High Resource Utilization Research Articles

A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems.

Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm2 per channel.

Sexual and Gender Diverse Healthcare Navigation Model: A Community-Participatory Delphi Adaptation of the Transitional Care Model for Sexual and Gender Diverse Individuals

ABSTRACT Health care transitions, movements between providers or settings, can lead to poor outcomes, higher resource utilization, and even higher costs. Health care transitions can be complex experiences for sexual and gender diverse (SGD) individuals, especially when there is a need for ongoing health care (e.g. gender-affirming therapy, depression). Engaging new providers can lead to setbacks in trust requiring self-disclosure SGD-identities all over again. The Transitional Care Model (TCM) supports improvement of outcomes associated with health care transitions; however, it has not been tailored to address the unique needs of SGD populations. A sample of adult SGD patients (n = 17) and SGD health experts (n = 11) from across the US completed a six cycle, electronic, modified Delphi process to adapt the TCM for the SGD population. The iterative process produced a TCM for SGD populations including eight refined and renamed components with SGD-specific descriptions and exemplars. The resulting model, the Sexual and Gender Diverse Healthcare Navigation Model, provides an eight-component adaptive model of transitional care that centers SGD patients and their support systems across complex health care systems. The consensus panel provided descriptions for each of the eight resulting components—identifying three foundational components—the right staffing, awareness of needs and risks, and engaging individuals and their support systems throughout transitions in care. This model stands to support improved health outcomes among SGD through a multi-component, patient centered approach to health care navigation.

Abstract 4135852: Safety and Post-operative Complications of Endovascular Versus Surgical Versus Follow-up and Medical Treatment for Patients with Vertebrobasilar Artery Stenosis: Propensity Score Weighting and a Machine Learning Driven Analysis

Background: Vertebrobasilar artery stenosis (VBAS) can cause posterior circulation strokes (PCS). Optimal management is controversial, with options including medical therapy (MT), endovascular stenting (ES), and surgical revascularization (SR). This study compares outcomes of these treatments and evaluates the correlation between clinical features and medical history with 30-day outcome. Methods: Patients with VBAS were identified from the 2017-2018 National Inpatient Sample (NIS). Propensity scores adjusted for baseline differences. Outcomes included mortality, neurological complications (NC), discharge destination (DD), length of stay (LOS), total charges (TC), and procedural complications (PC). Predictive ability of clinical variables was assessed using logistic regression (LR) and machine learning techniques (MLT). Results: Of 1,343 patients, 1,061 (79.0%) received NI, 234 (17.4%) underwent ES, and 24 (1.8%) had SR. Mean age was 69.45 years, with 64.1% male. Demographics: 69.8% White, 14.9% Black, 10.0% Hispanic, and 5.3% other races. Hypertension (HTN, 85.4%) and diabetes (DM, 18.9%) were prevalent. After propensity weighting, ES was associated with higher odds of mortality, surgical/medical complications (SMC), and device/graft complications (DGC) compared to NI. SR showed a non-significant trend toward higher non-home discharges (NHD). ES and SR groups had higher resource utilization with longer LOS and greater TC. Clinical variables alone were weak predictors, with AUC values ranging from 0.454 to 0.71 across different outcomes and models. Conclusion: ES of VBAS was associated with higher mortality and complication rates compared to MT alone, with inconsistent benefits for NC. SR also carried elevated risks without clear advantages over MT. These results support that current clinical independent variables from the NIS are weak predictors. This highlights the limitation of the database in relying solely on clinical and medical history, and suggests that future use of radiological and anatomical features can improve predictions of outcomes and determination of subgroups that can benefit from certain treatment. More studies should be conducted, including post hoc analyses based on radiological and anatomical features, to better inform treatment decisions and determine subgroups that can benefit from intervention or surgery. These findings suggest a need for judicious patient selection and reinforce the role of optimal MT.

Optimization Study on Hardness of Cold Rolled Strip Steel Based on a Data-Driven Approach

As a key index to measure product quality and processability, the hardness of cold-rolled strip steel has an important influence on the production efficiency of iron and steel enterprises. However, due to the coupling between the process parameters at each stage of the continuous annealing process, and the setting of traditional process parameters depending on the experience of the operator, the lack of scientific and systematic optimization methods, which easily leads to the problem of high hardness fluctuation and low resource utilization. In this study, the data-driven method is used to conduct a preliminary correlation analysis to further clarify the optimal combination of process parameters for achieving the target hardness. The Pearson correlation coefficient is used to determine that carbon content, quenching furnace temperature, and strip speed significantly affect strip hardness. To further clarify the optimal combination of process parameters to achieve the target hardness, a Genetic Algorithm (GA) was tried in this study. The results show that the optimized process parameter combination can effectively improve the stability and consistency of strip hardness, and provide a scientific basis for the process parameter setting in the cold rolled strip production process, which is helpful for enterprises to improve product quality, reduce cost, and enhance market competitiveness.

Economic Burden Associated with Pulmonary Arterial Hypertension in the United States.

Pulmonary arterial hypertension (PAH) is a progressive disease characterized by elevated pressure in the pulmonary arteries, commonly resulting in right heart failure. PAH is associated with a high economic burden throughout the duration of the disease. This retrospective cohort study of the Milliman Contributor Health Source Data, the Medicare 100% Research Identifiable Files, and the Merative Marketscan® Commercial dataset between 2018 and 2020 identified adult patients with prevalent PAH based on the earliest qualifying diagnosis date or medication date ('index date') between January 1, 2019 and November 30, 2020. Outcomes were assessed using patient data from index date through the earliest of end of enrollment, end of data, or death (Medicare fee-for-service [FFS] only). All-cause and PAH-related medical and pharmacy costs per-patient per-month (PPPM) and healthcare resource utilization per 1000 patients were summarized. The study included 11,670 Medicare FFS, 1021 Medicare Advantage, 274 Medicaid, and 1174 commercially insured patients in the US. The annual national burden to payers was estimated to be US$3.1 billion. The PPPM payer costs ranged from US$6500 to US$14,742; out-of-pocket (OOP) costs ranged from US$341 to US$907 PPPM. Inpatient utilization rate ranged from 435 to 770 per 1000 patients for all-cause admissions and from 15 to 58 per 1000 patients for PAH-related admissions. This study demonstrates that PAH continues to be associated with a high economic burden and healthcare resource utilization across all payer types within the US healthcare system.

Design and Implementation of Image Acquisition and Signal Transmission Functions based on FPGA

Abstract In recent years, video network monitoring systems have evolved rapidly, from the first generation of digital-analogue systems to the fully IP-based systems. Based on the FPGA core, this design focuses on image acquisition and information transmission. The FPGA expansion board contains several peripheral modules, including the OV7670 camera captures images, DRAM stores data, and FIFO handles cross-clock domain signal transmission. While the reference clock is 40ns, in this system, the clock period is 20ns, making two clock cycles equal to one reference clock cycle. Then, two counters are used, one is the reference clock and another is the line synchronization (cnt1) to count 800 clock cycles per line. The end of a line is indicated by end_cnt0, and FIFO data is stored in SDRAM. Configuring the VGA display driver allows image data to be transmitted and displayed on the VGA screen. The system features a simple structure, high resource utilization, and strong reliability.

Parallel Implementation of the CCSDS Turbo Decoder on GPU

This paper presents a software turbo decoder on graphics processing units (GPU). Unlike previous works, the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems (CCSDS) standard. However, the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism design. To mitigate this issue, we propose a padding method that inserts several bits before the information frame header. To obtain low-latency performance and high resource utilization, two-level intra-frame parallelisms and an efficient data structure are considered. The presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution (LTE) turbo codes with only small modifications. The proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50 Mbps throughputs for the code rates 1/6 and 1/2, respectively.

Security-Aware Deadline Constraint Task Scheduling using Hybrid Optimization of Modified Flying Squirrel Genetic Chameleon Swarm Algorithm

Cloud computing enables cost-effective resource sharing in hybrid clouds to tackle the problem of insufficient resources by elastically scaling the service capability based on the users’ needs. However, task scheduling (TS) in cloud environments is challenging due to deadline-based performance and security constraints. To remove the existing drawbacks based on deadline and security constraints, a Security-Aware Deadline Constraint TS (SADCTS) approach is presented using a hybrid optimization algorithm of the Modified Flying Squirrel Genetic Chameleon Swarm Algorithm (MFSGCSA). The proposed MFSGCSA is developed by integrating the genetic operators into CSA and combining it with the modified Flying Squirrel Optimization (FSO) algorithm in which the position update and global search equations are enhanced by adaptive probability factor to reduce the local optimum problem. In this SADCTS approach, the task assignment process is modeled into an NP-hard problem concerning a multi-level security model using user authentication, integrity, and confidentiality. This maximizes tasks’ completion rate and decreases the resource costs to process tasks with different deadline limitations. The optimal schedule sequence is obtained by MFSGCSA, where tasks are scheduled concurrently based on security constraints, demand, and deadlines to improve the prominence of cost, energy, and makespan. Experiments are simulated using the CloudSim toolkit, and the comparative outcomes show that the suggested SADCTS approach reduced the makespan, cost, and energy by 5-20% more than the traditional methods. Thus, SADCTS provides less task violation of 0.0001%, high energy efficiency of 700GHz/W, high resource utilization of 92%, less cost efficiency of 72GHz/$, and less makespan of 480minutes to satisfy the necessary security and deadline requirements for TS in shared resource hybrid clouds.

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.

Tao: Improving Resource Utilization while Guaranteeing SLO in Multi-tenant Relational Database-as-a-Service

It is an open challenge for cloud database service providers to guarantee tenants' service-level objectives (SLOs) and enjoy high resource utilization simultaneously. In this work, we propose a novel system Tao to overcome it. Tao consists of three key components: (i) tasklet-based DAG generator, (ii) tasklet-based DAG executor, and (iii) SLO-guaranteed scheduler. The core concept in Tao is tasklet, a coroutine-based lightweight execution unit of the physical execution plan. In particular, we first convert each SQL operator in the traditional physical execution plan into a set of fine-grained tasklets by the tasklet-based DAG generator. Then, we abstract the tasklet-based DAG execution procedure and implement the tasklet-based DAG executor using C++20 coroutines. Finally, we introduce the SLO-guaranteed scheduler for scheduling tenants' tasklets across CPU cores. This scheduler guarantees tenants' SLOs with a token bucket model and improves resource utilization with an on-demand core adjustment strategy. We build Tao on an open-sourced relational database, Hyrise, and conduct extensive experimental studies to demonstrate its superiority over existing solutions.

One-year budget impact of InTandem™: a novel neurorehabilitation system for individuals with chronic stroke walking impairment.

Aim: Chronic stroke walking impairment is associated with high healthcare resource utilization (HCRU) costs. InTandem™ is a neurorehabilitation system that autonomously delivers a rhythmic auditory stimulation (RAS)-based intervention for the at-home rehabilitation of walking impairment in adults in the chronic phase of stroke recovery. This study was conducted to estimate the budget impact of InTandem in comparison with currently available intervention strategies for improvement of gait/ambulation in individuals with chronic stroke walking impairment. Methods & materials: A budget impact analysis (BIA) for InTandem was conducted based on a 1-million-member US third-party payer perspective over a 1-year time horizon. Key inputs for the budget impact model were: costs for each intervention strategy (InTandem, physical therapy, self-directed walking and no treatment), HCRU costs for persons with chronic stroke and anticipated HCRU cost offsets due to improvements in gait/ambulatory status as measured by self-selected comfortable walking speed (based on functional ability). In addition to the reference case analysis, a sensitivity analysis was conducted. Results: Based on the reference case, introduction of InTandem was projected to result in overall cost savings of $439,954 in one year. Reduction of HCRU costs (-$2,411,778) resulting from improved walking speeds with InTandem offset an increase in intervention costs (+$1,971,824). Demonstrations of cost savings associated with InTandem were robust and were consistently evident in nearly all scenarios evaluated in the sensitivity analysis (e.g., with increased/decreased patient shares, increased HCRU costor increased InTandem rental duration). Conclusion: The InTandem system is demonstrated to improve walking and ambulation in adults in the chronic phase of stroke recovery after a five-week intervention period. The BIA predicts that introduction of InTandem will be associated with overall cost savings to the payer.

Racial disparities in end-of-life suffering within surgical intensive care units

BackgroundEnd-of-life (EOL) care is associated with high resource utilization. Recognizing and effectively communicating that EOL is near promotes more patient-centered care, while decreasing futile interventions. We hypothesize that provider assessment...

Effects of vitamin D supplementation on diabetic foot ulcer healing: a meta-analysis.

To systematically review the effect of vitamin D supplementation on diabetic foot ulcer (DFU) healing. The PubMed, Web of Science, Science direct, Ebsco host, CNKI, WanFang, VIP, and CBM databases were electronically searched to collect randomized controlled trials (RCTs) on the impact of vitamin D supplementation on DFUs from inception to 19 November 2022. Two researchers independently screened the literature, extracted the data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.3 software. A total of seven studies involving 580 patients were included. The results of meta-analysis showed that compared with control group, the wound healing efficiency rate (RR = 1.42, 95%CI 1.03 to 1.95, P= 0.03) and wound reduction rate (MD = 13.11, 95%CI 4.65 to 21.56, P < 0.01) of the experimental group were higher; the change values of the wound area (MD = -3.29, 95%CI -4.89 to 1.70, P < 0.01) and 25 (OH) D (MD = 9.63, 95%CI 6.96 to 12.31, P < 0.01) were larger. Supplementation of vitamin D on DFU patients can improve glucose metabolism and insulin indexes: hemoglobin A1c (MD = -0.44, 95%CI -0.62 to -0.26, P < 0.01), fasting insulin (MD = -3.75, 95%CI -5.83 to -1.67, P < 0.01), HOMA - β (MD = -5.14, 95%CI -8.74 to -1.54, P < 0.01), and quantitative insulin sensitivity check index (MD = 0.02, 95%CI 0.01 to 0.02, P < 0.01). It can also improve inflammation and oxidative stress markers: high sensitivity C-reactive protein (MD = -0.83, 95%CI -1.06 to -0.59, P < 0.01), erythrocyte sedimentation rate (MD = -15.74, 95%CI -21.78 to -9.71, P<0.01), nitric oxide (MD = 1.81, 95%CI 0.07 to 3.55, P= 0.04), and malondialdehyde (MD = -0.43, 95%CI -0.61 to -0.24, P<0.01). There was no statistically significant difference in changes of fasting plasma glucose, homeostasis model of assessment-insulin resistance, total antioxidant capacity, glutathione, very low density lipoprotein cholesterol, low density lipoprotein cholesterol, and high density lipoprotein cholesterol (P>0.05). The current evidence suggests that vitamin D supplementation can significantly promote DFU healing by lowering blood sugar and alleviating inflammation and oxidative stress. Key messages What is already known on this topic Diabetic foot ulcer (DFU) is a major complication of diabetes mellitus, with high morbidity, mortality and resource utilization. Vitamin D has the effect of lowering blood sugar, improving insulin sensitivity, and increasing anti-inflammatory response. Clinical research on vitamin D supplementation for the treatment of DFU is increasing, but due to the lack of combing and integration, the actual efficacy of vitamin D in patients is unclear. What this study adds This meta-analysis has shown that vitamin D supplementation can significantly promote DFU healing by lowering blood glucose and alleviating inflammation and oxidative stress. How this study might affect research, practice or policy This study preliminarily found the effectiveness of vitamin D supplementation on the healing of DFU, which can provide a reference for the treatment of DFU by medical staff.

Local Load Migration in High-Capacity Fog Computing

Fog computing brings storage and computational capabilities closer to the data source, which reduces latency and enhances efficiency in processing data. However, these capabilities are resource-constrained at the fog nodes as compared to the cloud core. This limitation can result in high computational loads that yield in saturation and congestion at the fog nodes when processing large traffic volumes. Hence, this paper introduces a novel load migration scheme for delay-sensitive and computation-intensive requests in fog computing without relaying to the cloud core, thus avoiding prolonged link delays. First, a grouped service function chain (SFC) provisioning framework is embedded on a heterogeneous architecture composed of super fog (SF) and ordinary fog (OF) nodes, where all the virtual network functions (VNFs) in the SFC are mapped on a single SF node to reduce resource use. Here, the SF nodes are always in active mode to receive incoming traffic, whereas the OF nodes are maintained in idle mode to save power and computing resources. When the SF node approaches a saturation threshold alarm, it diffuses its load (hosted VNFs) gradually to neighboring OF nodes in the vicinity, thus avoiding saturation at the SF node and providing service continuity. The framework is implemented on a resource-constrained network to achieve realistic operating conditions. Overall, the proposed work achieves high admission and resource utilization rates, reduced delays, and power consumption, as compared to key network architectures and provisioning schemes that separately map delay-sensitive and computation-intensive on hierarchical fog layers, which incur increased delays, network traffic, and power usage.

Enhanced Prairie Dog Optimization with Differential Evolution for solving engineering design problems and network intrusion detection system

This paper introduces a novel hybrid optimization algorithm, PDO-DE, which integrates the Prairie Dog Optimization (PDO) algorithm with the Differential Evolution (DE) strategy. This research aims to develop an algorithm that efficiently addresses complex optimization problems in engineering design and network intrusion detection systems. Our method enhances the PDO's search capabilities by incorporating the DE's principal mechanisms of mutation and crossover, facilitating improved solution exploration and exploitation. We evaluate the effectiveness of the PDO-DE algorithm through rigorous testing on 23 classical benchmark functions, five engineering design problems, and a network intrusion detection system (NIDS). The results indicate that PDO-DE outperforms several state-of-the-art optimization algorithms regarding convergence speed and accuracy, demonstrating its robustness and adaptability across different problem domains. The PDO-DE algorithm's potential applications extend to engineering challenges and cybersecurity issues, where efficient and reliable solutions are critical; for example, the NIDS results show significant results in detection rate, false alarm, and accuracy with 98.1%, 2.4%, and 96%, respectively. The innovative integration of PDO and DE contributes significantly to stochastic optimization and swarm intelligence, offering a promising new tool for tackling diverse optimization problems. In conclusion, the PDO-DE algorithm represents a significant scientific advancement in hybrid optimization techniques, providing a more effective approach for solving real-world problems that require high precision and optimal resource utilization.

Meta-Analysis of the Effects of Straw Incorporation on Maize Yield and Water Use Efficiency in China under Different Production Conditions

Maize plays a crucial role in China’s grain production, with a cultivation area reaching 44.22 million hectares and an annual yield of 289 million tons in 2023. However, the challenge remains on how to further increase maize yield and water use efficiency (WUE) without adding to the environmental burden. To systematically evaluate the impact of straw incorporation under varying production conditions on maize yield and WUE, this study collected experimental data from multiple locations across China. A meta-analysis was conducted to compare the effects of straw incorporation versus no incorporation, and the main influencing factors were identified using correlation analysis and a random forest model. The results indicate that straw incorporation significantly enhances both maize yield and WUE, with the most pronounced improvements observed under conditions of an average growing season temperature of 19–23 °C, soil pH of 6.5–7.5, low initial soil organic matter content, and deep plowing for straw incorporation. Additionally, moderate nitrogen application rates and straw incorporation amounts (9000–15,000 kg·ha−2) also significantly boost maize yield and WUE. Field management practices and meteorological conditions are identified as the primary factors affecting maize yield and WUE under straw incorporation conditions. Therefore, straw incorporation stands out as an effective agricultural practice for achieving high maize yields and efficient resource utilization. The findings of this study provide valuable insights for global food security and the sustainable development of agriculture.

Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

Recently, several edge deployment types, such as on-premise edge clusters, Unmanned Aerial Vehicles (UAV)-attached edge devices, telecommunication base stations installed with edge clusters, etc., are being deployed to enable faster response time for latency-sensitive tasks. One fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource utilization. Existing approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks, inter-dependencies of tasks and edge resource availability. These approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support, as well as provider lock-in. Therefore, we present EdgeColla, which is based on the integration of edge resources running across multi-edge deployments. EdgeColla leverages learning techniques to intelligently dispatch multi-dependent tasks, and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize them. Extensive experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

LSMOF-AD: Three-Stage Optimization Approach with Adaptive Differential for Large-Scale Container Scheduling

Container technology has gained a widespread application in cloud computing environments due to its low resource overhead and high flexibility. However, as the number of containers grows, it becomes increasingly challenging to achieve the rapid and coordinated optimization of multiple objectives for container scheduling, while maintaining system stability and security. This paper aims to overcome these challenges and provides the optimal allocation for a large number of containers. First, a large-scale multi-objective container scheduling optimization model is constructed, which involves the task completion time, resource cost, and load balancing. Second, a novel optimization algorithm called LSMOF-AD (large-scale multi-objective optimization framework with muti-stage and adaptive differential strategies) is proposed to effectively handle large-scale container scheduling problems. The experimental results show that the proposed algorithm has a better performance in multiple benchmark problems compared to other advanced algorithms and can effectively reduce the task processing delay, while achieving a high resource utilization and load balancing compared to other scheduling strategies.

BPNN‐based flow classification and admission control for software defined IIoT

AbstractFlow admission control (FAC) aims to efficiently manage the service requests while maximizing the network utilization. With multiple connection requests, access delay or even service interruption may occur. This paper proposes a novel FAC approach to reduce the contention between the end nodes and ensure high utilization of the networking resources for software defined IIoT. First, incoming flows are classified into different priorities using back propagation neural network based on selected features representing the current network status. Second, with the designed flow admission policies, bandwidth and buffer size are estimated with stochastic network calculus model. Finally, the thresholds of the proposed FAC scheme are dynamically decided based on the above two parameters. Various flows are admitted or rejected via the proposed FAC to maintain real time processing. Unlike traditional FAC schemes rely on static priority systems, the proposed scheme leverages machine learning technique for dynamic flow prioritization and the stochastic network calculus model for precise estimation. Computer simulation reveals that the proposed scheme accurately classifies the flows, and substantially decreases the transmission delay and improves the network utilization compared to the existing FAC schemes. This highlights the superiority of the proposed scheme meeting the demands of software defined IIoT.

Resource and Service Use after Discharge Among Infants 22-25 Weeks Estimated Gestational Age at the First High-Risk Infant Follow-Up Visit in California

To examine resource and service use after discharge among infants born extraordinarily preterm in California who attended high-risk infant follow-up (HRIF) clinic by 12months corrected age. We included infants born 2010-2017 between 22+0/7 and 25+6/7weeks' gestational age in the California Perinatal Quality Care Collaborative and California Perinatal Quality Care Collaborative-California Children's Services HRIF databases. We evaluated rates of hospitalization, surgeries, medications, equipment, medical service and special service use, and referrals. We examined factors associated with receiving ≥ 2 medical services, and ≥ 1 special service. A total of 3941 of 5284 infants received a HRIF visit by 12months corrected age. Infants born at earlier gestational ages used more medications, equipment, medical services, and special services and had higher rates of referral to medical and special services at the first HRIF visit. Infants with major morbidity, surgery, caregiver concerns, and mothers with more years of education had higher odds of receiving ≥ 2 medical services. Infants with Black maternal race, younger maternal age, female sex, and discharge from lower level neonatal intensive care units (NICUs) had lower odds of receiving ≥ 2 medical services. Infants with more educated mothers, multiple gestation, major morbidity, surgery, caregiver concerns, and discharge from lower level NICUs had increased odds of receiving a special service. Infants born extraordinarily preterm have substantial resource use after discharge. High resource utilization was associated with maternal/sociodemographic factors and expected clinical factors. Early functional and service use information is valuable to parents and underscores the need for NICU providers to appropriately prepare and refer families.