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This study aims to empirically investigate the impact of foreign technology transfer and R&D on total factor productivity and labour productivity of Indian firms. The results of production function analysis indicate that foreign technology plays a significant role in output growth. The results of growth the accounting approach also suggest a critical role of foreign technology in the productivity growth of firms. Interestingly, our findings indicate that while R&D does not significantly contribute to productivity growth, it shares a complementary relationship with foreign technology and drives a firm’s productivity. Our findings indicate that firms that engage in R&D and foreign technology transfer have a competitive edge in terms of productivity and output, but the role of R&D intensity cannot be established.

In this paper, Hybrid satellite-terrestrial systems (HSTC) are preferred in dynamic type environments with high mobility of nodes due to the performance enhancement features in multiple relay-based selective decode-and-forward (DF) approach. Because of the satellite link, aerial satellite to destination and satellite to relay links, are not the same due to time-selective shadowed Rician fading. Time-selective Rician fading depends on parameters like the angle of elevation of the satellite, the terrestrial relay node, and the node destination links, considered to be distinctively time-selective Nakagami faded. Here, per-frame average symbol error rate (SER) and outage probability are derived in a closed-form expression with consideration of M-ary PSK modulated symbols transmission. After evaluation, it was detected that the system performance is significantly degraded due to the time-varying nature of the links (dynamic environment). After various simulations and calculations, it was established that the error rate of the HSTC is significantly low by increasing the elevation angle of the satellite on the relay point. The performance enhancement can be observed by enhancing the satellite angle at the destination node of user equipment.

This article shows the heterogeneous network of Massive Multiple-Input Multiple-Output (mMIMO) system and Non-Orthogonal Multiple Access (NOMA) scheme in Downlink (DL) scenario. The performance of mMIMO systems using Maximal Ratio Transmission (MRT) and Zero-Forcing (ZF) precoding techniques has been investigated and compared with mMIMO-NOMA systems. The problem of Pilot Contamination (PC) arises when the channel is estimated at the Base Station (BS) due to the reuse of the same pilot matrix in co-channel cells. To reduce the co-channel interference, it has been shown that the Cell Sectoring (CS) of 120 degree and 60 degree can be employed. Sum-Rate (SR) capacities have been derived for the mMIMO and the mMIMO-NOMA systems for un-sectored and sectored cells considering both Perfect Channel State Information (PCSI), and Imperfect Channel State Information (ICSI). In a multi-cell scenario, it has been shown that the mMIMO-NOMA system exploits the precoding advantage along with successive interference cancellation and outperforms the standalone mMIMO system. Further, the ZF precoded mMIMO-NOMA system with 60 degree CS has been observed to be the most appropriate candidate amongst all three systems viz. 120 degree CS, 60 degree CS, and un-sectored system in terms of reduced interference.

The aim of this paper is to explore the macroeconomic determinants of corporate financial distress using a data from the Indian corporate sector. We also consider a set of quasi-macro variables to develop a hybrid model of financial distress. For this purpose, we use accounting information-based three-factor criteria to construct a series for probability of financial distress of firms. We initially use a systematic variable selection approach to develop alternative models of financial distress and then apply the bounds test to establish a long-run equilibrium relationship between financial distress of firms and its determinants. We find that macroeconomic factors play crucial role in determining financial distress. Results suggest that aggregate output, flow of international funds, international demand, and corporate profitability are negatively associated with probability of financial distress. However, periods of high inflation may not be beneficial as the results suggest that a high inflation leads to high financial distress. The findings are important in the sense that movement in these macroeconomic and quasi-macro factors can be useful in monitoring the buildup of risk or financial distress in the balance sheet of firms over time. The set of indicators identified in the study can be used to develop an efficient distress prediction models.

Linezolid-induced lactic acidosis is a rare, but life-threatening complication of a commonly used drug. Patients present with persistent lactic acidosis, hypoglycaemia, high central venous oxygen saturation and shock. Linezolid causes mitochondrial toxicity due to impaired oxidative phosphorylation. This is evidenced by cytoplasmic vacuolations in the myeloid and erythroid precursors of bone marrow smear as illustrated in our case. Discontinuation of the drug, administration of thiamine and haemodialysis reduces lactic acid levels.

PurposeThe main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.Design/methodology/approachIn this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.FindingsThe study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.Practical implicationsThe findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.Originality/valueThis study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

In this paper, we explore the possibilities and advantages of cooperative relaying with the addition of non-orthogonal multiple access (NOMA). First, the possibilities of NOMA for fifth-generation (5G) and beyond networks is discussed followed by the generalized structures for the cooperative NOMA. Then, advanced NOMA communication is investigated where NOMA is integrated with advanced transmission technologies for the further improvement in cooperative NOMA. Hereinafter, resource allocation is investigated and, finally, the major challenges and issues are highlighted.

In this work, the performance of the downlink non-orthogonal multiple access (NOMA) technique is investigated for two users considering optimal power allocation factors. The power domain NOMA differentiates the users based on channel gains by providing different power levels and it is demonstrated that optimal power allocation is only possible when the gain ratio is maximum. Further, the range of optimal power levels is derived for the strong user having better channel conditions. Furthermore, the outage probability (OP) has been derived for ordered NOMA in the downlink through the cumulative density function-based approach. The simulation results demonstrate the improvement in sum rate capacity for optimal power allocation as compared to random power allocation, and the OP reduces with the signal-to-noise ratio more sharply for the stronger user.

Any disease is curable if it is diagnosed at the early stages with the help of a little human effort. The disease breast cancer is the second leading cause of death among women after lung cancer. Mammography is one of the most mainstream clinical imaging modalities that are utilized for early recognition of breast cancer. Early breast cancer detection helps to alleviate unnecessary treatments as well as saving women’s lives. The speedy development in deep learning and some of the strategies of machine learning have invigorated abundant enthusiasm for their application to clinical imaging issues. This paper presents an improved convolutional neural network (CNN) model that consists of three convolutional layers where the starting layer searches for low-level features and the ending layer searches for high-level features. Two activation functions, that is, the Rectified Linear Unit and sigmoid functions, are utilized for the detection of breast cancer using digitized film mammograms from the Digital Database for Screening Mammography. The proposed convolutional neural system for identifying breast malignancy on mammogram imaging achieved praiseworthy execution on examination with prior models. The experimentation found that the model achieved has a true positive rate of 99% (accuracy = 97.20%, precision = 99%, true negative rate = 96%, F-score = 0.99, balanced classification rate = 0.975, Youden’s index = 0.95). The proposed improved CNN model can be used as a second opinion of doctors to detect breast cancer.

In this era of communication technology, it is desirable to increase the data rate while minimizing the error to improve the system’s reliability. One of these techniques is massive multiple-input multiple-output (mMIMO), which increases the spectral efficiency by providing the data to multiple users simultaneously through spatial multiplexing. The mMIMO system processes the received signal by prior estimation of the channel, which has a finite variance leading to imperfect channel state information (ICSI) at the receiver. In the fifth-generation technology, spectral efficiency using mMIMO may decrease as the number of subscribers increases, resulting in more interference and affecting system capacity. The ICSI provides another challenge, as the processed data at the receiver’s output may now be more erroneous. Thus, this article provides an insight into the impact of an increase in the number of users on the variation in bit error rate with the signal-to-noise ratio in multi-user mMIMO (MU-mMIMO) and low-density parity check (LDPC) codes concatenated MU-mMIMO systems having ICSI at the receiver for quadrature amplitude modulation (QAM) and 16-QAM as modulation techniques. It has been shown that the performance of the concatenated scheme outperforms the conventional mMIMO system.

The bottle gourd is a common vegetable in India subcontinental. Osmotic dehydration increases the storage ability of fruits and vegetables making them available throughout the year. Dehydrated products also play a great role in processed foods of all kinds and the way to achieve high quality dehydrated products are desired. The combination of osmosis with solar drying has been put forward, mainly for tropical fruit. A 24-hour cycle has been suggested combining osmodehydration, performed during the night, solar drying during the day. The well washed bottle gourd fruits were then peeled off with help of stainless-steel hand peeler. Pieces of bottle gourd cubes (1cmx1cmx1cm) were then cut out using a ruler and knife. The salt solution with 5, 10, 15, 20, and 25% (w/w) concentration were prepared. Bottle gourd cubes were immersed in the osmotic reagents with a fruit to osmotic solution mass ratio of 10:1, 20:1 and 30:1 soaking experiments were conducted at 400C, 500C and 600C in a 500 mi capacity water bath. The osmotic dehydration was done for a period of 1, 3, 5, 7 and 9 h and replicated thrice. The dependent variable studied was moisture content after dehydration. Moisture content of sample was determined by standard air oven method (Ranganna, 1995). The minimum moisture content was 46.9% (wb) for samples osmotically dehydrated at 600C for 9 h at 30:1 fruit to osmotic solution at 25% concentration. The moisture content of bottle gourd cubes observed decreases with higher concentration, temperature and mass ratio. Solar drying characteristics of 9 h bottle gourd cubes at 600C are determined by solar tray dryer. During the process of solar drying of 9 h bottle gourds cubes, the air temperature inside the cabinet varies 44 to 810C. and relative humidity inside the chamber was measures 34 to 58 percent. The minimum value of final moisture content of osmo-solar dried bottle gourd cubes was 8.3% at 600C for sample of 10:1 mass ratio and maximum value of final moisture content was 24.4% of at 600C for 30:1 sample.

Osmotic dehydration of fruits and vegetables an effective method for preservation. The Bottle gourd (Legeneria siceraria) belongs to Cucurbitaceous family. Average composition of fresh bottle gourd is constituted by water (96.0%), carbohydrates (2.5%), fibre (0.6%), proteins (0.2%) and calcium (20mg). The bottle gourd is a common vegetable in India subcontinental. It is moderately perishable vegetable. In this report, efforts were made to analyze the effect of different concentration, temperature and mass ratio of fruit to osmotic solution on osmotic dehydration process of bottle gourd cubes. comparative study for different parameters variables (solid gain weight reduction, water loss) was also studies. Five concentration levels of salt solution were 5, 10, 15, 20, and 25% (w/w) used in this study to compare the effect of the concentration of the osmotic agents on osmotic dehydration. Bottle gourd cubes were immersed in the osmotic reagents with a fruit to osmotic solution mass ratio of 10:1, 20:1 and 30:1 soaking experiment were conducted at 40, 50 and 600C in a 500 mi capacity water bath. The osmotic dehydration was done for a period of 1, 3, 5, 7 and 9 h and replicated thrice. Quality analysis of osmotically dehydrated bottle gourd cubes was done according to ISI (1971) and Ranganna (1991) with 9-point hedonic scale. The moisture content of bottle gourd cubes observed decreases with higher concentration, temperature and mass ratio. The weight reduction, water loss and solid gain were very fast during initial period of osmosis but equilibrium did reach even after 9h. The solid gain, water loss and weight reduction were observed higher for higher temperature, concentration and mass ratio of bottle gourd cubes. The sensory evaluation performed for the approximately central conditions processing parameters gave average scores of 7 or above.

This paper analyzes the performance of spatially modulated (SM) multiple-input-multiple-output (MIMO) system using k – μ fading channel. The Pairwise error probability (PEP) analysis of SM-MIMO over k – μ fading channel considering M-PSK modulation scheme has been done. Further, the analysis is done for different values of k and μ in k – μ fading channel.The SMMIMO PEP expressions for k – μ fading channel considering M-PSK modulation scheme has been derived. The Performance comparison of simulation results has been done for k – μ fading channels.The performance comparison of PEP has been done for M-PSK using k – μ fading channel.

RGB to gray conversion is an integral part of various computer vision applications such as face detection, object detection and surveillance systems. The resource required for the real time implementation of all these applications decreases to a great extent if computation is performed on gray images, which has 8 bit wide pixel, rather than color images, which has 24 bit wide pixel. In this paper, hardware efficient implementation of RGB to gray image is proposed which is realized on both FPGA and ASIC. FPGA realization is performed on digilent Zedboard having Artix-7 FPGA while the ASIC implementation is performed using Cadence Genus and Innovus tool at 45 nm process technology. ASIC implementation of proposed technique brings about total area utilization of 262 um2 and ADP of 18.078 um2 *ns which are respectively 81.42% and 96.55% less contrasted with existing design. The proposed system is seen to operate at high frequency of 3 GHz.

A collection of independent terminals instantaneously transmits data streams to a compact array of antennas. With imperfect channel state information (CSI), the antenna makes use of transmitted pilots to find the knowledge about CSI and the power dissipated by the terminals can be made reciprocally proportional to the square root of the number of antennas without a reduction in performances used at the base station. In contrast, when the CSI is known at the base station, the transmitted power is inversely proportional to the number of used antennas. Lower capacity bounds for zero-forcing (ZF) and maximum-ratio combining (MRC) detection are derived. A ZF receiver outperforms the MRC. A power scaling law is being taken into consideration in the analysis of the uplink sum rate with perfect and imperfect CSI. It is analyzed that the uplink sum rate for MRC and ZR receiver with perfect and imperfect CSI, considering the power scaling law. As the number base station antennas increases, the difference between the uplink sum rate for ZF and MRC decreases and if the number of base station antenna is very high, then both of these lines coincide with each other. Especially at a very large number of base station antennas, all bounds are very tight. In comparison to a single antenna system, the uplink sum rate (or spectral efficiency) can be improved in order of several magnitudes by using moderately large antenna arrays in massive multiple input multiple output (MIMO) system.

In this paper, we analyze the performance of spatially modulated (SM) multiple input multiple output (MIMO) system and spatially modulated MIMO cooperative system with multiple relays employing selective decode and forward (SDF) protocol. Here the analysis is done for pre-determined orthogonal channel for SM MIMO and SM MIMO cooperative system using M-PSK modulation and ML detection. Firstly Pairwise error probability (PEP) expression is derived and optimal, sub-optimal detection is done in a spatially modulated MIMO system. Further, the analysis of SDF spatially modulated MIMO cooperative system is extended to single relay and multiple relay configurations. PEP expression is also derived for SDF spatially modulated MIMO cooperative system. Simulation results for the SM MIMO system show optimal detection performs better than suboptimal detection and system performance improves as the number of antenna increases. The simulation results of PEP analysis in the SM MIMO cooperative system include the theoretical PEP, exact PEP and asymptotic PEP and Optimal power allocation (OPA) is also derived for the same. Optimal power allocation for theoretical PEP, exact PEP and asymptotic PEP outperforms the same at equal power allocation.

Fossil meteoritic records of short-lived, now-extinct radionuclides provide crucial high-resolution temporal information about the events, processes and activity of the Sun during the early phases of Solar System formation1. The proposed genesis of one such radionuclide,10Be, by spallation reactions of carbon and oxygen2–5 led to the hypothesis of enhanced irradiation in the early Solar System6–8. An alternative scenario of production of 10Be (half-life t1/2 = 1.386 ± 0.016 million years9) by a neutrino process in a supernova arising from the core collapse of a low-mass star (11.8 solar masses, M⊙) has recently been suggested10 and can explain the observed abundance of 10Be in the early Solar System. Here, we report well-resolved excesses in 7Li/6Li of up to ~21.5% in a calcium- and aluminium-rich inclusion (CAI) from the Efremovka meteorite that correlate with 9Be/6Li, suggestive of in situ decay of 7Be. The in situ decay of 7Be to 7Li, with a characteristic half-life of 53.12 ± 0.07 days11, entails multiple episodes of enhanced irradiation in the early Solar System, which have been observed recently in other Sun-like stars12,13. The short half-life of 7Be limits its production by interaction of solar energetic particles (SEPs) with the nebular gas and solids, and provides constraints on the genealogy and chronology of CAIs. Irradiation of the solid and gaseous precursors of CAIs of solar composition by a superflare (X-ray luminosity approximately 1032 erg s−1) during the terminal phase of class I or II of the pre-main-sequence stages of the Sun explains the isotopic properties, distinctive petrographic features and diffusivity constraints in the CAI. Li and Be isotopic measurements of calcium–aluminium inclusions (CAIs) in the Efremovka meteorite suggest that the solid and gaseous precursors of CAIs were irradiated by a superflare during the last stages of the pre-main-sequence evolution of the Sun.

In this article, the improvement in the performance of massive multiple input multiple output (mMIMO) systems has been shown using successive interference cancellation (SIC) at the base station (BS). The pilot contamination (PC) due to pilot reuse in multiple cells sharing the same frequency band results in the interference that worsens as the number of cells increases. The SIC at the BS reduces the impact of PC when compared with standalone decoding using maximal ratio combining (MRC). The performance in terms of sum rate (SR) capacity has been obtained when the number of users has been varied. The MRC with mMIMO systems exhibits poor performance as the SR reduces with increase in the number of users while the MRCSIC scheme improves the SR since the symbol of a user which is being decoded is not considered in the decoding of next user symbol. The paper also shows the variation in the SR with increase in the number of BS antennas for MRC and MRCSIC schemes. The simulation results have shown that the MRC-SIC scheme outperforms the standalone MRC scheme.