Lobbying, as has been reported occasionally in various public processes in the last few decades in India, is being practiced vociferously by most of the politico-economic actors, including by the most revered ones. Their actions, however, largely remain discreet and subdued. Accordingly, very few academic literatures explaining various modus operandi of lobbying in India, is available let alone setting up any model of interaction among the economic actors. Besides, the parameters relied upon by the lobbying actors to evolve an appropriate strategy to undertake lobbying action have not been studied in Indian context. It is imperative that the lobbying actors and their strategies would significantly differ among various nations depending upon their politico-economic settings, India is no exception. The present article aims to propose a conceptual model of interaction among the economic actors in the Indian context. Further, it also examines the rationale of pivotal Indian lobbying actors for relying upon the outside and inside lobbying strategies, which thus far was limited to the lobbyists of the US and the EU in the academic parlance. Besides, it also presents the strategies and tools of lobbying to garner influences in India.

The goal of this research is to identify a tool for use in assessing Computational Thinking Skills in undergraduate Computing students. To measure the effectiveness of interventions for the development of Computational Thinking skills, there is a need for valid and reliable assessment tools. Much of the research to date has been concentrated in primary and secondary education with a shortfall in research for higher education settings. This research aims to try and address this gap and is comprised of three main stages: a literature review; a pre-pilot study; and a pilot study. The literature review identified a validated tool with potential for use with undergraduate Computing students. The pre-pilot and pilot studies invited participants to engage with the tool, but engagement was limited with low participation rates. This paper examines the reasons for this and explores possible ways to move the research forward considering these challenges.

Leukemia is a cancer originating in the bone marrow and leads to rapid proliferation of abnormal blood cells. The main objective of this study is to implement a Convolutional Neural Network (CNN) to detect and classify leukemia from microscopic cell images. The proposed framework combines a Generative Adversarial Network (GAN) that generates synthetic images of healthy cells to address class imbalance and training on a balanced leukemia dataset, with four different CNN architectures (InceptionV3, ResNet50, EfficientNetB3 and InceptionV4) - the effectiveness of this approach is validated on a Breast Cancer tumor dataset consisting of ultrasound images. Unlike prior studies that rely on standard augmentation, our approach incorporates synthetic image quality metrics (FID, IS, SSIM) to validate realism and structural fidelity.The results reveal GAN architecture achieving 16% higher performance on cell images compared to tumor images. Additionally, results obtained for each model were 76%, 80%, 75%, and 75% respectively, with RestNet50 attaining the best result. Obtained results underline potential contribution of deep learning in cancer detection and improving clinical outcomes through GAN-augmentation, addressing class imbalance effectively.

Ensuring the safety and well-being of elderly and vulnerable people in assisted living environments is a critical concern. Computer vision presents an innovative approach to predicting health risks through video monitoring, employing human action recognition (HAR) technology. However, real-time prediction of human actions with high performance and efficiency is a challenge. This research proposes a real-time HAR model that combines a deep learning model and a live video prediction and alert system, to predict falls, staggering and chest pain for residents in assisted living. Six thousand RGB video samples from the NTU RGB+D 60 dataset were selected to create a dataset with four classes: Falling, Staggering, Chest Pain, and Normal, which comprises 40 daily actions. Four state-of-the-art HAR models, namely UniFormerV2, TimeSformer, I3D, and SlowFast, were trained in a total of six variants on a GPU using transfer learning. Results are presented based on class-wise and macro performance metrics, inference efficiency, model complexity and computational cost. The optimal model, TimeSformer, achieved a macro F1 score of 95.33

Breast cancer is still one of the most prevalent causes of cancer death globally, indicating that there is a critical need for early and precise diagnostic methods. While histopathological examination remains best method for breast cancer diagnosis, its accuracy and reliability depend significantly on the experience and subjective interpretations of pathologists. This research examined three state-of-the-art deep neural network architectures—Inception-v4, EfficientNet-B0, and Vision Trans former (ViT)—for the binary classification of breast histology images into benign and malignant categories. This research utilized the BreakHis dataset containing 7909 histopathological images as the training dataset, in addition, an external validation BACH dataset was used consisting of 400 images. Diagnostic performance on the external test set was further increased using a novel stacking ensemble method that combined predictions from individual models via Random Forest as a meta-classifier. The stacking ensemble method significantly outperformed individual models, achieving an accuracy of 96.0% and ROC-AUC of 0.99 on the external BACH dataset. Robustness analysis was also conducted to evaluate performance against common imaging artifacts, and visual interpretability was provided through Grad-CAM analyses, enhancing the clinical relevance of the models. The study also checked how the models performed when there were common image issues or distortions. Grad-CAM visualizations were used to see which areas in the image the models relied on for making predictions. In this work, CNNs were used to capture fine details from the images, while the Vision Transformer helped to recognize the overall structure. Bringing both models together improved the accuracy and made the results clearer for medical use.

The Research Supervision Framework was designed to provide quality research supervision to increasing numbers of taught master's students. The framework was applied in six sessions from May 2020 to January 2023. 15 out of 59 students successfully published their research in peer-reviewed international conferences. A survey was conducted involving the 15 students to understand their experience and if the framework supported students in publishing their research. Findings suggest that all students had a positive experience of writing and presenting an international conference paper. Rewarding, exciting, significant milestone, and prized achievements are some of the words that students use to describe their experience. Results demonstrate that the students felt supported throughout their research supervision including publishing their research The research presented in this paper is of interest to faculty in the research supervision of master's students to gain insight into supporting students to publish in international conferences.

An emerging trend known as lifelogging is a process of digitally documenting and processing the data of an individual’s daily experiences. Lifelogging creates data which is continuous but can can be noisy; hence, it is challenging to find a comprehensive means of retrieving events or moments of interest to the public. This research proposes a deep learning framework to improve memory retrieval from lifelogging data. The proposed framework combines text-image embeddings and ensembles of a zero-shot deep learning model. The framework is implemented using three versions of the Contrastive Language-Image Pre-training (CLIP) model based on the combination of 12 datasets created by seven users containing more than 100,000 images. The results are evaluated based on the average precision@k metric for different values of k. Specifically, on the given dataset, the ensemble model consisting of ResNet50x64 and ViT-L/14 in the ratio 3:1 gives highest precision of 0.90 at k = 5. The proposed retrieval framework can be used to help people with Alzheimer’s and other forms of dementia for recalling useful information.

A continuous photo-flow protocol for the anaerobic oxidative cleavage of styrenes using nitroarene as the oxygen transfer reagent.

This innovative practice full paper describes a home-based summer family learning programme, STEM Play & Learn, designed to support kindergarten-aged children and their families by engaging them in Science, Technology, Engineering and Mathematics (STEM) activities. 'Summer Learning Loss' is the phenomenon where children experience a decrease in academic knowledge and skills when they take an extended break from education, particularly during the summer holidays. Children from lower socio-economic status backgrounds are disproportionately affected by summer learning loss. This research investigates the efficacy of a novel STEM Summer Family Learning Programme for 4-6-year-old children in a socio-economically disadvantaged community. The novelty of our programme centres around the practice and pedagogy delivered in the child's home with parental engagement at its core. The programme is delivered once a week over six weeks by Home Visitors. Child-directed open-ended play-based STEM activities are modelled to encourage children to develop higher-order thinking skills. Parents and children are encouraged to explore these educational STEM Play and Learn activities further between visits. Through a community action research methodology, this research employs a cyclical process of observing, reflecting, acting, evaluating, and modifying. Results of parent and child evaluation data show promise in improving the children's developmental skills and positively influencing the home learning environment. Results also indicate there was an increase in the parents' confidence in teaching their children STEM at home. The findings contribute insights into how an innovative STEM family learning programme for early years educators and parents enhances educational outcomes for kindergarten-aged children and mitigates summer learning loss.

Since Jeanette Wing’s influential 2006 article, research into developing Computational Thinking (CT) skills has surged. Despite ongoing debates about CT definitions and components, reliable and valid assessment tools are crucial for measuring CT skill impact and evaluating related interventions. This systematic literature review seeks to identify CT assessment tools suitable for undergraduate computing students, by investigating the central question "What tools are available for the assessment of CT skills?". A comprehensive search of academic databases for CT assessment publications since 2006 yielded 202 unique articles. After applying exclusion criteria, 72 relevant articles were analyzed. The selected publications were categorized by tool type, and focus, examining tool reliability and validity. This paper presents initial findings from this review.

Herein we report a flow chemistry method for the synthesisof tert-butyl nitrite. The flow process provided95% yieldin a very short residence time of 1 min corresponding to a space timeyield of 13 g/h/mL. In addition, we used tert-butylnitrite for metal-free stereoselective nitration of alkenes. The reportedprocess had a higher batch cycle time of 12 to 24 h, limiting itsapplication for commercial production. We developed a continuous-flownitration process using a fixed bed column reactor. In the process,we provided the continuous air purging to ensure adequate supply ofoxygen in the reaction. This has helped us to reduce the reactiontime from 12 h in batch to 3 min in flow. The optimized nitrationprocess conditions were applied on a series of styrene and acrylatederivatives. The methodology has potential for industrial-scale productionof tert-butyl nitrite and its use as a nitratingagent.

Computing Support is the provision of academic supports such as individual tutoring and support classes to students studying computing at third level. Students can struggle with computing as it requires practice involving trial and error. This work proposes a research informed tutoring framework to support computer science students at third level. The tutoring framework combines three pillars; staff and training, pedagogies and activities. Support is put in place to help students develop technical and programming skills. Essential tutoring is provided for those who might otherwise drop out of college. The framework was applied to first and second-year undergraduate programmes and to the first semester in Higher Diploma conversion programmes. Results demonstrate that students' attendance has increased by 14%. In addition, student feedback shows that the framework supports students in computer science. This framework is a useful resource for academic managers that wish to implement computing support at third level.

Academic Support 360 Framework is a strategy to support the professional development of faculty. Such a strategy can be applied to solving full-time and adjunct faculty issues that relate to pedagogy, systems and processes in Higher Education. The faculty that this framework supports includes professors, lecturers, programme/course directors and classroom assistants. Adjunct faculty are part-time lecturers who complete many of the same tasks as a full-time professor/lecturer whilst also working in industry. For both new full-time and adjunct faculty, understanding pedagogy, systems and processes is a challenge. New faculty may require support around teaching effectively, creating assessments, using the Learning Management System, entering grades into the student management system and so on. For adjunct faculty, the challenge is compounded by their time commitments to both their professional and academic careers. Programme/Course Directors need support in understanding college processes such as course validation, exam boards, reading broadsheets, student requests to defer modules or move courses and more. Classroom assistants require induction, training and support on the Learning Management System, in-class student queries and payroll systems. This research proposes an Academic Support 360 Framework that provides the knowledge, skills and competence for full-time and adjunct faculty to apply pedagogy, use systems and follow processes. The framework is comprised of Induction, Mentoring, Online Resources and Just-in-Time training. This framework provides continual support to faculty which commences before the start of a semester, continues throughout the teaching weeks and is available during the grading process after the end of the semester. The framework shows promise as demonstrated by the increase in the number of users, induction sessions, attendance at weekly Q&A sessions, video views, queries in teams' channels and induction events. This research is useful to academic management that would like to induct and train full-time and adjunct faculty in systems, processes and teaching and learning.

Remember the old saying “Crisis doesn’t build character, it reveals it”. For many business houses, the COVID-19 pandemic is a crisis of a kind they haven’t faced before. It has put a screeching halt to economic activities and movement of people, affecting both short- and long-term objectives of organizations or even threatening their whole existence altogether in some cases. This is concerning as the “2021 Deloitte Global Resilience Report” states that more than three-fourths of Chief Executive Officers (CEOs) expect more disruptions to follow owing to the looming climate crisis (Deloitte, 2021). They also believe that environmental sustainability and climate change will be the most critical societal issues for the businesses to tackle in the coming decades. The challenges ahead of us are mammoth, and clearly there is no user manual or playbook to deal with them. Organizations need to make fundamental changes in the way they operate and prioritize what to focus on as we move forward.Business houses have been traditionally designed to accrue wealth to shareholders, and those who lead the organizations are often incentivized to improve the return on investment. In contrast, building a resilient and sustainable organization that goes on to withstand crises like COVID-19 and continue to serve society for generations to come requires forgoing short-term profits for the sake of more sustained performance in the long run. One thing that COVID-19 has revealed clearly is that companies with a strong commitment toward people, the environment and communities have overcome the crisis rather spectacularly. This perspective chapter highlights how companies can leverage culture to build sustainable business practices. Companies reflect the values of the leaders who run them; hence, organizational culture can’t be discussed in the absence of leadership.

E-Commerce is the activity of electronically purchasing or selling products in an online platform. E-Commerce recommender systems provide suggestions of products based on the consumer sentiment and ratings. There is often a mismatch between consumer rating and their sentiment. Identifying the accuracy of the mismatch is a challenge in machine learning. This research proposes a Novel Hybrid Machine Learning Framework to Recommend E-Commerce Products based on consumer sentiment and product descriptions. This proposed framework combines a text embeddings model, sentiment analysis model and a rating engine. The text embeddings model is implemented using gensim doc2vec for consumer reviews and product descriptions. Further it uses neural networks for capturing the consumer product interactions for collaborative filtering. The sentiment analysis model is implemented by inputting distributed text embeddings into neural networks that are trained to capture content feature of products and sentiment of consumer evaluations. The rating engine is implemented by aggregating several embeddings as attention weights for consumers and products, then outputting the prediction score for the consumer–product interaction. This research makes use of the real-world Amazon product category semi structured baby and digital music semi structured datasets, each of which contains information on consumer reviews and product metadata. Mean absolute error (MAE) and root mean-square error (RMSE) are considered to evaluate the recommendation performance, thereby measuring the accuracy of prediction ratings. Experimental results on Amazon distinct product dataset demonstrate an accuracy metric MAE value of 0.5909 and RMSE value of 0.8080. These results demonstrate that the proposed framework performs better on rating prediction in enhancing consumers experience in order to find their preferences for e-commerce products. Consequently, e-commerce platforms can enhance sales and consumer satisfaction by using machine learning frameworks to recommend which products a consumer will be interested in based on their past purchasing behavior.

Customer Lifetime Value (CLV) measures the average revenue generated by a customer over the course of their association with the firm. The Recency Frequency Monetary (RFM) Model is used to calculate the CLV. Recency is the latest item purchased. The number of times an item is purchased is the Frequency. Monetary is the price spent on the product by customers. CLV is measured using previous customer transactions of RFM factors. This research proposes a Deep Learning Customer Retention Framework to predict the Customer Lifetime Value in order to retain customers through an effective Customer Relationship Management strategy. The proposed framework combines clustering and regression models to analyze the significant variables for predicting the lifetime value of customers. Customers are categorized into levels such as high medium and low profitable customers based on their lifetime value. This research compares Deep Neural Network models, Machine Learning models and Probabilistic models. The Deep Neural Network is ANN. The machine learning models are Linear Regression, Random Forest, Gradient Boosting. The probabilistic models are Gamma-Gamma and Betageometric/negative binomial. The models are compared in order to predict the level of profitable customers. Results demonstrate that Deep Neural Network (DNN) model outperforms the other models with 71% accuracy. Improved prediction model for CLV and segmentation assists the firms to plan and decide relevant CRM strategies such as customer profitability analysis, cross-selling and one to one marketing for the future.

Practicing yoga poses in a home-based environment has increased due to Covid19. Yoga poses without a trainer can be challenging, and incorrect yoga poses can cause muscle damage. Smart home-based yoga teaching systems may aid in performing accurate yoga poses. However, the challenge with such systems is the computational time required to detect yoga poses. This research proposes a real-time machine learning framework for teaching accurate yoga poses. It combines a pose estimation model, a pose classification model, and a real-time feedback mechanism. The dataset consists of five popular yoga poses namely the downdog pose, the tree pose, the goddess pose, the plank pose, and the warrior pose. The BlazePose model was used for yoga pose estimation which transforms the image data into 3D landmark points. The output of the pose estimation model was then passed to the pose classification model for yoga pose detection. Four machine learning classifiers namely, Random Forest, Support Vector Machine, XGBoost, Decision Tree, and two neural network classifiers LSTM and CNN were evaluated based on accuracy, latency and size. Results demonstrate that XGBoost outperforms other models with an accuracy of 95.14 percentage, latency of 8 ms, and size of 513 KB. The output of the XGBoost Classifier was then used to correct yoga poses by displaying real-time feedback to the user. This novel framework has the potential to be integrated into mobile applications which can be used by people for the unsupervised practice of yoga at home.

Nitrate esters are important organic compounds having wide application in energetic materials, medicines and fuel additives. They are synthesized through nitration of aliphatic polyols. But the process safety challenges associated with nitration reaction makes the production process complicated and economically unviable. Herein, we have developed a continuous flow process wherein polyol and nitric acid are reacted in a microreactor to produce nitrate ester continuously. Our developed process is inherently safer and efficient. The process was optimized for industrially important nitrate esters containing two, three and four nitro groups. Substrates include glycol dinitrates: 1,2-propylene glycol dinitrate (PGDN), ethylene glycol dinitrate (EGDN), diethylene glycol dinitrate (DEGDN), triethylene glycol dinitrate (TEGDN); trinitrates: trimethylolethane trinitrate (TMETN), 1,2,4-butanetriol trinitrate (BTTN); and tetranitrates: erythritol tetranitrate (ETN). The optimized process for each molecule provided yield >90 % in a short residence time of 1 min corresponding to a space time yield of >18 g/h/mL of reactor volume.

Shuttlecock tracking is required for examining the trajectory of the shuttle-cock in badminton matches. Player Service Fault Detection identifies service faults during badminton matches. The match point scored by players is analyzed by the first referee based on the shuttlecock landing point and player service faults. If the first referee cannot decide, they use technology such as a third umpire system to assist. The current challenge with the third umpire system is based on the high number of marginal errors in predicting the match score. This research proposes a Machine Learning Framework to improve the accuracy of Shuttlecock Tracking and player service fault detection. The proposed framework combines a shuttlecock trajectory model and a player service fault model. The shuttlecock trajectory model is implemented using a pre-trained Convolutional Neural Network (CNN), namely Track-Net. The player service fault detection model uses Google MediaPipe Pose. A Random Forest classifier is used to classify the player's service faults. The framework is trained using the badminton world federation channel dataset. The dataset consists of 100000 images of badminton players and shuttlecock positions. The models are evaluated using a confusion matrix based on loss, accuracy, precision, recall, and F1 scores. Results demonstrate that the optimized TrackNet model has an accuracy of 90%, which is 5% more with 2.84% less positioning error compared to the current state of the art. The player service fault detection model can classify player faults with 90% accuracy using Google MediaPipe Pose, 10% more compared to the Openpose model. The machine learning framework for shuttlecock tracking and player service fault detection is of use to referees and the Badminton World Federation (BWF) for improving referee decision-making.

Since the Government is talking about revamping the higher education structure in our country under the generic basket of New Education Policy (NEP), Management Education seems to provide the basic structural element towards the same. Moreover, the focus on the skill element in the ‘being implemented’ policy further accentuates the need and role of management education across various disciplines irrespective of whether already existing or thought off in the years to come depending upon the shape and character of the Indian Economy supposed to lead the world in a way where predictions are a bit difficult. The current situation is like ‘Amrit Manthan’ and this will take some time before things settle down to pave way for a clearer path to such an issue! But then, as it is well known across academic and industry peers, no other education is complete without management education. This is so because two important ingredients: resources and the environment, have no alternatives. They will remain forever and will keep playing a vital role towards maintaining what we intend to call ‘Grey’ shade of Management Education. This article covers some very basic issues that may help us understand the colour ‘Grey’, synonymous with the extensive coverage and maturity that we all envisage in our country of multiple colours. Moreover, it looks to some thoughts on the ‘Management’ field itself being grey!