Search Results (257)

This study investigates the encroachment risk in the Makause informal settlement by analysing resident survey data to identify key contributing factors and build predictive models. Encroachment threatens the water infrastructure through damage, contamination, and service disruptions, highlighting the need for informed, community-based planning. The data was collected from 105 residents, with responses (“Yes,” “No,” “Unsure”) analysed using descriptive statistics and a one-way ANOVA to identify significant differences across categories. The ReliefF algorithm was used to rank the importance of features predicting the encroachment risk. These inputs were then used to train, validate, and test an Artificial Neural Network (ANN) model. The Artificial Neural Network demonstrated a high predictive accuracy, achieving correlation coefficients above 95% and low mean squared errors. The ANOVA identified statistically significant mean differences for selected variables, while ReliefF helped determine the most influential predictors. A high agreement level (p > 0.900) between predicted and actual responses confirmed the model’s validity. This research introduces an innovative, data-driven framework that integrates machine learning and a statistical analysis to support municipalities and utility providers in engaging informal communities to protect infrastructure. While this study is limited to Makause and may be affected by a self-reported bias, it demonstrates the potential of Artificial Neural Networks and ReliefF in enhancing the risk analysis and infrastructure management in informal settlements. Full article

The fast expansion of cloud computing has raised carbon emissions and energy usage in cloud data centers, so creative solutions for sustainable resource management are more necessary. This work presents a new algorithm—Carbon-Aware, Energy-Efficient, and SLA-Compliant Virtual Machine Placement using Deep Q-Networks (DQNs) and Agglomerative Clustering (CARBON-DQN)—that intelligibly balances environmental sustainability, service level agreement (SLA), and energy efficiency. The method combines a deep reinforcement learning model that learns optimum placement methods over time, carbon-aware data center profiling, and the hierarchical clustering of virtual machines (VMs) depending on resource constraints. Extensive simulations show that CARBON-DQN beats conventional and state-of-the-art algorithms like GRVMP, NSGA-II, RLVMP, GMPR, and MORLVMP very dramatically. Among many virtual machine configurations—including micro, small, high-CPU, and extra-large instances—it delivers the lowest carbon emissions, lowered SLA violations, and lowest energy usage. Driven by real-time input, the adaptive decision-making capacity of the algorithm allows it to dynamically react to changing data center circumstances and workloads. These findings highlight how well CARBON-DQN is a sustainable and intelligent virtual machine deployment system for cloud systems. To improve scalability, environmental effect, and practical applicability even further, future work will investigate the integration of renewable energy forecasts, dynamic pricing models, and deployment across multi-cloud and edge computing environments. Full article

In contrast to brittle normal-strength concrete (NSC), high-performance steel-fiber-reinforced concrete (HPSFRC) provides better tensile and shear resistance, enabling enhanced bridge girder design. To achieve a balance between cost efficiency and quality, reducing conventional reinforcement is a viable cost-saving strategy. This study focused on the flexural behavior of a type of pre-tensioned precast HPSFRC girder without longitudinal and shear reinforcement. This type of girder consists of HPSFRC and prestressed steel strands, balancing structural performance, fabrication convenience, and cost-effectiveness. A 30.0 m full-scale girder was randomly selected from the prefabrication factory and tested through a four-point bending test. The failure mode, load–deflection relationship, and strain distribution were investigated. The experimental results demonstrated that the girder exhibited ductile deflection-hardening behavior (47% progressive increase in load after the first crack), extensive cracking patterns, and large total deflection (1/86 of effective span length), meeting both the serviceability and ultimate limit state design requirements. To complement the experimental results, a nonlinear finite element model (FEM) was developed and validated against the test data. The flexural capacity predicted by the FEM had a marginal 0.8% difference from the test result, and the predicted load–deflection curve, crack distribution, and load–strain curve were in adequate agreement with the test outcomes, demonstrating reliability of the FEM in predicting the flexural behavior of the girder. Based on the FEM, parametric analysis was conducted to investigate the effects of key parameters, namely concrete tensile strength, concrete compressive strength, and prestress level, on the flexural responses of the girder. Eventually, design recommendations and future studies were suggested. Full article

Virtual machine (VM) placement in cloud datacenters is a complex multi-objective challenge involving trade-offs among energy efficiency, carbon emissions, and network performance. This paper proposes NCRA-DP-ACO (Network-, Cost-, and Renewable-Aware Ant Colony Optimization with Dynamic Power Usage Effectiveness (PUE)), a bio-inspired metaheuristic that optimizes VM placement across geographically distributed datacenters. The approach integrates real-time solar energy availability, dynamic PUE modeling, and multi-criteria decision-making to enable environmentally and cost-efficient resource allocation. The experimental results show that NCRA-DP-ACO reduces power consumption by 13.7%, carbon emissions by 6.9%, and live VM migrations by 48.2% compared to state-of-the-art methods while maintaining Service Level Agreement (SLA) compliance. These results indicate the algorithm’s potential to support more environmentally and cost-efficient cloud management across dynamic infrastructure scenarios. Full article

This paper presents a joint optimization framework for multi-period planning in a Manufacturing–Remanufacturing–Transport Supply Chain (MRTSC), focusing on carbon emission reduction and economic efficiency. A novel Mixed Integer Linear Programming (MILP) model is developed to coordinate procurement, production, remanufacturing, transportation, and returns under environmental constraints, aligned with carbon tax policies and the Paris Agreement. To address uncertainty in future demand and the number of returned used products (NRUP), a two-stage approach combining forecasting and optimization is applied. Among several predictive methods evaluated, a hybrid SARIMA/VAR model is selected for its accuracy. The MILP model, implemented in CPLEX, generates optimal decisions based on these forecasts. A case study demonstrates notable improvements in cost efficiency and emission reduction over traditional approaches. The results show that the proposed model consistently maintained strong service levels through flexible planning and responsive transport scheduling, minimizing both unmet demand and inventory excesses throughout the planning horizon. Additionally, the findings indicate that carbon taxation caused a sharp drop in profit with only limited emission reductions, highlighting the need for parallel support for cleaner technologies and more integrated sustainability strategies. The analysis further reveals a clear trade-off between emission reduction and operational performance, as stricter carbon limits lead to lower profitability and service levels despite environmental gains. Full article

Background: Rural communities in the United States experience significant barriers in accessing healthcare, including inadequate numbers of providers and local healthcare facilities. These barriers are exacerbated during seasons with high rates of respiratory diseases when rural clinics and providers may be overwhelmed. When mild, many of these respiratory diseases may be managed effectively in alternate settings, including community pharmacies. Investigators interviewed pharmacists in Washington State to explore the capacity of pharmacists and pharmacies to provide test-to-treat services for COVID-19, influenza, and strep throat. Methods: A qualitative study design was used to conduct key informant interviews with pharmacists who precepted student pharmacists from a local university. Twenty interviews were conducted, transcribed, and qualitatively evaluated to identify themes. The 5 A’s of Access were utilized as a theoretical framework. This framework describes five domains of access, including affordability, availability, accessibility, accommodation, and acceptability. Results: Qualitative analysis identified several themes that described the benefits of offering test-to-treat services in rural communities, such as reducing geographical barriers to accessing care, reducing wait times for patients, and reducing the number of patients seeking higher levels of care for basic treatments. Barriers to offering test-to-treat services identified by pharmacist participants included difficulties with receiving payment for services, challenges with adequate staffing, and the lack of awareness among many people in rural communities that pharmacies offer test-to-treat services. Conclusions: Rural communities experience challenges with the limited capacity of healthcare providers to meet the needs of patients in their communities. The results of this qualitative analysis may be useful to pharmacists in U.S. states where collaborative drug therapy agreements or collaborative practice agreements allow the provision of test-to-treat services. By providing test-to-treat services, pharmacists can increase access to care for rural patients and alleviate the burden of offering these services from other healthcare providers. Full article

Cloud computing faces growing challenges in energy consumption due to the increasing demand for services and resource usage in data centers. To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. The proposed method, RLVMP, classifies VMs as sensitive or insensitive and dynamically allocates resources to minimize energy consumption while ensuring compliance with service level agreements (SLAs). Experimental results using the CloudSim simulator, adapted with data from Microsoft Azure, show that our model significantly reduces energy consumption. Specifically, under the lr_1.2_mmt strategy, our model achieves a 5.4% reduction in energy consumption compared to PABFD, 12.8% compared to PSO, and 12% compared to genetic algorithms. Under the iqr_1.5_mc strategy, the reductions are even more significant: 12.11% compared to PABFD, 15.6% compared to PSO, and 18.67% compared to genetic algorithms. Furthermore, our model reduces the number of live migrations, which helps minimize SLA violations. Overall, the combination of Q-learning and the Firefly algorithm enables adaptive, SLA-compliant VM placement with improved energy efficiency. Full article

This study introduces a novel approach to optimal Service Level Agreement (SLA) tier selection in online services by incorporating client-side obsolescence factors into effective SLA planning. We analyze a comprehensive dataset of 600 million records collected over four years, focusing on the lifecycle patterns of browsers published into the iPhone and Samsung ecosystems. Using Gaussian Process Regression with a Matérn kernel and exponential decay models, we model browser version adoption and decline rates, accounting for data sparsity and noise. Our methodology includes a centroid-based filtering technique and a quadratic decay term to mitigate bot-related anomalies. Results indicate distinct browser delivery refresh cycles for both ecosystems, with iPhone browsers showing peaks at 22 and 42 days, while Samsung devices exhibit peaks at 44 and 70 days. We quantify the support duration required to achieve various SLA tiers as follows: for 99.9% coverage, iPhone and Samsung browsers require 254 and 255 days of support, respectively; for 99.99%, 360 and 556 days; and for 99.999%, 471 and 672 days. These findings enable more accurate and effective SLA calculations, facilitating cost-efficient service planning considering the full service delivery and consumption pipeline. Our approach provides a data-driven framework for balancing aggressive upgrade requirements against generous legacy support, optimizing both security and performance within given cost boundaries. Full article

Reducing the carbon intensity of well-being (CIWB) is essential for advancing environmental sustainability and socio-economic development. The expansion of digital service trade has emerged as a novel engine of global economic growth and a promising pathway for pollution reduction and carbon mitigation. This study investigates the nonlinear impact of digital service trade on CIWB, identifying an inverted U-shaped relationship—initially increasing CIWB, then reducing it beyond a certain threshold. In the financial digital service trade sector, this effect is mediated by energy structure transition, whereas in the technology-intensive sector, it is driven by green technological innovation. In contrast, digital service trade in the insurance, pension, and audiovisual sectors directly suppresses CIWB. Moreover, rising public environmental awareness helps leverage and strengthen the inhibitory effect of digital service trade on CIWB. Regionally, except for North America (which displays a consistently inhibitory effect), Asia, Africa, Europe, and Oceania reflect patterns similar to the overall sample. In regions with higher economic and internet development levels, the inverted U-shaped curve is steeper, and its turning point is located further to the left. Temporally, the relationship mirrors the full-sample patterns prior to the enforcement of the Paris Agreement, while an inhibitory effect emerges afterward. These findings offer policy implications for achieving the United Nations’ 2030 Sustainable Development Goals. Full article

Background: Yemen faces significant challenges related to drug smuggling and counterfeiting, exacerbated by socio-economic hardships and a fragile healthcare and regulatory system. These conditions create an environment conducive to illicit drug trafficking. This study aims to explore the perspectives of healthcare employees working in drug-related departments in the Capital Sana’a, Yemen, focusing on the factors contributing to drug smuggling and the broader challenges within Yemen’s pharmaceutical sector. Methods: A cross-sectional study was conducted among health employees in drug-related departments in the Capital Sana’a. Data were collected through a self-administered questionnaire and analyzed using SPSS version 22.0. Descriptive and inferential statistical analyses were performed to examine group differences, including t-tests and ANOVA. A significance level of p < 0.05 was considered statistically significant. Results: The t-test indicated significant disagreement among participants (50.3%) regarding the existence of a comprehensive pharmaceutical policy (p < 0.001). High levels of agreement were observed on commonly smuggled drugs (74.7%) and the underlying reasons for drug smuggling and counterfeiting (76%, p < 0.001). A significant gender difference emerged regarding perceptions of the Supreme Board of Drugs’ role, with males scoring lower (mean = 2.86, SD = 0.81) than females (mean = 3.43, SD = 0.42, p = 0.002). However, ANOVA results showed no significant differences within or between groups based on educational qualifications, professional roles, or years of service concerning pharmaceutical policy, the Supreme Board of Drugs, registration requirements, or drug smuggling and counterfeiting (p > 0.05). Conclusions: This study highlights critical challenges in Yemen’s pharmaceutical sector, including systemic weaknesses, policy gaps, and the prevalence of drug smuggling, while emphasizing the pivotal role of health employees in addressing these issues. Strengthening their capacity through targeted interventions—such as training, awareness campaigns, robust regulatory frameworks, equitable enforcement, and enhanced stakeholder engagement—is essential. Given the cross-border nature of drug smuggling, these findings underscore the urgent need for strengthened international cooperation, harmonized regulatory policies, and intelligence-sharing mechanisms to combat illicit pharmaceutical trade. Addressing these challenges at both national and international levels is vital for ensuring drug safety, protecting public health, and mitigating the global impact of counterfeit and smuggled medicines. Full article

Fog computing is one of the growing distributed computing platforms incorporated by Industries today as it performs real-time data analysis closer to the edge of the IoT network. It offers cloud capabilities at the edge of the fog networks through improved efficiency and flexibility. As the demands of Internet of Things (IoT) devices keep varying, it is important to rapidly modify the resource allocation policies to satisfy them. Constant fluctuation of the demands leads to over or under provisioning of resources. The computing capability of the fog nodes is small, and hence there is a necessity to develop resource provisioning policies that reduce the delay and bandwidth consumption. In this paper, a novel large language model (LLM)-guided Q-learning framework is designed and developed. The uncertainty in the fog environment in terms of delay incurred, bandwidth usage, and heterogeneity of fog nodes is represented using the LLM model. The reward shaping of a Q-learning agent is enriched by considering the heuristic value of the LLM model. The experimental results ensure that the proposed framework is good with respect to processing delay, energy consumption, load balancing, and service level agreement violation under a finite and infinite fog computing environment. The results are further validated through the expected value analysis statistical methodology. Full article

Background/Objectives: The objective of this study is to assess overall patient satisfaction with hospital services, including cleanliness, ward conditions, and food quality. Another key goal is to determine patient willingness to return for future medical services and identify the factors influencing this decision. Moreover, the study explores the relationship between patient satisfaction and continuity of care, as indicated by previous hospitalizations. Methods: We conducted a retrospective cohort study to evaluate patient satisfaction at the Bihor County Emergency Clinical Hospital in Oradea, Romania. A standardized 40-item questionnaire was developed in accordance with the Framework Agreement on the provision of medical assistance within the Romanian healthcare system. The survey, which was administered over a four-year period (2019–2022), covered seven domains: demographic data, accessibility, hotel conditions, quality of care, patient safety and rights, overall satisfaction, and feedback. A total of 12,802 patients completed the questionnaire, and all statistical analyses were performed using R Studio. Results: This study analyzes patient-reported satisfaction and experiences in a large healthcare facility, based on data from 12,802 participants. Overall, 91% of respondents rated the hospital positively, with 62% giving an excellent score. Spiritual assistance was well received (71%), and 70% of patients expressed willingness to return for future medical needs. Hospital cleanliness and ward conditions were rated highly, with 71% of respondents reporting excellent experiences. Food quality was positively reviewed by 66% of participants. Most patients (95%) confirmed proper hygiene practices by medical staff, and 95% were informed about their diagnosis. However, only 67% were aware of the complaint submission process. The dataset spans 2019–2022, with the highest hospitalizations in 2020 (36%) and obstetrics, cardiology, and general surgery being the most common specialties. Conclusions: This dataset reflects a high level of patient satisfaction across multiple dimensions of hospital services, including cleanliness, quality of care, and patient information. However, areas such as complaint handling and transparency in medication handling require further attention to improve the overall patient experience. The findings underscore the hospital’s strong performance in meeting patient expectations while identifying key areas for continued improvement. Full article

The confluence of the Internet of Things (IoT) and cloud computing heralds a paradigm shift in data-driven applications, promising unprecedented insights and automation across critical sectors like healthcare, smart cities, and industrial automation. However, this transformative synergy introduces a complex tapestry of security vulnerabilities stemming from the intrinsic resource limitations of IoT devices and the inherent complexities of cloud infrastructures. This survey delves into the escalating threats—from conventional data breaches and Application programming interface (API) exploits to emerging vectors such as adversarial artificial intelligence (AI), quantum-resistant attacks, and sophisticated insider threats—that imperil the integrity and resilience of IoT–cloud ecosystems. We critically evaluated existing security paradigms, including encryption, access control, and service-level agreements, juxtaposed with cutting-edge approaches like AI-driven anomaly detection, blockchain-secured frameworks, and lightweight cryptographic solutions. By systematically mapping the landscape of security challenges and mitigation strategies, this work identified the following critical research imperatives: the development of standardized, end-to-end security architectures, the integration of post-quantum cryptography for resource-constrained IoT devices, and the fortification of resource isolation in multi-tenant cloud environments. A comprehensive comparative analysis of prior research, coupled with an in-depth case study on IoT–cloud security within the healthcare domain, illuminates the practical challenges and innovative solutions crucial for real-world deployment. Ultimately, this survey advocates for the development of scalable, adaptive security frameworks that leverage the synergistic power of AI and blockchain, ensuring the secure and efficient evolution of IoT–cloud ecosystems in the face of evolving cyber threats. Full article

Effective crew scheduling in hospitals with multiple personnel groups is essential for time efficiency and fair workload distribution. This study focuses on optimizing shift scheduling for a team of nurses, doctors, and caregivers working in the Pediatric Intensive Care Unit (PICU) of a university hospital. The model is implemented and solved using GAMS 23.5 software to minimize total staffing costs while ensuring balanced shift allocations. The scheduling process in PICUs is influenced by multiple factors, including staff skills, experience levels, personal preferences, contractual agreements, and hospital demands. Since these factors affect doctors, nurses, and caregivers differently, the model considers each personnel group separately while integrating them into a unified optimization framework. The proposed model successfully generates an annual optimal shift schedule for 10 doctors, 14 nurses, and 9 caregivers, ensuring equitable workload distribution and compliance with hospital regulations. By implementing this scheduling approach, employee satisfaction is enhanced, service quality is improved, and administrative workload is reduced. Additionally, the model ensures a well-balanced distribution of responsibilities, minimizes scheduling inefficiencies, and significantly reduces the time required for shift planning. Ultimately, this study provides a fast, fair, and cost-effective solution for hospital workforce management. Full article

Cloud gaming has rapidly transformed the gaming industry, allowing users to play games on demand from anywhere without the need for powerful hardware. Cloud service providers are striving to enhance user Quality of Experience (QoE) using traditional assessment methods. However, these traditional methods often fail to capture the actual user QoE because some users are not serious about providing feedback regarding cloud services. Additionally, some players, even after receiving services as per the Service Level Agreement (SLA), claim that they are not receiving services as promised. This poses a significant challenge for cloud service providers in accurately identifying QoE and improving actual services. In this paper, we have compared our previous proposed novel technique that utilizes a deep learning (DL) model to assess QoE through players’ facial expressions during cloud gaming sessions in a virtual reality (VR) environment. The EmotionNET model technique is based on a convolutional neural network (CNN) architecture. Later, we have compared the EmotionNET technique with three other DL techniques, namely ConvoNEXT, EfficientNET, and Vision Transformer (ViT). We trained the EmotionNET, ConvoNEXT, EfficientNET, and ViT model techniques on our custom-developed dataset, achieving 98.9% training accuracy and 87.8% validation accuracy with the EmotionNET model technique. Based on the training and comparison results, it is evident that the EmotionNET model technique predicts and performs better than the other model techniques. At the end, we have compared the EmotionNET results on two network (WiFi and mobile data) datasets. Our findings indicate that facial expressions are strongly correlated with QoE. Full article