Although Asian economies have registered strong economic growth over the last few decades, their growing pollution emissions raise concerns among the policymakers about the sustainability of this ...output growth. This paper tests the causal relationship between economic development, energy consumption, trade openness, financial development, FDI, government expenditures, institutional quality. and pollution emissions for 41 Asian economies from 1996 to 2015. Further, we separately test the impact of political and economic institutions on pollution emissions of the sample economies. Our estimated outcomes, based on the panel cointegration method and panel vector error correction models (VECM), substantiate the presence of a cointegration relationship among all the selected variables. While economic development, energy use, trade openness, and FDI augment environmental degradation, financial development and better economic institutions help the selected countries in reducing their pollution emissions. Moreover, better economic and political institutions also mediate the adverse impact of income, trade openness, and FDI on pollution emissions. The VECM model shows that per capita GDP is the only variable having a causal effect on pollution emissions in all the models. For all the other variables, the causal effect is significant only in a few cases. These outcomes have some important policy recommendations for the sample economies.
•We test the direct and indirect effect of institutional quality on CO2 emissions.•Institutional quality contributes to the reduction of CO2 emissions in Asia.•Institutional quality also moderates the adverse effects of income on CO2 emissions.•Policy makers should incorporate the role and efficiency of institutional quality.
With the rapid development in computer vision domain, research on object tracking has directed more attention by scholars. Out of view (OV) is an important challenge often encountered in the tracking ...process of objects, especially in Internet of Things surveillance. Therefore, this paper proposes a fuzzy-aided solution for OV challenge. This solution uses a fuzzy-aided system to detect whether the target is poorly tracked by using the response matrix of samples. When poor tracking occurs, the target is relocated according to the stored template. The proposed solution is tested on OTB100 dataset, where the experimental results show that the auxiliary solution is effective for the OV challenge. The proposed solution also ensures the tracking speed and overall success rate of visual tracking as well as improves the robustness to a certain extent for IoT-assisted complex environment.
Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in ...the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.
Owing to increased egg consumption globally, a corresponding surge of about 18% in egg production has been recorded during the last decade as reported by WATT Global Media's Executive Guide to World ...Poultry Trends. Up till 2017, global egg production has hit 80-million metric-ton mark with China, USA and India, being the leading egg-producing countries contributing their share of 458, 109 and 95 billion eggs per annum, respectively. Global egg production for the year 2018 was 78 million metric tons, contributing approximately 8.58 million metric ton of eggshells which are being discarded mostly as waste. This calcium-rich commodity is dumped into landfills, leading to various environmental issues, and therefore should be tackled properly.
Eggshells are obtained from egg processing plants, egg stations, chicken hatcheries, industries and homes in millions of tonnes and can be employed in a myriad of fields. The following review article provides a brief insight into various applications of eggshells in our society, such as their use in medicinal supplements, bone graft substitute and denture base. Eggshells can also be employed in constructing floor tiles and in cement to enhance compressive strength. Other diverse applications of eggshell may include animal feed, plant fertiliser, batteries, inkjet printers, biodiesel production and removal of heavy metals from soil and water.
The increasing urbanisation and industrialization with amplified waste generation have wreaked havoc on our climate; thus, making it necessary to take certain extravagant measures to ensure the safety and sustainability of our planet.
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•Egg consumption is expected to increase from 7951.8 million dozens in 2019 to over 8000 million dozens in 2020.•USDA projects consumption statistics to reach up to 8917 million dozens by 2028.•Leading eggshell producing countries are China, USA and India.•It is the need of hour to convert waste into useful commodities for sustainable development.
The optimum economic outcome of financial system development depends on its level of efficiency. The purpose of this study is to investigate the effect of institutional quality on financial system ...efficiency. For empirical analysis, we have used a panel dataset of 108 countries from 1996–2020 and employed fixed effect regression and two stages least squares (2SLS) regression methods. The empirical results show that institutional quality has a significant positive effect on financial system efficiency. Particularly all the constituting elements—voice and accountability, political stability and absence of violence, regulatory quality, government effectiveness, rule of law, and control of corruption—of institutional quality are found to have a significant positive impact on financial system efficiency. Moreover, we found that the effect of institutional quality is more pronounced in countries with low-income levels and strong institutional quality. These findings are robust across several robustness tests conducted using additional controls, alternative methodologies, an alternative measure of institutional quality, and financial system efficiency. The results of the study suggest that policy makers should prioritize both enhancing and sustaining institutional quality to promote the efficiency of the financial system, which is crucial for sustainable growth and development.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Damage is an inevitable occurrence in metallic structures and when unchecked could result in a catastrophic breakdown of structural assets. Non-destructive evaluation (NDE) is adopted in industries ...for assessment and health inspection of structural assets. Prominent among the NDE techniques is guided wave ultrasonic testing (GWUT). This method is cost-effective and possesses an enormous capability for long-range inspection of corroded structures, detection of sundries of crack and other metallic damage structures at low frequency and energy attenuation. However, the parametric features of the GWUT are affected by structural and environmental operating conditions and result in masking damage signal. Most studies focused on identifying individual damage under varying conditions while combined damage phenomena can coexist in structure and hasten its deterioration. Hence, it is an impending task to study the effect of combined damage on a structure under varying conditions and correlate it with GWUT parametric features. In this respect, this work reviewed the literature on UGWs, damage inspection, severity, temperature influence on the guided wave and parametric characteristics of the inspecting wave. The review is limited to the piezoelectric transduction unit. It was keenly observed that no significant work had been done to correlate the parametric feature of GWUT with combined damage effect under varying conditions. It is therefore proposed to investigate this impending task.
Tactile Internet can combine multiple technologies by enabling intelligence via mobile edge computing and data transmission over a 5G network. Recently, several convolutional neural networks (CNN) ...based methods via edge intelligence are utilized for fire detection in certain environment with reasonable accuracy and running time. However, these methods fail to detect fire in uncertain Internet of Things (IoT) environment having smoke, fog, and snow. Furthermore, achieving good accuracy with reduced running time and model size is challenging for resource constrained devices. Therefore, in this paper, we propose an efficient CNN based system for fire detection in videos captured in uncertain surveillance scenarios. Our approach uses light-weight deep neural networks with no dense fully connected layers, making it computationally inexpensive. Experiments are conducted on benchmark fire datasets and the results reveal the better performance of our approach compared to state-of-the-art. Considering the accuracy, false alarms, size, and running time of our system, we believe that it is a suitable candidate for fire detection in uncertain IoT environment for mobile and embedded vision applications during surveillance.
This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the ...attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.
SARS-COV-2 is a virulent respiratory virus, first identified in China (Wuhan) at the end of 2019. Scientists and researchers are trying to find any possible solution to this deadly viral disease. ...Different drug source agents have been identified, including western medicine, natural products, and traditional Chinese medicine. They have the potential to counteract COVID-19. This virus immediately affects the liver and causes a decrease in oxygen levels. In this study, multiple vacciome approaches were employed for designing a multi-epitope subunit vaccine for battling against SARS-COV-2. Vaccine designing, immunogenicity, allergenic, and physico-chemical assessment were performed by using the vacciome approach. The vaccine design is likely to be antigenic and produce potent interactions with ACE2 and NSP3 receptors. The developed vaccine has also been given to in-silico cloning models and immune response predictions. A total number of 12 CTL and 12 HTL antigenic epitopes were predicted from three selected covid-19 virulent proteins (spike protein, nucleocapsid protein, and membrane proteins, respectively) based on C-terminal cleavage and MHC binding scores. These predicted epitopes were amalgamated by AYY and GPGPG linkers, and a β-defensins adjuvant was inserted into the N-terminus of this vaccine. This analysis shows that the recommended vaccine can produce immune responses against SARS-COV-2. Designing and developing of the mentioned vaccine will require further experimental validation.
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