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The incidence of prostate cancer is increasing from the past few decades. Various studies have tried to determine the survival of patients, but metastatic prostate cancer is still not extensively explored. The survival rate of metastatic prostate cancer is very less compared to the earlier stages. The study aims to investigate the survivability of metastatic prostate cancer based on the age group to which a patient belongs, and the difference between the significance of the attributes for different age groups.

Data of metastatic prostate cancer patients was collected from a cancer hospital in India. Two predictive models were built for the analysis-one for the complete dataset, and the other for separate age groups. Machine learning was applied to both the models and their accuracies were compared for the analysis. Also, information gain for each model has been evaluated to determine the significant predictors for each age group.

The ensemble approach gave the best results of 81.4% for the complete dataset, and thus was used for the age-specific models. The results concluded that the age-specific model had the direct average accuracy of 83.74% and weighted average accuracy of 79.9%, with the highest accuracy levels for age less than 60.

The study developed a model that predicts the survival of metastatic prostate cancer based on age. The study will be able to assist the clinicians in determining the best course of treatment for each patient based on ECOG, age and comorbidities.

While the world grappled with the COVID-19 pandemic and its externalities, scientists have recommended that the global community brace against potential future pandemics. The need to build resilient systems has never been this urgent. The world, especially emerging economies, faces acute food insecurity, high food prices, failing health infrastructure and rampant misinformation spread, among others. Since blockchain technology (BCT) has been discussed in the supply chain resilience context, and it offers the potential to develop resilient systems, we aim to outline the potential of BCT to help build resilience against ongoing and future pandemics. Mainly, we focus on BCT for healthcare management, disruption management of food supply chains, human resource management, modern education and certification and governance and administration.

Influenced by the constantly changing manufacturing environment, no single dispatching rule (SDR) can consistently obtain better scheduling results than other rules for the dynamic job-shop scheduling problem (DJSP). Although the dynamic SDR selection classifier (DSSC) mined by traditional data-mining-based scheduling method has shown some improvement in comparison to an SDR, the enhancement is not significant since the rule selected by DSSC is still an SDR.

This paper presents a novel data-mining-based scheduling method for the DJSP with machine failure aiming at minimizing the makespan. Firstly, a scheduling priority relation model (SPRM) is constructed to determine the appropriate priority relation between two operations based on the production system state and the difference between their priority values calculated using multiple SDRs. Subsequently, a training sample acquisition mechanism based on the optimal scheduling schemes is proposed to acquire training samples for the SPRM. Furthermore, feature selection and machine learning are conducted using the genetic algorithm and extreme learning machine to mine the SPRM.

Results from numerical experiments demonstrate that the SPRM, mined by the proposed method, not only achieves better scheduling results in most manufacturing environments but also maintains a higher level of stability in diverse manufacturing environments than an SDR and the DSSC.

This paper constructs a SPRM and mines it based on data mining technologies to obtain better results than an SDR and the DSSC in various manufacturing environments.

The authors originally explore the factors for blockchain technology (BCT) adoption in agricultural supply chains (ASCs) to enhance circularity and understand the dependencies, hierarchical structure and causalities between these factors.

Based on an extant literature review and expert opinion, the present study identified ten enablers for adopting BCT to leverage the circular economy (CE) practices in the ASCs. Then, using an integrated interpretive structural modeling and decision-making trial and evaluation laboratory (ISM-DEMATEL) approach, hierarchical and cause–effect relationships are established.

It was observed that traceability is the most prominent enabler from the CE perspective in ASCs. However, traceability, being a net effect enabler, will be realized through the achievement of other cause enablers, such as seamless connectivity and information flow and decentralized and distributed ledger technology. The authors also propose a 12 Rs framework for enhancing circularity in ASC operations.

The paper identifies enablers to BCT adoption that will enhance circularity in ASC operations. The ISM hierarchical model is based on the driving and dependence powers of the enablers, and DEMATEL aids in identifying causal relationships among the enablers.

The study's findings and proposed 12 Rs framework may help the practitioners and policymakers devise effective BCT implementation strategies in ASCs, thereby empowering sustainability and circularity.

This study enriches the literature by identifying and modeling enablers for BCT adoption in ASCs. The study also proposes a new 12 Rs framework to help enhance ASC circularity.

Land value in urban areas in India is influenced by regulations, bylaws and the amenities associated with them. Planning interventions play a significant role in enhancing the quality of the neighbourhood, thereby resulting in a change in its value. Land is a distinct commodity due to its fixed location, and planning interventions are also specific to certain locations. Consequently, the factors influencing land value will vary across different areas. While recent literature has explored some determinants of land value individually, conducting a comprehensive study specific to each location would be more beneficial for making informed policy decisions. Therefore, this article aims to examine and identify the critical factors that impact the value of residential land in the National Capital Territory of Delhi, India.

The study employed a combination of semi-structured and structured interview methods to construct a Relative Importance Index (RII) and ascertain the critical determinants affecting residential land value. A sample of 36 experts, comprising property valuers, urban planners and real estate professionals operating within the National Capital Territory of Delhi, India, were selected using snowball sampling techniques. Subsequently, rank correlation and ANOVA methods were employed to evaluate the obtained results.

Location and stage of urban development are the most critical determinants in determining residential land values in the National Capital Territory of Delhi, India. The study identifies a total of 13 critical determinants.

A scenario planning approach can be developed to achieve an equitable distribution of values and land use entropy. A land value assessment model can also be developed to assist professional valuers.

There has been a lack of emphasis on assessing the impact of planning interventions and territorial regulation on land values in the context of Delhi. This study will contribute to policy decision-making by developing a rank list of planning-based determinants of land value.

Blockchain technology is spreading its roots and growing rapidly. With the advancement of blockchain technology, government organizations are focusing toward adopting blockchain technology. The purpose of this study is to identify the factors that affect the adoption of blockchain technology and provide a conceptual framework to adopt blockchain technology in government organizations.

To answer the research questions, this study has used systematic literature review (SLR). For achieving the quality and transparent process, this study has applied the PRISMA framework in the SLR.

With the help of SLR, this study has identified 72 factors that influence the adoption of blockchain technology. After critically examining the factors, this study has developed the conceptual framework for the adoption of blockchain technology in government organizations with four factors of the unified theory of acceptance and use of technology model and four additional factors, i.e. facilitating conditions, social influence, effort expectancy, performance expectancy, trust, transparency, cost and security.

To perform SLR, this study has used two databases: Web of Science and Google Scholar; future studies can consider other databases. This study has provided the framework but not validated it; future studies can use structural equation modeling and artificial neural network to validate the framework.

This study helps to provide insights about blockchain technology and help government and policymakers to take decisions for the adoption of blockchain technology and in determining the future action plan.

Few studies are available in the literature that have used SLR to identify blockchain technology adoption in government organizations.

Blockchain technology (BCT) can be used for a wide variety of applications across domains and can bring many benefits. BCT-based applications can be beneficial for the government as well as businesses. Despite the many promises, BCT implementation lags behind. The purpose of this research is to identify a roadmap of critical implementation challenges that influence BCT implementation by governments.

The study develops an ISM-based model spread across seven levels to analyze the inter-relationship among the selected BCT challenges. The MICMAC analysis further helps in evaluating the variables based on their driving power and dependencies.

The findings show that all challenges have a strong impact on implementing BCT. The foundation for implementation BCT is to define standards and develop appropriate regulations. Next, the findings show the need for a shared infrastructure meeting the basic technical and societal requirements and developing viable business models to advance BCT implementation. Many challenges hinder the development of blockchain applications meeting the technical and ethical requirements.

Existing research has analyzed the relationship among challenges. To the best of the authors' knowledge this is the first paper to collate these implementation challenges and incorporate them to develop a hierarchical model using interpretive structural modeling technique. The results can be used to prioritize the tackling of the challenges.

The purpose of this study is to explore the potential impact of blockchain technology on supply chain performance (SCP). This study further delves into the enablers of blockchain adoption (BA) in SCM and investigates both the direct and mediated effects of blockchain assimilation on garnering a competitive edge in the supply chain and bolstering innovation proficiency, ultimately enhancing SCP.

This study used a quantitative approach, leveraging partial least squares structural equation modelling. Empirical data were sourced from 500 validated data sets obtained through questionnaires.

The results indicate that technological readiness and knowledge sharing are key drivers for integrating blockchain into supply chains, with technology readiness displaying a substantially stronger influence. Furthermore, BA significantly enhances supply chain innovation capabilities (SCIC), competitive performance (CP) and overall supply chain efficiency. Notably, both SCIC and CP mediate and amplify the positive effects of blockchain on SCP, emphasising the vital role of innovation and competition in optimising the benefits of blockchain.

To the best of the authors’ knowledge, this study is the first to bridge the gap in the literature connecting SCM and blockchain. The established model augments the theoretical discourse on the SCM-blockchain, offering scholars a validated framework that can be adapted and built upon in future studies.

The purpose of the paper is to make use of multiple parameters namely; residual energy, closeness to centre and mobility of detection point (DP) for the selection of detection point network (DPN). In the novel method proposed, the path will have less number of DPs participating in the entire DPN.

The proposed novel method will find out the special detection point (SDP) based on three criteria, namely, the amount of mobility for DP, the amount of remaining energy and the amount of distance between two DPs. This proposed method is an attempt to resolve the network lifetime problems during the communication of DPs over a period of time. It is developed for increasing the lifetime ratio, throughput, residual energy, number of alive nodes.

The simulation results of the novel method show the improvement over the existing methods investigated based on the lifetime ratio, throughput, remaining energy and alive nodes.

In the proposed method, the communication is done between different DPs in the network. The commutation is done using SDPs only from one cluster to another cluster. It is proposed for the implementation of energy efficient data sensing in mobile communication networks.

It is a significant mechanism for energy efficient data sensing of one DP to another DP of different clusters in the network. The total energy consumed for a period of time by the network is significantly reduced from the novel method.

Blockchain is one of the most significant emerging technologies that is set to transform many aspects of industry and society. However, it has several major technical, social, legal, environmental and ethical complexities that offer significant challenges for mainstream use within the public sector. The coronavirus disease 2019 (COVID-19) pandemic has compelled many public sector employees to work remotely, highlighting a number of challenges to blockchain adoption within the Indian context signifying the pertinence of this research topic in the post-pandemic era. This study offers insight to researchers and policymakers alike on how such challenges are interdependent within this important subject.

We explored 16 unique sets of challenges selected from the literature and gathered data from nine experts from government settings, healthcare and education sectors and academia who have significant knowledge and experience of blockchain implementation and use in their respective organisations. The implementation of Interpretive Structural Modelling (ISM) and Matriced' Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) provided a precise set of driving, linkage and dependent challenges that were used to formulate the framework.

The developed ISM framework is split into six different levels. The results suggest that the bottom level consists of challenges such as “Lack of standards (C9)” and “Lack of validation (C10)” form the foundation of the hierarchical structure of blockchain adoption. However, the topmost level consists of a highly dependent challenge termed “adoption of blockchain in the public sector (C16)”. The research filters the selected set of five challenges to develop a parsimonious model and formulated six propositions to examine the impact of “lack of standard (C9)”, “lack of validation (C10)” on “security issues (C3)” and “privacy concerns (C2)”, which eventually determine individuals' “reluctance to use blockchain technology (C12)”.

This research fills a key gap in exiting research by exploring the key challenges in blockchain adoption within the public sector by developing a valuable framework to model this important topic. To the best of our knowledge, this is the first paper to address these challenges and develop a parsimonious model for challenges of blockchain adoption in the public sector settings.