The WRKY gene family plays a very diverse role in plant growth and development. These genes contained an evolutionarily conserved WRKY DNA binding domain, which shows functional diversity and extensive expansion of the gene family. In this study, we conducted a genome-wide comparative analysis to investigate the evolutionary aspects of the WRKY gene family across various plant species and revealed significant expansion and diversification ranging from aquatic green algae to terrestrial plants. Phylogeny reconstruction of WRKY genes was performed using the Maximum Likelihood (ML) method; the genes were grouped into seven different clades and further classified into algae, bryophytes, pteridophytes, dicotyledons, and monocotyledons subgroups. Furthermore, duplication analysis showed that the increase in the number of WRKY genes in higher plant species was primarily due to tandem and segmental duplication under purifying selection. In addition, the selection pressures of different subfamilies of the WRKY gene were investigated using different strategies (classical and Bayesian maximum likelihood methods (Data monkey/PAML)). The average dN/dS for each group are less than one, indicating purifying selection. Our comparative genomic analysis provides the basis for future functional analysis, understanding the role of gene duplication in gene family expansion, and selection pressure analysis.

This research aims to comprehensively investigate contemporary practices and factors influencing global tax avoidance and evasion through a systematic literature review (SLR) and bibliometric analysis. This study methodology uses SLR with bibliometric analysis to map this research trend. Relevant literature was collected and systematically analyzed to identify key themes, methodologies, and findings regarding tax avoidance and evasion. The study’s findings identify various tax avoidance practices, including profit shifting, transfer pricing strategies, and the use of tax havens by multinational corporations. The report also highlights various forms of tax evasion, such as money laundering and illicit financial flows. Key factors influencing tax behavior are ethical considerations, government policies, economic factors, and corporate governance practices. This analysis highlights the complex interactions between these factors in shaping tax practices globally. The study’s conclusions show that effective tax governance requires a multifaceted approach integrating ethical standards, a strong regulatory framework, and international cooperation. Ethical corporate behavior and transparent reporting are essential in promoting tax compliance and fairness. This study emphasizes the critical role of policymakers in designing and enforcing tax policies that discourage aggressive tax planning while encouraging economic growth and equality. Recommendations from these findings are to increase transparency in multinational operations, strengthen international tax cooperation, and improve global tax administration capabilities. In addition, increasing taxpayer education and awareness can foster a culture of compliance and ethical tax practices. Future research should prioritize longitudinal studies and case-specific analyses to deepen understanding and develop targeted interventions to address tax avoidance and evasion challenges.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

This study investigates the interaction between audit firms and key audit matters (KAMs) to measure their impact on financial reporting quality in Palestine, thereby enriching the discourse on financial reporting. A descriptive statistical method was used to analyze the audit reports of listed Palestinian firms from 2018 to 2022. A methodology that scrutinizes the clarity and informativeness of KAMs across different audit firms and KAM types, the research investigates how audit procedures and risk assessments contribute to the comprehensibility of KAM disclosures. The findings highlight a significant disparity in the readability of KAMs attributable to audit firm selection, with the non-Big Four firms exhibiting distinct approaches. This understanding, gathered through multivariate analysis, offers valuable contributions to the ongoing discourse on financial reporting quality, emphasizing the essential role of audit firms in shaping the effectiveness of audit reports and KAM disclosures.

This work investigated the photocatalytic properties of polymorphic nanostructures based on silica (SiO₂) and magnetite (Fe₃O₄) for the photodegradation of tartrazine yellow dye. In this sense, a fast, easy, and cheap synthesis route was proposed that used sugarcane bagasse biomass as a precursor material for silica. The Fourier transform infrared (FTIR) spectroscopy results showed a decrease in organic content due to the chemical treatment with NaOH solution. This was confirmed through the changes promoted in the bonds of chromophores belonging to lignin, cellulose, and hemicellulose. This treated biomass was calcined at 800 ℃, and FTIR and X-ray diffraction (XRD) also confirmed the biomass ash profile. The FTIR spectrum showed the formation of silica through stretching of the chemical bonds of the silicate group (Si-O-Si), which was confirmed by DXR with the predominance of peaks associated with the quartz phase. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) confirmed the morphological and chemical changes due to the chemical and thermal treatments applied to this biomass. Using the coprecipitation method, we synthesized Fe₃O₄ nanoparticles (Np) in the presence of SiO₂, generating the material Fe₃O₄/SiO₂-Np. The result was the formation of nanostructures with cubic, spherical, and octahedral geometries with a size of 200 nm. The SEM images showed that the few heterojunctions formed in the mixed material increased the photocatalytic efficiency of the photodegradation of tartrazine yellow dye by more than two times. The degradation percentage reached 45% in 120 min of reaction time. This mixed material can effectively decontaminate effluents composed of organic pollutants containing azo groups.