Integer plane multiflow maximisation: one-quarter-approximation and gaps

Mathematical Programming - In this paper, we bound the integrality gap and the approximation ratio for maximum plane multiflow problems and deduce bounds on the flow-multicut-gap. We consider...     

Adsorption applications of synthetically prepared PANI-CuO based nanocomposite material

In this work, we have used a polyaniline and cupric oxide-based nanocomposite material (PANI-CuO) for the adsorption of Pb²⁺, Cd²⁺, and Cr⁶⁺ ions from contaminated water. FTIR, FESEM, EDX and XRD methods were used in the characterisation of PANI-CuO. The maximum percentage removal of Pb²⁺, Cd²⁺, and Cr⁶⁺ ions was found to be 79.9%, 78.9% and 82.1% at 1 g of PANI-CuO for 100 mL of contaminated water. The suitability order of the isotherm model was observed as Langmuir > Freundlich > Elovich, whereas the pseudo first order model was best fitted as compared to the pseudo second order model. The rate constants were evaluated as 1.441, 1.801 and 1.661 g mg^?1 min ^?1, respectively. The values of ΔG⁰ were calculated to be ?287.4, ?849.6, and ?728.9 kJ mol^?1 for Pb²⁺, Cd²⁺ and Cr⁶⁺ ions, respectively.     

Design, synthesis, and structural aspects of chalcogen-substituted pyridine dicarboxamide donors and their reactions

The design and synthesis of a new family of potentially pentadentate N₃Se₂ or N₃Te₂ type donors bearing a 2,6-disubstituted pyridine dicarboxamide moiety as the central fragment [-NH-C(O)-pyridine-C(O)-NH-] functionalized with chalcogen as additional donors in the appended arms of the pyridine ring through the alkyl spacers and their potential applications and reactivity toward d⁸ and d¹⁰ metal ions have been demonstrated.     

Low-energy Formulations of Support Vector Machine Kernel Functions for Biomedical Sensor Applications

Although physiologically-indicative signals can be acquired in low-power biomedical sensors, their accurate analysis imposes several challenges. Data-driven techniques, based on supervised machine-learning methods provide powerful capabilities for potentially overcoming these, but the computational energy is typically too severe for low-power devices. We present a formulation for the kernel function of a support-vector machine classifier that can substantially reduce the real-time computations involved. The formulation applies to kernel functions employing polynomial transformations. Using two representative biomedical applications (EEG-based seizure detection and ECG-based arrhythmia detection) employing clinical patient data, we show that the polynomial transformation yields accuracy performance comparable to the most powerful available transformation (i.e., the radial-basis function), and the proposed formulation reduces the energy by over 2500× in the arrhythmia detector and 9.3-198× in the seizure detector (depending on the patient).     

Network energy optimization and intelligent routing in WSN applicable for IoT using self-adaptive coyote optimization algorithm

The Internet of Things (IoT) is a recent wireless telecommunications platform, which contains a set of sensor nodes linked by wireless sensor networks (WSNs). These approaches split the sensor nodes into clusters, in which each cluster consists of an exclusive cluster head (CH) node. The major scope of this task is to introduce a novel CH selection in WSN applicable to IoT using the self-adaptive meta-heuristic algorithm. This paper aids in providing the optimal routing in the network based on direct node (DN) selection, CH selection, and clone cluster head (CCH) selection. DNs are located near the base station, and it is chosen to avoid the load of CH. The adoption of the novel self-adaptive coyote optimization algorithm (SA-COA) is used for the DN selection and CCH selection. When the nodes are assigned in the network, DN and CCH selection is performed by the proposed SA-COA. Then, the computation of residual energy helps to select the CH, by correlating with the threshold energy. CCH is proposed to copy the data from the CH to avoid the loss of data in transmitting. By forming the CCH, the next CH can be easily elected with the optimal CCH using SA-COA. From the simulation findings, the best value of the designed SA-COA-LEACH model is secured at 1.14%, 3.17%, 1.18%, and 7.33% progressed than self-adaptive whale optimization algorithm (SAWOA), cyclic rider optimization algorithm (C-ROA), krill herd algorithm (KHA), and COA while taking several nodes 50. The proposed routing of sensor networks specifies better performance than the existing methods.     

DDC-OMDC: Deadline-based data collection using optimal mobile data collectors in Internet of Things

Mobile data collectors (MDCs) are very efficient for data collection in internet of things (IoT) sensor networks. These data collectors collect data at rendezvous points to reduce data collection latency. It is paramount to determine these points in an IoT network to collect data in real time. It is important to consider IoT network characteristics to collect data on a specific deadline. First, the disconnected IoT sensor network is a real challenge in IoT applications. Second, it is essential to determine optimal data collection points (DCPs) and MDCs simultaneously to collect data in real time. In this study, Deadline-based Data Collection using Optimal Mobile Data Collectors (DDC-OMDC) scheme is proposed that aims to collect data in a disconnected network with the optimal number of mobile data collectors in a specific deadline for delay-intolerant applications. DDC-OMDC works in two phases. In the first phase, the optimal number of MDCs is determined to collect data at the optimal data collection points to guarantee one-hop data collection from each cluster. The optimal mobile data collectors are determined using optimal DCPs, data collection stopping time, and a specific deadline. In the second phase, the optimal data collection trajectory is determined for each MDC using the nearest neighbor heuristic algorithm to collect data in real time. The simulation results show that the proposed scheme outperforms in collecting data in real time and determines optimal mobile data collectors and optimal data collection trajectory to collect data in a specific deadline for delay-intolerant applications.     

Synthesis, characterization, crystal and molecular structure analysis of N-(2,4-dimethylphenyl)-6-methyl-4-(3-nitrophenyl)-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxamide

A novel dihydropyrimidine (DHPM) derivative bearing a carbamoyl moiety was synthesized by an efficient three-component Biginelli reaction and was characterized spectroscopically and finally confirmed by X-ray diffraction studies. The title compound C₂₀H₂₀N₄O₄ crystallizes in the monoclinic space group P2₁/c with cell parameters a=12.8970(12) Å, b=13.6210(11) Å, c=11.8420(13) Å, β=115.860(3)°, Z=4 and V=1872.0(3) Å³. The conformation of the dihydropyrimidine ring is unusual; it is planar instead of the usual boat-like conformation. The 3-nitrophenyl ring is orthogonal to the 3,4-DHPM ring. The carbonyl group is in an anti-clinal conformation.     

Synthesis and Crystal Structure of [2-(4-chlorobenzoyloxy)-5-methylphenyl]-(4-methylphenyl)methanone

[2-(4-Chlorobenzoyloxy)-5-methylphenyl]-(4-methylphenyl)methanone was synthesized and characterized by spectroscopic and X-ray diffraction methods. The molecule crystallizes in the monoclinic space group P2₁/c with cell parameters a = 13.7530(19) Å, b = 10.0560(19) Å, c = 13.469(3) Å, β = 100.630(7)° and Z = 4. There is a large discrepancy between the carbonyl group bond lengths of ketone and the ester. The structure exhibits intermolecular hydrogen bonds of the type C–H···O.