THE REACTIONS OF SOME 2-NITROPHENYL SULPHIDES IN 98% SULPHURIC ACID

Abstract

The coloured solutions of 2-nitrobenzenesulphenyl derivatives in 98% sulphuric acid have previously been suggested to contain either the conjugate acid of the sulphenyl derivative¹ or the sulphenium ion in equilibrium with the starting material. ¹⁹F n.m.r. studies on solutions of 4-trifluoromethyl-2-nitrobenzenesulphenyl derivatives show that these compounds react in sulphuric acid to give the corresponding 2-amino- and 2-nitro-benzenesulphonic acids together with other products.     

LTE‐Based Passive Bistatic Radar System for Detection of Ground‐Moving Targets

Use of a passive bistatic radar (PBR) system in the surveillance or monitoring of an area has its advantages. For example, a PBR system is able to utilize any available signal of opportunity (for example, broadcasting, communication, or radio navigation signals) for the purposes of surveillance. With this in mind, there are potentially many research areas to be explored; in particular, the capability of signals from existing and future communication systems, such as 4G and 5G. Long‐Term Evolution (LTE) is the world's most current communication system. Given this fact, this paper presents the latest feasibility studies and experimental results from using LTE signals in PBR applications. Details are provided about aspects such as signal characteristics, experimental configurations, and SNR studies. Six experimental scenarios are carried out to investigate the detection performance of our proposed system on ground‐moving targets. The ability to detect is demonstrated through use of the cross‐ambiguity function. The detection results suggest that LTE signals are suitable as a source signal for PBR.     

The A-integral and restricted Ahlfors–Beurling transform

In this paper we prove that the restricted Ahlfors–Beurling transform of a Lebesgue integrable function is A-integrable and derive an analogue of Riesz's equality holds.     

Synthesis,Characterization, Molecular Docking,and Anticancer Evaluation of 4‐Thiazolidinone Analogues

Scaffolds containing one or more heteroatoms are ubiquitous in nature and are responsible for almost every biological processes in flora and fauna. The fact that heterocyclic cores have high propensity for biological targets, several nitrogen, oxygen, and sulfur‐containing heterocyclic compounds have been reported in the last few decades. One such intriguing class is 4‐thiazolidinone that exhibit diverse range of pharmacological activities. In the present study, we report synthesis, characterization, molecular docking, and anticancer evaluation of 10 new 4‐thiazolidinone analogues ( 5 – 14 ). One of the compounds ( 11 ) was structurally characterized using single crystal X structure. Anticancer evaluation against hepatocellular carcinoma cell line (HepG2) revealed that compound ( 7 ) was the most potent (IC₅₀ = 75 μM) while other showed moderate to low activity (85–530 μM). To underpin the druggability and possible modes of action, drug‐likeness was determined and docking studies were carried out against β‐carbonic anhydrase receptor (PDB ID: 3IA). Attempts have also been made to establish a structure–activity relationship of the reported compounds.     

Joint routing,link capacity dimensioning,and switch port optimization for dynamic traffic in optical networks

This paper considers a challenging problem: to simultaneously optimize the cost and the quality of service in opaque wavelength division multiplexing (WDM) networks. An optimization problem is proposed that takes the information including network topology, traffic between end nodes, and the target level of congestion at each link/node in WDM networks. The outputs of this problem include routing, link channel capacities, and the optimum number of switch ports locally added/dropped at all switch nodes. The total network cost is reduced to maintain a minimum congestion level on all links, which provides an efficient trade-off solution for the network design problem. The optimal information is utilized for dynamic traffic in WDM networks, which is shown to achieve the desired performance with the guaranteed quality of service in different networks. It was found that for an average link blocking probability equal to 0.015, the proposed model achieves a net channel gain in terms of wavelength channels ( ) equal to 35.72 , 39.09 , and 36.93 compared to shortest path first routing and equal to 29.41 , 37.35 , and 27.47 compared to alternate routing in three different networks.     

Transverse Momentum Distributions of Pions,Kaons and Protons in p − p Interactions at 2.76 TeV

The spectra and particle yield ratios of π^±, K^± meson, protons and anti-protons in proton-proton (p − p) interactions at \( \sqrt{s}=2.76 \) TeV are presented. CRMC generators: EPOS-LHC, EPOS1.99 and QGSJETII-04, are used for simulations and the results are compared with LHC-CMS experimental data. The particles are identified in the transverse momentum range of p_T ≈ 0.1 − 1.7 GeV/c and absolute rapidity, ∣y∣ < 1. In simulation, same cuts in p_T and y are used as in the experiment. The EPOS1.99 model predicts the data well as compared to the other two models, which predict in some of the p_T regions only. The different models used could not effectively describe the experimental data in all p_T range.

     

Design,implementation, and evaluation of learning algorithms for dynamic real-time network monitoring

Network monitoring is necessary so as to ensure high reliability and availability in telecom networks. One of the main challenges posed by state-of-the-art monitoring tools is the creation of network baselines. Such baselines include thresholds that can be used to determine whether monitored values (with a given context, e.g., time) represent normal network operation or not. The size and complexity of current (and future) networks make it infeasible to manually determine and set baselines for each network operator and metric, let alone adapting the thresholds to changes in network conditions. This leads to the use of default baselines and/or setting baselines only once and never changing them throughout the lifetime of network elements. This does not only cause inefficient operation but could have implications for network reliability and availability. In this paper, we present the design, implementation, and evaluation of DARN: a collection of analytics and machine learning-based algorithms aimed at ensuring that network baselines are automatically adapted to different metric evolution. DARN has been comprehensively evaluated on a deployment with real traffic to confirm accuracy of generated baselines, a 22% improvement in accuracy due to baseline adaptation and a 72% reduction in false alarms.