Synthèse de complexes de platine(II) avec des ligands mixtes amines et pyrimidine et caractérisation par spectroscopies infrarouge de résonance magnétique multinucléaire

Les complexes disubstitués du Pt(II) avec des ligands azotés sont connus depuis assez longtemps, mais la chimie de coordination du platine est encore peu développée. Il y a plusieurs publications sur la chimie des dérivés de la pyrimidine, à cause de l'importance biologique de ces molécules. Cependant, il y a peu de travaux dans la littérature sur la chimie de coordination de la pyrimidine non substituée. Quelques nouvelles méthodes ont été développées dans ce projet pour la préparation de complexes contenant des ligands mixtes, amine et pyrimidine. Plusieurs amines aliphatiques et cycliques possédant un encombrement stérique différent ont été choisies pour l'étude. Les nouveaux complexes de types cis- et trans- \ud Pt(amine)(pyrimidine)X₂ (X = Cl et I) et des dimères à pont pyrimidine de type trans, trans- X₂(amine)Pt(µ-pm)Pt(amine)X₂ ont été synthétisés et caractérisés par différentes techniques spectroscopiques. Les composés diiodo ont été synthétisés via l'intermédiaire du dimère à ponts iodo, I(amine)Pt(µ-I)₂Pt(amine)I et peut être utilisée pour la plupart des amines. La formation du dimère est très longue, car le produit initial \ud Pt(amine)₂I₂ est insoluble dans l'eau, tout comme le dimère. La réaction entre le dimère à ponts iodo et la pyrimidine en milieux aqueux dans les proportions 1 : 2 donne des composés de type Pt(amine)(pm)I₂ d'isomérie cis. Si les ligands sont encombrés, il y aura une isomérisation cis-trans. La deuxième méthode (X = Cl) implique la formation de l'intermédiaire K[Pt(amine)CI₃], qui a été préparé par 2 méthodes différentes, dont une est limitée à des amines encombrées. Le composé ionique réagit avec la pyrimidine dans les proportions 1 : 2 pour produire Pt(amine)(pm)CI₂. Le premier produit formé est l'isomère cis, mais il peut y avoir isomérisation si l'amine est encombrée. La même réaction dans des proportions 2 : 1 a conduit à des dimères à pont pyrimidine Cl₂(amine)Pt(µpm)Pt(amine)Cl₂ de géométrie trans,trans. Les complexes ont été caractérisés à l'état solide par spectroscopie infrarouge et en solution dans l'acétone par résonance magnétique multinucléaire (¹H, ¹³C et

Addressing Trust Challenges in Blockchain Oracles Using Asymmetric Byzantine Quorums

Distributed Computing in Blockchain Technology (BCT) hinges on a trust assumption among independent nodes. Without a third-party interface or what is known as a Blockchain Oracle, it can not interact with the external world. This Oracle plays a crucial role by feeding extrinsic data into the Blockchain, ensuring that Smart Contracts operate accurately in real time. The Oracle problem arises from the inherent difficulty in verifying the truthfulness of the data sourced by these Oracles. The genuineness of a Blockchain Oracle is paramount, as it directly influences the Blockchain's reliability, credibility, and scalability. To tackle these challenges, a strategy rooted in Byzantine fault tolerance {\phi} is introduced. Furthermore, an autonomous system for sustainability and audibility, built on heuristic detection, is put forth. The effectiveness and precision of the proposed strategy outperformed existing methods using two real-world datasets, aimed to meet the authenticity standards for Blockchain Oracles.Comment: 12 pages, 16 figure

Distributed Fault-Tolerant Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a set of tiny autonomous and interconnected devices. These nodes are scattered in a region of interest to collect information about the surrounding environment depending on the intended application. In many applications, the network is deployed in harsh environments such as battlefield where the nodes are susceptible to damage. In addition, nodes may fail due to energy depletion and breakdown in the onboard electronics. The failure of nodes may leave some areas uncovered and degrade the fidelity of the collected data. Therefore, establish a fault-tolerant mechanism is very crucial. Given the resource-constrained setup, this mechanism should impose the least overhead and performance impact. This paper focuses on recovery process after a fault detection phase in WSNs. We present an algorithm to recover faulty node called Distributed Fault-Tolerant Algorithm (DFTA).The performance evaluation is tested through simulation to evaluate some factors such as: Packet delivery ratio, control overhead, memory overhead and fault recovery delay. We compared our results with referenced algorithm: Fault Detection in Wireless Sensor Networks (FDWSN), and found that our DFTA performance outperforms that of FDWSN

A short survey on fault diagnosis in wireless sensor networks

Fault diagnosis is one of the most important and demand- able issues of the network. It makes the networks reliable and robust to operate in the normal way to handle almost all types of faults or failures. Additionally, it helps sensor nodes to work smoothly and efficiently till the end of their lifetime. This short survey paper not only presents a clear picture of the recent proposed techniques, but also draws comparisons and contrasts among them to diagnose the potential faults. In addition, it proposes some potential future-work directions which would lead to open new research directions in the field of fault diagnosis

An LSTM-Based Outlier Detection Approach for IoT Sensor Data in Hierarchical Edge Computing

International audienceOutlier detection in sensor data has recently gained significant recognition, particularly with the proliferation of wireless sensor networks (WSNs) and the Internet of Things (IoT). Several challenges face outlier detection in WSNs and IoTs, including sensor nodes' limited energy and processing capabilities and high communication costs. This paper presents a novel deep learning-based outlier detection approach for IoT sensor data in hierarchical edge computing. First, we proposed a hierarchical edge computing framework to save energy, provide load balance, and low latency data processing at sensor ends. Then, we designed an outlier detection algorithm that resides on each edge server. The proposed algorithm consists of two modules: a predictor model and an outlier detector. The predictor module uses Long Short-Term Memory (LSTM) networks to predict the subsequent data measurements of sensor nodes. The predicted values are then passed to an outlier detector module, which decides whether a data point is an outlier

FDS: Fault detection Scheme for Wireless Sensor Networks

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On the Fusion of Probabilistic Network

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