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PurposeFinancial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction.Design/methodology/approachThis study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity.FindingsThis study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics.Research limitations/implicationsThe major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency.Practical implicationsFinally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020).Originality/valueThis study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.

Visible light communication (VLC) is a promising candidate that is expected to revolutionize indoor environment communications performance and fulfill fifth generation and beyond (5GB) technologies requirements. It offers high and free bandwidth, electromagnetic interference immunity, low-cost front end and low power consumption. Also, VLC has dual functions that could be utilized in both illumination and communication concurrently. The number of optical attocells (OAs) and their deployment in the room represent the main issue that should be taken into consideration in designing an optimal VLC system. In this paper, we have introduced a new model of five OAs in the typical room. In addition to an investigation of various optical attocells (OAs) deployment models, in which a multi-variable evaluation was performed in terms of received power, illumination, SNR and RMS delay spread in order to determine the optimal OAs model. Also, various modulation schemes performances were investigated which included NRZ-OOK, BPSK, and QPSK in order to improve the BER performance. Results indicated that BPSK modulation had superior BER performance when compared with all OAs models. Further, a comprehensive results analysis and comparison of all proposed models was conducted over various parameters, in which our new proposed OAs model achieved an optimal performance in comparison with the other models.

The COVID-19 pandemic has impacted the world population adversely, posing a threat to human health. In the past few years, various strains of SARS-CoV-2, each with different mutations in its structure, have impacted human health in negative ways. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations influence the virulence, antibody evasion, and Angiotensin-converting enzyme 2 (ACE2) affinity of the virus. These mutations are essential to understanding how a new strain of SARS-CoV-2 has changed and its possible effects on the human body. This review provides an insight into the spike mutations of SARS-CoV-2 variants. As the current scientific data offer a scattered outlook on the various type of mutations, we aimed to categorize the mutations of Beta (B.1.351), Gamma (P.1), Delta (B.1.612.2), and Omicron (B.1.1.529) systematically according to their location in the subunit 1 (S1) and subunit 2 (S2) domains and summarized their consequences as a result. We also compared the miscellany of mutations that have emerged in all four variants to date. The comparison shows that mutations such as D614G and N501Y have emerged in all four variants of concern and that all four variants have multiple mutations within the N-terminal domain (NTD), as in the case of the Delta variant. Other mutations are scattered in the receptor binding domain (RBD) and subdomain 2 (SD2) of the S1 domain. Mutations in RBD or NTD are often associated with antibody evasion. Few mutations lie in the S2 domain in the Beta, Gamma, and Delta variants. However, in the Omicron variant many mutations occupy the S2 domain, hinting towards a much more evasive virus.

In this paper, we are proposing a detection scheme known as spectral direct detection technique implemented with Fiber Bragg Grating (FBG) act as encoder/decoder. This FBG based is used to encode and decode the spectral amplitude coding namely modified double weight (MDW) code in Optical Code Division Multiple Access (OCDMA). This code is used due to its flexibility where its weight can be any even number that greater than two. Moreover, it can maintain the cross-correlation parameter equal to one. The performance of spectral direct detection technique against AND-subtraction technique which is both implemented with FBG based encoder/decoder is compared via simulation in downstream and upstream access network at point to multipoint (P2MP) configuration. The simulation will be carried out using OptiSystem version 6.0 and the performance is characterized through bit error rate (BER) and power received at various bit rate.

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Filter Bank multicarrier (FBMC) has been modified for VLC. FBMC is the main filter suitable for high-speed data transfer using multicarrier modulation (MCM). Therefore, the FBMC is an appropriate alternative to the OFDM system which suffers from several weaknesses like limited bandwidth due to the presence of the cyclic prefix (CP). FBMC has been adapted to IM / DD compliant using Hermitian Symmetry to make the real signal, but this method increases system complexity and power consumption. In this paper, FBMC was adapted using a different technique than the above which consisted of generating an original complex FBMC signal. The signal was then dismantled into two real and imaginary parts (juxtaposing the real and imaginary parts of the complex signal in the time domain), thus the imaginary signal turned real. The proposed technique saved 50% in the number of the FFT/IFFT (Inverse Fast Fourier Transformer/Fast Fourier Transformer) operations, which resulted in a great decrease in power consumption and the occupied chip area.