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Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

The purpose of the study is to analytically as well as numerically investigate the weight of throughflow on the onset of Casson nanofluid layer in a permeable matrix. This study examines both the marginal and over stable kind of convective movement in the system.

A double-phase model is used for Casson nanofluid, which integrates the impacts of thermophoresis and Brownian wave, whereas for flow in the porous matrix the altered Darcy model is occupied under the statement that nanoparticle flux is disappear on the boundaries. The resultant eigenvalue problem is resolved analytically as well as numerically with the help of Galerkin process with the Casson nanofluid Rayleigh–Darcy number as the eigenvalue.

The findings revealed that the throughflow factor postpones the arrival of convective flow and reduces the extent of convective cells, whereas the Casson factor, the Casson nanoparticle Rayleigh–Darcy number and the reformed diffusivity ratio promote convective motion and also decrease the extent of convective cells.

Controlling the convective movement in heat transfer systems that generate high heat flux is a real mechanical challenge. The proposed framework proved that the use of throughflow is one of the most important ways to control the convective movement in Casson nanofluid. To the best of the authors’ knowledge, no inspection has been established in the literature that studies the outcome of throughflow on the Casson nanofluid convective flow in a porous medium layer. However, the convective flow of Casson nanofluid finds many applications in improving heat transmission and energy efficiency in a range of thermal systems, such as the cooling of heat-generating elements in electronic devices, heat exchangers, pharmaceutical practices and hybrid-powered engines, where throughflow can play a significant role in controlling the convective motion.

The purpose of this paper is to investigate experimentally the effect of several input process factors, namely, feed rate, spindle speed, ultrasonic power and coolant pressure, on hole quality measures (penetration rate [PR] and chipping diameter [CD]) in rotary mode ultrasonic drilling of macor bioceramic material.

The main experiments were planned using the response surface methodology (RSM). Scanning electron microscopy was also used to examine and study the microstructure of machined samples. This study revealed the existence of dominant brittle fracture and little plastic flow that resulted in a material loss from the base work surface. Experiment findings have shown the dependability and adequacy of the proposed mathematical model.

The percentage of brittle mode deformation rises as the penetration depth of abrasives increases (at increasing levels of feed rate). This was due to the fact that at greater depths of indentation, material loss begins in the form of bigger chunks and develops inter-granular fractures. These stated causes have provided an additional advantage to increasing the CD over the machined rod of bioceramic. The desirability method was also used to optimize multi-response measured responses (PR and CD). The mathematical model created using the RSM method will be very useful in industrial revelation. Furthermore, the investigated answers’ particle swarm optimization (PSO) and teacher-learner-based optimization (TLBO) make the parametric analysis more relevant and productive for real-life industrial practices.

Macor bioceramic has been widely recognized as one of the most highly demanded innovative dental ceramics, receiving expanded industry approval because of its outstanding and superior characteristics. However, effective and efficient processing remains a problem. Among the available contemporary machining methods introduced for processing typical and advanced materials, rotary mode ultrasonic machining has been identified as one of the best suitable candidates for precise processing of macor bioceramics, as this process produces thermal damage-free profiles, as well as high accuracy and an increased material removal rate. The optimized combined setting obtained using PSO is feed rate = 0.16 mm/s, spindle speed = 4,500 rpm, ultrasonic power = 60% and coolant pressure = 280 kPa with the value of fitness function is 0.0508. The optimized combined setting obtained using TLBO is feed rate = 0.06 mm/s, spindle speed = 2,500 rpm, ultrasonic power = 60% and coolant pressure = 280 kPa with the value of fitness function is 0.1703.

This paper aims to provide a critical review of water generation from atmospheric air by using desiccant materials. Over the past few years, there has been very high stress on water scarcity, especially in Asian and African countries. Because of this insecurity, many countries are focusing on their research in the field of water technologies. Water generation from atmospheric air by using desiccant materials is one of the techniques among the air-to-water generators (AWGs).

A structured and systematic literature review has been presented to observe and understand the past trend/patterns in the field of water generation from atmospheric air by using desiccant materials. To understand the water generation technologies based on desiccant materials, the research papers from the years 1987 to 2022 have been studied and included.

The properties of the different and most probable desiccant materials in the field of AWGs have been discussed. A detailed review of testing reports of collected water samples has also been presented in tabular form. Finally, the economic analysis has been done and future prospects have been discussed. It is also found that the capacity of solid desiccant materials to adsorb the water is less as compared to liquid desiccant materials. But, the adsorption capacity can be improved by using composite desiccant materials.

The uniqueness of this manuscript lies in the compiling and examination of the existed published research papers, including variables such as author, year and geographical location, experimental/simulative, types of desiccant material, type of setup, desiccant material type and quantity and type of concentrator. This manuscript provides critique to the empirical and conceptual research in AWG technologies and also stimulates researchers to explore the topic very carefully.

Industry 4.0 has received significant attention in today's competitive business market, necessitating a restructuring of functional domains in nearly every manufacturing organization. A comprehensive strategy to improve performance in preparation for Industry 4.0 implementation necessitates several steps, one of which is the establishment of performance outcomes (POs). The aim of this paper is to identify and rank the POs realized due to the adoption of Industry 4.0 enablers.

Based on an extensive literature review and inputs received from experts, a comprehensive list of enablers and the POs was prepared and finalized. This paper proposes a framework based on hybrid solution methodology, namely Neutrosophic Analytical Hierarchy Process (N-AHP) and Neutrosophic Combined Compromise Solution (N-CoCoSo), to rank the POs realized due to the adoption of Industry 4.0 enablers. The N-AHP methodology has been adopted to calculate the relative weights of the Industry 4.0 enablers. In comparison, the N-CoCoSo method has been adopted to rank the POs of Industry 4.0.

The proposed framework is applied to an Indian manufacturing organization to test the organization's practical applicability. Additionally, sensitivity analysis is also carried out to check the steadiness of the proposed framework. The findings of this study revealed that “Improved responsiveness to market conditions in today's competitive business environment” is the top-ranked PO of Industry 4.0, followed by “Enhanced competitiveness and better market share”, “Better product quality, through smart management of production process” and “Reduction in manufacturing waste and environmental sustainability” which could be realized due to adoption of its enablers.

This research would aid practitioners by enhancing the practitioners' capacity to understand and prioritize the various POs resulting from implementing Industry 4.0 enablers. Embracing a clear strategic plan will further assist practitioners in improving the efficiency of Industry 4.0 implementation.

Previous literature has only addressed the relationship between Industry 4.0 enablers and POs in a limited way. This paper attempts to compile a comprehensive list of Industry 4.0 enablers relevant to manufacturing organizations in order to fill this knowledge and research gap.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

Reverse logistics has attracted many industries due to product recalls, enormous waste generation, competitive reasons, vast opportunity in the waste management market, and to get the maximum value out of waste recovery. Selection of the right implementation strategy is vital for reverse logistics to function efficiently. Therefore, this research aims to evaluate the criteria for selecting reverse logistics strategy and help to choose the preferred strategy for its implementation.

Three reverse logistics implementation strategies, namely, in-house, joint venture and outsourcing, are proposed. A novel hybrid fuzzy analytical hierarchy process (F-AHP) and fuzzy measurement of alternatives and ranking according to COmpromise Solution (F-MARCOS) based framework is developed to fulfil the research objective. A survey is performed on Indian manufacturing industry to demonstrate the applicability of the proposed framework.

The result shows that government policy and regulations, reverse logistics risks and reduced emission have prime importance for a manufacturing industry which needs to implement reverse logistics into its supply chain. Outsourcing is the preferred reverse logistics strategy followed by joint venture and in-house that a manufacturing firm in India can implement.

The research results are based on the responses of the survey received. This research considers various industry sectors to test the applicability of the framework. However, for actual implementation, this survey must first be limited to a particular industry as the results will apply to that industrial sector only.

This developed framework simplifies the procedure of selecting the strategy when the industry needs to implement reverse logistics. For industries working with a smaller set of criteria, this framework is a powerful and dynamic approach for reducing and choosing the most pertinent one that helps accomplish their objectives of reverse logistics implementation strategy selection.

Based on the literature and current applicability of reverse logistics, this research proposes three models to implement reverse logistics in Indian industries. A novel hybrid F-AHP and F-MARCOS based framework is developed to handle the selection of suitable reverse logistics strategy.

The purpose of this paper is to investigate capacity coordination in services supply chain (SSC). It provides discussion and application of various contracts in a two-stage single period SSC.

This paper considers a two-stage serial supply chain with demand uncertainty and price insensitivity. A model is developed to represent a global IT SSC incorporating services specific factors like over-capacity cost and higher degree of substitution resulting in flexibility to meet unplanned demand. At first, centralized and competitive solutions of the model are studied. Then, the paper studies coordination in this supply chain using some of widely used contract templates.

This paper finds several key insights for the researchers and practitioners in this area around adverse impact of over-capacity cost on demand, positive effect of delivery team’s exposure to market on contracting terms and better understanding of efficient frontiers for selected contracting mechanism.

This paper has limited its analysis to three key and most widely used contracts and made assumptions about risk-neutrality of the firms. Future research can study other contracting templates and/or relax for the model as laid out in this paper.

An automated software agent can be built leveraging the closed form equations developed here to help decide on optimal capacity investment and devise coordinating contracts.

This paper established that because of higher degree of substitution, perishability and non-trivial over-capacity cost, SSC behave bit differently than the physical goods supply chain and coordination of participating firms needs to be studied in a services specific context for improving system-wide performance.

Global trade depends on more complex, prolonged and larger port systems, where port logistics is a backbone for such operations. Ports are responsible for transferring more than 80 percent of the global trade. Port logistics are prone to being risk-oriented. The study proposes a model to study various port logistics barriers and their associated risks for emerging economies in the Industry 4.0 era.

The study develops a framework by integrating the fuzzy set theory, the evidential reasoning approach and the expected utility theorem for identifying the severity value of port logistics barriers under the Industry 4.0 era for emerging economies and prioritize them based on various perspectives. The study identifies multiple risks associated with the barriers, and intensity-based categorization of the risks is performed for risk profiling.

The study reveals that poor infrastructure, nonsupportive policy ecosystem, and lack of research and development are the top barriers that need immediate attention. A new approach has been proposed that changes the importance of perspectives, and 192 analytical experiments were done to study the changing behavior of barriers. The study also presents various types of risks associated with the selected barriers.

In future studies, other barriers can be discovered and studied to develop such models. To cover the entire spectrum of possibilities, belief degrees of the barriers could be used to study the barriers instead of changing the weights.

This study presents a quantification model to prioritize the barriers based on environmental, economic and operational perspectives. Further, the model helps create scenarios for decision-makers to improve port logistics performance and achieve sustainability. The study identifies various risks associated with port logistics barriers and allows decision-makers to take proactive actions.

This study contributes significantly to the literature on port logistics by developing a framework for determining the severity of the barriers in the Industry 4.0 era for emerging economies. Further, the study pinpoints various risks associated with port logistics, and risk profiling is carried out.