Breast cancer is one of the most significant causes of death for women around the world. Breast thermography supported by deep convolutional neural networks is expected to contribute significantly to early detection and facilitate treatment at an early stage. The goal of this study is to investigate the behavior of different recent deep learning methods for identifying breast disorders. To evaluate our proposal, we built classifiers based on deep convolutional neural networks modelling inception V3, inception V4, and a modified version of the latter called inception MV4. MV4 was introduced to maintain the computational cost across all layers by making the resultant number of features and the number of pixel positions equal. DMR database was used for these deep learning models in classifying thermal images of healthy and sick patients. A set of epochs 3–30 were used in conjunction with learning rates 1 × 10–3, 1 × 10–4 and 1 × 10–5, Minibatch 10 and different optimization methods. The training results showed that inception V4 and MV4 with color images, a learning rate of 1 × 10–4, and SGDM optimization method, reached very high accuracy, verified through several experimental repetitions. With grayscale images, inception V3 outperforms V4 and MV4 by a considerable accuracy margin, for any optimization methods. In fact, the inception V3 (grayscale) performance is almost comparable to inception V4 and MV4 (color) performance but only after 20–30 epochs. inception MV4 achieved 7% faster classification response time compared to V4. The use of MV4 model is found to contribute to saving energy consumed and fluidity in arithmetic operations for the graphic processor. The results also indicate that increasing the number of layers may not necessarily be useful in improving the performance.

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Consensus protocols stand behind the success of blockchain technology. This is because parties that distrust each other can make secure transactions without the oversight of a central authority. The first consensus protocol emerged with Bitcoin. Since then, many others have appeared. Some of them have been implemented by official blockchain platforms, whereas others, for the time being, remain as proposals. A blockchain consensus is a trade-off. The new solutions promise to overcome the known drawbacks of blockchain, but they may also bring new vulnerabilities. Moreover, blockchain performance metrics are not clearly defined, as some metrics, such as delay and throughput, which are key factors for the efficiency of standard networks, are purposely constrained by most mainstream blockchain platforms. The main body of this paper consolidates knowledge of blockchains, focusing on the seminal consensus protocols in large-scale market capitalization platforms, and how consensus is achieved for large-scale, decentralized, blockchain architectures. The benefits, limitations, and tradeoffs, as well as the subsequent trend in current consensus development, and its limitations as a general paradigm, are highlighted. The paper also sheds light on overlooked potential performance metrics, and it proposes some novel solutions to some of the identified problems.

Wearable antennas have grown in popularity in recent years as a result of their appealing features and prospects to actualize lightweight, compact, low-cost and adaptable wireless communications and surroundings. These antennas have to be conformal and made of lightweight materials in a low-profile arrangement when attached to various parts of the human body. Near-body operation of these antennas should be possible without degradation. When these characteristics are taken into account, the layout of wearable antennas become challenging, especially when textile substrates are investigated, high conductivity materials are used during manufacturing procedures and body binding scenarios have an impact on the design's performance. Several of these issues arise in the context of body-worn deployment, despite modest changes in magnitude between implementations. This paper examines the multiple issues and obstacles encountered in the construction of wearable antennas as well as the range of materials used, and the Specific Absorption Rate (SAR) effects employed as well as the bending scheme. An overview of the innovative features and their separate approaches to addressing the difficulties lately raised by work in this field conducted by the scientific community is provided as an appendix.

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