Low Resource Utilization Research Articles

An optimal algorithm for energy harvesting in optical networks

Optical fiber cables, which make up the majority of the world's telecommunications infrastructure, now carry the vast majority of digital data. However, because to the Internet's exponential expansion and a slew of bandwidth-hungry new services like online gaming, 3D or high-definition television, and cloud computing, this infrastructure will soon run out of capacity. The growing need for faster transmission speeds by clients has led to the development of heterogeneous optical networks. High throughput, portability, and future information interconnects may all be accommodated by such a system. Delivering such high-capacity connections to a mobile client should need heterogeneous optical networks. Different multiple access user technologies have been suggested to meet the needs of a group of future heterogeneous optical network users. In order to address the high energy consumption, poor QoS and low resource utilization in cloud radio access network, this paper proposed a novel algorithm. The main idea is to allocate virtual resources with energy-awareness and hybrid energy supply. Then, the energy arrival and consumption models were established based on different network devices. Simulation results show that the proposed algorithm effectively improved the energy consumption performance and QoS as compared with existing methods.

Dynamic Network Resource Autonomy Management and Task Scheduling Method

Satellite network resource management and scheduling technology are significant to constructing integrated information networks in heaven and earth. The difficulty in realizing this technology lies in improving resource utilization efficiency while ensuring the service quality of satellites and efficiently coordinating complex satellite network systems and services. This paper proposes a model, A Dynamic task scheduling method based on a UNified resource Management architecture(DUNM), based on the designed resource management architecture supported by dynamic scheduling algorithms to address the problems of low resource utilization, resource allocation, and task completion rate. First, with sufficient resources, the task execution time to complete a task is calculated based on the number of resources, task transmission time, task waiting time, etc. Secondly, based on the tasks assigned to satellites, the execution time of all functions with different transmission rates of communication links between satellites is calculated, and the total sum of all time consumption is analyzed. Finally, after simulation experiments and comparison with various baseline algorithms, about a 40% reduction in time to complete scheduled tasks and an almost 25% reduction in the average cost to finish a scheduling task, our method has higher scheduling efficiency and lower task completion revenue. It also guarantees a higher task completion rate while completing the tasks. Our approach attained a nearly 100% completion rate for scheduling tasks, which means that our algorithm can achieve the scheduling tasks faster and at high task revenue, thus improving the efficiency and economic efficiency of the whole system. Therefore, it validates the advantages of our method, such as high efficiency and high revenue.

Improved Grant-Free Access for URLLC via Multi-Tier-Driven Computing: Network-Load Learning, Prediction, and Resource Allocation

Grant-Free (GF) access has been recognized as a promising candidate for Ultra-Reliable and Low-Latency Communications (URLLC). However, even with GF access, URLLC still may not effectively gain high reliability and millimeter-level latency, simultaneously. This is because the network load is typically time-varying and not known to the base station (BS), and thus, the resource allocated for GF access cannot well adapt to variations of the network load, resulting in low resource utilization efficiency under light network load and leading to severe collisions under heavy network load. To tackle this problem, we propose a multi-tier-driven computing framework and the associated algorithms for URLLC to support users with different QoS requirements. Especially, we concentrate on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K $ </tex-math></inline-formula> - repetition GF access in light of its simplicity and well-balanced performance for practical systems. In particular, our framework consists of three tiers of computation, namely network-load learning, network-load prediction, and adaptive resource allocation. In the first tier, the BS can learn the network-load information from the states (success, collision, and idle) of random-access resources in terms of resource blocks (RB) and time slots. In the second tier, the network-load variation is effectively predicted based on estimation results from the first tier. Finally, in the third tier, by deriving and weighing the failure probabilities of different groups of users, their QoS requirements, and the predicted network loads, the BS is able to dynamically allocate sufficient resources accommodating the varying network loads. Simulation results show that our proposed approach can estimate the network load more accurately compared with the baseline schemes. Moreover, with the assistance of network-load prediction, our adaptive resource allocation offers an effective way to enhance the QoS for different URLLC services, simultaneously.

Design of Spectrum Processing Chiplet Based on FFT Algorithm

With the rapid development of electronic information and computer science, the fast Fourier transform (FFT) has played an increasingly important role in digital signal processing (DSP). This paper presented a spectrum processing chiplet design method to solve slow speed, low precision, and low resource utilization in spectrum processing of general-purpose spectrum chips and field programmable gate array (FPGA). To realize signal processing, the Radix-2 4096-point FFT algorithm with pipeline structure is used to process spectral signals extracted from the time domain. To reduce the harm caused by spectrum leakage, a windowing module is added to optimize the input data, and the clock gating unit (CGU) is used to perform low-power management on the entire clock reset. The result shows the chiplet takes 0.368 ms to complete a 4096-point frequency sweep under a clock frequency of 61.44 MHz. The chiplet significantly improves speed and accuracy in spectrum processing, which has great application potential in wireless communication.

Scheduling optimization for production of prefabricated components with parallel work of serial machines

Scheduling optimization of manufacturing prefabricated components is critical for construction in enhancing efficiency and productivity. However, problems of low formulation efficiency and low resource utilization still exist, impeding productivity and increasing costs. Although numerous efforts have been devoted to scheduling optimization, limited research has considered the parallel work of serial machines in production lines, leading to a deviation between actual situations and expectations. Therefore, a new optimization method is proposed with two major contributions. First, a Prefabricated Components Production Scheduling (PCPS) model considering the parallel work of serial machines is established, containing 3 new constraint conditions. a Position Algorithm (PA) is also designed to facilitate a constraint related to the locations of component groups. Second, a genetic algorithm-based method is designed to seek potential optimal scheduling schemes. The model is demonstrated and evaluated in an actual case. The results show that: (1) Optimal schedules calculated by the PCPS model can reduce penalty cost by 28.6% and total completion time by 14.9% compared to the case without considering the parallel work of serial machines. (2) Optimal schedules can effectively reduce penalty cost by 63.9% and total completion time by 34.4% compared to the empirical method of ascending order by the due date. The optimal schedules can provide a solution for more detailed and practical scheduling. For future improvement, other factors affecting productivity, such as operator proficiency, will be added to the model constraints.

Prehospital emergency medical service utilization and associated factors among critically ill COVID-19 patients treated at centers in Addis Ababa, Ethiopia.

The majority of populations in developing countries are living in areas of no access or limited access to prehospital emergency medical services (EMS). In Addis Ababa, the reported prehospital EMS utilization were ranging from zero to thirty-eight percent. However, there is limited research on reasons for the low utilization of prehospital resources in Ethiopia. This study aimed to assess factors associated with prehospital EMS utilization among critically ill COVID-19 patients in Addis Ababa, Ethiopia. A hospital-based cross-sectional study was conducted to collect primary data from 421 COVID-19 patients in Addis Ababa between May and July 2021. Logistic regression was used to identify factors associated with prehospital service utilization. Andersen's Behavioral Model was implemented to address independent variables, including predisposing, enabling, need, and health behaviors-related variables. The level of prehospital care utilization was 87.6%. Being married [AOR 2.6(95%; CI:1.24-5.58)], belief that self-transport is quicker than the ambulance [AOR 0.13(95%; CI: 0.05-0.34)], and perceptions that ambulance provides transportation service only [AOR 0.14(95%; CI:0.04-0.45)] were predisposing factors associated with prehospital service utilization while the source of referrals [AOR 6.9(95%; CI: 2.78-17.30)], and prior knowledge on the availability of toll-free ambulance calling numbers [AOR 0.14(95%; CI: 0.04-0.45)] were identified as enabling factors. Substantial proportions of critically ill COVID-19 patients used prehospital services to access treatment centers. Prehospital EMS utilization in this study varies by predisposing and enabling factors, particularly: marital status, source of referral, prior knowledge on the availability of toll-free ambulances, belief that self-transport is quicker than ambulances, and perceptions that ambulance provides transportation service only. Our findings call for further actions to be taken by policymakers including physical and media campaigns focusing on the identified factors.

SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Networks

The effectiveness of Recurrent Neural Networks (RNNs) for tasks such as Automatic Speech Recognition has fostered interest in RNN inference acceleration. Due to the recurrent nature and data dependencies of RNN computations, prior work has designed customized architectures specifically tailored to the computation pattern of RNN, getting high computation efficiency for certain chosen model sizes. However, given that the dimensionality of RNNs varies a lot for different tasks, it is crucial to generalize this efficiency to diverse configurations. In this work, we identify adaptiveness as a key feature that is missing from today’s RNN accelerators. In particular, we first show the problem of low resource utilization and low adaptiveness for the state-of-the-art RNN implementations on GPU, FPGA, and ASIC architectures. To solve these issues, we propose an intelligent tiled-based dispatching mechanism for increasing the adaptiveness of RNN computation, in order to efficiently handle the data dependencies. To do so, we propose Sharp as a hardware accelerator, which pipelines RNN computation using an effective scheduling scheme to hide most of the dependent serialization. Furthermore, Sharp employs dynamic reconfigurable architecture to adapt to the model’s characteristics. Sharp achieves 2×, 2.8×, and 82× speedups on average, considering different RNN models and resource budgets, compared to the state-of-the-art ASIC, FPGA, and GPU implementations, respectively. Furthermore, we provide significant energy reduction with respect to the previous solutions, due to the low power dissipation of Sharp (321 GFLOPS/Watt).

Design and numerical analysis of a composite system of a household biogas digester with pretreatment

The rural household biogas digesters are studied in this paper, and numerical analysis is used to simulate the effects of light climates in the cold season and the heat of sunlight on the temperature distribution in the pool. The numerical results show that the uneven distributions of light intensity and temperature in the biogas digester have a great influence on its cumulative gas production. The central area has a significant heat collection effect, the inner wall is weaker, and the temperature near the top is slightly higher than that in the lower area, and the vertical temperature changes in a decreasing trend. Aiming at the different photo-climatic conditions of the household biogas digester and the uneven heat collection and temperature distribution in different periods, the traditional slag pumping work is cumbersome, and the utilization rate of the biogas residue is low, so a new household with pretreatment is proposed. An intelligent temperature control system is designed for the biogas tank to analyze the adverse effects of straw fermentation at low temperature, insufficient gas production and low resource utilization.

Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

In Information Centric Networking (ICN) where content is the object of exchange, in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks. Setting up cache space at any node enables users to access data nearby, thus relieving the processing pressure on the servers. However, the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents. To address the issues mentioned, a cooperative caching strategy (CSTL) for remote sensing satellite networks based on a two-layer caching model is proposed. The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station. Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users. A content classification method based on hierarchical division is proposed in the satellite network, and differential probabilistic caching is employed for different levels of content. The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content. In the two-layer caching model, ground stations and satellite networks collaboratively cache to achieve global planning of cache contents, rationalize the utilization of cache resources, and reduce the propagation delay of remote sensing data. Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load, which satisfies the timeliness requirement of remote sensing data transmission.

Optimization of Electric Bus Scheduling for Mixed Passenger and Freight Flow in an Urban-Rural Transit System

Transport accessibility and urban-rural connectivity are seen as critical aspects of rural economic development. In the transit network, passenger flow between urban-rural corridors demonstrates directional imbalances and low utilization of scarce resources. Freight transportation, on the other hand, lags due to poor geography, high operating costs, and scattered demand. This paper proposes a new mode of public transit that integrates passenger and freight transport, providing a carrier for logistics while compensating for the low utilization of passenger transport. In this mode, each timetabled round trip is divided into one dedicated passenger trip with high demand and one mixed-flow trip with on-demand requests. A space-time-state network is constructed considering the picking-up time window, loading/unloading service time, and electric bus energy replenishment. A mixed-integer linear programming model is developed to optimize the bus schedule that covers the travel demands and the charging requests with minimized travel costs. A Lagrangian relaxation framework with a dynamic programming algorithm and sub-gradient method is presented for problem-solving. The real-life rural-urban transport instance and a simulated network demonstrate the operation of the new mode and validate the efficiency of the proposed method. The innovative concept and the optimization framework are expected to serve as a reference for public administration to alleviate passenger and freight transportation bottlenecks in the urban-rural context.

Integrating LCA and network model to understand food-energy-water nexus in the Ningxia, China

It is necessary to link and feedback the FEW-Nexus in order to find a sustainable method of food, energy, and water (FEW) supply. We better understand nexus by integrating LCA and network models based on previous work in this paper. The results show that (1) 1.16 billion kg of grain were traded in Ningxia in 2020. The grain export area is primarily in Ningxia’s central and southern regions, and grain trade relieves the pressure on grain demand in the north. (2) Grain trade has resulted in the movement and reconfiguration of water footprints and carbon footprints, alleviating some of the pressure on local water resources. However, some counties have low agricultural resource utilization efficiency. For example, Xiji failed to use water effectively in water-rich areas to meet the needs of water-scarce areas. (3) The spatial association network of the FEW-Nexus is unbalanced, and the associated channels should be enriched. Ningxia can improve the utilization of energy and water resources by strengthening key nodes of food flow.

ORSAS: An Output Row-Stationary Accelerator for Sparse Neural Networks

Various pruning skills and network compression methods make modern neural networks sparse for both weights and activations. However, GPUs (graphics processing units) and most of the customized CNN (convolutional neural networks) accelerators have not taken advantage of the sparsity of neural networks, and the accelerators for sparse neural networks in these years are suffering from low computational resource utilization. This paper first proposes an output row-stationary dataflow that exploits the sparsity of both weights and activations. It allows the accelerator to process weights and activations in their compressed form, leading to the high utilization of computational resources, i.e. multipliers. Besides, a low-cost compression algorithm is adopted for both weights and input activations to reduce the power consumption of data access. Secondly, the Y-buffer is proposed to eliminate repeated reading of input activations caused by halo effects, which emerge when tiling large-size input feature maps (ifmaps). Third, an interleaved broadcasting mechanism is introduced to alleviate the imbalance problems caused by the irregularity of sparse data. Finally, a prototype design called ORSAS is synthesized and placed and routed with a SMIC 55 nm process. The evaluation results show that ORSAS occupies the smallest logical cell area, and has the highest multiplier utilization among the peers’ works when the sparsity is large. ORSAS keeps the utilization of multipliers in a range from 60% to 90% over the convolutional layers of popular sparse CNNs and achieves ultra-low power consumption and highest efficiency.

Online Homework Intelligent Platform Based on Self-Regulated Learning (SRL): Essential for Sustainable Development of Online Higher Education

Online higher education has become a steadily more popular way of learning for university students in the post-pandemic era. It has been emphasized that active learning and interactive communication are key factors in achieving effective performance in online learning. However, due to the lack of learning motivation of students and the lack of feedback data in online learning, there are numerous problems, such as the weak self-discipline of students, unsatisfactory learning experience, a high plagiarism rate of homework, and the low utilization of online teaching resources. In this study, an online homework intelligent platform implemented by information technology (IT) was proposed. It was based on the pedagogical self-regulated learning (SRL) strategy as a theoretical foundation, and information technology as a driver. Through setting online homework assignments, a sustainable means of promoting the four components of the SRL strategy, i.e., self-disciplinary control, independent thinking, reflective learning, and interest development, can be provided to university students. Therefore, this study explained the “4A” functions in the platform and analysed the details of their implementation and value, such as assistance in locating resources, assignment of differentiated homework, assessment of warning learning, and achievement of sharing. After three years of continuous improvements since COVID-19, this online platform has been successfully applied to students and teachers at our university and other pilot universities. A comparison of student teaching data, questionnaire responses and teacher interviews from the Computer Composition Principles course illustrated the sustainability as well as the effectiveness of the method.

A Resource-Efficient Predictive Resource Provisioning System in Cloud Systems

In cloud systems, demand-prediction based resource provisioning schemes help assure the SLOs (service level objectives) of cloud tenants. We notice that if a provisioning scheme does not exclude bursts from historical resource demands in normal demand prediction or always uses a large padding to correct under-prediction, it will lead to resource over-provisioning and low resource utilization. To improve the previous schemes, in this paper, we present a Resource-efficient Predictive Resource Provisioning system in cloud systems (RPRP) that excludes bursts in demand prediction and has algorithms to specifically handle bursts to avoid resource over-provisioning. Rather than setting padding to a possibly high value, RPRP has a load-dependent padding algorithm that adaptively determines padding based on predicted demands. To handle bursts, RPRP has a burst-resilient shared padding algorithm that reserves resource shared by multiple co-located VMs rather than for individual VMs. It also embodies a responsive padding algorithm that adaptively adjusts padding to recover from both under-provisioning and over-provisioning. We implemented RPRP on top of Xen and conducted both trace-driven simulation and real-world testbed experiments. The experimental results show that RPRP achieves higher resource utilization, more accurate demand predictions, and fewer SLO violations than previous schemes.

Improving documentation of anticoagulation and antiplatelet recommendations after outpatient endoscopy

Clear documentation of instructions for resuming anticoagulant and antiplatelet (AC/AP) medications after gastrointestinal endoscopy is essential for high-quality postprocedure care. Yet, these recommendations are frequently absent, which may impact patient...

A Virtual Machine Consolidation Algorithm Based on Dynamic Load Mean and Multi-Objective Optimization in Cloud Computing

High energy consumption and low resource utilization have become increasingly prominent problems in cloud data centers. Virtual machine (VM) consolidation is the key technology to solve the problems. However, excessive VM consolidation may lead to service level agreement violations (SLAv). Most studies have focused on optimizing energy consumption and ignored other factors. An effective VM consolidation should comprehensively consider multiple factors, including the quality of service (QoS), energy consumption, resource utilization, migration overhead and network communication overhead, which is a multi-objective optimization problem. To solve the problems above, we propose a VM consolidation approach based on dynamic load mean and multi-objective optimization (DLMM-VMC), which aims to minimize power consumption, resources waste, migration overhead and network communication overhead while ensuring QoS. Fist, based on multi-dimensional resources consideration, the host load status is objectively evaluated by using the proposed host load detection algorithm based on the dynamic load mean to avoid an excessive VM consolidation. Then, the best solution is obtained based on the proposed multi-objective optimization model and optimized ant colony algorithm, so as to ensure the common interests of cloud service providers and users. Finally, the experimental results show that compared with the existing VM consolidation methods, our proposed algorithm has a significant improvement in the energy consumption, QoS, resources waste, SLAv, migration and network overhead.

Consolidating Bus Charger Deployment and Fleet Management for Public Transit Electrification: A Life-Cycle Cost Analysis Framework

Despite rapid advances in urban transit electrification, the progress of systematic planning and management of the electric bus (EB) fleet is falling behind. In this research, the fundamental issues affecting the nascent EB system are first reviewed, including charging station deployment, battery sizing, bus scheduling, and life-cycle analysis. At present, EB systems are planned and operated in a sequential manner, with bus scheduling occurring after the bus fleet and infrastructure have been deployed, resulting in low resource utilization or waste. We propose a mixed-integer programming model to consolidate charging station deployment and bus fleet management with the lowest possible life-cycle costs (LCCs), consisting of ownership, operation, maintenance, and emissions expenses, thereby narrowing the gap between optimal planning and operations. A tailored branch-and-price approach is further introduced to reduce the computational effort required for finding optimal solutions. Analytical results of a real-world case show that, compared with the current bus operational strategies and charging station layout, the LCC of one bus line can be decreased significantly by 30.4%. The proposed research not only performs life-cycle analysis but also provides transport authorities and operators with reliable charger deployment and bus schedules for single- and multi-line services, both of which are critical requirements for decision support in future transit systems with high electrification penetration, helping to accelerate the transition to sustainable mobility.

Abstract 14448: On-Table Extubation Associated With Lower Resource Utilization and Non-Inferior Outcomes After Coronary Bypass

Introduction: Minimizing the duration of postoperative ventilation facilitates early discharge from the intensive care unit (ICU) thus promoting enhanced recovery after cardiac surgery (ERAS). Hence, we sought to investigate the impact of on-table extubation on outcomes and resource utilization after coronary artery bypass graft (CABG) surgery. Methods: We performed a retrospective review of a prospectively collected local database, including all patients undergoing CABG surgery at a large academic center from January 2020 to December 2021. Patients were stratified into two groups, on-table (before leaving the operating room) extubation versus early (&lt; 6 hours after procedure) extubation. In-hospital mortality and postoperative complications were compared using univariate analyses. Length of stay (LOS), 30-day readmission and high resource utilization (HRU), a binary outcome defined as direct procedure cost of the highest quartile, were investigated. Results: A total of 691 patients were included in our analysis, 267 (38.6%) of which were extubated on-table. The two groups had similar median age and preoperative ejection fractions. However, on-table extubation patients were more likely to be males (86.9% vs 76.6%, p&lt;0.001) with smaller BMIs (27.7% vs 28.5%, p=0.002), lower STS risk scores (0.75% vs 0.95%, p=0.022) and lower cerebrovascular disease prevalence (8.2% vs 14.2%, p=0.017). Compared to early extubation, on-table extubation patients had similar rates of in-hospital mortality (0 % vs 1.4 %, p=0.051), postoperative blood transfusion (7.5% vs 9.0%, p=0.494), reintubation (2.6% vs 3.5%, p=0.499) and pneumonia (1.9% vs 3.3%, p=0.263). As hypothesized, on-table extubation patients had shorter ICU LOS (32hrs vs 39hrs, p=0.02), shorter postoperative LOS (4.9 days vs 5.9 days, p=0.003), and lower incidence of high resource utilization (18.0% vs 29.5%, p&lt;0.001). ICU readmission (3.8% vs 3.5%, p=0.887) and 30-day readmission (5.2% vs 7.3%, p=0.277) were similar in the two groups. Conclusions: On-table extubation is a safe and effective ERAS practice, that is associated with lower ICU LOS, postoperative LOS, and resource utilization, without affecting rates of reintubation, ICU readmission, 30-day readmission or operative mortality.

Research on Nickel-Cobalt-Copper Productive Collaboration and Intelligent Decision-Making Technology in Symbiotic Coupling Enterprises

This study investigates the research on nickel-cobalt-copper productive collaboration and intelligent decision-making technology for symbiotic coupling enterprises in the Gansu Province of China. The aim is to address the problems of low resource utilization efficiency, weak production collaboration, and an insufficient intelligent decision-making level in the nonferrous metallurgy industry. First, the present situation of nickel-cobalt-copper industry chain-level collaboration in the agglomeration area is analyzed extensively, and the corresponding problems are proposed. Second, the functional framework of productive collaboration and intelligent decision-making is presented from the industrial chain and industrial agent levels. In addition, the design methods of various balance strategies in the production collaboration within the industrial agent are provided. These can realise the daily balance of material, metal, and energy data in an individual industrial agent. Finally, with regard to intelligent decision-making at the industrial chain level, six key measures surrounding different themes are provided to support the implementation of productive collaboration and intelligent decision-making in the nonferrous metallurgy agglomeration area.

Energy Efficient and Low dynamic power Consumption TCAM on FPGA

Artificial Intelligence [AI] and networking applications extensively incorporate Field Programmable Gate Arrays [FPGA] - based Ternary Content Addressable Memories [TCAM]. Since FPGAs cannot support TCAMs, they must be emulated with SRAM-based memories, requiring FPGA resources. Compared to state-of-the-art designs, the proposed FPGA-based TCAM implementation will save significant resources. This methodology makes use of the Lookup Table RAMS (LUTRAMs), slice carry-chains, and flip-flops (FF) allowing simultaneous mapping of rules and deeper pipelining respectively. The TCAM implementation results in lower power consumption, lesser delays, and lower resource utilization. It outperformed conventional FPGA-based TCAMs in terms of energy efficiency (EE) and performance per area (PA) by at least 3.34 and 8.4 times respectively, and 56% better than existing FPGA designs. The proposed method outperforms all previous approaches due to its low dynamic power consumption when considering the huge size of TCAM emulation on SRAM-based FPGAs.