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In today's corporate world, a company's long-term viability and prosperity depend on its corporate governance practices. The present study investigates the interplay between financial misrepresentation, earnings management, and corporate governance within the context of Pakistan. To estimate the financial data of enterprises obtained from non-financial organizations listed on the Pakistan Stock Exchange a panel regression analysis was conducted. The analysis covered the time from 2009 to 2020 and employed quantitative data. The findings of the study show that the different aspects of corporate governance mechanisms have varying levels of influence. Specifically, remuneration paid to directors had a significant impact on financial misstatement, while the size of the board strongly impacts the earning management. The financial misstatement was also found affected by the earning management. The M score (statistical model used to predict the probability of financial misstatement) positively influenced when board diligence was incorporated in the mediation of earning management. It is important to note that this study only considers the internal governance mechanisms of firms, suggesting that future research could benefit from the inclusion of external governance mechanisms for a more holistic model. This study is aligned with the ESG's governance aspects and SDG-17, providing valuable insights for specialists, financial backers, policymakers, and experts. The results of this study catalyze further research in this area and can aid in achieving SDG 17 by raising awareness of the significance of good governance practices, ethical reporting that leads to sustainable firm performance, and ensuring long-term economic growth and development. [ABSTRACT FROM AUTHOR]

The key objective of this paper is to investigate the possible impact of Ni weight percentage doping at various temperatures as a function of frequency on their impedance, dielectric, electric, complex electric modulus, and optical properties. X-ray diffraction patterns revealed that all the fabricated samples dominate ZnO hexagonal wurtzite structure. The dielectric and electric properties were studied through complex impedance spectroscopy with frequency at temperatures ranging from 140 to 280 K and investigated the impedance spectra in equivalent circuits relating to bulk resistance and bulk capacitance. The bulk resistance decreased with the rise in temperature and nickel concentrations. The ac conductivity improved with temperature. Both impedance and modulus studies reveal the temperature-dependent non-Debye type relaxation conduction phenomenon. The Shifting of the absorption edge to lower wavelength and blue shift of the bandgap is observed in DRS spectroscopy of the Ni-doped ZnO nanoparticles. [ABSTRACT FROM AUTHOR]

The complex chemical and structural arrangement of lignocellulosic biomass demand a pretreatment step to dismantle its coherent nature. The current study was designed to study H2SO4 impregnation before hydrothermal pretreatment (HP) of sugarcane bagasse for biogas production using response surface methodology based on defining factors: temperature (160, 180, 200 °C), time (5, 12, 19 min), and H2SO4 concentration (1, 2, 3% w/v). The H2SO4 impregnation had a paramount impact in combination with temperature and treatment time parameters. The lowest solid yield 12.3% was recorded in pretreatment run A-HSO (200 °C, 19 min, 3% H2SO4), up to 100% xylan removal was noted in four pretreatment runs (A-HSO; 200 °C, 19 min, 3% H2SO4, B-HSO; 200 °C, 19 min, 1% H2SO4, C-HSO; 200 °C, 5 min, 3% H2SO4, and O-HSO; 213.6 °C, 12 min, 2% H2SO4), maximum klason lignin increase in insoluble solid fraction (ISF) verified was 358.85% in pretreatment run O-HSO at 213.6 °C, while glucan content reduction (33.18–74.14%) was also observed in pretreatment runs A-HSO, C-HSO, and O-HSO. Results verified maximum methane recovery of 160.16 NmL (g TVS)−1 in pretreatment run I-SHO (180 °C, 12 min, 2% H2SO4). Microbial community analysis observed the predominance of Flavobacterium and Desulfosporosinus genera of Bacterial domain and Methanolinea of Archaea domain. H2SO4 impregnation before HP though not suitable for methane production from ISF could be employed in a biorefinery perspective to obtain valuables (chemicals and fuels) due to the production of organic acids rich liquid stream and lignin-rich solid fraction. [ABSTRACT FROM AUTHOR]

Tetracycline pollution is a growing global threat to aquatic and terrestrial biodiversity due to its unprecedented use in aquaculture, livestock, and human disease prevention. The influx of tetracycline may annihilate the microbial ecology structure in the environment and pose a severe threat to humans by disturbing the food chain. Although significant research data are available in the literature on various aspects of tetracycline, including detection techniques, degradation mechanisms, degradation products, and policy statements to curtail the issue, there is a scarcity of a report to compile the recent data in the literature for better analysis and comparison by the policymakers. To achieve this paucity in knowledge, the current study aims at collecting data on the available degradation strategies, mechanisms involved in biodegradable and non-biodegradable routes, the main factor affecting degradation strategies, compile novel detection techniques of tetracycline antibiotics in the environment, discuss antibiotic resistance genes and their potential role in degradation. Finally, limitations in the current bioremediation techniques and the future prospects are discussed with pointers for the decision-makers for a safer environment. [ABSTRACT FROM AUTHOR]

Power transmission lines are the key network that transmits energy from the generation side to load. The complexity and uncertainty in the power system increase continuously due to the evolution of the smart grid, which needs an effective and accurate protection system. The faults in transmission lines affect the whole power system and also the consumers' side. Therefore, accurate and precise identification of faults in transmission lines minimizes the losses and maximizes the functionality and reliability of the power network. Due to the recent advances in digital technology, an online scheme is used to locate the fault in transmission lines. In this paper, machine learning-based discrete wavelet transform and double-channel extreme learning machine method are proposed to locate and classify the faults in transmission lines. Db4 wavelet is used as a mother wavelet in the discrete wavelet transform for feature extraction up to nine levels. The proposed method validated on real-time data which achieves higher classification accuracies and less fault detection time. Results show that high-impedance non-linear faults have no effect on the proposed technique. [ABSTRACT FROM AUTHOR]

Worldwide industrial development has released hazardous polycyclic aromatic compounds into the environment. These pollutants need to be removed to improve the quality of the environment. Chemotaxis mechanism has increased the bioavailability of these hydrophobic compounds to microorganisms. The mechanism, however, is poorly understood at the ligand and chemoreceptor interface. Literature is unable to furnish a compiled review of already published data on up-to-date research on molecular aspects of chemotaxis mechanism, ligand and receptor-binding mechanism, and downstream signaling machinery. Moreover, chemotaxis-linked biodegradation of aromatic compounds is required to understand the chemotaxis role in biodegradation better. To fill this knowledge gap, the current review is an attempt to cover PAHs occurrence, chemical composition, and potential posed risks to humankind. The review will cover the aspects of microbial signaling mechanism, the structural diversity of methyl-accepting chemotaxis proteins at the molecular level, discuss chemotaxis mechanism role in biodegradation of aromatic compounds in model bacterial genera, and finally conclude with the potential of bacterial chemotaxis for aromatics biodegradation. [ABSTRACT FROM AUTHOR]

To improve the efficiency of hydrothermal pretreatment for better methane production and minimizing sugarcane bagasse recalcitrance, hydrogen peroxide was studied as an impregnation agent before hydrothermal pretreatment. Hydrothermal pretreatment was conducted employing three factors: temperature (160, 180, 200 °C), time (5, 12, 19 min) and hydrogen peroxide concentration (2, 4, 6 % v/v) by central composite design. Pretreated solid fraction resulted in a maximum of 73.1% delignification, 75.8% xylan removal besides 139.5% glucan increase. Liquid fraction was observed with 3005.63 ppm of total phenolic content, 17.7 g L−1 CODs (Soluble Chemical Oxygen Demand) along with minimal concentrations of 5-hydroxymethylfurfural (0.01–0.06 mg mL−1), furfural (00.06 mg mL−1), and acetic acid (0.11–0.58 mg mL−1). Digestion experiments resulted in 323.3 NmL g−1 TVS methane in comparison to 147.8 NmL g −1 TVS for raw bagasse. Most abundant bacterial genera identified were uncultured AUTHM297 (Thermotogales) whereas Methanosaeta were the abundant methanogenic archaeal genera. The study resulted in a 118.64% increase in methane production in comparison to raw bagasse indicating the importance of the impregnation step before hydrothermal pretreatment. [ABSTRACT FROM AUTHOR]

Metallic nanoparticles (MNPs) with their diverse physical and chemical properties have been applied in various biomedical domains. The increasing demand for MNPs has attracted researchers to develop straightforward, inexpensive, simple, and eco-friendly processes for the enhanced production of MNPs. To discover new biomedical applications first requires knowledge of the interactions of MNPs with target cells. This review focuses on plant and microbial synthesis of biological MNPs, their cellular uptake, biocompatibility, any biological consequences such as cytotoxicity, and biomedical applications. We highlighted the involvement of biomolecules in capping and stabilization of MNPs and the effect of physicochemical parameters particularly the pH on the synthesis of MNPs. Recently achieved milestones to understand the role of synthetic biology (SynBiol) in the synthesis of tailored MNPs are also discussed. [ABSTRACT FROM AUTHOR]

The use of wavelet-coupled data-driven models is increasing in the field of hydrological modelling. However, wavelet-coupled artificial neural network (ANN) models inherit the disadvantages of containing more complex structure and enhanced simulation time as a result of use of increased multiple input sub-series obtained by the wavelet transformation (WT). So, the identification of dominant wavelet sub-series containing significant information regarding the hydrological system and subsequent use of those dominant sub-series only as input is crucial for the development of wavelet-coupled ANN models. This study is therefore conducted to evaluate various approaches for selection of dominant wavelet sub-series and their effect on other critical issues of suitable wavelet function, decomposition level and input vector for the development of wavelet-coupled rainfall-runoff models. Four different approaches to identify dominant wavelet sub-series, ten different wavelet functions, nine decomposition levels, and five different input vectors are considered in the present study. Out of four tested approaches, the study advocates the use of relative weight analysis (RWA) for the selection of dominant input wavelet sub-series in the development of wavelet-coupled models. The db8 and the dmey (Discrete approximation of Meyer) wavelet functions at level nine were found to provide the best performance with the RWA approach. [ABSTRACT FROM AUTHOR]

Reference 1 Ahmad F, Zhu D, Sun J. Environmental fate of tetracycline antibiotics: degradation pathway mechanisms, challenges, and perspectives. Correction to: Environ Sci Eur (2021) 33:1 https://doi.org/10.1186/s12302-021-00505-y Following publication of the original article [[1]], the authors identified an error in the author name of Fiaz Ahmad. [Extracted from the article]

Auxin is an essential phytohormone that plays a crucial role in the growth and development of plants in stressful environments. Here, we analysed the auxin/indole-3-acetic acid (Aux/IAA) gene family, which produces auxin in pepper, and succeeded in identifying 27 putative members containing four conserved domains (I. II. III and IV) in their protein sequences. Sequence analysis, chromosomal mapping and motif prediction of all identified CaAux/IAA genes were performed. It was observed that these genes contained four conserved motifs divided into nine different groups and distributed across nine chromosomes in pepper plants. RNA-seq analysis revealed the organ specific expression of many CaAux/IAA genes. However, the majority of genes were expressed with high expression levels in the early stages of fruit development. However, the maximum expression level of the CA03g34540 gene was observed in the breaker stage. Moreover, thirteen CaAux/IAA genes were labelled as early responsive genes to various phytohormone and abiotic stresses. Furthermore, RNA-seq analysis in response to pathogen inoculation (PepMoV, TMV strains P0/P1, and Phytophthora capsici) showed distinct expression profiles of all identified genes, suggesting the diverse expression nature of genes under these stress conditions. Overall, this study provides insight into the dynamic response of CaAux/IAA genes under environmental and phytohormones stress conditions, providing bases to further explore the importance of these genes through mutant/transgenic analysis in pepper. [ABSTRACT FROM AUTHOR]

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2 , RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead., Competing Interests: Conflict of interestNo conflict of interest., (© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.)