In modern urban energy communities, diverse natured loads (homes, schools, hospitals, malls, etc.) are situated in the same locality and have self-electricity generation/management facilities. The power systems of these individual buildings are called smart microgrids. Usually, their self-electricity generation is based on renewable energy sources, which are uncertain due to their environmental dependency. So, the consistency of self-energy generation throughout the day is not guaranteed; thus, the dependency on the central utility grid is continued. To solve this, researchers have recently started working on interoperable smart microgrids (ISMs) for urban communities. Here, a central monitoring and control station captures the energy generation/demand information of each microgrid and analyzes the availability/requirement, thereby executing the energy transactions among these ISMs. Such local energy exchanges among the ISMs reduce the issues with uncertain renewable energy and the dependency on the utility grid. To establish such useful ISMs, a well-established communication mechanism has to be adopted. In this view, this paper first reviews various state-of-the-art developments related to smart grids and then provides extensive insights into communication standards and technologies, issues/challenges, and future research perspectives for ISM implementation. Finally, a discussion is presented on advanced wireless technology, called LoRa (Long Range), and a modern architecture using the LoRa technology to establish a communication network for ISMs is proposed.

In light of the expansion of Internet of Things (IoT) bias, there is a great opportunity to improve environmental monitoring skills across a variety of fields, including but not limited to husbandry, civic planning, and artificial safety. The incorporation of artificial intelligence methods, such as machine literacy algorithms and deep literacy models, makes it possible to do real-time analysis and interpretation of environmental data that is gathered by Internet of Things cameras. This paper examines the approaches and technologies that are currently being utilized in Internet of Things (IoT)-based environmental monitoring systems. The focus is placed on the role that artificial intelligence plays in the process of perfecting data quality, predictive analytics, and decision-making procedures. Case studies and experimental findings illustrate the efficiency of the system in detecting and forecasting changes in the environment, which makes it easier to implement imaginative actions for environmental conservation and operation. It is important to note that the findings highlight the potentially disruptive outcome of merging Internet of Things (IoT) and artificial intelligence (AI) technologies to address urgent environmental concerns and pave the way for sustainable development enterprise.

Innovations in electric vehicle technology have led to a need for maximum energy storage in the energy source to provide some extra kilometers. The size of electric vehicles limits the size of the batteries, thus limiting the amount of energy that can be stored. Range anxiety amongst the crowd prevents the entire population from shifting to a completely electric mode of transport. The extra energy harnessed from the kinetic energy produced due to braking during deceleration is sent back to the batteries to charge them, a process known as regenerative braking, providing a longer range to the vehicle. The work proposes efficient machine learning-based methods used to harness maximum braking energy from an electric vehicle to provide longer mileage. The methods are compared to the energy harnessed using fuzzy logic and artificial neural network techniques. These techniques take into consideration the state of charge (SOC) estimation of the battery, or the supercapacitor and the brake demand, to calculate the energy harnessed from the braking power. With the proposed machine learning techniques, there has been a 59% increase in energy extraction compared to fuzzy logic and artificial neural network methods used for regenerative energy extraction.

Developing successful embedded vision applications necessitates a detailed review of various algorithmic optimization trade-offs and a wide variety of hardware design options. This makes it difficult for developers to navigate the solution space and find design points with the best performance trade-offs. In neural machine translation, large Transformer frameworks have produced state-of-the-art results and have become the industry standard. We will clarify the mathematical mechanisms behind the paper's efficient interpretation for neural machine translations and look for the best combination of known techniques to improve interpretation speed without compromising recognition accuracy in this paper. We perform an empirical study that compares various approaches and shows that using a hybrid of decoder self-attention replacement with simplified recurrent units, a deep encoder and shallow decoder architecture, and multiple head attention reseeding can achieve up to higher accuracy. By replacing heavy functions with lighter ones and enhancing the autoencoder's layers structure, excellent results can be achieved in a harmonious mix of time series, network architecture, and probabilistic solutions.

Artificial intelligence (AI) is a machine intelligence technology with vast potential for a smart industrial revolution. This study aims to create new electric-powered transportation such as bicycles, motorbikes, and other vehicles. The combination of high-speed, resiliency, and low-latency connection, as well as AI and Internet of Things (IoT) technologies, will enable the transformation to fully autonomous vehicles. This electric motorcycle tracks its speed, location, and other data using IoT logic. A smart charging system has also been developed to keep them ready to use effectively. The proposed research also reduces the damage to the environment efficiently.

The sturdy demands in orthopaedics are not yet fulfilled, in both bone replacement and joint substitution and also in other repairs of bone defects. Metals are used for bone repair and replacement for a long period of time instead of biomaterial. Most of the countries are still now practising bone replacement with metals like titanium and stainless steel only due to the lag of technology advancement. A lot of research and development paves the way to composite biomaterial, as it is evolving in the domain of treatment of orthopaedics. There was massive research undergoing in the biomaterial field recently, and various methods of fabrication have been tested and implemented. Nanocomposites provide a higher surface‐to‐volume ratio, surface chemistry and nanoscale reinforcement, flexible production techniques, good corrosion and erosion resistance, and cheaper costs. The nanocomposite biomaterials were intended for biomedical purposes. Because of the intricacy of biological structures (tissue or organ) and additive manufacturing techniques for tissue engineering, scaffolding has wide scope for instant remedies in bone implant. The design restrictions and physical attributes of fast prototyping structures are then evaluated in terms of input factors like design elements, material choices, and additive manufacturing processing parameters. As a result of this survey, the needs and application importance of additively manufactured implants for the regeneration of various biomaterial types as well as the attempts undertaken to mitigate their medical impairment are suggested.