Resource Utilization Rate Research Articles

Analysis of COVID-19 patients hospitalized in general wards and intensive care units: Insights from the RIMAC cardiopulmonary imaging registry across Latin American centers.

This study aimed to compare various aspects among coronavirus disease 2019 (COVID-19) patients admitted to general wards versus intensive care units (ICUs) in Latin American (LATAM), including demographics, comorbidities, imaging and laboratory findings, complications, treatments, and predictors of mortality. Data from the LATAM cardiopulmonary imaging registry of hospitalized COVID-19 patients (RIMAC) were analyzed. RIMAC is a prospective observational study conducted from March to December 2020 across 12 tertiary centers in nine LATAM countries. Out of 1,435 patients, 49.34% were admitted to general wards and 50.66% to ICUs. Significant differences were observed between the two groups. ICU patients had a higher incidence of comorbidities, elevated biomarker levels, and complications such as kidney and heart failure, and required more intensive treatments. They also showed more severe imaging findings and had longer hospital stays with higher mortality rates compared to ward patients. The study highlighted significant disparities between COVID-19 patients admitted to general wards and ICUs in LATAM. These disparities encompassed clinical severity, resource utilization, and mortality rates. Older age, obesity, extensive lung infiltrates, and kidney failure emerged as predictors of mortality. These findings underscore the need for tailored management strategies based on the severity of illness among COVID-19 patients in LATAM.

A dynamic resource allocation optimization method for compilation subsystems in power IoT environment

Abstract Affected by the resource allocation skew problem, which leads to the low utilization rate of resources, we propose to study the optimization method of dynamic resource allocation of compiler subsystem under the environment of electric power IoT (Internet of Things). The skewness willingness function is constructed to reflect the imbalance degree of the compiler subsystem partitioning in the power IoT environment, and at the same time, quantitative indexes are provided for evaluating the effectiveness of the compiler subsystem partitioning algorithm in the power IoT environment. In the resource allocation stage, combined with the compiler subsystem resource tilt allowable threshold, the dynamic allocation of resources is carried out based on the existing compiler tasks in accordance with the priority, with the goal of minimizing the additional waiting time (tilt). In the test results, the compiler subsystem is tested for the distribution of ηEE under different types of requests is always within 0.25 J/bit. Compared with the control group, it has obvious advantages in processing performance and stability.

Advancing Data Center Operations Through AI and Machine Learning : A Comprehensive Analysis of Predictive Maintenance and Resource Optimization

This article systematically analyzes the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on modern data center operations. Through an extensive review of implemented cases and empirical data from multiple data centers, the article demonstrates how AI-driven solutions significantly enhance operational efficiency, reduce maintenance costs, and improve infrastructure reliability. The findings indicate that predictive maintenance algorithms achieve a 47% reduction in unexpected equipment failures, while ML-based resource optimization leads to a 31% improvement in resource utilization rates. The article examines integrating deep learning models for real-time energy management, resulting in an average 23% reduction in cooling costs and a 0.15 improvement in Power Usage Effectiveness (PUE). Additionally, the article analyzes the implementation of AI-powered security frameworks, which demonstrated a 92% accuracy rate in anomaly detection and reduced false positives by 76% compared to traditional rule-based systems. The article also presents a novel framework for capacity planning using neural networks, achieving an 89% accuracy in demand forecasting over 12 months. These findings provide valuable insights for data center operators and establish best practices for implementing AI/ML solutions in mission-critical infrastructure environments. The article concludes with recommendations for overcoming integration challenges and a roadmap for future technological adoption.

Higher Education Teachers Digital Literacy Competencies Exploration of Cultivation Models - Based on New Liberal Arts Contextual Perspectives

In the context of the new liberal arts, teachers' digital literacy ability has become a key factor affecting the effectiveness of education. At present, some colleges and universities are still facing a lot of problems in cultivating teachers' digital literacy ability, which are mainly manifested in the low utilization rate of digital teaching resources, imperfect teaching digital evaluation system, lack of personalized and systematic cultivation of teachers' digital literacy ability, and teachers' cross-fusion ability of disciplines need to be strengthened and so on. In view of these difficulties encountered in the cultivation of teachers' digital literacy, three new cultivation modes are proposed, namely, the implementation of the "cloud platform" cultivation mode, the establishment of the "research-practice-competition" integrated cultivation mode, and the promotion of the "research-practice-competition" integrated cultivation mode. Three new cultivation modes are proposed, namely the implementation of the "cloud platform" cultivation mode, the establishment of the "research-practice-competition" integration cultivation mode, and the promotion of the "market mechanism" cultivation mode, and we hope to provide reliable suggestions for the cultivation of the digital literacy competence of college teachers in China.

Physical education teaching scheduling technology based on chaotic genetic algorithm

In the modern educational environment, rational and efficient course scheduling is of great significance in ensuring teaching quality and improving resource utilization. The traditional sports scheduling methods are faced with the challenges of diversified demands and complex constraints. For this reason, the study proposes a new physical education course scheduling model after mathematically modeling the physical education scheduling model and improving the genetic environment based on chaotic genetic algorithm, and then proposes a new physical education course scheduling model. The experiment outcomes denote that the average computing time of the improved chaotic genetic algorithm is 28 s, and when the number of iterations is 175–200, the optimal number of individuals is 35 at most, and the value of the optimal fitness is 9.4. The simulation test outcomes denote that the new scheduling model can reasonably arrange the course in the 5th-6th section, which meets the needs of students. Meanwhile, when the amount of scheduling teachers is 4 and the amount of courses is 5, the average utilization rate of scheduling resources at this time is the highest 82.5%. Compared with the same type of scheduling model, the new model has the highest superiority of 90.7%, the highest stability of 90.1%, and the highest robustness of 91.6%. The proposed method can meet the diversified and dynamically changing teaching needs, and provides an effective optimization tool for physical education scheduling.

A Comparative Analysis of the Sustainable Development of Urban Agriculture in Chengdu and Singapore

With the deepening global concern for sustainable development, urban agriculture has received more and more attention as an important combination of urban development and ecological environmental protection. This paper provides a comprehensive evaluation and comparison of two areas, Chengdu and Singapore, in terms of their capacity for sustainable development of urban agriculture through an empirical analysis method. To make a multidimensional evaluation index system, the study uses the hierarchical analysis method (AHP) and the fuzzy comprehensive evaluation method. The index covers important areas like the level of agricultural development, the intensity of applied inputs, the rate of energy use, the rate of resource utilization, and the level of agricultural factors. The results show that Chengdu performs significantly in terms of resource utilization rate and agricultural factor level, while Singapore performs better in terms of energy utilization rate. The study provides targeted recommendations, including strengthening scientific and technological innovation, policy support, and ecological and environmental protection, with the aim of providing references and lessons for the sustainable development of urban agriculture in the two places and other areas around the world.

Dynamic Event-Driven Finite-Horizon Optimal Consensus Control for Constrained Multiagent Systems.

This article investigates the event-driven finite-horizon optimal consensus control problem for multiagent systems with symmetric or asymmetric input constraints. Initially, in order to overcome the difficulty that the Hamilton-Jacobi-Bellman equation is time-varying in finite-horizon optimal control, a single critic neural network (NN) with time-varying activation function is applied to obtain the approximate optimal control. Meanwhile, for minimizing the terminal error to satisfy the terminal constraint of the value function, an augmented error vector containing the Bellman residual and the terminal error is constructed to update the weight of the NN. Furthermore, an improved learning law is proposed, which relaxes the tricky persistence excitation condition and eliminates the requirement of initial stability control. Moreover, a specific algorithm is designed to update the historical dataset, which can effectively accelerate the convergence rate of network weight. In addition, to improve the utilization rate of the communication resource, an effective dynamic event-triggering mechanism (DETM) composed of dynamic threshold parameters (DTPs) and auxiliary dynamic variables (ADVs) is designed, which is more flexible compared with the ADV-based DETM or DTP-based DETM. Finally, to support the effectiveness of the proposed method and the superiority of the designed DETM, a simulation example is provided.

Drip Fertigation Improves Maize Yield, Resource Utilization, and Economic Benefits by Increasing Light Interception Under Dense Planting in Southwest China

ABSTRACTSeasonal drought and traditional water‐fertilizer management limit the increase in the grain yield of summer maize in Southwest China. Drip fertigation mode (HM) can effectively improve crop yields. However, research on drip fertigation has not been conducted in Southwest China. A 2‐year field experiment about HM was carried out with the traditional water‐fertilizer management mode (FM) as control. The plant densities were 5.25 × 104 plants ha−1 and 8.25 × 104 plants ha−1 in 2022 and 6.00 × 104 plants ha−1 and 9.00 × 104 plants ha−1 in 2023. The effects of HM on the aboveground biomass, leaf area index, yield, and resource utilization rate of summer maize were studied. Compared with the FM treatment, the HM treatment significantly increased the yield (25.18%), aboveground biomass (25.58%), leaf area index (34.87%), and leaf area duration (29.60%). HM optimized the canopy structure with an 11.05% improvement in light transmission at the top and a significant 61.32% increase in cumulative light radiation interception per unit area at the bottom of the canopy. The nitrogen partial factor productivity (NPFP), radiation utilization efficiency (RUE), heat utilization efficiency (HUE), and economic benefits of the HM treatment significantly increased by 39.58%, 49.45%, 25.92%, and 32.53%, respectively. In addition, dense planting increased the irrigation water use efficiency (IWUE) by 14.25%. In summary, drip irrigation combined with water and fertilizer can significantly improve maize grain yield, resource utilization efficiency, and economic benefits by increasing light interception in Southwest China. This study will lay a theoretical foundation for filling the relevant research gap in the region.

US Clinical Practice Experience with Eculizumab in Myasthenia Gravis: Acute Clinical Events and Healthcare Resource Utilization.

The terminal complement inhibitor eculizumab is approved in the USA for the treatment of patients with acetylcholine receptor antibody-positive generalized myasthenia gravis (MG). The ELEVATE study aimed to examine clinical-practice outcome data on eculizumab effectiveness in US adults with MG (generalized or ocular). This paper reports the findings on MG exacerbations and crises and associated healthcare resource utilization, and the use of rescue therapy. A retrospective chart review was conducted of US adults with MG who initiated eculizumab. Outcomes assessed for up to 2 years before and after eculizumab initiation included percentages and rates per patient per year (PPPY) of exacerbations and crises (the latter defined as intubation/impending intubation), healthcare resource utilization, and rescue therapy administration. A total of 119 patients diagnosed with MG were enrolled in the study; 92 patients had ≥3 months of data both before and during eculizumab therapy and were included in the analyses. The mean rate of MG exacerbations decreased from 0.385 PPPY before eculizumab initiation to 0.152 PPPY during eculizumab treatment (p = 0.0034); the mean rate of MG crises decreased from 0.411 to 0.056 PPPY (p = 0.0018). Rates of healthcare resource utilization and rescue therapy use also decreased significantly during eculizumab treatment. This retrospective chart review analysis provides evidence for a beneficial impact of eculizumab treatment on the incidence of MG exacerbations and crises and associated healthcare resource utilization in clinical practice, and on rescue therapy use. These data further support the therapeutic benefits of eculizumab in patients with MG.

Automatic Scheduling Method for Customs Inspection Vehicle Relocation Based on Automotive Electronic Identification and Biometric Recognition

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally.

Preparation of excellent building materials using geopolymer instead of traditional cement

In order to improve the resource utilization rate of aluminum ash, high-quality building materials were prepared by replacing traditional cement with aluminum ash, and the performance of building materials under different conditions and factors was studied. The experimental results show that when the pressure was 300 MPa and the natural curing time was 3 days, the comprehensive performance of the brick reaches its optimum (compressive strength of 60 MPa, flexural strength of 1.3 MPa, and softening coefficient of about 0.9), far superior to other reported methods for preparing building materials. SEM-EDS, Particle size analysis and XRD confirmed that the crystal structure in aluminum ash undergoes a transformation under high-intensity mechanical pressure, forming cement-based active substances. This study not only obtained a new method for preparing building materials, but also further promoted the research on the resource utilization of aluminum ash, providing a new approach for the treatment and disposal of hazardous waste.

Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment

To efficiently realize the parallel task scheduling of SaaS platform in large-scale cloud service environment, this paper studies the parallel task scheduling algorithm of SaaS platform based on dynamic adaptive particle swarm optimization in cloud service environment. Users access the cloud through the user access interface module, and issue task scheduling instructions or send task scheduling requests. After the service management module provides diversified application service support according to the scheduling requirements, the core service module determines the SaaS platform parallel scheduling objective function, and uses dynamic adaptive particle swarm optimization to solve the objective function to obtain the SaaS platform parallel task scheduling results. The test results show that the algorithm has better multi-objective solving ability and can obtain higher quality objective solutions, and the test results of the total execution time of parallel scheduling tasks and the total transmission time of task data on SaaS platform are all within 30 s. The results of virtual machine resource load balancing degree are all below 15%; the utilization rate of virtual machine resources is above 92.2%.

Reservation and allocation model considering the user cost and the utilization of parking space

In order to alleviate the problem of imbalance between parking supply and demand by improving the utilization rate of parking resources, and considering the game relationship between user benefits and system benefits in parking allocation, the optimization objective function of maximum parking space utilization and minimum user cost in parking reservation mode is established from the two aspects which are system optimization and user optimization respectively. Then, the optimal allocation parking integer programming model (OPA) considering users' preferences is established. An Augmented Lagrangian-Alternating Direction Method of Multipliers Algorithm is designed to solve the optimal solution of the model. Finally, in order to test the validity of the model, the proposed model and the classical distribution model are compared and analyzed under different supply and demand conditions. The results show that the performance of the proposed model in three performance metrics which are parking utilization, average user cost and request acceptance rate is significantly better than that of the classical allocation model. The research results of this paper can provide theoretical reference for the management of parking reservation platform.

Effects of environmentally friendly soil remediation materials on soil quality and maize production

In the precent, the reclaimed raw soil of newly renovated abandoned homestead was considered as the research object, and study the effects of different environmentally friendly soil remediation material combinations on the quality of reclaimed raw soil and maize crop production. A total of 6 improved material treatments were set up, including decomposed chicken manure(YJ), fly ash(FMH), ferrous sulfate curing agent(SHJ), ferrous sulfate curing agent+organic fertilizer (SYJ), fly ash+decomposed chicken manure(FYJ), and control treatment(CK). The research results showed that, the organic carbon content and water storage of new reclaimed raw soil exhibited a significant increasing trend after the application of different environmentally friendly modified materials(P < 0.05). Among them, the organic carbon and water storage of reclaimed soil under FYJ improved, which increased by 132.3 and 14.4 %, respectively compared with CK. During the growth period of summer maize, the plant height and yield exhibited a trend of FYJ> SYJ> YJ> FMH> SHJ> CK. The yield of maize under SYJ and FYJ treatments increased by 38.8 and 53.4 %, respectively, compared with CK. The appropriate combination of environmentally friendly soil remediation materials is helpful to improve the quality of new reclaimed raw soil, promote soil carbon sequestration, increase the comprehensive utilization rate of waste resources, increase the production performance of crops, and help rural revitalization and beautiful rural construction. Bangladesh J. Bot. 53(3): 673-679, 2024 (September) Special

Outcomes and Cost-Effectiveness of Hospital Outpatient Versus Ambulatory Surgery Center Lumbar Decompression Surgery.

Retrospective Chart Review. Outpatient spinal surgeries in Ambulatory Surgery Centers (ASCs) have gained traction due to their potential cost efficiencies and improved perioperative processes. This study aims to compare the cost-effectiveness and patient outcomes of lumbar laminectomies performed in hospital settings vs ASCs. A retrospective analysis was conducted on 771 patients who underwent 1 or 2-level outpatient laminectomy between 2019 and 2023. Patient demographics, 90-day and one-year clinical and patient-reported outcomes (PROs), and one-year episode of care costs were evaluated. A one-year cost-effectiveness analysis was performed using the EQ-5D to measure quality-adjusted life years (QALYs). ASC patients demonstrated lower body mass index and American Society of Anesthesiologists (ASA) scores, with a higher prevalence of 1-level laminectomies compared to hospital patients. ASC-based laminectomy was associated with lower initial surgery cost and one-year episode of care costs ($5662 ± 4748 vs $10229 ± 9202, P < 0.001), with similar rates of complications and postoperative resource utilization. These trends remained after controlling for patient demographics, comorbidities, and number of levels treated. In patients completing baseline and 1-year EQ-5D scores, ASC-based laminectomy was over twice as cost-effective as hospital procedures ($64873/QALY gained vs $152630). The findings support the safety and one-year cost effectiveness of ASCs for appropriately selected patient populations undergoing lumbar laminectomy. Additional studies are needed to replicate these findings across institutions, and to assess the cost effectiveness of ASC-based laminectomy beyond one-year postoperatively.

Physiological and molecular responses of juvenile silver crucian carp (Carassius gibelio) to long-term high alkaline stress: Growth performance, histopathology, and transcriptomic analysis

Salty-alkaline waters are widely distributed globally, and how to effectively develop saline-alkaline water to improve the utilization rate of water resources has become a global challenge. This study examined the survival performance of juvenile silver crucian carp (Carassius gibelio) under alkaline gradient acclimation conditions and explored the regulatory mechanisms for their adaptation to high alkalinity. Compared to directly immersing in high concentrations, alkaline gradient acclimation significantly improved the survival rate of juvenile silver crucian carp. Long-term high alkaline stress caused significant reduction in the growth performance of juvenile silver crucian carp. After alkaline stress, the enzyme activities of Na+-K+-ATP (NKA) and Ca2+-Mg2+-ATP (CMA) in the gills were significantly decreased. Meanwhile, histopathology of the gills showed structural changes, indicating that the physiological functions of juvenile silver crucian carp were impaired under alkaline conditions. Transcriptomic analysis found that the expression of genes related to metabolism and immune response pathways in the gill and kidney underwent significant changes. Genes related to carbohydrate metabolism were upregulated while those related to protein metabolism were downregulated, indicating that alkaline stress caused the organism to utilize carbohydrates rather than proteins as much as possible to meet enhanced metabolism. The HIF pathway that maintains the body's survival was significantly up-regulated, at the expense of high energy consumption. Dysregulation of genes related to the formation of neutrophil extracellular traps (NETosis) pathway indicated that the immune function of the fish was suppressed. Overall, this study revealed the physiological and molecular responses of juvenile silver crucian carp to alkaline stress, deepening our understanding of their adaptation strategies and the potential impact of alkaline environments on aquatic life.

Distributed Ledger-Based Authentication and Authorization of IoT Devices in Federated Environments

One of the main security challenges when federating separate Internet of Things (IoT) administrative domains is effective Identity and Access Management, which is required to establish trust and secure communication between federated IoT devices. The primary goal of the work is to develop a “lightweight” protocol to enable authentication and authorization of IoT devices in federated environments and ensure the secure communication of IoT devices. We propose a novel Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI) which takes advantage of the unique fingerprint of IoT devices based on their configuration and additional hardware modules, such as Physical Unclonable Function, to provide flexible authentication and authorization based on Distributed Ledger technology. Moreover, LAAFFI supports IoT devices with limited computing resources and devices not equipped with secure storage space. We implemented a prototype of LAAFFI and evaluated its performance in the Hyperledger Fabric-based IoT framework. Three main metrics were evaluated: latency, throughput (number of operations or transactions per second), and network resource utilization rate (transmission overhead introduced by the LAAFFI protocol). The performance tests conducted confirmed the high efficiency and suitability of the protocol for federated IoT environments. Also, all LAAFFI components are scalable as confirmed by tests. We formally evaluated LAAFFI security using Verifpal as a formal verification tool. Based on the models developed for Verifpal, we validated their security properties, such as message secrecy, authenticity, and freshness. Our results show that the proposed solution can improve the security of federated IoT environments while providing zero-day interoperability and high scalability. Compared to existing solutions, LAAFFI is more efficient due to the use of symmetric cryptography and algorithms adapted for operations involving IoT devices. LAAFFI supports multiple authorization mechanisms, and since it also offers authentication and accountability, it meets the requirements of Authentication, Authorization and Accounting (AAA). It uses Distributed Ledger (DL) and smart contracts to ensure that the request complies with the policies agreed between the organizations. LAAFFI offers authentication of devices belonging to a single organization and different organizations, with the assurance that the encryption key will be shared with another device only if the appropriate security policy is met. The proposed protocol is particularly useful for ensuring the security of federated IoT environments created ad hoc for special missions, e.g., operations conducted by NATO countries and disaster relief operations Humanitarian Assistance and Disaster Relief (HADR) involving military forces and civilian services, where immediate interoperability is required.

Most important performance evaluation methods of production lines: A comprehensive review on historical perspective and emerging trends

Production is one of the most significant building blocks that strengthen the sustainable economy of companies and thus contribute to the countries’ welfare. Performance indicators of the production line affect planning operations and the efficiency of the supply chain to which the factory is connected. The key indicators for production line designers and performance analysts to monitor and improve include production rate, resource utilization rate, and average inventory level. The production rate is the most important indicator closely affecting an industrial plant's productivity and efficiency levels. From this perspective, accurate and fast estimation of this indicator is very critical. Production rate can be calculated by simulation, analytical technique, or artificial intelligence methods according to the production line characteristics. In this comprehensive review, the most important performance evaluation methods are discussed historically and systematically about the buffer allocation problem using the snowball sampling method. With this explicit motivation, 145 papers were reviewed and classified according to production line topology, hypothetical/real-case line, machine reliability, previous method on which the method is based, and originality and/or line characteristics. To present a comprehensive comparison, the methods considered were analyzed according to different criteria. This review provides general/in-depth qualitative and quantitative discussions and highlights insights to practitioners and scholars. In addition, the impact of recent key work on production line analysis in the field is assessed along with emerging trends, evolving manufacturing paradigms are discussed, and the challenges associated with performance analysis are addressed.

Mitigating Risk of Acute Kidney Injury Among Children With Methicillin-resistant Staphylococcus aureus Osteomyelitis.

Children with acute hematogenous osteomyelitis (AHO) from methicillin-resistant Staphylococcus aureus (MRSA) are treated with vancomycin despite the risk of acute kidney injury (AKI). This study evaluates the rate of AKI and resource utilization for children with or without AKI when vancomycin is used in this setting. Children with MRSA AHO treated with vancomycin were retrospectively studied. AKI was assessed by clinical diagnosis and Kidney Disease Improving Global Outcomes (KDIGO) criteria. Cohorts of children with or without AKI were compared for differences in treatment, resource utilization, and outcomes. Multivariate logistic regression analysis assessed factors associated with risk for AKI. Cost analysis was performed using the Pediatric Health Information System and Healthcare Cost and Utilization Project databases. Among 85 children studied, 14 (16.5%) had chart-diagnosed AKI and 24 (28.2%) met KDIGO criteria. Children with AKI had more febrile days and higher thrombosis rates. They had longer vancomycin treatment (8 vs 5d), higher troughs (27.8 vs 17.5mg/L), and prolonged hospitalization (19.9 vs 11.1d). Multivariate analysis found a maximum vancomycin trough level (odds ratio: 1.05, P = 0.003) with a cutoff of 21.7mg/L predicted AKI.Only 2 of 20 (10%) children who had MRSA isolates with a minimum inhibitory concentration of 2 achieved therapeutic vancomycin levels. Pediatric Health Information System data of 3133 children with AHO treated with vancomycin identified 75 (2.4%) with AKI who had significantly longer lengths of stay (13 vs 7d) and higher billed charges ($117K vs $51K) than children without AKI. Chart documentation of AKI (16.5%) grossly underestimated KDIGO-defined occurrence (28.2%). This study showed that vancomycin-associated AKI required substantially greater resource utilization and higher health care costs. Lowering the targeted trough range, shortening the duration of vancomycin therapy, and considering alternative antibiotics when minimum inhibitory concentration ≥2 will reduce the risk and cost of AKI among children with MRSA AHO. Level III-retrospective comparative therapeutic study.

Enhancing Cloud Task Scheduling with Multi-Objective Optimization Using K-Means Clustering and Dynamic Resource Allocation

The scheduling and resource allocation procedure is an essential component of cloud resource management. Effective resource allocation is severely hampered by the task arrival rates' erratic and unclear behavior. To prevent under or overusing resources, an effective scheduling strategy is necessary. To improve scheduling and allocation performance, a multi-objective optimization technique is presented for the best resource allocation and task scheduling inside scientific workflow datasets in a heterogeneous environment. In the first stage, the system calculates four key metrics: Communication Cost, Computation Cost, Earliest Finished Time on a particular VM, and Total Task Length for a specific scientific workflow dataset. These metrics provide a comprehensive understanding of the resource requirements and help make informed scheduling decisions. In the second stage, tasks are clustered using the K-Means clustering algorithm. This clustering groups similar tasks together, making managing and scheduling them easier. In the third stage, the proposed resource allocation algorithm allocates the clustered tasks to the appropriate VMs. This step ensures that the tasks are assigned to the best-suited resources, optimizing the overall system performance and resource utilization. By following this multi-stage process, the system aims to achieve optimal resource allocation and task scheduling, thereby improving the efficiency and effectiveness of cloud resource management. The proposed method significantly outperforms PSO, CSO, and GWO by consistently achieving lower Makespan—under 400 units at 50 tasks—while maintaining high resource utilization rates above 0.75, demonstrating superior efficiency in task execution and resource management.