Selection of Cluster Heads for Wireless Sensor Network in Ubiquitous Power Internet of Things

This paper designs a selection algorithm of cluster heads (CHs) in wireless sensor network (WSN) under the ubiquitous power Internet of Things (UPIoT), aiming to solve the network failure caused by premature death of WSN sensors and overcome the imbalance in energy consumption of sensors. The setting of the cluster head node helps to reduce the energy consumption of the nodes in the network, so the choice of cluster head is very important. The author firstly explains the low energy adaptive clustering hierarchy (LEACH) and the distance and energy based advanced LEACH (DEAL) protocol. Compared with the LEACH, the DEAL considers the remaining nodal energy and the sensor-sink distance. On this basis, the selectivity function-based CH selection (SF-CHs) algorithm was put forward to select CHs and optimize the clustering. Specifically, the choice of CHs was optimized by a selectivity function, which was established based on the remaining energy, number of neighbors, motion velocity and transmission environment of sensors. Meanwhile, a clustering function was constructed to optimize the clustering, eliminating extremely large or small clusters.Finally, the simulation proves that the DEAL protocol is more conducive to prolonging the life cycle of the sensor network. The SF-CHs algorithm can reduce the residual energy variance of nodes in the network, and the network failure time is later, which provides a way to improve the stability of the network and reduce energy loss

A prediction model for N2 disease in T1 non–small cell lung cancer

ObjectiveControversy remains over the routine use of mediastinoscopy or positron emission tomography in T1 non–small cell lung cancer without lymph node enlargement on computed tomography because the risk of N2 involvement is comparatively low. We aimed to develop a prediction model for N2 disease in cT1N0 non–small cell lung cancer to aid in the decision-making process.MethodsWe reviewed the records of 530 patients with computed tomography–defined T1N0 non–small cell lung cancer who underwent surgical resection with systematic lymph node dissection. Correlations between N2 involvement and clinicopathologic parameters were assessed using univariate analysis and binary logistic regression analysis. A prediction model was built on the basis of logistic regression analysis and was internally validated using bootstrapping.ResultsThe incidence of N2 disease was 16.8%. Four independent predictors were identified in multivariate logistic regression analysis and included in the prediction model: younger age at diagnosis (odds ratio, 0.974; 95% confidence interval, 0.952-0.997), larger tumor size (odds ratio, 2.769; 95% confidence interval, 1.818-4.217), central tumor location (odds ratio, 3.204; 95% confidence interval, 1.512-6.790), and invasive adenocarcinoma histology (odds ratio, 3.537; 95% confidence interval, 1.740-7.191). This model shows good calibration (Hosmer–Lemeshow test: P = .784), reasonable discrimination (area under the receiver operating characteristic curve, 0.726; 95% confidence interval, 0.669-0.784), and minimal overfitting demonstrated by bootstrapping.ConclusionsWe developed a 4-predictor model that can estimate the probability of N2 disease in computed tomography–defined T1N0 non–small cell lung cancer. This prediction model can help to determine the cost-effective use of mediastinal staging procedures

Energy Optimization for WSN in Ubiquitous Power Internet of Things

This paper attempts to solve the problems of uneven energy consumption and premature death of nodes in the traditional routing algorithm of rechargeable wireless sensor network in the ubiquitous power Internet of things. Under the application environment of the UPIoT, a multipath routing algorithm and an opportunistic routing algorithm were put forward to optimize the network energy and ensure the success of information transmission. Inspired by the electromagnetic propagation theory, the author constructed a charging model for a single node in the wireless sensor network (WSN). On this basis, the network energy optimization problem was transformed into the network lifecycle problem, considering the energy consumption of wireless sensor nodes. Meanwhile, the traffic of each link was computed through linear programming to guide the distribution of data traffic in the network. Finally, an energy optimization algorithm was proposed based on opportunistic routing, in a more realistic low power mode. The experimental results show that the two proposed algorithms achieved better energy efficiency, network lifecycle and network reliability than the shortest path routing (SPR) and the expected duty-cycled wakeups minimal routing (EDC). The research findings provide a reference for the data transmission of UPIoT nodes

Association between High-Sensitivity C-Reactive Protein and N-Terminal Pro-B-Type Natriuretic Peptide in Patients with Hepatitis C Virus Infection

Background. Prior study showed HCV-infected patients have increased serum N-Terminal Pro-B-Type Natriuretic Peptide (NT-proBNP) and a possible left ventricular diastolic dysfunction. The objectives of the present paper were to investigate the characteristics of hs-CRP and its correlation with clinical profiles including NT-proBNP and echocardiographic variables in HCV-infected patients. Methods and Results. A total of 106 HCV-infected patients and 106 control healthy individuals were enrolled. The level of serum hs-CRP (median 1.023 mg/L, range 0.03∼5.379 mg/L) was significantly lower in all 106 patients than that in controls (median 3.147 mg/L, range 0.08~7.36 mg/L, P = 0.012). Although hs-CRP did not correlate significantly with NT-proBNP when all patients and controls were included (r = 0.169, P = 0.121), simple regression analysis demonstrated a statistically significant linear correlation between hs-CRP and NT-proBNP in HCV-infected patients group (r = 0.392, P = 0.017). Independent correlates of hs-CRP levels (R2 = 0.13) were older age (β′ = 0.031, P = 0.025) and NT proBNP (β′ = 0.024, P = 0.017). Conclusions. Although the level of serum hs-CRP decreased significantly, there was a significant association between hs-CRP and NT-proBNP in HCV-infected patients

Effects of blood flow restriction training on bone turnover markers, microstructure, and biomechanics in rats

ObjectiveThe present study aimed to investigate the effects of blood flow restriction training on muscle strength, bone tissue structure material, and biomechanical properties in rats applying various exercise interventions and to analyze the process by identifying the bone turnover markers, it provides a theoretical basis for the application of BFRT in clinical rehabilitation.MethodsA total of 24, 3-month-old male SD (Sprague Dawley) rats were randomly divided into pressurized control group (CON, n=6), low-intensity training group (LIRT, n=6), high-intensity training group (HIRT, n=6), and blood flow restriction training group (LIBFR, n=6) for 8-week ladder-climbing exercises. The pressured control group were given only ischemia treatments and did not undertake any burden. The low-intensity training group was allowed to climb the ladder with 30% of the maximum voluntary carrying capacity (MVCC). The rats in the high-intensity training group were allowed to climb the ladder with 70% MVCC. The blood flow restriction training group climbed the ladder with 30% MVCC while imposing blood flow restriction. Before sampling, the final MVCC was measured using a ladder-climbing protocol with progressively increasing weight loading. The serum, muscle, and bone were removed for sampling. The concentrations of the bone turnover markers PINP, BGP, and CTX in the serum were measured using ELISA. The bone mineral density and microstructure of femur bones were measured using micro-CT. Three-point bending and torsion tests were performed by a universal testing machine to measure the material mechanics and structural mechanics indexes of the femur bone.ResultsThe results of maximum strength test showed that the MVCC in LIRT, HIRT, and LIBFR groups was significantly greater than in the CON group, while the MVCC in the HIRT group was significantly higher than that in the LIRT group (P<0.05). According to the results of the bone turnover marker test, the concentrations of bone formation indexes PINP (amino-terminal extension peptide of type I procollagen) and BGP (bone gla protein) were significantly lower in the CON group than in the HIRT group (P<0.01), while those were significantly higher in the LIRT group compared to the HIRT group (P<0.01). In terms of bone resorption indexes, significant differences were identified only between the HIRT and other groups (P<0.05). The micro-CT examination revealed that the HIRT group had significantly greater bone density index values than the CON and LIRT groups (P<0.05). The results of three-point bending and torsion test by the universal material testing machine showed that the elastic modulus and maximum load indexes of the HIRT group were significantly smaller than those of the LIBFR group (P<0.05). The fracture load indexes in the HIRT group were significantly smaller than in the LIBFR group (P<0.05).Conclusion1. LIRT, HIRT, LIBFR, and CON all have significant differences, and this training helps to improve maximum strength, with HIRT being the most effective. 2. Blood flow restriction training can improve the expression of bone turnover markers, such as PINP and BGP, which promote bone tissue formation. 3. Blood flow restriction training can improve muscle strength and increase the positive development of bone turnover markers, thereby improving bone biomechanical properties such as bone elastic modulus and maximum load