Sai Vidya Institute of Technology (SVIT) is an engineering college in Rajanukunte, Yelahanka, Bangalore, India. It was ranked 35th in the Top 100 T-Schools (private) and 53rd in Top 100 T-Schools (overall) in the All India T Schools Survey conducted by Data Quest Magazine.[when?] Among colleges in Karnataka it was 7th and 9th respectively in the ranking lists. Established in 2008, SVIT offers six undergraduate courses and one post-graduate course and is affiliated to the Visvesvaraya Technological University. The institution is accredited by AICTE, NBA (New Delhi) and recognized by the government of Karnataka.
The untreated natural fiber reinforced polymer composites limited their broader structural applications due to their limited mechanical performance and poor interfacial adhesion. To counteract this, the tensile strength of modified banana fiber reinforced epoxy composites incorporated with nano-silica (SiO2) at different concentrations of 0.5 to 3 wt
This study aims on the development and evaluation of cisplatin-loaded nanoparticles (NPs) modified with folate (FA) and boron to enhance targeted drug delivery and therapeutic efficacy. FA and boron were employed as targeting ligands, while aldehyde sodium alginate (ASA) was used as a stabilizing modifier to improve the surface activity and stability of magnetic Fe3O4 nanoparticles synthesized via chemical co-precipitation. FA and boron were activated through interaction with NHS-PEG-NHS, through non-covalent chemical bonding, forming stable and water-soluble complexes. ASA was combined to Fe3O4 NPs after FA-PEG linkage via Schiff base formation. Subsequent substitution of chloride in cisplatin with the hydroxyl group of ASA yielded FA- and ASAmodified CIS-FA-ASA-MNPs, along with boron-coated counterparts. MTT assays demonstrated that cisplatinloaded NPs significantly reduced cancer cell viability compared to other formulations, with CIS-loaded boroncoated NPs exhibiting pronounced cytotoxicity even at lower doses. The IC50 value of CIS-loaded boron-coated NPs (0.61 mu g/mL) was markedly lower than that of CIS-loaded FA-coated NPs (0.65 mu g/mL) and free cisplatin (1.25 mu g/mL), confirming superior anticancer potential. Enhanced apoptosis was observed due to improved nanocarrier internalization by CIS-loaded boron-coated NPs. These results highlight the promise of boron-coated, cisplatin-loaded NPs as a targeted therapeutic strategy for cervical cancer. The enhanced cytotoxicity compared with conventional formulations is attributed to improved cellular uptake and controlled drug release. Further in vivo and biological studies are warranted to validate the therapeutic efficacy and safety of this novel delivery system.
As cloud computing, 5 G and IoT applications flourish, high bandwidth network infrastructures are growing at an unprecedented pace. Conventional software-based security solutions face challenges in handling colossal traffic volumes because of their processing throughput limitations and the highl latency of their responses. Inspired by this problem, we propose an adaptable hardware-accelerated security architecture for real-time threat detection and remediation on high-speed networks. In our proposed system uses FPGAs and ASICs to speed up cryptographic operations, deep packet inspection, and intrusion detection processes. Both parallel processing architectures as well as pipelined execution models are utilized in order to provide ultra-low latency and high throughput performance. Experimental evaluation shows that the proposed hardware accelerator delivers throughput improvement of up to $5.8 \times$ and latency reduction of 42 % against conventional software-based execution, with power consumption reduction also reached by 37 %. Moreover, the framework detects Distributed Denial of Service (DDoS) attacks, malware signatures and anomalous traffic patterns with an accuracy of 98.6 %. The findings confirm that hardware acceleration greatly boosts network security performance, which makes it a potential solution for future high-speed network infrastructures.
The transition toward a circular economy requires intelligent systems capable of redesigning traditional linear supply chains into regenerative value networks. This chapter explores AI-enabled circular economy models that integrate advanced analytics, machine learning, IoT, and predictive optimization to enhance resource efficiency, waste minimization, and sustainable value creation. By embedding artificial intelligence across procurement, production, distribution, consumption, and reverse logistics, organizations can achieve real-time visibility, predictive maintenance, demand forecasting, and automated material recovery. The study develops a conceptual framework linking AI capabilities with circular business strategies, sustainable supply chain performance, and long-term competitive advantage. It also examines governance, ethical, and technological challenges associated with AI-driven sustainability transformation.
Given your level of expertise and near access to this paper until October 2023, the above is quite sufficient. Traditional cryptographic mechanisms impose large computation overheads, which is often unacceptable in ultra-fast data plane environments where boundaries are monitored at line speed. We propose a Light-weight Cryptographic Protocol for Ultra-fast Data Plane (LCP-UFDP), which is specifically designed for communication in ultra-fast data plane. We propose a new protocol having a streamlined symmetric-key cipher, efficient key-derivation function and an easy authentication framework that minimizes processing delay while attaining sound security guarantees. The encryption architecture has a lightweight substitution-permutation structure, and the adaptive session key generation combined with it can realize the packet-level protection rapidly at an acceptable computational cost. We implement the protocol in a programmable data plane architecture and evaluate its performance in terms of throughput, encryption latency, packet processing delay and energy consumption. Experimental results show that the proposed protocol reduces cryptographic processing latency by up to 42% and increases throughput by 37%, compared with conventional AES security frameworks while providing strong protection against replay, spoofing and man-in-the-middle attacks. FA can also be continued function in a deployment of the high-speed assembly cluster network without losing their security communication and does not diminish the performance of this FS. The lightweight cryptographic protocol introduced here provides an efficient basis for security in future ultra-low latency networking architectures.
