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The mechanical properties and insulation damage of stator end windings in permanent magnet synchronous generator (PMSG) are investigated by theoretical analysis, finite element analysis (FEA) and experiment verification. First, a theoretical model of the winding electromagnetic force (EF) and vibration response before and after eccentricity faults are established. Then, the stress/strain distribution of the end windings under different working conditions is analyzed. Finally, the insulation damage behavior of end windings with initial cracks is studied and characterized using the stress intensity factor (SIF). The results indicate that the winding EF and vibration responses vary under different faults. The stress/strain at the joint of the winding is the largest, and the insulation layer has the maximum stress/strain under the same section. Additionally, the SIF amplitude of the crack at the winding joint is the largest. And the SIF amplitude of the crack will increase with the increase of fault degree/crack depth/crack length. The contribution of this paper lies in the comprehensive analysis of the winding EF distribution, vibration response, stress and strain, and insulation damage before and after eccentricity. This analysis can provide valuable reference for the health monitoring of PMSG eccentricity faults and the prevention of winding insulation damage.

Static air-gap eccentricity (SAGE) is a common fault in generators. Current researches primarily focus on the parallel SAGE types, while the inclined static air-gap eccentricity (ISAGE) has been rarely investigated. This paper proposes an improved air-gap eccentricity model and presents a study on the electromagnetic-mechanical properties in synchronous generators under typical ISAGE running conditions. Such electromagnetic-mechanical properties include not only the magnetic flux density (MFD) variations, but also the mechanical responses of the electromagnetic torque (EMT) and rotor unbalanced magnetic pulls (UMP). The typical ISAGE factors include: 1) a varied rotor inclined angle θ, and 2) different values of axial inclined distance z (indicates the offset combination case of the two rotor ends). This work is based on the MFD analysis calculation. Theoretical analysis, finite element analysis, and experimental study are carried out, respectively, by taking a 5 kVA two-pole prototype generator as the study object. It is shown that the electromagnetic-mechanical properties of the generator will change when the inclined eccentricity occurs. The EMT, and the rotor UMP as well as the vibrations will increase as the rotor inclined θ and z increase. Specifically, the 2nd harmonic has the most significant variation.

Summary: Background: Clinical severity scores can identify patients at risk of severe disease and death, and improve patient management. The modified early warning score (MEWS), the quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA), and the Universal Vital Assessment (UVA) were developed as risk-stratification tools, but they have not been fully validated in low-resource settings where fever and infectious diseases are frequent reasons for health care seeking. We assessed the performance of MEWS, qSOFA, and UVA in predicting mortality among febrile patients in the Lao PDR, Malawi, Mozambique, and Zimbabwe. Methods: We prospectively enrolled in- and outpatients aged ≥ 15 years who presented with fever (≥37.5 °C) from June 2018–March 2021. We collected clinical data to calculate each severity score. The primary outcome was mortality 28 days after enrolment. The predictive performance of each score was determined using area under the receiver operating curve (AUC). Findings: A total of 2797 participants were included in this analysis. The median (IQR) age was 32 (24–43) years, 38% were inpatients, and 60% (1684/2797) were female. By the time of follow-up, 7% (185/2797) had died. The AUC (95% CI) for MEWS, qSOFA and UVA were 0.67 (0.63–0.71), 0.68 (0.64–0.72), and 0.82 (0.79–0.85), respectively. The AUC comparison found UVA outperformed both MEWS (p

ABSTRACT Blood cultures are central to the management of patients with sepsis and bloodstream infection. Clinical decisions depend on the timely availability of laboratory information, which, in turn, depends on the optimal laboratory processing of specimens. Discrete event simulation (DES) offers insights into where optimization efforts can be targeted. Here, we generate a detailed process map of blood culture processing within a laboratory and use it to build a simulator. Direct observation of laboratory staff processing blood cultures was used to generate a flowchart of the blood culture laboratory pathway. Retrospective routinely collected data were combined with direct observations to generate probability distributions over the time taken for each event. These data were used to inform the DES model. A sensitivity analysis explored the impact of staff availability on turnaround times. A flowchart of the blood culture pathway was constructed, spanning labeling, incubation, organism identification, and antimicrobial susceptibility testing. Thirteen processes in earlier stages of the pathway, not otherwise captured by routinely collected data, were timed using direct observations. Observations revealed that specimen processing is predominantly batched. Another eight processes were timed using retrospective data. A simulator was built using DES. Sensitivity analysis revealed that specimen progression through the simulation was especially sensitive to laboratory technician availability. Gram stain reporting time was also sensitive to laboratory scientist availability. Our laboratory simulation model has wide-ranging applications for the optimization of laboratory processes and effective implementation of the changes required for faster and more accurate results.IMPORTANCEOptimization of laboratory pathways and resource availability has a direct impact on the clinical management of patients with bloodstream infection. This research offers an insight into the laboratory processing of blood cultures at a system level and allows clinical microbiology laboratories to explore the impact of changes to processes and resources.

The static air gap eccentricity is one of the prevalent faults in permanent magnet synchronous generators. In previous studies, the variation of magnetic flux density, stator current and vibration have been studied mostly. However, the temperature characteristic of winding insulation under 3D static air gap eccentricity fault is rarely investigated. As a supplement, this paper comprehensively analyzes the influence of radial static air gap eccentricity, axial static air gap eccentricity and the combined static air gap eccentricity (radial static air gap eccentricity and axial static air gap eccentricity) fault on the winding insulation thermal response. The whole work is based on not only the loss of core and windings but also the temperature rise of the winding insulation, which is carried out through theoretical analysis, finite element calculation, and experiment verification. It is shown that radial static air gap eccentricity will increase the winding insulation temperature whatever in singe radial static air gap eccentricity or combined static air gap eccentricity, while axial static air gap eccentricity has the opposite effect. Specifically, the winding insulation temperature in radial static air gap eccentricity 0.1 mm, 0.2 mm and 0.3 mm cases are 57.18℃, 59.10℃ and 61.04℃, respectively, which is 3.29℃, 5.21℃ and 7.15℃ higher than that under normal condition. However, under axial static air gap eccentricity, winding insulation temperature will decrease by 1.84℃, 4.11℃ and 5.9℃, respectively. When axial static air gap eccentricity is unchanged and radial static air gap eccentricity is increased, the winding insulation temperature will increase by 1.5℃ and 3.23℃, respectively. On the contrary, when radial static air gap eccentricity is unchanged and axial static air gap eccentricity increases, the winding insulation temperature decreases by 2.54℃ and 5.66℃, respectively. The end part and the join between the end and line parts of the winding insulation are the most dangerous positions to suffer from static thermal wear.

This study presents a sizing methodology in the frame of the multidisciplinary design of interior permanent magnet synchronous machines. The proposed approach targets at obtaining an initial electromagnetic design of the machine, including diameters, axial length, rotor barrier shape, stator slot geometry as well as the winding layout. The analytical design workflow consists of an iterative sizing procedure based on a robust geometric model, consistent equations, and no correction factors. The process is carried out starting from very basic design parameters such as rated voltage, torque, and magnetic material properties. The modular structure of the proposed design algorithm can be easily integrated with optimization routines obtaining a final optimized design, which contemplates various multiobjective constraints.

ABSTRACTEffective policy to address the global threat of antimicrobial resistance requires robust antimicrobial susceptibility data. Traditional methods for measuring minimum inhibitory concentration (MIC) are resource intensive, subject to human error, and require considerable infrastructure. AIgarMIC streamlines and standardizes MIC measurement and is especially valuable for large-scale surveillance activities. MICs were measured using agar dilution for n = 10 antibiotics against clinical Enterobacterales isolates (n = 1,086) obtained from a large tertiary hospital microbiology laboratory. Escherichia coli (n = 827, 76%) was the most common organism. Photographs of agar plates were divided into smaller images covering one inoculation site. A labeled data set of colony images was created and used to train a convolutional neural network to classify images based on whether a bacterial colony was present (first-step model). If growth was present, a second-step model determined whether colony morphology suggested antimicrobial growth inhibition. The ability of the AI to determine MIC was then compared with standard visual determination. The first-step model classified bacterial growth as present/absent with 94.3% accuracy. The second-step model classified colonies as “inhibited” or “good growth” with 88.6% accuracy. For the determination of MIC, the rate of essential agreement was 98.9% (644/651), with a bias of −7.8%, compared with manual annotation. AIgarMIC uses artificial intelligence to automate endpoint assessments for agar dilution and potentially increases throughput without bespoke equipment. AIgarMIC reduces laboratory barriers to generating high-quality MIC data that can be used for large-scale surveillance programs.IMPORTANCEThis research uses modern artificial intelligence and machine-learning approaches to standardize and automate the interpretation of agar dilution minimum inhibitory concentration testing. Artificial intelligence is currently of significant topical interest to researchers and clinicians. In our manuscript, we demonstrate a use-case in the microbiology laboratory and present validation data for the model’s performance against manual interpretation.

Abstract Electric machines are widely employed in a variety of sectors, including automotive and aerospace industry. With the rising trend towards electrification in aviation, electric machines with increased power density and efficiency are becoming key enabling technologies, requiring the development of innovative design procedures. In this study, a preliminary design procedure for Surface Mounted Permanent Magnet Synchronous Machines (SPMSMs) intensively cooled by means of oil channels placed in the stator slots is proposed. The analytical derivation of SPMSM sizing equations, including the sub‐domain model and optimisation algorithm will be reported in detail. Indeed, both the electromagnetic and thermal equations of the machine are considered for the derivation of the sizing tool. As a result, the latter allows to capture variation of the machine design not only in terms of mechanical demands, such as power and speed, but also in terms of coolant characteristics, and can be effectively employed to perform trade‐off studies at a preliminary design stage. The proposed design tool will be used for a specific case study: The design of a high‐speed machine for an aerospace hydraulic actuator. The accuracy of the proposed design tool is validated for different parameters by comparing the results to experimental measurements performed on an existing 8‐pole 9‐slot prototype SPMSM.

The control system of PMa-SynRMs (Permanent Magnet-assisted Synchronous Reluctance Machines) exhibit susceptibility to external disturbances, thereby emphasizing the utmost significance of employing an observer to effectively observe and suppress system disturbances. Meanwhile, disturbances in load are sensitive to control system noise, which may be introduced from current sensors, hardware circuit board systems, sensor-less control, and analog position sensors. To tackle these problems, this paper proposes an A-DESO (anti-disturbance extended state observer) to improve the dynamic performance, noise suppression, and robustness of PMa-SynRMs in both the constant torque and FW (flux weakening) regions. With the proposed A-DESO, lumped disturbance in torque could be detected, and speed could be extracted in position with unmeasurable noise. Thanks to the merits of the small observation error in the low-frequency region and the excellent anti-disturbance performance in the high-frequency region of the proposed A-DESO, the control of the PMa-SynRM features the advantages of a fast response and good noise immunity ability within the same observation error range. The proposed A-DESO presents a shorter convergence time and a better noise suppression ability, which leads to a better dynamic response of the PMa-SynRM when encountering an unknown load disturbance compared to the traditional ESO-based control, according to simulations and experiments.