A. K. Dasarathy

Smart cities are pioneering sustainability by integrating renewable energy sources. This review explores the dynamic interplay between renewables, sustainability challenges, and climate solutions in smart cities. Urban populations’ demands for energy efficiency are met by adopting renewables like solar, wind, and hydroelectric power, reducing reliance on fossil fuels and fostering economic growth. Sustainable building materials and green designs are promoted, along with electric vehicles for enhanced mobility and reduced emissions. Challenges include technological adoption, economic constraints, and effective regulation. Despite these, smart cities remain committed to sustainable urban development. The future holds promise with the expansion of renewables, the adoption of circular economy principles, and the prevalence of electric vehicles in urban transit. Emerging technologies like AI and IoT will optimize energy use, provide real-time insights, and enhance governance. Smart cities lead the way towards a more sustainable urban future.

This manuscript explores the capability of deep learning technology within the software of emotional text analysis (ETA). In particular, we consciousness of using lengthy quick-time period reminiscence (LSTM), convolutional neural networks (CNN), and different deep mastering fashions in identifying emotional content in text. We review the present literature concerning ETA, presenting an in-depth assessment of fundamentals, which includes sentiment evaluation, factor-based total sentiment analysis, emotion recognition, and multi-characteristic sentiment evaluation. We also speak about current datasets available for schooling, check out deep getting-to-know fashions, and provide implementation details. Subsequently, we perceive demanding situations inside the application of deep mastering fashions for ETA, which includes idea flow and shortage of guide annotation. In the end, we offer directions for future research on deeper evaluation of the emotional content material of textual content and its programs.

The advent of widespread AI in the field of corporate planning has sparked a revolutionary shift, ushering in previously unimaginable possibilities for efficacy and data-driven decision-making. This evolution, however, is not without of moral consequences. Our increasingly AI-powered environment has elevated the need of considering the ethical implications of AI application in company strategy. Incorporating sophisticated fairness criteria, bias reduction strategies, transparency evaluations, and legal framework compliance, this study examines the varied terrain of AI ethics.The research shows that achieving justice and openness in AI systems requires constant juggling, with model accuracy typically taking a backseat. Concerns about potentially unfair AI-driven conclusions are met with effective solutions for bias reduction, such as reweighing and resampling. Improving AI model interpretability by feature-level transparency evaluation using SHAP values. Also, AI has been shown to be ethically and legally compliant, as shown by its high compliance ratings with legislation like the General Data Protection Regulation and the Equal Employment Opportunity Commission’s requirements.These results provide useful insights for corporate executives, legislators, and the general public as they pertain to using AI in an ethical manner. The full potential of AI may be realized by enterprises while maintaining ethical standards if the correct balance is struck between fairness and openness, investment is made in bias prevention, and regulatory compliance is encouraged. In this study, we focus on how artificial intelligence might be reimagined from its current state as a potentially biased and opaque planning tool to one that drives ethical excellence in corporate strategy.This study acts as a catalyst for change by advocating for ethical and responsible AI deployment, which in turn creates a climate in which judgments influenced by AI are more fair, open, and lawful. These findings will serve as a springboard for other studies and activities that will push for more ethical AI development and use in company strategy.

The characteristics of interlocking blocks made with recycled expanded polystyrene (EPS) foam as a partial substitute for coarse aggregates and Metakaolin in place of fly ash are presented in this study. This is a list of the blocks' properties. In the study, a multi-criteria decision model was also used to find a solution to waste issues and rising building material costs. Pulverized EPS was used to replace coarse aggregates by 10%, 15%, and 20% by volume, respectively. To make the block lighter, metakaolin and EPS foam are used. For comparison, a control specimen was prepared. Laboratory tests were performed to compare the physical and mechanical properties of the proposed interlocking block to those of a conventional concrete hollow block (CHB) in terms of physical and mechanical properties, material cost, and construction time. Finally, a comparison of parameters between the conventional and proposed blocks revealed that the proposed block is superior in terms of material, cost, and construction time.

Noise pollution is becoming more and more acute, and hence many researchers are studying the noise attenuation effect and prevention of noise. In this study an attempt has been made to find the reduction in noise levels at National Highway 45 near peerkankaranai in Chennai. Two sensitive places were selected along NH 45 by examining attenuation of noise by providing noise barriers in the form of concrete structures. The primary goal of this project was to identify innovative design of noise barrier that has the potential to be implemented in NH 45, Chennai. Based on the research and evaluation conducted for this study, it was recommended that two innovative barrier designs be implemented in Chennai. First, a noise prediction is made at the specified location on the highway under certain traffic conditions in order to determine the noise level by measurement and decide on the barrier requirement. The installation of sound barriers is feasible enough to cause a significant decrease in noise pollution at the roads. Considerable noise attenuation is achieved by providing concrete noise barrier. The paper provides Leq at the time of traffic data recorded was 105.1 dBA at NH45 and 91.108 dBA at NH5 during the time of observation at installation of barrier the values are 70.09 dBA 79.11 dBA, respectively. Noise reduction is possible and noise reduction is predominantly reduced by providing barrier.