Simulation and experimental analysis of a brushless electrically excited synchronous machine with a hybrid rotor

Electrically excited synchronous machines with brushes and slip rings are popular but hardly used in inflammable and explosive environments. This paper proposes a new brushless electrically excited synchronous motor with a hybrid rotor. It eliminates the use of brushes and slip rings so as to improve the reliability and cost-effectiveness of the traction drive. The proposed motor is characterized with two sets of stator windings with two different pole numbers to provide excitation and drive torque independently. This paper introduces the structure and operating principle of the machine, followed by the analysis of the air-gap magnetic field using the finite-element method. The influence of the excitation winding's pole number on the coupling capability is studied and the operating characteristics of the machine are simulated. These are further examined by the experimental tests on a 16 kW prototype motor. The machine is proved to have good static and dynamic performance, which meets the stringent requirements for traction applications

TDCMR: Triplet-Based Deep Cross-Modal Retrieval for geo-multimedia data

Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method

Study from the United States: increased prevalence of kidney stones in patients with high weight-adjusted waist index

ObjectiveUsing data from NHANES 2007–2018, to examine the association between WWI (weight-adjusted waist index) index and prevalence of kidney stones.MethodsUsing multiple logistic regression analysis of the National Health and Nutrition Examination Survey (NHANES) 2007–2018, we evaluated the association between WWI index and the prevalence of kidney stones, followed by subgroup analysis of sensitive populations. Smooth curve fitting was used to determine whether there was a non-linear relationship between the WWI index and kidney stone prevalence, and threshold effect analysis was used to test this relationship.ResultsAmong 29,280 participants, 2,760 self-reported renal calculi. After adjustment for all confounders, there was a positive association between WWI and kidney stone prevalence (OR = 1.20, 95% CI: 1.12, 1.28), and this positive association was stronger with increasing WWI (and P = 0.01 for trend). Our results indicate a non-linear positive correlation between WWI index and kidney stones, with the saturation threshold effect analysis and the most important threshold value at 11.02. According to subgroup analysis, WWI showed the strongest association with kidney stone prevalence in participants aged 20–39 years, males, other US ethnic groups, and participants without hypertension and diabetes.ConclusionIncreased WWI is positively associated with increased incidence of kidney stones, and increased WWI is a high risk for kidney stones that should be treated with caution. This association should be more pronounced in people between the ages of 20 and 39 years, in men, in other US ethnic populations, and in participants who do not have hypertension or diabetes