Non-intrusive RF sensing for early diagnosis of spinal curvature syndrome disorders.

The recent developments in communication and information ease people's lives to sit in one place and access any information from anywhere. However, the longevity of sitting and sitting in different postures raises the issues of spinal curvature. It necessitates a physical examination to identify the spinal illness in its early stages. This article aims to develop an intelligent monitoring framework for detecting and monitoring spinal curvature syndrome problems based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing actual patients. The proposed SDRF-based system identifies irregular spinal curvature syndrome and offers feedback signals when an incorrect posture is identified. We design the system using wireless university software-defined radio peripheral (USRP) kits to transmit and receive RF signals and record the wireless channel state information (WCSI) for kyphosis, Lordosis, and scoliosis spinal disorders. The statistical measures are extracted from the WCSI and apply machine learning algorithms to identify and classify the type of disorders. We record and test the system using 11 subjects with the spinal disorders kyphosis, Lordosis, and scoliosis. We acquire the WCSI, extract various statistical measures in terms of time and frequency domain features, and evaluate machine learning classifiers to identify and classify the spinal disorder. The performance comparison of the machine learning algorithms showed overall and each spinal curvature disorder recognition accuracy of more than 99%.

Mechanical Performance of Polymeric ARGF-Based Fly Ash-Concrete Composites: A Study for Eco-Friendly Circular Economy Application.

At present, low tensile mechanical properties and a high carbon footprint are considered the chief drawbacks of plain cement concrete (PCC). At the same time, the combination of supplementary cementitious material (SCM) and reinforcement of fiber filaments is an innovative and eco-friendly approach to overcome the tensile and environmental drawbacks of plain cement concrete (PCC). The combined and individual effect of fly ash (FA) and Alkali resistance glass fiber (ARGF) with several contents on the mechanical characteristics of M20 grade plain cement concrete was investigated in this study. A total of 20 concrete mix proportions were prepared with numerous contents of FA (i.e., 0, 10, 20, 30 and 40%) and ARGF (i.e., 0, 0.5, 1 and 1.5%). The curing of these concrete specimens was carried out for 7 and 28 days. For the analysis of concrete mechanical characteristics, the following flexural, split tensile, and compressive strength tests were applied to these casted specimens. The outcomes reveal that the mechanical properties increase with the addition of fibers and decrease at 30 and 40% replacement of cement with fly ash. Replacement of cement at higher percentages (i.e., 30 and 40) negatively affects the mechanical properties of concrete. On the other hand, the addition of fibers positively enhanced the flexural and tensile strength of concrete mixes with and without FA in contrast to compressive strength. In the end, it was concluded that the combined addition of these two materials enhances the strength and toughness of plain cement concrete, supportive of the application of an eco-friendly circular economy. The relationship among the mechanical properties of fiber-reinforced concrete was successfully generated at each percentage of fly ash. The R-square for general relationships varied from (0.48-0.90) to (0.68-0.96) for each percentage of FA fiber reinforced concrete. Additionally, the accumulation of fibers effectively boosts the mechanical properties of all concrete mixes.

Utilization of Polymer Concrete Composites for a Circular Economy: A Comparative Review for Assessment of Recycling and Waste Utilization.

Polymer composites have been identified as the most innovative and selective materials known in the 21st century. Presently, polymer concrete composites (PCC) made from industrial or agricultural waste are becoming more popular as the demand for high-strength concrete for various applications is increasing. Polymer concrete composites not only provide high strength properties but also provide specific characteristics, such as high durability, decreased drying shrinkage, reduced permeability, and chemical or heat resistance. This paper provides a detailed review of the utilization of polymer composites in the construction industry based on the circular economy model. This paper provides an updated and detailed report on the effects of polymer composites in concrete as supplementary cementitious materials and a comprehensive analysis of the existing literature on their utilization and the production of polymer composites. A detailed review of a variety of polymers, their qualities, performance, and classification, and various polymer composite production methods is given to select the best polymer composite materials for specific applications. PCCs have become a promising alternative for the reuse of waste materials due to their exceptional performance. Based on the findings of the studies evaluated, it can be concluded that more research is needed to provide a foundation for a regulatory structure for the acceptance of polymer composites.

Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases.

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2, RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.