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This work aims to study the time-frequency relationship between the recent COVID-19 pandemic and instabilities in oil price and the stock market, geopolitical risks, and uncertainty in the economic policy in the USA, Europe, and China. The coherence wavelet method and the wavelet-based Granger causality tests are applied to the data (31st December 2019 to 1st August 2020) based on daily COVID-19 observations, oil prices, US-EPU, the US geopolitical risk index, and the US stock price index. The short- and long-term COVID-19 consequences are depicted differently and may initially be viewed as an economic crisis. The results illustrate the reduced industrial productivity, which intensifies with the increase in the pandemic’s severeness (i.e., a 10.57% decrease in the productivity index with a 1% increase in the pandemic severeness). Similarly, indices for oil demand, stock market, GDP growth, and electricity demand decrease significantly with an increase in the pandemic severeness index (i.e., a 1% increase in the pandemic severeness results in a 0.9%, 0.67%, 1.12%, and 0.65% decrease, respectively). However, the oil market shows low co-movement with the stock exchange, exchange rate, and gold markets. Therefore, investors and the government are recommended to invest in the oil market to generate revenue during the sanctions period.

Silver nanoparticles (AgNPs) of different shapes and sizes were prepared by solution-based chemical reduction routes. Silver nitrate was used as a precursor, tri-sodium citrate (TSC) and sodium borohydride as reducing agents, while polyvinylpyrrolidone (PVP) was used as a stabilizing agent. The morphology, size, and structural properties of obtained nanoparticles were characterized by scanning electron microscopy (SEM), UV-visible spectroscopy (UV-VIS), and X-ray diffraction (XRD) techniques. Spherical AgNPs, as depicted by SEM, were found to have diameters in the range of 15 to 90 nm while lengths of the edges of the triangular particles were about 150 nm. The characteristic surface plasmon resonance (SPR) peaks of different spherical silver colloids occurring in the wavelength range of 397 to 504 nm, whereas triangular particles showed two peaks, first at 392 nm and second at 789 nm as measured by UV-VIS. The XRD spectra of the prepared samples indicated the face-centered cubic crystalline structure of metallic AgNPs. The in vitro antibacterial properties of all synthesized AgNPs against two types of Gram-negative bacteria, Pseudomonas aeruginosa and Escherichia coli were examined by Kirby–Bauer disk diffusion susceptibility method. It was noticed that the smallest-sized spherical AgNPs demonstrated a better antibacterial activity against both bacterial strains as compared to the triangular and larger spherical shaped AgNPs.

This study examines the effect of financial development (FD) and foreign direct investment (FDI) on the environmental quality for the panel of 90 belt and road countries from 1990 to 2017. This study advances the knowledge of financial development by using the new comprehensive index, which is based on access, depth, and efficiency of financial markets and financial institutions and incorporated foreign direct investment as an important determinant of environmental quality. By applying the Driscoll-Kraay standard error pooled ordinary least square method, the empirical findings reveal that FD deteriorates the environmental quality by increasing the CO 2 emissions, while FDI improves environmental quality and the relationship between economic growth (EG) and CO 2 emissions is inverted U-shaped, i.e., presence of EKC hypothesis. The energy consumption and urbanization pollute the environment, while trade openness enhances the quality of the environment. Furthermore, the Dumitrescu-Hurlin (DH) panel causality test result confirms that the bidirectional causality exists among FD, trade openness, energy consumption, and urbanization with CO 2 emissions. The empirical results provide new insights for policymakers and also have several implications for the betterment of environmental quality.

Studying and analyzing energy consumption and structural changes in Pakistan’s major economic sectors is crucial for developing targeted strategies to improve energy efficiency, support sustainable economic growth, and enhance energy security. The logarithmic mean Divisia index (LMDI) method is applied to find the factors’ effects that change sector-wise energy consumption from 1990 to 2019. The results show that: (1) the change in mixed energy and sectorial income shows a negative influence, while energy intensity (EI) and population have an increasing trend over the study period. (2) The EI effects of the industrial, agriculture and transport sectors are continuously rising, which is lowering the income potential of each sector. (3) The cumulative values for the industrial, agricultural, and transport sectors increased by 57.3, 5.3, and 79.7 during 2019. Finally, predicted outcomes show that until 2035, the industrial, agriculture, and transport incomes would change by -0.97%, 13%, and 65% if the energy situation remained the same. Moreover, this sector effect is the most crucial contributor to increasing or decreasing energy consumption, and the EI effect plays the dominant role in boosting economic output. Renewable energy technologies and indigenous energy sources can be used to conserve energy and sectorial productivity.

This study aims to examine the impact of public-private partnerships (PPP) investment in energy, technological innovations (TI), economic growth (EG), exports, and foreign direct investment (FDI) on CO 2 emissions in Brazil over the period from 1984 to 2018. In doing so, we employ the Ng-Perron unit root test to examine the stationarity and autoregressive lag distributed (ARDL) model for cointegration between CO 2 emissions and its determinants. The outcomes are as follows: first, in the long run, the PPP investment in energy deteriorates the environmental quality by increasing CO 2 emissions, while TI has a significant negative effect on CO 2 emissions. It is also found that the exports and FDI degrade the environmental quality and the relationship between EG and CO 2 emissions is inverted U-Shaped, presence of the EKC hypothesis. Second, in the short run, PPP investment in the energy sector is negatively influencing, while TI has a positive association with carbon emissions. The empirical findings provide new insights for policymakers to regulate PPP investment in the energy sector for the improvement of environmental quality in Brazil. Graphical abstract

The present study reports a hydrogel-based sunlight-assisted synthesis of gold nanoparticles (Au NPs) with enhanced antimicrobial and wound healing potential. The hydrogel extracted from the seeds of Cydonia oblonga was used as a reducing and capping agent to synthesize Au NPs for the first time. The as-synthesized Au NPs were characterized for an average size, shape, surface functionalization, antimicrobial, and wound healing capabilities. The cubic and rectangular-shaped Au NPs with an average edge length of 74 ± 4.57 nm depicted a characteristic surface plasmon resonance band at 560 nm. The hydrogel-based Au NPs inhibited the growth of microorganisms in zones with 12 mm diameter. In-vitro experiments showed that a minimum inhibitory concentration of Au NPs (16 µg/mL) was sufficient to mimic the 95% growth of pathogenic microorganisms in 24 h. In vivo treatment of wounds with Au NPs in murine models revealed a 99% wound closure within 5 days. Quantitative PCR analysis performed to decipher the role of Au NPs in enhanced wound healing showed an increase in the expression levels of NANOG and CD-34 proteins.

Heavy metal stress is a leading environmental issue reducing crop growth and productivity, particularly in arid and semi-arid agro-ecological zones. Cadmium (Cd), a non-redox heavy metal, can indirectly increase the production of reactive oxygen species (ROS), inducing cell death. A pot experiment was conducted to investigate the effects of different concentrations of Cd (0, 5, 25, 50, 100 µM) on physiological and biochemical parameters in two sorghum (Sorghum bicolor L.) cultivars: JS-2002 and Chakwal Sorghum. The results showed that various concentrations of Cd significantly increased the Cd uptake in both cultivars; however, the uptake was higher in JS-2002 compared to Chakwal Sorghum in leaf, stem and root. Regardless of the cultivars, there was a higher accumulation of the Cd in roots than in shoots. The Cd stress significantly reduced the growth and increased the electrolyte leakage (EL), hydrogen peroxide (H2O2) concentration and malondialdehyde (MDA) content in both cultivars, but the Chakwal Sorghum showed more pronounced oxidative damage than the JS-2002, as reflected by higher H2O2, MDA and EL. Moreover, Cd stress, particularly 50 µM and 100 µM, decreased the activity of different antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). However, the JS-2002 exhibited higher SOD, POD and CAT activities than the Chakwal Sorghum under different Cd-levels. These findings revealed that JS-2002 had a stronger Cd enrichment capacity and also exhibited a better tolerance to Cd stress due to its efficient antioxidant defense system than Chakwal Sorghum. The present study provides the available information about Cd enrichment and tolerance in S. bicolor, which is used as an important agricultural crop for livestock feed in arid and semi-arid regions.

Object detection is a vital research direction in machine vision and deep learning. The object detection technique based on deep understanding has achieved tremendous progress in feature extraction, image representation, classification, and recognition in recent years, due to this rapid growth of deep learning theory and technology. Scholars have proposed a series of methods for the object detection algorithm as well as improvements in data processing, network structure, loss function, and so on. In this paper, we introduce the characteristics of standard datasets and critical parameters of performance index evaluation, as well as the network structure and implementation methods of two-stage, single-stage, and other improved algorithms that are compared and analyzed. The latest improvement ideas of typical object detection algorithms based on deep learning are discussed and reached, from data enhancement, a priori box selection, network model construction, prediction box selection, and loss calculation. Finally, combined with the existing challenges, the future research direction of typical object detection algorithms is surveyed.

A topological index is a numerical value derived from the structure of a molecule or graph that provides useful information about the molecule’s physical, chemical, or biological properties. These indices are especially important in chemo-informatics and QSAR/QSPR (Quantitative Structure-Activity Relationship/Quantitative Structure-Property Relationship) studies, where they are used to predict a wide range of properties without the need for experimental measurements. In essence, a topological index is a way to quantify the molecular structure in a form that can be used in mathematical models to estimate the molecule’s behavior, activity, or properties. In terms of chemical graph theory and chemo-informatics, entropy-based indices quantify the structural complexity or disorder in a molecule’s connectivity. These indices are useful for modeling and predicting molecular properties and biological activities. In this paper, we established a QSPR analysis of colorectal drugs between entropy indices and their physical properties and developed a relationship. Through a comprehensive analysis of these drugs, we gain essential insights into their molecular properties, which are vital for predicting their behavior and effectiveness in treating colorectal cancer. These models are compared with existing degree-based models, highlighting the superior performance of our approach. The QSPR study is performed using curvilinear regression models including linear, quadratic, cubic exponential and logarithmic models. Additionally, we propose the integration of machine learning (ML) techniques to further enhance the predictive accuracy and robustness of our models. By leveraging advanced ML algorithms, we aim to uncover more complex, non-linear relationships between topological indices and drug efficacy, potentially leading to more accurate predictions and better-informed drug design strategies.