The research aims to discover the influence of news indicator on the volatility of precious metals prices. It highlights an essential aspect by focusing on comparing pre and during COVID-19 period. For this purpose, an advanced econometric technique, i.e., Generalized Autoregressive Conditional Heteroskedasticity variant of Mixed Data Sampling (GARCH MIDAS), has been employed. The full sample results demonstrate that news relating to any of the precious metals is likely to affect their volatilities, except palladium. In the case of during the COVID-19 sample, the outcomes reveal that fear-induced news raises the return volatilities of gold and palladium; thereby, both are highly sensitive to the recent pandemic. In contrast, silver and platinum are found to have less impact.
Optimal coordination and settings of directional overcurrent relays (DOCRs) are crucial task to ensure reliable and effective protection in power system networks. The settings of DOCRs are plug setting current (PSC) and time multiplier setting (TMS). The DOCRs coordination is well-known as a highly constrained nonlinear optimization problem. Its complexity in terms of non-linearity increases along with the network size increment. This paper proposes a hybrid optimization algorithm which consists of Firefly Algorithm and Linear Programming (FA-LP) to relax the search space by linearizing the equation of the DOCRs coordination to attain an optimal solution. Furthermore, this paper also considers a mixed type of IEC relay characteristics to attain effective relay operation. The proposed method is tested on the IEEE 8-, 15- and 30-bus test systems. The results show the reduction of total relay operating time between 15.6% and 85.5% as compared to other techniques in the literature and also being verified using the ETAP software.
The main objective of this study is to check the short-term stress of COVID-19 pandemic on the American, European, Asian, and Pacific stock market indices and furthermore the correlation between all the stock markets during the pandemic. Secondary data of 41 stock exchange from 32 countries have been collected from investing.com website from 1st July 2019 to 14th May 2020 for the stock market and the COVID-19 data has been collected according to the first cases reported in the country, stocks market are classified either developed or emerging economy, further divided according to the subcontinent i.e. America, Europe, and Pacific/Asia, the main focus in the data is the report of first COVID-19 cases. The study reveals that there is volatility in the all the 41 stock market (American, Europe, Asia, and Pacific) after reporting of the first case and volatility increase with the increase of COVID-19 cases, moreover, there is a significant negative relationship between the number of COVID-19 cases and 41 major stock indices of American, Europe, Asia and Pacific, European subcontinent market found more effected from the COVID-19 than another subcontinent, there is Clustering effect of COVID-19 on all the stock market except American's stock market due to smart capital investing.
The investigation explores whether COVID-19 influences oil and precious metals prices by comparing data extracted from online searching trends and actual events. The study utilizes the Linear Granger causality test & non-parametric causality-in-quantiles method and uses data from January (2020) to March (2021). We have incorporated four mostly trading metals (i.e., Gold, Palladium, Silver, & Platinum) & Crude oil. Although outcomes of Linear Granger causality test show no causal relation between COVID-19 & oil and precious metal prices for both cases (i.e., online searching trends and actual events), the outcomes of the non-parametric test revealed the existence of non-linear association among constructs. Non-parametric test results revealed that COVID-19 significantly influences the prices of oil and precious metals. Therefore, we conclude that policymakers need to contemplate pandemic risk as most critical risk factor for stability of market when developing policies for the market and economy. Furthermore, through this study, investors and policymakers will get noteworthy awareness for thinking out of the box during the crisis.
Establishing sustainable and balanced development for green financing is critical for improving financial sustainability and banks’ capability. Banks struggle to achieve economic sustainability in the current highly competitive business environment. This research examines the impact of income diversification on financial sustainability proxy by return on assets (ROA) by applying the quantile regression technique to the data from banks of ASEAN countries over the period 2008-2019. In addition, liquidity risk, bank size, interest and non-interest incomes, and market capitalization are studied as control variables. The empirical findings indicate that income diversification positively impacts return on assets at all countries’ lower, middle, and upper quantiles, even though sizes can differ across countries and quantiles. Moreover, market capitalization, non-interest income, and banks’ size favorably impact banks’ performance. In contrast, liquidity risk and interest incomes are negatively linked to the performance of banks for all countries at each quantile. These results have significant strategic implications for managers, regulators, and policymakers who share a common interest in boosting financial sustainability and performance and significantly shaping green recovery.
China's sustainable development relies vastly on the country's ability to produce safe and reliable resources which can help the country meet its carbon neutrality target by 2060. In this study, we therefore deem it necessary to examine the role of nuclear energy, hydro, and biomass energy on China's overall ecological footprint in the presence of important control variables like GDP, trade, industrialization, human capital and government stability. Quarterly data from 1970Q1 to 2020Q4 is included and quantile-based causality in mean and variance is employed for empirical analysis. The findings suggest the presence of strong causal linkages between all three forms of clean energy toward ecological footprint in China. However, the predictive power of clean energies for ecological footprint is insignificant at the upper quantiles of its distribution. Implications of our results point toward a quick and wide-scale deployment of clean energy sources before the ecological footprint reaches at peak levels. Otherwise, the usefulness of nuclear, biomass, and hydro energies in bringing down the emissions may decline. Government authorities and environmentalists in China need to be vigilant in deploying clean energies before its too late. Finally, policy directions are discussed in line with sustainable development goals (SDGs).
The presence of a thick waxy envelop, the drug efflux and the maintenance of a reductive environment via mycothione reductase pathway help Mycobacterium tuberculosis (M.tb) to survive intracellularly and serve as major contributing factors towards development of drug resistance. Bearing this in mind, in this work cholesterol conjugated thiolated stereocomplexed nanomicelles (thPEI(CH)-ScM) were prepared to increase the transport of rifampicin into M.tb (H37-Rv) infected alveolar macrophages along with efflux pump and mycothione reductase (Mtr) inhibition. Rifampicin-loaded cholesterol decorated thPEI(CH)-ScM showed improved in vitro drug release, intestinal mucopenetration, macrophage uptake, mycobacterial inhibition potential and biocompatibility compared to cholesterol conjugated stereocomplexed nanomicelles (PEI(CH)-ScM) and stereocomplexed nanomicelles (ScM). The minimum inhibitory concentration (MIC) of thPEI(CH)-ScM against M.tb (H37-Rv) was found to be significantly lower than that of the free drug and ScM. These thiolated micelles also exhibited efflux pump inhibition and prolonged retention in alveolar macrophages along with Mtr inhibition with an inhibitor constant (Ki) value of 2.74 and half-maximum inhibition concentration (IC50) of 4.73 µg/ml. In vivo studies demonstrated significantly higher reduction in M.tb (H37-Rv) CFU by thPEI(CH)-ScM, a preferential uptake in lungs along with improved pharmacokinetics and reduced dosing frequency compared to PEI(CH)-ScM and ScM. Thus, rifampicin-loaded thPEI(CH)-ScM can be regarded as a stable, biocompatible and efficient nanocarrier system with better drug uptake due to cholesterol conjugation as well as improved drug retention and accumulation within infected alveolar macrophages via efflux pump and Mtr inhibition.
Materials and methods: The cross-sectional survey was conducted; primary data were collected from asthmatic patients in different hospitals and clinics of allopathic, homeopathic, and herbal practitioners in Karachi, Pakistan. The study duration was from January 2020 to December 2020. Asthmatic patients aged over 13 years were selected for the study. A written informed consent was taken from the patients before the interview. Collected data were analyzed by the Statistical Package of Social Sciences (SPSS) 22. Result: Among 255 asthmatic patients; 51.4% (n = 131) were male and 48.6% (n = 124) were female. For control of acute attacks of asthma 88.2% (p = 0.0001) of patients significantly preferred allopathic treatment while 6.3% (p = 0.008) used homeopathic treatment and 5.5% chose herbal treatment. For maintenance of asthma, 78.8% (p = 0.0001) patients used allopathic treatment while 12.4% (p = 0.0001) homeopathic and 8.8% (p = 0.0001) patients used herbal treatment. About 63.4% (p = 0.0001) of the asthmatic patients used short-acting β-2 agonists for managing acute asthmatic episodes while long-acting β-2 agonists (p = 0.0001) and inhaled corticosteroids (p = 0.0001) were found to be the preferred medicines for maintenance therapy. Effectiveness of treatment (p = 0.004) and cost effectiveness (p = 0.0001) significantly act as contributing factors for the selection of the treatment. The majority of the patients were satisfied with their chosen treatments for control of asthmatic symptoms. Conclusion: Most asthmatic patients preferred allopathic treatment for the management of acute episodes and control of asthmatic symptoms. It was found that the major factors for selecting a specific treatment include effectiveness, cost, and minimal side effects.
Intellectual Capital (IC) is a driving force behind the financial performance of non-financial firms. Investing in intellectual and physical capital allows companies to optimize their financial performance by maximizing resource utilization. This study aims to determine whether IC efficiency impacts the financial performance of listed Pakistani and Indian companies between 2010 and 2020. Return on Assets (ROA) and Return on Equity (ROE) are used to calculate financial performance, and IC is calculated using the modified Value-Added Intellectual Coefficient (MVAIC) model. Regression analysis is performed using the STATA software developed by the South Texas Art Therapy Association. Human Capital (HC), Structural Capital (SC), and Capital Employed (CE) have a significant impact on Pakistani and Indian firms’ financial performance. Resource-based theory (RBT) supports these findings. The findings should provide management with a prompt to improve financial performance and emphasize the importance of IC. A rare study has addressed the impact of IC on firm financial performance using the MVAIC model, rather than the VAIC model, in Pakistan and India.
Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection.
The present study was aimed to assess the efficacy of individual and combined effects of novel fuller earth, rock phosphate, and biochar (grapefruit peel) at 1% dosage on maize plant growth, soil chemical properties anduptake of toxic metals (TMs), such as Cu, Zn, Fe, and Cd, by maize plant sown in Korangi (district of Karachi, Pakistan) heavily polluted and Korangi less polluted (K-HP and K-LP) soils. The obtained results indicate that the dry biomass of maize crop increased by 14.13% with combined (FE1% + GBC1%) on K-HP soil and 18.24% with combined (FE 1% + GBC 1%) effects on K-LP soil. The maximum immobilization of Cu, Zn, Fe, and Cd was observed by 36% with GBC1%, 11.90% with FE1%, 98.97% with combined RP1% + GBC1%, 51.9% with FE1% + GBC1% for K-HP, 11.90% with FE1%, 28.6% with FE1%, 22.22% with RP1% + GBC1%, and 57.05% with FE 1% + GBC 1% for K-LP soil. After the addition of proposed substances, modification of soil OM, SOC, TOC, and pH level appeared this lead to the changes in the phyto-availability of Cu, Zn, Fe, and Cd in maize plant. It was concluded that the application of individual and combined effects of novel fuller earth, rock phosphate, and biochar (grapefruit peel) have potential to stabilize pollutants from multi-metal polluted soils for safe crop production.
Feature selection is the process of identifying the most relevant features from the given data having a large feature space. Microarray datasets are comprised of high-quality features and very few samples of data. Feature selection is performed on such datasets to identify the optimal feature subset. The major goal of feature selection is to improve the accuracy by identifying a minimal feature subset. For this purpose, the proposed research focused on analyzing and identifying effective feature selection algorithms. A novel framework is proposed which utilizes different feature selection methods from filters, wrappers, and embedded algorithms. Furthermore, classification is then performed on selected features to classify the data using a support vector machine (SVM) classifier. Two publically available benchmark datasets are used, i.e., the Microarray dataset and the Cleveland Heart Disease dataset, for experimentation and analysis, and they are archived from the UCI data repository. The performance of SVM is analyzed using accuracy, sensitivity, specificity, and f-measure. The accuracy of 94.45% and 91% is achieved on each dataset, respectively.
This study analyzes the causal connection between financial inclusion and carbon emission in selected South Asian countries through a quantile technique–based linear Granger and non-parametric causality test. The analysis of the study covers quarterly data from 1980 Q1 to 2019 Q4. However, the linear Granger causality assessment outcome does not indicate any causal relationship between financial inclusion and carbon emission. In contrast, results from non-parametric assessment reveal a non-linear connection between the variables. The non-parametric test results of the South Asian countries exhibit that financial inclusion leads to carbon emission, which instigates the deterioration of the environment, except for Bhutan. Subsequently, creating awareness by promoting renewable energy resources is essential while investing in fuel-efficient technology to reduce the dependence on fossil fuels. The results of this study provide significant information to the governments and policymakers in emerging countries to improve financial literacy among people to reduce the risk of global warming by encouraging investment in energy-efficient resources.
The goal of this study is to examine the food and oil price nexus from January 1993 to September 2020. To have a broader aspect, we decompose oil prices into demand and supply shocks and food price index into sub-indices such as Meat, Dairy, Cereal, and sugar price indices. The findings show that the association between the food prices and indices with oil prices is bidirectional. Also, results show that the oil prices, demand, and supply shocks are the main contributors to volatility transmission compared to food prices and their sub-indices. The outcome of this study will help the agricultural sector's policymakers develop reliable and sound policy designs that will help control the influence of oil prices on food prices.
This study aimed to investigate the anti-neuropathic pain activity and its underlying molecular mechanism of Ajugarin-I (Aju-I) in a rat model of diabetic neuropathic pain. The rats were given a single injection of 60 mg/kg of streptozotocin (STZ) intraperitoneally (i.p.) to induce diabetic neuropathic pain. After two weeks, rats were given Aju-I (1 and 5 mg/kg/day) i.p. for four consecutive weeks. The results demonstrated that in diabetic rats, treatment with Aju-I decreased STZ-induced hyperglycemia. It reduced the pain hypersensitivity (mechanical, thermal, and cold nociception) caused by STZ. It effectively restored STZ-associated pathological changes in the pancreas. In the sciatic nerve and spinal cord, it attenuated STZ-associated histopathological alterations and DNA damage. It suppressed oxidative stress by increasing the expression of nuclear factor-erythroid factor 2-related factor 2 (Nrf2), thioredoxin (Trx), and heme oxygenase (HO-1), but decreasing the immunoreactivity of Kelch-like ECH-associated protein 1 (Keap1). Additionally, TRPV1 (transient receptor potential vanilloid 1) and TRPM8 (transient receptor potential melastatin 8) expression levels were considerably reduced by Aju-I treatment. it enhanced antioxidant levels and suppressed inflammatory cytokines production. Taken together, this research suggests that Aju-I treatment reduces pain behaviors in the STZ model of diabetic neuropathy via modulating Nrf2/Keap-1/HO-1 signaling and TRPV1/TRPM8 nociceptors.
Prior research shows that firms' geographical location is critical for financial decisions. However, it is still unclear whether firms mimic the unethical behavior of their local peers and whether firms' corporate social responsibility (CSR) and product market competition mitigate such behavior. We examine a US sample of 23,605 observations and find that firms' likelihood of misconduct is positively related to the average level of misconduct in the local metropolitan area. The analysis shows that firms with strong CSR do not mimic their local peers' fraudulent behavior. However, firms operating in industries with greater competition imitate their local peers’ unethical behavior. The channel analysis reveals that fraudulent peer effects are only prevalent in small firms, young firms, and those with low institutional ownership, suggesting that information asymmetry and weak monitoring drive our findings. The results also suggest stakeholders should pay more attention to firms operating in areas where misconduct is widespread.
This paper presents an effective planning methodology for electric vehicle (EV) fast‐charging stations (CS) using a multi‐objective binary version of the atom search optimization (ASO) algorithm. The proposed method uses quantum operations to binarize the algorithm and achieve a higher convergence rate than the existing binary ASO algorithm. Additionally, a modified atom selection function is used to improve the searching capability of the ASO algorithm. Furthermore, the nondominated sorting procedure and pareto concepts are infused to solve the CS location problem (CSLP) considering the EV travel time, CS costs, and grid power loss as independent multi‐objectives. The efficacy of the proposed multi‐objective quantum ASO (MO‐QASO) algorithm is evaluated using performance metrics namely, inverted generational distance (IGD), spacing (SP), and maximum spread (MS). The MO‐QASO simulation results are compared with the results of other heuristic algorithms. MO‐QASO achieves the best IGD (0.0021), SP (0.0002), and MS (0.9982) values, verifying the convergence and diversity of the algorithm. Importantly, the best CS planning solution obtained from MO‐QASO is similar to the true solution obtained from the exhaustive search method. The MO‐QASO efficiency is further validated by solving a CSLP from literature. Thus, the MO‐QASO algorithm is a promising optimization tool for solving CSLP.
The upcoming models of vehicles will be able to communicate with each other and will thus be able to share and/or transfer information. A vehicular ad hoc network (VANET) is an application of this vehicular communication that leads to an intelligent transportation system (ITS). Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) are the two distinct types of vehicular ad hoc networks (VANET). V2V and V2I technologies are together known as V2X and are recently being tested. Continuous research to enhance routing considers different characteristics and exciting aspects of VANETs. The proposed schemes are classified based on the operational scenario. A survey of proposed routing schemes in the last eight years is presented to determine the design considerations and the approach used in every proposed system, along with their shortcomings. This survey will assist new scholars in this field to analyze existing state-of-the-art systems. The table at the end of each routing scheme shows the proposed routing scheme’s simulation, routing, and scenario parameters. This paper also reviews VANET technology, its role in the intelligent transportation system, recent development in the field, and the timeline for implementation of the system.
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