Fast Neighbor Discovery for Wireless Ad Hoc Network with Successive Interference Cancellation

Neighbor discovery (ND) is a key step in wireless ad hoc network, which directly affects the efficiency of wireless networking. Improving the speed of ND has always been the goal of ND algorithms. The classical ND algorithms lose packets due to the collision of multiple packets, which greatly affects the speed of the ND algorithms. Traditional methods detect packet collision and implement retransmission when encountering packet loss. However, they does not solve the packet collision problem and the performance improvement of ND algorithms is limited. In this paper, the successive interference cancellation (SIC) technology is introduced into the ND algorithms to unpack multiple collision packets by distinguishing multiple packets in the power domain. Besides, the multi-packet reception (MPR) is further applied to reduce the probability of packet collision by distinguishing multiple received packets, thus further improving the speed of ND algorithms. Six ND algorithms, namely completely random algorithm (CRA), CRA based on SIC (CRA-SIC), CRA based on SIC and MPR (CRA-SIC-MPR), scan-based algorithm (SBA), SBA based on SIC (SBA-SIC), and SBA based on SIC and MPR (SBA-SIC-MPR), are theoretically analyzed and verified by simulation. The simulation results show that SIC and MPR reduce the ND time of SBA by 69.02% and CRA by 66.03% averagely.Comment: 16 pages, 16 figure

CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics

Online segmentation of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference. Fully supervised semantic segmentation algorithms have achieved state-of-the-art performance, but rely on plenty of pixel-level labels, which are time-consuming and labor-consuming to produce. LayerCAM, an advanced weakly supervised semantic segmentation algorithm, can generate pixel-accurate results using only image-level labels, but its scattered and partially under-activated class activation regions degrade segmentation performance. In this paper, we propose a weakly supervised semantic segmentation method with Continuous Gradient CAM and its nonlinear multi-scale fusion (CG-fusion CAM). The method redesigns the way of back-propagating gradients and non-linearly activates the multi-scale fused heatmaps to generate more fine-grained class activation maps with appropriate activation degree for different sizes of damage sites. Experiments on our dataset show that the proposed method can achieve segmentation performance comparable to that of fully supervised algorithms

Association between apathy in patients with maintenance dialysis and hospitalization or mortality: a prospective cohort study

BackgroundPatients receiving maintenance dialysis experience increased rates of hospitalization and mortality. Apathy is associated with reduced quality of life and increased hospitalization, institutionalization, and death. Whether apathy contributes to poor outcomes in population undergoing maintenance dialysis remain unknown.MethodsWe conducted a prospective cohort study of maintenance dialysis population who were consecutively recruited at the Dialysis Center of Shanghai General Hospital between July 2017 and August 2018 and were followed up for 3 year. Apathy status was measured by the Apathy Evaluation Scale. The study outcomes were the occurrence of death and first hospitalization.ResultsA total of 647 participants included in this study, 274 (42.3%) had a current apathy and 373 (57.7%) were not. During the follow-up period, 394 (60.9%) were hospitalized, and 169 (26.1%) died. Kaplan–Meier analysis showed that the risks of hospitalization and mortality were significantly higher in individuals with apathy than in those without apathy (both p < 0.001). Apathy at baseline was associated with hospitalization and death both in univariate analysis and in all multivariable models (all p < 0.001).ConclusionApathy was highly prevalent and independently correlated with an increased risk of poor outcomes in patients with maintenance dialysis

Effects of Octenyl-Succinylated Chitosan—Whey Protein Isolated on Emulsion Properties, Astaxanthin Solubility, Stability, and Bioaccessibility

The synthesis of octenyl-succinylated chitosan with different degrees of substitution resulting from chemical modification of chitosan and controlled addition of octenyl succinic acid was investigated. The modified products were characterized using 1H NMR, FTIR, and XRD, and the degree of substitution was also determined. The properties of the modified chitosan oligosaccharide in solution were evaluated by surface tension and dye solubilization, finding that the molecules self-assembled when they are above the critical aggregation concentration. The two methods yielded consistent results, showing that the self-assembly was reduced with higher levels of substitution. The antimicrobial activity of the octanyl-succinylated chitosan oligosaccharide (OSA-COS) derivatives against Staphylococcus aureus, Escherichia coli, and Fusarium oxysporum f.sp cucumerinum was investigated by the Oxford cup method. While the acetylated COS derivatives were not significantly effective against either E coli or S. aureus, they showed significant antifungal activity toward F. oxysporum that was superior to that of COS. The modified product was found to form a stable emulsion when mixed with whey protein isolate. The emulsion formed by the highly substituted derivatives have a certain stability and loading efficiency, which can be used for the encapsulation and delivery of astaxanthin

Urinary Aromatic Amino Acid Metabolites Associated With Postoperative Emergence Agitation in Paediatric Patients After General Anaesthesia: Urine Metabolomics Study

Background: Emergence agitation (EA) is very common in paediatric patients during recovery from general anaesthesia, but underlying mechanisms remain unknown. This prospective study was designed to profile preoperative urine metabolites and identify potential biomarkers that can predict the occurrence of EA.Methods: A total of 224 patients were screened for recruitment; of those, preoperative morning urine samples from 33 paediatric patients with EA and 33 non-EA gender- and age-matched patients after being given sevoflurane general anaesthesia were analysed by ultra-high-performance liquid chromatography (UHPLC) coupled with a Q Exactive Plus mass spectrometer. Univariate analysis and orthogonal projection to latent structures squares-discriminant analysis (OPLS-DA) were used to analyse these metabolites. The least absolute shrinkage and selection operator (LASSO) regression was used to identify predictive variables. The predictive model was evaluated through the receiver operating characteristic (ROC) analysis and then further assessed with 10-fold cross-validation.Results: Seventy-seven patients completed the study, of which 33 (42.9%) patients developed EA. EA and non-EA patients had many differences in preoperative urine metabolic profiling. Sixteen metabolites including nine aromatic amino acid metabolites, acylcarnitines, pyridoxamine, porphobilinogen, 7-methylxanthine, and 5′-methylthioadenosine were found associated with an increased risk of EA, and they all exhibited higher levels in the EA group than in the non-EA group. The main metabolic pathways involved in these metabolic changes included phenylalanine, tyrosine and tryptophan metabolisms. Among these potential biomarkers, L-tyrosine had the best predictive value with an odds ratio (OR) (95% CI) of 5.27 (2.20–12.63) and the AUC value of 0.81 (0.70–0.91) and was robust with internal 10-fold cross-validation.Conclusion: Urinary aromatic amino acid metabolites are closely associated with EA in paediatric patients, and further validation with larger cohorts and mechanistic studies is needed.Clinical Trial Registration:clinicaltrials.gov, identifier NCT0480799

Protein functional module identification method combining topological features and gene expression data

Article conducting an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. The experimental results show that the ECTG algorithm can detect protein functional modules better

Analysis of the Criminalization of the Production and Sale of Online Game Plug-ins and the Regulation of Users

With the advent of the Internet information age, the game industry is booming, and online game plug-ins have come into being as cheats. From the definition and classification of online game cheats, it is found that the crimes involved mainly include the crimes of destroying computer information systems, copyright infringement, illegal business operations, and illegal computer intrusion under the Criminal Code of China, as well as the Personal Information Protection Law and the Unfair Prevention Law touched by the Japanese Criminal Law. This paper analyzes the possibility of criminalization and punishment of online game plug-in producers and sellers from three aspects: the main body of the online game market, policy orientation, and legal improvement. Comparing the criminalization and punishment of Chinese and Korean network plug-in users, it is possible to refer to the legal measures in South Korea

Improving Transportation Information Resilience: Error Estimation for Networked Sensor Data

Nowadays, the effectiveness of any smart transportation management or control strategy would heavily depend on reliable traffic data collected by sensors. Two problems regarding sensor data quality have received attention: first, the problem of identifying malfunctioning sensors; second, reconstruction of traffic flow. Most existing studies concerned about identifying completely malfunctioning sensors whose data should be discarded. This project focuses on the problem of error detection and data recovery of partially malfunctioning sensors that could provide valuable information. By integrating a sensor measurement error model and a transportation network model, the authors propose a Generalized Method of Moments (GMM) based estimation approach to determine the parameters of systematic and random errors of traffic sensors in a road network. The proposed method allows flexible data aggregation that ameliorates identification and accuracy. The estimates regarding both systematic and random errors are utilized to conduct hypothesis test on sensor health and to estimate true traffic flows with observed counts. The results of three network examples with different scales demonstrate the applicability of the proposed method in a large variety of scenarios. This research improves fundamental knowledge on transportation data analytics as well as the effective management of data and information infrastructure in transportation practice.View the NCST Project Webpag

Assessment of Hydrodynamic Performance and Motion Suppression of Tension Leg Floating Platform Based on Tuned Liquid Multi-Column Damper

To address the unstable motion of a tension leg platform (TLP) for floating wind turbines in various sea conditions, an improved method of incorporating a tuned liquid multi-column damper (TLMCD) into the TLP foundation is proposed. In order to evaluate the control effect of TLMCD on the motion response of the floating foundation, a multiphase flow solver based on a viscous flow CFD method and overlapping grid technique is applied to model the coupled multi-body dynamics interaction problem involving liquid tanks, waves, and a spring mooring system. This method has been proven to accurately capture the high-frequency motions of the structure and account for complex viscous interferences affecting the geometric motions. Additionally, the volume of fluid (VOF) method and the first-order linear superposition method are used to model the focused wave, enabling a simulation of the effects of transient wave loads on the floating foundation. The results show that the tuned damping effect of TLMCD on the TLP is mainly in the pitch motion, with the maximum pitch amplitude control volume ratio of TLMCD reaching up to 86% and the maximum surge amplitude control volume ratio of TLMCD reaching up to 25.2% under the operating conditions. These findings highlight the potential for additional research on and implementation of TLMCD technology