The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people's writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.

Fine grained AZ31 magnesium alloy sheets were produced by groove pressing at 250 °C processing temperature targeted for biomedical implant applications. Microstructural studies revealed a grain refinement up to 12.5 ± 6.3 μm from 64 ± 14.5 μm. Higher hardness (95.1 ± 2.1 HV0.1) was measured for groove pressed AZ31 compared with AZ31 (58.7 ± 4.9 HV0.1). From the x-ray diffraction studies, basal dominated texture appeared in groove pressed samples. Corrosion performance assessed by conducting electrochemical studies using simulated body fluid (SBF) showed lower corrosion current density demonstrates increased corrosion resistance after groove pressing due to decreased grain size and texture. The post corrosion mechanical performance was assessed by conducting tensile tests after subjecting the samples to corrosion in SBF for 72 h. Grain refined AZ31 alloy exhibited improved strength with marginal decreased ductility before corrosion. Post corrosion experiments revealed better mechanical performance for the grain refined alloy as reflected in higher strength and % of elongation than the base alloy. The findings demonstrate the potential of grain refinement by groove pressing on enhancing resistance towards corrosion initiated mechanical failure of AZ31 in the presence of corrosion defects.

In this study, multiple intelligent control systems for direct torque-controlled Synchronous motors are implemented and compared. Using a lookup table to pick a vector through the inverter voltage space, the direct torque control (DTC) system can be obtained. To replicate the state selector in relation to the look-up table, intelligent controllers are deployed. Intelligent logic controllers like fuzzy and neural are used to regulate the performance of permanent magnet synchronous motors (PMSM). In steady-state applications, neural and fuzzy controllers reduce the torque ripple and stator current harmonic distortion. These outcomes are compared with those obtained when the synchronous motor was put under the basic direct torque control method using a proportional integral (PI) controller. The accuracy and effectiveness of the suggested control topologies have been verified using computer simulation software like MATLAB/Simulink.

This paper explored how generative artificial intelligence (AI) could enhance the digital accessibility of individuals with visual, auditory, and cognitive impairments. It aims to develop an adaptive and context-sensitive system to dynamically customize content in accordance with users’ needs. The proposed system creates text simplification with generative AI models like Generative Pretrained Transformer 3 (GPT-3), and caption images with Contrastive Language–Image Pre-Training (CLIP). It adapts users’ reactions with reinforcement learning, to enable the generation of real-time and personalized content. This project tested the system performance with mixed data, including texts, images, and videos. The outcomes revealed that the accessibility of the content had been significantly increased. At the same time, the Flesch-Kincaid Grade Level was reduced by 50% through text simplification, and the bilingual evaluation understudy (BLEU) score was ranked at 0.74 in the case of image captioning. User satisfaction had increased by 15% after feedback corrections. In addition to these results, the system demonstrated high effectiveness in supporting auditory-impaired users by achieving a subtitle synchronization accuracy of 94.6% in video content, and increasing auditory user satisfaction by 18% during accessibility evaluations. This study helped develop AI-based accessibility and provide more inclusive online environment for people with disabilities, thus facilitating their access to online content. In conclusion, the proposed system is more convenient and could offer a broader range of individual and time-sensitive user experiences, compared to the current accessibility models.

The increasing demand for faster, robust, and efficient device development of enabling technology to mass production of industrial research in circuit design deals with challenges like size, efficiency, power, and scalability. This paper, presents a design and analysis of low power high speed full adder using negative capacitance field effecting transistors. A comprehensive study is performed with adiabatic logic and reversable logic. The performance of full adder is studied with metal oxide field effect transistor (MOSFET) and negative capacitance field effecting (NCFET). The NCFET based full adder offers a low power and high speed compared with conventional MOSFET. The complete design and analysis are performed using cadence virtuoso. The adiabatic logic offering low delay of 0.023 ns and reversable logic is offering low power of 7.19 mw.