High Resource Utilization Research Articles

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 (< 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<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<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.

High-performance and balanced parallel graph coloring on multicore platforms

Graph coloring is widely used to parallelize scientific applications by identifying subsets of independent tasks that can be executed simultaneously. Graph coloring assigns colors the vertices of a graph, such that no adjacent vertices have the same color. The number of colors used corresponds to the number of parallel steps in a real-world end-application. Therefore, the total runtime of the graph coloring kernel adds to the overall parallel overhead of the real-world end-application, whereas the number of the vertices of each color class determines the number of the independent concurrent tasks of each parallel step, thus affecting the amount of parallelism and hardware resource utilization in the execution of the real-world end-application. In this work, we propose a high-performance graph coloring algorithm, named ColorTM, that leverages Hardware Transactional Memory (HTM) to detect coloring inconsistencies between adjacent vertices. ColorTM detects and resolves coloring inconsistencies between adjacent vertices with an eager approach to minimize data access costs, and implements a speculative synchronization scheme to minimize synchronization costs and increase parallelism. We extend our proposed algorithmic design to propose a balanced graph coloring algorithm, named BalColorTM, with which all color classes include almost the same number of vertices to achieve high parallelism and resource utilization in the execution of the real-world end-applications. We evaluate ColorTM and BalColorTM using a wide variety of large real-world graphs with diverse characteristics. ColorTM and BalColorTM improve performance by 12.98times and 1.78times on average using 56 parallel threads compared to prior state-of-the-art approaches. Moreover, we study the impact of our proposed graph coloring algorithmic designs on a popular end-application, i.e., Community Detection, and demonstrate the ColorTM and BalColorTM can provide high-performance improvements in real-world end-applications across various input data given.

МЕТОДИКА СОЗДАНИЯ ТОПОЛОГИЧЕСКИХ ОГРАНИЧЕНИЙ ПРИ ВЫСОКОЙ УТИЛИЗАЦИИ РЕСУРСОВ ПЛИС

In the paper we consider the problem of achieving high real performance of reconfigurablecomputer systems in implementing computationally expensive tasks from various problem areas.The parameters of the programs executed on reconfigurable systems determine their real performance.The main component of these programs is the computing data processing structures implementedas FPGA configuration files. At the same time, one of the key parameters of any computingstructure is the clock frequency of its operation, which directly affects its performance. However,there are several problems concerning the achievement of high clock rates, and they cannot be solvedwith the help of modern CAD tools. The reason is the non-optimal topological placement of functionalblocks of the computing structure within the field of FPGA primitives, especially with high resourceutilization. Due to this, the load on the FPGA switching matrix is increasing, and, as a result,the connections among functionally dependent FPGA primitives turn out to be much longer than isacceptable. In addition, excessive connection length is observed when tracing connections amongprimitives that are placed on different FPGA chips or are physically separated by on-chip peripherals.In the paper we describe a methodology which provides optimization of the placement of computingstructure elements on FPGA primitives, and minimizes the length of traces among primitives,and also minimizes the number of traces among physically separated FPGA topological sections.To prove the proposed methodology, we implemented the test task "FIR-filter" on a reconfigurablecomputer "Tertius." We have demonstrated the main problems concerning reaching the target clockrate and have described a method for their solution. Owing to our methodology, it is possible toincrease the clock rate; hence, the performance of Tertius will increase by 25% without revisingthe functional circuit of the task’s computing structure. According to our current research of thesuggested methodology and its efficiency, we claim that CAD tools, used for creating topologicalrestrictions and based on our methodology, will significantly reduce the time for developing programswith the required characteristics for reconfigurable computer systems.

RT-SEAT: A hybrid approach based real-time scheduler for energy and temperature efficient heterogeneous multicore platforms

The demand for heterogeneous multicore platforms is growing at a rapid pace in modern gadgets. Such platforms help cater to various types of applications and thus provide high resource utilization. As each task has a different execution time on different types of cores, it is very challenging to schedule tasks on such platforms. With the advancement in technology, it has become imperative to manage energy consumption and temperatures of cores in multicore platforms. Hence, in this work, we propose RT-SEAT, a hybrid real-time scheduler for energy and temperature-efficient heterogeneous multicore systems. Through extensive experimental analysis, we have found that RT-SEAT is able to schedule more tasks (up to 46.75%), save more energy (up to 6.89%), and reduce the average temperature of the cores by 20.45%, when the system workload varies from 50% to 100%, with respect to the state-of-the-art.

71 Barriers to providing optimal care to children with medical complexity in the emergency department: A physicians' perspective

Abstract Background Children with medical complexity (CMC) are a subset of the pediatric population with chronic medical conditions requiring specialized care and high health resource utilization. Due to the nature of their underlying diseases, along with the medical devices that they are reliant on, they account for a large proportion of annual pediatric emergency department (ED) visits in tertiary centres and about one third of the children admitted. ED physicians are often the first to interact with CMC patients and thus the quality of care they provide influences the hospital course of those patients. Objectives To determine and explore the barriers that ED physicians face when providing care to CMC in the ED and identify potential areas of improvement in care provided in the ED. Design/Methods Semi-structured personal interviews were conducted virtually with 20 pediatric emergency medicine physicians and fellows. Each participant was asked the same set of questions and given extra time to elaborate. Interviews were transcribed verbatim and manual coding was conducted to identify commonalities across the interviews. Results We identified four major themes and 12 sub-themes as barriers to optimal care of CMC in the ED. Major themes included time, training, medico-legal concerns, and care plans. The two most commonly identified barriers were time required to care for CMC patients, identified by 90% (n=18) of the participants, and limited exposure during fellowship training, identified by 80% (n=16) of the participants. Only 20% (n=4) identified medico-legal challenges as a unique barrier when managing CMC. All the participants (n=20) emphasized the importance of a care plan, a detailed document given to caregivers of CMC by the primary care team with details about their medical condition, and if not available, 85% (n=17) indicated that it will affect the efficiency and quality of care provided. Even if a care plan is available, 50% (n=10) of the participants would still contact the complex care team for further management recommendations. Conclusion Care plan accessibility and time restraint in the ED are major barriers faced by PEM physicians in the ED when caring for CMC. Additional training during fellowship in complex care medicine and providing patients with an updated care plan are two interventions suggested to optimize care of CMC in the ED.

Optimized scheduling of multi‐user Map‐Reduce jobs in heterogeneous environment

SummaryMap‐Reduce is a programming paradigm widely used for data intensive applications in distributed computing. Divisible load theory (DLT) is another model to partition divisible load for parallel processing. In this work, a new job scheduler named virtual job scheduler (VJS) is developed to schedule the MapReduce jobs based on DLT. VJS constructs a virtual job set from the queue of jobs awaiting execution by considering the CPU and IO resource utilization levels of each job. The core of VJS is in its partitioning algorithm. Two novel partitioning algorithms, namely, two level successive partitioning (TLSP) and predictive partitioning (PRED) have been proposed. TLSP applies DLT to both map and reduce phase in succession. This second load partitioning performed during the comparatively longer reduce phase, exploits the advantage of DLT better. PRED is a modification of TLSP aimed at optimizing the overall schedule length, by taking the idle time of the reducers into consideration. Evaluation of these two models across various execution environments has indicated a profound decrease in the wait time of reducers and as a result has shown a significant reduction in the makespan of the whole job as such. VJS altered to incorporate PRED partitioning algorithm has proved to be suitable for heterogeneous environment, with high resource utilization compared with the default Hadoop MapReduce scheduler.

An Adaptive Elastic Multi-model Big Data Analysis and Information Extraction System

With the diverse applications to industry and domain-specific context, multi-source information extraction on semi-structured and unstructured data, as well as across data models, is becoming more common. However, multi-model information extraction often requires the deployment of multiple data model management, storage, and analysis subsystems on the cloud, many subsystems are not high-resource utilization at the same time, and the resource waste phenomenon is often serious. Therefore, an adaptive scalable multi-model big data analysis and information extraction system is designed and implemented in this paper, which can support data maintenance and cross-model query of relational, graph, document, key and other data models, and can provide efficient cross-model information extraction. On this basis, we can achieve the system resource allocation on demand and fast scaling mechanism, according to the real-time requirements of multi-model big data analysis, and dynamic adjustment of each subsystem resource allocation. Therefore, our solution not only guarantees multi-model query and information extraction performance and quality of service, but also significantly reduces the total consumption of system resources and cost.

Mental health resources and its equity in Central South of China: A case study of Hunan Province.

ObjectivesMental health resources are an important basis for coping with mental health services. The equity is an important index of a reasonable allocation of health resources. This study aims to evaluate the mental health resources and its equity allocation in Hunan Province, which is one of the typical central south areas of China, so as to provide reference for the development of mental health in China and other areas.MethodsData related to mental health resources was obtained from the Project of Mental Health Resources in Hunan Province, which was conducted by the Department of Hunan Mental Health Center in 2019. The Gini coefficient, the Theil index and other indicators were employed to quantitatively evaluate the equity of mental health resources’ allocation.ResultsBy the end of 2018, there were a total of 141 mental health institutions in Hunan Province of China, the bed density was 5.31 beds per 10,000 people, the ratio of doctors to nurses was 2.20, the number of outpatients of mental health institutions was 1288,047 per year. The mental health resources’ allocation in terms of demographic dimension were in a preferred status with the Gini values all less than 0.3, and the Gini values for mental health resources`allocation in terms of geographical dimension ranged from 0.24 to 0.35. The Theil index for mental health allocation in terms of demographic dimension was lower than 0.05, and the Theil index for mental health allocation in terms of geographical dimension ranged from 0.04 to 0.11.ConclusionsThe shortage of mental health resources is still the priority issue to be increased and optimized by policy-makers in Hunan in the future, especially the human resources. Moreover, the utilization of mental health resources was low though its equity was fair. Policy-makers need to consider the high utilization and geographical accessibility of health resources among different regions to ensure people in different regions could get access to available health services.

Design and Fabrication of an Additively Manufactured Aluminum Mirror with Compound Surfaces.

Microsatellites have a great attraction to researchers due to their high reliability, resource utilization, low cost, and compact size. As the core component of the optical payload, the mirror directly affects the system package size. Therefore, the structural design of mirrors is critical in the compact internal space of microsatellites. This study proposes a closed-back mirror with composite surfaces based on additive manufacturing (AM). Compared with the open-back mirror, it provides excellent optomechanical performance. In addition, AM significantly reduces the intricate mechanical parts’ manufacturing difficulty. Finally, the roughness was better than 2 nm. The surface shape of the AM aluminum mirror reached RMS 1/10λ (λ = 632.8 nm) with the aid of ultra-precision machining technologies such as single-point diamond turning (SPDT), surface modification, and polishing, and the maximum deviation of the surface shape was about RMS 1/42λ (λ = 632.8 nm) after the thermal cycle test, which verified the optical grade application of AM.

Highly targeted stabilization and release behavior of hazardous substances in phosphogypsum

In this paper, an innovative method for highly targeted solidification/stabilization (S/S) of harmful substances in phosphogypsum (PG) was proposed. The release behavior of hazardous substances in PG after mixed reaction was explored using lime or carbide slag (CS) as alkaline regulator and then polyaluminium chloride (PAC), polyferric sulfate (PFS) and polyacrylamide (PAM) as highly targeted S/S agents. The experimental results show that lime has a long-term buffering effect on the pH of PG and a significant stabilizing effect on P, F and heavy metals. When more than 5 % lime was added, the leaching concentration of heavy metals was less than 0.1 mg/L. Moreover, PAM, PAC and PFS have remarkable S/S effects on P, F and heavy metals in the order of PAM > PFS > PAC. When the dosage of PAM was more than 5 %, the leaching concentration of P and F was less than 0.2 mg/L and 1 mg/L, respectively, and that of heavy metals tends to be 0 mg/L, indicating that PAM demonstrates a significant continuous S/S effect and thus provides a feasible pretreatment method for harmless filling, soil utilization and high value-added resource utilization of PG. The high targeted S/S technology of hazardous substances studied in this paper directly provides theoretical and data support for the harmless and resource-based treatment of large-scale solid waste PG produced, alleviates the pressure of solid waste environmental protection in the phosphorus chemical industry, and provides a new research idea for the harmless treatment of other toxic and harmful large-scale solid wastes, reducing the cost of solid waste treatment and simplifying the treatment process.

Cross-Domain Resource Allocation Scheme with Unified Control Architecture in Software Defined Optical Access Network

With the rapid development of communication and the rise of new network services, the resource provisioning of the optical access network becomes more significant than before, especially under the multiple-domain networking situation with the requirement of cross-domain service support. For the sake of high resource utilization to establish more connections of services, this paper proposes a cross-domain resource allocation (CDRA) scheme in a software-defined optical access network to meet the huge bandwidth supporting the requirement of new network services. To achieve this purpose, the global evaluation strategy with the consideration of the traffic situation in each node in its domain is presented in the CDRA scheme, and its interaction process makes decisions for comprehensive optimal resource allocation by integrating radio resources and optical aggregation resources of the entire access network. Furthermore, to manage the resources among multiple domains and support the interaction process of the CDRA scheme, a cross-domain unified control architecture is firstly upgraded by using software-defined networking technology, which includes the further design of CDRA function modules with the global evaluation strategy. The simulation results verify the feasibility of the upgraded architecture and further show that the proposed CDRA scheme can effectively decrease the blocking probability with a 29.35% improvement, balance the network load, and enhance the utilization of network resources of the network.

DynamAP: Architectural Support for Dynamic Graph Traversal on the Automata Processor

Dynamic graph traversals (DGTs) currently are widely used in many important application domains, especially in this big-data era that urgently demands high-performance graph processing and analysis. Unlike static graph traversals, DGTs in real-world application scenarios require not only fast traversal acceleration itself but also, more importantly, a runtime strategy that can effectively accommodate the ever-evolving nature of the graph structure updates followed by a diverse range of graph traversal algorithms . Because of these special features, state-of-the-art designs on conventional compute-centric architectures (e.g., CPU and GPU) struggle to provide sufficient acceleration for DGT processing due to the dominating irregular memory access patterns in graph traversal algorithms and inefficient platform-specific update mechanisms. In this article, we explore the algorithmic features and runtime requirements of real-world DGTs and identify their unique opportunities of acceleration on the recent Micron Automata Processor (AP), an in-situ memory-centric pattern-matching architecture. These features include the natural mapping between traversal algorithms’ path exploration pattern to classic non-deterministic finite automata processing, AP’s architectural and compilation support for DGTs’ evolving traversal operations, and its inherent hardware fitness. However, despite these benefits, enabling highly efficient DGT execution on AP is non-trivial and faces several major challenges. To tackle them, we propose DynamAP , the first AP framework design that enables fast processing for general DGTs. DynamAP is oblivious to periodical traversal algorithm changes and can address the significant overhead caused by frequent graph updates and AP recompilation through our novel hybrid macro designs and associated efficient updating strategies. We evaluate DynamAP against the current DGT designs on a CPU, GPU, and AP with a range of widely adopted DGT algorithms and real-world graphs. For a single update request , our DynamAP achieves an average speedup of 21.3x (up to 39.2x ) over the state-of-the-art implementation on host-AP architecture; an average speedup of 9.2x (up to 14.7x ) and 1.7x (up to 2.8x ) over two highly optimized DGT design frameworks on a 64-GB Intel(R) Xeon CPU and a 32-GB NVIDIA Tesla V100 GPU. DynamAP also maintains high performance and resource utilization for high graph update ratios, and can significantly benefit natural graphs that present a high average vertex degree.

Information security of flowmeter communication network based on multi-sensor data fusion

With the progress of society, the use of intelligent flowmeters has become more and more widespread. The powerful functions of intelligent flowmeters have made people’s production and living more convenient. Still, at the same time, public data, including industry and agriculture privacy and information security, have exposed huge security risks. This paper aims to study the information security of flowmeter communication networks based on multi-sensor data fusion and artificial intelligence-driven. The present study introduces network information security and its development direction, proposes a cloud computing-based network security situation prediction algorithm, uses the cloud model to predict the security situation of the cloud computing network, and gives a cloud computing network security situation assessment system model. At the same time, it analyzes and explores network information security, and explores network security based on multi-sensor data fusion and artificial intelligence driving. The experimental research results show that due to the unique advantages of the universal flowmeter intelligent processing system, such as high resource utilization, uniform standards, and wide application range, it has a lot of room for development. With the development of science and technology and application fields, and the continuous progress and expansion of general flowmeters, the future space of general category flowmeters is huge. The popularity of the Internet is getting faster and faster, and the requirements for information security are getting higher and higher, and the information security issues faced in the future will become more and more complicated. Therefore, it is necessary to speed up the construction of an information security system, and take measures to strictly manage from the aspects of law, management, technology and talents.

Efficient adsorption of La(III) by magnetic carbon nitride based ZIF-8 nanocomposites

The enrichment and recovery of low concentration rare earth ions from wastewater resources has become an important research direction to achieve high quality utilization of rare earth resources. In this experiment, graphitic carbon nitride was used as the substrate, which was firstly compounded with Fe3O4 to become magnetic graphitic carbon nitride material by alcohol heat method, and then in situ compounded with ZIF-8 in aqueous phase at room temperature to form the composite g-C3N4/Fe3O4@ZIF-8. Finally, the composite was characterized. The adsorption performance of g-C3N4/Fe3O4@ZIF-8 and the control g-C3N4/Fe3O4 and ZIF-8 for La(III) under various conditions was investigated by adsorption experiment. In addition, the types and mechanism of La(III) adsorption were discussed by kinetic, isothermal adsorption line and thermodynamic modeling studies. Finally, the stability performance and reusability of the composite were evaluated by regeneration experiments. The result show that g-C3N4/Fe3O4@ZIF-8 has good adsorption performance on lanthanide ions. g-C3N4/Fe3O4@ZIF-8 reached adsorption equilibrium at about 20 min. When pH is 6 and the solid–liquid ratio of adsorbent is 0.25 g/L, the adsorption amount was 176.68 mg/g. g-C3N4/Fe3O4@ZIF-8 had good selectivity for lanthanum(III), and it retains 81 % of initial adsorption performance after five times of reuse.

S1428 Outcomes of Pyogenic Liver Abscess (PLA) on Mortality and Hospitalization Cost Amongst Patients With History of Alcohol Use Disorder (AUD): Retrospective Study Using NIS (2016 – 2019)

Introduction: Liver is the most common site for intra-abdominal abscess. Pyogenic liver abscess is more common in men compared to women. Risk factors include diabetes mellitus and chronic hepatobiliary disease. Alcohol abuse is known to cause bone marrow suppression and compromised immune system. Limited data are available regarding clinical outcomes in patients with history of alcohol use disorder who are admitted with pyogenic liver abscess. Methods: Using National Inpatient Sample databases from 2016 to 2019. We identified patients presenting with pyogenic liver abscess, the population were then divided based on the presence and absence of alcohol use disorder using appropriate ICD-10-CM/PCS codes. STATA 16.0 software was used for the analysis. Pearson’s Chi-Square test was used to analyze categorical variable, whereas the student t-test was used to analyze continuous variables. Univariate and multivariate logistic regression was used to adjust for potential confounders. Primary outcome was in hospital mortality due to Pyogenic liver abscess in patients with alcohol use disorder. Results: Amongst total of 19,830 patients admitted with pyogenic liver abscess, 1,090 patients had history of alcohol use disorder and 18,740 patients did not. Both groups consisted predominantly of white male patients. Mean LOS was higher in alcohol use disorder group (8±6.08 days) than in non-alcohol use disorder group (7.35±6.34 days ) (P=< 0.001 ). Mean total hospitalization charges were higher in AUD group ($ 20,968) than in the non-AUD group ($ 18,994) (P= < 0.001) Conclusion: Patient with Pyogenic liver abscess with alcohol use disorder history had higher resource utilization, but less odds of mortality. (Table) Table 1. - In-patient outcome comparison of patients with history of alcohol use disorder presenting with pyogenic liver abscess PLA WITH AUD, N(%) PLA WITHOUT AUD, N(%) P value Total 1090(8.92) 18740(91.07) Primary Outcome OR 95 CI P value Mortality 0.511 0.067 - 3.86 < 0.001 Secondary Outcomes Mean Mean P value LOS(days) 8+/-6.08 7.35+/-6.34 < 0.001 Charges(US$) 20968 18994 < 0.001 Demographics Mean Age 57.65+/- 11.45 61.47+/-15.65 Female(%) 205(18.81) 7404(41)

Feedback Learning in Software-Defined Mobile Network for Resource Aware Load Balancing Under Fault Tolerance Conditions

Fault-tolerant-based load balance resource allocation can help in the explosive data flow in the mobile network. System parameter function, system load factors, network-level configuration, network characteristics, and routing parameters are all affected by volatile data. In today's era of Big Data, one of the most important areas of study in mobile communications is how to adapt to traffic flow. The accessibility of load balancing sensors helps eliminate delays, which in turn helps reduce energy consumption and shorten execution time. In this research, we present a load balancing method for software-defined mobile networks (SDMNs) that is known to maximize the utility of the sensors by considering both the processing power of the sensors and the requirements of their sources. A proactive action technique that takes advantage of the wireless facility is proposed, and a wireless load balancing design solution using the learning method is then utilized in the designed framework. To achieve high resource utilization, we use a method based on convergence. Intelligent resource utilization by multiple sensor devices can help to cope with high bandwidth applications such as multimedia in mobile networks. Compared to conventional methods, the model has the potential to achieve better results.

Highly efficient neuromorphic learning system of spiking neural network with multi-compartment leaky integrate-and-fire neurons.

A spiking neural network (SNN) is considered a high-performance learning system that matches the digital circuits and presents higher efficiency due to the architecture and computation of spiking neurons. While implementing a SNN on a field-programmable gate array (FPGA), the gradient back-propagation through layers consumes a surprising number of resources. In this paper, we aim to realize an efficient architecture of SNN on the FPGA to reduce resource and power consumption. The multi-compartment leaky integrate-and-fire (MLIF) model is used to convert spike trains to the plateau potential in dendrites. We accumulate the potential in the apical dendrite during the training period. The average of this accumulative result is the dendritic plateau potential and is used to guide the updates of synaptic weights. Based on this architecture, the SNN is implemented on FPGA efficiently. In the implementation of a neuromorphic learning system, the shift multiplier (shift MUL) module and piecewise linear (PWL) algorithm are used to replace multipliers and complex nonlinear functions to match the digital circuits. The neuromorphic learning system is constructed with resources on FPGA without dataflow between on-chip and off-chip memories. Our neuromorphic learning system performs with higher resource utilization and power efficiency than previous on-chip learning systems.

Double-Lead Content Search And Producer Location Prediction Scheme For Producer Mobility In Named Data Networking

Abstract In recent years, Named Data Network (NDN) has become a popular network architecture because of high resource utilization, strong security and high transmission efficiency. Meanwhile, mobile multimedia communication has become the mainstream with the popularization and application of smart terminals. Most of the research on NDN mobility is focused on consumer mobility without taking producer mobility into account. To solve the delay and high cost carried by producer moving, we propose a Double-Lead content search algorithm based on neighbor and proxy and a location prediction algorithm based on traffic features. We use a neural network model to predict a new location of producers and calculate route before switching, which can save the rerouting latency in advance when predicting accurately. In a few cases of inaccurate predictions, we select different search methods according to the distance of the producer’s movement, to complete the Double-Lead search between the producer and the consumer. Experimental results show that DLPNDN can reduce the delay and traffic overhead well in NDN when the producer moves.

Factors Associated with Length of Stay and Cost among Pediatric Hospitalizations with a Primary Ophthalmic Diagnosis

ABSTRACT Purpose To investigate factors associated with prolonged length of stay and high cost among pediatric hospitalizations with a primary ophthalmic diagnosis. Methods This retrospective, cross-sectional study utilized data on pediatric admissions with a primary ophthalmic diagnosis from the multicenter 2016 Kids’ Inpatient Database. Multivariable logistic regression models adjusted for demographic, hospital, and admission characteristics were used to evaluate factors associated with prolonged stay and high cost, defined as exceeding the 75th percentile (>4 days and $12,642, respectively). Results An estimated 6,811 pediatric hospitalizations with a primary ophthalmic diagnosis in the United States in 2016 were included. On adjusted analysis, a prolonged length of stay was more likely with Medicaid (vs. private insurance, OR = 1.19, 95% CI: [1.02, 1.40], p = .03), non-trauma (vs. trauma, OR = 2.77, 95% CI: [2.12, 3.63], p < .001) and urban teaching hospitals (vs. rural, OR = 3.48, 95% CI: [1.04, 11.69], p = .04). A high cost of stay was more likely with higher income levels (Quartile 3 vs. 1, OR = 1.30, 95% CI: [1.02, 1.67], p = .04; Quartile 4 vs. 1, OR = 1.49, 95% CI: [1.08, 2.05], p = .02), private insurance (vs. Medicaid, OR = 1.26, 95% CI: [1.04, 1.53], p = .02), Western hospitals (vs. South, OR = 2.74, 95% CI: [1.83, 4.12], p < .001), and trauma (vs. non-trauma, OR = 3.29, 95% CI: [2.57, 4.21], p < .001). Children and young adults had higher odds of prolonged stay, while adolescents and young adults had higher odds of high cost compared to toddlers (p < .05 for all). Conclusions Additional work addressing the factors associated with higher resource utilization may help promote the delivery of quality inpatient pediatric eye care.

A review on shellfish polysaccharides: Extraction, characterization and amelioration of metabolic syndrome.

Shellfish are diverse, widely distributed organisms that are a rich source of biological resources. Polysaccharides are an important components in shellfish, hence a great deal of attention has been directed at isolation and characterization of shellfish polysaccharides because of their numerous health benefits. Differences in shellfish species, habits, and environment result in the diversity of the structure and composition of polysaccharides. Thus, shellfish polysaccharides possess special biological activities. Studies have shown that shellfish polysaccharides exert biological activities, including antioxidant, antitumor, immune-regulation, hypolipidemic, antihypertensive, and antihyperglycemic effects, and are widely used in cosmetics, health products, and medicine. This review spotlights the extraction and purification methods of shellfish polysaccharides and analyses their structures, biological activities and conformational relationships; discusses the regulatory mechanism of shellfish polysaccharides on hyperlipidemia, hypertension, and hyperglycemia caused by lipid metabolism disorders; and summarizes its alleviation of lipid metabolism-related diseases. This review provides a reference for the in-depth development and utilization of shellfish polysaccharides as a functional food to regulate lipid metabolism-related diseases. To achieve high value utilization of marine shellfish resources while actively promoting the development of marine biological industry and health industry.