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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.
This paper presents a novel inventory model for non-instantaneously deteriorating items with demand dependent on the selling price. The model strategically utilizes the cost-free storage period offered by ports as a temporary buffer before transferring goods to owned warehouses, enabling cost-effective inventory management within a two-warehouse distribution system. Unlike traditional supply chains that depend solely on rented or owned storage, the proposed approach minimizes holding costs and improves resource utilization by taking advantage of the port’s free storage window. Additionally, the model incorporates investments in energy-efficient green technologies to reduce carbon emissions during transportation between the port, warehouse, and industry. Numerical experiments confirm the model’s ability to significantly reduce total inventory costs, while sensitivity analysis highlights its robustness under varying selling prices. The inclusion of green technology further enhances environmental sustainability. Implemented in MATLAB R2024a, the model provides valuable insights for managing inventory efficiently in price-sensitive and environmentally regulated supply chains.
Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface Methodology (RSM) with meta-heuristic optimization techniques to enhance engine performance and emissions characteristics using Tectona Grandi’s biodiesel with Elaeocarpus Ganitrus as an additive. Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy. The study identified the Teak biodiesel blend (TB20) with a 5 ml Elaeocarpus Ganitrus additive (TB20 + R5) as the optimal formulation, achieving the highest Brake Thermal Efficiency and reduced Brake-Specific Fuel Consumption. Desirability analysis further confirmed the blend’s superior performance and emissions characteristics, with a desirability rating of 0.9282. This work highlights the potential of hybrid optimization approaches for improving biodiesel performance and emissions without engine modifications, contributing to the advancement of sustainable energy practices in internal combustion engines.
Acoustic emission (AE) detection is a nonintrusive technique for monitoring transformer conditions by capturing emissions from partial discharges (PDs), hotspots, and noise. Machine learning (ML) has been widely used in PD diagnostics in power transformers. While effective in managing complex data characteristics, traditional ML algorithms cannot quantify uncertainty in classification. This study uses Bayesian networks integrated with a classification algorithm to quantify uncertainty in acoustic signal classification. Bayesian deep learning (BDL) and ensemble models are the two commonly used techniques for uncertainty quantification (UQ). BDL models with multiple Bayesian layers are more prone to convergence issues with difficulty interpreting the sources of uncertainty. The performance of the ensemble method is based on model diversity with a higher risk of overfitting and does not offer insights into aleatoric uncertainty. The present work proposes architectures with a single Bayesian layer to quantify both aleatoric and epistemic uncertainties in acoustic signal classification. A Bayesian convolutional neural network (CNN) layer combined with an ensemble architecture demonstrates comparatively higher performance among the considered Bayesian architectures. Raw acoustic signals are transformed into spectrogram images to enhance feature representation, capturing time-frequency characteristics. The proposed method is assessed using laboratory and field-measured data, demonstrating significant improvements in estimating uncertainty in classification.
Underwater image enhancement poses unique challenges due to poor visibility, color distortion, and haze caused by light absorption and scattering in water. In this paper, we propose an ensemble model, Ensemble Pyramid-based Convolutional Neural Network and Deep Channel Prior Dehazing Network (EPCNN-DCPDN), which combines Pyramid-based Convolutional Neural Networks (CNNs) and the Deep Channel Prior Dehazing Network (DCPDN) to address these challenges. The model operates in two ways: sequentially, by first applying DCPDN for haze removal followed by Pyramid-based CNNs for multi-scale feature refinement, or in parallel, with outputs from both models fused using a weighted average or learned fusion mechanism. We evaluated the proposed model on multiple underwater datasets and compared its performance against nine state-of-the-art models, including CLAHE, FUnIE-GAN, WaterGAN, and Haze-Line Prior Model. The EPCNN-DCPDN model achieved superior results with a PSNR of 28.34 dB, SSIM of 0.902, and UIQM of 3.56. It also demonstrated outstanding accuracy in challenging underwater conditions, with an accuracy of 97.92% on shallow, deep, and low-light underwater datasets, outperforming existing models such as WaterGAN and Haze-Line Prior Model. The results highlight the effectiveness of the proposed model in restoring color, contrast, and fine details in underwater images. The model’s ability to handle a wide range of underwater conditions makes it an ideal solution for applications in underwater exploration, marine research, and object detection.
