Search Results (1,707)

Metastatic spinal cord compression (MSCC) is a common complication in cancer patients, occurring in 3–5% of diagnosed cases annually, and serves as the initial manifestation of malignancy in 20% of patients. Timely diagnosis and management are critical due to the risk of irreversible neurological damage and the significant impact on both quality and quantity of life. The National Institute for Health and Care Excellence (NICE) recommends that patients presenting with back pain accompanied by neurological signs and/or symptoms undergo whole-spine magnetic resonance imaging (MRI) within 24 h. This retrospective study at Medway Maritime Hospital in England aimed to assess adherence to these guidelines by reviewing the time from presentation to MRI for patients exhibiting symptoms and/or signs of MSCC. Data for 69 patients were collected over one year using electronic patient records and the acute oncology service database. Analysis revealed that MRI was conducted within 24 h in only 43 out of 69 cases (62%), and 16 out of 25 delayed cases (i.e., MRI done beyond the recommended 24 h window) experienced delays of more than 48 h. To improve guideline adherence, interventions such as informational flyers and regular MSCC training sessions, including trainee teaching and presentations during grand rounds, were implemented. A follow-up re-audit involving 113 patients over one year demonstrated improved adherence to the 24 h MRI guideline, with 81 out of 113 cases (71%) meeting the target. The second cycle also documented reasons for delays, identifying patient compliance and pain control as primary factors. Additionally, the timing of steroid administration following suspicion of MSCC was recorded. Future studies should re-assess adherence, focus on better documentation of delay causes, enhance pain management before MRI scans, and ensure prompt steroid administration. Full article

Household energy consumption is one of the major drivers of carbon emissions, and an in-depth analysis of its influencing factors, along with forecasting carbon emission trajectories, is crucial for achieving China’s carbon emission targets. This study reviews the research progress on urban household energy-related carbon emissions (HErC) in China since 2000, with a focus on the latest developments in influencing factors. The study categorizes these factors into five major groups: household characteristics, economic attributes, energy consumption features, awareness and norms, and policies and interventions. The findings indicate that income levels, energy efficiency, and household size are the key determinants of urban HErC of China and are commonly used as core assumptions in scenario-based forecasts of emission trends. In addition, although environmental awareness and government services have increasingly garnered attention, their specific effects require further investigation due to the challenges in quantification. A synthesis of existing forecasting studies suggests that, without the implementation of effective measures, HErC will continue to rise, and the peak emission period will be delayed. Enhancing building and energy efficiency, promoting low-carbon consumption and clean energy applications, and implementing multidimensional coordinated policies are considered the most effective pathways for emission reduction. Full article

This study discusses the issue of train rescheduling in high-speed railways (HSR) when unexpected interruptions occur. These interruptions can lead to delays, cancellations, and disruptions to passenger travel. An optimization model for train rescheduling under uncertain-duration interruptions is proposed. The model aims to minimize both the decline in passenger service quality and the total operating cost, thereby achieving sustainable rescheduling. Then, a hybrid optimization algorithm combining rolling horizon optimization with a deep-learning-embedded NSGA-II algorithm is introduced to solve this multi-objective problem. This hybrid algorithm combines the advantages of each single algorithm, significantly improving computational efficiency and solution quality, particularly in large-scale scenarios. Furthermore, a case study on the Beijing–Shanghai high-speed railway shows the effectiveness of the model and algorithm. The optimization rates are 16.27% for service quality and 15.58% for operational costs in the small-scale experiment. Compared to other single algorithms or algorithm combinations, the hybrid algorithm enhances computational efficiency by 26.21%, 15.73%, and 25.13%. Comparative analysis shows that the hybrid algorithm outperforms traditional methods in both optimization quality and computational efficiency, contributing to enhanced overall operational efficiency of the railway system and optimized resource utilization. The Pareto front analysis provides decision makers with a range of scheduling alternatives, offering flexibility in balancing service quality and cost. In conclusion, the proposed approach is highly applicable in real-world railway operations, especially under complex and uncertain conditions, as it not only reduces operational costs but also aligns railway operations with broader sustainability goals. Full article

Background: This study aims to analyze the vaccination status and factors influencing delayed vaccination among toddlers born to hepatitis B surface antigen (HBsAg)-positive mothers. Methods: Data of HBsAg-positive mothers between 1 January 2021 and 31 December 2022 were provided by the Suzhou Maternal and Child Health Care and Family Planning Service Center. The vaccination records were obtained from the Jiangsu Province Immunization Service Management Information System. Logistic regression analysis was used to analyze influencing factors of delayed vaccination. Results: A total of 4250 toddlers born to HBsAg-positive mothers were documented. The data revealed that the first dose of the hepatitis B vaccine was administered to 100% of the toddlers. In addition, the coverage of the National Immunization Program (NIP) vaccines among these toddlers ranged from 92.9% to 99.4%. The proportion of delayed NIP vaccination varied between 0% and 12.2%. The proportion of delayed Bacillus Calmette–Guérin (BCG) vaccination was 11.3%, with the delay predominantly observed between 4 and 6 months. Notably, the proportion of delayed BCG vaccination among the toddlers born to HBsAg-positive mothers was significantly higher than that in the general population. Additionally, the proportion of the first dose of non-NIP vaccines was 3.3–36.4%, and the proportion of DTaP-IPV/Hib was 27.0%. Logistic regression analysis revealed that the regional level, the mother’s human papillomavirus (HPV) vaccination status, and the infant’s birth weight were significant factors influencing the timeliness of vaccination. Conclusions: Although the vaccination status of toddlers born to HBsAg-positive mothers in Suzhou city remains stable, the issue of delayed vaccination requires attention. It is essential to continue strengthening targeted vaccine education to reduce vaccine hesitancy and improve the rate of timely vaccination. Full article

The disruptive innovation of artificial intelligence (AI) chatbots is affecting educational dominance, which must be considered by higher educational institutions. Open and Distance Learning (ODL) becomes imperative for the effective and interactive communication between the institutions and learners. Drawbacks of isolation, motivation, insufficient time to study, and delay feedback mechanisms are some of the challenges encountered by ODL learners. The consequences have led to an increase in students’ attrition rate, which is one of the key issues observed by many authors facing ODL institutions. The National Open University of Nigeria (NOUN), one of the ODL institutions in Nigeria, is limited to an existing e-ticketing support system which is manually operated. A study on 2000 students of the NOUN using an online survey method revealed that 579 students responded to the questionnaire, equalling 29%. Further findings revealed significant delay time responses and inadequate resolutions as major barriers affecting the e-ticketing system in the NOUN. However, despite the quantitative method employed in the study, an artificial intelligence chatbot for automatic responses was also developed using Python 3.8+, ChatterBot (Version 1.0.5) Chatbot Framework, SQLite (default ChatterBot Storage, NLTK, and Web Interface: Flask (for integration with a web application). In testing the system, out of the 579 respondents, 370, representing 64% of the respondents, claimed that the chatbot was extremely helpful in resolving their issues and complaints. The adaptation of an AI chatbot in an ODL institution as a support system reduces the attrition rate, thereby revolutionising support services’ potential in Open and Distance Learning systems. Full article

Maritime transport is a pivotal logistics mode for the long-distance and bulk transportation of goods. However, the intricate planning involved in this mode is often hindered by uncertainties, including weather conditions, cargo diversity, and port dynamics, leading to increased costs. Consequently, accurate estimation of the total (stay) time of the vessel and any delays at the port are essential for efficient planning and scheduling of port operations. This study aims to develop predictive analytics to address the shortcomings in the previous works of port operations for a vessel’s Stay Time and Delay Time, offering a valuable contribution to the field of maritime logistics. The proposed solution is designed to assist decision-making in port environments and predict service delays. This is demonstrated through a case study on Brazil’s ports, where the best performance is observed for tree-based methods. Additionally, feature analysis is used to understand and interpret key factors impacting maritime logistics, enhancing the overall understanding of the complexities involved in port operations. Full article

To meet the growing needs of automobile users, and to provide services on demand with stable and efficient paths across different bands amidst this proliferation of users, an integrated approach to the software-defined vehicular network (SDVN) is proposed in this paper. Due to the significant increase in users, DSRC is already considered insufficient to fulfill modern needs. Hence, to enhance network performance and fulfill the growing needs of users in SDVN environments, we implement cognitive technology by integrating the DSRC, mmWave, and THz bands to find stable paths among different nodes. To manage these different technologies, an SDN controller is employed as the main controller (MC), recording the global state of all nodes within the network. Channel sensing is conducted individually for each technology, and sensing results—representing the number of available bands for secondary communications—are updated periodically in the MC. Consequently, the MC manages connections by switching between DSRC, mmWave, and THz bands, providing stable paths between the source and destination. The switching decision is taken by considering both the distance from the MC and the availability of channels among these three technologies. This cognitive integration of bands in SDVN improves performance in terms of network delay, packet delivery, and overhead ratio. Full article

Avian scavengers, particularly vultures, play a crucial role in ecosystem health by efficiently consuming carcasses, thereby reducing pathogen abundance and limiting disease transmission to wildlife, livestock, and humans. In addition to the indispensable role of vultures, they are a particularly threatened group of birds. This study investigates the environmental factors that optimize this ecosystem service by examining the scavenging dynamics of vultures and other species at deer carcasses in a hunting area in Sierra Madrona, Ciudad Real, Spain. Carcasses were placed in habitats with different vegetation densities (open vs. dense) and altitudes (high vs. low) and were monitored for 30 days using camera traps. Data on scavenger diversity, arrival times, and carcass persistence were analyzed using Bayesian multilevel models. Results reveal that vegetation density and altitude significantly influence vulture arrival times and carcass duration, with dense vegetation and low altitudes delaying scavenger access. These findings provide actionable insights for game management to enhance vulture conservation and improve both public and ecosystem health through timely and effective carcass removal. Full article

Early intervention services are a critical support for young children experiencing developmental delays and disabilities. Due to myriad negative social and economic conditions, some infants and young children, namely Black, Indigenous, and other children of color, as well as those experiencing poverty, are at greater risk of experiencing a developmental delay or disability and experiencing issues of access to needed services and supports within and beyond early intervention programs. Due to these systemic issues, these infants and young children are more likely to have caregivers experiencing mental health concerns and issues of access to services and supports. Early childhood serving programs are faced with meeting the behavioral health needs of families experiencing cumulative vulnerabilities. Some early intervention (EI) programs are partnering with infant and early childhood mental health (IECMH) providers to meet mental health needs. IECMH consultation (IECMHC) is a multi-level support that aims to build the capacity of early childhood programs to meet the needs of young children, families, caregivers, and staff. IECMHC has an intentional focus on promoting and ensuring equity, specifically more equitable systems. It focuses on addressing inequities impacting young children and their caregivers, thus strengthening these essential collaborations. This paper highlights research demonstrating the importance and collective power of IECMHC in early intervention programs to advance behavioral health and equity. Full article

In view of the future of the Internet of Things (IoT), the number of edge devices and the amount of sensing data and communication data are expected to increase exponentially. With the emergence of new computing-intensive tasks and delay-sensitive application scenarios, terminal devices need to offload new business computing tasks to the cloud for processing. This paper proposes a joint transmission and offloading task scheduling strategy for the edge computing-enabled low Earth orbit satellite networks, aiming to minimize system costs. The proposed system model incorporates both data service transmission and computational task scheduling, which is framed as a long-term cost function minimization problem with constraints. The simulation results demonstrate that the proposed strategy can significantly reduce the average system cost, queue length, energy consumption, and task completion rate, compared to baseline strategies, thus highlighting the strategy’s effectiveness and efficiency. Full article

This paper proposes a frame aggregation with a simple block acknowledgement (FASBA) mechanism to provide a strict QoS guarantee to life-saving emergency traffic in wireless local area networks. This work builds on our previous work on a multi-preemptive enhanced distributed channel access protocol called MP-EDCA. The main difference between FASBA and MP-EDCA is that MP-EDCA does not provide a strict QoS guarantee to life-saving emergency traffic (e.g., ambulance calls), especially in high-load conditions. Our proposed FASBA protocol solves the problems of achieving a strict QoS guarantee to life-saving emergency traffic. The strict QoS guarantee is achieved by aggregating multiple frames with a two-bit block acknowledgement for transmissions. FASBA assures guaranteed network services by reducing MAC overheads; consequently, it offers higher throughput, lower packet delays, and accommodates a larger number of life-saving emergency nodes during emergencies. The performance of the proposed FASBA is validated by Riverbed Modeler and MATLAB 2024a-based simulation. Results obtained show that the proposed FASBA offers about 30% lower delays, 17% higher throughput, and 60% lower retransmission attempts than MP-EDCA under high-traffic loads. Full article

Benchmarking railway infrastructure managers (RIMs) has become a crucial tool in the context of European transport market liberalization, facilitating efficiency improvements and strategic decision-making. RIMs face challenges in increasing capacity, optimizing operations, and ensuring competitive, safe, and economically sustainable services. To address these challenges, this study proposes a hybrid benchmarking model that integrates Principal Component Analysis (PCA) to identify key performance indicators (KPIs) and reduce data dimensionality, the Grey Best–Worst Method (G-BWM) to determine KPI weight coefficients based on expert evaluations, and Assurance Region Data Envelopment Analysis (AR-DEA) to assess the relative efficiency of RIMs while incorporating real-world constraints. The research findings confirm that RIM8 is the most efficient unit, driven by high electrification levels, strong accident prevention measures, and optimal use of infrastructure. In contrast, RIM2 and RIM4 record the lowest efficiency scores, primarily due to poor safety performance, high infrastructure-related delays, and suboptimal resource utilization. By introducing weight constraints through AR-DEA, the model ensures that efficiency assessments reflect actual operational conditions, rather than relying on unrestricted weight allocations. The main contribution of this study lies in developing a systematic and objective framework for evaluating RIM efficiency, ensuring consistency and reliability in performance measurement. The practical implications extend to policy development and operational decision-making, providing insights for infrastructure managers, regulatory bodies, and policymakers to optimize resource allocation, enhance infrastructure resilience, and improve railway sector sustainability. The results highlight key efficiency factors and offer guidance for targeted improvements, reinforcing benchmarking as a valuable tool for long-term railway infrastructure management and investment planning. By offering a quantitatively grounded efficiency assessment, this model contributes to the competitiveness and sustainability of railway networks across Europe. Full article

Connected and automated vehicles (CAV) are growing in popularity and could have potential implications on the transportation system. The effects of CAVs have yet to be fully realized because of the newness of the technology. Anticipated effects include increased capacity, faster travel time, improved level of service, increased safety, and overall effectiveness of the transportation system. The Highway Capacity Manual (HCM) published by the Transportation Research Board of the National Academies has incorporated some of these impacts by developing capacity adjustment factors (CAFs) for various scenarios for freeway segments, signalized intersections, and roundabouts. This study builds upon the HCM study of signalized intersections by analyzing the effect CAVs have on a coordinated signalized corridor. Using PTV VISTRO and PTV VISSIM software a seven-intersection corridor along Eagle Road in Boise/Meridian, Idaho was modeled and analyzed with increasing penetration rates of CAVs. Approach delay, queue length, level of service, and travel time along the corridor were studied as CAV penetration rates increased. It was found that approach delay, queue length, and level of service (LOS) improved as the number of CAVs increased. As CAVs increased from 0% to 100%, the LOS increased from an E to an A at small intersections and from a D or F to C at large intersections. The travel time from one end of the corridor to the other decreased. Full article

Background/Objectives: Early detection and diagnosis are important when treating gallbladder (GB) diseases. Poorer clinical outcomes and increased patient symptoms may result from any error or delay in diagnosis. Many signs and symptoms, especially those related to GB diseases with similar symptoms, may be unclear. Therefore, highly qualified medical professionals should interpret and understand ultrasound images. Considering that diagnosis via ultrasound imaging can be time- and labor-consuming, it may be challenging to finance and benefit from this service in remote locations. Methods: Today, artificial intelligence (AI) techniques ranging from machine learning (ML) to deep learning (DL), especially in large datasets, can help analysts using Content-Based Image Retrieval (CBIR) systems with the early diagnosis, treatment, and recognition of diseases, and then provide effective methods for a medical diagnosis. Results: The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature—the developed model combines features obtained from three different pre-trained architectures for feature extraction. The cosine method was preferred as the similarity measurement metric. Conclusions: Our proposed CBIR model achieved successful results from six other different models. The AP value obtained in the proposed model is 0.94. This value shows that our CBIR-based model can be used to detect GB diseases. Full article

Internet services are increasingly being deployed using cloud computing. However, the workload of an Internet service is not constant; therefore, the required cloud computing resources need to be allocated elastically to minimize the associated costs. Thus, this study proposes a proactive cloud resource scheduling framework. First, we propose a new workload prediction method—named the adaptive two-stage multi-neural network based on long short-term memory (LSTM)—which can adaptively route prediction tasks to the corresponding LSTM sub-model according to the workload change trend (i.e., uphill and downhill categories), in order to improve the predictive accuracy. To avoid the cost associated with manual labeling of the training data, the first-order gradient feature is used with the k-means algorithm to cluster and label the original training data set automatically into uphill and downhill training data sets. Then, based on stochastic queueing theory and the proposed prediction method, a maximum cloud service profit resource search algorithm based on the network workload prediction algorithm is proposed to identify a suitable number of virtual machines (VMs) in order to avoid delays in resource adjustment and increase the service profit. The experimental results demonstrate that the proposed proactive adaptive elastic resource scheduling framework can improve the workload prediction accuracy (MAPE: 0.0276, RMSE: 3.7085, $R^2$: 0.9522) and effectively allocate cloud resources. Full article