Investigation of a New Flux-Modulated Permanent Magnet Brushless Motor for EVs

This paper presents a flux-modulated direct drive (FMDD) motor. The key is to integrate the magnetic gear with the PM motor while removing the gear inner-rotor. Hence, the proposed FMDD motor can achieve the low-speed high-torque output and high-speed compact design requirements as well as high-torque density with a simple structure. The output power equation is analytically derived. By using finite element analysis (FEA), the static characteristics of the proposed motor are obtained. Based on these characteristics, the system mathematical model can be established. Hence, the evaluation of system performances is conducted by computer simulation using the Matlab/Simulink. A prototype is designed and built for experimentation. Experimental results are given to verify the theoretical analysis and simulation

Space-Time Hybrid Model for Short-Time Travel Speed Prediction

Short-time traffic speed forecasting is a significant issue for developing Intelligent Transportation Systems applications, and accurate speed forecasting results are necessary inputs for Intelligent Traffic Security Information System (ITSIS) and advanced traffic management systems (ATMS). This paper presents a hybrid model for travel speed based on temporal and spatial characteristics analysis and data fusion. This proposed methodology predicts speed by dividing the data into three parts: a periodic trend estimated by Fourier series, a residual part modeled by the ARIMA model, and the possible events affected by upstream or downstream traffic conditions. The aim of this study is to improve the accuracy of the prediction by modeling time and space variation of speed, and the forecast results could simultaneously reflect the periodic variation of traffic speed and emergencies. This information could provide decision-makers with a basis for developing traffic management measures. To achieve the research objective, one year of speed data was collected in Twin Cities Metro, Minnesota. The experimental results demonstrate that the proposed method can be used to explore the periodic characteristics of speed data and show abilities in increasing the accuracy of travel speed prediction

Anisotropic elastic constants calculation of stainless steel cladded layers of pressure vessel steel plate

Cladding stainless steel layer on the inner surface of ferrite pressure vessel is a common method to improve the corrosion resistance and save the economic cost. However, the movement of heat source and temperature gradient in the process of cladded welding will lead to the anisotropy of cladded layer material. When measuring the residual stress of pressure vessel steel plate with stainless steel cladded layers (SSCL) by contour method, it is necessary to know the elastic mechanical properties of stainless steel cladded layers accurately. The assumption of transversely isotropy (TI) was employed, and the relationship between the material compliance matrix and the elastic modulus of transversely isotropic material was utilized. Based on the elastic modulus of each cladded layer and the whole steel plate from the longitudinal direction (0°) until the transverse direction (90°) measured by the experiment, the independent constants S11, S13, S33 and S44 in the compliance matrix of each cladded layer and the whole steel plate were obtained by regression analysis method. Furthermore, by using the relationship between the independent constants of the stiffness matrix of the transversely isotropic material and the single crystal material, the independent constant S12 in the compliance matrix of each stainless steel cladded layer and the whole steel plate were obtained. And then the independent constants of the stiffness matrix of each cladded layer and the whole steel plate were acquired. Hence, a method for calculating the anisotropic elastic constants of the stainless steel cladded layer and the whole steel plate was proposed. The results will provide material data support for measuring residual stress of pressure vessel steel plate with stainless steel cladded layers by contour method

TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems

Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of microservice systems. However, performing RCA on modern microservice systems can be challenging due to their large scale, as they usually comprise hundreds of components, leading significant human effort. This paper proposes TraceDiag, an end-to-end RCA framework that addresses the challenges for large-scale microservice systems. It leverages reinforcement learning to learn a pruning policy for the service dependency graph to automatically eliminates redundant components, thereby significantly improving the RCA efficiency. The learned pruning policy is interpretable and fully adaptive to new RCA instances. With the pruned graph, a causal-based method can be executed with high accuracy and efficiency. The proposed TraceDiag framework is evaluated on real data traces collected from the Microsoft Exchange system, and demonstrates superior performance compared to state-of-the-art RCA approaches. Notably, TraceDiag has been integrated as a critical component in the Microsoft M365 Exchange, resulting in a significant improvement in the system's reliability and a considerable reduction in the human effort required for RCA

Automatic Root Cause Analysis via Large Language Models for Cloud Incidents

Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years

Influence of Traffic Activity on Heavy Metal Concentrations of Roadside Farmland Soil in Mountainous Areas

Emission of heavy metals from traffic activities is an important pollution source to roadside farmland ecosystems. However, little previous research has been conducted to investigate heavy metal concentrations of roadside farmland soil in mountainous areas. Owing to more complex roadside environments and more intense driving conditions on mountainous highways, heavy metal accumulation and distribution patterns in farmland soil due to traffic activity could be different from those on plain highways. In this study, design factors including altitude, roadside distance, terrain, and tree protection were considered to analyze their influences on Cu, Zn, Cd, and Pb concentrations in farmland soils along a mountain highway around Kathmandu, Nepal. On average, the concentrations of Cu, Zn, Cd, and Pb at the sampling sites are lower than the tolerable levels. Correspondingly, pollution index analysis does not show serious roadside pollution owing to traffic emissions either. However, some maximum Zn, Cd, and Pb concentrations are close to or higher than the tolerable level, indicating that although average accumulations of heavy metals pose no hazard in the region, some spots with peak concentrations may be severely polluted. The correlation analysis indicates that either Cu or Cd content is found to be significantly correlated with Zn and Pb content while there is no significant correlation between Cu and Cd. The pattern can be reasonably explained by the vehicular heavy metal emission mechanisms, which proves the heavy metals’ homology of the traffic pollution source. Furthermore, the independent factors show complex interaction effects on heavy metal concentrations in the mountainous roadside soil, which indicate quite a different distribution pattern from previous studies focusing on urban roadside environments. It is found that the Pb concentration in the downgrade roadside soil is significantly lower than that in the upgrade soil while the Zn concentration in the downgrade roadside soil is marginally higher than in the upgrade soil; and the concentrations of Cu and Pb in the roadside soils with tree protection are significantly lower than those without tree protection. However, the attenuation pattern of heavy metal concentrations as a function of roadside distance within a 100 m range cannot be identified consistently

Gene-Expression Signatures Can Distinguish Gastric Cancer Grades and Stages

Microarray gene-expression data of 54 paired gastric cancer and adjacent noncancerous gastric tissues were analyzed, with the aim to establish gene signatures for cancer grades (well-, moderately-, poorly- or un-differentiated) and stages (I, II, III and IV), which have been determined by pathologists. Our statistical analysis led to the identification of a number of gene combinations whose expression patterns serve well as signatures of different grades and different stages of gastric cancer. A 19-gene signature was found to have discerning power between high- and low-grade gastric cancers in general, with overall classification accuracy at 79.6%. An expanded 198-gene panel allows the stratification of cancers into four grades and control, giving rise to an overall classification agreement of 74.2% between each grade designated by the pathologists and our prediction. Two signatures for cancer staging, consisting of 10 genes and 9 genes, respectively, provide high classification accuracies at 90.0% and 84.0%, among early-, advanced-stage cancer and control. Functional and pathway analyses on these signature genes reveal the significant relevance of the derived signatures to cancer grades and progression. To the best of our knowledge, this represents the first study on identification of genes whose expression patterns can serve as markers for cancer grades and stages