Akshaya College of Engineering and Technology (Tamil:அக்ஷயா பொறியியல் மற்றும் தொழில் நுட்பக்கல்லூரி) is a private self-financing engineering college in Kinathukadavu, Coimbatore, Tamil Nadu, India. The college is approved by AICTE and affiliated to Anna University.
This paper presents empirical information regarding the transformational aspect of Artificial Intelligence (AI) in supporting sustainable business ecosystems based on digital innovation and integration in the emerging economies with the circular economy. Based on a mixed-method design, 220 organizations in manufacturing, energy and technology sectors in India, Brazil, Vietnam, and South Africa served as the source of data. The assessment of the research was performed through quantitative analysis employing Structural Equation Modeling (SEM) and qualitative data collected with the help of expert interviews and case analysis. The significant results also indicate that the mature AI governance produces a significant improvement in the scalability of the circular economy initiatives ($\beta=0.72, \mathrm{p}<.001$). The quality of regulation and digital infrastructure was identified as intermediaries that mitigated the barriers to adoption ($\boldsymbol{\beta}=0.24,\mathbf{p}<.001$) and projects that made a net-positive impact on the environment were found to be more viable in the long run and supported by the stakeholders ($t=8.27, p<.001$). Adaptive resilience ($b=0.68, p<.001$) was greatly associated with structured human-AI collaboration, and the data transparency was found to be positively related with trust and fairness in value distribution ($\mathrm{r}=0.66-0.71, \mathrm{p}<.01$). The paper finds that digital transformation is achievable in sustainable manner, but requires not only technological preparedness, but also ethical regulation, collaborative models and institutional assistance. The study provides a confirmed, generalizable construct to policymakers, industry executives, and sustainability planners who are in need of putting into practice AI-based AI-led models of green expansion that will balance growth and innovation, stability, and eco-friendliness.
In today’s highly interconnected digital landscape, secure identity verification and authentication have become essential to ensure data privacy and confidentiality, but also for maintaining trust, regulatory compliance, and protection against sophisticated cyber threats. As cloud environments continue to expand and host increasingly sensitive and large-scale data, the demand for more robust and future-proof security mechanisms has intensified. Addressing this challenge, this article proposes a novel framework using Quantum Key Distribution (QKD) for identity verification and authentication, offering a resilient solution against both classical and quantum-era cyberattacks. The cloud system integrates key entities, including Cloud Service Providers (CSPs), data users, and data owners, and operates through five distinct phases: Initialization and Registration, Quantum Key Generation and Distribution, Identity Verification and Registration, key agreement, and password change. In the initialization phase, security parameters are established, and the registration phase ensures the enrollment of all entities. Then, a Quantum Key is generated by the Dense Network and Quantum Key Techniques (DNet-QKT) for exchanging keys. Then, Identity verification and authentication are reinforced using Advanced Encryption Standard (AES), hashing, Exclusive OR (XOR), and cryptographic keys. Finally, the password update phase provides a dependable and secure mechanism for users to modify their credentials, reinforcing the system’s integrity and ensuring resilience against emerging security threats. For a key size 8, DNet-QKT established a computational cost of 2.766 s (sec), and memory of 20.400 Megabytes (MB).
Wireless Sensor Networks (WSNs) are increasingly employed in fields such as precision agriculture, where the need for dependable data transmission and energy conservation is critical. Nonetheless, WSNs encounter significant challenges owing to the restricted battery life of sensor nodes and the detrimental effects of erroneous data on the aggregation and routing efficiency. Existing cluster-based routing and fault detection techniques often fail in dynamic settings and have high false alarm rates, which diminish the overall reliability of the network. To address these challenges, this study introduces a hybrid energy-efficient framework consisting of three primary stages: clustering based on fuzzy logic, selection of a Cluster Head (CH) using the Adaptive Human Evolutionary Optimization Algorithm (HEOA), and energy-aware routing through the Horned Lizard Optimization Algorithm (HLOA). To enhance fault resilience, a Deep Graph-Gated Recurrent Unit (GraphGRU) network is incorporated at the CH level to anticipate and filter out faulty data, thereby enhancing data integrity prior to aggregation. The proposed model was assessed against several existing techniques including BFOABMS, COA, IFCM-CHBCO, ASSO-SSO, and PFCRE. The results demonstrate significant performance improvements: the Measure of Dispersion (0.4314), Packet Delivery Ratio (99
AA7075/6%B4C aluminium metal matrix composites were fabricated by stir casting and subjected to cryogenic soaking durations of 0-36 h. Machining performance was evaluated using EDM under an L27 orthogonal array design. Response Surface Methodology was applied for single-objective analysis, while a hybrid Entropy Weight-Grey Relational Analysis optimized multiple responses. The optimal condition was identified as a 12-hour soaking duration with machining parameters of gap 0.2 mm, current 60 A, pulse on 100 mu s, and pulse off 70 mu s, yielding MRR 0.5086 g/min, TWR 0.0014 g/min, and SR 3.912 mu m. Validation with an Adaptive Neuro-Fuzzy Inference System confirmed high predictive accuracy. This study establishes cryogenic soaking duration as a critical factor in enhancing EDM machinability of B4C-reinforced AMMCs and demonstrates ANFIS as an effective tool for multi-objective machining optimization.
Steel reinforcement in concrete structures is highly susceptible to corrosion which leads to significantly durability concerns. To address this challenge, this study numerically investigates the flexural performance of concrete beams reinforced with glass fiber-reinforced polymer (GFRP) rebars as a replacement to steel. In this study, a finite element (FE) model was developed in ANSYS mechanical APDL to simulate the behavior of M-30 grade concrete beams with two mix designs by including 20
