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Coal particles, a typical of organic matter with high carbon content, widely diffused in soil environment due to massive consumption of coal energy for centuries. Even in small amounts presented in soils, coal particles can produce obvious overestimation of soil organic carbon (SOC) and thus increase an uncertainty of soil C sequestration assessment. However, there is a lack of determination methods for distinguishing coal-derived C from SOC. This study takes the Jiaozuo mining area as the study area, which has a history of anthracite mining for more than 100 years and where coal-contaminated soils are widespread. The hyperspectral characteristics of the mine soils were analyzed by collecting coal particles and coal-free soils and then mixing known quantities of the two samples manually. Based on eight spectral mathematical transformation and two spectral feature screening methods (i.e., traditional correlation coefficiencies, and comprehensive competitive adaptive reweighted sampling (CARS)), six inversion models including spectral feature index models (i.e., deviation of arch (IDOA), difference index (ID), ratio index (IR)), and three machine learning models (i.e., partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF)), were established to inversing coal-derived C content. The applicability of the established optimal inversion model was also examined. It was found that in the wavelength range from 350 nm to 2500 nm, the spectral curves of coal particles are obviously different from those of plant-derived organic matter and coal-free soils. Moreover, the spectral reflectance (R) of coal-contaminated soils decreases with increasing coal-derived C content. These findings provide a theoretical basis for the application of hyperspectral remote sensing technology to quantitatively inverse coal-derived C of mine soils. The CARS-extracted feature wavebands of coal-derived C distributed evenly in the wavelength range between 350 nm and 2500 nm, and the number of feature waveband extracted by the CARS was far higher than that by traditional correlation coefficiency methods. After mathematical transformation of R data, the estimated accuracies of coal-derived C content produced by the IDOA, ID and IR inversion models were significantly enhanced, among which, the DI model of 1/R spectral transformation exhibited the highest estimation accuracy. Compared with the traditional index models, the three machine learning models combined with CARS further enhanced estimation accuracy of coal-derived C content. Among the three machine learning models, the 1/R-CARS-RF inversion model produced the highest estimation accuracy, showing \begin{document}$R_{\rm{m}}^2 $\end{document}=0.998, RMSE=0.348, RPD=29.943 for its validation set. The applicability test showed that the 1/R-CARS-RF model exhibited a good applicability for different coal-contaminated soils in the Jiaozuo mining area as observed 1.88% for RMSE and 4.97 for RPD. Therefore, it can be expected that hyperspectral remote sensing technology has a promising application prospect for determining coal-derived C content in mine soils. In addition, this study can provide a methodology support for distinguishing coal-derived C from SOC in mine soils and accurately assessing soil carbon sequestration.

Electric vehicles are having significant priority in transportation including goods, public transport etc. among many technologies in electric vehicle, brushless DC (BLDC) motors operated eclectic drives are mostly used. Speed sensorless control is required to implement for obtaining a best performance. At the same time, a sliding mode controller (SMC) can be able to produce the ultimate response under various conditions. Hence sensorless speed control with SMC of BLDC motor is implemented in this paper to drive the electric vehicle. In order to develop speed sensorless of motor, a model reference adaptive control (MRAC) system is developed. However, obtaining a fastest response is very important in electric vehicle speed control. Hence a TS-Fuzzy model is developed to use in the place of conventional PI controllers. Extensive results are collected for presentation by establishing Hardware – in the – Loop (HIL) with the help of two OPAL-RT devices.

Grid connected renewable energy sources based electric power generation systems are increasing day by day to meet the electrical demand without burning any fossil fuels. Doubly fed induction generators (DFIGs) are widely used in wind turbines due to advantages that they can allow the amplitude and frequency of their output voltages to be maintained at a constant value, no matter the speed of the wind blowing on the wind turbine rotor. Moreover, the DFIG provides a huge possibility to improve network dynamic behavior by using the power electronic converters with sufficient control arrangements. Many different structure and control algorithms can be used for control of power converters. However, conventional controllers cannot perform with full efficiency during rapid changes in wind speed. Hence, Takagi–Sugeno–Kang (TSK) Fuzzy controllers based novel control technique is proposed in this paper for performing many tasks on grid connected DFIG systems. The proposed control scheme is tested under variations of voltage, frequency, wind speed, and reactive power load. To achieve the realistic responses, the Hardware-in the-Loop (HIL) is implemented with the help of OPAL-RT modules for presenting extensive results.

This review article delves into the strategic integration of renewable energy sources within smart cities, emphasizing the role of electric vehicles (EVs) in ensuring stability. Smart cities, at the forefront of sustainable urban development, prioritize energy systems that are both clean and renewable. The integration of various renewable energy resources, including solar, wind, geothermal, hydropower, ocean, and biofuels, into the energy systems of smart cities is explored, considering both technical and economic aspects. The rapid adoption of these technologies provides a foundation for a low-carbon economy, ensuring a cleaner and more sustainable urban environment. However, challenges persist, particularly in optimizing energy systems for renewable integration, stability, operational range, and cost-effectiveness. Additionally, the evolution of smart cities has brought forth advanced energy conservation systems, addressing energy management at both building and city levels. Innovative solutions, such as advanced infrastructure and energy trading strategies in distributed systems, are proposed. The role of EVs in the smart grid is also highlighted, addressing challenges posed by renewable energy’s intermittent output. The potential of EVs to counteract these fluctuations, especially under the vehicle-to-grid (V2G) framework, is discussed, offering insights into future research avenues for a harmonious and sustainable energy ecosystem in smart cities.

Monogenic syndromes are associated with neurodevelopmental changes that result in cognitive impairments, neurobehavioral phenotypes including autism and attention deficit hyperactivity disorder (ADHD), and seizures. Limited studies and resources are available to make meaningful headway into the underlying molecular mechanisms that result in these symptoms. One such example is DeSanto-Shinawi Syndrome (DESSH), a rare disorder caused by pathogenic variants in the WAC gene. Individuals with DESSH syndrome exhibit a recognizable craniofacial gestalt, developmental delay/intellectual disability, neurobehavioral symptoms that include autism, ADHD, behavioral difficulties and seizures. However, no thorough studies from a vertebrate model exist to understand how these changes occur. To overcome this, we developed both murine and zebrafish Wac/wac deletion mutants and studied whether their phenotypes recapitulate those described in individuals with DESSH syndrome. We show that the two Wac models exhibit craniofacial and behavioral changes, reminiscent of abnormalities found in DESSH syndrome. In addition, each model revealed impacts to GABAergic neurons and further studies showed that the mouse mutants are susceptible to seizures, changes in brain volumes that are different between sexes and relevant behaviors. Finally, we uncovered transcriptional impacts of Wac loss of function that will pave the way for future molecular studies into DESSH. These studies begin to uncover some biological underpinnings of DESSH syndrome and elucidate the biology of Wac, with advantages in each model.