The research extensively examines mechanical testing and optimization of basalt fiber-reinforced nylon (BF-nylon) materials. All samples were produced by FFF 3D printer, specifically the Delta Wasp company’s model 2040 industrial X. Crucial mechanical evaluations such as compression testing, tensile testing, Izod impact testing and wear resistance analysis were performed to analyze the composite’s stiffness, strength, toughness and wear resistance. Minimal wear rate were 0.0293 mm³ m⁻¹, while highest compression, impact and tensile resistance strengths reached were 48.2 MPa, 58.4 MPa, and 40.79 J m⁻¹, respectively, in accordance with the major process parameters outlined in experimental matrix. Data obtained at experimental evaluation was employed to evaluate and train ANN model. The efficiency of BF-nylon were enhanced through (MOGA) multi-objective optimization employing a genetic algorithm. The impact resistance, wear rate, compression strength, and tensile strength, of 0.016275777 mm³/m, 33.11 J/m, 56.70 MPa, and 47.40 MPa, have been attained at layer height 335 mm, infill density 90
In the Internet of Things (IoT) era, energy efficiency and reliability of static random-access memory (SRAM) play a critical role in achieving low-power and robust system performance. This paper introduces a novel SRAM topology that mitigates read and half-select disturbances while simultaneously enhancing read static noise margin (RSNM) and write static noise margin (WSNM). The proposed design improves RSNM through a singleended isolated read path, and WSNM is strengthened using a power-rail switching scheme, in which the inverter's VDD is temporarily connected to ground. To further reduce power consumption, transistor stacking is incorporated in both inverters and write paths, eliminating read bitline leakage and removing the need for dedicated write bitlines. Compared to prior SRAM architectures, the proposed design achieves up to 1.95 & times; improvement in RSNM and 1.47 & times; in WSNM, while reducing read power, write power, and leakage power by at least 17.13%, 25.81%, and 34.78%, respectively. Moreover, under severe process variations, the proposed design demonstrates significantly lower variability in RSNM, WSNM, and leakage power. Further, the superiority of the proposed SRAM design is established through its electrical quality metric, a key measure of overall performance. All evaluations were performed using Synopsys HSPICE with 16-nm CNTFET technology, considering a 64 & times; 64 SRAM array.
The Smart Roadways system transforms electric vehicle (EV) infrastructure by integrating Wireless Power Transmission (WPT) and IoT-based billing to enable seamless, on-the-go charging. A wireless power transmitter embedded in the road transfers energy to an EV’s receiver coil, ensuring continuous power without stopping at charging stations. The EV system, powered by an ESP32 microcontroller, interfaces with a battery, voltage sensor, LCD, buzzer, motor driver, and DC geared motors for real-time monitoring and control. The vehicle can be activated via a touch sensor, and upon entering the charging lane, it automatically begins wireless charging. Charging details, including voltage and cost, are displayed and monitored in real time via Blynk IoT integration. This innovation reduces range anxiety, enhances EV adoption, and contributes to a sustainable and intelligent transportation ecosystem.
This paper presents a novel design of a ternary full adder (TFA) implemented using multi-threshold carbon nanotube field-effect transistors (CNTFETs) and unary operators. Leveraging the inherent capability of CNTFET technology to precisely tune threshold voltage through nanotube diameter variation, the proposed architecture efficiently realizes the multiple voltage levels required for ternary logic without complex body-biasing or multi-oxide techniques. The design methodology eliminates conventional decoders and binary logic blocks by systematically employing negative ternary inverters (NTI), positive ternary inverters (PTI), biased ternary inverters (BTI), and shifting operators to directly derive the Sum and Carry functions. The circuit is simulated using the Stanford CNTFET model in the HSPICE environment under a 0.9 V supply voltage and 1 GHz operating frequency. Performance metrics including delay, average power consumption, power–delay product (PDP), and transistor count are evaluated and compared with four state-of-the-art TFA designs. The proposed TFA employs only 52 transistors, achieving the lowest power consumption and PDP among the compared designs. Specifically, it reduces power consumption by up to 64.8% and PDP by up to 59.7% relative to existing counterparts, while incurring a moderate delay overhead. Monte Carlo simulations with 1000 samples further confirm improved robustness against process variations. Moreover, the proposed design has higher noise immunity. The proposed TFA was then tested on an image-processing task. It produced higher image fidelity and the highest figure of merit defined by hardware efficiency and image quality among all compared designs.
In the health environment world, heart disease is a leading one for the rate of death. Preventing heart disease relies on its early and accurate detection and prediction in order to mitigate risk for patients and to improve their health outcomes. Recently, advances made in AI, particularly Deep learning, transfer learning, and ensemble learning, are changing how we predict heart disease. These learning methods operate on various types of datasets, patient records and genetic data that can inform predictions. Recent investigations have discovered that learning models can exploit knowledge from other areas of medicine, and this advancement aids in circumventing the issue of bare labelled data in heart disease studies. This review paper highlights the current deep learning, along with several machine learning techniques for feature extraction and detecting heart disease from medical images and data from medical records. It will also evaluate the performance of these techniques and provide an excellent analysis of their approach, results and limitations. This analysis leaves strong indications as to the validation of deep learning as a learning method, and ensemble model approaches provide a statistically improved diagnosis accuracy. Further, there is a suggestion to engage further heart disease datasets with broader origins and more in-depth features in order to build superior machine learning models. The current predictive model’s development for heart disease has been relatively limited. Accordingly, there’s a need for more sophisticated models with data across various geographical locations while improving early-stage heart disease prediction accuracy.
