Search Results (3,834)

This study examines institutional processes in digital justice through a mixed conceptual approach that integrates bibliometric analysis and technology-adoption modeling, incorporating artificial intelligence (AI) as a projected component rather than an implemented system. A corpus of approximately 200 Scopus-indexed documents (2003–2024) was analyzed, identifying five dominant thematic clusters: advanced technologies, institutional justice, digital government, judicial information management, and digital criminal justice. The results reveal persistent gaps in the literature, particularly in rural and underserved communities, where connectivity barriers and the limited application of adoption models hinder inclusive digital transformation. As an institutional contribution, the study presents the conceptual design of the digital solution “Travel Permits—Accessible Justice”, developed under a Service-Oriented Architecture (SOA) and projected for future integration with AI-supported components to automate judicial authorizations through biometric validation, electronic signatures, and digital delivery. To evaluate its potential acceptance, the Technology Acceptance Model (TAM) is analytically adapted and extended to the community-based judicial context, framing institutional learning processes as a prospective form of learning analytics focused on user interaction, perceived usefulness, perceived ease of use, and behavioral intention. Taken together, the integration of bibliometric evidence with an extended TAM, along with the projected incorporation of AI-supported institutional learning processes, offers a coherent foundation for future studies on inclusive digital innovation in justice environments. Full article

Extracellular vesicles (EVs) mediate intercellular communication by delivering proteins and RNAs, with their molecular cargo often reflecting the biological context of their source. Perinatal tissues are promising sources of EV-related biomaterials with potential dermatologic applications. In this study, we compared EV-related molecular cargo from two umbilical cord-associated sources, umbilical cord mesenchymal stem cell (UCMSC)-derived EVs and cord blood plasma (CBP), to investigate whether these materials exhibit distinct functional effects on melanogenesis. UCMSC-derived EVs were isolated from conditioned culture medium and characterized using nanoparticle tracking analysis (NTA), cryo-electron microscopy (cryo-EM), and canonical EV marker detection, while cord blood samples were processed to obtain plasma following centrifugation and filtration, containing EVs together with soluble plasma components. Functional assays in the murine melanocyte cell line B16F10 demonstrated that UCMSC-derived EVs suppressed melanin production, whereas CBP treatment enhanced melanogenesis. Integrative omics analyses combining microRNAs (miRNAs) microarray profiling and proteomic characterization revealed distinct molecular signatures between UCMSC-derived EVs and CBP samples. Functional validation using miRNA mimic assays showed that selected miRNAs, including miR-6862-5p, miR-3622b-5p, miR-7847-3p, miR-6774-5p, and miR-4685-5p, reduced melanin production, whereas others, including miR-203a-3p, miR-126-3p, miR-139-5p, and miR-15b-5p, increased melanin levels. Pathway analysis using Ingenuity Pathway Analysis (IPA) (QIAGEN Inc.) associated these miRNA subsets with signaling pathways involved in melanogenesis. Together, these findings indicate that UCMSC-derived EVs and CBP exhibit opposite functional effects on melanogenesis and possess distinct miRNA and protein cargo profiles, providing potential molecular targets for modulating pigmentation and supporting the development of EV-related therapeutic strategies for pigmentation disorders. Full article

Egg quality during storage is a critical factor influencing consumer acceptance and food safety. However, the effects of storage methods on eggshell translucency and surface microbiota remain insufficiently understood. In this study, three common packaging methods, paper pulp trays (PPT), expanded polyethylene foam trays (EPE), and transparent plastic boxes (TPB), were evaluated to assess their impact on egg translucency, internal quality, and microbial communities. Egg quality traits were measured, and microstructural and elemental characteristics were examined using scanning electron microscopy and compositional analysis. In addition, 16S rRNA sequencing was performed to characterize the eggshell surface microbiota. The packaging method significantly influenced translucency development, with EPE mitigating mottling better than PPT and TPB. Storage duration was the predominant driver of internal quality deterioration, particularly affecting the albumen height and Haugh units. Translucency was not associated with shell thickness or mineral content but was likely associated with moisture dynamics. Distinct microbial communities are shaped by different packaging materials. These findings provide new insights into the mechanisms underlying translucency and microbial ecology during egg storage. This highlights the practical implications of optimizing packaging strategies to maintain egg quality, extend the shelf life, and ensure microbial safety. Full article

Background/Objectives: Mental fatigue in adolescents is a growing concern in educational contexts, positioning physical education (PE) teachers as key agents in designing effective mitigation strategies. This study examined the perceptions of Spanish high school PE teachers regarding the causes, consequences, and potential countermeasures for students’ mental fatigue. Methods: A total of 116 in-service teachers (81 males and 35 females; mean teaching experience 7.8 ± 5.3 years) from 12 autonomous communities throughout Spain completed a comprehensive 34-item electronic questionnaire. The instrument assessed the perceived existence, etiology, and outcomes of mental fatigue through multiple-choice, dichotomous (yes/no), and five-point Likert scale questions, with particular attention given to the role of physical activity (PA) in symptom alleviation. A quantitative frequency analysis was conducted to examine the data. Results: The main findings reveal a strong consensus among the teachers (77.6% to 87.9%) on the prevalence of mental fatigue, with its primary causes attributed to academic pressure and sedentarism. The consequences were identified as increased irritability and reduced cognitive performance. The teachers overwhelmingly endorsed moderate intensity PA as the most effective countermeasure. However, a significant gap was identified between this theoretical awareness and the systematic implementation of targeted strategies within schools. Conclusions: These results underscore the critical need for professional development programs and structural support to translate teacher knowledge into practical intervention, suggesting important directions for future research. Full article

To meet the increasingly strict requirements of modern communication, radar detection and electronic measurement systems for wide-bandwidth, low-insertion-loss and high-isolation signal routing, this paper presents a 16 × 16 programmable switch matrix that simultaneously achieves wideband operation (DC-40 GHz), low insertion loss (≤0.9 dB maximum), high isolation (>50 dB typical), and systematic modular scalability, a combination not found in existing implementations. The matrix, constructed with high-quality coaxial switches and optimized RF circuitry and electromagnetic structures, provides flexible and stable single-pole multi-throw (SPMT) signal routing across an ultra-wide frequency range from DC to 40 GHz. The switch matrix features a modular architecture, integrating multiple RF switching units, drive control circuits, and communication interface modules. This architecture achieves minimal signal path depth while maintaining full connectivity between any input and output port, directly minimizing cumulative insertion loss. Through precise impedance matching design and isolation structure optimization, the system still exhibits outstanding transmission characteristics at the 40 GHz high-frequency end: typical insertion loss does not exceed 0.9 dB, and the isolation between channels is better than 50 dB, effectively ensuring the integrity of signals in complex multi-channel environments. To meet the requirements of automated testing and remote control, the equipment integrates dual communication interfaces (serial port/network port), supports the SCPI command set and TCP/IP protocol, and can be conveniently embedded in various test platforms to achieve instrument interconnection and test process automation. Experimental verification shows that this matrix exhibits excellent switching stability and signal consistency across the entire 40 GHz, with a switching action time of less than 10 ms. Furthermore, it is capable of real-time topology reconfiguration via a microcontroller or FPGA. These innovations collectively deliver a switch matrix that meets the demanding requirements of 5G communication, millimeter-wave radar, and aerospace defense systems—applications where bandwidth, signal integrity, and system flexibility are paramount. Full article

This study presents a scalable decision support system (DSS) framework designed to meet the growing demands of instant data-driven decision-making environments. The architecture integrates key technologies, including Apache Kafka for parallel data streaming, a Python-based data analytics module for distributed processing, JWT-based secure user authentication, and WebSocket communication for instantaneous prediction delivery. The system performs mitochondrial localization in electron microscopy (EM) images using multiple versions of the YOLO (You Only Look Once) object detection model. The publicly available CA1 Hippocampus dataset was used for detection evaluation. Among the evaluated models, YOLOv10x achieved the highest detection performance, yielding a mean average precision (mAP) score of 95.2%. Experimental evaluations of the DSS were conducted under simulated load conditions using the Artillery tool to assess the system’s scalability and responsiveness. Empirical results indicate consistent low-latency performance across varying consumer group sizes, confirming the architecture’s ability to scale the analytics module horizontally without compromising responsiveness. These findings validate the system’s suitability for just-in-time decision support applications. In particular, the system may support clinicians in the task of mitochondrial analysis, where structural abnormalities can be indicative of pathological conditions, including cancer. By enabling early detection of such abnormalities, the proposed framework has the potential to contribute to the timely diagnosis of diseases such as cancer. The proposed study differs from existing studies by combining deep learning with real-time scalable data processing technologies, such as Kafka and WebSocket, in a web-based DSS application for mitochondria detection. Full article

Background: Oral healthcare workers play a pivotal role in exploring the significant potential of integrating oral healthcare with overall health within a healthcare system. This review aims to identify the main barriers and facilitators to integrating oral health into primary and general healthcare from the perspectives of oral healthcare professionals. Methods: The study adhered to the Arksey and O’Malley methodological framework for scoping reviews. Five main databases were systematically searched, namely Web of Science, PubMed, Scopus, ProQuest, and Embase, spanning from 1 January 2000 to 31 December 2024. The Rainbow Model served as the framework for content analysis, organizing the advantages, disadvantages, barriers, and facilitators into micro, meso, and macro levels. Results: Five integration domains were identified across macro, meso, and micro levels, illustrating how oral health can be systematically embedded within general health through the utilization of oral healthcare professionals. These domains encompassed chronic disease management (screening, counseling, and referral), emergency management, electronic health records, interprofessional education, and tele-dentistry, highlighting policy, organizational, and workforce levers for strengthening care integration, enhancing system efficiency, and improving access and equity. Conclusions: This scoping review synthesizes five integration domains and four cross-cutting strategic directions for embedding oral health within broader healthcare systems. By conceptualizing integration across macro, meso, and micro levels, the study provides a structured framework that may serve as a reference for policymakers, educators, and health service leaders. The findings highlight potential enablers, such as coordinated governance, workforce development, digital infrastructure, and community engagement, which could support integration. Full article

In this paper, we propose a downlink (DL) channel estimation scheme for frequency-division duplex (FDD) multi-antenna orthogonal frequency-division multiplexing (OFDM) systems, leveraging atomic norm minimization (ANM) and deep neural networks (DNN). Unlike time-division duplex (TDD) systems, where uplink (UL) and DL channels are reciprocal, FDD systems do not share this reciprocity, leading to increased channel training overhead. However, both theoretical analyses and empirical evidence reveal that key channel characteristics—such as angles of arrival and departure, path delays, and the number of propagation paths—exhibit partial reciprocity between UL and DL. Building on this insight, we design a DL channel estimation scheme that exploits frequency-independent UL parameters along with estimated DL channel gains. Our method integrates ANM with DNN to enhance estimation accuracy and efficiency. Specifically, ANM formulates the estimation problem while avoiding the off-grid errors inherent in traditional grid-based methods. To further mitigate performance degradation in clustered-path channels and reduce computational complexity, we introduce a DNN-based architecture that predicts channel parameters. The DNN captures hidden relationships between received pilot signals and frequency-independent channel parameters, enabling accurate estimation with linear time complexity. During training, ANM assists in serving users, ensuring reliable performance. Once the DNN is fully trained, it takes over to balance quality of service (QoS) and latency, providing an efficient and accurate solution for DL channel estimation in FDD-OFDM systems. Full article

Exosomes, a major subpopulation of small extracellular vesicles (sEV), are conserved mediators of intercellular communication, yet the properties of their endosomal precursors, intraluminal vesicles (ILV), have not been systematically quantified across species or imaging modalities. This study systematically evaluates ILV sizes across diverse eukaryotic species and modalities while assessing their relationship to secreted sEV sizes. We carried out two complementary meta-analyses of ILV sizes based on transmission electron microscopy (TEM) and cryogenic electron microscopy (cryo-EM) data across species. This was followed by in situ assessment of sEVs secreted by HEK293T cells with TEM, nanoparticle tracking analysis and super-resolution microscopy characterization. Across species, imaging modalities, and cellular contexts, ILV sizes were under approximately 200 nm, with a mean diameter of 100.5 nm, overlapping with the size range of sEVs. This study addresses an existing knowledge gap by systematically evaluating ILV size across species and revealing an upper size limit of approximately 200 nm. Full article

Janus nanoparticles (JNPs) synthesis has caught the scientific community’s attention due to their amphiphilic properties and extensive areas of application. In this work, different new copper–silica-based and silica-based JNPs were synthesized using a novel masking methodology and a self-assembly method based on sol–gel procedures, respectively. Moreover, various techniques were used to characterize the developed nanomaterials, including scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). Two types of copper–silica-based Janus nanoparticles were synthesized with a 40 to 70 nm size, while SiO₂-based JNPs of around 135 nm were obtained. The duality of different JNPs was confirmed by SEM and by a simple and economical route based on an emulsion stabilization path: analyzing dispersion/aggregation and associated behavior at the immiscible solvent interface. JNPs exhibited an extended residence time over 20 days at an immiscible solvent interface, thereby enhancing the resulting emulsion interface stability. This behavior highlighted their amphiphilic characteristics in comparison to conventional nanoparticles. Consequently, a procedure to determine nanoparticle amphiphilicity could be further standardized. Full article

Background/Objectives: Children and adolescents undergoing Haematopoietic Stem Cell Transplantation (HSCT) experience complex symptoms, often under-reported by patients and undetected by clinicians, which cause distress. Patient-Reported Outcome Measures (PROMs) offer a way to capture symptom experiences directly from patients, with the potential of supporting effective symptom assessment and management, yet their routine use in paediatric HSCT remains unclear. This systematic review synthesises evidence on PROMs used during inpatient paediatric HSCT care, examining their role in symptom monitoring and clinical decision-making, and identifying gaps to strengthen person-centred, developmentally appropriate care. Methods: We searched the MEDLINE, CINAHL, Embase, APA PsychINFO, and Cochrane Library in October 2024 for studies published in English between 2014 and 2025 describing the use of PROMs during inpatient paediatric (0–18 years) HSCT admission (up to Day +100 post HSCT). In March 2025, prior to data extraction, we added additional studies published by authors of included studies. Two-stage independent screening and data extraction were conducted, and the Quality Assessment with Diverse Studies (QuADS) tool was used to appraise each study. Narrative syntheses informed by Symptom Management Theory were used to compare PROM use, clinical integration, and reported impacts. Results: Seventeen studies met inclusion criteria, describing 20 PROMs used during paediatric HSCT hospitalisation. PROMs captured a wide range of physical and psychological symptoms, with pain and nausea most frequently reported. While PROMs reportedly improve symptom detection and communication, integration into routine paediatric HSCT clinical care was rare; and only two studies systematically used PROMs data to guide symptom management. Evidence of PROMs-driven improvements in HSCT clinical outcomes was scarce, and longitudinal data on symptom trajectories were limited. Conclusions: PROMs are not routinely used to inform clinical practice in paediatric HSCT, and current evidence provides only a partial understanding of symptom trajectories and lived symptom experiences during the paediatric acute transplant admission. To realise the full potential of PROMs in enhancing symptom assessment and management, systematic PROMs integration into clinical workflows is required, supported by electronic health record integration, clinician training, and longitudinal research designs that capture symptom evolution across the transplant continuum. Full article

In today’s world, electronics and networked systems, such as IoT devices, embedded platforms and smart environments, are increasingly popular and widespread. As a result, these systems become more exposed to cyber threats. The malicious URL is also one of the most widespread yet perilous vectors of cyberattack, as it is widely used in phishing, malware distribution, and command-and-control communication. The security of these electronic systems necessitates real-time, lightweight and intelligent detection techniques that must be efficient in resource-constrained environments. In order to meet this requirement, we propose SwiftURL, a lightweight deep learning model to detect malicious URLs that can be specifically deployed in modern electronic environments. SwiftURL leverages knowledge distillation from a transformer-based ELECTRA-Small teacher model, transferring detection capability into a smaller and faster student model while maintaining high performance. Experimental results on a public Kaggle dataset of malicious URLs demonstrate that SwiftURL achieves an accuracy of 94.38%, reduces computational overhead by 35%, and accelerates training time by 15%. These findings highlight SwiftURL’s effectiveness as a practical solution for enhancing cybersecurity in electronic and networked systems through efficient, on-device URL threat detection. Full article

Bioelectromethanogenesis, the microbial conversion of carbon dioxide (CO₂) into methane (CH₄) using a cathode, offers a promising route for biogas upgrading and renewable energy storage. The flow field is an essential factor influencing the performance of bioelectromethanogenesis, and the stability and efficiency of the biocathode play important roles in this process. This study systematically investigated the effect of different internal-circulation flow rates on the biocathode initiated without the electric field and the reactor effluent. It was found that the methane production of the biocathode initiated without the electric field was increased by around 30% at an internal-circulation flow rate of 18 mL/min, which was stronger than that of the biocathode initiated by the reactor effluent. The relative content of the extracellular polymeric substance (EPS) heme was increased by 4%, while the EPS electron accepting capacity was much higher than that initiated by reactor effluent. Furthermore, the microbial community analysis showed that the functional methanogen on the biocathode initiated without an electric field was Methanosaeta (17%) and Methanobacterium (8%). This study could provide support for the dynamic operation of biogas upgrading in microbial electrolysis cells. Full article

With the rapid development of 5G communication technology, 5G networks are designed to achieve three major objectives: higher bandwidth, support for a greater number of connected devices, and lower latency. It is necessary to meet the requirements of the three primary 5G application scenarios: Enhanced Mobile Broadband, Massive Machine-Type Communications, and Ultra-Reliable and Low Latency Communications (uRLLC). To meet the stringent requirements for time synchronization and low latency, 5G is being integrated with Ethernet-based Time-Sensitive Networking (TSN) technologies. TSN plays an important role in achieving time determinism in uRLLC scenarios and ensures low-latency and high-reliability Ethernet communication through the transmission of time signals that are also known as the Precision Time Protocol. We applied TSN technology in the Institute of Electrical and Electronics Engineers 802.1Qbv standard and evaluated its transmission delay performance. Modifying the gate control list (GCL) to accommodate varying network traffic ensures low-latency transmission for high-priority traffic. We propose two GCL configurations for TSN that incorporate time-aware shaper to achieve efficient traffic scheduling. Full article

Remote sensing (RS) images are often degraded by atmospheric haze, which compromises both visual interpretation and downstream applications. To address this, we introduce CSGL-Former, a novel Cross-Stripes Global–Local Fusion Transformer for RS image dehazing. Our model efficiently captures anisotropic long-range dependencies using cross-stripes attention (CSA) and aggregates hierarchical global semantics via a Multi-Layer Global Aggregation (MLGA) module. In the decoder, global context is adaptively blended with fine-grained local features to restore intricate textures. Finally, inspired by the atmospheric scattering model, a soft reconstruction head restores the clear image by predicting spatially varying affine parameters, strictly preserving content fidelity while effectively removing haze. Trained end-to-end, CSGL-Former demonstrates a compelling balance of accuracy and efficiency. Extensive experiments on the RRSHID and SateHaze1K benchmarks show that our model achieves state-of-the-art or highly competitive performance against representative baselines. Ablation studies further validate the effectiveness of each proposed component. Full article