and sensor-based devices are increasingly used to quantify mobility, load, and recovery in orthopaedic patients, yet clinicians lack practical guidance on selection, implementation, and interpretation. This qualitative expert consensus study synthesized real-world experiences from leaders in orthopaedics, rehabilitation, biomechanics, and digital health who implemented wearables at scale. Methods: Semi-structured interviews were conducted with 16 experts (64% response rate) recruited via hybrid purposive and snowball sampling. Participants included orthopaedic surgeons and research scientists with 124 cumulative years of wearable experience across over 9000 monitored patients. Interviews addressed device selection, clinical workflow, data management, and adoption barriers. Data were charted into a structured extraction matrix and analyzed using Inductive Thematic Analysis and a Framework Approach, reported per COREQ guidelines. Results: Experts utilized diverse sensor platforms across arthroplasty, trauma, spine, and sports medicine. Four key themes emerged: (1) device selection prioritized usability and patient compliance over technical sophistication; (2) workflow required defined team roles to manage data volume and avoid clinical burden; (3) patient engagement favored simplified, actionable feedback amid divergent views on data transparency; (4) future outlook anticipated AI-driven proactive risk prediction. Conclusions: No single wearable suits all orthopaedic practices; success hinges on aligning sensor placement with clinical questions, rigorous data quality checks, and integration into care plans. This study offers a practical checklist and roadmap for point-of-care adoption. Full article

Pitahaya (Hylocereus spp.) production is increasingly affected by climatic factors, as well as by phytopathogens and abiotic stress, leading to delays in agronomic interventions and reduced productivity. The objective was to design, implement, and validate a multimodal system (CARYPAR) that enables early disease detection and agile decision-making, characterized by low latency and reduced dependence on cloud connectivity. The methodology integrates climate reanalysis from NASA POWER, biophysical remote sensing variables derived from Sentinel-1/2, and proximal computer vision captured via mobile devices using a late fusion architecture and an optimized convolutional neural network, EfficientNet-V2B0, which discriminates between optimal and pathological conditions in vegetative tissues and fruit. The results of the experimental validation carried out in 160 georeferenced units achieved an overall accuracy of 80.0% and an F1 score of 0.8645 for Bad Fruit. The McNemar test and the operational agreement with agro-industrial experts yielded a Cohen’s Kappa index of κ = 0.6831, with an inference latency reduced to 22.00 ms. It is concluded that the multimodal integration of satellite bio-environmental data with edge computer vision achieves substantial agreement with agronomic expert judgment under heterogeneous field conditions (Cohen’s κ = 0.6831), supporting its role as a decision-support tool rather than a replacement for expert assessment. Therefore, its adoption can enhance real-time irrigation management and crop protection, while contributing to traceability and sustainable resource management in agricultural regions with limited connectivity. Full article

The integration of electric mobility and energy systems has emerged as a key research domain in the transition toward sustainable energy and decarbonized transport, yet the literature is lacking systematic quantitative overviews of its scientific development. This study addresses this gap by conducting a bibliometric analysis of research activities across five domains central to electric vehicle–energy system integration: central energy management systems; renewable energy, hydrogen production, and large-scale storage; industrial applications; smart energy communities, virtual power plants, and vehicle-to-X; and urban high-power charging parks with local storage. Using publication data from Web of Science and Scopus, performance analysis and science mapping techniques were applied to examine publication dynamics, thematic structures, and intellectual linkages. Results indicate strong growth and consolidation around smart grids and decentralized flexibility solutions, particularly within energy management, renewable integration, and community-based energy systems, while industrial applications and high-power charging infrastructures remain comparatively underrepresented. The findings suggest a maturing interdisciplinary field characterized by expanding connections between mobility and energy research, alongside emerging opportunities related to industrial integration, charging infrastructure, and vehicle-to-grid deployment. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems, enabling a differentiated understanding of research dynamics. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems. The findings highlight priority areas for future research, particularly industrial integration and scalable charging infrastructure, and offer insights for policymakers and industry stakeholders. Full article

Agentic artificial intelligence—systems that reason, plan, and act autonomously within governed workflows—is converging with autonomous electric mobility and urban robotics to reshape how cities govern, move, and manage physical space. We argue that the simultaneous arrival of these three vectors is triggering a transformation comparable in scope to the Industrial Revolution. Cities that deploy across all three domains are becoming the new hubs of innovation: they concentrate talent, accelerate knowledge circulation, enable cross-fertilisation, and generate hybrid proposals that no single vector could produce alone. Just as Manchester, Birmingham, and the Ruhr became the defining centres of industrialisation because steam, textiles, iron, and coal recombined through the proximity of the engineers and entrepreneurs who moved between them, a small number of cities today are pulling ahead because they host the shared talent pool around which agentic governance, autonomous mobility, and urban robotics co-evolve. Conceptually, we extend the mirroring hypothesis in two directions: dynamically, arguing that organisations and urban ecosystems converge toward the configurations new technologies make possible; and ontologically, arguing that agentic AI introduces non-human agents into organisational architectures, requiring hybrid human–AI coordination. We formalise this dynamic as five propositions (P1–P5) of cumulative recursive hybridisation (CRH), operating through four reinforcing feedback loops—data, regulation, infrastructure, and talent. Together, these loops explain why the emerging urban order is path-dependent: early movers accumulate compounding advantages, while latecomers face exponentially rising costs of entry. We demarcate CRH from adjacent frameworks—general-purpose technologies, organisational complementarities, and complex adaptive systems—and test it against counterfactual evidence from failed, stalled, and Global South trajectories (Sidewalk Toronto, the Cruise rollback, Songdo, Bengaluru). We also examine its political-economy, equity, and surveillance limits. Drawing on comparative evidence from public-sector chatbot deployments, autonomous mobility ecosystems in the United States and China, and emerging urban robotics cases, we conclude that what is at stake is not incremental modernisation but the construction of a new urban order. The cities that act as innovation hubs for the agentic AI era will shape global standards, attract global talent, and define the institutional templates that others eventually adopt—much as the industrial cities of the eighteenth and nineteenth centuries did. Full article

Heterotopic ossification (HO), the pathological formation of mature bone in non-skeletal soft tissues (e.g., muscles, tendons), severely impairs patient mobility and quality of life. Despite decades of research, systematic analysis of signaling networks across HO subtypes (acquired traumatic HO, hereditary Fibrodysplasia Ossificans Progressiva (FOP), Progressive Osseous Heteroplasia (POH)) remains insufficient, and clinical therapies suffer from high recurrence and severe side effects. This review synthesizes recent advances in HO pathogenesis: FOP involves gain-of-function activin A receptor type I (ACVR1) mutations (mostly R206H), disrupting bone morphogenetic protein (BMP)/Activin A signaling; POH arises from paternal guanine nucleotide-binding protein, alpha-stimulating activity polypeptide (GNAS) loss-of-function mutations, derepressing Hedgehog signaling via reduced cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) activity; tHO features trauma-induced inflammation/hypoxia activating BMP/transforming growth factor–beta (TGF-β) pathways. Key signaling crosstalk (e.g., BMP-Yes-associated protein (YAP)-Indian hedgehog (IHH)) is integrated, and novel therapies (ACVR1 inhibitors, Activin A antibodies, retinoic acid receptor gamma (RARγ) agonists, adeno-associated virus (AAV)-mediated ACVR1 silencing) are highlighted, with emphasis on subtype-specific efficacy. A stratified, mechanism-based HO management framework is proposed, aiming to accelerate precision therapy development and advance understanding of aberrant tissue regeneration. Full article

Climate change significantly impacts agricultural ecosystems through rising temperatures, changing precipitation patterns, increasing atmospheric CO₂ levels, and more frequent extreme weather events. These environmental changes have a pronounced effect on plant-parasitic nematodes (PPNs; phylum Nematoda), which cause serious crop losses on a global scale. This review aims to provide a comprehensive evaluation of current knowledge on how major climate change drivers influence the biology, population dynamics, host–plant interactions, and geographic distribution of PPNs in agricultural systems. Recent studies show that rising temperatures accelerate nematode development, increasing the number of generations within a production season and facilitating the spread of many economically important species toward higher latitudes and elevations. Changes in precipitation patterns and soil moisture directly affect nematode survival, mobility, and infection success, and these effects often vary depending on regional conditions and nematode species. Elevated atmospheric CO₂ levels modify plant–nematode interactions by increasing root biomass, altering rhizosphere processes, and regulating plant defense pathways (e.g., jasmonic acid and salicylic acid signaling), which may enhance host susceptibility and infection intensity. Furthermore, extreme climate events can disrupt the natural balance in soil ecosystems, weakening natural antagonist–nematode relationships. However, responses of PPNs to climate change are not uniform, and contrasting findings across studies indicate that these responses are strongly shaped by species-specific traits and environmental variability. In addition, future research should focus on long-term and multi-factorial field studies to better capture the combined effects of climate drivers. Overall, climate change is expected to increase PPN prevalence and drive shifts in their geographic distribution, highlighting the need for climate-sensitive and regionally adapted nematode management strategies. Full article

Background: Μetabolic syndrome (MetS)—comprises central adiposity, elevated blood pressure, dyslipidaemia, and dysglycaemia, increasing the risk of type 2 diabetes and cardiovascular disease. Exercise training improves cardiorespiratory fitness and several MetS components, but real-world effectiveness is limited by poor adherence, restricted supervision, and insufficient personalisation. Objective: This scoping review mapped the clinical intervention evidence on technology-enhanced exercise and structured physical activity relevant to MetS, while distinguishing direct MetS evidence from translational evidence. Methods: In accordance with PRISMA-ScR, we searched PubMed and extended the search to Scopus and Web of Science; a supplementary IEEE Xplore search and a post hoc Embase check were also conducted. Eligible studies were interventions using web-based delivery, wearables, telemonitoring/mobile health (mHealth), artificial intelligence (AI) coaching, virtual reality (VR)/exergaming, or continuous glucose monitoring (CGM) alongside exercise training or structured physical activity. Results: Nineteen studies met the eligibility criteria. The evidence base was weighted toward wearable/app-based feedback and telemonitoring/mHealth/web-based approaches, with fewer studies on VR/exergaming, CGM-enabled exercise, and AI coaching. Most studies were randomised or cluster-randomised, but interventions were usually short term. Across categories, technology most consistently supported adherence, self-monitoring, accountability, remote supervision, and, in selected cases, physiology-informed personalisation. Direct MetS evidence was strongest for wearables with structured feedback, telemonitoring, mHealth, and web-based delivery, whereas AI coaching and CGM were supported by adjacent translational evidence. Conclusions: Technology-enhanced exercise and structured physical activity show promising but heterogeneous and still preliminary potential for MetS management. Key limitations include short follow-up, uneven representation across categories, inconsistent reporting of exercise dose/intensity fidelity and adverse events, and limited equity and implementation outcomes. Full article

This research enhances the literature on bank capital structure by combining financial intermediation theory with technological innovation to analyse the impact of FinTech adoption and liquidity management on leverage choices in South African banks. Utilising panel data spanning 2015 to 2024 and applying the Generalised Method of Moments (GMM) to tackle endogeneity and dynamic persistence, the research presents new findings from an overlooked emerging market setting. The results show a diverse effect of technology on leverage. Conventional banking systems, represented by automated teller machines (ATMs), show a positive relationship with the total debt ratio (TDR), suggesting a capital-intensive nature of tangible assets. Conversely, digital technologies such as mobile banking and a composite FinTech Index display a notable negative correlation with leverage, indicating that digital transformation improves efficiency, strengthens internal funding capacity, and reduces dependence on external debt. Moreover, increased liquidity levels are negatively correlated with leverage, suggesting that well-capitalised banks with robust liquidity rely less on debt funding. By examining FinTech and liquidity dynamics, the research contributes to both theory and practice, emphasising digital innovation as an alternative to external funding and stressing the importance of sound liquidity management amid evolving regulatory environments such as Basel III. Full article