Pesticides are extensively used in agriculture to protect crops from pests and diseases, but their persistence in soil can pose environmental and health hazards. Sustainable pest control and environmental preservation depend on an understanding of the variables that affect pesticide degradation. This study synthesizes current knowledge on the biotic and abiotic factors impacting pesticide degradation in agricultural soils. Key determinants include soil physicochemical parameters such as pH, texture, and organic matter, ambient conditions including temperature, moisture, and light, as well as biological activity and farming practices.

The degradation of pesticides plays a crucial role in mitigating their environmental impact and determining their persistence in soil and water systems. This review explores the underlying mechanisms and pathways involved in the degradation of various pesticides, emphasizing hydrolysis, photolysis, oxidation, and microbial degradation processes. The influence of environmental factors such as pH, temperature, and light on degradation rates is also discussed. Understanding these mechanisms is essential for developing effective pesticide management strategies and designing environmentally friendly compounds.

This paper examined the impact of Business Intelligence Systems (BIS) on decision-making quality among top-level management in the Jordanian banking sector, with Artificial Intelligence Capability as a moderating variable, to enhance understanding of the link between business intelligence and decision-making quality. A questionnaire was used to gather data from 297 employees of Jordanian Commercial Banks. The research hypotheses were developed and tested using partial least squares structural equation modeling (PLS-SEM). The results revealed a positive relationship between system quality, data quality, and decision-making quality, with service quality showing a particularly strong impact. Effective system quality techniques were found to enhance decision-making quality, underscoring the importance of BI resource management. The findings also indicated a significant effect of data quality on strategic decision-making quality, while data visualization did not show a statistically significant impact. Furthermore, the results confirmed the moderating role of Artificial Intelligence Capability in the relationships between system quality, data quality, data visualization, and decision-making quality. Overall, the findings of this study contribute valuable insights to the literature on business intelligence and strategic decision-making.

The question of cybersecurity has turned into a necessity of credible digital finance, defining the expansion capacity of fintech sectors to increase access to secure and safe financial services. The phenomenon of fintech inclusion is becoming subject to the impact of institutional and corporate protection factors, which minimize perceived risk, and safeguard users, as well as confidence in digital financial channels. This paper is based on institutional and risk governance views and explores the influence of cybersecurity practices on fintech inclusion and suggests digital policies as a mediating factor that converts cybersecurity preparedness to inclusive fintech performance. Although increased attention is paid to cybersecurity and the modernization of regulations, there is still a lack of empirical data on the impact of cybersecurity practices on the inclusion of fintech based on policy-driven processes in a unified analytical framework. The 300 respondents who worked in the positions of information security managers, compliance and risk officers, fintech product managers, IT governance specialists, and senior executives were sampled to gather the data across the fintech-enabled organizations. The proposed hypothesized relationships were tested using Structural Equation Modeling based on the PLS-SEM. These results suggest that cybersecurity practices affect considerably digital policies and fintech inclusion, whereas digital policies affect considerably fintech inclusion. The mediation analysis ensures that the effect of cybersecurity practices on fintech inclusion is partly transmitted through the digital policies, which indicate that a better result is observed when backed by the clear governance structure and policy frameworks. The findings have practical implications to managers and policymakers who aim to improve the inclusion of fintech using secure and policy-driven digital finance environments.

The digital transformation has been a main engine of economic growth by transforming the organizational structures, operations and strategic decisions with the help of sophisticated digital technologies. The mediating role of Big Data Analytics in this transformation is that it helps organizations to transform digital initiatives into real economic performance. It will utilize the Technology Acceptance Model and recent literature on the concept of digital transformation and the capabilities of data to identify the effects of digital transformation on economic growth in the mediator Big Data Analytics. The research paper is based on three aspects of digital transformation: preparations of digital infrastructure, process integration based on technology and data-driven decisions support. A sample of 300 respondents was taken, who are representatives of digitally intensive organizations, including senior executives, technology managers, and analytics specialists. Structural Equation Modeling on the Partial Least Squares algorithm was used to test the research framework. The results prove that digital transformation has a positive impact on economic growth and improves the capabilities of Big Data Analytics to a large extent. Big Data Analytics, in its turn, can help improve economic outcomes due to its contribution to enhancing efficiency, the ability to innovate, and responsiveness. The mediation analysis supports the claims and confirms that Big Data Analytics is a major transmission channel upon which the digital transformation initiatives create sustainable economic value. The research has its implications on organizational leaders, technology strategy, and policymakers. Digital transformation can only be maximized by strategic investment in digital infrastructure, development of analytics capability and data governance. To create the digital policies that can support both the data-driven innovation and inclusive economic growth, policymakers can rely on these findings.

Benzimidazole continues to be an intriguing scaffold in recent drug discovery, owing to its broad spectrum of pharmacological effects. In recent years, a variety of its derivatives, which included chalcone imines, hydrazones, and thiosemicarbazones, were actively investigated for their antitumor potential. In the search for new agents capable of treating kidney cancer, an analysis of a small series of 2-substituted benzimidazoles (45) using 3D-QSAR modelling was performed to determine the antiproliferative activities against cancer cell lines A-498. The biological activity was sufficient to establish a meaningful structure–activity relationship, providing a foundation for the design of more potent compounds. The activity-favouring and activity-disfavoring structural regions were clearly revealed using contour maps generated by the models. The CoMSIA/SHD model was one of the best developed, and its high statistical robustness (q2 = 0.751) and predictive power (R2 pred = 0.924) indicated its reliability. We designed five new derivatives of benzimidazole based on the QSAR results, which demonstrated potent inhibitory potential. Molecular docking studies were performed in order to investigate in detail their interaction modes with the aromatic receptor, and stable binding conformations at the active site have been found. The in silico pharmacokinetic studies suggested that these compounds have a favourable ADMET and bioavailability profile, reinforcing their suitability for in vitro testing. Two leads, L15 and L22, with better PKs properties and high-predicted activities, were subjected to a 100-ns MD simulation in complex with the aromatase target to investigate their stability. We also conducted a retrosynthetic analysis for L15 and L22, suggesting potential synthetic routes for experimental validation. Overall, these findings suggest that benzimidazole analogues could be promising candidates for treating RCC and possibly for blocking aromatase.

The rising usage of E-Auditing and its effect on businesses through new technology developments and rules demonstrates how audit systems can maximize operational efficiency and business decision quality. Researchers are undergoing a study to determine E-Auditing acceptance rates. The TOE model represents “Technological, Organizational, and Environmental” variables that function as key examination areas in organizational analysis and management practices when researchers study technological implementation and adoption patterns in industrial environments. The research proposes that these three aspects (technical aspects with both Relative advantage (RA) and Technology Compatibility (TC) and organizational aspects including top management support (TMS) and readiness (R)) along with environmental aspects such as competitive pressure (CP) contribute to e-auditing adoption. Auditor trust appeared in this study as the suggested moderating factor. A total of 235 participants provided info outside random sampling while the analysis used SPSS software. The experimental results proved that factors associated with TOE provide legitimate grounds for E-auditing acceptance. Evidence demonstrates that TOE variables provide justification for why organizations would accept E-auditing technology. Data show that trust functions as a supportive variable for the relationship between TOE and e-auditing but provides minimal strength. E-Auditing adoption research needs further investigation within emerging economies to understand better how users adopt this tool. The objective for decision-makers should focus on expanding user understanding of E-Auditing adoption along with educating decision-makers about the benefits of implementing this system.

A thorough analysis of developments in machine learning (ML) and deep learning (DL) technologies for skin cancer diagnosis is provided in this research. It investigates how ML and DL could improve the precision and effectiveness of melanoma, basal cell carcinoma, and squamous cell carcinoma detection. By looking at current studies, the study emphasizes the use of neural networks, convolutional neural networks (CNNs), support vector machines (SVM), random forests, and k-nearest neighbors (KNN) in the diagnosis of skin cancer. Key findings show that DL models, including VGG, ResNet, and Inception benefit from huge datasets and sophisticated data augmentation strategies to attain high accuracy, sensitivity, and specificity. The paper also discusses the challenges and limitations associated with these technologies, such as the requirement for extensive annotated datasets. The study concludes with a call for collaboration to overcome current challenges and enhance the practical application of ML and DL in skin cancer detection.

Diabetes mellitus is a serious health disease that affects people all over the world. The number of persons identified with diabetes mellitus rises each year. α -Glucosidase is a digestive enzyme used to control diabetes mellitus. The searching for new potent α-glucosidase inhibitors capable of delaying carbohydrate digestion in the human body is an important strategy towards control of diabetes mellitus. In this work, a series of quinoline-based Schiff base derivatives already identified as α-glucosidase inhibitory activity was studied by using 2D/3D-QSAR approach. The best HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA models were constructed using thirteen molecules in the training set, resulting in favorable values of Q2 (0.834 and 0.607), and high values of R2 (0.985 and 0.912), respectively. The generated HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA contour plots were precious for designing and enhancing the α-glucosidase inhibitory activity of quinoline-based Schiff base molecules. Considering these results, two novel α-glucosidase compounds were designed to possess significant activity. The newly suggested molecules showed good outcomes in the preliminary in silico ADME/Tox evaluations. Molecular docking results revealed that the new designed inhibitors have a good stability in the active pocket of the studied receptor compared to voglibose, clinically used as an α-glucosidase inhibitor. MD simulation and MM-GBSA results confirmed the molecular docking outcomes. Finally, DFT analysis was useful in determining the most electrophilic and nucleophilic centers of the two designed α-glucosidase inhibitors.

This article aims to study technological intelligence in innovative startups in Algeria using Kerr’s model. Technological intelligence consists of four main dimensions: intelligent systems, competitive intelligence, market intelligence, and intelligent processes. To collect data, a questionnaire was distributed to a sample of 255 innovative startups in Algeria, and the data were analyzed using confirmatory factor analysis (CFA) with Smart PLS software. The results indicated that the two-dimensional model combining intelligent systems and competitive intelligence provided the best fit, with a relationship value of 0.605 between these two dimensions. On the other hand, the relationship between market intelligence and competitive intelligence was weak, with a value of 0.281, reflecting the limited use of analytical methods by startups to monitor competitors. Based on these findings, the study recommends that innovative startups in Algeria enhance their use of competitive intelligence and intelligent systems to improve decision-making processes. Additionally, these startups should make better use of available market technologies to develop their products and services, while focusing on continuous competitor analysis and identifying opportunities. In conclusion, technological intelligence is a strategic element for startups, helping them improve their performance and achieve a competitive edge in the changing business environment in Algeria.