Kinetic theory of acoustic-like modes in nonextensive pair plasmas

The low-frequency acoustic-like modes in a pair plasma (electron-positron or pair-ion) is studied by employing a kinetic theory model based on the Vlasov and Poisson's equation with emphasizing the Tsallis's nonextensive statistics. The possibility of the acoustic-like modes and their properties in both fully symmetric and temperature-asymmetric cases are examined by studying the dispersion relation, Landau damping and instability of modes. The resultant dispersion relation in this study is compatible with the acoustic branch of the experimental data [W. Oohara, D. Date, and R. Hatakeyama, Phys. Rev. Lett. 95, 175003 (2005)], in which the electrostatic waves have been examined in a pure pair-ion plasma. Particularly, our study reveals that the occurrence of growing or damped acoustic-like modes depends strongly on the nonextensivity of the system as a measure for describing the long-range Coulombic interactions and correlations in the plasma. The mechanism that leads to the unstable modes lies in the heart of the nonextensive formalism yet, the mechanism of damping is the same developed by Landau. Furthermore, the solutions of acoustic-like waves in an equilibrium Maxwellian pair plasma are recovered in the extensive limit ($q\rightarrow1$), where the acoustic modes have only the Landau damping and no growth.Comment: Accepted for publication in Astrophysics and Space Scienc

Reducing Production Lead Time Through Value Stream Mapping

This research addresses the application of lean manufacturing concepts to the discrete production sector of a metal fabrication company in Malaysia. The goal of this research was to investigate the application of lean manufacturing tools to reduce production lead time for a dedicated product family. This study is applied to a small metal-based manufacturing company (referred to as ABC). Root-cause analysis based on behavioral and informational factors was developed for seven waste indicators to identify the waste in managerial-level stage. Value Stream Mapping (VSM) is prescribed as part of the lean production portfolio of tools and has been applied in a variety of industries to identify lean tools to try to eliminate the waste. VSM was used to first map the current state in order to realize the current production lead time and to identify sources of wastes at the worker-level stage. Then a future state map was developed using lean tools and techniques and answering the eight questions stated by Rother and Shook (1999). The future state map identifies the reduced production lead time based on lean concept. Two detailed simulation models using system modeling corporation’s Arena 7 package was developed. The current state simulation verifies the current production lead time from current state map and the future state simulation which was developed based on the future state map quantifies reduction of production lead time. It is concluded that using Value Stream Mapping (VSM), the production lead time of 200 units of ‘back plate indoor’ reduced from less than 15 days in current state map to less than 9 days in future state map. Also, the simulation shows that production lead time reduction to eight days could be accomplished at ABC if lean tools are utilized. Comparing the production lead time of eight days in simulated model and less than nine days in Value Stream Mapping (VSM) showed that VSM is a reliable tool for production schedule and estimating production lead time in lean production system

GALP: A hybrid artificial intelligence algorithm for generating covering array

Today, there are a lot of useful algorithms for covering array (CA) generation, one of the branches of combinatorial testing. The major CA challenge is the generation of an array with the minimum number of test cases (efficiency) in an appropriate run-time (performance), for large systems. CA generation strategies are classified into several categories: computational and meta-heuristic, to name the most important ones. Generally, computational strategies have high performance and yield poor results in terms of efficiency, in contrast, meta-heuristic strategies have good efficiency and lower performance. Among the strategies available, some are efficient strategies but suffer from low performance; conversely, some others have good performance, but is not such efficient. In general, there is not a strategy that enjoys both above-mentioned metrics. In this paper, it is tried to combine the genetic algorithm and the Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity to produce the appropriate test suite in terms of efficiency and performance. Also, a simple and effective minimizing function is employed to increase efficiency. The evaluation results show that the proposed strategy outperforms the existing approaches in terms of both efficiency and performance

Optimization of removal of toluene from industrial wastewater using RSM Box– Behnken experimental design

The study is concerned with the adsorption of toluene from real wastewater using granular beads of activated carbon. The adsorbent was analyzed before and after the process using Scanning Electron Microscope analysis to characterize its surface characteristics. The adsorption parameters including solution pH, contact time, dosage of adsorbent, temperature and toluene initial concentration were optimized using response surface methodology (RSM) Box-Behnken experimental design to maximize the toluene adsorption. The adsorption capacity of the adsorbent was 298 mg g−1 and the maximum toluene removal was 99.5% which was achieved in the following optimal conditions: pH: 2, 100 min, adsorbent dosage: 0.7 g L−1, 40 °C and initial concentration: 30 mg L−1. The adjusted coefficient of determination of the model was over 0.99 which denotes that the model was quite appropriate and accurate and also it was effective in the optimization of toluene adsorption. Finally, the activated carbon adsorbent was applied to remove toluene from a real sample of wastewater under the optimal operating conditions and the uptake percentage of 96.9% was achieved which was in accordance with the output of the removal of toluene from synthetic wastewater

MULTIDIMENSIONAL ANALYSIS OF PEOPLE'S BEHAVIOR IN ONLINE SOCIAL NETWORKS

L\u2019impressionante crescita in popolarit\ue0 delle Online Social Networks (OSNs), evidenziata dall\u2019enorme numero di utenti oggi legati ai social network pi\uf9 popolari, offre un\u2019opportunit\ue0 unica per comprendere i comportamenti online degli individui. In questa tesi, analizziamo i comportamenti delle persone sulle OSNs considerando che tali comportamenti sono il risultato della combinazione di esperienze ed attitudini sia online che offline. Dapprima, eseguiamo una analisi multidimensionale degli utenti attraverso diversi social media per fornire una descrizione complessiva dei comportamenti online e comprendere come questi cambino quando pi\uf9 media sono disponibili contemporaneamente. I risultati che presentiamo rappresentano uno dei primi esempi di esplorazione dei comportamenti umani su diversi social media. Ad esempio, utilizzando lo user degree su 5 diversi social network, evidenziamo che l\u2019importanza di ogni individuo cambia da piattaforma a piattaforma. La natura longitudinale del nostro dataset \ue8 anche stata sfruttata per studiare l\u2019attivit\ue0 di posting degli utenti, evidenziando una leggera correlazione positiva sulla frequenza con cui gli utenti pubblicano su social media differenti e confermando la natura bursty delle attivit\ue0 di posting mediante l\u2019uso di serie temporali multidimensionali. Inoltre, durante la tesi abbiamo sviluppato un metodo di identificazione innovativo per collegare le persone attraverso le diverse piattaforme social. Facendo riferimento agli attributi pubblici comuni, attraverso l\u2019uso di application programming interface (API) dei diversi social network, costruiamo le istanze negative in tre modi diversi, superando la selezione randomica abitualmente adottata, allo scopo di valutare la robustezza del nostro algoritmo di identificazione su diversi dataset. I risultati mostrano che l\u2019approccio porta ad un metodo di identificazione molto efficace per costruire dataset affidabili. Uno scenario reale costruito su Google+ e Facebook \ue8 stato utilizzato come testbed per la validazione del metodo. I risultati che riportiamo dimostrano i vantaggi ottenibili con il nuovo metodo rispetto ad altri metodi da letteratura. Infine, la tesi compie un primo passo verso una miglior comprensione degli effetti degli eventi offline sulla struttura del grafo delle social network in cui sono pubblicizzati. Pi\uf9 precisamente, svolgiamo una analisi temporale della social network legata all\u2019evento, comprendendo le persone che dichiarano di partecipare all\u2019evento tramite facebook, e valutiamo come questa evolva durante l\u2019intervallo temporale dell\u2019eventi stesso. I risultati mostrano che nuove amicizie nascono durante l\u2019evento e che la creazione di questi nuovi legami sociali \ue8 una delle cause principali di chiusura triangolare e che il grado maggiore si osserva durante l\u2019ultimo giorno dell\u2019evento stesso.The unprecedented and quickly increasing popularity of Online Social Networks (OSNs) is evidenced by the huge number of users who are turning to Facebook, Twitter and other social networks. The rapid growth of these online social networks provides a unique chance to study and understand the online behavior of the people. In this thesis, we analyze people's behavior in online social network considering the fact that online behavior of people is influenced by different factors which derive from the combination of their offline and online life. First, we perform a multidimensional analysis of users across multiple social media sites to give an all-around picture of people\u2019s online behavior. While people in their online life have access to a wide portfolio of social platforms, little is known about users\u2019 behavior when they have different online communication media available. Our findings represent some novel insights about people\u2019s behavior across social media. Having at our disposal users\u2019 degree in five different social networks, we find that the individuals\u2019 importance changes from medium to medium. The longitudinal nature of our dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Second, we develop an innovative identification methodology for connecting people across multiple social platforms. Relying on common public attributes available through the official application programming interface (API) of social networks, we construct negative instances in three different ways, going beyond the commonly adopted random selection to evaluate the robustness of our identification algorithm on different datasets. Results show that the approach can lead to a very effective identification method and methodology for building reliable datasets. Moreover, we analyzed the success of our method in a real scenario built on Google+/Facebook neighborhoods. Experiments reveal the advantages of the proposed method in comparison to previous methods in the literature. Finally, we take the first step towards understanding the effect of offline events on the graph structure of the social network where they are advertised. More precisely, we perform a temporal analysis of the event social network, constituted by people declaring to attend the event on Facebook and the links between them, and evaluated how it evolves during the event time period. The results show that new friendships are created during events and that this new friendships creation is one of the main reasons of triangle closure and the higher degrees observed in the last day of the events period

Experimental investigation of rheological and filtration properties of water-based drilling fluids in presence of various nanoparticles

The authors are grateful to the Department of Petroleum Engineering at Science and Research Branch, IAU, for providing adequate facilities to conduct this study and also to the Research Management Center of IAU for their financial support of this research.Peer reviewedPostprin

Analysis of ergosterol and gene expression profiles of sterol ∆5,6-desaturase (ERG3) and lanosterol 14α-demethylase (ERG11) in Candida albicans treated with carvacrol

Introduction: Usually, for treatment of fungal infections, antifungals such as azoles are used, but one of the biggest problems faced in clinical practice is the emergence of resistance for most of these drugs. Antifungal drugs derived from plants may alleviate this problem. The aims of this study were to analyse the ergosterol and gene expression profiles of ERG genes in Candida albicans treated with carvacrol. Methods:We used carvacrol and conducted a series of follow-up studies to examine the inhibitors of Candida species isolated from immunocompromised patients. Antifungal susceptibility test, time-kill study, ergosterol binding assay and ergosterol content were investigated. Eventually, the expression of ERG3 and ERG11genes was carried out to investigate the inhibitory properties of antifungal activity against Candida albicans using quantitative real time RT-PCR. Results: Carvacrol was able to inhibit Candida species and reduce time-kill kinetic in C. albicans. This phytoconstituent acted by binding to ergosterol in the fungal membrane and caused a reduction of 52% of the ergosterol content compared to the untreated growth control. Finally, carvacrol displayed significant down-regulation of ERG3 and ERG11genes in C. albicans. Conclusion: These results provide proof of concept for the implementation of carvacrol inhibitors of Candida species. In addition, ERG3 and ERG11 genes could be probable target of carvacrol against C. albicans

Performance of a campus photovoltaic electric vehicle charging station in a temperate climate

A photovoltaic (PV) array can be combined with battery energy storage to satisfy the electrical demand of lightweight electric vehicles. Measured solar resource and vehicle energy consumption, together with locational, mechanical and electrical constraints were used to design a vehicle charging station comprised of a 63 m2 10.5 kW AC PV array, with a 9.6 kWh lithium-ion battery. PV output, battery charge and discharge, electricity flows were monitored over one year. Deviations between measured and calculated annual AC generation averaged to 14%. Average annual direct consumption, self-consumption and system self-sufficiency were 8.47%, 30.3% and 74.36% respectively