Association between antiinflammatory cytokine,IL‐10, and sleep quality in patients on maintenance hemodialysis

Elevated proinflammatory cytokines have been attributed to poor sleep quality in patients receiving hemodialysis. This is the first investigation about the relationship between sleep quality and circulating levels of antiinflammatory markers in these patients. A total of 72 patients who were receiving maintenance hemodialysis were enrolled in this cross‐sectional study. The Pittsburgh Sleep Quality Index (PSQI) was used to measure sleep quality. Patients were divided into two groups: good sleepers (PSQI score < 5) and poor sleepers (PSQI score ≥ 5). Assessments were made for serum biochemical parameters (albumin, parathyroid hormone), inflammatory (interleukin [IL]‐6, tumor necrosis factor‐alpha [TNF‐α], and high‐sensitivity c‐reactive protein [hs‐CRP] ) and antiinflammatory (IL‐10) markers. Fifty‐four patients (75%) were classified as poor sleepers. Poor sleepers showed significantly lower levels of serum IL‐10 and higher serum triglyceride and parathyroid hormone concentrations. These patients were more likely to have more comorbidities. The global PSQI score was significantly correlated with serum IL‐10 (p = 0.03) and triglyceride levels (p = 0.01). Multivariate logistic regression analysis showed a direct correlation between PSQI and having comorbidities (p = 0.011, odds ratio [OR] = 3.918; confidence interval 95% [CI] = 2.742–19.031), between PSQI and serum triglyceride (p = 0.027, OR = 1.027 [95% CI = 1.007–1.048] ), and an inverse correlation between PSQI and serum IL‐10 level (p = 0.021, OR = 0.424 [95% CI = 0.195–0.922]). Reduced circulating levels of the antiinflammatory cytokine IL‐10 were significantly associated with poor sleep quality in hemodialysis patients. Factors including serum IL‐10 and triglyceride concentrations and having comorbidities may predict patients prone to poor sleep quality.     

A novel Hα control strategy for design of a robust dynamic routing algorithm in traffic networks

In this paper novel centralized and decentralized routing control strategies based on minimization of the worst-case queuing length are proposed. The centralized routing problem is formulated as an H_infin optimal control problem to achieve a robust routing performance in presence of multiple and unknown fast time-varying network delays. Unlike similar previous work in the literature the delays in the queuing model are assumed to be unknown and time-varying. A Linear Matrix Inequality (LMI) constraint is obtained to design a delay-dependent H_infin controller. The physical constraints that are present in the network are then expressed as LMI feasibility conditions. Our proposed centralized routing scheme is then reformulated in a decentralized frame work. This modification yields an algorithm that obtains the "fastest route", increases the robustness against multiple unknown time-varying delays, and enhances the scalability of the algorithm to large scale traffic networks. Simulation results are presented to illustrate and demonstrate the effectiveness and capabilities of our proposed novel dynamic routing strategies.     

On selection of forwarding nodes for long opportunistic routes

Opportunistic routing is a promising routing paradigm which increases the network throughput. It forces the sender’s neighbors, who successfully overheard the transmitted packet, to participate in the packet forwarding process as intermediate forwarding nodes. As a seminal opportunistic routing protocol, MORE combines network coding idea with opportunistic routing to eliminate the need for strict coordination among active forwarding nodes. In this paper, we show that MORE performance does not scale well with the route length, especially when the route length goes beyond two hops. Also, we found that MORE fails to establish a working opportunistic route in sparse networks. Clearly, the network throughput is directly influenced by both the quantity and quality of forwarding nodes, and their cooperation order. In this paper, we propose a new forwarder selection mechanism which considers the route length, link qualities, the distance from the source, and nodes density. It eliminates the occasional route disconnectivity happening in MORE and improves the quality of the established opportunistic routes. The simulation result indicates that our proposal always outperforms MORE when dealing with long opportunistic routes.

     

Blind downlink channel estimation for TDD‐based multiuser massive MIMO in the presence of nonlinear HPA

In time division duplex (TDD)‐based multiuser massive multiple input multiple output (MIMO) systems, the uplink channel is estimated and the results are used in downlink for signal detection. Owing to noisy uplink channel estimation, the downlink channel should also be estimated for accurate signal detection. Therefore, recently, a blind method was developed, which assumes the use of a linear high‐power amplifier (HPA) in the base station (BS). In this study, we extend this method to a scenario with a nonlinear HPA in the BS, where the Bussgang decomposition is used for HPA modeling. In the proposed method, the average power of the received signal for each user is a function of channel gain, large‐scale fading, and nonlinear distortion variance. Therefore, the channel gain is estimated, which is required for signal detection. The performance of the proposed method is analyzed theoretically. The simulation results show superior performance of the proposed method compared to that of the other methods in the literature.     

Optimization of Run-of-River Hydropower Plant Design under Climate Change Conditions

The assessment of climate change and its impacts on hydropower generation is a complex issue. This paper evaluates the application of representative concentration pathways (RCPs, 2.6, 4.5, and 8.5) with the change factor (CF) method and the statistical downscaling method (SDSM) to generate six climatic scenarios of monthly temperature and rainfall over the period 2020–2049 in the Karkheh basin, Iran. The identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data (IHACRES) model was employed to simulate runoff for the purpose of designing a run-of-river hydropower plant in the Karkheh basin. The non-dominated sorting genetic algorithm (NSGA)-II was employed to maximize yearly energy generation and the plant factor, simultaneously. Results indicate the runoff scenarios associated with the SDSM lead to higher run-of-river hydropower generation in 2020–2049 compared to the CF results.     

Preparation of Au nanoparticles by Q switched laser ablation and their application in 4-nitrophenol reduction

Clean Technologies and Environmental Policy - This work reports the fabrication of Au nanoparticles (NPs) by laser ablation of a gold metal plate immersed in water in the absence of stabilizing...