Institute of Chartered Financial Analysts of India

The wireless sensor network (WSN) contains a huge number of cost-effective and small energy-constrained nodes for network communication. Moreover, the clustering is considered as a major part of WSNs routing. Hence, this paper develops the Cosine Lotus Effect Algorithm (CLEA) for Cluster Head (CH) selection, and the Fractional Cosine Lotus Effect Algorithm (FCLEA) for routing. Initially, the network simulation is carried out, and the CH is done using the proposed CLEA with the fitness components such as link lifetime (LLT), delay, inter, and cluster distance, trust factor, and energy, in which, the radial basis function network (RBFN) is employed for energy prediction. The proposed FCLEA is utilised for routing, where fitness factors such as energy, delay, distance, and trust factors are utilised. Moreover, the FCLEA-based routing obtained the better average residual energy, distance, and throughput of 1.719 J, 7.068 m, and 431.8.

Automated reporting of chest radiographs is an emerging task at the intersection of medical image analysis and natural language generation. In this problem, a model receives a chest X-ray and produces a clinically meaningful textual description, including the presence or absence of respiratory diseases. Conventional systems rely on an encoder–decoder pipeline in which a convolutional neural network (CNN) encodes the image and a recurrent neural network (RNN) decodes the representation into a report word by word. Recent work has shown that reinforcement learning can further align generated reports with sequence-level objectives. To overcome this limitation, the proposed CXR-CapsNet model adapts deep reinforcement learning with embedded rewards to the domain of chest radiographic report generation by modifying the underlying CNN for medical imaging and by introducing an enhanced reward mechanism based on clinical and linguistic evaluation metrics. The policy network provides local guidance for predicting the next token in the report, while the value network provides global guidance over possible continuations of the partially generated sequence. A reward network, built on visual–semantic embeddings, combines similarity in the joint image– text space with a linear combination of metrics such as BLEU, CIDEr, ROUGE, and METEOR to better reflect the quality of the report. The policy and value networks are first pre-trained separately, with the reward network trained independently. In a benchmark chest radiographic data set, the proposed CXR-CapsNet achieves performance comparable to or better than strong baselines, while offering a substantial improvement over previous reinforcement-learning-based approaches for the generation of medical reports.

Research methodology Information from secondary sources was used to develop this case study. The sources of the data include the organization’s website, annual reports, news releases, published reports and online documents. Case overview/synopsis As of 2024, Uganda was one of the world’s leading producers and consumers of bananas. Banana was a major food crop in the country, with approximately 75% of farmers cultivating it.9 It contributed up to 25% of the daily calorie intake of people in the rural areas. [1] A tremendous amount of bananas had to be harvested to meet the demand, and a substantial amount of trash was generated in the process, as the stems of the plants had to be severed and discarded. This trash ended up in landfills and decayed there, producing methane and polluting the environmental ecosystem. While working on a project on the banana plant value chain, Juliet Tumusiime (Juliet) saw the enormous amount of trash generated from banana cultivation. She observed farmers throwing away the banana stems after harvesting the fruit, unaware of the potential hidden in what they considered trash. This sparked in her an interest in exploring ways to recycle the discarded stems. Another practice that bothered her was the use of synthetic hair extensions. This was because she realized how these hair extensions, made of microplastics, were adding to the plastic pollution in Uganda when they were discarded after use. As of 2024, the country was generating 600 tons of plastic waste daily, of which only 6% was collected or recycled.7 Juliet’s concern for the environment and her passion to create something valuable out of the banana waste motivated her to start Cheveux Organique Limited (Cheveux) – a company to create eco-friendly hair extensions from banana fibers. However, converting banana stem into fiber was a labor-intensive process that consumed time and involved manual work. Every stage in the production process increased the costs, with the result that the final product had to be sold at a price that most Ugandans could not afford. Cheveux produced about 5 kg of banana fiber hair a month, priced at approximately US$50 per 150 gm. This was much higher than the price of imported synthetic extensions. The major challenge for Juliet was to make the product affordable to Ugandans. Cheveux was not the only player in the market. There was stiff competition from companies who were operating in the natural hair extension segment in Uganda. Also, there was competition from synthetic hair manufacturers. Despite these challenges, Juliet was hopeful of leading the hair extension industry with her eco-friendly hair extensions to keep the environment free of plastic. Juliet stood at a crossroads as she was considering scaling up operations to meet the growing demand. One way forward involved investing in automation – an option that could reduce production costs, increase production and make the products competitive in regional and international markets. However, adopting this route would result in lower job creation and displacement of some employees. How will Juliet weigh the long-term sustainability and growth of her business against her commitment to job creation and community empowerment? The question remains: How will Juliet find a path to scale up her business amidst balancing innovation and inclusion? Complexity academic level This case is intended for use in MBA, Post-Graduate (PG)/Executive level programs as part of the Business Strategy, Entrepreneurship and Sustainability courses.

This study explores the relationship between carbon performance (CP) and financial performance (FP) in Indian firms, with a further examination of the moderating role of CEO duality. Using data from 130 listed non-financial firms in the BSE500 index from fiscal year ending 2016 to 2022, the study employs the system generalized method of moments (SGMM) model based on information from annual reports, sustainability reports and the Ace Equity Database. Here, CP is proxied by carbon intensity (CI), suggesting that higher CI is associated with lower CP. The findings reveal that a unit increase in CP leads to an increase of 0.002 units in ROA and a decrease of 0.001 units in MBR. Furthermore, the positive moderating role of CEO duality between CP and ROA is established, whereas CEO duality is found to have an adverse moderating role between CP and MBR. Given the limited number of empirical studies on this subject in the Indian context, this study contributes to the literature by highlighting the heterogeneous influence of carbon management on accounting and market-based firm performance (FP). These insights offer practical guidance for corporate managers aiming to align sustainability initiatives with financial decision-making.

Traditional approaches heavily rely on sentiment polarity classification. This approach groups opinions as ”positive,” ”negative,” or ”neutral”; however, while effective for the implementation of a basic opinion mining strategy, this rudimentary classification fails to identify the large amounts of diverse, rich, and emotional data necessary for understanding the huge volume and variety of sentiments being expressed by human beings through large amounts of unstructured data. The growth of social media, online reviews, and digital communication has presented a need for advanced Context-Aware Emotion Intelligence Systems capable of detecting complex contextual emotional patterns in large heterogeneous data sources. This research proposes a scalable transformer-based emotion intelligence framework for contextual emotion classification using distributed Big Data processing. The framework integrates distributed preprocessing, transformer-based contextual representation learning, and emotion dependency modeling to classify text into six target emotional categories. The proposed framework utilizes distributed computing infrastructure and transformer-based contextual embeddings to efficiently process large-scale unstructured textual data and generate contextual emotion-aware sentiment intelligence. Unlike traditional polarity-based sentiment analysis systems, the suggested framework combines distributed Big Data processing with transformer-based contextual emotion modelling to develop a scalable and contextualized Emotion Intelligence Model. The emotion intelligence framework gets a macro-F1 score of 0.91 on the GoEmotions (58,009 instances) and SemEval emotion datasets; it outperforms emotion-aligned and polarity-based baseline models by about 4-6% and more than 10%, respectively. The distributed Spark-based implementation demonstrates scalable processing behaviour with substantial processing-time reduction under large-scale distributed workloads. The proposed framework formulates emotion prediction as a dominant-emotion single-label multi-class contextual classification problem while incorporating inter-emotion dependency modelling for enhanced contextual representation learning.