Adaptive Energy-Efficient Power Allocation in Green Interference Alignment Based Wireless Networks

Interference alignment (IA) is a promising technique for interference management in wireless networks. However, the sum rate may fall short of the theoretical maximum especially at low signal-to-noise ratio (SNR) levels since IA mainly concentrates on mitigating the interference, instead of improving the quality of desired signal. Moreover, most of the previous works focused on improving spectrum efficiency, but the energy efficiency aspect is largely ignored. In this paper, an adaptive energy-efficient IA algorithm is proposed through power allocation and transmission-mode adaptation for green IAbased wireless networks. The power allocation problem for IA is first analyzed, then we propose a power allocation scheme that optimizes the energy efficiency of IA-based wireless networks. When SNR is low, the transmitted power of some users may become zero. Thus the users with low transmitted power are turned into the sleep mode in our scheme to save energy. The transmitted power and transmission mode of the remaining active users are adapted again to further improve the energy efficiency of the network. To guarantee the interests of all the users, fairness among users is also considered in the proposed scheme. Simulation results are presented to show the effectiveness of the proposed algorithm in improving the energy efficiency of IAbased wireless networks

An improved robust function correction-adaptive extended Kalman filtering algorithm for SOC estimation of lithium-ion batteries.

State of Charge (SOC) is one of the key indicators for evaluating the state of electric vehicles. In order to cope with the uncertainty of random noise in nonlinear systems, an improved robust function correction-adaptive extended Kalman filtering (RFC-AEKF) algorithm is proposed for SOC prediction. Using FFRLS method to verify the Dual Polarization model established in this paper. The robust function is an abstract method that describes system state noise and observation noise, and performs real-time correction, combined with adaptive methods to estimate SOC. The experimental results show that the proposed RFC-AEKF algorithm has the smallest mean absolute error (MAE) and root mean square error (RMSE) compared to other algorithms. Under the Beijing bus dynamic stress test (BJDST) conditions, the MAE and RMSE of the RFC-AEKF are 0.354% and 0.658%, respectively, indicating that the RFC-AEKF algorithm can improve SOC estimation accuracy and enhance robustness

Colchicine protects against acute pancreatitis via downregulation of cytokine levels

Purpose: To investigate the effect of colchicine on acute pancreatitis (AP) in a rat model, and the molecular mechanism involved. Methods: Acute pancreatitis (AP) was induced in rats by injection with 5 % sodium taurocholate. Changes in histology of pancreatic tissues were determined following treatment with colchicine. Serum amylase activity was measured using Automated Biochemistry Analyser. Results: Hematoxylin and eosin (H & E) staining showed that colchicine prevented histopathological changes such as infiltration of interstitial leukocytes and erythrocytes, cell necrosis, oedema formation and vacuolization in the rat pancreas. Treatment of AP rats with colchicine significantly and dosedependently decreased ascite volume in the abdominal cavity. Serum amylase activity was significantly suppressed in AP rats on treatment with 100 mg/kg colchicine. Furthermore, treatment of the AP rats with colchicine caused marked decrease in the expressions of interleukin 6 and tumor necrosis factor α, and upregulated expressions of IL 10 in serum. Colchicine treatment of AP rats also caused significant increase in CGRP level in the plasma. Conclusion: Colchicine prevents pancreatic tissue damage induced by AP by down-regulating proinflammatory cytokines, upregulation of anti-inflammatory cytokines, and enhancing CGRP release. Therefore, colchicine may be useful for the treatment of acute pancreatitis

Sodium glucose cotransporter-2-inhibitor dapagliflozin improves nonalcoholic fatty liver disease by ameliorating dipeptidyl-peptidase-4 protein expression in diabetic mice

Introduction: Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. It can progress from simple steatosis to nonalcoholic steatohepatitis and may even develop into liver fibrosis, hepatocirrhosis, or hepatocellular carcinoma, but there is no effective treatment. Material and methods: Wild-type (wt) and diabetic (db/db) mouse NAFLD-induced models were used to investigate the hepatoprotective effects and potential mechanisms of dapagliflozin (a new oral hypoglycaemic drug) on type 2 diabetes mellitus (T2DM) complicated with NAFLD, and to establish wt and db/db mouse NAFLD-induced and dapagliflozin treatment models. Results: Dapagliflozin reduces blood glucose, glycosylated haemoglobin, blood lipids, and serum transaminase levels in db/db mice and improves T2DM-related liver injury accompanied by NAFLD; the mechanism may be related to the decrease in dipeptidyl-peptidase-4 (DPP4) protein expression and improvement in liver enzymes. Further mechanism-related studies by our team revealed that dapagliflozin can also downregulate the expression of DPP4 proteins in the liver and reduce serum soluble DPP4 enzyme levels, thereby improving the hepatic steatosis and insulin resistance of NAFLD. Conclusion: Dapagliflozin may be an effective drug for the treatment of T2DM-induced NAFLD and NAFLD, providing a reliable laboratory basis and new treatment methods for the clinical treatment of NAFLD