International Journal Of Nonlinear Analysis And Applications
Volume:11 Issue: 1, Winter-Spring 2020

This study was aimed to present a model for social responsibility of audit firms using the grounded theory method. To this end, the components and dimensions of social responsibility model of audit firms were identified and explained. The research method was applied in terms of purpose, mixed (qualitative-quantitative)exploratoryin termsof datatype, anddescriptive-correlationalinterms of data collection time. The statistical population of the qualitative part of research included experts and managers of audit firms and top professors in auditing in the country, among whom, 30 people were selected as sample size using purposive sampling method and saturation principle. The second group of the statistical population included all financial and audit managers of Shasta subsidiary firmas, among whom 145 people were selected as sample using relative stratified random sampling method. The results showed that the model of social responsibility measurement in audit firms has five dimensions of causal conditions (audit firm size, audit quality, auditor’s expertise, corporate size,auditfeesandtimebudget),axialphenomena(trust,technicalandcomparability,willingnessto invest, law-orientedness and business ethics), contextual conditions (internal controls, voluntary disclosure of financial statements, transparency of financial statements), intervening conditions (CEO’s personalitytraits,accountingconservatism,andpoliticalconsiderations)andconsequences(financial development, information validity, information quality, trust and social participation, and corruption).

This study aimed to present a model for portfolio risk premium assessment of companies listed in Tehran Stock Exchange. In order to achieve this purpose, monthly data of 150 companies listed in Tehran Stock Exchange during 2007-2017 was used. In this study, the predictive powers of FamaFrench three-factor model [11], Carhart four-factor model [1], Fama - French five-factor model [24], Brousseau five-factor model [18] and Roy and Shijin six-factor model [44] have been evaluated using variables used in the mentioned models and then an optimal model has been developed for portfolio risk assessment using stepwise regression. Findings showed that the Carhart four-factor model has higher predictive ability (48.3%) than other mentioned models in the Tehran Stock Exchange. According to the results of stepwise regression, seven variables have been selected as effective variables on portfolio risk premium. The explanatory power and predictive ability of the model developed in the Tehran Stock Exchange was 55.7% indicating higher predictive ability respect to previous models on portfolio risk premium. Investigation of the coefficients of the developed model showed that market risk premium, size factor, value factor, momentum factor and accounting quality factor have positive and significant effects on portfolio risk premium while investment factor and liquidity risk factor have significant negative impacts on portfolio risk premium.

The success or failure of a project in achieving predefined objectives is largely dependent on the suitability of its execution system. An important decision in the early stages of a project is to investigate different possible ways for executing projects and selecting the best one. This requires the identification of risk taking of projects. Risk is, in fact, the same as uncertainty and a multidimensional concept affecting the project's objectives. Risk management is defined as the risk identification and assessment process and application of specific methods to reduce risks to an acceptable level. Therefore, the initial objective of project risk management is risk identification, evaluation and control for the success of projects.  The risk management standard published by the Project Management Institute (PMI), entitled Project Management Body of Knowledge (PMBOK) was used in this study as the basic method for describing risk management. The general objective of this study was to develop a risk management model for water and sewage projects using Interpretive Structural Modeling (ISM). The statistical population included all experts involved in the field of water and sewage projects. The Delphi method was used for risk identification. Eventually, data was analyzed with the help of the ISM and driving power-dependence power diagram (MICMAC). According to the results, allocation of Islamic Treasury documents and bills, imprecise conduction of preliminary studies, the lack of coordination between project designers, irrelevant maps and details, imprecise initial project estimation, the lack of adequate maps and details and project failure and its conversion into some small projects with a high level of impact-dependence are highly prioritized risks needed to be controlled.

Nowadays, tracking objects has become one of the basic needs of security systems. Deep learning based methods has dramatically improved results in tracking objects. Meanwhile, the quality of the videos captured by camera is effective on the accuracy of the trackers. All images captured by camera inevitably contain noise. The noise is usually created due to various reasons such as the underlying media, weather condition, and camera vibrations in the wind and so on. This paper deals with the issue. In this paper, tracking objects is performed by Yolu 3 architecture in deep learning. Cycle spinning method is also employed to eliminate noise.

One of the most important topics studied in the field of market microstructures is to measure information asymmetry in the capital market. In recent years, the Probability of Informed Trading (PIN) has been introduced to measure information asymmetry. The use of private information in stock exchanges reduces stock liquidity, thereby increasing the cost of equity capital. The main purpose of this study is to investigate the relationship between the probability of informed trading and cost of capital as well as to examine the moderating role of family ownership in the relationship between the probability of informed trading and cost of capital in 113 companies listed on the Tehran Stock Exchange during 2012-2016. The independent variable of information asymmetry is measured by the probability of informed trading criterion and the dependent variable of cost of capital by the criterion of cost of equity and cost of debt, and the weighted average cost of capital, and the moderating variable is family ownership. The research method is correlational and the multivariate regression using combined data with fixed effect regression model approach is used. The research findings show that there is a positive and significant direct relationship between the probability of informed trading with the cost of equity and the weighted average cost of capital, as well as the variable of family ownership has a positive and significant direct effect on the relationship between the probability of informed trading and the weighted average cost of capital. When the probability of trading by private information holders increases, due to the increase in information asymmetry between informed and uninformed investors, uninformed investors demand higher returns to cover the investment risk, thereby increasing the company's cost of capital. This will increase the cost of financing through bonds.

Balanced scorecard (BSC) is a quantified interpretation of organization strategy, which is delineated as a strategy map and describes the organizational performance on four perspectives of financial, customer, internal business process and learning and growth. The measures and objectives of BSC are derived from mission, vision and organization strategy. Organization strategy indicates how an organization creates value for the shareholders, customers and citizens. Furthermore, strategy is the very factor that guides an organization towards achieving its vision. In the same vein, the internal business process perspective describes the processes and measures that eventually lead to the desired level of performance in financial and customer perspectives. As a result, the existence of cause-and-effect relationships outlined in the form of strategy map are deemed as the guideline for the organization achievement. The cause-and-effect relationships determine the accurate route for strategy realization. Without having such associations, the organization is merely has access to a set of financial and non-financial measures. Taking the above into account, the present research has transformed the key measures in Iran’s banking industry to quantified values and by using Granger causality test, it explores the cause-and-effect associations between the measures of process and financial perspectives. Eventually, it analyzes and describes how organization vision is associated with organization’s operations processes.

Nowadays due to the variety of heating systems, efforts are being made to optimize each of these systems. Due to the fact that people spend most of their time in their home or indoors, providing cleaner and more comfortable air should also be of interest to researchers and developers of these systems. The floor heating system was initially introduced as the most ideal heating system and is used by many people. After the growth of floor heating systems, theories that this system, like other common heating systems such as radiator or heater systems, can move particles in the floor of living spaces and cause diseases such as allergies; has been raised. But some experts argue that radiation is the main mechanism of heat transfer in the floor heating system and that the system is unable to move and lift particles. A response to these two contradictory theories must be examined in order to further optimize these systems. In the present study, the effect of floor heating systems on the behavior of dust on the floor of the study area is investigated. The present work has been validated using the results of an experimental work in which the airflow within the desired geometry is investigated. In this study software (ANSYS-Fluent 19.1) and discrete phase model two-phase is used to simulate this model. By examining the results, it is found that the free displacement caused by the floor heating system and the inlet air from the window is capable of lifting and moving the particles on the floor at specified directions. Also, by examining the effective parameters on the displacement rate of these particles, including diameter, density and velocity of inlet air, it was observed that particles with less than 1 micrometer diameter and less than 750 kg/m3 density range can move in the geometry space studied.

In recent years, online shopping has benefited great importance and popularity across the world. The purpose of this study is to identify factors affecting consumers’ online shopping intention. A paper-based survey questionnaire was distributed among the residents of Tehran and ultimately 355 questionnaires were used for modeling. The results of the structural equation modeling revealed that factors such as trust, positive attitudes towards online shopping, traffic restrictions, consumer resources could have their own positive effects on individuals’ online shopping intention; in contrast, perceived risk, sense of place, and positive attitudes towards in-store shopping could bring about negative impacts on such orientations. The effects of some socio-demographic variables and product-related ones were also further discussed. The findings of this study could be used for online stores in order to attract more consumers as well as for transport system planners in order to reduce demands for shopping trips and thus control traffic.

False rumors and news are always published as purposeful approaches with social, economic, political intents in order to provide false information and deceive people in the communities. This leads to a lack of trust in news and information. Differentiating real news from rumor has been considered as one of the most important aspects of news evaluation and different approaches have been used to identify and distinguish fake news from real one. Among them, the use of artificial intelligence and machine learning methods has been more important due to the successes achieved. Due to this advantage, the present study has attempted to use machine learning algorithms including SVM, k-NN, decision tree, random forest and MLP, to identify and classify fake and real news in the data set collected from Persian Twitter messenger. Based on the results of the confusion matrix implementation and functional evaluation of learning algorithms, it has been determined that Randomized decision trees and decision tree have the highest accuracy in evaluations with 90.25 and 90.20 as in the next step, the accuracy of the random forest is 89.99%. This indicates the ability of tree decision-making algorithms in optimal evaluation and better identification of fake news on Persian Twitter. Also, random forest and Randomized decision trees algorithms have the highest precision in implementation with 92%, and after these two algorithms, decision tree with 90.20% is in the third rank of precision.

Telescopic Self-Centering braces are one of the very successful examples of Self-Centering braces which perform well in seismic loading. In this study, a new example of Telescopic Self-Centering brace is introduced, which has superior features over other telescopic braces. These include: high axial load capacity, use of shorter cables in brace construction, simplicity of construction, use of separate cables for compressive and traction modes, less fatigue in cyclic loads and, allowing for more dynamic loading cycles. In this paper, a sample was designed with an axial force capacity of 300kN. Modeling of behavior (DT-SCED) was accurately expressed using numerical relationships. nonlinear incremental stiffness analysis method was also used to calculate the hysteresis brace behavior. The cyclic load test was applied to this brace and the result showed complete Self-Centering behavior. Then, a sample building with the double telescoping Self-Centering Energy-Dissipative Brace (DT-SCED) was subjected Single direction pushover analyses and the results are compared with the sample buildings with the Self-Centering Energy-Dissipative Bracing (SCED) and the Telescoping Self-Centering Energy-Dissipative Bracing (T-SCED). The results of the analysis and comparing with other samples confirm the seismic superiority of performance of the DT-SCED brace over other samples.

The pricing bubble is one of the issues facing the capital markets, which occurs at different stages in the capital markets and with its emergence and fall, many changes occur in the capital markets and the situation of investors. This article seeks to investigate the bubble formation and its fall in the Tehran Stock Exchange using the State-Space Model of the Markov Switching Method. To investigate this issue, the space-state system, two models of Wu [22], Campbell & Shiller [4, 5] have been used, which in one case considers bubble formation and in the other case bubble falling.The studied data were from April 2011 to September 2018 and on a daily basis, which was extracted from the archives of the Tehran Stock Exchange.The stock market has witnessed the bubble formation process a total of 19 times in the period under review, so that in 2011, 4 times, in May, December, February and March in 2012, 5 times in May, July, October, November and February price bubble occurred. Also, in 2013, a price bubble occurred 4 times, which included the months of May, July (2 times), and January. This sequence for 2014, including once in March and in 2015, occurred twice in April and February. In 2016 and 2017, the price bubble did not occur, and in 2018, in June, July and August, there were 3 price bubbles so far.

The experience of receiving desirable banking services can have a significant psychological and behavioral impact on customers. The present study was aimed to design a desirable model of banking services based on customer experience using the grounded theory.

The present research is applied in terms of purpose, descriptive- survey in terms of data collection method and a qualitative research based on the data nature. The study was conducted using the Strauss and Corbin’s theory [1]. The statistical population included Shahr Bank customers and the statistical sample was 20 customers using Shahr Bank services who were selected using purposeful sampling method. The instrument of study was an open-ended questionnaire based on open interview protocols. SPSS statistical software was used for descriptive analysis and chart plotting. In the grounded theory section, Atlas.TI software was used.

The results of the analysis of research findings led to the design of an optimal model of banking services based on customer experience using the grounded theory, in which the concepts and categories of the optimal model of banking services based on customer experience in Shahr Bank, including 230 codes, 63 concepts and 15 core categories were identified. The causal conditions include 4 categories; context 2 categories; strategies 2 categories; intervening conditions 3 categories and consequences include 4 core categories.

The present study indicated the complexity of the components of the optimal model of banking services based on customer experience in Shahr Bank. However, the conceptual model enables the managers to make appropriate executive decisions.

Either natural or man-made a disaster is defined as unexpected destructive event that causes damages and malfunction of existing systems and services all around the disaster area, these destructive effects are unfortunately beyond the capability of local authorities to recover and respond immediately, the disaster recovery plans are immediately initiated so that rescue and aid operations can help those who are trapped in disaster area to survive, those efforts need to be controlled and coordinated with reliable communication systems that are more likely partially or fully disabled due to the disaster, the capabilities of cognitive radio technology enables it to play a significant role in providing efficient communication services for the rescue teams and headquarters as well as trapped victims, in this paper, we survey the cognitive radio architectures that can replace the Software Defined Radio SDR in order to reduce the network expenses in terms of network size and network computational complexity .

Real time vibrational signal processing is one of the fault detection methods for the mechanical systems. The Hilbert–Huang Transform (HHT) is a newly developed robust method for analyzing nonlinear and non-stationary vibrations based on time-frequency distribution. This approach is based on Empirical Mode Decomposition (EMD) and Hilbert spectral analysis. This paper presents a state-of-the-art method for decomposing a signal into a set of so-called Intrinsic Mode Functions (IMF). The proposed alternative method is based on the change in the screening algorithm. This modified method is useful to mitigate end effects and reduces the calculation load and time. The effectiveness of this method was validated by numerical simulation. The results show the accuracy and reliability of this method.

An appropriate rating system in banks can clarify the status and performance of banks for its users. Although many national and international rating institutions have been established, its absence is felt in our country. In a rating system, it is necessary to take into account the economic and environmental conditions of the country in order to evaluate the banks. For this purpose, in this research, 35 criteria are selected according to the opinion of 34 banking and academic experts, and using them, 15 banks in the 5-year period of 2014-2018 are ranked by the TOPSIS method. The findings show that the indicators related to financial dimensions (liquidity, profitability, capital and asset risk), qualitative dimensions (complexity and behavior of banks) and environmental dimensions (economic variables, government support and industry characteristics) are effective in the indigenous model of bank rating; In this regard, the financial health system and stock prices of banks are used to evaluate the indigenous model; The results show that the indigenous model has a positive and significant correlation with the financial health system; so that by identifying the position of the bank in the indigenous model, its position can be relatively described within the financial health system; Also, the results of the indigenous model show a positive and significant relationship with the stock prices of banks. This evidence draws attention to the proposed indicators for evaluating and rating banks.

In all developed countries, energy systems are being adapted to employ sustainable energies as such these countries are developing some programs to reduce the usage of fossil energy as much as possible in order to avoid environmental pollution and make the world a better place to live. The use of electrical vehicle (EV) is one of the appropriate options in this regard. In this paper, the power of charging stations, load uncertainty, and the uncertainty of electricity price in power systems were modeled using the behaviors of EV owners and a two-point estimate method, respectively. Then the contribution coefficient of charging stations and wind generation units as a distribution system were optimized using the NSBSA algorithm. Simulation was performed in MATLAB software, and IEEE 9-bus test system validated the efficiency of this algorithm.

Portfolio management is portfolio management created on behalf of the investor by the financial assets to ensure maximum efficiency within the risk rate and duration set by the investors. The most important goal in creating and managing managed portfolios to achieve maximum efficiency is to reduce the risk. This is equivalent to selecting the optimal portfolio from the portfolio of possible portfolios, which is called portfolio selection problem. The dynamic portfolio optimization model solves the complexities caused by the effects of various factors on the problem by focusing step by step on various factors and then combining the results of these investigations. The main issue in this research is the use of a new tool for selecting investment portfolios in view of the lack of high liquidity or low liquidity of firms and portfolio selection models. The statistical sample is considered for 27 active enterprises in the real of time from the beginning of March to 2014. The results show that the use of asset liquidity index to optimize portfolio using two Taylor series expansion methods has created a significant difference in the weight, yield and risk of portfolio compared to the Markowitz model. Also, the results of calculating the trainer criterion showed that the optimization model obtained from the expansion of the Taylor series of value function has a higher performance than the portfolios obtained from the Taylor process.

Intelligent transportation systems (ITS), especially in metropolitan areas, can play a crucial role in reducing traffic and its flow and finally reducing the average travel time of vehicles. Due to the high capabilities of intelligent agents in modeling and simulation, they have been used significantly to model complex urban environments and traffic control systems in recent years. In fact, base operating systems are appropriate for modeling intelligent transportation systems, especially in changing urban spaces, and simulating related facilities and equipment. However, most researches in this area have not been comprehensive. As it was mentioned, in this research, a comprehensive agent-based modeling of intelligent urban transportation system is used to control and manage urban traffic using instantaneous traffic information of streets and finally various scenarios have been proposed and implemented. Also, all vehicles have been equipped with GPS and communication devices are simulated. In this study, more emphasis has been placed on intelligent traffic lights using traffic information than routing methods using traffic information and the results have shown high effect of the smartization of traffic lights compared to the change in vehicles routing methods in management and reduction of urban traffic in implemented scenarios.

Decentralized renewable energy resources have been identified as one of the promising ways to sustain the future energy demands. However, most of the energy produced by renewable energy resources, particularly the Photovoltaic (PV) system is intermittent in nature and often fluctuates. In this regard, this paper utilizes Malaysian Reference Networks model, with the aim to analyse the effects of solar PV integration with medium voltage (MV) network under various solar variability conditions. For validation, network losses and voltage profiles had been evaluated on various PV variability profiles using DIgSILENT power factory software. The impact of solar generation variability on transformer On-Load Tap Changer (OLTC) had been investigated through the utilization of five types of solar variability day in Malaysia; compiled as one-minute resolution. In addition, urban, sub-urban, and rural MV networks had been considered in this study. The results presented in this paper show that proper allocation of PV plants can help to reduce network losses and improve voltage profiles. Rural network incurred the highest number of tap changes in OLTC to control the voltage level, compared to urban and sub-urban networks.

The need for the deployment of reliable and efficient telecommunication systems in extreme emergency scenarios such as disaster response networks imposes a set of emerging unusual communication and routing challenges and obstacles that questions the performance of existing traditional and commercial telecommunication systems and networks in such scenarios, the revolution of telecommunication and networks industry witnessed the development of enormous telecommunication and networking services and systems that shaped their implementations in various domains of applications , in this paper, we study most of these communication standards in terms of their pros and cons, we also analyze the potentials of these standards in for Disaster Response networks in comparison with Cognitive Radio technology that has distinct capabilities and functionalities that enabled such a technology to be highly applicable for such harsh and unexpected scenarios.

Studying and recognizing the behaviour of securities returns has always been the focus of investors and researchers since the dawn of capital markets. Based on the statistics of the last decade, the stock market has been one of the major centres for making investment and getting high returns. Analysis and forecasting the price of financial assets has always been an intriguing topic in both scientific and practical disciplines which created various challenges for financial analysis. Chaos theory and fractal analysis are the latest theories in this regard. The present study has reviewed the information on Tehran Stock Exchange (TSE) companies’ returns between 2014 and 2018 in monthly intervals to measure the multifractal system, long-term memory (LTM), and weak-form efficiency of the stock return variable. The aim of the study has been addressed by using Hurst’s rescaled range (R/S) statistic model. Any R/S larger than 0.5 indicated a correlation between future stock returns and previous returns and that previous data influence the market. Consequently, it is not a random market but has an LTM, a fractal dimension, and is relevant to the Efficient Market Hypothesis (EMH) that confirms these variables.

Loan deferment is a negative consequence of the activities of financial institutions. Increase in the amount of deferred loans can diminish productivity in the banking sector. The purpose of the present research is to cluster bank customers in order to prevent loan deferment and identify and classify customers with varying levels of loan repayment risk. In the proposed method, k-means, two-step, and Kohonen techniques are used for clustering and determining the behavior of each cluster. The results indicate that the k-means model with five clusters has the highest clustering accuracy. Clustering is also used to determine underlying feauture. loan term, loan value, and collateral value are respectively identified as the most influential feauture . Customers are clustered after removing non-significant feautures. Eight different machine learning techniques are used for clustering. These techniques are ranked in terms of efficiency based on certain evaluation criteria and using data envelopment analysis. The results indicated that support vector machine (SVM) and artificial neural networks (ANN) are the most efficient of the examined techniques

Happiness at the workplace refers to how satisfied people are with their workplace and lives. Happy people are productive people while those people who are unhappy may not pay full attention to any task. Furthermore, employees in the public organizations in UAE are not well satisfied due to poor workplace conditions. In some ministries in UAE must equipment are not well maintained to ease the work of an employer. The main aim of this paper to develop a Model of happiness at the workplace in promoting employee happiness in public organization using structural equation Model. The data were obtained from the employee of Dubai Electricity and Water Authority in UAE. Since the population of this study is 11787 therefore, the sample size was 370. surveyed through the self-administered Google form and by posting. The data was screened, and out of the 370 questionnaires distributed, 260 were completed and were received. Analysis of Moment Structures (AMOS) in Structural Equation Modelling (SEM) confirmatory factor analysis (measurement and structural measurement models) were used to analyzed the data. Although this study has conceptualized the improving happiness at the workplace in promoting employee happiness in public organization. Additional research is needed among other states in UAE such Dubai. The motivational formulation of employee happiness, job involvement, work place climate and its impact on organizational performance is a point of future research.

This research examines a conceptual framework of the smart educational system for improving instructors’ efficiency and student outcomes in the UAE. The study mainly focused on the precarious framework of smart learning and key features of smart learning settings through earlier studies. There was a sample of 326 learners who were examined on the academic impact of the Smart Education System. Nevertheless, the results demonstrated that the use of the smart education system has a positive effect on teachers’ efficiency and educational outcomes. The political implications of this research are that it is anticipated that smart education will steadily improve, particularly in the UAE context.

Real-time marketing is an influential factor in creating a high engagement audience and is identified as event-driven marketing by instantly connecting with the customer based on events. Modern audiences are more expectant than ever, and the time has come when no one waits 24 hours for an update. Instant forms of communication (e.g., short message service, instant messaging, and social media services) remind us of the need for real-time marketing. The present study aimed to design a strategic real-time marketing model based on the Internet of Things (IoT) in smart cities in the fourth industrial revolution (4IR) in order to enable timely action on events and triggers on digital channels to capture pure moments with automation platforms and analytical and cognitive tools. In the current research, we applied a mixed-method and grounded theory with a systematic approach attributed to Strauss & Corbin to analyze the data and designed a model using three open, axial, and selective coding techniques. Data were collected using library and snowball sampling methods. The questionnaire was provided to managers and specialists following the identification of dimensions, criteria, and related sub-criteria, and the results were analyzed in an independent t-test. Moreover, the dimensions and components were prioritized applying the AHP technique. According to the results, entering the era of the 4IR, social media and mobile phones has shifted the common paradigms and the realization of scientific myths. Destructive technological stimuli with exponential growth and super-tendencies resulting from these transformations will create new images in the media, which highlights the optimization of micro-moments at the time of need to express goals and turn them into the content.

In this paper, we study a positive-additive functional equation in intuitionistic fuzzy C ∗ -algebras. Using fixed point methods, we approximate the positive-additive functional equation in intuitionistic fuzzy C ∗ -algebras.

In this paper we discussed the concept of new triangle in Morrey spaces which is defined by using Wilson angle and I−angle. We also discuss about some fundamental properties of a triangle in Morrey spaces

There is a useful approach for multiple objects tracking easy and efficient that is called simple online and real time tracking(SORT). SORT algorithm performance can be improved by adding visual information. This can reduce the number of identity switches. Because the main framework of the algorithm has a lot of computational complexity, a deep network has been used that is offline on a large data set of trained pedestrians. the focus of this article is on the architecture of this deep network in order to extract more and higher quality visual information that can help the object recognition algorithm. The paper also used a particle filter instead of a Kalman filter to improve data association performance. We tested our proposed method on two standard datasets, MOT16 and MOT17, and compared its performance with other available methods. The results show that the tracking accuracy(52.2) on the MOT17 dataset is improved compared to the existing methods in this field. Experimental evaluation shows that our proposed architecture improves the number of identity switches and ideally tracks goals in complex environments.

The purpose of the study is to examine the practices implemented by Bangladeshi listed companies on the Dhaka Stock Exchange (DSE) in the knowledge of working capital management. The author collects data from 97 financial managers using a self-structured questionnaire consisting of 32 questions on a Likert scale, and uses different statistical methods to evaluate for statistical significance. The result suggested that behavioral bias exerts significant impact on all parameters of working capital, but a poor relationship for account payable, in the context of Bangladeshi manufacturing and service companies. Cross-redundancy value should be greater than zero in order to determine the predictive efficiency of the system within this research. The study result reflects that the values of a cross-validated redundancy indicate a fair prediction standard for the model. Although dealing with various manufacturing and service sectors in Bangladesh, responses from across those sectors are very difficult to produce. The evidence diminishes questions about non-response bias and the willingness of Bangladeshi companies to generalize the results. This research fills a void in the literature by providing understandings into performs followed by Bangladeshi companies in dealing WCM and its mechanisms, by updating and expanding previous work on WCM.

In this research, we are looking to present a pricing model in the two-level supply chain using game theory, assuming there is advertising and in terms of uncertainty. In this research, it was tried to provide different models for different conditions in the supply chain, taking into account various supply chain competition strategies and conditions. The issue was designed with the goal of optimizing the pricing of two alternative products provided by two different manufacturers. The survey environment was considered highly competitive. The discussion of advertising in the chain and the uncertainty in the problem parameters were also considered in order to bring the model closer to real-world conditions. Then the model was solved for different strategies and optimal strategies for each side of the chain were identified. Finally, numerical issues were presented, solved and analyzed in different dimensions.

The concern for destruction of environment has been turned into a critical problem in recent years and in addition to profitability and sales the marketers concern with consumer’s health. Dynamism and importance for protection of environment, serious competition, rising concern of consumer, public regulations and increased awareness in customers caused enterprises to produce green products to acquire competitive advantage and to attract eco-friendly customers to protect from it. Thus, coincided with rising concerns and importance of environmental issues, development and analysis of related concepts to green marketing development and improvement are assumed necessary for the post generations. He studied statistical population is composed of customers or food consumers of foods at shopping centers in Tehran and statistical population, including 384 subjects, were tested using conceptual model by structured equation modeling and least partial square methods with AMOS software in which the findings showed that variables of macro environment, marketing mix, social responsibility, environmental knowledge management, green branding, social marketing, interactive marketing, security and vale affected development of green marketing. The results of current research could propose a local model with respect to conditions in Iran.

A primary aspect of human aging is progressive neurological dysfunction. Due to the fundamental variations in aging in mice and humans, it is difficult to obtain and research effective mouse models. There are two types of tissue phenotypes that are distinct; one is the tissue for retina and one for the hippocampus. Each form has three strains. A variational formulation for sparse approximations is introduced in this work, inferring both the kernel hyper-parameters and inducing inputs by maximising a lower bound of probability of true log marginal. In order to account for more complexity with the time series, a model is built on this series with a correlated human model performance. The molecular senescence of the hippocampus and retina, both with accelerated neurological senescence (SAMP10 and SAMP8) models were presented. The purpose of the study is to specify the relationship between these genes or pathways that would provide insight into the mechanism for this phenotype which will be superior to the current incomplete state-of-the-art approximations. Furthermore, the combined study of the essential features of inbred strains and profiling of gene expression can help determine which genes are essential for complex phenotypes. However, the identification, sequencing and gene expression of full-genome polymorphism of inbred mouse strains with intermediate.

Today, metropolises have been exposed to process of urban stagnation and decline that was led to creation of ineffecient and worn-out spaces in the city. In order to resolve these problems, several approaches have been expressed from perspective of theorists where the urban recreation is placed on top of them. One may refer to smartening of smart cities as the foremost attitudes relatibg to regeneration in which revaluation of human pure life is assumed as the foremost preferences in this concept. The grounded theory has been utilized in this study in order to identify parameters of smartening of new cities based on justice-oriented indicators. Similarly, the methodology is of descriptive-analytical type. The new urban development and properties and definition of smart city were initially extracted by attribution to the existing references and they were concluded by analytical technique. Then the related principles were formulated for the theories and at the end these principles were compared and parameters were obtained using analytical-comparative method. Primarily, using mathematical analysis, normalization and standardization methods and parameters were arranged by means of McGranhan method and weighting in descending order to identify which of principles and parameters might play significant role in research result. The findings of this study suggest that justice principle (weight= 0.272) has devoted the highest weight in realization of smartening of smart cities based on justice-oriented urban development.

In this paper, an adaptive control schemes to solve the output tracking control problem of a class of nonlinear switched systems in presence of the disturbance is proposed. First, an nonlinear disturbance observer (NDO) is designed and the  backstepping scheme is constructed based on the standard Lyapunov function method for tracking purpose. With the propose scheme, the existence of a standard Lyapunov function for all subsystems with unknown parameters infers the global uniform asymptotic stability of this more generic switched system The switching parameters used in the switching system are defined differently for each subsystem. Analyzing the system's stability proved that the closed-loop signal boundedness under arbitrary switching is well ensured. It is shown that the proposed adaptive anti-disturbance control scheme based on a nonlinear disturbance observer is a suitable control approach for a class of nonlinear switched systems.

Traffic lights play an important role in urban transportations, but they also cause delays in streets leading to junctions. Scheduling of traffic light, therefore, puts a considerable effect on urban traffics and routing delays. The proposed routing aims to route the vehicle agent so that the driver arrives at its destination by the fastest path. In this paper, a new intelligent algorithm is proposed for scheduling traffic lights to decrease traffic density and less delay in routing. This algorithm considers traffic flow density and the presence of emergency vehicle agents. The algorithm evaluates the status of the traffic flow by fuzzy logic. The evaluation is done by considering traffic flow speed and density. The output of fuzzy logic is used by Gradational Search Algorithm (GSA). GSA considers the status of the flow, the priority of the traffic flow, and the distance of the emergency vehicle to the traffic light. The simulation results prove that the proposed algorithm has better performance.

Micro-expression as the main way of non-verbal communication occurs quickly and subtle in highrisk situations. Since it cannot be misleading, it discloses the real human aim. Nonetheless, feature extraction is an arduous task due to its two particular features. To resolve this problem in this paper, we propose Local Binary Pattern on Four Diagonal Planes. In fact, we utilize it after motion magnification for feature extraction in apex detection task. Also, we combine it and optical flow for micro-expression recognition. Simulation results show that automatic micro-expression apex frame detection and micro-expression recognition is promising using our method on CASME2 and CASME in comparison with other methods.

Online media requires to pay attention to various levers for building an appropriate identity and the image of interest of a brand to achieve sustainable competitive advantages. Meanwhile, paying attention to the brand DNA is one of the requirements of the business environment for building a sustainable identity. The main objective of this research was to design a genetic model (i.e., brand DNA) in the field of internet businesses. This research was an applied study based on the exploratory sequential mixed method. Participants for the first section were 30 experts in the field of branding in the online media, which were identified and chosen using a judgmental approach and snowball sampling. Participants for the quantitative section included managers of 293 online business companies in the science and technology parks (STPs) of Tehran universities. The sample size was determined by Cochran’s formula equal to 166 participants who have been chosen by simple random sampling. The data collection tool was a semi-structured interview (for the quantitative section) and a 51-item questionnaire (for the quantitative section). The content analysis approach was adopted for analyzing the data gathered in the qualitative section. For the quantitative section, data were analyzed using SMART PLS. From the results, a total of 17 sub-themes and four main themes were confirmed. Sub-themes included owner attributes, value, demands, fundamental competencies, brand story, brand promise, brand sense, personality, style, product, competitive advantage, objective, holographic culture, strategy, brand experience, communication, and standard of performance, which were known in the form of two phenotype and genotype types. Furthermore, the results of the qualitative section inconsiderably confirmed the accuracy of the designed model.

Nowadays, organizations that combine marketing and entrepreneurial activities act better in discovering and exploiting new opportunities of the market. One of the main advantages of the entrepreneurship concept in the marketing is its capability to respond to an ever-changing environment. When entrepreneurs are looking for new opportunities regarding limited resources, they must adopt an innovative approach to dealing with these uncertainties. This study aims to identify, prioritize and validate the components affecting entrepreneurship in marketing in the microelectronics industry. In this research, confirmatory factor analysis has been used to analyze the tools of study. Next, descriptive indicators of research variables (i.e. mean and standard deviation) have been reported. Then, structural equation method has been utilized in order to evaluate the causal relationships between variables. Finally, Friedman test has been applied to prioritize the research variables. The findings showed that 22% of employment variance, 31% of market performance variance, 28% of customer orientation variance, 46% of product marketing variance, 34% of marketability variance and 53% of entrepreneurial marketing variance can been explained by the variables of the research model. Also, the fit indices obtained for the tested model showed that the RMSEA index in the estimated model has an acceptable level with value of 0.064 and other fit indices such as CFI, GFI, NFI, and AGFI are equal to 0.97, 0.94, 0.95 and 0.92, respectively indicating good levels. These characteristics for goodness of fit confirm that the data of current study fits well with the factor structure of the model and hence the entrepreneurial marketing is valid and applicable in the microelectronics industry.

Traditional technique for determining the soil texture and other soil properties is performed in laboratory which is a time consuming task. In this paper, machine learning algorithms are deployed to classify the soil texture and its properties without any intervention of laboratory equipment using the satellite images recorded by Landsat 8. These images are used to extract the terrain properties of the region which is integrated with weather data for the specific region and the vegetation index which are the major factors affecting the soil condition. A major aim of this paper is to design a robust technique for extracting, transforming Landsat images to numerical data and pre-processing the data for classifying the soil property. Minimum Noise Fraction (MNF) is utilized to segregate and remove noise from the Landsat images for subsequent processing. A significant amount of noise is present in the raw data which affects the accuracy of the analysis. Terrain features are extracted after noise removal from the MNF transformed images and merged with the weather data, and vegetation index for a period of time and then classified using voting classifier of the ensemble modeling or analysis of the soil texture of the region. The voting is performed by integrating the results of logistic regression, support vector machine and decision tree. With this study, the consolidated dependence of the soil texture on the environmental factors is analyzed and a cross validation accuracy of 94.44% is obtained.

This paper studies a novel technique based on the use of two effective methods like modified Laplace- variational method (MLVIM) and a new Variational method (MVIM)to solve PDEs with variable coefficients. The current modification for the (MLVIM) is based on coupling of the Variational method (VIM) and Laplace- method (LT). In our proposal there is no need to calculate Lagrange multiplier. We applied Laplace method to the problem .Furthermore, the nonlinear terms for this problem is solved using homotopy method (HPM). Some examples are taken to compare results between two methods and to verify the reliability of our present methods.