The College of Technology and Engineering (CTAE), is a public engineering college located in Udaipur, Rajasthan, India. It is one of the top ranking engineering institute of the state offering varied courses in engineering.
The complexity and difficulty of the ongoing and unstoppable cybercrimes in the traditional or conventional Artificial Intelligence (AI) system create the worst problems for the recent day’s modern cyberspace. The traditional systems are always dependent on centralized systems, which have insufficient performance to prevent and reveal DDoS attacks and modern cybersecurity challenges. The traditional systems of artificial intelligence (AI), machine learning (ML), and Deep Learning (DL) are limited only to the task of detecting DDoS attacks; they are not able to protect the client–server network cyberspace from DDoS attacks. Centralized-based property of the traditional systems produces a particular failure that makes them more attractive victims in cyberspace by DDoS attack criminals. The difficulty of deploying Artificial Intelligence (AI) cybersecurity techniques often results in poor DDoS attack mitigation performance, leaving organizations vulnerable and susceptible to DDoS attacks. The principal and major goal of the study is to formulate a strong Blockchain Technology-based Cyber Security emerging technology-oriented technique, particularly designing and modeling for protecting client–server network cyberspace from DDoS-based cyber-attacks. We have a few numbers of system methodologies like data collection (i.e., related literature review, dataset selection and collection, and experimental data collection), selection of performance evaluation parameters, validation process, experimental equations of performance evaluation metrics, and selection and collection of hardware and software resources. Different models are trained and tested on the CIC-DDoS2019 dataset; we have seen that there are different values of performance evaluation metrics for different models. The CNN model showed the value of 98.5
This paper reviews the design of customized 3D printed (also referred to as additively manufactured) implants. A focus is placed on the information flow of design as it is processed, starting from a patient’s scan and culminating with the 3D printing compatible customized implant’s design. We discuss the challenges related to the introduction of 3D printing technologies into the design of the implant, the variabilities encountered, and opportunities for standardization. The paper identifies research and standardization gaps in four stages of a 3D printed customized implant’s design process, namely, medical imaging, constructing CAD (3D) model of VOI, design, and 3D printing compatible file formatting. We hope the paper will help drive research to overcome future challenges encountered in the design process of 3D printed customized medical implants.
Accurate and real-time estimation of battery State of Charge (SOC) and State of Health (SOH) is essential for reliable Battery Management Systems (BMS), particularly on resource-constrained embedded platforms. Existing neural-network-based approaches achieve promising estimation accuracy but often involve computationally intensive architectures that limit on-device deployment. To address this limitation, this study proposes a ResourceAware Dual-Head Temporal Convolutional Network (RA-DHTCN) for simultaneous SOC–SOH estimation. The proposed framework uses temporal and dilated convolutions with residual learning and dual estimation heads, followed by structured pruning, knowledge distillation, and INT8 quantization for lightweight deployment. The model is implemented using Python and PyTorch and evaluated using the Kaggle EV Battery Charging & Thermal Runaway Dataset, containing 500 timestamped observations. The proposed model achieves 0.70% MAE, 0.84% RMSE, 0.76% MAPE, and for SOH estimation. The optimized architecture is designed to reduce model size, memory consumption, and inference latency while retaining estimation accuracy. Compared with conventional deep-learning baselines, the framework targets an [X%] improvement in estimation accuracy and supports realtime embedded BMS operation. Overall, RA-DHTCN provides a compact and scalable solution for on-device joint SOC–SOH estimation.
This research investigates the influence of single and hybrid mineral nanofillers on the morphological, thermal, and electrical properties of epoxy-based glass fiber composites. Nanocomposites were fabricated using hand lay-up technique, incorporating nano-calcium carbonate (CaCO₃) and nano-talc with particle size of 50 to 100 nm into an epoxy matrix with 5-ply E-glass/S-glass fiber. The study evaluates composites with 2-8 wt% of nano-CaCO₃, and hybrid systems containing nano-CaCO₃ (2-8 wt%) and nano-talc (1-4 wt%). Morphological analysis via Scanning Electron Microscopy (SEM) assessed nanofiller dispersion, while thermal properties were analyzed using Differential Scanning Calorimetry (DSC) for glass transition temperature (Tg). Electrical performance was evaluated through dielectric breakdown voltage (ASTM D149) and surface resistivity (ASTM D257). SEM analysis showed filler dispersion depended on concentration, with optimal distribution at 4-6 wt% before significant agglomeration at higher concentrations. DSC results revealed increased Tg with nanofillers, peaking for the composite with 6 wt% CaCO₃ and 3 wt% talc, indicating enhanced thermal stability. The dielectric breakdown voltage peaked at 17.6 kV for 2 wt% CaCO₃ and 1 wt% talc composite, while surface resistivity was highest for 4 wt% CaCO₃ and 2 wt% talc (73.74×1012Ω). All formulations maintained UL 94 V-1 flammability rating. The study concludes that the synergistic interaction between nano-CaCO₃ and nano-talc enables significant tailoring of thermal and electrical properties for advanced applications.
