A hierarchical fuzzy system for modeling driver’s behavior

The study of human behavior during driving is of primary importance for improving the driver??s security. In this study, we propose a hierarchical driver_vehicle_environment fuzzy system to analyze driver??s behavior under stress conditions on a road. We include climate, road and car conditions in fuzzy modeling. For obtaining fuzzy rules, experts?? opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. The number of fuzzy rules is optimized by Particle Swarm Optimization (PSO) algorithm. Also the frequency of pressing on brake and gas pedals and the number of car??s direction changes are used to determine the driver??s behavior under different conditions. Three different positions are considered for driving and decision making; one position in driving lane and two positions in opposite lane. A fuzzy model called Model 1 is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. The behaviors of different drivers under two stress conditions are investigated. Also we obtained two other models based on fuzzy rules called Model 2 and Model 3 by using Sugeno fuzzy inference. Model 2 has two linguistic terms and Model 3 has four linguistic terms for estimating the time distances with other cars. The results of three models are compared. The comparative studies have shown that simulation results are in good agreement with the real world situations.     

A comparative study of two approaches for supporting optimal network location queries

Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald’s) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L₁ and L₂/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.     

Identification and classification of ERP critical failure factors in Iranian industries

Implementation of Enterprise Resource Planning systems (ERPs) is a complex and costly process, which usually results in serious failures. The majority of previous studies and research projects have been conducted in identifying ERP Critical Success Factors (CSFs) rather than Critical Failure Factors (CFFs). On the other hand, most studies have been devoted to developed countries, while in developing countries, many companies have moved towards using such systems. The majority of IT/IS management standards and guidelines have been developed by technologically-leading countries. But developing countries, which mostly confront with especial challenges, have a different condition from the implicit assumptions of leading countries. Iran is one of these countries that many of its ERP implementation projects led to failure. There is no comprehensive study that identifies ERP CFFs in Iranian industries. The main purpose of this study is to identify such factors and classify them to help other industries, consultants and implementers to prevent failures in the implementation of ERP projects. So, at first, with the semi structured interviews and literature reviews, 47 failure factors were identified. After that a questionnaire developed and sent to ERP project team members in Iranian industries that failed in their ERP projects. Robust Exploratory Factor Analysis (EFA) has been used for data analysis, which finally classified critical failure factors in seven groups named as vendor and consultant, human resources, managerial, project management, processes, organizational and technical. The results of this study have provided a very useful reference for scholars and managers to identify the relevant issues of ERP projects failure in developing countries.     

A benzoate coprecipitation route for synthesizing nanocrystalline GDC powder with lowered sintering temperature

Electrolyte powders with low sintering temperature and high-ionic conductivity can considerably facilitate the fabrication and performance of solid oxide fuel cells (SOFCs). Gadolinia-doped ceria (GDC) is a promising electrolyte for developing intermediate- and low-temperature (IT and LT) SOFCs. However, the conventional sintering temperature for GDC is usually above 1200 °C unless additives are used. In this work, a nanocrystalline powder of GDC, (10 mol% Gd dopant, Gd_0.1Ce_0.9O_1.95) with low-sintering temperature has been synthesized using ammonium benzoate as a novel, environmentally friendly and cost-effective precursor/precipitant. The synthesized benzoate powders (termed washed- and non-washed samples) were calcined at a relatively low temperature of 500 °C for 6 h. Physicochemical characteristics were determined using thermal analysis (TG/DTA), Raman spectroscopy, FT-IR, SEM/EDX, XRD, nitrogen absorptiometry, and dilatometry. Dilatometry showed that the newly synthesized GDC samples (washed and non-washed routes) start to shrink at temperatures of 500 and 600 °C (respectively), reaching their maximum sintering rate at 650 and 750 °C. Sintering of pelletized electrolyte substrates at the sintering onset temperature for commercial GDC powder (950 °C) for 6 h, showed densification of washed- and non-washed samples, obtaining 97.48 and 98.43% respectively, relative to theoretical density. The electrochemical impedance spectroscopy (EIS) analysis for the electrolyte pellets sintered at 950 °C showed a total electrical conductivity of 3.83 × 10^?2 and 5.90 × 10^?2 S cm^?1 (under air atmosphere at 750 °C) for washed- and non-washed samples, respectively. This is the first report of a GDC synthesis, where a considerable improvement in sinterability and electrical conductivity of the product GDC is observed at 950 °C without additives addition.     

Wire electrical discharge machining of ASP30 tool steel

Wire electrical discharge machining is a non-traditional cutting process for machining of hard and high strength materials. This study analyzed the effects of the main input parameters of wire electrical discharge machining of ASP30 steel (high alloyed Powder metallurgical [PM] high speed steel) as the workpiece on the material removal rate and surface roughness. The input parameters included spraying pressure and electric conductivity coefficient of the dielectric fluid, linear velocity of the wire and wire tension. The machined surface quality was evaluated using SEM pictures. Results indicated that increasing the spraying pressure of dielectric fluid leads to a higher material removal rate and surface roughness and that increasing the wire tension, linear velocity of wire, and electric conductivity of the dielectric fluid decreases the material removal rate and surface roughness.

     

Highly efficient supported AlCl3-based cationic catalysts to produce polyα-olefin oil base stocks

The main aim of this research is to decrease the amount of AlCl₃ content that is very corrosive and hazardous in the catalytic system, required for the α-olefin oligomerization without substantial change of final oil features. This was successfully achieved by supporting AlCl₃ on different carriers. More precisely, a series of supported bimetallic catalysts was synthesized by immobilization of AlCl₃ and TiCl₄ onto Al₂O₃, SiO₂, and mixed supports, that is, Al₂O₃/FeCl₃ and SiO₂/FeCl₃. It was found that silica and alumina-based catalysts had higher catalytic activities compared to support free AlCl₃; however, this enhancement for silica-based supports was more significant. According to gel permeation chromatography (GPC) results, the use of single supports, that is, Al₂O₃ and SiO₂, increased oligomer's molecular weight, while the application of mixed supports resulted in the decrease of molecular weight of the oligomers. Viscosity characteristics of the synthesized oligomers have also been studied at two different temperatures of 40 and 100°C (KV⁴⁰ and KV¹⁰⁰). The viscosity index (VI) values, derived from KV⁴⁰ and KV¹⁰⁰, of the prepared oligomers were in the range of 126–145. The molecular weight and termination mechanisms of the oligomers were studied by ¹H-NMR spectroscopy. The obtained results disclosed that the employed reaction conditions led to the production of oligomer chains with various structures including vinylidene (Vd), and di and three-substituted vinylene (2Vn, 3Vn) structures.     

An Overview on SARS‑CoV‑2(COVID‑19)and Other Human Coronaviruses and Their Detection Capability via Amplification Assay,Chemical Sensing,Biosensing,Immunosensing,and Clinical Assays

A novel coronavirus of zoonotic origin(SARSCoV-2)has recently been recognized in patients with acute respiratory disease.COVID-19 causative agent is structurally and genetically similar to SARS and bat SARS-like coronaviruses.The drastic increase in the number of coronavirus and its genome sequence have given us an unprecedented opportunity to perform bioinformatics and genomics analysis on this class of viruses.Clinical tests like PCR and ELISA for rapid detection of this virus are urgently needed for early identification of infected patients.However,these techniques are expensive and not readily available for point-of-care(POC)applications.Currently,lack of any rapid,available,and reliable POC detection method gives rise to the progression of COVID-19 as a horrible global problem.To solve the negative features of clinical investigation,we provide a brief introduction of the general features of coronaviruses and describe various amplification assays,sensing,biosensing,immunosensing,and aptasensing for the determination of various groups of coronaviruses applied as a template for the detection of SARS-CoV-2.All sensing and biosensing techniques developed for the determination of various classes of coronaviruses are useful to recognize the newly immerged coronavirus,i.e.,SARS-CoV-2.Also,the introduction of sensing and biosensing methods sheds light on the way of designing a proper screening system to detect the virus at the early stage of infection to tranquilize the speed and vastity of spreading.Among other approaches investigated among molecular approaches and PCR or recognition of viral diseases,LAMP-based methods and LFAs are of great importance for their numerous benefits,which can be helpful to design a universal platform for detection of future emerging pathogenic viruses.     

Poly1‐hexene: New impact modifier in HIPS technology

Poly1‐hexene was prepared using a conventional heterogeneous Ziegler–Natta catalyst and its stereoregularity was characterized using ¹³C‐NMR analysis. New kind of high impact polystyrene (HIPS) was prepared by radical polymerization of styrene in the presence of different amounts of synthesized poly1‐hexene (PH) as impact modifier (HIPS/PH) and compared with conventional high impact polystyrene with polybutadiene (HIPS/PB) as rubber phase. Scanning electron microscopy (SEM) revealed that the dispersion of poly1‐hexene in polystyrene matrix was more uniform compared with it in HIPS/PB. The impact strength of HIPS/PH was 29–79% and 80–289% higher than that in HIPS/PB and neat polystyrene, respectively. FTIR was used to confirm more durability of HIPS/PH samples toward ozonation. To study the effect of rubber type and amount on the T_gs of polystyrene, differential scanning calorimetry was employed. Results obtained from TGA demonstrated higher thermal stability of HIPS/PH sample in comparison with conventional HIPS/PB one. Our obtained results suggest new high impact polystyrene that in all studied aspects has better performance than the conventional HIPS. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43882.