Thermo-volumes allow the design engineer to expediently understand the thermal resistance of a given cooling solution (an indicator of performance) along with its flow resistance (an indicator of the pumping power, or energy consumption, which will be required by the fluid handler). In the present work, we expand upon thermo-volumes by including the lifetime exergy cost (in units of Joules of availability destroyed) as a means to enable the consideration of resource consumption (and thus the environmental sustainability) of the cooling solution. To achieve these exergo-thermo-volumes, we reinterpret previous definitions of thermo-volumes in terms of the entropy generated during heat transfer and fluid flow. The Guoy–Stodola theorem is used to convert this entropy generation into an ‘operational’ exergy loss. Next, based on the material choice and assembly processes used in creating the product, an embedded exergy consumption that accounts for the amount of exergy destroyed during extraction, transportation and disposal of the material is attached to the operational exergy loss. Thus, the total ‘cradle-to-cradle’ exergy loss of the solution is devised. In this framework, the optimal solution will be that which destroys the minimal amount of exergy. Correspondingly, instead of relying upon the coefficient of performance (which is focused on operational consumption), we propose evaluation of cooling solutions in terms of the heat removal capacity per unit lifetime exergy consumption. The paper concludes by illustrating applicability of the method to the design of an enterprise server. It should be noted that although the paper is focused on electronics cooling solutions, the methodology is designed to be sufficiently general for use in any thermal management application. Copyright © 2009 John Wiley & Sons, Ltd.

Human being normally uses natural language in form of written or spoken in their daily life. It is an entirely concept based on Artificial Intelligence, Computer Science and Linguistic resources. In Indian languages, Gujarati is an Indo - Aryan language with rich and high inflection. In these languages, several words have common root that's need to be reduced by using stemmer. Stemmer is used to reduce a word to its root without understanding of semantic meaning. In Natural Language Processing, it is initial step to identify the root or base of the word. A word can be simple or compound. For example, the word is simple word because it can't be decomposed. While, the word is a compound word, because the word is made up of two parts: root and suffix. We have developed an algorithm for reducing affixes from the Gujarati words and get optimal output as compare to the previous hybrid algorithm. The GUJSTER (GUJarati STEmmeR) also check the stem word into online Gujarati Dictionary.

Cyber-Physical Systems (CPS) are increasingly integral to critical infrastructure, industry, and daily life, but their growing complexity and connectivity render them susceptible to a wide array of cyber threats. This paper explores advanced defense mechanisms designed to enhance the security of CPS, focusing on innovative approaches that address both cyber and physical vulnerabilities. Key strategies discussed include the implementation of robust anomaly detection algorithms, integration of machine learning for predictive threat analysis, and the development of resilient system architectures that can withstand and recover from attacks. Additionally, the paper highlights the importance of adaptive security frameworks that can evolve in response to emerging threats, as well as the role of cross-disciplinary collaboration in creating comprehensive defense strategies. By leveraging these advanced defense mechanisms, the security and reliability of Cyber-Physical Systems can be significantly improved, ensuring their safe and effective operation in an increasingly interconnected world.

The learning tools necessary to prepare the next generation of students must be shaped by the socio-economic needs of the 21st century. The needs of the 21st century – from rebuilding city scale physical infrastructure to personalized healthcare – not only require learning from the wealth of global information available on the Internet, but also the building of a strong grounding in fundamentals. History has shown that the depth in fundamentals has been achieved through conventional books. Indeed, authoritative books in physical fundamentals have been penned in the 19th century and early 20th century. We present a 21st century cyber-physical learning platform that combines the best of physical books with information systems. The systemic instantiation of the platform combines modern optics and computing to view books, scan objects, and enable interactive learning – while simultaneously benefitting from the vast pool of information on the Internet. The hybrid learning platform preserves the best of the past to pave the way for the future. It also enables future research such as meta-data and descriptive tagging of the large number of images available on the web.

This paper explores the potential of Twitter, a popular social media platform, as a tool for predicting election outcomes. Sentiment analysis has emerged as a powerful tool for predicting election outcomes, with numerous studies showcasing its effectiveness in various countries. For instance, research has utilized sentiment analysis to forecast election results in nations like the USA, India, Pakistan and other countries, demonstrating the utility of social media data in gauging public opinion and predicting electoral results [1]. Elections in India are always considered important events that most people look forward to the rapid growth of social media in the past has provided end users with powerful tools to share their ideas. Twitter, which is one such platform, provides daily updates on political events through various hashtags and trends. People react to political events and give their opinions. Our approach is to collect tweets from top political parties contesting the Gujarat Assembly Elections 2022, and then calculate sentiment scores. The database includes a variety of recent and well-liked tweets about a specific political party. Party tweets are generated with specific keywords like “BJP”, “AAP”,“Congress” and so on. In the context of India, Twitter sentiment analysis tools and classification have been used to predict the outcomes of state assembly elections, underscoring the potential of social media data in forecasting electoral results within the country [2]. We used standard machine learning algorithms like VADER sentiment analyzer on Random Forest and Decision Tree for our classification and testing data to classify tweets as positive and negative. As a result, this work uses sentiment analysis to evaluate tweets gathered from Twitter and forecast election outcomes. This work shows the growing influence of social media on politics and the feasibility of using such platforms for predictive analysis. The findings of this study could provide valuable insights for political parties, policymakers, and researchers interested in the intersection of social media and politics. Random Forest and Decision Tree models performed well in predicting election outcomes based on sentiment analysis on Twitter data with 89% and 86% respectively.

Healthcare CDSS uses machine learning and analytics for real-time insights, risk projections, and decision support. The chapter covers CDSS development, deployment, and evaluation using machine learning for early illness prediction and personalised care. NLP analyses unstructured medical data to identify risks and schedule staff efficiently. Data scientists detect abnormalities to prevent healthcare fraud. CDSS machine learning speeds drug discovery with diagnoses, treatments, and insights. CDSS machine learning and analytics improve patient care, personalisation, and outcomes.