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Purpose To compare renal sinus fat volume (RSFV) separately within the right and left kidneys between bilateral nephrolithiasis patients and healthy controls. Methods This cross-sectional study analyzed patients who underwent unenhanced abdominal computed tomography (CT) divided into nephrolithiasis (n = 102) and healthy control (n = 130) groups. Age, sex, blood pressure [systolic blood pressure (SBP) and diastolic blood pressure (DBP)], estimated glomerular filtration rate (eGFR), body weight, and height of each participant were extracted. Volumetric renal sinus adipose tissue was measured separately for both kidneys on CT images. Urea, serum creatinine (Scr), uric acid (UA), total serum cholesterol (TCH), serum triglyceride (TG), and serum high- and low-density lipoprotein (HDL and LDL, respectively) cholesterol levels were obtained. Results Overall, 232 participants (mean age 47 years, 50% women) were enrolled. There were no differences in sex, DBP, urea, and LDL-cholesterol between the two groups (all p > 0.05). However, nephrolithiasis patients had higher age, BMI, SBP, and RSFV; higher Scr, UA, TCH, and TG serum levels; and lower HDL-cholesterol level and eGFR. Average left RSFV was significantly higher than right RSFV in healthy controls (4.56 ± 2.29 versus 3.34 ± 1.90 cm3, p Conclusions Our data showed renal sinus adipose tissue accumulation and the relationship among RSFV, age, BMI, and LDL-cholesterol in bilateral nephrolithiasis patients.

The power of a range-extended electric bus comes from its battery and range-extender. How to design the range-extender working point for the vehicle in the process of running is the key factor to achieve energy conservation and emission reduction. To solve this problem, a vehicle model was built by using AVL Cruise simulation software. Through Cruise and Isight co-simulation optimisation, a multi-objective optimisation model for per 100-km fuel consumption and pollutant emission is established. Optimal variables include upper and lower limits of the power unit and working point of the range-extender. Adaptive mutation genetic algorithm (AMGA) was used as optimisation algorithm. Results showed that fuel consumption and pollutant emissions were effectively reduced. The per 100-km fuel consumption decreased by 48.0%, carbon monoxide emission decreased by 49.6%, hydrocarbon emission decreased by 47.28%, and nitrogen oxide emission decreased by 51.1%. The economics of range-extended electric bus have been greatly improved.

The aim of the present study was to optimise the cycle fuel consumption and energy consumption of a range-extended electric bus. The optimal variables included the final drive ratio of the main decelerator, the upper and lower limits of the power unit and the speed threshold. NSGA-II was used as the optimisation algorithm. The optimisation results showed that the overall economy behaviour of the bus was improved, while climbing performance increased by 43.77%. The stability of dynamic performances was acceptable and only slight fluctuations were found in the maximum speed and the acceleration duration from 0 km/h to 50 km/h. Although the power consumption increased by 0.6%, per 100 km fuel consumption decreased by 32.98%. The results showed that the optimisation strategies, optimisation objectives and optimisation variables applied in this paper were reasonable and effective.

Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.