The deterioration of railway wheel tread causes unexpected breakdowns with increasing risk of operational failure leading to higher maintenance costs. The timely detection of wheel faults, such as wheel flats and false flanges, leading to varying conicity levels, helps network operators schedule maintenance before a fault occurs in reality. This study proposes a multiple model-based novel technique for the detection of railway wheelset conicity. The proposed idea is based on an indirect method to identify the actual conicity condition by analyzing the lateral acceleration of the wheelset. It in fact incorporates a combination of multiple Kalman filters, tuned on a particular conicity level, and a fuzzy logic identification system. The difference between the actual conicity and its estimated version from the filters is calculated, which provides the foundation for further processing. After preprocessing the residuals, a fuzzy inference system is used that identifies the actual conicity of the wheelset by assessing the normalized rms values from the residuals of each filter. The proposed idea was validated by simulation studies to endorse its efficacy.

Conicity is an important characteristic that helps the railway vehicle to steer itself down the track. However during the operation, the conicity tends to change inconsistently due to frictional contact at the wheel-rail interface. Safety, reliability and ride comfort which are utmost importance for journey are adversely affected due to the changes in conicity level beyond certain limit. Several techniques have been employed for monitoring the health of the railway wheelset however still a significant potential exists to investigate the wheelset conicity. This paper presents a model based technique to monitor the wheelset condition which contributes to the wheel flats due to decrease in conicity level and the problem of false flanges due to increased level of conicity. In this paper an unconstrained solid axle railway wheelset is considered for study. The dynamic behavior of the wheelset is analyzed at different conicity levels to understand the effect of the conicity on the wheelset. In order to demonstrate the potential of this research work a simulation model is developed in Matlab/ Simulink to mimic the behavior of an actual wheelset. Simplified linearized model of the wheelset is used to estimate the dynamics of the wheelset. From the simulation results it is evident that the frequency of vibration is changing with the changes in conicity level. In this way using the proposed method the conicity level is indirectly identified. The results produced by simulation model are satisfactory.

In this paper, integration of silicon oxycarbide (SiOC) and silicon nitride (Si3N4) platforms was demonstrated to realize ultra-efficient thermal tuning of photonic integrated circuits. Si3N4 being a fascinating photonic material with moderate refractive index (n ≈ 2) and ultra-low loss, lacks thermo-optic coefficient that makes thermal phase actuators long and dissipate high powers. Integration of SiOC coating with a comparable refractive index (n = 2.2) boosts the effective thermo-optic efficiency of Si3N4 photonic circuits by almost an order of magnitude with no additional loss. An SiOC layer was deposited by the reactive RF magnetron sputtering technique from the SiC target at room temperature. The structural, chemical and optical characterizations of the sputter deposited SiOC layer were performed with SEM, AFM, EDS and spectroscopic ellipsometry. The results of SiOC-coated Si3N4 and pristine Si3N4 photonic devices were discussed and compared. SiOC was demonstrated as an enabling platform for low-loss and power-efficient thermal phase actuators in conventional photonic technologies with application in reconfigurable photonic systems.

The majority of cytoplasmic proteins and vesicles move actively primarily to dynein motor proteins, which are the cause of muscle contraction. Moreover, identifying how dynein are used in cells will rely on structural knowledge. Cytoskeletal motor proteins have different molecular roles and structures, and they belong to three superfamilies of dynamin, actin and myosin. Loss of function of specific molecular motor proteins can be attributed to a number of human diseases, such as Charcot-Charcot-Dystrophy and kidney disease.  It is crucial to create a precise model to identify dynein motor proteins in order to aid scientists in understanding their molecular role and designing therapeutic targets based on their influence on human disease. Therefore, we develop an accurate and efficient computational methodology is highly desired, especially when using cutting-edge machine learning methods. In this article, we proposed a machine learning-based superfamily of cytoskeletal motor protein locations prediction method called extreme gradient boosting (XGBoost). We get the initial feature set All by extraction the protein features from the sequence and evolutionary data of the amino acid residues named BLOUSM62. Through our successful eXtreme gradient boosting (XGBoost), accuracy score 0.8676%, Precision score 0.8768%, Sensitivity score 0.760%, Specificity score 0.9752% and MCC score 0.7536%.  Our method has demonstrated substantial improvements in the performance of many of the evaluation parameters compared to other state-of-the-art methods. This study offers an effective model for the classification of dynein proteins and lays a foundation for further research to improve the efficiency of protein functional classification.