The real-world use cases of blockchain technology, such as faster cross-border payments, identity management, smart contracts, cryptocurrencies, and supply chain–blockchain technology are here to stay and have become the next innovation, just like the Internet. There have been attempts to formulate digital money, but they have not been successful due to security and trust issues. However, blockchain needs no central authority, and its operations are controlled by the people who use it. Furthermore, it cannot be altered or forged, resulting in massive market hype and demand. Blockchain has moved past cryptocurrency and discovered implementations in other real-life applications; this is where we can expect blockchain technology to be simplified and not remain a complex concept. Blockchain technology’s desirable characteristics are decentralization, integrity, immutability, verification, fault tolerance, anonymity, audibility, and transparency. We first conduct a thorough analysis of blockchain technology in this paper, paying particular attention to its evolution, applications and benefits, the specifics of cryptography in terms of public key cryptography, and the challenges of blockchain in distributed transaction ledgers, as well as the extensive list of blockchain applications in the financial transaction system. This paper presents a detailed review of blockchain technology, the critical challenges faced, and its applications in different fields. Blockchain in the transaction system is explained in detail with a summary of different cryptocurrencies. Some of the suggested solutions are given in the overall study of the paper.

The principal aim of this paper is to study the solvability of infinite system of integral equations in two variables of Hammerstein type in the Banach spaces C0 and ℓ1 using Meir-Keeler condensing operators and measure of noncompactness. In this study we give some examples.

Institute of Mathematics of the Czech Academy of Sciences

Deep learning is a great success primarily because it encodes large amounts of data and manipulates billions of model parameters. Despite this, it is challenging to deploy these cumbersome deep models on devices with limited resources, such as mobile phones and embedded devices, due to the high computational complexity and the amount of storage required. Various techniques are available to compress and accelerate models for this purpose. Knowledge distillation is a novel technique for model compression and acceleration, which involves learning a small student model from a large teacher model. Then, that student network is fine-tuned on any downstream task to be applicable for resource-constrained applications. This paper explores various state-of-the-art model compression techniques, including knowledge distillation, for compressing large deep neural networks to make them deployable on resource-constrained devices.