Next generation legged robot locomotion: A review on control techniques.

The next generation of autonomous-legged robots will herald a new era in the fields of manufacturing, healthcare, terrain exploration, and surveillance. We can expect significant progress in a number of industries, including inspection, search and rescue, elderly care, workplace safety, and nuclear decommissioning. Advanced legged robots are built with a state-of-the-art architecture that makes use of stereo vision and inertial measurement data to navigate unfamiliar and challenging terrains.

Potential smart grid vulnerabilities to cyber attacks: Current threats and existing mitigation strategies.

A novel concept in the realm of conventional electricity grids, known as the "smart grid," has emerged to explore the most effective methods for integrating green and renewable energy sources. By leveraging existing technologies for its communication network, the Smart Grid also inherits their associated drawbacks. Exploiting these vulnerabilities can lead to severe consequences such as privacy breaches, cascading failures, or even system-wide blackouts.

Towards net zero: A technological review on the potential of space-based solar power and wireless power transmission.

The global need for energy is increasing at a high rate and is expected to double or increase by 50%, according to some studies, in 30 years. As a result, it is essential to look into alternative methods of producing power. Solar photovoltaic (PV) power plants utilize the sun's clean energy, but they're not always dependable since they depend on weather patterns and requires vast amount of land.

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives.

Digital twin: Data exploration, architecture, implementation and future.

A Digital Twin (DT) is a digital copy or virtual representation of an object, process, service, or system in the real world. It was first introduced to the world by the National Aeronautics and Space Administration (NASA) through its Apollo Mission in the '60s. It can successfully design a virtual object from its physical counterpart.

A review on brain tumor segmentation based on deep learning methods with federated learning techniques.

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation.

A review on self-healing featured soft robotics.

Soft robots are becoming more popular because they can solve issues stiff robots cannot. Soft component and system design have seen several innovations recently. Next-generation robot-human interactions will depend on soft robotics.

Selectivity of Sol-Gel and Hydrothermal TiO Nanoparticles towards Photocatalytic Degradation of Cationic and Anionic Dyes.

Titanium dioxide (TiO) nanoparticles have been extensively studied for catalyzing the photo-degradation of organic pollutants, the photocatalyst being nonselective to the substrate. We, however, found that TiO nanoparticles prepared via the sol-gel and hydrothermal synthetic routes each possess a definite specificity to the charge of the substrate for photodegradation. The nanoparticles were characterized by SEM, FTIR, XRD, TGA, and UV-visible spectra, and the photocatalytic degradation under UV-B (285 nm) irradiation of two model compounds, anionic methyl Orange (MO) and cationic methylene blue (MB) was monitored by a UV-visible spectrophotometer.

Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.

Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia have spread over the world, killing millions of people. Medical specialists have experienced challenges in correctly identifying these diseases due to their subtle differences in Chest X-ray images (CXR). To assist the medical experts, this study proposed a computer-aided lung illness identification method based on the CXR images.

Savonius wind turbine blade design and performance evaluation using ANN-based virtual clone: A new approach.

The drag based Savonius wind turbine (SWT) has shown immense potential for renewable power generation in built-up areas under complex urban wind conditions. While a series of studies have been conducted on improving SWT's efficiency, optimal performance has yet to be achieved using traditional design approaches such as experimental and/or computational fluid dynamics methods. Recently, artificial intelligence and machine learning have been widely used in design optimization.

Submicron Aerosol Composition and Source Contribution across the Kathmandu Valley, Nepal, in Winter.

The Kathmandu valley experiences an average wintertime PM concentration of ∼100 μg m and daily peaks over 200 μg m. We present ambient nonrefractory PM chemical composition, and concentration measured by a mini aerosol mass spectrometer (mAMS) sequentially at Dhulikhel (on the valley exterior), then urban Ratnapark, and finally suburban Lalitpur in winter 2018. At all sites, organic aerosol (OA) was the largest contributor to combined PM (C-PM) (49%) and black carbon (BC) was the second largest contributor (21%).

Wintertime Air Quality across the Kathmandu Valley, Nepal: Concentration, Composition, and Sources of Fine and Coarse Particulate Matter.

The Kathmandu Valley in Nepal experiences poor air quality, especially in the dry winter season. In this study, we investigated the concentration, chemical composition, and sources of fine and coarse particulate matter (PM, PM, and PM) at three sites within or near the Kathmandu Valley during the winter of 2018 as part of the second Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE 2). Daily PM concentrations were very high throughout the study period, ranging 72-149 μg m at the urban Ratnapark site in Kathmandu, 88-161 μg m at the suburban Lalitpur site, and 40-74 μg m at rural Dhulikhel on the eastern rim of the Kathmandu Valley.

Polymorphisms in Gene and Susceptibility of Colorectal Cancer in Bangladeshi Population: A Case-Control Analysis.

Objective: Interleukin-17A () genetic polymorphisms are associated with multiple cancer types, including colorectal cancer (CRC). However, no previous study was performed in the Bangladeshi population to evaluate the association. Our study aimed to find the association between two variants (rs10484879 C/A and rs3748067 G/A) and susceptibility of CRC.

Generating binomial coefficients in a row of Pascal's triangle from extensions of powers of eleven.

Sir Isaac Newton noticed that the values of the first five rows of Pascal's triangle are each formed by a power of 11, and claimed that subsequent rows can also be generated by a power of 11. Literally, the claim is not true for the row and onward. His genius mind might have suggested a deep relation between binomial coefficients and a power of some integer that resembles the number 11 in some form.

Plastic Burning Impacts on Atmospheric Fine Particulate Matter at Urban and Rural Sites in the USA and Bangladesh.

To better understand the impact of plastic burning on atmospheric fine particulate matter (PM), we evaluated two methods for the quantification of 1,3,5-triphenylbenzene (TPB), a molecular tracer of plastic burning. Compared to traditional solvent-extraction gas chromatography mass spectrometry (GCMS) techniques, thermal-desorption (TD) GCMS provided higher throughput, lower limits of detection, more precise spike recoveries, a wider linear quantification range, and reduced solvent use. This method enabled quantification of TPB in fine particulate matter (PM) samples collected at rural and urban sites in the USA and Bangladesh.

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images.

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death.

Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach.

Recently the most infectious disease is the novel Coronavirus disease (COVID 19) creates a devastating effect on public health in more than 200 countries in the world. Since the detection of COVID19 using reverse transcription-polymerase chain reaction (RT-PCR) is time-consuming and error-prone, the alternative solution of detection is Computed Tomography (CT) images. In this paper, Contrast Limited Histogram Equalization (CLAHE) was applied to CT images as a preprocessing step for enhancing the quality of the images.