High Resource Utilization Research Articles

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.

A 146-dB-Ohm Gain 14-pARMS Noise Patch-Clamp Amplifier for Whole-Cell Ion Current Detection

The membrane clamp technique is an important tool to reflect the electrophysiological characteristics of cells by recording the ionic currents of cellular channels. Embedded systems work in systems designed for specific user groups and are used to implement specific functions. The membrane clamp amplifier built with embedded technology has the advantages of miniaturization, specialization, low power consumption, high integration, high resource utilization, and a long life cycle, which can avoid the inconvenience to developers and experimenters due to the update of general-purpose computer software and hardware. In this paper, a patch-clamp amplifier (PCA) based on a transimpedance amplifier (TIA) is proposed, which includes a glass microelectrode series resistance/capacitance compensation circuit and feedback resistor parasitic capacitance compensation. The prototype is designed using 180 nm CMOS technology and occupies an area of 720 μ m × 630 μ m . The prototype achieves a transimpedance gain of 145.6 dB-Ohm, a -3 dB bandwidth greater than 15 kHz, and an input reference noise current of 13.98 pARMS. Experimental results show that the proposed PCA is capable of compensating up to 30 pF of electrode capacitance and up to 10 MΩ for 86% of the series resistance, respectively. In addition, when the feedback resistor parasitic capacitance compensation circuit is enabled, the overshoot phenomenon disappears, overcoming the shortcomings of the conventional diaphragm clamp amplifier current clamp and maintaining the original key performance.

A home-based, multidisciplinary liver optimisation programme for the first 28 days after an admission for acute-on-chronic liver failure (LivR well): a study protocol for a randomised controlled trial

BackgroundAcute-on-chronic liver failure (ACLF) represents a rising global healthcare burden, characterised by increasing prevalence among patients with decompensated cirrhosis who have a 28-day transplantation-free mortality of 33.9%. Due to disease complexity and a high prevalence of socio-economic disadvantage, there are deficits in quality of care and adherence to guideline-based treatment in this cohort. Compared to other chronic conditions such as heart failure, those with liver disease have reduced access to integrated ambulatory care services. The LivR Well programme is a multidisciplinary intervention aimed at improving 28-day mortality and reducing 30-day readmission through a home-based, liver optimisation programme implemented in the first 28 days after an admission with either ACLF or hepatic decompensation. Outcomes from our feasibility study suggest that the intervention is safe and acceptable to patients and carers.MethodsWe will recruit adult patients with chronic liver disease from the emergency departments, in-patient admissions, and an ambulatory liver clinic of a multi-site quaternary health service in Melbourne, Australia. A total of 120 patients meeting EF-Clif criteria will be recruited to the ACLF arm, and 320 patients to the hepatic decompensation arm. Participants in each cohort will be randomised to the intervention arm, a 28-day multidisciplinary programme or to standard ambulatory care in a 1:1 ratio. The intervention arm includes access to nursing, pharmacy, physiotherapy, dietetics, social work, and neuropsychiatry clinicians. For the ACLF cohort, the primary outcome is 28-day mortality. For the hepatic decompensation cohort, the primary outcome is 30-day re-admission. Secondary outcomes assess changes in liver disease severity and quality of life. An interim analysis will be performed at 50% recruitment to consider early cessation of the trial if the intervention is superior to the control, as suggested in our feasibility study. A cost-effectiveness analysis will be performed. Patients will be followed up for 12 weeks from randomisation. Three exploratory subgroup analyses will be conducted by (a) source of referral, (b) unplanned hospitalisation, and (c) concurrent COVID-19. The trial has been registered with the Australian New Zealand Clinical Trials Registry.DiscussionThis study implements a multidisciplinary intervention for ACLF patients with proven benefits in other chronic diseases with the addition of novel digital health tools to enable remote patient monitoring during the COVID-19 pandemic. Our feasibility study demonstrates safety and acceptability and suggests clinical improvement in a small sample size. An RCT is required to generate robust outcomes in this frail, high healthcare resource utilisation cohort with high readmission and mortality risk. Interventions such as LivR Well are urgently required but also need to be evaluated to ensure feasibility, replicability, and scalability across different healthcare systems. The implications of this trial include the generalisability of the programme for implementation across regional and urban centres.Trial registrationAustralian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12621001703897. Registered on 13 December 2021.WHO Trial Registration Data Set. See Appendix 1

Flow-through and forming mechanism of laser shock micro-coining

Sheet-bulk metal forming (SBMF) is an advanced technology to efficiently produce high-quality parts based on high resource utilization. Coining, a kind of SBMF, can be used to fabricate functional and decorative microstructures. In this paper, a new method called laser shock micro-coining (LSMC) is proposed based on the unique advantages of intense pulsed laser of high strain rate dynamic loading characteristics and suitable for micro-forming. However, smaller sheet thickness will lead to the emergence of flow-through. In this paper, ALE model and SPH-FEM model are established for numerical simulation to study LSMC. Through numerical simulation and experiment, the forming mechanism and improvement methods of flow-through are studied, and the appropriate research parameters are determined. The material flow, forming mechanism, stress wave propagation mechanism and forming uniformity are studied. Results show that the lack of material near the projections and the unique inverse plastic deformation in dynamic forming are the important factors leading to flow-through. Suitable laser energy and large sheet thickness as well as the existence of the punch can inhibit the formation of flow-through from one or two aspects. Under dynamic forming, the material has high forming rate and good filling properties. Material deformation is related to the propagation of stress wave. The propagation of stress wave and the height of sheet metal result in a unique short rod mechanism. The process has good forming uniformity, and a more uniform forming effect can be obtained by appropriately increasing the laser energy.

Enhanced oxygen reduction of porous N-doped carbon nanosheets with graphitic N and defects obtained from coal-based graphene quantum dots

Metal-free carbon-based electrocatalysts for oxygen reduction reaction (ORR) have received considerable attention owing to their abundance and low cost. However, designing carbon-based electrocatalysts with excellent performance in a facile manner remains a challenge. In this work, porous nitrogen-doped carbon nanosheet (PNCNs) catalysts with high contents of graphitic N and defects were readily prepared via a simple self-doping method, in which coal-based graphene quantum dots (GQDs) were used as the carbon and nitrogen resource. Based on the results, PNCNs-900 (obtained at 900 °C) displayed good ORR performance, with a half-wave potential (E1/2) of 0.80 V. It also exhibited high stability and methanol tolerance compared with commercial Pt/C. Experimental investigations and theoretical calculations prove that the increased ORR electrocatalytic activity of PNCNs-900 is due to the synergistic effect of a large number of defects and graphitic N. Furthermore, its higher electrocatalytic activity for ORR also benefits from the porous lamellar structure, which facilitates the mass transfer process. This study offers a novel approach for developing effective ORR catalysts and for the high value-added utilization of coal resources.

Patients with Chronic Obstructive Pulmonary Disease and Evidence of Eosinophilic Inflammation Experience Exacerbations Despite Receiving Maximal Inhaled Maintenance Therapy.

BackgroundSome patients with chronic obstructive pulmonary disease (COPD) experience frequent exacerbations despite maximal inhaled therapy (“triple therapy”), possibly leading to high health care resource utilization (HCRU).AimDescribe characteristics, future HCRU, and mortality of patients with COPD who experience frequent exacerbations despite triple therapy; characterize individuals who may be candidates for biologic therapies.MethodsThis descriptive observational study used primary care data of patients aged ≥40 years in the United Kingdom receiving maintenance therapy for COPD who had ≥1 year of data prior to index date and ≥1 year of follow-up data. We described these patients’ clinical and demographic characteristics, including blood eosinophil counts (BEC), pattern of exacerbations, hospitalizations, and corticosteroid exposure, as well as future exacerbations, hospitalizations, and death.ResultsOf 43,753 patients with maintenance-treated COPD, 6480 experienced exacerbations despite ≥3 months of triple therapy. Of these, 5669 had available BEC: 1287 (22.7%) had BEC ≥250 cells/µL and ≥3 exacerbations in the year prior to the index date; 471 (36.6%) received ≥4 acute courses of oral corticosteroids. Patients with a pattern of high disease burden continued to have high disease burden: 51.1% experienced ≥3 exacerbations and 2.6% experienced ≥3 hospitalizations. Patients who experienced exacerbations despite triple therapy had a significantly higher risk of COPD-related death than other maintenance-treated patients (5.8% vs 2.1%).ConclusionNearly one-quarter of patients receiving triple therapy for COPD who experienced frequent exacerbations had elevated BEC and ≥3 exacerbations, suggesting a potential mechanism of persistent eosinophilic inflammation that could be a target for eosinophil-depleting biologic therapy.

Profiling Disease and Economic Burden in CRSwNP Using Machine Learning.

PurposeChronic rhinosinusitis with nasal polyps (CRSwNP) is associated with high healthcare resource utilization (HRU) and economic cost; however, heterogeneity of clinical burden among patients with differing clinical characteristics has not been fully elucidated. Here, an unsupervised machine learning approach supported by clinical validation identified distinct clusters of patients with CRSwNP and compared healthcare burden.Patients and MethodsThis retrospective analysis identified adult patients with ≥2 claims for CRSwNP and date of first diagnosis (index date) between January 2015 and June 2019 from a healthcare database. Patients were required to have enrollment in the database 6-months pre- and 12-months post-index. Patients were assigned to clusters using latent class analysis. All-cause and nasal polyp (NP)-related HRU and costs were compared between clusters.ResultsAmong 12,807 patients, 5 clusters were identified: cluster 1: no surgery/low comorbidity/low medication use (n = 4076); cluster 2: no surgery/low comorbidity/high medication use (n = 2201); cluster 3: no surgery/high comorbidity/high medication use (n = 2093); cluster 4: surgery/low comorbidity/moderate medication use (n = 3168); cluster 5: surgery/high comorbidity/high medication use (n = 1269). All-cause HRU was similar across clusters. NP-related HRU was highest in the surgical clusters (clusters 4 and 5). All-cause costs were similar in clusters 1–3 ($15,833–$17,461) and highest in clusters 4 ($31,083) and 5 ($31,103), driven by outpatient costs. Total NP-related costs were also highest for clusters 4 and 5 ($14,193 and $16,100, respectively).ConclusionSubstantial heterogeneity exists in clinical and economic burden among patients with CRSwNP. Machine learning offers a novel approach to better understand the diverse, complex burden of illness in CRSwNP.

Effect of leaving against medical advice on 30-day infective endocarditis readmissions

ABSTRACT Background The burden of against medical advice (AMA) discharges on the readmission rate of infective endocarditis (IE) patients has been largely ignored. Methods We used the National Readmissions Database, years 2016 to 2019, to identify IE patients and categorized them into those who left AMA (IE AMA) and those who were discharged to home or skilled nursing facility (SNF)/other facility (IE non-AMA). The primary outcome was 30-day all-cause readmissions difference per AMA status. Results Of 26,481 patients with IE who met the inclusion criteria, 4,310 (16.3%) left the hospital AMA. IE AMA patients were younger (mean years; 43.7 vs 34.2; p < 0.01) and had a higher prevalence of injection drug use (IDU) (89.4% vs 45.2%; p < 0.01) but fewer comorbidities compared to IE non-AMA. In adjusted analyses, IE AMA had higher hazards for 30-day readmissions compared to IE non-AMA [hazards ratio (HR): 3.1 (2.9–3.5); p < 0.01]. Conclusion IE AMA are at increased risk of 30-day readmissions and higher resource utilization at the time of readmission compared to IE non-AMA. Considering the high prevalence of IDU in IE AMA, the role of mental health to curb the burden of IE readmissions is an area of further research.

Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation.

Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD) data. We propose a lightweight OOD detection method that leverages the Mahalanobis distance in the feature space and seamlessly integrates into state-of-the-art segmentation pipelines. The simple approach can even augment pre-trained models with clinically relevant uncertainty quantification. We validate our method across four chest CT distribution shifts and two magnetic resonance imaging applications, namely segmentation of the hippocampus and the prostate. Our results show that the proposed method effectively detects far- and near-OOD samples across all explored scenarios.

Healthcare utilization and health-related quality of life of severe asthma patients in Singapore

Background Notwithstanding unequivocal consensus on the disproportionate effect of severe asthma (SA) on asthma morbidity, healthcare utilization, quality of life, work impairment and socioeconomic burden, the burden of SA patients in Singapore has not been appraised. Objectives To determine the burden of disease and extent of quality of life impairment in SA patients in Singapore. Methods A cross-sectional analysis of SA patients seen in Singapore General Hospital (2020–2021) to investigate emergency healthcare utilization, oral corticosteroid (OCS) burden and health-related quality of life (HRQoL) with primary endpoint EuroQoL-5 Dimension three-level (EQ-5D-3L) scores. The empirical measurement properties of the EQ-5D utility index in SA were comprehensively assessed through multivariate regression analyses. Results A total of 336 SA patients were recruited, 51.2% of SA patients had at least one acute healthcare resource utilization during the previous year, with 25.6% of patients having an emergency healthcare visit to the hospital. Overall mean (SD) EQ-5D-3L and EQ-5D-3L utility scores in SA patients were 6.22 (1.51) and 0.77 (0.30), respectively. EQ-5D utility scores were 0.14 lower in uncontrolled vs controlled asthma and 0.09 lower in the presence of severe exacerbation, whereas barely changed by maintenance OCS dose and airflow limitation. Conclusion SA patients were found to have high disease burden, high healthcare resource utilization and OCS use, low biologics usage, poor HRQoL and utility in comparison with other chronic diseases.

Study on the influence of particle size on the flotation separation of kaolinite and quartz

The influences of particle size on the flotation separation of kaolinite and quartz was explored by flotation tests of full-size and various sizes, zeta potential analysis. The flotation separation effects of kaolinite and quartz mixed minerals under different conditions were analyzed by calculating the characteristic parameters such as K, R ∞ , Km, SI and SE. Results show that in the sodium oleate scenario, kaolinite has better floatability under neutral conditions, while quartz has better floatability at pH 12. Ca 2+ has little effect on the floatability of kaolinite but it does inhibit the floatability of quartz under acid and weak alkali conditions. As well it can improve the floatability of quartz at a pH 12. In the two mineral flotation separation tests, reverse flotation had better selectivity for coarse-grained minerals. The maximum separation efficiency of the mixed ore with a particle size of 0.125–0.075 mm was 71.88%, the recovery of kaolinite in the concentrate was 84.97%, and the recovery of quartz amounted to 13.10%. It is of great significance to the research on the flotation separation of kaolinite and the high resource utilization of kaolinite. • 0.250–0.030 mm can be separated through positive and reverse flotation. • −0.030 mm fine kaolinite and quartz can't be separated by sodium oleate flotation. • When pH is less than 8, Ca 2+ is conducive to positive flotation separation. • Conforms the first kinetic model, reverse flotation has high separation efficiency.

208 The burden of alopecia areata (AA) vs psoriasis (PsO) in the United States

The well-established disease burden of PsO can provide a benchmark for understanding the disease burden of other immune-mediated dermatologic disorders that may be associated with high health care resource utilization (HCRU), such as AA. The objective was to describe the economic burden of patients with AA vs PsO aged ≥12 years. A retrospective analysis using IBM MarketScan Commercial & Medicare databases was conducted to identify patients aged ≥12 years with ≥2 claims of either AA (ICD-10-CM: L63.x) or PsO (ICD-10-CM: L40.x) recorded between 1 Jan 2016–31 Dec 2019. The date of first recorded diagnosis for either AA or PsO (patients with both were excluded) was the index date, with ≥12 months continuous enrollment before and after the index date. Demographic and clinical characteristics were used to propensity match AA patients to PsO patients using a 1:2 ratio. Descriptive analyses were performed with the matched cohorts for demographic and clinical characteristics, treatment, and healthcare resource use in the pre-index period. Annualized mean all-cause costs were compared in both groups in the post-index period. The matched analysis included 17,081 and 33,687 patients with AA and PsO, respectively. Mean age was 40.8 and 41.3 years and females comprised 61.6% and 63.6% of patients, respectively. Mean (SD) medical costs during the post-index period were $7,457 ($31,992) and $10,310 ($33,392), respectively (standardized difference [stdiff]: 0.09). Mean (SD) pharmacy costs were $2,470 ($8,851) and $11,421 ($23,746) in the post-index period, respectively (stdiff: 0.5). This analysis of patients with matched demographic and clinical characteristics demonstrated the substantial economic burden of AA, with medical costs approaching those of PsO. Differences observed in pharmacy costs may be due to the lack of approved therapies for AA. As treatment options for AA are developed, it will be important to continue evaluating the impact of effective therapies on cost burden.

Cadmium-Rich Plant Powder/PAN/PU Foams with Low Thermal Conductivity.

Treating and utilizing heavy metal enriched plants have become growing problems. In this work, a series of composite foams were made from the powder of Cadmium-rich plant, polyacrylonitrile (PAN) and polyurethane (PU). Test results indicated that the addition of plant powder can not only increase the specific surface area, but also improve the apparent density and thermal stability of the foams. Besides, compared with the foam without plant powder, the powder-added foams exhibited a decreasing trend for thermal conductivity, and the minimum was 0.048 w/(m·k), which indicated that the addition of plant powder can help to enhance the thermal insulation of composite foam. More importantly, the results of leaching experiment showed that the leaching rate of heavy metal cadmium in the composite foam with 50% plant powder content was as low as 0.14% after being immersed in the acidic (pH = 3) solution for 5 days, which implies that the foam materials are very safe. This study provides a new way to realize high value-added resource utilization of heavy metal-enriched plants.

EdgePipe: Tailoring Pipeline Parallelism With Deep Neural Networks for Volatile Wireless Edge Devices

As intelligence recently moves to the edge to tackle the problems of privacy, scalability, and network bandwidth in the centralized intelligence, it is necessary to construct an efficient yet robust deep learning model viable at edge devices, which are usually volatile in wireless links and device functionality. The intensive computation burden for deep learning at the edge side necessitates some level of parallel processing via acceleration. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EdgePipe</i> , a deep learning framework based on deep neural networks (DNNs) with a mixture of model parallelism and pipeline training for high resource utilization over volatile wireless edge devices. To tackle the volatility problem in wireless links and device functionality, a concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">super neuron</i> is defined to be a group of neurons across adjacent layers, which is the basis of model partitioning at edge devices. The relatively loss-resilient neuron structure prevents the entire forward or backward training paths from being totally broken down due to only some intermittent link or device failure caused by one or few devices. Furthermore, we design a subsequent pipeline training mechanism based on the prior super-neuron-based model partitioning for fast convergence with more training data in a fixed timeline. The experimental results have demonstrated that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EdgePipe</i> outperforms several counterpart algorithms including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PipeDream</i> under the volatile wireless lossy or device malfunctioning environments, while preserving the low interlayer communication overhead.

Dynamic Collaboration Model of Production Network Based on Cloud Service Bus

The IT system of manufacturing enterprises usually has many problems, such as complex industrial software, different development languages, diverse communication protocols, and complex operation environment. Cloud service bus (CSB) technology based on service model encapsulates various applications existing in enterprises by means of the integration of cloud service bus and micro services, which can realize the rapid cloud migration and deployment of heterogeneous industrial application software. Theoretically, after the production system is connected to CSB, it can be arranged arbitrarily by service choreography technology to produce any possible products. However, in the context of industrial Internet, the production system connected to CSB corresponds to the equipment, materials, personnel, and other resources on the production line one by one. These production nodes need to consider the production capacity and cost of production service nodes and the output value of the whole production network, and cannot be combined arbitrarily. Therefore, when integrating production service nodes, we should not only consider the technical integration, but also consider whether the production conditions support this integration. To solve the problem of production node integration in CSB, a dynamic collaboration model of production network based on cloud service bus is proposed in this paper. The model takes the capacity, cost, and production relationship of production nodes as constraints, and the overall efficiency of production network as the optimization goal. The model can calculate the new creation, modification, deletion, and other scheduling operations of production line services in real time and give the production plan with the highest resource utilization and the greatest value in the current production network. The model can improve the rationality and economy of service choreography and give full play to the value of production network. Taking an enterprise with 11 production nodes and 5 production lines as an example, this paper discusses in detail how to use this model to calculate the optimal production organization scheme and the maximum output value of the enterprise.

HotFuzz: Discovering Temporal and Spatial Denial-of-Service Vulnerabilities Through Guided Micro-Fuzzing

Fuzz testing repeatedly assails software with random inputs in order to trigger unexpected program behaviors, such as crashes or timeouts, and has historically revealed serious security vulnerabilities. In this article, we present HotFuzz, a framework for automatically discovering Algorithmic Complexity (AC) time and space vulnerabilities in Java libraries. HotFuzz uses micro-fuzzing, a genetic algorithm that evolves arbitrary Java objects in order to trigger the worst-case performance for a method under test. We define Small Recursive Instantiation (SRI) as a technique to derive seed inputs represented as Java objects to micro-fuzzing. After micro-fuzzing, HotFuzz synthesizes test cases that triggered AC vulnerabilities into Java programs and monitors their execution in order to reproduce vulnerabilities outside the fuzzing framework. HotFuzz outputs those programs that exhibit high resource utilization as witnesses for AC vulnerabilities in a Java library. We evaluate HotFuzz over the Java Runtime Environment (JRE), the 100 most popular Java libraries on Maven, and challenges contained in the DARPA Space and Time Analysis for Cybersecurity (STAC) program. We evaluate SRI’s effectiveness by comparing the performance of micro-fuzzing with SRI, measured by the number of AC vulnerabilities detected, to simply using empty values as seed inputs. In this evaluation, we verified known AC vulnerabilities, discovered previously unknown AC vulnerabilities that we responsibly reported to vendors, and received confirmation from both IBM and Oracle. Our results demonstrate that micro-fuzzing finds AC vulnerabilities in real-world software, and that micro-fuzzing with SRI-derived seed inputs outperforms using empty values in both the temporal and spatial domains.

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.

Novel Workload-Aware Approach to Mobile User Reallocation in Crowded Mobile Edge Computing Environment

A mobile edge computing (MEC) paradgim is evolving as an increasingly popular means for developing and deploying smart-city-oriented applications. MEC servers can receive a great deal of requests from devices of mobile users, especially in crowded scenes, e.g., a city’s central business district and school areas. It thus remains a great challenge for appropriate scheduling and managing strategies to avoid hotspots, guarantee load-fairness among MEC servers, and maintain high resource utilization at the same time. To address this challenge, we propose a coalitional-game-based and location-aware approach to MEC service migration for mobile user reallocation in crowded scenes. Our proposed method includes: 1) dividing MEC servers into multiple coalitions according to their inter-Euclidean distance by using a modified <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> -means clustering method; 2) discovering hotspots in every coalition area and scheduling services based on their corresponding cooperations; and 3) migrating services to appropriate edge servers to achieve high utilization and load-fairness among coalition members. Experimental results based on a real-world mobile trajectory dataset for crowded scenes, and an urban-edge-server-position dataset demonstrate that our method outperforms existing ones in terms of load fairness, number of migrations, and utilization rate of edge servers.

Efficient design and implementation of a distributed temperature sensor for field apparatus

The signal acquisition and processing ability of distributed temperature sensor based on Raman scattering (DTS-Raman) directly determines the accuracy, spatial resolution, temperature resolution, and other key indicators of the entire system. Real-time and effective processing is the key to ensuring the practicability of the field system. Owing to the long distance of the DTS system, the energy of the weak Raman scattering is further reduced when it reaches the avalanche photodiode detector because of fiber loss, which results in a decrease in the signal-to-noise ratio (SNR) and an increase in the temperature measurement error. To improve the SNR and ensure the accuracy of the demodulation temperature, the signal acquisition and processing module must be able to perform real-time and fast processing of large amounts of data. The existing processing structure based on the acquisition board requires a special personal computer and host processing software, which limits the application of DTS system. Therefore, a real-time acquisition and processing scheme based on field-programmable gate array is proposed in this paper. A set of cyclic shift register sequence (CSRS) with Ping-Pang functions is designed to improve the storage efficiency. According to the period of the laser source, the accumulated results stored in the CSRS and echo pulse data are accumulated and shifted in real time to realize circular utilization of storage resources. A DTS-Raman system prototype and test platform are built to verify the effects of the proposed scheme. The experimental results show that the scheme can realize a temperature accuracy within in real time and has a high resource utilization efficiency.

Approach to Development Sustainable Green City

Cities are centers of high natural resource utilization activities that contribute to the development of global environmental issues. Urban planning should be integrated with environmental planning in order to achieve sustainable development. This study aims to examine the position of the ecological urban concept in the concept of city ecology and sustainable cities. The method used in this study is a literature review of publications related to the concept of ecocity, urban ecology and sustainable city. The concept of green city development seeks to answer the issue of climate change through adaptation and mitigation actions, through attributes: green planning and design, green open space, green waste, green transportation, green water, green energy, green building, and green community.