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This research explores the application of machine learning to optimize pricing strategies in the aftermarket sector, particularly focusing on parts with no assigned values and the detection of outliers. The study emphasizes the need to incorporate technical features to improve pricing accuracy and decision-making.

The methodology involves data collection from web scraping and backend sources, followed by data preprocessing, feature engineering and model selection to capture the technical attributes of parts. A Random Forest Regressor model is chosen and trained to predict prices, achieving a 76.14% accuracy rate.

The model demonstrates accurate price prediction for parts with no assigned values while remaining within an acceptable price range. Additionally, outliers representing extreme pricing scenarios are successfully identified and predicted within the acceptable range.

This research bridges the gap between industry practice and academic research by demonstrating the effectiveness of machine learning for aftermarket pricing optimization. It offers an approach to address the challenges of pricing parts without assigned values and identifying outliers, potentially leading to increased revenue, sharper pricing tactics and a competitive advantage for aftermarket companies.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

The Dissolved Oxygen (DO) level of the rivers is a unique indicator of aquatic health. Decrease of DO values will adversely impact the biotic community sustained by the aquatic ecosystems. We analysed the DO level at six sites of the River Ganges adjacent to the megacity of Kolkata during the COVID-19 induced lockdown period in April 2020 and compared it with data collected from 2015 to 2019 without altering the sampling and monitoring protocols. The normal decreasing trend of DO during the time phase of 2015–2019 suddenly exhibited a sharp spike during the COVID-19 lockdown phase. The DO values hiked significantly during COVID-19 lockdown phase in all the stations with the spatial trend Botanical Garden (6.44 mg/l) > Ramkrishna Ghat (6.32 mg/l) > Shibpur Ghat (6.21 mg/l) > 2nd Hooghly Bridge (6.19 mg/l) > Princep Ghat (6.09 mg/l) > Babughat (5.79 mg/l). The data pattern confirms a straightforward improvement of water quality during the lockdown phase in the context of the DO level, which is congenial for aquatic biodiversity of the River Ganges at the local scale.

This paper aims to present a comprehensive overview of open-access publications by Indian non-profit organizations over the past two decades. The study explores the growth, licensing patterns, citations, authorship patterns and other parameters to understand the scholarly output.

The study involves data collection from OpenAlex scholarly catalog. Data analysis uses OpenRefine, a data carpentry tool, to examine and extract various aspects of scholarly output. A total of 89,149 scholarly outputs from 2004 to 2023 were analyzed using statistical and bibliometric methods.

The findings revealed a positive publication growth trend, with 57.74% open access. Gold OA dominates, with 69.61% of papers in 2023. Licensing patterns reveal that 63.75% of OA papers have licenses. Most papers have multiple authors, with 24.83% of over ten authors receiving 60.12% of citations. “Medknow” is the leading publisher, and “The Indian Journal of Ophthalmology” tops journals. Contributions from repositories like SSRN and PubMed are significant. The study also examines citation patterns across different OA types and identifies the top 30 research areas, emphasizing “Medicine” as the most prevalent.

The identified trends and patterns offer valuable insights for policymakers, researchers and organizations to enhance accessibility and impact. This study stresses sustained efforts for transparency and democratization of knowledge in the non-profit sector.

This study filled a gap in existing research by focusing on Indian non-profits, highlighting their roles and impacts often overlooked in scholarly literature. This study provides insights into the growth of open-access publications and their implications in the non-profit sector.

Soil is an essential component of road construction and is used in the form of subgrade materials. It ensures the stability and durability of the road under adverse conditions; being one of the important parameters, poor judgment of the engineering properties of soil can lead to pavement failure. Geopathic stress (GS) is a subtle energy in the form of harmful electromagnetic radiation. This study aims to investigate the effect of GS on soil and concrete.

A total of 23 soil samples from stress zones and nonstress zones were tested for different engineering properties like water content, liquid limit, plastic limit, specific gravity and California bearing ratio. Two concrete panels were placed on GS zones, and their quality was monitored through nondestructive testing for a period of one year.

The result shows that the engineering properties of soil and pavement thickness are increasing in stress zones as compared with nonstress zones. For concrete panels, as time passes, the quality of the concrete gets reduced, which hints toward the detrimental effect of GS.

This research is a systematic, scientific, reliable study which evaluated subgrade characteristics thus determining the detrimental impact of the GS on soil and pavement thickness. On a concluding note, this study provides a detailed insight into the performance of the road segment when subjected to GS. Through this investigation, it is recommended that GS should be considered in the design of roads.

This qualitative research examines the varied reasons for relocation to old age homes (OAHs) in contemporary India. The purpose of this study investigates the acceptance of institutional living in Lucknow (a Tier II city of India) and whether migration to OAHs is a voluntary decision. This study also examines the lifeworld of the older adult in these OAHs in an attempt to find out whether OAHs are conducive to positive ageing. Derivatively, the authors study their engagement/time use pattern and social networking patterns in the OAHs. Finally, the research seeks to learn whether OAHs are slowly substituting older adult care given within the family by offering the best of the facilities and services.

This qualitative research was conducted in two private OAHs in Lucknow, India. The findings of the study are based on 28 qualitative interviews conducted with the inmates, administrative staff and caretakers. The interviews were unstructured and open-ended and were supported by observations. The observation was not only made of the social setting but also the reaction of the participants. The idea was to develop an emic view of the subject by exploring valid narratives. Pseudonyms were used to report the finding so as to maintain the confidentiality of the research subjects.

This research moves beyond the traditional wisdom that people move to OAH because of the push factors within the family. OAHs in India have evolved over the years and high-end OAHs are equipped with modern amenities to cater to the upper class in their twilight years. Residents were found to lead active lives in OAHs and their common habitus and bonding capital helped them to face the vagaries of old age more confidently. Their active life and membership in various civic organizations challenge the contention of the role theory that the aged are more prone to lose rather than gain roles.

The originality of the research lies in the fact that the authors are extending the arguments made by the role theory of social ageing. The theory proposes that aged people are more likely to lose out roles rather than gain new ones. This study finds that the elderly tend to live a very active life in OAHs and engaged various civic organizations. Although they may lose/voluntarily give up the roles like the head of the household, spouse, etc., they acquire new roles in the context of OAHs.

This case describes the challenges faced by Amul in organising dairy farmers into a co-operative and creating continuous opportunities for value addition. Participants in the case discussion are required to review the developments in the organisation and recommend a strategy for the future.

The world is seeing a large deficit in employable workforce. An employable workforce is developed with appropriate combination of academic and practical skills. Practical skills are better developed with industry intervention rather than in classrooms. Changing trends of economic activity have steadily changed the business thought. The purpose of this paper is to assess the role of industry in developing employability by studying employability from the value chain and “Demand-Supply” of workforce models. The scope is limited to Indian context. This paper is a part of the research on factors influencing employability in India.

Literature review, expert interviews and authors’ own experiences and understanding.

It has been found that most of the countries in the world are facing a shortage of skilled and employable workforce. We examined various literature content and studied specific instances in the Industry. The study has shown that in the cases where industry has been actively involved in identifying training needs, the supply of manpower has been adequate. So, it is found that industry's role in employability is crucial and impacts on other macro policies for human development.

As this paper is a part of a larger study on “Factors impacting employability in India,” it is of paramount importance to Indian researchers, students and policy makers. The paper and the research are oriented to identifying causes to the problem of employability, so that systemic changes can be identified.

This paper is based on large amount of literature that is existing in various sources. All the literature has been thoroughly read and assimilated. Suitable references have cited and others have been acknowledged. Most importantly this is an original work of the authors and their views.