Search Results (43,736)

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

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

The widespread prevalence of Salmonella underscores the urgent need for rapid, sensitive, and reliable detection methods to ensure food safety and protection of public health. In this study, we successfully developed an integrated detection system that combines immunomagnetic separation with surface plasmon resonance (SPR) analysis. This system achieved high capture efficiencies, exceeding 96.04% in phosphate-buffered saline and over 91.66% in milk samples artificially spiked with S. Typhimurium at concentrations below 4.2 × 10⁴ CFU/mL. However, direct SPR detection of the isolated S. Typhimurium showed limited sensitivity, with a limit of detection (LOD) of 4.2 × 10⁷ CFU/mL. Incorporating a sandwich assay with antibody-conjugated gold nanoparticles significantly enhanced sensitivity, lowering the LOD by six orders of magnitude to 4.2 × 10¹ CFU/mL. The whole integrated process, integrating immunomagnetic separation with SPR analysis, was completed within 50 min. These results demonstrate that this AuNP-enhanced SPR platform offers both the rapidity and sensitivity essential for effective monitoring of food safety and traceability in Salmonella-related foodborne outbreaks, particularly in products such as milk. Full article

With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article

Borehole trajectory control is a fundamental task for directional well engineers. Now that there are inevitable errors about single-stand control in the field situation, it is difficult to deal with the complex underground problems in real time. In order to improve the efficiency of directional operation and the accuracy of wellbore trajectory control, this paper presents an improved Sparrow Search algorithm by integrating the multi-strategy model and Constant-Toolface models to calculate the single-stand control scheme for single-bent-housing motors in directional drilling. To evaluate the performance of the algorithm, the Particle Swarm algorithm, the Sparrow Search algorithm, and the improved Sparrow Search algorithm (LCSSA) are used to optimize the process parameters for each drilling, respectively. Numerical tests based on drilling data show that all three algorithms can predict the drilling parameters. In contrast, the LCSSA exhibits the fastest convergence and the smallest error after optimizing single-stand control, attaining an average convergence time of 0.08 s. It accurately back-calculated theoretical model parameters with high accuracy and met engineering requirements when applied to actual drilling data. In field applications, the LCSSA reduces the deviation from the planned trajectory by over 25%, restricting the deviation to within 0.005 m per stand; additionally the total drilling time was reduced by at least 18% compared to previous methods. The integration of the LCSSA with the drilling system significantly enhances drilling operations by optimizing trajectory accuracy and boosting efficiency and serves as an advanced tool for designing process parameters. Full article

Hibiscus sabdariffa (H. sabdariffa) is a high-value economic and functional crop, limited by agroclimatic conditions and low technological adoption. This systematic review examines the current state of artificial intelligence applications in agricultural management, analyzing 2111 records, selecting 82, and synthesizing 22 studies that meet the inclusion criteria. This review adopts a holistic framework aligned with three priority areas in agriculture—resource and climate management, crop productivity and quality, and sustainability—to explore how AI addresses key challenges in the cultivation and post-harvest processing of Hibiscus sabdariffa. The results show a predominance of classical machine learning techniques, with limited implementation of deep learning models. The most common applications include image classification, yield prediction, and analysis of bioactive compounds. However, limitations remain in the availability of open data, reproducible code, and standardized metrics. The narrative synthesis identified clear opportunities to integrate emerging technologies, such as deep neural networks and the Internet of Things (IoT), particularly in water management and stress monitoring. The review concludes that strengthening interdisciplinary research and promoting data openness is key to achieving a more resilient, sustainable, and technologically advanced crop. Full article

Background/Objectives: Family caregivers in palliative care often face complex physical, emotional, and logistical challenges, which can result in a significant caregiving burden. Dyadic interventions—designed to support both the patient and the caregiver simultaneously—have emerged as a promising holistic approach to enhancing well-being and quality of life. This systematic review aimed to evaluate the effects of dyadic support interventions in reducing caregiver burden among middle-aged and older adults receiving palliative care. Methods: A systematic literature search was conducted following PRISMA guidelines across five databases (CINAHL, MEDLINE, Web of Science, Scopus, and Google Scholar for grey literature) covering the period from 2019 to January 2025. Results: Of 653 records identified, 8 studies met the inclusion criteria. Interventions were typically delivered by multidisciplinary teams and included weekly in-person consultations, telephone follow-up, telemedicine, physical exercise sessions, laughter therapy, and music therapy over durations ranging from 16 weeks to 6 months. These programs resulted in reduced anxiety and depressive symptoms (PHQ-4, HADS, SDS, BAI, SAS), improved functional and social performance (SF-36), and/or enhanced quality of life (MQLQ, QOL-AD, KCCQ-12, EORTC QLQ-C30). In patients, they contributed to better symptom control (ESAS, CFS), while in caregivers, they effectively reduced burden (ZBI-12, FCBSI, CBI) and/or supported the anticipatory grief process (PGQ, AGS). However, not all studies reported consistently positive outcomes. Conclusions: Structured dyadic interventions that involve both patients and caregivers significantly improve outcomes in palliative care for middle-aged and older adults. Future research should examine their long-term impact and explore the integration of artificial intelligence to optimize intervention delivery. Full article

Background: Perinatal research faces significant challenges in understanding placental biology and maternal–fetal interactions due to limited access to human tissues and the lack of reliable models. Emerging technologies, such as liquid biopsy and single-cell analysis, offer novel, non-invasive approaches to investigate these processes. This scoping review explores the current applications of these technologies in placental development and the diagnosis of pregnancy complications, identifying research gaps and providing recommendations for future studies. Methods: This review adhered to PRISMA-ScR guidelines. Studies were selected based on their focus on liquid biopsy or single-cell analysis in perinatal research, particularly related to placental development and pregnancy complications such as preeclampsia, preterm birth, and fetal growth restriction. A systematic search was conducted in PubMed, Scopus, and Web of Science for studies published in the last ten years. Data extraction and thematic synthesis were performed to identify diagnostic applications, monitoring strategies, and biomarker identification. Results: Twelve studies were included, highlighting the transformative potential of liquid biopsy and single-cell analysis in perinatal research. Liquid biopsy technologies, such as cfDNA and cfRNA analysis, provided non-invasive methods for real-time monitoring of placental function and early identification of complications. Extracellular vesicles (EVs) emerged as biomarkers for conditions like preeclampsia. Single-cell RNA sequencing (scRNA-seq) revealed cellular diversity and pathways critical to placental health, offering insights into processes such as vascular remodeling and trophoblast invasion. While promising, challenges such as high costs, technical complexity, and the need for standardization limit their clinical integration. Conclusion: Liquid biopsy and single-cell analysis are revolutionizing perinatal research, offering non-invasive tools to understand and manage complications like preeclampsia. Overcoming challenges in accessibility and standardization will be key to unlocking their potential for personalized care, enabling better outcomes for mothers and children worldwide. Full article

The increasing demand for high-throughput, real-time, and single-molecule protein analysis in precision medicine has propelled the development of novel sensing technologies. Among these, nanopore-based methods have garnered significant attention for their unique capabilities, including label-free detection, ultra-sensitivity, and the potential for miniaturization and portability. Originally designed for nucleic acid sequencing, nanopore technology is now being adapted for peptide and protein analysis, offering promising applications in biomarker discovery and disease diagnostics. This review examines the latest advances in biological, solid-state, and hybrid nanopores for protein sensing, focusing on their ability to detect amino acid sequences, structural variants, post-translational modifications, and dynamic protein–protein or protein–drug interactions. We critically compare these systems to conventional proteomic techniques, such as mass spectrometry and immunoassays, discussing advantages and persistent technical challenges, including translocation control and signal deconvolution. Particular emphasis is placed on recent advances in protein sequencing using biological and solid-state nanopores and the integration of machine learning and signal-processing algorithms that enhance the resolution and accuracy of protein identification. Nanopore protein sensing represents a disruptive innovation in biosensing, with the potential to revolutionize clinical diagnostics, therapeutic monitoring, and personalized healthcare. Full article

Introduction: Polio supplementary immunisation activities (SIA) are implemented to rapidly increase vaccination coverage and interrupt the transmission of poliovirus in a specified geographical area. Polio SIA complements routine immunisation and is crucial for the eradication of the disease by increasing population immunity. However, several contextual factors (i.e., implementation determinants) can influence the success or failure of polio SIA implementation; as such, understanding their dynamics can enhance proactive planning for practice improvement. This study aimed to explore and map the contextual factors of polio SIA implementation in the African region using a critical systems thinking approach. Methods: A rapid review of published and grey literature was conducted. The search included the Global Polio Eradication Initiative library for programmatic reports and two databases (PubMed and Google Scholar). Data extraction was performed using a structured tool. Thematic analysis was performed to categorise the identified contextual factors according to the domains and constructs of the Consolidated Framework for Implementation Research (CFIR). Then, a causal loop diagram (CLD) was used to map the linkages between the identified factors. Results: A total of seventy-eight contextual factors across the five CFIR domains were identified: three for innovation, twenty for outer setting, sixteen for inner setting, twenty-six for individuals, and thirteen for the implementation process. A system map of all the factors using CLD revealed multiple contingent connections, with eleven reinforcing loops and four balancing loops. Conclusions: This study identified the multilevel nature of the contextual factors that influence polio SIA, including their dynamics. The integration of CLD and CFIR in this study offers critical insights into the potential feedback loops that exists between the contextual factors which can be used as leverage points for policy and practice improvements, including tailoring strategies to enhance polio campaign implementation effectiveness, especially with the expanded use of the novel Oral Polio Vaccine type 2 (nOPV2) across countries in the region. Full article

Prime editing (PE), a novel “search-and-replace” genome editing technology, demonstrates significant potential for crop genetic improvement due to its precision and versatility. However, since its initial application in plants, PE technology has consistently faced challenges of low and variable editing efficiency, representing a major bottleneck hindering its broader application. Therefore, this study conducted a systematic review following the PRISMA 2020 guidelines. We systematically searched databases—Web of Science, PubMed, and Google Scholar—for studies published up to June 2025 focusing on enhancing PE performance in crops. After a rigorous screening process, 38 eligible primary research articles were ultimately included for comprehensive analysis. Our analysis revealed that early PE systems such as PE2 could perform diverse edits, including all 12 base substitutions and small insertions or deletions (indels), but their efficiency was highly variable across species, targets, and edit types. To overcome this bottleneck, researchers developed four major optimization strategies: (1) engineering core components such as Cas9, reverse transcriptase (RT), and editor architecture; (2) enhancing expression and delivery via optimized promoters and vectors; (3) improving reaction processes by modulating DNA repair pathways or external conditions; and (4) enriching edited events through selectable or visual markers. These advancements broadened PE’s targeting scope with novel Cas9 variants and enabled complex, kilobase-scale DNA insertions and rearrangements. The application of PE technology in plants has evolved from basic functional validation, through systematic optimization for enhanced efficiency, to advanced stages of functional expansion. This review charts this trajectory and clarifies the key strategies driving these advancements. We posit that future breakthroughs will increasingly depend on synergistically integrating these strategies to enable the efficient, precise, and predictable application of PE technology across diverse crops and complex breeding objectives. This study provides an important theoretical framework and practical guidance for subsequent research and application in this field. Full article

Traditional hydraulic-fracturing models are restricted by low computational efficiency, insufficient field data, and complex physical mechanisms, causing evaluation delays and failing to meet practical engineering needs. To address these challenges, this study innovatively develops a dynamic hydraulic-fracturing monitoring method that integrates machine learning with numerical simulation. Firstly, this study uses GOHFER 9.5.6 software to generate 12,000 sets of fracture geometry data and constructs a big dataset for hydraulic fracturing. In order to improve the efficiency of the simulation, a macro command is used in combination with a Python 3.11 code to achieve the automation of the simulation process, thereby expanding the data samples for the surrogate model. On this basis, a parameter sensitivity analysis is carried out to identify key input parameters, such as reservoir parameters and fracturing fluid properties, that significantly affect fracture geometry. Next, a neural-network surrogate model is established, which takes fracturing geological parameters and pumping parameters as inputs and fracture geometric parameters as outputs. Data are preprocessed using the min–max normalization method. A neural-network structure with two hidden layers is chosen, and the model is trained with the Adam optimizer to improve its predictive accuracy. The experimental results show that the efficiency of automated numerical simulation for hydraulic fracturing is significantly improved. The surrogate model achieved a prediction accuracy of over 90% and a response time of less than 10 s, representing a substantial efficiency improvement compared to traditional fracturing models. Through these technical approaches, this study not only enhances the effectiveness of fracturing but also provides a new, efficient, and accurate solution for oilfield fracturing operations. Full article

High-performance musical instrument training is a demanding discipline that engages cognitive, neurological, and physical skills. Professional musicians invest substantial time and effort into mastering their repertoire and developing the muscle memory and reflexes required to perform complex works in high-stakes settings. While existing surveys have explored the use of music in therapeutic and general training contexts, there is a notable lack of work focused specifically on the needs of professional musicians and advanced instrumental practice. This topical review explores the potential of EEG-based brain–computer interface (BCI) technologies to integrate real-time feedback of biomechanic and cognitive features in advanced musical practice. Building on a conceptual framework of technology-enhanced musical practice (TEMP), we review empirical studies of broad contexts, addressing the EEG signal decoding of biomechanic and cognitive tasks that closely relates to the specified TEMP features (movement and muscle activity, posture and balance, fine motor movements and dexterity, breathing control, head and facial movement, movement intention, tempo processing, ptich recognition, and cognitive engagement), assessing their feasibility and limitations. Our analysis highlights current gaps and provides a foundation for future development of BCI-supported musical training systems to support high-performance instrumental practice. Full article

Apolipoprotein A (ApoA) proteins, ApoA-I, ApoA-II, ApoA-IV, and ApoA-V, play critical roles in lipid metabolism, neuroinflammation, and blood–brain barrier integrity, making them pivotal in neurological diseases such as Alzheimer’s disease (AD), stroke, Parkinson’s disease (PD), and multiple sclerosis (MS). This review synthesizes current evidence on their structural and functional contributions to neuroprotection, highlighting their dual roles as biomarkers and therapeutic targets. ApoA-I, the most extensively studied, exhibits anti-inflammatory, antioxidant, and amyloid-clearing properties, with reduced levels associated with AD progression and cognitive decline. ApoA-II modulates HDL metabolism and stroke risk, while ApoA-IV influences neuroinflammation and amyloid processing. ApoA-V, although less explored, is implicated in stroke susceptibility through its regulation of triglycerides. Genetic polymorphisms (e.g., APOA1 rs670, APOA5 rs662799) further complicate disease risk, showing population-specific associations with stroke and neurodegeneration. Therapeutic strategies targeting ApoA proteins, including reconstituted HDL, mimetic peptides, and gene-based approaches, show promise in preclinical models but face translational challenges in human trials. Clinical trials, such as those with CSL112, highlight the need for neuro-specific optimization. Further research should prioritize human-relevant models, advanced neuroimaging techniques, and functional assays to elucidate ApoA mechanisms inside the central nervous system. The integration of genetic, lipidomic, and clinical data offers potential for enhancing precision medicine in neurological illnesses by facilitating the generation of ApoA-targeted treatments and bridging current deficiencies in disease comprehension and therapy. Full article

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article