Phase Transitions on Fixed Connected Graphs and Random Graphs in the Presence of Noise

In this paper, we study the phase transition behavior emerging from the interactions among multiple agents in the presence of noise. We propose a simple discrete-time model in which a group of non-mobile agents form either a fixed connected graph or a random graph process, and each agent, taking bipolar value either +1 or -1, updates its value according to its previous value and the noisy measurements of the values of the agents connected to it. We present proofs for the occurrence of the following phase transition behavior: At a noise level higher than some threshold, the system generates symmetric behavior (vapor or melt of magnetization) or disagreement; whereas at a noise level lower than the threshold, the system exhibits spontaneous symmetry breaking (solid or magnetization) or consensus. The threshold is found analytically. The phase transition occurs for any dimension. Finally, we demonstrate the phase transition behavior and all analytic results using simulations. This result may be found useful in the study of the collective behavior of complex systems under communication constraints.     

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.     

Fuzzy and Deep Belief Network Based Malicious Vehicle Identification and Trust Recommendation Framework in VANETs

Wireless Personal Communications - This work aims to implement a clustering scheme to separate vehicles into a cluster that is based on various parameters, such as the total number of relay nodes,...     

CP2EH: a comprehensive privacy-preserving e-health scheme over cloud

The Journal of Supercomputing - In the Attribute-Based Encryption (ABE) scheme, patients encrypt their electronic health record (EHR), attach the appropriate attributes with it, and outsource them...     

Optimization of process parameters affecting surface roughness in magnetic abrasive finishing process

Development in manufacturing technology enhances the mechanical behavior of machined parts and improves the surface finish with high precision, which conveys the progressive importance of magnetic abrasive finishing (MAF) process. In current research work, magnetic abrasive particles were used as finishing tools during the MAF process. However, these magnetic abrasives are fabricated by special techniques, i.e., the adhesive bonding-based method, the sintering method, the plasma-based method and so on. The present study explores the basic finishing characteristics of the magnetic abrasive produced by the sintering process. After the sintering process, improved quality of magnetic abrasives was obtained, where the abrasive particle sticks on the base metal matrix. The abrasive particle used is alumina powder and the magnetic particle is iron powder. Experiments were performed on Stainless Steel 202 to inspect the sound effects of several process parameters such as rotational speed, electromagnet voltage, machining gap and abrasive particle size on machining performance. Apart from that, surface roughness was also measured, which revealed the influence of the abrasive particle on the machined surface in terms of surface finish. It is observed from this study that appropriate size of magnetic abrasive particle optimizes the surface finish.     

Chemically deposited nickel oxide as counter electrode for dye sensitized solar cell

Nickel oxide (NiO) thin films have been synthesized by simple and inexpensive chemical bath deposition at low temperature. The synthesized thin films were annealed at 623 K and used for further characterization. Structural and morphological properties of the NiO thin film were characterized using X-ray diffraction and scanning electron microscope (SEM), respectively. The structural study shows the simple cubic formation of NiO thin films with average crystallite size of 9 nm. Honeycomb like surface morphology with porous structure was observed from the SEM study. NiO thin film electrode has been used as a counter electrode in dye sensitized solar cell. Finally, photovoltaic parameters such as short circuit current density (J_sc), open circuit voltage (V_oc), Fill Factor (FF) and efficiency (η) have been studied.     

K–La–MgO as heterogeneous catalyst for synthesis of 3-(2-hydroxyethyl)-1,3-oxazolidin-2-one from diethanol amine and carbon dioxide

Cost-effective valorization of carbon dioxide into bulk and specialty chemicals using catalysis will be attractive in the foreseeable future. 1,3-Oxazolidin-2-one derivatives are one of the important classes of heterocyclic compounds which have wide applications in pharmaceutical industries due to their biological activities such as antibacterial, antimicrobial, antiseptic. Various synthetic routes are employed to prepare these compounds which include phosgenation, oxidative carbonylation, etc., which make use of polluting chemicals and homogeneous catalysts. The heterogeneous catalytic processes to synthesize these derivatives are quite limited. Thus, developing a green route which is environmental friendly is highly desirable. The current work deals with development of a heterogeneous reusable catalyst and its application to synthesize 1,3-oxazolidin-2-one derivatives using carbon dioxide as a C1 source. The fact that no use of promoter or organic co-catalyst is made in the current process makes the synthesis route more favorable. Pure La–MgO and K–La–MgO with different K loading (1, 3, 5, and 7 wt%) synthesized by combustion route were screened for carbonylation of diethanol amine. 5% K–La–MgO was found to be the best catalyst. The catalyst was well characterized in virgin form and after use by various analytical techniques like TEM, SEM, XRD, CO₂ and NH₃-TPD, BET surface area analysis. With 5% K–La/MgO, 72% conversion of diethanol amine was achieved with 100% selectivity of the desired product at optimum conditions, i.e., 150 °C, 5 wt% K–La/MgO catalyst loading of 0.02 g/cm³ and 2.0 MPa CO₂ pressure. Reaction mechanism was proposed and kinetic model developed. The apparent activation energy was calculated as 18.76 kcal/mol. The catalyst was robust and recyclable. The process is clean and green.