Pace Institute of Technology and Sciences

The cognitive workload (CWL) triggers neural activity, which is crucial for understanding the brain’s response to mental stress or stimuli that induce stress. Electroencephalogram (EEG) signals were collected from a mental arithmetic task (MAT), simultaneous task EEG workload datasets, and segmented into 4-s intervals. These segmented signals were then transformed into images using time–frequency conversion methods (TF) called superlet transform (SLT). The resulting TF images were fed into convolutional neural networks (CNNs), such as VGG16, ResNet50, Xception, EfficientNetB0, AlexNet, GoogLeNet, SqueezeNet, VGG16 + LSTM, VGG16 + BiLSTM, FNet, gMLP, and multilayer perceptrons (MLP) mixer. CNN models were trained using the Adam optimizer to detect cognitive load. The preprocessing involved normalization, and scaling in both phases. Among the models tested, the SLT-based TFEEG with the MLP Mixer outperformed other CNN architectures. It helps reduce overfitting and vanishing gradients, enhances performance with new data, improves GPU acceleration, and reduces computational cost due to its simpler architecture. However, the SLT effectively handles non-stationary data through its adaptive multiresolution approach, making it ideal for EEG analysis. The proposed SLT + MLP Mixer achieved an accuracy of 98.69

Difficulties are still present in developing sustainable and biodegradable composites with respect to their mechanical property improvement of natural-fibre-reinforced composites. Some of the limitations that would affect the use of natural fibre-reinforced composites in structural applications have things attached to their poor mechanical properties, high moisture absorption, and non-uniform fibre characteristics. This investigation involved the reinforcement of polylactic acid with pineapple leaf fibre and hemp fibre to produce a biocomposite with improved strength and durability. The composite is fabricated from compression moulding by optimizing the process with an enhanced Taguchi approach. Fine-tuning is made on key parameters like fiber content, moulding temperature, and pressure settings prior to the maximum performance achieved. Mechanical research included tensile test, flexural strength, and impact resistance. Grey relational analysis is used to identify the most significant parameters while principal component analysis helped in reducing the complexity of data for better optimization. Simulation and validation are carried out through MATLAB so that parameters like stress distribution, fiber orientation, and multi-objective optimization could be interpreted. The results showed that with the use of pineapple leaf and hemp fiber reinforced polylactic acid (PALHF-re-PLA) composite, an increase of 73.50

Due to technological advancements, the energy demand on electronic devices has been reduced from milliwatts (mW) to microwatts (µW), and this microwatt power will be manageable with the usage of thermoelectric generators (TEGs). The TEGs work on the Seebeck effect and generate electricity due to temperature differences. To ascertain the temperature difference required for the power generation, phase change materials (PCMs) are widely recommended to integrate with TEGs. It is desired to prefer the PCMs of low temperature and high temperature types for cold and hot sides of TEGs, and as a result, it could be indeed beneficial to achieve a larger temperature difference for the power generation. The power output from TEGs could be sufficient to feed low power devices such as sensors, IoTs, ships, locomotive industries, wearable devices and signal indicators. The latest research progresses on the materials used for fabricating TEGs, placement of TEGs in the waste heat areas so as to achieve a maximum conversion efficiency, and heat transfer enhancement of PCMs used in TEGs. Due to their environmental sustainability, reliability, minimal maintenance costs, and direct power generation, TEGs are widely employed in various industries. The Internet of Things periphery devices are classified into three categories: Smart Home, Smart Factory, and Energy Efficiency. The high thermal capacity of PCM protects TEG and prevents device failure. The expansion of PCM-TEG's cooling capacity enhances its efficacy. The results indicate that the operating duration is extended by higher thermal power levels and that PCM reduces output voltage fluctuations. Inadequate heat source power may lead to partial PCM melting, which could result in a reduction in electricity output during non-heating periods. This study illustrates the great potential of thermoelectric power generators to herald in a new era of Internet of Things sensing devices by extracting energy from the ambient temperatures. This work could portray the wide area from the development of the novel generators and materials for better performance (Figure of merit), less space, and economically feasible, and the mechanism of heat transfer, critical analysis, importance of IoT, applications, advantages to drawbacks of TEG-PCM module.

The evolution of Digital Signal Processing (DSP) systems within Very Large Scale Integration (VLSI) era has significantly impacted computational speed, chip size, and power consumption, consequently influencing the overall cost of systems. Typically, sophisticated DSP systems, including peak cancellation with infinite impulse response (PC-IIR) filters, require multiple arithmetic units. Therefore, the performance of PC-IIR filters can be significantly improved by efficient design of arithmetic logic circuits. In this paper, a new multiple-stage fast ripple hybrid adder (MS-FRHA) and Compressor-based divide-and-conquer vector multiplier (CDCVM) is introduced for PC-IIR filter. Multiple single-stage carry select structures are combined in the proposed MS-FRHA to increase area, delay, and power performance. Also, CDCVM effectively handles huge numbers by dividing the multiplication problem into smaller sub-problems and uses compressor-based Vedic multiplication (CVM) for each sub-problem. According to simulation data, the proposed arithmetic logic circuits use the least amount of space, time, and power of all the earlier designs. Furthermore, a comparison to the most advanced PC-IIR filter shows that the proposed model can reduce delays and resource consumption.

Biocomposite materials have gained significant attention in recent decades due to their sustainability, lightweight nature, and ability to valorize waste biomass into high-value engineering materials. This study investigates the mechanical, thermal conductivity, and wear behaviour of vinyl ester-based biocomposites reinforced with silane-treated Caryota urens mat fibre and biochar derived from Sterculia foetida fruit shells. Biochar was produced via a slow pyrolysis process, while composite laminates were fabricated using the hand lay-up technique. The composites were evaluated according to ASTM standards to assess tensile, flexural, impact, interlaminar shear strength (ILSS), hardness, wear rate, coefficient of friction, and thermal conductivity. Results indicate that silane-treated fibre reinforcement significantly enhances mechanical performance compared to neat resin, achieving tensile strength of 54 MPa, flexural strength of 86 MPa, impact strength of 3.4 J, ILSS of 18.6 MPa, and hardness of 72 Shore-D. Among the hybrid composites, the specimen containing 40 vol% fibre and 2 vol% biochar exhibited the highest overall mechanical performance, with tensile, flexural, impact, and ILSS values of 96 MPa, 148 MPa, 5.4 J, and 28.1 MPa, respectively. Higher biochar loading (4 vol%) led to a marginal reduction in mechanical properties but improved wear resistance, frictional behaviour, and thermal conductivity. These results demonstrate the potential of the developed biocomposites for lightweight structural applications in automotive interiors, housing components, and consumer products.