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The purpose is to derive the most effective place in the air for an aerial robot, viz., drone to use as an alternative communication system during disasters.

In this technology-driven era, various concepts are becoming the area of interest for multiple researchers. Drone technology is also one of them. The researchers, with interest in drones, are therefore trying to understand the various uses of employing drones in diverse applications which are mind-boggling, starting from civil applications (viz., an inspection of power lines, counting wildlife, delivering medical supplies to inaccessible regions, forest fire detection, and landslide measurement) to military applications (viz., real-time monitoring, surveillance, patrolling, and demining). However, one area where its usage is still to be exploited in many countries is using drones as a relay when communication lines are disrupted due to natural calamities. This will be particularly helpful in rescuing the affected people as the aerial node will enable them to communicate to the rescue team using mobiles/ordinary landline telephones even when regular communication towers are destroyed due to disastrous natural calamities, for example, tsunamis, earthquakes, and floods. Various algorithms, namely, water filling algorithm, advanced water filling algorithm, equal power distribution algorithm, and particle swarm optimization, were therefore studied and analyzed using simulation in addition to various path loss models to realize the desired place for an aerial robot, viz., drone in the air, which will eventually be used as an alternative communication system for badly hit ground users due to any disaster.

It was found that the effective combination of the water filling algorithm and particle swarm optimization algorithm may be done to place the drone in the air to increase the overall throughput of the affected ground users.

The research is original. None of the parts of this research paper has been published anywhere.

The purpose is to design a system in which drones can control traffic most effectively using a deep learning algorithm.

Drones have now started entry into each facet of life. The entry of drones has made them a subject of great relevance in the present technological era. The span of drones is, however, very broad due to various kinds of usages leading to different types of drones. Out of the many usages, one usage which is presently being widely researched is traffic monitoring as traffic monitoring can hover over a particular area. This paper specifically brings out the basic algorithm You Look Only Once (YOLO) which may be used for identifying the vehicles. Consequently, using deep learning YOLO algorithm, identification of vehicles will, therefore, help in easy regulation of traffic in streetlights, avoiding accidents, finding out the culprit drivers due to which traffic jam would have taken place and recognition of a pattern of traffic at various timings of the day, thereby announcing the same through radio (namely, Frequency Modulation (FM)) channels, so that people can take the route which is the least jammed.

The study found that the object(s) detected by the deep learning algorithm is almost the same as if seen from a naked eye from the top view. This led to the conclusion that the drones may be used for traffic monitoring, in the days to come, which was not the case earlier.

The main research content and key algorithm have been introduced. The research is original. None of the parts of this research paper has been published anywhere.

This study, based on the instrumental approach of the stakeholder theory, examines the firm performance of public and private sector firms in the mandatory corporate social responsibility (CSR) expenditure regime in India. CSR was legislated in India in the year 2014.

The study hypothesizes that firms which fulfill the mandatory CSR expenditure requirement will have a higher firm performance and uses one-way ANOVA and post-hoc test for analysis. Firm performance is examined with respect to firm value and market performance.

The instrumental approach of the stakeholder theory is not supported in the mandatory CSR expenditure regime in India. The public sector firms that comply with the mandatory CSR expenditure requirement have a lower firm performance. Further, the private sector firms that meet the mandatory CSR expenditure criterion do not have a significantly different firm performance than the private sector firms that do not fulfill this criterion.

The study indicates as to why some firms fail to meet the CSR expenditure compliance. It also gives suggestions on how regulators and government agencies can solicit the participation of the Indian firms to undertake CSR initiatives. The study further suggests how firms may reap maximum benefit from the CSR expenditure.

Since CSR expenditure has been made mandatory only in the year 2014 in India, hardly any study has examined firm performance in the mandatory CSR expenditure regime in India.

The purpose of this paper is to identify the significant corporate social responsibility (CSR) issues and to analyze the interaction among them in order to improve supply chain performance (SCP) system of an organization.

To achieve the objective, a preference rating approach (synthesized in company preference rating (CPR) and company satisfaction assessment (CSA) approaches) has been used in two stages as: stage 1, relative importance rating (RIR) of identified issues has been computed using CPR approach. For this, preference graphs have been developed based on the field expert’s opinions using graph-theory-based representation technique. In stage 2: competitive priority ratings of CSR issues with respect to competitive strategies have been calculated using CSA approach.

Based on RIRs (stage 1), it has been noticed that issue namely “social benefits for employees” is more desirable among all considered issues. In stage 2, based on competitive priority ratings, the issue “social benefits for employees” is more important for an organization to build a better brand reputation through CSR activities and issue “degree of CSR implementation” is more significant in order to improve the SCP of an organization. The issue “environmental protection” helps the organization to reduce their overall environmental impact and also provides an aid in achieving long-term sustainable goals of an organization.

The findings of present research gives an idea about the importance of CSR issues, based on this, managers can decide, which issue is more significant and can help in taking decisions and framing strategies in context of CSR for improving the SCP of their organization effectively and efficiently.

As per the best knowledge, recognized CSR issues have been considered first time to quantify the CSR-based SCP of Indian automobile industries located near around Delhi region, India. This paper may provide an aid to the managers in making the CSR-based policies to enhance their SCP.

This paper seeks to propose a methodology based on Petri nets to evaluate the reliability parameters of a screening system in paper industry. The effects of failures and courses of action on the system performance have also been investigated.

Generalized stochastic Petri nets (GSPN), a class of Petri nets, has been used to model the interactions amongst the active/standby units of the system; and Markovian approach has been used to evaluate the reliability parameters. The data related to equipments' operational behavior were collected, processed and quantified. Using the data, reliability analysis of system in the long run conditions has been carried out. The sensitivity analysis has been performed to study the effect of failure/repair rates of each unit of the system on system performance.

The methodology adopted in this paper provides a better understanding on the behavior of the system through its graphical representation. The reachability graph generated with Petri net model helps to identify the state space evolution of the system.

Reliability analysis of a screening system of the paper industry presented in this paper will help management in deciding upon the maintenance strategy to be adopted with the objective of improving the performance of the system and consequently reducing the operational and maintenance costs.

This paper aims to study whether exporting enterprises are more productive in export-intensive industries. It also aims to identify the action area and policy direction for enhancing productivity in Indian textile manufacturing. Global integration has increased the volume of international trade. It is crucial for countries to have competitive enterprises to capture a larger share of the global economy. Improvement in productivity performance not only enhances competitiveness but also promotes growth in an economy.

A productivity analysis for the Indian textile manufacturing industry using firm-level panel data is conducted. The data are collected for 160 firms relevant to the period from 2007-2008 to 2012-2013 from Ace Equity database. Using the technique of data envelopment analysis, the output oriented Malmquist productivity index is computed and the sources of productivity change are identified. Also, a comparison between the productivity performance of the exporting and non-exporting firms has been made.

The results suggest that exporting firms are exhibiting better productivity performance and resource utilisation during the study period. Technology change and scale efficiency seem to be the major sources of productivity gain for exporting firms.

The research is limited to a single industry, reference database and methodology. There is scope for further in-depth, micro-level research to analyze the differences in drivers of productivity for exporting and non-exporting firms.

This paper provides validation to export promotional policies in the Indian textile industry by establishing better productivity performance of exporting firms. It also provides direction for managerial action by identifying efficiency component as the factor pulling down productivity.

The purpose of this research article is to explore the behavioural intention of consumers to use app-based shopping for green-tech products in the emerging economy of Bangladesh. The study investigates the indirect effects of perceived ease of use, usefulness, perceived delivery and perceived security on the behavioural intention to use app-based shopping for purchasing green-tech products by considering the mediating role of perceived trust.

A quantitative research approach was applied to collect data from the respondents who had previously used app-based shopping for green-tech products in Bangladesh. An online, self-administered survey questionnaire was used to collect data from 348 respondents. The survey data was analysed using SmartPLS-4 to measure the reliability and validity of the constructs. In addition, partial least squares structural equation modelling (PLS-SEM) was employed to test the research model and hypotheses.

The study's results reveal that perceived usefulness, ease of use, security and delivery positively and significantly influence perceived trust, leading to a higher behavioural intention to use app-based shopping for green-tech products. Additionally, perceived trust significantly mediates the relationship between the behavioural intention to use app-based shopping and perceived usefulness, perceived ease of use, perceived security and perceived delivery.

The study's findings have important implications for app-based shopping services to support customers interested in purchasing green-tech products in an emerging economy. The results also indicate that green-tech product companies must adopt new service delivery channels and ensure consumers' convenience and cost and time savings. The present research findings suggest that green-tech product companies need to ensure that they integrate digital technologies into their services for secure and timely delivery of products, improving customer convenience.

The study's findings can be insightful for app-based shopping service providers to foster their businesses by focussing on developing a positive trust perception in the consumer's mind, leading to a positive intention to use the app-based shopping services. The present study will enrich the current literature by investigating how consumers' perceived trust affects their behavioural intention to use app-based online shopping for purchasing green-tech products. It will also expand the existing knowledge on app-based shopping by exploring how perceived delivery impacts perceived trust, which subsequently affects customers' intentions to adopt the purchase of green-tech products.

Additive manufacturing (AM) is expected to significantly transform the operations in manufacturing sector. It is also proposed to have optimistic applications in the medical supply chains (SC). However, its adoption in medical sector is faced with a range of barriers. Motivated by the need to establish an AM-based medical SC in a developing economy, the present paper analyses the potential barriers that would hinder the adoption of AM in medical SC.

Based on an extensive literature review and expert discussions, 12 significant barriers have been identified, which are analysed using an integrated interpretive structural modelling–analytical network process (ISM–ANP) methodology. An interrelationship between these barriers using ISM has been analysed to determine the driving-dependence power of these barriers using MICMAC (Matrice d' Impacts Croises-Multiplication Applique' e a' Classement) analysis. The barriers are then ranked using the ANP approach.

It has been focussed that the non-availability of a variety of materials, lack of education and training to designers and workers and production technology limitation are the most critical barriers. The results suggest that the managers should give greater significance to the technological and organizational barriers.

An approach to overcome these barriers can help the managers and organizations to develop successful AM-based SCs. The study is the first to identify and analyse the barriers for successful adoption of AM in medical SC context.

This paper aims to enhance the surface finish of the fused deposition modeling (FDM) part using the vapor smoothening (VS) post-processing method and to study the combined effect of FDM and VS process parameters on the quality of the part.

Analysis of variance method is used to understand the significance of the FDM and VS process parameters. Following this, the optimized parameter for multiple criteria response is reported using the technique for order preference by similarity to ideal solution. The process parameters alternatives are build orientation angle, build surface normal and exposure time and the criteria are surface roughness and dimensional error percentage.

The result observed contradicts the result reported on the independent parameter optimization of FDM and VS processes. There is a radical improvement in the surface finish on account of the coating process and an increase in the exposure time results in the decrease of the surface roughness. Minimum surface roughness of 0.11 µm is observed at 1,620 build angle and the least dimensional error of 0.01% is observed at build orientation angle 540. The impact of VS on the up-facing surface is different from the down-facing surface due to the removal of support material burrs and the exposure of the surface to vapor direction.

A study on the multi-criteria decision-making to ascertain the effect of post-processing on FDM component surface normal directed both to downward (build angle 0°–90°) and to upward (build angle 99°–180°) are reported for the first time in this article. The data reported for the post-processed FDM part at the build angle 0°–180° can be used as a guideline for selecting the optimal parameter and for assigning appropriate tolerance in the CAD model.

Sarcasm is a sentiment in which human beings convey messages with the opposite meanings to hurt someone emotionally or condemn something in a witty manner. The difference between the text's literal and its intended meaning makes it tough to identify. Mostly, researchers and practitioners only consider explicit information for text classification; however, considering implicit with explicit information will enhance the classifier's accuracy. Several sarcasm detection studies focus on syntactic, lexical or pragmatic features that are uttered using words, emoticons and exclamation marks. Discrete models, which are utilized by many existing works, require manual features that are costly to uncover.

In this research, word embeddings used for feature extraction are combined with context-aware language models to provide automatic feature engineering capabilities as well superior classification performance as compared to baseline models. Performance of the proposed models has been shown on three benchmark datasets over different evaluation metrics namely misclassification rate, receiver operating characteristic (ROC) curve and area under curve (AUC).

Experimental results suggest that FastText word embedding technique with BERT language model gives higher accuracy and helps to identify the sarcastic textual element correctly.

Sarcasm detection is a sub-task of sentiment analysis. To help in appropriate data-driven decision-making, the sentiment of the text that gets reversed due to sarcasm needs to be detected properly. In online social environments, it is critical for businesses and individuals to detect the correct sentiment polarity. This will aid in the right selling and buying of products and/or services, leading to higher sales and better market share for businesses, and meeting the quality requirements of customers.