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One of the most challenging issues in the world of ship control is automatic ship berthing. In order for the berthing to be completed automatically and safely, the control systems must account for the ship’s dynamics at low speeds. Artificial neural networks (ANNs) are frequently used to address this need because of their capacity to mimic and carry out all the functions of the human brain throughout the ship berthing procedure. However, there are still certain drawbacks when employing this theory to create the automatic system for ship berthing, and this makes it more challenging to develop the control system for practical ship applications. This study reviews the pros and cons of employing ANNs along with some other deep learning algorithms in automatic ship berthing systems. Some directions for further research into automatic ship-berthing systems are also suggested.

Globally, the demand for energy from renewable sources is growing due to the increasing electricity consumption and the pollution of fossil fuels. The government has framed various policies to facilitate green energy generation, encouraging renewable energy source usage through PV installations in multiple sectors, including educational institutions. The primary objective of this paper is to propose a methodological approach for analysing the performance of the installed PV system on the rooftop of a university building in Tamil Nadu, India. The site selected is favourable for electricity generation from PV systems with an average global solar radiation of 5.82 kWh/m2day. Solar energy changes periodically with annual and daily variations and is not steady due to seasonal changes. The step-by-step performance assessment and the annual performance of the 100-kW solar PV system, which was instituted in 2019, with the forecasted parameters, are presented in this paper. Therefore, the assessment analysis is carried out in four phases: feasibility assessment, Energy yield assessment, Life cycle assessment, and Power quality assessment. To improve the solar PV output and efficiency, considering the solar irradiation, temperature, wind velocity, etc., PV yield is measured to evaluate the PV system's energy metrics. This paper also considers the carbon credits earned, solar power generated in the location, and the payback period. The power quality assessment is carried out in this paper to test the PV plant's compliance with effective grid integration.

The energy transition is a crucial effort from many sectors and levels to create a more integrated, carbon-neutral society. More than 20% of all greenhouse gas emissions are attributed to the transportation sector, predominantly concentrated in metropolitan areas. As a result, various technological hurdles are encountered and overcome. It facilitates the adoption of electric vehicles (EVs) run on renewable energy, making them a practical option in the fight against climate change and the completion of the energy revolution. Recent developments suggest that EVs will replace internal combustion engine (ICE) during the next few months. The EV either gets all of its power from batteries and ultra capacitors or some of it from both. In a plug-in electric vehicle, the battery or ultra-capacitor is charged by an AC supply connected to a grid line. In a hybrid electric vehicle, the ICE charges the battery or ultra-capacitor. Regenerative braking is another way to charge the battery from the traction motor. In a plug-in electric vehicle, the energy from of the battery or ultra-capacitor is put back into the AC grid line. Electronic converters are essential to converting power from the grid line to the traction motor and back again. This paper examines the current state of the electric vehicle market throughout the world and its potential future developments. Power electronics converters (PEC) and energy storage devices significantly impact electric vehicles’ efficiency. Furthermore, general opinions about EVs are soon in this sector, as well as research topics that are still open to industry and University researchers.

Lithium-ion battery management requires an accurate determination of the batteries’ remaining usable lives. A novel hybrid method used for the RUL and short-term capacity prediction of batteries is presented in this manuscript. The proposed technique is combined with a White Shark Optimizer (WSO) with the Multi-kernel Support Vector Machine (MSVM); therefore, it is called the WSO-MSVM technique. The major goal of this WSO-MSVM method is to attain effective future capacities and RUL forecast for the Lithium-Ion Battery (LIB) with reliable management of uncertainty. The hybrid technique solves the difficulty of forecasting the RUL of LIB. This proposal trains the WSO-MSVM model using the LIB data. The trained model is utilized to forecast the capacity and RUL of LIBs. In this proposed system, capacity, current, and voltage are considered in the discharge operation. The proposed technique validates good reliability and prediction accuracy with this suitable mechanism. Compared to the existing techniques like artificial neural network (ANN), ant lion optimizer-optimized artificial neural network, and adaptive network-based fuzzy inference system, the proposed technique shows fewer errors under charge and discharge conditions. In addition, the uncertainty intervals of the proposed WSO-MSVM technique for the predicted RUL may include the actual remaining life of the battery. The proposed technique’s charging error is lower the other exciting methods. [ABSTRACT FROM AUTHOR]

Lymphatic filariasis (LF), a neglected tropical disease is targeted for elimination globally by 2020. National Health Policy of India set the goal by 2017 and annual single-dose mass drug administration (MDA) with anti-filarial drugs is in operation in endemic districts since 2004. Performance and effectiveness of MDA was diverse across the nation and prevalence of infection continues above threshold level in 50% of endemic districts which requires alternative strategies. National programme aims at achieving >65% consumption for transmission control. Post 10 rounds of MDA in an endemic district of Kerala state, a three-arm study identified determinants in gap in outreach and consumption, based on which context-dependent intervention using social group work and networking (SGWN) was implemented and impact assessed. A spill over effect with overall increase in coverage and consumption rates irrespective of arms apparently is due to inclusive improvement in MDA programme whereas, significant relative improvement in intervention arm reflects effectiveness of SGWN strategy. Though reduction in microfilaria prevalence in all arms was observed, it was significant between intervention and comparison arm. This study highlights need for context-dependent communication strategy to improve MDA for accelerating LF elimination by the targeted 2020.

The delivery of electricity employing an electromagnetic field that extends across an intervening region is called a wireless power transfer (WPT). This approach paves the way for electric vehicles (EVs) to use newly available options to reduce their environmental impact. This article is a review that examines the WPT technology for use in electric vehicle applications from both the technical aspect and the environmental impact. This review will attempt to accomplish the following objectives: (1) describe the present state of the technology behind the development and application of a WPT across the transportation industry; (2) substantiate the actual implementation of WPT EV systems; and (3) estimate the functioning of the autonomous system, as well as detect the potential stumbling blocks and openings for enhancement. The most recent advancements and implementation in compensating topologies, power electronics converters, and control techniques are dissected and debated scientifically to improve the system’s performance. To evaluate the performance from a sustainable perspective, energy, environmental, and economic factors are utilized, and at the same time, policy drivers and health and safety problems are researched.