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The demand-side view of creditor rights posits a negative association between creditor rights and corporate borrowings. The purpose of this paper is as follows: first, the author examines whether the demand-side effect is more pronounced amongst firms with excess promoter shareholding. Subsequently, the authors analyze the impact of high promoter holdings on investment decisions owing to bankruptcy reforms.

To answer the above questions, the authors exploit the passage of the Insolvency and Bankruptcy Code (IBC) (2016) that strengthens the creditor rights of lenders, which impacts the borrowings and financing activities of Indian corporates. Using a panel of listed Indian firms over the period of 2012–2019, the authors analyze how the IBC affects firms’ borrowings and financing decisions with excess promoter holdings.

The authors find that bankruptcy reforms led to a statistically significant decline in the use of borrowed funds (primarily secured and long-term debt) by firms with high concentrated holdings. The analysis also indicates that firms with excess promoter ownership face an increased cost of debt due to bankruptcy reforms. As a result, firms with excess promoter holdings curtail their investments. Overall, the results indicate that India’s bankruptcy reforms significantly affect the firms’ financing and investment decisions with highly concentrated ownership.

While past research has explored the relationship between bankruptcy reforms and demand for/supply of debt, the authors provide novel empirical evidence on the role of promoter holdings that affects the relationship between bankruptcy law and financing and investment decisions. To the best of the author’s knowledge, this study is the first to investigate the role of ownership structure in the context of bankruptcy reforms by using a quasi-natural experiment.

This study aims to examine the role of economic political uncertainty (EPU) on various corporate policies, namely, cash reserves, investment, capital structure and operating activities of Indian listed firms.

To assess the influence of policy-related uncertainties, the author uses the India-specific EPU news-based index constructed by Baker et al. (2016) as a proxy for policy uncertainties. This study uses data from listed Indian firms spanning the period 2003 to 2019. The author uses panel regression models with firm-fixed effects to analyze the impact of EPU on corporate policies, including cash reserves, leverage and CAPEX, while considering key control variables.

In response to heightened EPU, firms tend to increase their cash reserves, curtail their investment activities and favour secured financing options. Notably, this study aligns with the “real options” framework, demonstrating that firms with substantial investment irreversibility significantly reduce their capital expenditures during periods of elevated EPU. Additionally, the results reveal that rising EPU corresponds to heightened borrowing costs and increased operating expenses for firms.

In contrast to prior research that predominantly investigated the impact of EPU on the decisions of listed firms in developed markets, this study delves into the role of EPU on corporate policies among listed firms in India. This focus is particularly relevant, given the significant policy changes that have transpired in the Indian business landscape in recent years.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

A wireless body area network (WBAN) plays a crucial role in the health-care domain. With the emergence of technologies like the internet of things, there is increased usage of WBAN for providing quality health services. With wearable devices and sensors associated with human body, patient’s vital signs are captured and sent to doctor. The WBAN has number of sensor nodes that are resource constrained. The communications among the nodes are very crucial as human health information is exchanged. The purpose of this paper aims to have Quality of Service (QoS) with energy aware and control overhead aware. Maximizing network lifetime is also essential for the improved quality in services. There are many existing studies on QoS communications in WBAN.

In this paper, with the aim of energy-efficient WBAN for QoS, a cross-layer routing protocol is designed and implemented. A cross-layer routing protocol that is ad hoc on-demand distance vector (AODV)-based, energy and control overhead-aware (AODV-ECOA) is designed and implemented for energy-efficient routing in WBAN. The cross-layer design that involves multiple layers of open systems interconnection reference model, which will improve energy efficiency and thus QoS.

Implementation is simulated using the network simulator tool, i.e. NS-2. The proposed cross-layer routing protocol AODV-ECOA shows least bandwidth requirement by control packets, leading to less control overhead, highest packet delivery ratio and energy efficiency. The experimental results revealed that AODV-ECOA shows better performance over existing protocols such as AODV and POLITIC.

An efficient control overhead reduction algorithm is proposed for reducing energy consumption further and improves performance of WBAN communications to realize desired QoS.