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.

Manufacturers employ AI for monitoring vehicle mileage, inspecting components, and scheduling maintenance. Past studies underscore the need for auto-related plans to prioritize environmental protection, concentrating on AI-driven environmental solutions promoted by AI for Good. AI enhances brand success by improving investment, technology, and promotional capabilities. This study emphasizes consistency in AI application across the automotive value chain for brand sustainability. A web-based poll surveyed 120 AI users in marketing, HR, sustainability, as well as 180 sustainability specialists and regulators. The primary goal is to assess, via structural model evaluation, how extraneous variables affect the development of AI-powered brand sustainability strategies. The study highlights AI's sustainability benefits in the automotive industry improving transportation safety, forecasting maintenance, and creating eco-friendly vehicles. However, challenges involve over-reliance on AI, predicting human behavior, and addressing sustainability threats. AI development should consider regional differences, prioritizing openness, policy harmony, and consumer agency. These findings aid marketing and HR professionals in devising customer-centric long-term plans.

This paper examined technology information as the factor that drives productivity among Small and Medium Enterprises (SMEs), particularly the effect of innovation on the performance of SMEs and the effect of Customer Relationship Management (CRM) Technology on industrial performance. The mediation of CRM Technology on the effect of innovation on company performance was examined as well. Survey questionnaire was distributed online to 544 firms, and data were analyzed through structural equation models using Amos version 23. Results show that innovation increases productivity (performance) of small businesses and plays a positive role on information systems. However, innovation had an insignificant role on industrial Performance. Meanwhile, CRM Technology shows no mediation in the relationship between innovation and performance. Generally, results proved the significance of innovation in enhancing company performance.

The objective of this research was to gain a deeper comprehension of how individuals perceive and respond to cybersecurity and how various internal and external factors influence these behaviors and attitudes. Conducted at ABC organization in Saudi Arabia, the study employed the qualitative methodology. Two online focus groups were employed featuring open-ended questions. The data were subsequently analyzed thematically using inductive and deductive coding techniques. Several theories were used as theoretical lenses to analyze the data. After the collected data had been analyzed, three main themes emerged: (a) perceived safeguards and threats, (b) personal and professional experience in information security, and (c) necessity of education and raising awareness. Additionally, two sub-themes were revealed: (a) costs and benefits and (b) necessity of safeguard measures and attaining trust. The study’s identified themes and sub-themes offer a thorough comprehension of the demographic, social, cultural, and internalized factors influencing cybersecurity-related behavior. The identified themes could potentially be applicable to other settings. Future qualitative research could further explore the transferability of these findings by conducting similar studies in different organizational, cultural, and linguistic contexts. It is also recommended for future quantitative research to delve deeper than surface-level data and consider underlying meaning, factors, connections, or relationships that may skew the results. It is crucial to delve into hidden meanings, not just accept data at face value.

This study aims to examine the factors affecting Jordanian Islamic banks’ customers’ adoption of electronic banks. The study relied on quantitative methods, where the study designed a questionnaire based on the Technology Acceptance Model (TAM) which derived from the Theory of Reasoned Action (TRA). The study sample consisted of 470 respondents. The study applied the Partial Least Squares Structural Equation Modeling (PLS-SEM), where the results of the Chi-square test and the standardized root mean square residual (SRMR) test showed the validity of the model for analysis. The results showed that usefulness, privacy, and awareness affect the adoption of e-banking, and that ease to use and electronic skills affect the adoption of e-banking through usefulness. The results also show that Infrastructure does not affect the adoption of e-banking, and Convenience does not affect the adoption of e-banking through Intention to use e-banking. The study advises Islamic banks to spread awareness about the importance of electronic banking and design easy-to-use electronic services that enjoy privacy and security.

The current study aims to determine the factors influencing the intention to employ digital libraries among postgraduate students in Jordanian universities. The data were gathered through questionnaires in the format of Google Forms. The questionnaires were distributed to postgraduate students enrolled in Jordanian universities with digital libraries, who were recruited via purposive sampling. A total of 261 responses were received, with 67 deemed unsuitable for the study analysis and subsequently excluded. Resultantly, 194 valid questionnaires were finalized for data analysis. The SMART-partial least squares (PLS) software was utilized to conduct structural equation modelling (SEM) to test the study hypotheses. This study discovered that the variables, namely performance expectancy, effort, and facilitating conditions expectancy, were significantly and positively associated with the outcome variable, which is the intention to employ digital libraries. Meanwhile, social influence was revealed to be insignificant. Performance expectancy also significantly mediated the correlations between technological readiness and the intention to utilize and between online self-efficacy and the intention to utilize. The present study focused only on potential users among postgraduate students at Jordanian universities with digital libraries. The findings contributed valuable insights into the academic sector, especially library management to enhance the rate of digital library or e-library adoption at Jordanian universities. Digital library managers and policymakers could leverage the findings to design pertinent strategies that increase user engagement within digital library environments. The UTAUT model was demonstrated to be capable of predicting users’ intentions of employing digital libraries and corroborated the mediation role of performance expectancy on the associations between technological readiness and the intention to utilize and between online self-efficacy and the intention to use.