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The purpose of this paper is to provide an overview of unmanned aerial vehicle (UAV) developments, types, the major functional components of UAV, challenges, and trends of UAVs, and among the various challenges, the authors are concentrating more on obstacle sensing methods. This also highlights the scope of on-board vision-based obstacle sensing for miniature UAVs.

The paper initially discusses the basic functional elements of UAV, then considers the different challenges faced by UAV designers. The authors have narrowed down the study on obstacle detection and sensing methods for autonomous operation.

Among the various existing obstacle sensing techniques, on-board vision-based obstacle detection has better scope in the future requirements of miniature UAVs to make it completely autonomous.

The paper gives original review points by doing a thorough literature survey on various obstacle sensing techniques used for UAVs.

Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation.

The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing.

This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization.

The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.

This study examines the performance effect of working capital for a large sample of Indian manufacturing firms in light of supply chain disruption, i.e. the COVID-19 pandemic.

This study is based on secondary data collected from the Prowess database on Indian manufacturing firms listed on the Bombay Stock Exchange (BSE) 500. Panel data regression analyses are used to estimate all models. Moreover, this study has employed robust standard errors to consider for heteroscedasticity concerns.

The results challenge the current notion of working capital investment and reveal that higher working capital has a positive and significant impact on firm performance. Further, it highlights that Indian manufacturing firms suffered financially post-COVID-19 as they significantly lack the working capital to run day-to-day operations.

This research contributes to the scant literature by examining the association between working capital financing and firm performance in light of the COVID-19 pandemic, representing typical developing economies like India.

In real-time entertainment processing applications, processing of the multiple data streams demands high efficient multiple transfers, which leads to the computational overhead for system-on-chip (SoC), which runs the artificial intelligence algorithms. High-performance direct memory access controller (DMAC) is incorporated in SoC to perform the multiple data transfers without the participation of main processors. But achieving the area-efficient and power-aware DMAC suitable for streaming the multiple data remains to be a daunting challenge among the researchers.

The purpose of this paper to provide the DMA operations without intervention of central processing unit (CPU) for bulk video data transmissions.

The proposed DMAC has been developed based on the hybrid advanced extensible interface (AXI)-PCI bus subsystem to handle the multiple data streams from the video sources. The proposed model consists of bus selector module, user control signal, status register, DMA-supported address and AXI-PCI subsystems to achieve better performance in analysing the video frames.

The extensive experimentation is carried out with Xilinx Zynq SoC architecture using Very High Speed integrated circuit hardware description language (VHDL) programming, and performance metrics such as utilization area and power are calculated and compared with the other existing DMA controllers such as Scatter-DMA, Gather-DMA and Enhanced DMA. Simulation results demonstrate that the proposed DMAC has outperformed other existing DMAC in terms of less area, less delay and power, which makes the proposed model suitable for streaming multiple video streams.

The purpose of the study was to construct a Leadership Mindset Scale (LMS) and to assess its reliability and construct validity. Participants were 100 employees in a variety of leadership and non-leadership positions at various organizations in three states. An item and factor analysis on the 13 LMS items led to a scale with 11 items (Cronbach α = .80). A Principal Axis Factor analysis with Promax rotation suggested three factors: Leadership Mindset Teachability (LMS-T), a belief in leadership teachability; Leadership Mindset Improvability (LMS-I), a belief in leadership improvability over time; and Leadership Mindset Predictability (LMS-P), a belief that leadership cannot be predicted at an early age. Convergent validity of LMS-Total and Teachability was evidenced by significant correlations with the implicit theories of intelligence and anxiety scales, and developmental leadership and transactional leadership scales. Divergent validity was evidenced by a non-significant correlation with social desirability. The results suggest that the LMS measures a construct different from those of other leadership scales used in the study. The LMS can be helpful in leadership training programs to promote a growth mindset about the trainability of leadership skills.

The purpose of air-intake duct used in combat aircrafts is to decelerate the inlet flow and concurrently raise the static pressure recovery at the compressor inlet. Because of side-slip movement during sharp maneuvers of the aircrafts, the airflows ingested into twin air-intake ducts are not same and symmetric at its two inlets but are asymmetric in nature. The asymmetric inlet flow conditions at the twin air-intakes thus caused instabilities and deteriorated aerodynamic performance of aircraft components such as compressors and other downstream components. This study aims to investigate the flow control in a twin air-intake with asymmetric inflows.

The continuity and momentum equations are solved with second-order upwind scheme for computing finite-volume method-based unsteady computational fluid dynamics simulation.

Performance parameters are deteriorated with the increase of inflow asymmetry in the twin air-intake duct. Slotted synthetic jets are used to manage flow separation, thereby increasing aerodynamic performance of the air-intake. A variety of vortical structures are generated from the rectangular slots, convected downstream of the twin air-intake. The use of slotted synthetic jets increases static pressure recovery by 64 per cent whereas reducing total pressure loss coefficient by 63 per cent, distortion coefficient by 58 per cent and swirl coefficient by 55 per cent which is an indicative of better aerodynamic performance of twin air-intake.

The study stresses the need of robust flow control technique to improve the performance of combat air-intake system under extreme maneuvering conditions. The results can be useful in designing air-intake satisfying the stealth features for modern combat aircrafts.

The authors' study aims to examine to what extent managerial religiosity does influence human resource (HR) managers' attitudes towards women as managers (ATWM), and whether such posi(nega)tive attitudes can facilitate or impede the adoption of supportive HR practices (SHRP).

This study empirically examines a theoretical model by employing partial least squares-based structural equation modelling (PLS-SEM) using quantitative survey data from 182 HR managers in Bangladesh.

The authors' findings reveal that individual religiosity may adversely affect HR managers' attitudes towards recognising women as managers, and such stereotyped attitudes, in turn, may attenuate the adoption of supportive HR practices in organisations operating particularly in highly religious socio-culture environments.

The findings of the authors based on self-report, cross-sectional survey data collected from HR managers/equivalent working in the Bangladeshi organisations may unlikely to predict the ATWM held by the top leaders in organisations and other employees in similar socio-cultural settings.

The authors' findings suggest that religiosity cannot be ignored in management development and recruitment processes for HR managers, particularly in a society characterised by relatively weaker formal institutions and people with a higher degree of religiosity.

To the best of the authors' knowledge, this study is the first attempt explicating how top management's religiosity interacts with the attitudes towards the acceptance of women as managers and how such attitudes can influence the adoption of supportive HR practices.

The purpose of this paper is to present a way to determine the optimum values of design parameters in a cylindrical heat sink with branched fins. Investigations into the effect of design parameters, such as the number of fins, length of fin, height of fin and outer diameter of the heat sink on heat transfer are reported here. In this analysis, branch angle (α = 10°) is considered.

The Taguchi method, a powerful tool to design optimization, is applied for the tests and standard L9 orthogonal array with three factors, and three levels for each factor are selected. Nine test samples are analyzed in which the total heat transfer rate for each test sample is found. Contribution ratios for each parameter are also found. The results obtained from this analysis are used to find the optimum design parameter values relating to the heat sink performance.

The optimum design parameters are analyzed in this paper. The reliability of the optimum test samples is verified. Also, the variation of the average heat transfer rate of optimum sample is reported when it is compared with the reference sample.

Effective design of a cylindrical heat sink has been reported for cooling light-emitting diode (LED) lights, which have recently attracted the attention of the illumination industry. In this analysis, the contribution ratios have an important role to set out the performance characteristics of a heat sink.

The reliability of the optimum test samples is verified. Also, the variation of the average heat transfer rate of optimum sample is reported when it is compared with the reference sample.

The purpose of this paper is to improve the yield of a particular model of a car windshield, as the organization faces losses due to poor performance and rejection.

The Six Sigma DMAIC (define, measure, analyze, improve and control) methodology is used to reduce variation and defects in the process. It is a methodology based on data-driven and fact-based analysis to find out the root cause of the problem with the help of statistical analysis. A worst performing model is selected as a case study through the scoping tree. The preprocess, printing, bending and layup process defects are reduced by analyzing the potential causes and hypothesis testing.

This paper describes Six Sigma methodology in a glass manufacturing industry in India for automotive applications. The overall yield of a car windshield achieved 93.57 percent against the historical yield of 88.4 percent, resulting in saving 50 lacs per annum. Due to no rework or repairing in the glass, low first-time yield causes major losses. Process improvement through focused cross-functional team reduces variation in the process. Six Sigma improves profitability and reduces defects in the automotive glass manufacturing process.

This case study is applied in automotive glass manufacturing industries. For service and healthcare industries, a similar type of study can be performed. Further research on the common type of processor industry would be valuable.

The case study can be used as a problem-solving methodology in manufacturing and service industries. The tools and techniques can be used in other manufacturing processes also. This paper is useful for industries, researchers and academics for understanding Six Sigma methodology and its practical implementation.

This case study is an attempt to solve automobile glass manufacturing problems through DMAIC approach. The paper is a real case study showing benefits of Six Sigma implementation in the manufacturing industry and saving an annual cost of 50 lacs due to rejections in the process.

The purpose of this study is to compare the classification learning ability of our algorithm based on boosted support vector machine (B-SVM), against other classification techniques in predicting the credit ratings of banks. The key feature of this study is the usage of an imbalanced dataset (in the response variable/rating) with a smaller number of observations (number of banks).

In general, datasets in banking sector are small and imbalanced too. In this study, 23 Scheduled Commercial Banks (SCBs) have been chosen (in India), and their corresponding corporate ratings have been collated from the Indian subsidiary of reputed global rating agency. The top management of the rating agency provided 12 input (quantitative) variables that are considered essential for rating a bank within India. In order to overcome the challenge of dataset being imbalanced and having small number of observations, this study uses an algorithm, namely “Modified Boosted Support Vector Machines” (MBSVMs) proposed by Punniyamoorthy Murugesan and Sundar Rengasamy. This study also compares the classification ability of the aforementioned algorithm against other classification techniques such as multi-class SVM, back propagation neural networks, multi-class linear discriminant analysis (LDA) and k-nearest neighbors (k-NN) classification, on the basis of geometric mean (GM).

The performances of each algorithm have been compared based on one metric—the geometric mean, also known as GMean (GM). This metric typically indicates the class-wise sensitivity by using the values of products. The findings of the study prove that the proposed MBSVM technique outperforms the other techniques.

This study provides an algorithm to predict ratings of banks where the dataset is small and imbalanced. One of the limitations of this research study is that subjective factors have not been included in our model; the sole focus is on the results generated by the models (driven by quantitative parameters). In future, studies may be conducted which may include subjective parameters (proxied by relevant and quantifiable variables).

Various stakeholders such as investors, regulators and central banks can predict the credit ratings of banks by themselves, by inputting appropriate data to the model.

In the process of rating banks, the usage of an imbalanced dataset can lessen the performance of the soft-computing techniques. In order to overcome this, the authors have come up with a novel classification approach based on “MBSVMs”, which can be used as a yardstick for such imbalanced datasets. For this purpose, through primary research, 12 features have been identified that are considered essential by the credit rating agencies.