A Community-Based Event Delivery Protocol in Publish/Subscribe Systems for Delay Tolerant Sensor Networks

The basic operation of a Delay Tolerant Sensor Network (DTSN) is to finish pervasive data gathering in networks with intermittent connectivity, while the publish/subscribe (Pub/Sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, extension of Pub/Sub systems in DTSNs has become a promising research topic. However, due to the unique frequent partitioning characteristic of DTSNs, extension of a Pub/Sub system in a DTSN is a considerably difficult and challenging problem, and there are no good solutions to this problem in published works. To ad apt Pub/Sub systems to DTSNs, we propose CED, a community-based event delivery protocol. In our design, event delivery is based on several unchanged communities, which are formed by sensor nodes in the network according to their connectivity. CED consists of two components: event delivery and queue management. In event delivery, events in a community are delivered to mobile subscribers once a subscriber comes into the community, for improving the data delivery ratio. The queue management employs both the event successful delivery time and the event survival time to decide whether an event should be delivered or dropped for minimizing the transmission overhead. The effectiveness of CED is demonstrated through comprehensive simulation studies

Can technology demonstration promote rural households’ adoption of conservation tillage in China?

Under the uncertainty of conservation tillage on output, technology demonstration, as an information disclosure mechanism, is very worthy of attention for its effects on rural households’ conservation tillage adoption. This study constructs a three-stage technology adoption model to discuss the theoretical relationship between technology demonstration and rural households’ conservation tillage adoption decision, and then empirical analyzed it using a sampling rural household data from six provinces in the main grain-producing areas of China. The results show that: First, the cognition of conservation tillage is the pre-determined stage for the adoption and its intensity. Second, technology demonstration has significant positive effect on rural households’ cognition of conservation tillage, but it strongly negative related to the adoption and adoption intensity. Third, extending the technology demonstration time cannot change the rural households’ adoption decision. Fourth, the technological demonstration has similar effects on the conservation tillage adoption of small-scale and large-scale farmers. Fifth, increasing land size helps rural households to adopt conservation tillage, while land fragmentation hinders their adoption

Filter Pruning For CNN With Enhanced Linear Representation Redundancy

Structured network pruning excels non-structured methods because they can take advantage of the thriving developed parallel computing techniques. In this paper, we propose a new structured pruning method. Firstly, to create more structured redundancy, we present a data-driven loss function term calculated from the correlation coefficient matrix of different feature maps in the same layer, named CCM-loss. This loss term can encourage the neural network to learn stronger linear representation relations between feature maps during the training from the scratch so that more homogenous parts can be removed later in pruning. CCM-loss provides us with another universal transcendental mathematical tool besides L*-norm regularization, which concentrates on generating zeros, to generate more redundancy but for the different genres. Furthermore, we design a matching channel selection strategy based on principal components analysis to exploit the maximum potential ability of CCM-loss. In our new strategy, we mainly focus on the consistency and integrality of the information flow in the network. Instead of empirically hard-code the retain ratio for each layer, our channel selection strategy can dynamically adjust each layer's retain ratio according to the specific circumstance of a per-trained model to push the prune ratio to the limit. Notably, on the Cifar-10 dataset, our method brings 93.64% accuracy for pruned VGG-16 with only 1.40M parameters and 49.60M FLOPs, the pruned ratios for parameters and FLOPs are 90.6% and 84.2%, respectively. For ResNet-50 trained on the ImageNet dataset, our approach achieves 42.8% and 47.3% storage and computation reductions, respectively, with an accuracy of 76.23%. Our code is available at https://github.com/Bojue-Wang/CCM-LRR

Development and comparison of two computational intelligence algorithms for electrical load forecasts with multiple time scales

Electricity load forecasting provides the critical information required for power institutions and authorities to develop rational, effective, and economic dispatch plans. The load forecasting at the regional power system is important for optimal management and accommodating local renewable energy sources, which is a challenging task as the demand variations are more sensitive to local weather changes (such as temperature, humidity, precipitation, and wind speed) and consumers' activities and behaviours. The paper aims to develop a new prediction method using intelligent computational algorithms. Long Short-Term Memory (LSTM), a deep recurrent neural network, explores the long-term dependency of network memory sequence data to identify intrinsic variations in both horizontals (time series) and vertical (network depth) dimensions over a longer historical period. Support Vector Machine (SVM) is a typical learning method that has been successfully implemented to solve nonlinear regression and time series problems. This paper studies the two methods and adapts the two methods to become suitable algorithms for load prediction. The paper presents the algorithms, their applications and prediction results. The prediction performance is compared for using LSTM and SVM at ultra-short, short-term, medium-term, and long-term forecasting. The results show that LSTM has higher prediction accuracy than SVM in both ultra-short and short-term forecasts, but SVM is more capable of medium-term and long-term forecasting. Finally, the epoch time for LSTM and SVM is also calculated and compared

High-resolution load forecasting on multiple time scales using Long Short-Term Memory and Support Vector Machine

Electricity load prediction is an essential tool for power system planning, operation and manage-ment. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applica-tions. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.501%, 3.577%, 25.073% and 69.947% for four prediction time horizons delineated. For the SVM, the MAPE values are 2.531%, 5.039%, 7.819% and 10.841%, respectively. It is found that both methods are suitable for shorter time horizon predictions. The results show that LSTM is more capable of ultra-short and short-term forecasting, while SVM has a higher prediction accuracy in medium-term and long-term forecasts. Further investigation is per-formed via blind tests and the test results are consistent

Evolution and reform of UK electricity market

Electricity Market is structured to fund reliable electricity supply, meet the need of consumers, ensure the affordability of end-users, and support national economic development. In recent years, to meet challenging emission target set by Government, power system in the UK has a rapid increase of integration with various-scale Renewable Energy Sources (RESs) and energy storage systems (ESSs), which pushes the electricity market reform to accommodate the changes, encourage renewable energy integration, adopt new technologies, stimulate consumers participation, and ensure the power system resilience. The paper reviews the history of UK electricity market evolution, driving factors of reform, and the trend of current electricity market reform. In history, the UK electricity wholesale market has experienced three significant reform stages, which are introducing the Electricity Pool of England & Wales (the Pool) in the 1980s, implementing the New Electricity Trading Arrangements (NETA) in the 2000s, and performing the Electricity Market Reform (EMR) in 2013. To address the new emerging challenges in decarbonising power generation, the paper explains and analyses on-going electricity market changes and the trend for future electricity market reform

Moderate increase of serum uric acid within a normal range is associated with improved cognitive function in a non-normotensive population: A nationally representative cohort study

Background: Associations between serum uric acid (SUA) and changes in cognitive function are understudied in non-normotensive populations, and many previous studies only considered the baseline SUA at a single time point. We aimed to examine the effects of baseline SUA and 4-year changes in SUA on cognitive changes in the non-normotensive population. Materials and methods : In the China Health and Retirement Longitudinal Study (CHARLS), cognitive function was measured based on executive function and episodic memory in four visits (years: 2011, 2013, 2015, and 2018). We identified two study cohorts from CHARLS. The first cohort included 3,905 non-normotensive participants. Group-based single-trajectory and multi-trajectory models were applied to identify 7-year cognitive trajectories. Adjusted ordinal logistics models were performed to assess the association between baseline SUA and 7-year cognitive trajectories, and subgroup analyses were conducted according to the presence of hyperuricemia or SUA levels. The second cohort included 2,077 eligible participants. Multiple linear regression was used to explore the effect of a 4-year change in SUA on cognitive change during the subsequent 3-year follow-up. Results: Four distinct single-trajectories of global cognitive performance and four multi-trajectories of executive function and episodic memory were identified. Higher baseline SUA levels were significantly associated with more favorable cognitive single-trajectories (ORQ4 vs. Q1 : 0.755; 95 % CI: 0.643, 0.900) and multi-trajectories (ORQ4 vs. Q1: 0.784; 95 % CI: 0.659, 0.933). Subgroup analyses revealed that the protective effect of SUA was significant in the non-hyperuricemia groups or the low-level SUA groups. Additionally, changes in SUA could influence future cognitive changes. Compared with non-hyperuricemia participants with elevated SUA, non-hyperuricemia participants with decreased SUA and patients with persistent hyperuricemia had a higher risk for cognitive decline. Furthermore, only the Q3 group of changes in SUA could enhance global cognitive function compared with the Q1 group (β: 0.449; 95 % CI: 0.073, 0.826). Conclusion: Our study indicates that the maintenance of normal SUA levels and a moderate increase of SUA were advantageous in improving cognitive function or trajectories in a non-normotensive population. Conversely, SUA may impair cognitive function in patients with persistent hyperuricemia

Association of TyG index and TG/HDL-C ratio with arterial stiffness progression in a non-normotensive population

Background: Cross-sectional studies have reported that insulin resistance (IR) is associated with arterial stiffness. However, the relationship between IR and arterial stiffness progression remains unclear. This study aims to evaluate the association of triglyceride glucose (TyG) index and triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio with arterial stiffness progression in a non-normotensive population. Methods: A total of 1895 prehypertensive (systolic pressure 120–139 mmHg or diastolic pressure 80–90 mmHg) or hypertensive (systolic pressure ≥ 140 mmHg or diastolic pressure ≥ 90 mmHg or using antihypertensive medication) participants were enrolled in 2013 and 2014, and followed until December 31, 2019. Arterial stiffness progression was measured by brachial-ankle pulse wave velocity (baPWV) change (absolute difference between baseline and last follow-up), baPWV change rate (change divided by following years), and baPWV slope (regression slope between examination year and baPWV). Results: During a median follow-up of 4.71 years, we observed an increasing trend of baPWV in the population. There were linear and positive associations of the TyG index and TG/HDL-C ratio with the three baPWV parameters. The difference (95% CI) in baPWV change (cm/s) comparing participants in the highest quartile versus the lowest of TyG index and TG/HDL-C ratio were 129.5 (58.7–200.0) and 133.4 (52.0–214.9), respectively. Similarly, the evaluated baPWV change rates (cm/s/year) were 37.6 (15.3–60.0) and 43.5 (17.8–69.2), while the slopes of baPWV were 30.6 (9.3–51.8) and 33.5 (9.0–58.0). The observed association was stronger in the hypertensive population. Conclusion: Our study indicates that the TyG index and TG/HDL-C ratio are significantly associated with arterial stiffness progression in hypertensive population, not in prehypertensive population

Glycated hemoglobin and risk of arterial stiffness in a Chinese Han population: A longitudinal study

Background and Aims: Glycated hemoglobin (HbA1c) associates with the risk of arterial stiffness, and such association can be found between fasting blood glucose (FBG), postprandial blood glucose (PBG), triglyceride-glucose index (TyG index), and arterial stiffness. However, the results were inconsistent, longitudinal studies were sparse, and comparison of these glycemic parameters was less conducted. We aimed to explore the longitudinal relationship between HbA1c and arterial stiffness and compare the effect of the parameters. Methods: Data were collected from 2011 to 2019 in Beijing Health Management Cohort (BHMC) study. Cox proportional hazard models were fitted to investigate the association between the parameters and arterial stiffness. A generalized estimation equation (GEE) analysis was conducted to investigate the effect of repeated measurements of glycemic parameters. A receiver operating characteristic (ROC) analysis was performed to compare the predictive value of glycemic parameters for arterial stiffness. Results: Among 3,048 subjects, 591 were diagnosed as arterial stiffness during the follow-up. The adjusted hazard ratio (HR) [95% confidence interval (CI)] for arterial stiffness of the highest quartile group of HbA1c was 1.63 (1.22–2.18), which was higher than those of FBG, PBG, and TyG index. The nonlinear association of arterial stiffness with HbA1c and PBG was proved. The robust results of the sensitivity analysis were obtained. Conclusions: HbA1c is an important risk factor of arterial stiffness compared with PBG, FBG, and TyG index, and has a strong predictive ability for arterial stiffness among non-diabetics and the general population

Bidirectional associations between daytime napping duration and metabolic syndrome: A nationally representative cohort study

Background: We aimed to examine the bidirectional associations between daytime napping duration and metabolic syndrome (MetS). Methods: Using data from the China Health and Retirement Longitudinal Study from 2011 to 2015, modified Poisson regression models were performed to explore the longitudinal associations of baseline napping duration with the occurrence and remission of MetS. Generalized estimating equation was conducted to explore the association between baseline MetS status with subsequent changes in daytime napping duration. Cross-lagged panel analysis was performed to further verify their bidirectional relationships. Results: During the four-year follow-up, among 5041 participants without MetS at baseline, extended naps were significantly associated with MetS occurrence, compared with non-napping. This association was only significant in individuals with adequate night-time sleep duration or good sleep quality of the 2898 participants with MetS at baseline. Excessive napping duration may be not favorable for MetS remission especially for adequate night-time sleepers. With respect to reverse associations, baseline MetS status significantly increased the napping duration during the subsequent follow-up period. Finally, there were significant bidirectional cross-lagged associations between napping duration and MetS severity score after adjusting for all covariates. Conclusions: Our study indicates bidirectional relationships exist between daytime napping duration and MetS. Interestingly, longer napping duration was detrimental to cardiometabolic health only in those with sufficient night-time sleep duration or good sleep quality