Unilateral Diaphragmatic Paralysis in a Patient With COVID-19 Pneumonia.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is best known for causing febrile pneumonia with lung parenchymal involvement. However, that is often not the only disease presentation, as many studies have shown that coronavirus disease 2019 (COVID-19) can present with other complications involving the cardiovascular and neurologic systems. Here, we report a case of COVID-19 pneumonia presenting with a peculiar finding of unilateral diaphragmatic paralysis. The patient presented with dyspnea requiring oxygen support via a nasal cannula. He was managed with the hospital's COVID-19 treatment protocols and clinically improved within 14 days of admission. This case helps shine some light on the neuroinvasive potential of SARS-CoV-2.

A Diagnostic Dilemma of Dysphonia: A Case Report on Laryngeal Myasthenia Gravis.

An autoimmune neuromuscular junction disorder, myasthenia gravis, occurs when antibodies are produced against postsynaptic membrane acetylcholine receptors. Late-onset myasthenia gravis, a rare variant of the disease found in the elderly, has become a diagnostic challenge on account of its atypical presentation. We proffer a case of a 60-year-old man that presented with progressive dysphonia and weakening of cough, which was eventually followed by difficulty in swallowing and nasal regurgitation. Examination and laboratory workup came out unremarkable apart from a positive acetylcholine receptor antibody test, due to which a diagnosis of laryngeal myasthenia, an uncommon presentation of late-onset myasthenia gravis was made. Following treatment with pyridostigmine and prednisolone saw a relief of the active complaints. This article highlights the need for physicians to stay alert and have a high suspicion of such probability for timely diagnosis.

Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography.

Breast cancer is one of the deadly diseases among women. However, the chances of death are highly reduced if it gets diagnosed and treated at its early stage. Mammography is one of the reliable methods used by the radiologist to detect breast cancer at its initial stage. Therefore, an automatic and secure breast cancer detection system that accurately detects abnormalities not only increases the radiologist's diagnostic confidence but also provides more objective evidence. In this work, an automatic Diverse Features based Breast Cancer Detection (DFeBCD) system is proposed to classify a mammogram as normal or abnormal. Four sets of distinct feature types are used. Among them, features based on taxonomic indexes, statistical measures and local binary patterns are static. The proposed DFeBCD dynamically extracts the fourth set of features from mammogram images using a highway-network based deep convolution neural network (CNN). Two classifiers, Support Vector Machine (SVM) and Emotional Learning inspired Ensemble Classifier (ELiEC), are trained on these distinct features using a standard IRMA mammogram dataset. The reliability of the system performance is ensured by applying 5-folds cross-validation. Through experiments, we have observed that the performance of the DFeBCD system on dynamically generated features through highway network-based CNN is better than that of all the three individual sets of ad-hoc features. Furthermore, the hybridization of all four types of features improves the system's performance by nearly 2-3%. The performance of both the classifiers is comparable using the individual sets of ad-hoc features. However, the ELiEC classifier's performance is better than SVM using both hybrid and dynamic features.

A New Fractional Particle Swarm Optimization with Entropy Diversity Based Velocity for Reactive Power Planning.

Optimal Reactive Power Dispatch (ORPD) is the vital concern of network operators in the planning and management of electrical systems to reduce the real and reactive losses of the transmission and distribution system in order to augment the overall efficiency of the electrical network. The principle objective of the ORPD problem is to explore the best setting of decision variables such as rating of the shunt capacitors, output voltage of the generators and tap setting of the transformers in order to diminish the line loss, and improve the voltage profile index (VPI) and operating cost minimization of standard electrical systems while keeping the variables within the allowable limits. This research study demonstrates a compelling transformative approach for resolving ORPD problems faced by the operators through exploiting the strength of the meta-heuristic optimization model based on a new fractional swarming strategy, namely fractional order (FO)-particle swarm optimization (PSO), with consideration of the entropy metric in the velocity update mechanism. To perceive ORPD for standard 30 and 57-bus networks, the complex nonlinear objective functions, including minimization of the system, VPI improvement and operating cost minimization, are constructed with emphasis on efficacy enhancement of the overall electrical system. Assessment of the results show that the proposed FO-PSO with entropy metric performs better than the other state of the art algorithms by means of improvement in VPI, operating cost and line loss minimization. The statistical outcomes in terms of quantile-quantile illustrations, probability plots, cumulative distribution function, box plots, histograms and minimum fitness evaluation in a set of autonomous trials validate the capability of the proposed optimization scheme and exhibit sufficiency and also vigor in resolving ORPD problems.

Sliding mode control for a fractional-order non-linear glucose-insulin system.

By providing the generalisation of integration and differentiation, and incorporating the memory and hereditary effects, fractional-order modelling has gotten significant attention in the past few years. One of the extensively studied and utilised models to describe the glucose-insulin system of a human body is Bergman's minimal model. This non-linear model comprises of integer-order differential equations. However, comparison with the experimental data shows that the fractional-order version of Bergman's minimal model is a better representative of the glucose-insulin system than its original integer-order model. To design a control law for an artificial pancreas for a diabetic patient using a fractional-order model, different techniques, including feedback linearisation, have been applied in the literature. The authors' previous work shows that the fractional-order version of Bergman's model describes the glucose-insulin system in a better way than the integer-order model. This study applies the sliding mode control technique and then compares the obtained simulation results with the ones obtained using feedback linearisation.

Quinoa (Chenopodium quinoa Willd.): An Overview of the Potentials of the "Golden Grain" and Socio-Economic and Environmental Aspects of Its Cultivation and Marketization.

Quinoa (Chenopodium quinoa Willd.) is native to the Andean region and has attracted a global growing interest due its unique nutritional value. The protein content of quinoa grains is higher than other cereals while it has better distribution of essential amino acids. It can be used as an alternative to milk proteins. Additionally, quinoa contains a high amount of essential fatty acids, minerals, vitamins, dietary fibers, and carbohydrates with beneficial hypoglycemic effects while being gluten-free. Furthermore, the quinoa plant is resistant to cold, salt, and drought, which leaves no doubt as to why it has been called the "golden grain". On that account, production of quinoa and its products followed an increasing trend that gained attraction in 2013, as it was proclaimed to be the international year of quinoa. In this respect, this review provides an overview of the published results regarding the nutritional and biological properties of quinoa that have been cultivated in different parts of the world during the last two decades. This review sheds light on how traditional quinoa processing and products evolved and are being adopted into novel food processing and modern food products, as well as noting the potential of side stream processing of quinoa by-products in various industrial sectors. Furthermore, this review moves beyond the technological aspects of quinoa production by addressing the socio-economic and environmental challenges of its production, consumption, and marketizations to reflect a holistic view of promoting the production and consumption of quinoa.

Evaluating the effects of social networking sites addiction, task distraction, and self-management on nurses' performance.

AIMS: The purpose of this study was to explore the relationship of social networking sites (SNSs) addiction on nurses' performance and how this relationship was mediated by task distraction and moderated by self-management. DESIGN: This cross-sectional study is designed to empirically test the relationship of SNSs addiction, task distraction, and self-management with the nurses' performance. METHODS: Data were collected by conducting an online survey on nurses across the world using a web-based questionnaire developed through 'Google Docs' and distributed through Facebook from 13 August 2018 - 17 November 2018. The Facebook groups were searched using the selected key terms. In total, 45 groups were found to have relevance to this research; therefore, request was made to the admins of these groups to participate in this research and to post a link in their groups. Only 19 group admins responded positively by uploading a link of the research instrument on their respective group pages and 461 members of these groups participated in the research. RESULTS: Results of the data collected from 53 different countries indicated that SNSs addiction results in lowering the nurses' performance. This relationship is further strengthened by task distraction introduced as a mediating variable. The results show that self-management mediates the relationship between SNSs addiction and employees' performance. Moreover, the results of the study confirm that self-management reduces the negative impact of SNSs addiction on nurses' performance. CONCLUSION: Social networking sites (SNSs) addiction and task distraction reduce the nurses' performance, whereas self-management enhances nurses' performance. IMPACT: This study addresses the problem of using SNSs at the workplace and its potential effect on nurses' performance. Results demonstrate that SNSs addiction reduces the performance which is further decreased by task distraction; however, self-management of nurses can enhance the nurses' performance. The research has numerous theoretical and practical implications for hospital administration, doctors, and nurses.