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AbstractIn this paper, we performed thorough experimental and theoretical calculations to examine the interaction between Pt derivative, as an anticancer, and ct-DNA. The mode of DNA binding with [Pt(NH3)2(Isopentylgly)]NO3, where Isopentylgly is Isopentyl glycine, was evaluated by various spectroscopic methods, docking, and molecular dynamics simulation studies. UV-Vis and fluorescence spectroscopic titration results and CD spectra of DNA-drug showed this interaction is viagroove binding. Also, thermal stability studies or DNA melting temperature changes (ΔTm), as well as the quenching emissions monitoring proved it. Also, the thermodynamic parameter and binding constant displayed that complex-DNA formation is a spontaneous process, and H-binding and also groove binding were found to be the main forces. Theoretical studies stated [Pt(NH3)2(Isopentylgly)]NO3-DNA formation occurs on C-G center on DNA, along with rising DNA-compound stability. IC50value against the human breast cell line probably is due to the Isopentyl glycine ligand in the structure of the Pt compound, and it was obtained more than cisplatin and less than carboplatin against the MCF7 cell.Communicated by Ramaswamy H. Sarma

Blockchain-based peer-to-peer ET (P2P-ET) is expected to be a key element of the next-generation power system due to its decentralized nature, immutable data structure and smart contract capability that allows energy contracts to be made on the blockchain. However, blockchain suffers from performance, scalability and privacy challenges. To address these challenges, we propose MuLCOff, a multi-layer consensus and off-chain computation-based P2P-ET system. MuLCOff’s architecture consists of three network layers, where each layer consists of multiple blockchain network shards that can operate as an independent permissioned blockchain network. The network shards can participate in multiple layers of consensus simultaneously. Unlike previous systems, MuLCOff supports the concurrent processing of transactions across different consensus layers. Each network shard also has an associated off-chain infrastructure, which allows performing heavy computations and private energy negotiation outside the blockchain. Experimental results indicate that the proposed multi-layer consensus provides 87% better throughput compared to state of the art consensus algorithms. Moreover, MuLCOff’s off-chain-based ET scheme reduces the latency by 75 to 124 times and the computation cost by a factor of 80 to 94.6 respectively compared to its on-chain counterpart.

Software-defined networking (SDN) allows flexible and centralized control in cloud data centers. An elastic set of distributed SDN controllers is often required to provide sufficient yet cost-effective processing capacity. However, this introduces a new challenge: Request Dispatching among the controllers by SDN switches. It is essential to design a dispatching policy for each switch to guide the request distribution. Existing policies are designed under certain assumptions, including a single centralized agent, global network knowledge, and a fixed number of controllers, which often cannot be satisfied in practice. This article proposes MADRina, Multiagent Deep Reinforcement Learning for request dispatching, to design policies with high dispatching adaptability and performance. First, we design a multiagent system to address the limitation of using a centralized agent with global network knowledge. Second, we propose a Deep Neural Network-based adaptive policy to enable request dispatching over an elastic set of controllers. Third, we develop a new algorithm to train the adaptive policies in a multiagent context. We prototype MADRina and build a simulation tool to evaluate its performance using real-world network data and topology. The results show that MADRina can significantly reduce response time by up to 30% compared to existing approaches.

Hierarchical consensus protocols are one of the most promising approaches considered to improve the performance and scalability of blockchain-based peer-to-peer energy trading (P2P ET) systems. They allow a network to be divided into multiple clusters where each cluster can independently run its own local consensus on local transactions (i.e., transactions that belong to a single cluster) and also cooperate with other clusters for global transactions (i.e., transactions that affect more than one cluster). However, current solutions do not allow the parallel processing of local and global transactions, leading to low throughput and high latency. Moreover, they impose an additional burden on the leader node by assigning to it the responsibility for both local and global consensus. This paper proposes a hierarchical concurrent optimistic blockchain consensus protocol, called HiCoOB, which includes an optimistic local consensus and a deterministic global consensus that enables the concurrent execution of local and global transactions. Additionally, the local and the global consensus are coordinated by two distinct leaders, thereby reducing the overhead on the local leader node. HiCoOB also provides a synchronization method between local and global consensus to avoid the double spending of energy or money that arises from interdependent client transactions. Experimental results indicate that the proposed protocol achieves 87% higher throughput than the existing systems. Furthermore, it reduces the waiting time of the local transactions by a factor of 5x. Finally, HiCoOB has 10 times less overhead on the local leader node in terms of message exchange when compared to state-of-the-art systems.

The security of safety-critical IoT systems, such as the Internet of Vehicles (IoV), has a great interest, focusing on using Intrusion Detection Systems (IDS) to recognise cyber-attacks in IoT networks. Deep learning methods are commonly used for the anomaly detection engines of many IDSs because of their ability to learn from heterogeneous data. However, while this type of machine learning model produces high false-positive rates and the reasons behind its predictions are not easily understood, even by experts. The ability to understand or comprehend the reasoning behind the decision of an IDS to block a particular packet helps cybersecurity experts validate the system’s effectiveness and develop more cyber-resilient systems. This paper proposes an explainable deep learning-based intrusion detection framework that helps improve the transparency and resiliency of DL-based IDS in IoT networks. The framework employs a SHapley Additive exPlanations (SHAP) mechanism to interpret decisions made by deep learning-based IDS to experts who rely on the decisions to ensure IoT networks’ security and design more cyber-resilient systems. The proposed framework was validated using the ToN_IoT dataset and compared with other compelling techniques. The experimental results have revealed the high performance of the proposed framework with a 99.15% accuracy and a 98.83% F1 score, illustrating its capability to protect IoV networks against sophisticated cyber-attacks.

Simultaneous determination of adrenaline, paracetamol and tryptophan using AuNPs/poly(NDI) modified electrode