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. Space-based solar power (SBSP) has emerged as the potential solution to this issue. SBSP can provide 24/7 baseload carbon-free electricity with power density over 10 times greater than terrestrial alternatives while requiring far less land. Solar power is collected and converted in space to be sent back to Earth via Microwave or laser wirelessly and used as electricity. However, harnessing its full potential necessitates tackling substantial technological obstacles in wireless power transmission across extensive distances in order to efficiently send power to receivers on the ground. When it comes to achieving a net-zero goal, the SBSP is becoming more viable option. This paper presents a review of wireless power transmission systems and an overview of SBSP as a comprehensive system. To introduce the state-of-the-art information, the properties of the system and modern SBSP models along with application and spillover effects with regard to different sectors was examined. The challenges and risks are discussed to address the key barriers for successful project implementation. The technological obstacles stem from the fact that although most of the technology is already available none are actually efficient enough for deployment so with, private enterprises entering space race and more efficient system, the cost of the entire system that prevented this notion from happening is also decreasing. With incremental advances in key areas and sustained investment, SBSP integrated with other renewable could contribute significantly to cross-sector decarbonization.

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. However, the main function of a digital twin system is to provide a bidirectional data flow between the physical and the virtual entity so that it can continuously upgrade the physical counterpart. It is a state-of-the-art iterative method for creating an autonomous system. Data is the brain or building block of any digital twin system. The articles that are found online cover an individual field or two at a time regarding data analysis technology. There are no overall studies found regarding this manner online. The purpose of this study is to provide an overview of the data level in the digital twin system, and it involves the data at various phases. This paper will provide a comparative study among all the fields in which digital twins have been applied in recent years. Digital twin works with a vast amount of data, which needs to be organized, stored, linked, and put together, which is also a motive of our study. Data is essential for building virtual models, making cyber-physical connections, and running intelligent operations. The current development status and the challenges present in the different phases of digital twin data analysis have been discussed. This paper also outlines how DT is used in different fields, like manufacturing, urban planning, agriculture, medicine, robotics, and the military/aviation industry, and shows a data structure based on every sector using recent review papers. Finally, we attempted to give a horizontal comparison based on the features of the data across various fields, to extract the commonalities and uniqueness of the data in different sectors, and to shed light on the challenges at the current level as well as the limitations and future of DT from a data standpoint.

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. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues.

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. Soft material technologies integrate safety at the material level, speeding its integration with biological systems. Soft robotic systems must be as resilient as biological systems in unexpected, uncontrolled situations. Self-healing materials, especially polymeric and elastomeric ones, are widely studied. Since most currently under-development soft robotic systems are composed of polymeric or elastomeric materials, this finding may provide immediate assistance to the community developing soft robots. Self-healing and damage-resilient systems are making their way into actuators, structures, and sensors, even if soft robotics remains in its infancy. In the future, self-repairing soft robotic systems composed of polymers might save both money and the environment. Over the last decade, academics and businesses have grown interested in soft robotics. Despite several literature evaluations of the soft robotics subject, there seems to be a lack of systematic research on its intellectual structure and development despite the rising number of articles. This article gives an in-depth overview of the existing knowledge base on damage resistance and self-healing materials' fundamental structure and classifications. Current uses, problems with future implementation, and solutions to those problems are all included in this overview. Also discussed are potential applications and future directions for self-repairing soft robots.

Selectivity of Sol-Gel and Hydrothermal TiO2 Nanoparticles towards Photocatalytic Degradation of Cationic and Anionic Dyes.

Titanium dioxide (TiO2) nanoparticles have been extensively studied for catalyzing the photo-degradation of organic pollutants, the photocatalyst being nonselective to the substrate. We, however, found that TiO2 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. Untreated sol-gel TiO2 nanoparticles (Tsg) preferentially degraded MO over MB (90% versus 40% in two hours), while after calcination at 400 °C for two hours (Tsgc) they showed reversed specificity (50% MO versus 90% MB in one hour). The as-prepared hydrothermal TiO2 nanoparticles (Tht) behaved in the opposite sense of Tsg (41% MO versus 91% MB degraded in one and a half hours); calcination at 400 °C (Thtc) did not reverse the trend but enhanced the efficiency of degradation. The study indicates that TiO2 nanoparticles can be made to degrade a specific class of organic pollutants from an effluent facilitating the recycling of a specific class of pollutants for cost-effective effluent management.

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. For the first time, 17 different forms of lung disorders were considered and the study was divided into six trials with each containing two, two, three, four, fourteen, and seventeen different forms of lung disorders. The proposed framework combined robust feature extraction capabilities of a lightweight parallel convolutional neural network (CNN) with the classification abilities of the extreme learning machine algorithm named CNN-ELM. An optimistic accuracy of 90.92% and an area under the curve (AUC) of 96.93% was achieved when 17 classes were classified side by side. It also accurately identified COVID-19 and TB with 99.37% and 99.98% accuracy, respectively, in 0.996 microseconds for a single image. Additionally, the current results also demonstrated that the framework could outperform the existing state-of-the-art (SOTA) models. On top of that, a secondary conclusion drawn from this study was that the prospective framework retained its effectiveness over a range of real-world environments, including balanced-unbalanced or large-small datasets, large multiclass or simple binary class, and high- or low-resolution images. A prototype Android App was also developed to establish the potential of the framework in real-life implementation.

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. As such, an ANN-based virtual clone can be an alternative to traditional design methods for wind turbine performance determination. Therefore, the main goal of this study is to investigate whether ANN-based virtual clones are capable of determining the performance of SWTs with a shorter timeframe and minimal resources compared to traditional methods. To achieve the objective, an ANN-based virtual clone model is developed. Two sets of data (computational and experimental) are used to validate and determine the efficacy of the proposed ANN-based virtual clone model. Using experimental data, the model's fidelity is over 98%. The proposed model produces results in one-fifth the time of the existing simulation (based on the combined ANN + GA metamodel) method. The model also reveals the location of the dataset's optimized point for augmenting the turbine's performance.

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

The Kathmandu valley experiences an average wintertime PM1 concentration of ∼100 µg m-3 and daily peaks over 200 µg m-3. We present ambient nonrefractory PM1 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 PM1 (C-PM1) (49%) and black carbon (BC) was the second largest contributor (21%). The average background C-PM1 at Dhulikhel was 48 µg m-3; the urban enhancement was 120% (58 µg m-3). BC had an average of 6.1 µg m-3 at Dhulikhel, an urban enhancement of 17.4 µg m-3. Sulfate (SO4) was 3.6 µg m-3 at Dhulikhel, then 7.5 µg m-3 at Ratnapark, and 12.0 µg m-3 at Lalitpur in the brick kiln region. Chloride (Chl) increased by 330 and 250% from Dhulikhel to Ratnapark and Lalitpur on average. Positive matrix factorization (PMF) identified seven OA sources, four primary OA sources, hydrocarbon-like (HOA), biomass burning (BBOA), trash burning (TBOA), a sulfate-containing local OA source (sLOA), and three secondary oxygenated organic aerosols (OOA). OOA was the largest fraction of OA, over 50% outside the valley and 36% within. HOA (traffic) was the most prominent primary source, contributing 21% of all OA and 44% of BC. Brick kilns were the second largest contributor to C-PM1, 12% of OA, 33% of BC, and a primary emitter of aerosol sulfate. These results, though successive, indicate the importance of multisite measurements to understand ambient particulate matter concentration heterogeneity across urban regions.

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 (PM2.5, PM10, and PM10-2.5) 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 PM2.5 concentrations were very high throughout the study period, ranging 72-149 µg m-3 at the urban Ratnapark site in Kathmandu, 88-161 µg m-3 at the suburban Lalitpur site, and 40-74 µg m-3 at rural Dhulikhel on the eastern rim of the Kathmandu Valley. Meanwhile, PM10 ranged 194-309, 174-377, and 64-131 µg m-3, respectively. At the Ratnapark site, crustal materials from resuspended soil contributed an average of 11% of PM2.5 and 34% of PM10. PM2.5 was largely comprised of organic carbon (OC, 28-30% by mass) and elemental carbon (EC, 10-14% by mass). As determined by chemical mass balance source apportionment modeling, major PM2.5 OC sources were garbage burning (15-21%), biomass burning (10-17%), and fossil fuel (14-26%). Secondary organic aerosol (SOA) contributions from aromatic volatile organic compounds (13-23% OC) were larger than those from isoprene (0.3-0.5%), monoterpenes (0.9-1.4%), and sesquiterpenes (3.6-4.4%). Nitro-monoaromatic compounds-of interest due to their light-absorbing properties and toxicity-indicate the presence of biomass burning-derived SOA. Knowledge of primary and secondary PM sources can facilitate air quality management in this region.

Polymorphisms in IL-17A Gene and Susceptibility of Colorectal Cancer in Bangladeshi Population: A Case-Control Analysis.

OBJECTIVE: Interleukin-17A (IL-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 IL-17A variants (rs10484879 C/A and rs3748067 G/A) and susceptibility of CRC. METHODS AND MATERIALS: This retrospective case-control study comprised 292 CRC patients and 288 age, sex, and BMI matched healthy volunteers. Genotyping of both variants was done by the tetra-primer ARMS-PCR method, and the results were analyzed by the SPSS software package (version-25.0). RESULTS: Logistic regression analysis indicated that in case of IL-17A rs10484879 polymorphism, AC and AA genotype carriers showed 2.44- and 3.27-times significantly increased risk for CRC development (OR = 2.44, P = .0008 and OR = 3.27, P = .0133, individually). A significant association was also observed for AC + AA genotype (OR = 2.58, P = .0001). Again, over-dominant and allelic model revealed statistically significant link to CRC risk (OR = 2.13, P = .0035 and OR = 2.22, P = .001). For rs3748067 polymorphism, AG and AA genotype carriers showed 2.30- and 2.45-times enhanced risk for CRC (OR = 2.30, P = .005 and OR = 2.45, P = .031). A statistically significant association was also observed for AG + AA genotype (OR = 2.35, P = .001), over-dominant model (OR = 2.05, P = .014), and allelic model (OR = 2.11, P = .0004). CONCLUSION: This study highlights that IL-17A rs10484879 and rs3748067 polymorphisms may be associated with CRC development. However, further functional research with larger samples may reveal more statistically significant outcomes.

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 (PM2.5), 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 (PM2.5) samples collected at rural and urban sites in the USA and Bangladesh. These analyses demonstrated a measurable impact of plastic burning at 5 of the 6 study locations, with the largest absolute and relative TPB concentrations occurring in Dhaka, Bangladesh, where plastic burning is expected to be a significant source of PM2.5. Background-level contributions of plastic burning in the USA were estimated to be 0.004-0.03 µg m-3 of PM2.5 mass. Across the four sites in the USA, the lower estimate of plastic burning contributions to PM2.5 ranged 0.04-0.8%, while the median estimate ranged 0.3-3% (save for Atlanta, Georgia, in the wintertime at 2-7%). The results demonstrate a consistent presence of plastic burning emissions in ambient PM2.5 across urban and rural sites in the USA, with a relatively small impact in comparison to other anthropogenic combustion sources in most cases. Much higher TPB concentrations were observed in Dhaka, with estimated plastic burning impacts on PM2.5 ranging from a lower estimate of 0.3-1.8 µg m-3 (0.6-2% of PM2.5) and the median estimate ranging 2-35 µg m-3 (5-15% of PM2.5). The methodological advances and new measurements presented herein help to assess the air quality impacts of burning plastic more broadly.

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. Microscopy is a familiar process for diagnosing malaria, collecting the victim's blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.

Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images.

Diabetic Retinopathy (DR) is a major complication in human eyes among the diabetic patients. Early detection of the DR can save many patients from permanent blindness. Various artificial intelligent based systems have been proposed and they outperform human analysis in accurate detection of the DR. In most of the traditional deep learning models, the cross-entropy is used as a common loss function in a single stage end-to-end training method. However, it has been recently identified that this loss function has some limitations such as poor margin leading to false results, sensitive to noisy data and hyperparameter variations. To overcome these issues, supervised contrastive learning (SCL) has been introduced. In this study, SCL method, a two-stage training method with supervised contrastive loss function was proposed for the first time to the best of authors' knowledge to identify the DR and its severity stages from fundus images (FIs) using "APTOS 2019 Blindness Detection" dataset. "Messidor-2" dataset was also used to conduct experiments for further validating the model's performance. Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied for enhancing the image quality and the pre-trained Xception CNN model was deployed as the encoder with transfer learning. To interpret the SCL of the model, t-SNE method was used to visualize the embedding space (unit hyper sphere) composed of 128 D space into a 2 D space. The proposed model achieved a test accuracy of 98.36%, and AUC score of 98.50% to identify the DR (Binary classification) and a test accuracy of 84.364%, and AUC score of 93.819% for five stages grading with the APTOS 2019 dataset. Other evaluation metrics (precision, recall, F1-score) were also determined with APTOS 2019 as well as with Messidor-2 for analyzing the performance of the proposed model. It was also concluded that the proposed method achieved better performance in detecting the DR compared to the conventional CNN without SCL and other state-of-the-art methods.

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. After that, we developed a novel Convolutional Neural Network (CNN) model that extracted 100 prominent features from a total of 2482 CT scan images. These extracted features were then deployed to various machine learning algorithms - Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Finally, we proposed an ensemble model for the COVID19 CT image classification. We also showed various performance comparisons with the state-of-art methods. Our proposed model outperforms the state-of-art models and achieved an accuracy, precision, and recall score of 99.73%, 99.46%, and 100%, respectively.

HAS COUNTRYWIDE LOCKDOWN WORKED AS A FEASIBLE MEASURE IN BENDING THE COVID-19 CURVE IN DEVELOPING COUNTRIES?

In the absence of any effective vaccine and clinically proven treatment, experts thought that strict lockdown measures could be an effective way to slow down the spread of novel coronavirus. Despite the strict lockdown measures in several developing countries, the number of newly infected cases is getting unbridled as time progresses. This anomaly ignites questions about the effectiveness of the prolonged strict confinement measures. In light of the above view, with an aim to find the answer to this question, trends of four epidemiological parameters: growth factor of daily reported COVID-19 cases, daily incidence proportion, daily cumulative index and effective reproduction number in five developing countries named Bangladesh, Brazil, Chile, Pakistan and South Africa have been analysed meticulously considering the different phases of their national lockdowns. Any compelling evidence has not been found in favor of countrywide lockdown effectiveness in the above-mentioned countries. Numerical results illustrate that stringent nationwide lockdown measures have failed bringing the epidemic threshold (Re) of COVID-19 under unity. In addition, citizens of the aforementioned countries have been struggling with catastrophic socio-economic consequences due to prolonged confinement measures. Our study suggests that a new policy should be proposed for developing countries to battle against future disease outbreaks ensuring a perfect balance between saving lives and confirming livelihoods.