Search Results (15,941)

Introduction: Inequities in access to medicines persist for asylum seekers, refugees, and undocumented migrants in Europe. For vaccines, access gaps not only exist for these groups in childhood routine immunization, but also for life-course and catch-up vaccinations. As part of a broader project examining access to medicines and vaccines for migrants across all stages of the migration cycle, this scoping review synthesizes evidence on the determinants of access to vaccines. Methods: We conducted a scoping review across PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Database of Systematic Reviews, Scopus, and grey literature sources, covering the period 2000–2024. Sources were eligible if they addressed access to vaccines among migrants. We examined access to vaccines along the life course, and across phases of the migratory cycle, including departure, transit, reception and settlement, and return or deportation. Results: A total of 47 research studies and grey literature reports were included. Most studies focused on migrants in reception and settlement (destination) settings, with only twelve sources addressing other phases of the migratory cycle. Across European countries, migrants were frequently reported to have lower uptake of routine vaccines (e.g., measles–mumps–rubella (MMR), polio, diphtheria–tetanus–pertussis (DTP), and human papillomavirus (HPV)) and COVID-19 vaccines than host populations. The most frequently reported barriers were related to migrants’ legal status, administrative requirements, and lack of documentation, alongside poor affordability of vaccination, limited awareness of their rights, and mistrust in the health system. Conclusions: Health systems need to adopt innovative approaches to expand vaccine access for migrant populations. Further, protecting confidentiality is essential for building trust and reducing ethical and legal risks. Flexible and coordinated vaccination strategies are required to address migrants’ mobility across the different migration stages and settings. Our findings appeal for sustained improvements in access to vaccines among migrants in Europe, contingent on strong policy commitments to equity, data protection, and the adoption of life-course and catch-up vaccination strategies. Full article

The dense tree canopy in the complex orchard environment obstructs wireless positioning signals and generates NLOS interference, which reduces the positioning accuracy of agricultural mobile robots. This study investigates a localization method for agricultural mobile robots based on two UWB tags and an electronic compass. By analyzing the NLOS interference factors and error sources of UWB, a method for NLOS interference suppression and positioning correction employing two UWB tags tightly coupled with heading angle was proposed. The construction of the heading angle L2IB system and its comprehensive process were also introduced as follows. The proposed method constructs candidate localization domains for dual UWB tags based on multilateration and integrates the inter-tag distance and heading-angle constraints within an L2IB framework to suppress NLOS-induced errors and estimate the robot center position. Experiments were performed under four simulated scenarios, namely line-of-sight (LOS), single-anchor occlusion, multi-anchors occlusion, and single-tag occlusion. The proposed method was compared with the centroid and least-squares methods. The results demonstrate that the L2IB method effectively improves localization accuracy under NLOS conditions. Specifically, in the single-tag NLOS interference scenario, the MAE, RMSE, and maximum localization error were 3.7, 4.0, and 6 cm, respectively. These results indicated that the system could meet the positioning needs of most NLOS environments in the orchard. Therefore, the proposed method exhibits feasibility and provides a new alternative for high-precision localization of mobile robots in orchards under NLOS conditions. Full article

Educational information systems have evolved into highly interconnected digital landscapes that support learning management platforms, student information systems, institutional repositories, and online assessment environments. As these systems increasingly operate across cloud infrastructures and mobile devices, ensuring the confidentiality, integrity, and availability (CIA Triad) of educational data is critical for safeguarding institutional operations and maintaining trust in digital education services. This paper investigates how next-generation security protocols, such as adaptive multi-factor authentication and advanced access control and data protection mechanisms, can reinforce ISO/IEC 27001:2022 requirements within contemporary educational information systems. The analysis maps emerging protocol capabilities to relevant new ISO/IEC 27001:2022 control domains, illustrating how they mitigate threats associated with unauthorized access, data manipulation, and service disruption. The proposed framework is further supported by an implementation-oriented mapping and an illustrative operational architecture that demonstrates the feasibility of translating prioritized security determinants into practical mechanisms. The FAHP analysis identifies access control mechanisms, backup and recovery, and data validation as the three highest-weighted determinants, with aggregate weights of 0.061, 0.059, and 0.057, respectively. These determinants are translated into a determinant-driven Security Operationalization Matrix that connects ISO/IEC 27001:2022 control domains, CIA dimensions, and technology recommendations, and is complemented by implementation feasibility considerations tailored to the budgetary, infrastructural, and resource constraints characteristic of educational institutions. Based on the prioritization results and conceptual operationalization, the proposed integrative approach provides a structured and progressively adoptable foundation for CIA-oriented security governance in digital educational environments. Full article

Visual impairment affects approximately 2.2 billion people worldwide, yet existing assistive technologies remain fragmented and prohibitively expensive. This paper presents Munir, an integrated multimodal assistive system designed to enhance human–computer interaction through a combination of a mobile application and Bluetooth-enabled smart glasses. Munir leverages a hybrid machine learning architecture to provide inclusive, real-time support for daily living activities. The system integrates ten core capabilities—including face recognition, optical character recognition, and scene description—all accessible through a unified bilingual (Arabic/English) voice interface. By employing on-device processing for biometric tasks, Munir ensures user privacy and trust while maintaining high responsiveness. End-to-end system evaluation on the SCface dataset achieves a 96.69% recognition rate with 0% False Accept Rate. At an estimated first-year total cost of $806, Munir demonstrates a 4–5× cost advantage over commercial alternatives, providing a scalable and affordable multimodal solution for global digital inclusion. Full article

White adipose tissue (WAT) is essential for maintaining energy homeostasis during hibernation by supplying lipolysis-derived fatty acids as a major fuel source. In raccoon dogs (Nyctereutes procyonoides), the activity of brown adipose tissue is diminished, providing a unique model to investigate how WAT supports metabolic homeostasis in a largely non-thermogenic state. Here, we integrated physiological, histological, transcriptomic, and molecular analyses of back-fat and tail-fat depots during autumn fattening and winter sleep. Despite reduced food intake, body weight loss, and mild hypothermia, raccoon dogs maintained systemic glucose and lipid homeostasis. Both WAT depots exhibited adipocyte atrophy and the coordinated suppression of core metabolic and biosynthetic pathways, indicating a shared program of metabolic depression. However, the two depots adopted distinct remodeling strategies. Back-fat showed collagen densification and vascular-associated remodeling, suggesting a structural adaptation that may preserve tissue integrity during winter sleep. In contrast, tail-fat displayed enhanced innate immune signaling and M2 macrophage enrichment, indicating immune niche remodeling that may support tissue protection during prolonged lipid mobilization. Together, these findings reveal that raccoon dogs maintain metabolic homeostasis during shallow hibernation through a non-thermogenic, WAT-centered strategy characterized by shared metabolic depression and depot-specific structural and immunometabolic remodeling. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Aqueous precipitation and crystallization are central to impurity removal, product formation, and resource recovery in mineral and chemical processing, but robust inline monitoring remains challenging because supersaturation is not measured directly and conductivity signals are affected by temperature, composition drift, bubbles, solids, polarization, and fouling. Electrical conductivity (EC) is attractive as a low-cost, rugged process analytical tool, yet its usefulness depends on mechanistic interpretation: EC reflects charge-carrier concentration and mobility rather than supersaturation itself. This review organizes the literature into a layered framework covering (i) measurement integrity and deployment, (ii) bulk-signal extraction in multiphase media, (iii) estimation of latent variables such as dissolved concentration or supersaturation proxies, and (iv) control readiness based on conductivity-derived targets. Frequency-aware conductivity extraction, event-anchored verification, and observer-based estimation are treated as optional, complementary modules. A Ca-carbonate/CaCO₃ system is used as an illustrative case because its coupling among conductivity, pH/speciation, supersaturation, and precipitation is especially transparent, although the framework is intended for broader processing systems, including complex liquors and slurries. Opportunities are also highlighted for nanomaterials to improve both precipitation control and EC information content. Full article

Smart Mobility plays a key role in Smart Cities, given its ability to support the rollout of intelligent transport systems, allowing for more sustainable urban transportation and greater interoperability across diverse mobility modes. Furthermore, Smart Mobility is essential to maximize the quality of life for the community while advancing principles of sustainability, economic development, technological innovation, and collaborative governance. Real-Time Traffic Management (RTTM) emerges as a vital technology for optimizing traffic management in Smart Mobility. Using the PRISMA framework, the proposed systematic literature review examines 165 peer-reviewed publications related to RTTM research work published between 2019 and 2025. This review identified eleven application domains, with Urban Traffic Management Systems (36.97%) and Artificial Intelligence (AI) and Predictive Analytics (12.73%) representing the most prominent areas. A retrospective analysis of the literature on control architecture used in closed-loop feedback systems indicates that most studies (89%) have adopted a more dynamic control model, while 7.8% adopted a Digital Twin (DT)-based approach. However, several implementation barriers persist, including limited integration of online optimization and learning loops into RTTM systems, gaps in performance comparisons between simulation and reality, scalability issues due to heterogeneous environments, inconsistent data quality caused by various sensor types, and difficulties integrating sensors into a control system. In addition, this paper proposes a taxonomy of RTTM applications and control architectures, while outlining key practical barriers to implementation and charting future research directions for advancing Smart Mobility through robust RTTM. Full article

The city of Bologna (Italy) boasts an outstanding hydraulic heritage linked to the development of the silk industry, embodied in an extensive and valuable canal network. These public works, such as the Canale di Reno and the Canale Navile, were fundamental to the urban and economic shaping of the city from the Middle Ages onwards; however, many were concealed or dismantled from the 19th century. This article analyses recent heritage engagement and dissemination strategies regarding Bologna’s historic canals and proposes new tools to overcome their spatial fragmentation and enhance their interpretation as a continuous network. The methodology combines analysis, fieldwork or valorisation of the hydraulic system, proposing two complementary promotion actions: the design of a mobile application and the development of a straightforward urban intervention consisting of linear pavement marking of the underground canals layout. The proposed operational hypotheses suggest that integrating digital resources with on-site signage brings invisible heritage to light, improves the spatial understanding of the hydraulic system, and fosters both community and tourist engagement. The study concludes that these strategies reinforce the territorial understanding and social awareness of civil engineering heritage, offering a transferable approach for the outreach of hydraulic networks. Full article

Antirestriction proteins protect mobile genetic elements from the host’s restriction-modification systems. Here, we investigated the ability of ArdA and ArdB antirestriction proteins to regulate gene expression in an engineered E. coli K-12 MG1655-based biosensor strain. This biosensor strain harbors a lux-based reporter system controlled by the AllR-repressed promoter. Although structurally similar, DNA-mimic ArdA proteins interact with AllR differently. Recently described small sArdC and well-known ArdA from the conjugative plasmid R64 appear to bind AllR and open the promoter, while the other tested antirestriction proteins (small sArdN protein and various full-sized ArdA proteins from different sources) have no effect on gene expression under AllR-controlled promoter. Direct binding between ArdA and AllR was experimentally confirmed using pull-down assays with His-tagged ArdA. Our study opens up prospects for the specific use of antirestriction proteins for the regulation of gene expression. Surprisingly, ArdB, a non-DNA-mimic antirestriction protein used initially as a control, was also able to open the promoter, apparently through nonspecific interaction with DNA. We verified this effect with a distant ArdB homolog from a rhizobacterium, which was also able to open the promoter. Full article

Concussions exert a massive cost on our economic and healthcare systems. Many of the most commonly employed neurocognitive measures in concussion assessment have been shown to be psychometrically problematic. Additionally, norms are established from largely male populations. The present study investigates the use of a validated and reliable measure of concussion sequelae, the mobile half-version of the Connors Continuous Performance Test 3rd Edition (CCPT-3), on a representative population to study the influence of sex and age on normative values collected at baseline. Baseline data were analyzed from 71,976 participants across a wide range of academic and athletic contexts, as well as healthcare settings. Multiple regressions examined the influence of sex as a function of age in different developmental groups: children, adolescents, young adults, adults, and older adults. Sex effects emerged during childhood, peaked during adolescence, and decreased in adulthood. Females showed better accuracy (fewer commission and omission errors), whereas males had faster response speeds (hit-rate RT). Effect sizes were generally in the small to very small range (sex effect sizes ranged from Cohen’s d = 0.02 to 0.39). The findings highlight the importance of accounting for sex and age in cognitive test performance and underscore the impact of correcting for even small effects when working with large samples. Full article

Face super-resolution (FSR) aims to reconstruct high-quality high-resolution face images from low-resolution inputs. Although CNN–Transformer hybrid models have shown promising performance by jointly modeling local textures and global dependencies, their large parameter sizes and high computational costs hinder practical deployment in resource-constrained scenarios such as mobile devices and embedded systems. Meanwhile, existing lightweight SR models usually reduce complexity by simplifying network depth, channel dimensions, or convolutional operations, which may weaken feature representation capability and lead to insufficient recovery of fine facial structures. To address these issues, this paper proposes HCTIUNet, a lightweight CNN–Transformer hybrid network based on an inverted U-shaped architecture. Specifically, the proposed network integrates lightweight CNN branches for local facial texture extraction and Transformer branches for global dependency modeling, while introducing a multi-scale feature interaction strategy and a global feature refinement module to enhance facial structural details. Experimental results on the FFHQ, CelebA, and Helen datasets demonstrate that HCTIUNet achieves competitive performance under the ×8 face super-resolution setting, obtaining PSNR/SSIM/LPIPS values of 27.55 dB/0.765/0.225, 27.63 dB/0.761/0.212, and 27.53 dB/0.777/0.213, respectively. Moreover, HCTIUNet contains 10.5 M parameters, requires 9.9 G FLOPs, and achieves an inference time of 0.021 s. These results indicate that the proposed method achieves a favorable trade-off between reconstruction accuracy, perceptual quality, and computational efficiency, making it suitable for efficient face super-resolution applications. Full article

Autonomous mobile robots are used in optimizing warehouse logistics, yet achieving precise positioning during docking maneuvers and autonomous planning remains a technical challenge. This study presents a custom vision-based control system designed for an autonomous omnidirectional wheeled robot. The proposed methodology acquires visual feedback using a stereo camera integrated within the Robot Operating System framework. Two visual feedback control laws are formulated and rigorously evaluated: a Classic Position-Based Visual Servoing algorithm, which minimizes pose error using a quaternion-based approach, and a second solution that utilizes Dual Lie Algebra to compute the 3D visual sensor’s velocities, ensuring convergence towards the desired point-feature configuration. Experimental validation reveals that while both methods achieve docking, the dual pose-free approach enables more robust, effortless movement of the robot platform than Classic Position-Based Visual Servoing. Consequently, these findings indicate that integrating depth-based feature recovery with advanced algebraic strategies offers a stable control strategy for automated industrial scenarios. Full article

Traditional global localization systems frequently struggle with perceptual ambiguities in dynamic environments and structurally similar scenes, which severely compromises navigation robustness. Concurrently, conventional path planning methodologies rarely integrate proactive safety considerations regarding high-risk environmental features. To resolve these critical limitations, this paper introduces a comprehensive semantic-assisted framework for mobile robots to enhance both global localization and navigation. First, we develop a semantic-aware place representation derived from LiDAR point clouds. By explicitly filtering dynamic objects and assigning category-specific weights, this approach mitigates perceptual aliasing and ensures robust scene recognition. Furthermore, we implement a Hyper-Semantic Point Histogram (HyperSPH) to embed semantic encoding directly into local geometric features. A Semantic Geometric Consistency Filter is subsequently applied to eliminate matching outliers and maximize registration accuracy. For secure navigation, we propose the Semantic-guided Twin Delayed Deep Deterministic Policy Gradient with Long Short-Term Memory (S-TD3-LSTM) algorithm within a deep reinforcement learning architecture. This strategy extracts temporal correlations via Long Short-Term Memory networks and integrates a dedicated semantic cost function to optimize obstacle avoidance policies. Extensive experiments demonstrate that the proposed localization module achieves superior retrieval and pose estimation precision over conventional methods. In complex path planning scenarios, the S-TD3-LSTM algorithm ensures stable convergence and generates highly adaptive trajectories. By proactively identifying and bypassing semantic hazards, the integrated system drastically minimizes exposure to dangerous zones, successfully establishing a rigorous balance between path efficiency and execution safety. Full article