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The purpose of this study is to perform a numerical analysis of fiber-reinforced polymer (FRP) retrofitting in reinforced concrete (RC) joints at high temperatures and predict models using artificial neural networks (ANN). The aim was to gain insights into their structural behavior across a range of loading conditions from room temperature to 800°C. Additionally, the research assessed the efficiency of carbon fiber-reinforced polymer (CFRP), glass fiber reinforced polymer (GFRP) and aramid fiber reinforced polymer (AFRP) strengthening in enhancing the structural performance of the critical sections.

The linear numerical simulations were conducted to evaluate the performance of RC beam-column joints using finite element modelling (FEM) analysis. The ANN model demonstrated exceptional effectiveness in predicting the stiffness of frames with openings, establishing itself as the premier machine learning algorithm for frame stiffness estimation. In the conventional model, 300°C was proven to be an effective temperature approach. Subsequently, maintaining a constant temperature of 300°C, an in-depth analysis of nearly 30 models of three retrofitting techniques was conducted under thermomechanical loading.

The CFRP retrofits yielded 15% less deflection and 30% more stress than the remaining FRPs, and the ANN models predicted the deflection, main stresses, bending moment and shear force. The ANN model results were compared with those of other frequently used models. The R thresholds (R = 0.954, 0.981, 0.986, 0.968, 0.978 and 0.936) for training, testing and validation indicated that the ANN model achieved data variability. The findings indicate that the ANN model is more accurate because of the strong connection between the numerical model and the prediction.

To identify the pinpoint of critical segments within the rehabilitation section and determine the most effective wrapping method among the three laminates.

The complexity of atmospheric corrosion, further compounded by the effects of climate change, makes existing models inappropriate for corrosion prediction. The commonly used kinetic model and dose-response functions are restricted in their capacity to represent the non-linear behaviour of corrosion phenomena. The application of artificial intelligence (AI)-driven machine learning algorithms to corrosion data can better represent the corrosion mechanism by considering the dynamic behaviour due to changing climatic conditions. Effective use of materials, coating systems and maintenance strategies can then be made with such a corrosivity model. Accurate corrosion prediction will help to improve climate change resilience of the social, economic and energy infrastructure in line with the UN Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure) and 13 (Climate Action). This chapter discusses atmospheric corrosion prediction in relation to the SDGs and the influence of AI in overcoming the challenges.

The objective of the present study is to identify and analyze a set of critical success factors (CSFs) for ice-cream industry [cold chain management (CCM)] that helps in increasing the efficacy, quality, performance and growth of the supply chain organization.

A questionnaire survey with companies in ice-cream sector and a panel study with experts were conducted to identify and validate CSFs and their associated sub-factors. Eight CSFs identified from the cold chain domain vetted for the ice-cream industry and then prioritized by using one of the most well-known decision-making frameworks, Decision-Making Trial and Evaluation Laboratory. The general verdicts of the modelling and its application to the real-world case have been tested through an ice-cream company supply chain.

The result shows that the significant CSFs accountable for the growth of the ice-cream industry are the infrastructure and capacity building, consistent product improvement and operational efficiencies of the value chain. Subsequently, it was identified that the use of IT and related technologies and improved processes for operations also play a considerable role in the performance of ice-cream industry.

The study successfully outlines the effective CCM practices for critical issues. The proposed methodology and factor modelling case demonstration might be useful in analyzing the logistic chains of products such as fruits, drugs and meat.

The meritorious identification of critical areas and executing mitigation plans bring notable benefits to the firms such as improved operational efficiencies, improved time to market performance and product innovation, which bring additional benefits to the producers.

The purpose of this paper is to identify, analyze and model Internet of Things (IoT) enablers essential for the success of Industry 4.0.

IoT enablers for Industry 4.0 are identified from literature and inferable discussions with industry experts. Three different techniques namely, principal component analysis (PCA), interpretive structural modeling (ISM) and decision making trial and evaluation laboratory (DEMATEL) are applied to model IoT enablers. In addition to this, DEMATEL is also applied under two different situations representing the behavioral characteristic of experts involved. These are termed as optimistic (maximum) and pessimistic (minimum).

The integrated approach of PCA-ISM-DEMATEL shows that IoT ecosystem and IoT Big Data are the most influential or driving IoT enablers. These two enablers have been identified as the pillars for Industry 4.0. On the other side, IoT interchangeability, consumer IoT, IoT robustness and IoT interface and network capability have also been identified as the most dependent enablers for Industry 4.0.

The findings enable the industry practitioners to select the most appropriate driving enablers for an effective implementation of Industry 4.0.

The integrated approach-based hierarchical model and cause-effect relationship among IoT enablers are proposed which is a novel initiative for Industry 4.0. Moreover, two different variants of DEMATEL namely, pessimistic and optimistic are applied first time.

This paper investigates how the channel leadership strategies develop a post-coronavirus disease (COVID-19) resilient agri-supply chain, which reduces supplier and retailer's price loss and enhances the logistics service quality level considering logistics outsourcing of agri-product especially for the rapidly changing market condition.

Based on the classical leadership theory, two channel leadership strategies, i.e. LPL and SL, are considered. The proposed framework first derives the equilibrium price and service quality level decision among the supplier, the logistics provider and the retailer. Then it compares both leadership strategies in terms of the equilibrium prices and service quality theoretically. This article also presents a case study of Arabian dates pricing and supply chain to test the theoretically derived propositions.

Selection of suitable leadership strategy is a critical factor for profit maximization of the supply chain drivers and proper optimization of equilibrium price and service quality. Here, the product's quality and the market's socio-economic condition play an important role in selecting a suitable leadership strategy. A random transformation of the physical market to an e-commerce portal creates a wide variation of the market's socio-economic parameters, affecting the equilibrium pricing and the logistics provider's service quality.

This study proposes a post-COVID-19 resilient agri-supply chain framework considering price and quality-dependent stochastic market demand, incorporating a wide range of socio-economic factors in the model to counteract the effect of rapid behavior change of agri-market due to COVID-19 norms. This research examines the effect of different channel leadership strategies to facilitate suitable decisions on prices and service quality and retrieve the profit of the supplier, retailer and logistics provider. The future models can incorporate competitiveness in logistics outsourcing, fourth-party logistics (4PL) and contract farming in the agri-supply chain. Each of the extensions can open avenues in different directions.

As the post-COVID-19 market and the customer behavior is randomly changing, and the traditional market is rapidly converting into supermarkets and e-commerce portals, this paper examines the model with a wide variety of e-commerce portals with multi-variation of product. It is conclusive that the product's quality and the market's socio-economic behavior significantly impact the equilibrium decision. The drivers of the supply chain must take them into account before choosing a particular channel leadership strategy.

This study considers a multi-product and multi-market (e-commerce) model by integrating a wide variety of products and the market's socio-economic parameters. The model is tested in a price and quality-dependent stochastic market condition, contributing to the literature by reconciling two different channel leadership strategies into the global logistics of fresh agri-product.

This study aims to address the adoption issues of green and sustainable practices in the hotel industry. The study identifies critical performance indicators (CPIs) and utilizes Hotel Carbon Management Initiative (HCMI) framework to prioritize CPIs for achieving a robust adoption framework for green and sustainable practices.

The hotel industry is driven by changing ecological degradation, and it is necessary to achieve feasible development goals. This research article formulates the CPIs derived from HCMI and decision-making model is created using the Analytic Hierarchy Process (AHP).

In this research, CPIs of HCMI are considered and aim to formulate five major CPIs of HCMI, namely air pollution, energy efficiency, water conservation, noise pollution and waste management. The study identifies the need for better control and sustainable growth in the Indian hotel industry with minimum carbon emissions coupled with the green approach adoption.

The CPIs work on minimization of risks and maximizing optimality of return on investment. The development of the hotel industry will be improved and immensely welcomed by capping the carbon emission with the green initiatives. This research is limited as urban hotels are surveyed in this study.

This work makes a valid argument to establish HCMI as a model initiative for environment quality improvement and further extension of other activities in the hospitality sector and scale-up sustainable practices for future-ready circular economies.

The Indian marble and stone industry has got the potential to contribute well to the development of the emerging economy. However, unlike the other Indian industries, stone and marble industries are highly underrated sectors, which may become a critical factor for development. This paper analyses the sustainability factors in supply chain management practices.

A literature review is used to identify the barriers and drivers in sustainable supply chain management practices. Interpretive structural modeling has been used to obtain a hierarchy of barriers and drivers along with driving power and dependence power analysis. Further, MICMAC analysis is used for segregating the barriers and drivers in terms of their impact on sustainability.

The findings of the work of this research are that the attention of society, government, and commercial banks should be more toward the unorganized condition of stone and marble sector. There should be an increase in the commitment of stakeholders to reduce pollution and install safety, by enforcing more relevant laws and regulations and creating the importance of environmental awareness.

The main contribution of this research is to identify the barriers and drivers of sustainable supply chain management in a stone and marble industry. The paper proposes a sound mathematical model to prioritize the critical factors for responsible production and consumption of resources from sustainability perspectives of stone industry.

This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

This article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.

This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.

The proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.

The existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.

India is in the process to achieve an important place in $2,000bn global food trade. In order to achieve this goal, there is a need to develop a food safety system which is well written down in line with international practices that are highly coordinated based on self-compliance to assure consumer protection. Accordingly, many organizations undergo assessment of their food safety system to verify compliance internally as well as externally. The purpose of this paper is to provide insight on the critical factors and benefits by evaluating the food safety assessment practices.

A questionnaire-based survey is conducted among 96 Indian food business operators and regulators involved in assessment practices to obtain critical factors for the assessment of food safety practices. The questionnaire captures indicators for motivations or challenges and benefits of food safety assessment to identify critical factors using exploratory factor analysis. Model for the food safety assessment practices was developed based on multiple regression analysis by determining the impact of factors on the benefits of food safety assessment.

This paper identifies four factors responsible for assessing food safety practices, namely, business-centric approach, legislative needs, technical practices and organization resentment as a combination of reasons and challenges along with two benefits risk: protection and organization reinforcement. The regression analysis indicates that the organization reinforcement gets positively impacted by business and technical practices and negatively by organization resentment. Risk protection has a significant relationship with legislative needs.

This is the first attempt to systematically explore the factors around the assessment of food safety practices in India. This study provides inputs for the practical application of managers and regulators.

The primary objective of this article is to examine current procurement, inventory control and management practices in modern healthcare, with a particular focus on the procurement and management of surgical supplies in a prominent public, highly specialized healthcare sector.

This study was conducted in three phases. In Phase 1, the study team interacted with various hospital management stakeholders, including the surgical hospital store, examined the current procurement process and identified challenges. Phase 2 focused on selecting items for a detailed study and collected the qualitative and quantitative details of the store department of the healthcare sector chosen. A detailed study analyzed revenue, output/demand, inventory levels, etc. In Phase 3, a decision-making framework is proposed, and inventory control systems are redesigned and demonstrated for the selected items.

It was observed that the demand for many surgical items had increased significantly over the years due to an increase in disposable/disposable items, while inventories fluctuated widely. Maximum inventory levels varied between 50 and 75%. Storage and availability were important issues for the hospital. It is assumed the hospital adopts the proposed inventory control system. In this case, the benefits can be a saving of 62% of the maximum inventory, 20% of the average stock in the system and optimal use of storage space, improving the performance and productivity of the hospital.

This study can help the healthcare sector administration to develop better systems for the procurement and delivery of common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels, and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study can help the healthcare sector administration develop better systems for procuring and delivering common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.

This study is an early attempt to develop a decision framework and inventory control system from the perspective of healthcare inventory management. The gaps identified in real hospital scenarios are investigated, and theoretically based-inventory management strategies are applied and proposed.