Empirical studies pertaining to the effects of fiscal policy instruments on environmental quality have provided mixed evidence. We consider the asymmetric effects of fiscal policy instruments on environmental quality for the top ten Asian carbon emitters over the period 1981-2018. We go beyond the literature and claim that the effects could be asymmetric. More specifically, we found that a positive shock in government expenditure will worsen environmental quality in Malaysia, UAE, Thailand, Indonesia, Turkey, Iran, India, and China, and improve it in Japan. On the other hand, we found that cutting government expenditure will improve environmental quality in these economies and will worsen only in Japan. Moreover, a higher government income tax revenue uniquely increases the government's spending that increases the carbon emissions in Malaysia, UAE, Thailand, Indonesia, Turkey, Iran, India, and China, and decrease in Japan. The negative shock of government revenue has adverse results on carbon emissions in these economies. However, short-run asymmetric effects translate to long-run effects in most Asian economies.
A Finite Element Method (FEM) simulation study is conducted, aiming to scrutinize the sensitivity of Sezawa wave mode in a multilayer AlN/SiO₂/Si Surface Acoustic Wave (SAW) sensor to low concentrations of Volatile Organic Compounds (VOCs), that is, trichloromethane, trichloroethylene, carbon tetrachloride and tetrachloroethene. A Complimentary Metal-Oxide Semiconductor (CMOS) compatible AlN/SiO₂/Si based multilayer SAW resonator structure is taken into account for this purpose. In this study, first, the influence of AlN and SiO₂ layers’ thicknesses over phase velocities and electromechanical coupling coefficients (k²) of two SAW modes (i.e., Rayleigh and Sezawa) is analyzed and the optimal thicknesses of AlN and SiO₂ layers are opted for best propagation characteristics. Next, the study is further extended to analyze the mass loading effect on resonance frequencies of SAW modes by coating a thin Polyisobutylene (PIB) polymer film over the AlN surface. Finally, the sensitivity of the two SAW modes is examined for VOCs. This study concluded that the sensitivity of Sezawa wave mode for 1 ppm of selected volatile organic gases is twice that of the Rayleigh wave mode.
The demand for primary energy resources has increased significantly due to the rapid growth of the global economy and increasing greenhouse gas (GHG) emissions. Therefore, improving energy efficiency levels is essential for global energy, energy security, and environmental sustainability. In the context of the Asia-Pacific region, the study of energy efficiency among different countries can play a role in better energy utilization. These countries also provide a policy for the Asia-Pacific region to improve its energy utilization. This study's primary focus is to investigate the optimal efficiency score of 15 areas of the Asia-Pacific region, and the analysis is based on super-efficiency (radical) and super slacks-based measure (SBM) data in a nonparametric DEA model. Three areas in the Asia-Pacific are selected for energy efficiency measures: South Asia, East Asia, and Australasia. The results suggest that Bangladesh, Pakistan, China, Singapore, New Zealand, the Philippines, Japan, India, Indonesia, Malaysia, Thailand, and Vietnam obtain the most efficient score of 1 in both DEA models throughout the study period. Australia and Sri Lanka receive a low score during all study periods, while Hong Kong does not have data for all study years. The results of the study will help improve energy performance, cost-effectiveness, and environmental sustainability, increasing the competitiveness and scalability of efficient energy sources.
The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify & intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA.
The prime objective of the present research work was to evaluate the efficiency of bio-machine for the removal of Cadmium (Cd) from aquatic systems. Aspergillus niger fungus was used as bio-machine to remove Cd from aquatic systems. Twenty three different strains (IIB-1 to IIB-23) were isolated from industrial effluents and the Langmuir and Freundlich models were applied to the best Cadmium removal strain IIB-23 in order to obtain the adsorption parameters. Different parameters such as pH, temperature, contact time, initial metal concentratio, and biomass dosage on the biosorption of Cd were studied. The percent removal of Cd initially increased with an increase in pH ranging from 5.5-6.5 and then decreased by increasing pH from 7.0-7.5. An optimized pH used for Cd removal from aquatic systems was found to be 6.5. Additionally, an optimum amount of biomass was 1.33 g for the maximum removal of Cd from the aqueous solutions with initial metal concentration of 75 mg/L. The results obtained thus indicated that Langmuir model is the best suited for the removal of Cd from aquatic systems.
A low-energy plasma focus device was used as an electron beam source. A technique was developed to simultaneously measure the electron beam intensity and energy. The system was operated in Argon filling at an optimum pressure of 1.7 mbar. A Faraday cup was used together with an array of filtered PIN diodes. The beam-target X-rays were registered through X-ray spectrometry. Copper and lead line radiations were registered upon usage as targets. The maximum electron beam charge and density were estimated to be 0.31 μC and 13.5 × 10(16)/m(3), respectively. The average energy of the electron beam was 500 keV. The high flux of the electron beam can be potentially applicable in material sciences.
Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF's. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts-resulting in the formation of more communicative teams with high expertise levels.
Lately, wearable applications featuring photonic on-chip sensors are on the rise. Among many ways of controlling and/or modulating, the acousto-optic technique is seen to be a popular technique. This paper undertakes the study of different multilayer structures that can be fabricated for realizing an acousto-optic device, the objective being to obtain a high acousto-optic figure of merit (AOFM). By varying the thicknesses of the layers of these materials, several properties are discussed. The study shows that the multilayer thin film structure-based devices can give a high value of electromechanical coupling coefficient (k2) and a high AOFM as compared to the bulk piezoelectric/optical materials. The study is conducted to find the optimal normalised thickness of the multilayer structures with a material possessing the best optical and piezoelectric properties for fabricating acousto-optic devices. Based on simulations and studies of SAW propagation characteristics such as the electromechanical coupling coefficient (k2) and phase velocity (v), the acousto-optic figure of merit is calculated. The maximum value of the acousto-optic figure of merit achieved is higher than the AOFM of all the individual materials used in these layer structures. The suggested SAW device has potential application in wearable and small footprint acousto-optic devices and gives better results than those made with bulk piezoelectric materials.
Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research.
This study presents an extended thermodynamic and phenomenological combined model to mitigate the environmental hazardous acid gas over composite membranes. The model has been applied to an acid gas such as carbon dioxide (CO2) for its permeation through polyetherimide incorporated montmorillonite (Mt) nanoparticles hollow fiber asymmetric composite membranes. The well-established non-equilibrium lattice fluid (NELF) model for penetrating low molecular weight penetrant in a glassy polyetherimide (PEI) was extended to incorporate the other important polymer/filler system features such as tortuosity in acid gas diffusion pathways resulted from layered filler aspect ratio and concentration. The model mentioned above predicts the behavior of acid gas in PEI-Mt composite membranes based on thermodynamic characteristics of CO2 and PEI and tortuosity due to Mt. The calculated results are compared to experimentally determined values of CO2 permeability through PEI-Mt composite asymmetric hollow fiber membranes at varying transmembrane pressures and Mt concentrations. A reasonable agreement was found between the model predicted behavior and experimentally determined data in terms of CO2 solubility, Mt concentration and aspect ratio were calculated based on average absolute relative error (%AARE). The proposed modified model efficiently predicts the CO2 permeance across MMMs up to 3 wt% Mt loadings and 6 bar pressure with ± 10%AARE.
Deep neural networks have made tremendous strides in the categorization of facial photos in the last several years. Due to the complexity of features, the enormous size of the picture/frame, and the severe inhomogeneity of image data, efficient face image classification using deep convolutional neural networks remains a challenge. Therefore, as data volumes continue to grow, the effective categorization of face photos in a mobile context utilizing advanced deep learning techniques is becoming increasingly important. In the recent past, some Deep Learning (DL) approaches for learning to identify face images have been designed; many of them use convolutional neural networks (CNNs). To address the problem of face mask recognition in facial images, we propose to use a Depthwise Separable Convolution Neural Network based on MobileNet (DWS-based MobileNet). The proposed network utilizes depth-wise separable convolution layers instead of 2D convolution layers. With limited datasets, the DWS-based MobileNet performs exceptionally well. DWS-based MobileNet decreases the number of trainable parameters while enhancing learning performance by adopting a lightweight network. Our technique outperformed the existing state of the art when tested on benchmark datasets. When compared to Full Convolution MobileNet and baseline methods, the results of this study reveal that adopting Depthwise Separable Convolution-based MobileNet significantly improves performance (Acc. = 93.14, Pre. = 92, recall = 92, F-score = 92).
Different parts of a plant (seeds, fruits, flower, leaves, stem, and roots) contain numerous biologically active compounds called "phytoconstituents" that consist of phenolics, minerals, amino acids, and vitamins. The conventional techniques applied to extract these phytoconstituents have several drawbacks including poor performance, low yields, more solvent use, long processing time, and thermally degrading by-products. In contrast, modern and advanced extraction nonthermal technologies such as pulsed electric field (PEF) assist in easier and efficient identification, characterization, and analysis of bioactive ingredients. Other advantages of PEF include cost-efficacy, less time, and solvent consumption with improved yields. This review covers the applications of PEF to obtain bioactive components, essential oils, proteins, pectin, and other important materials from various parts of the plant. Numerous studies compiled in the current evaluation concluded PEF as the best solution to extract phytoconstituents used in the food and pharmaceutical industries. PEF-assisted extraction leads to a higher yield, utilizes less solvents and energy, and it saves a lot of time compared to traditional extraction methods. PEF extraction design should be safe and efficient enough to prevent the degradation of phytoconstituents and oils.
Graphene as a material for optoelectronic design applications has been significantly restricted owing to zero bandgap and non-compatible handling procedures compared with regular microelectronic ones. In this work, nitrogen-doped reduced graphene oxide (N-rGO) with tunable optical bandgap and enhanced electrical conductivity was synthesized via a microwave-assisted hydrothermal method. The properties of the synthesized N-rGO were determined using XPS, FTIR and Raman spectroscopy, UV/vis, as well as FESEM techniques. The UV/vis spectroscopic analysis confirmed the narrowness of the optical bandgap from 3.4 to 3.1, 2.5, and 2.2 eV in N-rGO samples, where N-rGO samples were synthesized with a nitrogen doping concentration of 2.80, 4.53, and 5.51 at.%. Besides, an enhanced n-type electrical conductivity in N-rGO was observed in Hall effect measurement. The observed tunable optoelectrical characteristics of N-rGO make it a suitable material for developing future optoelectronic devices at the nanoscale.