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This review article delves into the environmental impact assessment of offshore wind farms in deep waters. Insights are drawn from lessons assessing the impacts of offshore wind projects on marine life, particularly marine mammals and seabirds. These lessons underscore the importance of collecting robust baseline data, understanding populationlevel implications, and learning from other industries to refine environmental risk assessments. Brazil’s emerging offshore wind industry serves as a backdrop to illustrate the complexities of balancing renewable energy ambitions with environmental considerations. Meanwhile, a qualitative review sheds light on potential environmental repercussions of deepwater, floating offshore wind facilities. Factors such as atmospheric changes, habitat disruptions, and underwater noise disturbances are examined. As the global pursuit of offshore wind energy intensifies, the review emphasises the need for strategic data collection, effective mitigation strategies, and informed decision-making to minimize environmental impacts whilst capitalising on renewable energy.

Solar energy is a promising renewable energy source, but its intermittent and variable nature poses significant challenges for accurate forecasting. Over the recent years, there has been a remarkable surge in research dedicated to improving the precision of solar energy forecasting models. This review article delves into the state-of-the-art in solar energy forecasting. Beginning with an exploration of the hurdles faced in forecasting solar radiation, we proceed to provide an extensive survey of various forecasting models that have been developed to tackle this complex problem. Factors influencing the accuracy of solar energy forecasts are discussed, and an insight into the future trends in solar energy forecasting is provided. Key areas of focus include machine learning techniques, artificial neural networks (ANNs), and support vector regression.

This article reviews two key areas in the field of thermoelectric superconductors used for gathering energy and cooling systems. The first area looks at a special kind of material called double perovskites. These materials are not only good for solar energy but also have unique features that make them useful for thermoelectric applications. For example, their structure and properties change depending on the type of halogen used, and they show promise for converting heat into electricity efficiently. The second area focuses on a new design of thermoelectric devices, made up of tiny energy generators called nanoTEGs. These are built on a silicon-basedmembrane and are designed to be highly efficient in managing heat. Initial tests have shown that these devices can generate a small amount of power, even with a modest temperature difference. This suggests that they could be used in future for tasks like measuring temperature or detecting airflow. Both of these areas offer exciting possibilities for improving how we harvest energy and manage heat, opening up new avenues for sustainable technology solutions.

Automobiles and motorcycles that run on conventional fuels are currently not the greatest for transportation because to the rising awareness of pollution and the energy shortage concerns. There is a demand for a less expensive and more effective mode of transportation because the cost of petroleum goods is soaring right now. Additionally, it is becoming more and more crucial to conserve energy in order to assess the issue of leftover fuel depletion. Even large-scale manufacturing and industrial operations restrict their employees' use of transportation within the premises in order to reduce the risk of air pollution. Research on environmentally friendly transport has increased to fulfil these demands. Technology for electrically powered vehicles is a step in the right direction. One such environmentally friendly vehicle that aids employees in covering large distances inside of their organisations is the monowheel. A monowheel is a single-track, one wheeled vehicle that looks a lot like a unicycle. The rider sits inside the wheel as opposed to being above it like on a unicycle. The wheel is a ring that is often propelled by smaller wheels contacting its inner rim. The smaller wheels are powered by an electric motor and a battery to turn. Changes to the frame's design and the addition of solar panels as a backup power source are proposed. Then, the car can also be powered by solar energy. Software from CATIA and ANSYS, respectively, are used for the design and analysis of the inner and exterior fames.

This study uses an organized experimental methodology to assess the security and robustness of blockchain-integrated systems within the framework of Industry 5.0. The R&D department's average salary increased by 10%, according to an analysis of personnel statistics, which reflects trends in remuneration. Interdepartmental transactions have increased by 20% according to blockchain transaction analysis, highlighting the significance of safe interdepartmental cooperation. Security issues highlight the need of ongoing watchfulness; in the R&D department, data breaches have increased by 30%. The Manufacturing department scored 85% on the resilience evaluation, which reveals diversity in departmental flexibility. Conclusively, this study offers crucial perspectives on blockchain's function in Industry 5.0 and underscores the need of security, cooperation, and adaptability in this dynamic environment.

Using simulation and empirical data analysis, this research examines the efficacy of real-time traffic control in smart cities. Traffic data collected in real time from strategically placed sensors shows that traffic volume was reduced by 8.33% on Main Street after a traffic light timing change was implemented. Traffic volume at Highway Junction was also significantly reduced by 5.56% as a result of traffic sign updates. On the other hand, interventions result in a relatively small decrease in traffic volume (2.78%) in the City Center. The influence of these actions is shown by the traffic simulation models, which show average vehicle speeds rising from 25 to 28 mph on Main Street, 45 to 50 mph at Highway Junction, and 30 to 32 mph in the Residential Area. The aforementioned research highlights the crucial function of data-driven decision-making in traffic management, guaranteeing effective distribution of resources and quantifiable enhancements in urban mobility. Urban planners and legislators may use these discoveries to build smart cities that are more accessible, sustainable, and efficient.

The incorporation of Industry 4.0 has integrated various innovations into fire safety management, thus changing the mode of identifying, assessing, and controlling fire risks. This review aims at how emerging technologies like IoT, AI, cloud technology, and BIM are making changes to fire safety in structural structures. With IoT-enabled sensors, data, and analytics coupled with predictive algorithms for real-time scenarios, fire safety systems have become dynamic systems where early detection, quick response, and risk management can be achieved. In addition, cloud web-based solutions improve the storage of information while providing the predictive aspect for certainty of safety measures. This paper also largely focuses on such activities through the likes of ISO/IEC 30141 and IEEE 802.15.4, thus making a critical role in maintaining effective connectivity between IoT devices, which is necessary for the effective performance of fire safety systems. Furthermore, the implementation issues, including the high costs, the difficulty in scaling up the projects, and the cybersecurity concerns, are considered and compared to the possible solutions, which include upgrading in stages and the possibility of subsidies from the government. The review also points out areas for further study, such as the creation of small cell networks with lower latency, the use of AI to carry out the maintenance of IoTs, and the enhancement of protection mechanisms of systems that are based on the IoTs. In general, this paper highlights the vast possibilities offered by Industry 4.0 technologies to support organizational fire safety management or decrease fire fatalities and improve built environment fire safety. [ABSTRACT FROM AUTHOR]

The paper aims to develop a machine learning-based system that can automatically convert text to audio and text to video as per the user’s request. Suppose Reading a large text is difficult for anyone, but this TTS model makes it easy by converting text into audio by producing the audio output by an avatar with lip sync to make it look more attractive and human-like interaction in many languages. The TTS model is built based on Waveform Recurrent Neural Networks (WaveRNN). It is a type of auto-regressive model that predicts future data based on the present. The system identifies the keywords in the input texts and uses diffusion models to generate high-quality video content. The system uses GAN (Generative Adversarial Network) to generate videos. Frame Interpolation is used to combine different frames into two adjacent frames to generate a slow- motion video. WebVid-20M, Image-Net, and Hugging-Face are the datasets used for Text video and LibriTTS corpus, and Lip Sync are the dataset used for text-to-audio. The System provides a user-friendly and automated platform to the user which takes text as input and produces either a high-quality audio or high-resolution video quickly and efficiently.

Today, it is typical to need to identify and validate a person in order to use a safe box, operate a vehicle, access a bank account via an ATM, or carry out other operations that call for the protection of personal information. The accuracy and dependability of traditional methods, such as ID card verification or signature, are lacking. The systems in use at these places must be rapid and dependable. The usage of ATMs, which allow users to conveniently exchange banknotes, is now met with a new challenge: guaranteeing that users can maintain a valid identification. Customers are experiencing financial losses as a result of the increase in criminal cases brought about by antiquated ATM identification procedures. Our system’s main objective is to improve the usability and security of online transactions. Today, the application of biometric technologies is expanding swiftly. Biometrics is used for personal identification. Here, we are allowing user’s access to an ATM by means of a biometric fingerprint scanner. The Bank enters a fingerprint’s information into a database during the enrolment procedure. The bank provides authentication to the customer, which they can use to conduct transactions. If a fingerprint match in the database occurs, transactions take place. The transaction will be cancelled if, following verification, the fingerprint does not match. The user of a fingerprint-based ATM machine can conduct secure transactions.

Given the extensive number of empirical studies pertaining to this subject, a bibliometric study was required to create an overview of the literature on green technology and environmental sustainability. The objective of the study is to identify the most cited research journals. Six research questions were developed to study the number of publications per year, the number of citations per article, the country-wise contribution, etc. Using a bibliometric analysis, this study analyses and summarises the related literature from 1997–2023. The paper provides an all-around intellectual structure for the literature. It was found out that the research paper by Sharifi et al. (2020), entitled “The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management”, published in the journal "The Science of the Total Environment," was most cited journal. China contributed the most as far as green technology and environmental sustainability are concerned. It was also found out that most of the papers were published in 2020.