Integrating cutting-edge technology with conventional farming practices has been dubbed “smart agriculture” or “the agricultural internet of things.” Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks.
Humanoid doctor is an AI-based robot that featured remote bi-directional communication and is embedded with disruptive technologies. Accurate and real-time responses are the main characteristics of a humanoid doctor which diagnoses disease in a patient. The patient details are obtained by Internet of Things devices, edge devices, and text formats. The inputs from the patient are processed by the humanoid doctor, and it provides its opinion to the patient. The historical patient data are trained using cloud artificial intelligence platform and the model is tested against the patient sample data acquired using medical IoT and edge devices. Disease is identified at three different stages and analyzed. The humanoid doctor is expected to identify the diseases well in comparison with human healthcare professionals. The humanoid doctor is under-trusted because of the lack of a multi-featured accurate model, accessibility, availability, and standardization. In this letter, patient input, artificial intelligence, and response zones are encapsulated and the humanoid doctor is realized.
Purpose The study aims to focus on the necessity for advanced transformational leadership and integration of technology in accomplishing sustainable goals through proactive, innovative approaches to thrive in a complex and environment-conscious world. The integration of metaverse technological adaption and information technological capabilities marks a significant evolution beyond traditional models; enhances the strategies and practices of organizations. Design/methodology/approach The three-wave study design included 448 IT leaders (CEOs, Directors and Managers) in India, Bangladesh, Bhutan and Indonesia. The data was analyzed using PLS-SEM 4 (v4.0.9.9) software. Findings The results suggest that green transformational leadership (GTL) positively influence green organizational agility (GOA). There is a positive relationship between GTL and GOA through green human resource management practices (green training and development and green compensation and rewards). Information technology capabilities of the leaders help in moderating organizational innovativeness and through this metaverse adoption moderate organizational agility. Research limitations/implications The innovative application of upper-echelon theory builds up a fresh perspective on leader’s role in the organization by shifting the emphasis from traits to attitudes influencing effectiveness in promoting green culture. Adopting metaverse in the organizations would help leaders in operationalizing flexible culture and managerial support. This helps employees in fostering team cohesion, feeling of belongingness enhancing productive culture through innovativeness. Originality/value The study provides novel perspectives of using metaverse adoption in organizations, where leaders approach comprised of traits, capabilities and attitudes toward organizational agility are studied.
A novel series of quinazolin-4(3H)-one derivatives (8a8l) were successfully synthesized using a multicomponent reaction of the substituted 1,2,4-oxadiazole (5a-5l) and 3-((5-mercapto-1,3,4-oxadiazol-2-yl)methyl)-8methylquinazolin-4(3H)-one (7) in the presence of K2CO3 and KI at room temperature and were characterized by FTIR, 1H NMR, 13C NMR and HRMS. The anticancer activity was performed through an MTT assay using doxorubicin as a standard against three human cancer cell lines: MCF-7, MDA-MB-231 (breast cancer) and A549 (lung cancer), which shows moderate to excellent activity. The anticancer evaluation displayed the significant sensitivity of the MCF-7 towards all the screened candidates for compounds 8b, 8e and 8k with IC50 values of 8.85 f 1.0, 7.30 f 1.4, and 9.50 f 1.5 mu M compared to doxorubicin (DXN) (IC50 = 13.41 f 0.7 mu M). The IC50 values of the novel scaffolds ranged from 7.26 f 1.1 mu M to 39.21 f 0.2 mu M whereas the DXN showed 8.44 f 1.8 mu M to 13.41 f 0.7 mu M respectively. The newly developed substituted quinazolinone-linked oxadiazole hybrids were exhibited strong anticancer activity based on percent inhibition values. Based on the molecular docking study, candidates 8b, 8e, and 8k all fit well within the breast cancer active site, with energy scores of -9.37, -9.12, and -8.28 kcal mol-1, respectively. The theoretical predictions by DFT and in silico docking analysis of physico-chemical and ADME/Tox properties were well supported by the experimental studies and antioxidant assays revealed strong activity, indicating the potential candidates for the future bioactive scaffolds.
Differing from AI and GenAI adoption, research on traditional systems emphasised extrinsic factors like utility, social influence and innovativeness as predictors of user behaviour. The role of proximal psychological factors like motivation, however, has been overlooked in this context, which becomes essential with this shift towards AI. In the educational sector, the students’ use of AI shows the possibility of intrinsic factors like motivation in shaping adoption behaviour. This study uses Self-Determination Theory (SDT) and its Organismic Integration Theory (OIT) extension to propose a conceptual map that examines the role of distinct motivational types in shaping students’ GenAI adoption behaviour. The adoption behaviour of 348 Indian students pursuing higher education was collected through a cross-sectional survey and analysed using structural equation modelling. Findings indicated that autonomous motivation, including intrinsic, identified, and integrated motivation, significantly predicts students’ intentions to use GenAI tools. The study further examined the moderating role of perceived compatibility, revealing that alignment between users’ lifestyles and GenAI usage strengthens the impact of controlled motivations. When students feel that AI fits well with their needs and learning requirements, showing high compatibility, external motivators have a stronger effect on their decision to adopt it. This makes compatibility an important new finding and provides additional insights into the motivational types of GenAI adoption in academic contexts. This study extends the body of knowledge by moving beyond the binary treatment of motivation and empirically distinguishing between specific types of motivation. It emphasises the importance of self-determined motivation while showing how the correlations between various motivation types and GenAI usage intentions are conditioned by perceived compatibility. The study also offers practical insights based on the significant results.
