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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.

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.

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.