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This paper develops a decision-analysis model to prioritize and select the site to establish a new hospital over different indicators such as cost, market conditions, environmental factors, government factors, locations and demographics. In this way, an integrated model is proposed under the intuitionistic fuzzy information (IFI), the standard deviation (SD), the rank-sum (RS) and the measurement of alternatives and ranking using the compromise solution (MARCOS) approach for ranking hospital sites (HSs).

The IF-SD-RS model is presented to obtain the combined weight with the objective and subjective weights of diverse sub-criteria and indicators for ranking sites to establish the hospital. The IF-MARCOS model is discussed to prioritize the various sites to establish the hospital over several crucial indicators and sub-criteria.

The authors implement the developed model on a case study of HSs assessment for the construction of new hospital. In this regard, inclusive set of 6 key indicators and 18 sub-criteria are considered for the evaluation of HSs. This study distinguished that HS (h₂) with combined utility function 0.737 achieves highest rank compared to the other three sites for the given information. Sensitivity analysis is discussed with different parameter values of sub-criteria to examine how changes in weight parameter ratings of the sub-criteria affect the prioritization of the options. Finally, comparative discussion is made with the diverse extant models to show the reasonability of the developed method.

This study aims to develop an original hybrid weighting tool called the IF-SD-RS model with the integration of IF-SD and IF-RS approaches to find the indicators' weights for prioritizing HSs. The developed integrated weighting model provides objective weight by IF-SD and subjective weight with the IF-RS model. The model presented in the paper deals with a consistent multi-attribute decision analysis (MADA) concerning the relations between indicators and sub-criteria for choosing the appropriate options using the developed IF-SD-RS-MARCOS model.

This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector.

From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure.

The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible.

Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

The success of construction projects can be indicated by measuring their performance. For effective project performance (PP), the successful execution of a construction projects is very important. A systematic review of the literature on performance areas and performance assessment models was undertaken. The purpose of this paper is to develop a mathematical formulation for construction PP areas to suit the Indian context by modifying the current project quarterback rating (PQR) model.

Based on the literature, the PQR model has not been validated for suitability in the Indian context. To validate the PQR model and modify the same for the Indian context, a survey instrument was used to collect data on performance areas and a multivariate data analysis technique was carried out to develop a modified model. Delphi technique was used to assign the weights for each performance metric in performance areas.

This study concluded the importance of three additional performance areas, namely, productivity, stakeholder satisfaction and environment for assessing PP for Indian construction projects. It also identified the interrelationship between the performance areas and the PP.

The developed modified PQR model (MPQR) will guide the concerned stakeholders to take corrective actions for improving the performance of construction projects.

The MPQR proposed in this paper covers ten areas and is a comprehensive single score that can be used to benchmark and compare performance over different projects to achieve continuous improvement.

This study investigates the impact of contract farming in onion, okra and pomegranate production on profits of smallholder farmers in India. It also investigates the determinants of farmers’ participation in contract farming. The study is based on a survey of 1,131 farmers from Maharashtra, India engaged in the cultivation of these three crops.

This study uses instrumental variable regressions and quasi-experimental methods to decipher the impact of contract farming.

The study reveals that contract farming ensures higher returns for smallholders, enables their access to high-end markets and brings in risk-sharing with protection during price fluctuations. Farm size and farmers’ risk perceptions are significantly associated with their participation in contract farming.

The study is based on cross-sectional data, which presents limitations on considering unobserved farmer-level individual heterogeneity.

The study shows that contracts highlight the functioning of the contractor/integrator on both the input and output sides of the market. By providing better-quality inputs on credit and at discounted prices and by providing training, the integrator helps small farmers meet international food safety and quality standards, a historically difficult challenge for smallholders in India.

This article aims to comprehensively review the measurement and management of supply chain performance (SCP) and strategic performance (SP). It strives to identify integrable features regarding frameworks, measurement approaches, practices and emerging research issues in these areas to integrate SCP and SP for measuring and managing performance. It intends to develop a dynamic-integrated-performance-system by incorporating integrable aspects of SCP and SP to link these domains for organizational performance improvement.

Using systematic-literature-review, this study analyzes 154 articles published in selected peer-reviewed international journals from 2000 to 2023 regarding SCP and SP. It assesses existing knowledge regarding research-design followed, challenging areas and imperatives in these critical business domains to investigate the prior conceptual, empirical, case study-based and literature-review-based articles.

The study identifies integrable features regarding key theoretical and measurement frameworks, critical objectives, significant measures, effective practices for measuring and managing SCP and SP and emerging research issues common to these areas. The findings help develop a dynamic-integrated-performance-system that uses the theoretical lenses of resource-based-view/dynamic-capability-theory and adopts a comprehensive framework like DBSC (system-dynamic-model with BSC perspectives). It incorporates identified integrable measures and best practices to monitor, measure, manage and improve organizational performance for sustainable competitive advantage. The article reveals that earlier studies have overlooked analyzing SCP and SP integration aspects.

From the theoretical viewpoint, the present SLR is unique in three ways: first, in investigating both the measurement and management of SCP and SP holistically; second, in identifying integrative features of these two; and third, in proposing a DIPS to link SCP and SP for performance improvement. The study reveals that existing literature has focused on measuring and managing SCP and SP in isolation without attempting a comprehensive and unified approach to integrate the respective domains. The present SLR adopts a holistic approach to link SCP and SP from SCM and strategic-management perspectives. The study proposes a dynamic-integrated-performance-system to measure, manage and improve performance in a unified method.

This study provides SC and strategy practitioners with an understanding of strategy-performance pathways for achieving strategic objectives and executing risk mitigation initiatives to counter disruptions. It enables SC managers to comprehend SC practices and SCP leading to dynamic SC capabilities development. Operationalizing the proposed DIPS will help firms link SCP and SP, align operational SC practices with strategic sustainability and circularity objectives and meet sustainable development goals while benefiting social and environmental stakeholders.

Assessing relationships and identifying a unified approach integrating SCP with SP have not been addressed earlier. This study's uniqueness is finding integrable features of SCP and SP and constructing a dynamic-integrated-performance-system to link these domains for achieving strategic competitiveness.

This paper aims to provide the security and privacy for Byzantine clients from different types of attacks.

In this paper, the authors use Federated Learning Algorithm Based On Matrix Mapping For Data Privacy over Edge Computing.

By using Softmax layer probability distribution for model byzantine tolerance can be increased from 40% to 45% in the blocking-convergence attack, and the edge backdoor attack can be stopped.

By using Softmax layer probability distribution for model the results of the tests, the aggregation method can protect at least 30% of Byzantine clients.

The Open API (application programming interface) architecture will play an important role in promoting future FinTech service applications; however, it involves user data, and the current specialization and progression are less visible. Therefore, an evaluation framework for Open API development in the FinTech service ecosystem is constructed in this study.

This study preliminarily selects the four most important key objects and factors of this ecosystem and conducts expert interviews to revise the evaluation framework. Then, this study uses the fuzzy analytic hierarchy process (FAHP) to evaluate the objects and their factor weights and finally uses the FAHP analysis results to further apply the evaluation based on distance from average solution (EDAS) approach to explore the strategy optimization scenarios.

According to the analysis results, the co-creation object and productivity object are the two most significant objects, with weights of 0.275 and 0.272, respectively. The analysis shows that FinTech-related companies expect to increase productivity through co-creation. Finally, the results also indicate that mobile payment is the best Open API application scenario in the FinTech service ecosystem, followed by online banking. These results illustrate strategic and management implications.

This study screens key evaluation criteria with a literature review and expert questionnaire interviews to process quantitative research. It can determine the weights of objectives and criteria to clarify the strength of influence between the objectives and criteria. Next, this study measures the probable performance of Open API applied in various FinTech service ecosystem scenarios.

This study aims to (1) develop a structural equation model in understanding the relationships between corporate social responsibility (CSR) performance and contractor competitiveness and (2) test the moderating effect of firm sizes on this relationship.

A literature review showed an urgent need to investigate the relationship between CSR implementation and contractor competitiveness holistically. CSR and contractor competitiveness variables were identified through the literature review and discussions with experienced professionals. Using a survey questionnaire, a total of 252 completed questionnaires were received. A structural equation modeling technique was then applied to analyze the data collected. Multigroup analysis was employed to test the moderating effect of firm sizes on the relationship between CSR implementation and contractor competitiveness.

The results indicated a strong relationship between CSR implementation and contractor competitiveness. This relationship is not moderated by firm size.

This research is one of the first studies to holistically explore the linkages between CSR implementation and contractor competitiveness. The findings can be served as a solid foundation to promote CSR performance in construction firms. Contractors of different sizes are suggested to implement CSR activities to foster competitiveness.

The use of social media is one of the new technological options that has been recommended as a potential new strategy for delivering high-quality, high-value cancer prevention and management services. Despite the increasing use of social media, little research has been done on the use of social media in brain tumors. Therefore, this systematic review aims to provide a comprehensive review of the use of social media in brain tumor research.

A systematic search was performed in PubMed, Scopus and Web of Science from inception to August 1, 2022. English full-text articles evaluating social media use, benefit or content in brain tumor were considered.

Sixteen documents satisfied the inclusion criteria and were included in the final analysis. Most of the included studies (n = 11/16) were conducted and published by researchers in the USA. In terms of social media platform, most studies focused on Twitter (8/16, 50%) and YouTube (8/16, 50%), followed by Facebook (6/16, 37.5%) and Instagram (4/16, 25%). Most studies (n = 7/12) analyzed the content of brain tumor information provided on social media, followed by patients’ use of social media (n = 3/12) and the quality of information on social media (n = 3/12). The other three articles also examined patient recruitment, crowdfunding and caregiver use of social media.

By identifying the use, benefits and content of social media platforms in different settings, patients, clinicians and policymakers can better benefit from harnessing the power of social media in different ways, leading to improved health-care services.

To the authors knowledge, this is the first study to systematically examine social media use, benefits and content status in brain tumors.

Global climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.

The authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.

The results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.

The findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.

The paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.