نمونه ترجمه های سایت ای ترجمه

  3.2. The Internet of Things and Its Consequences

Internet of Things became a popular topic of Industry 4.0. Many publications regarding these topics focus on large-, small- and medium-sized firms. Ashton introduced the term Internet of Things in 1999. It refers to a system in which the material world communicates with computers using sensors [12,49–58]. Several years later, the number of devices connected to the network exceeded the number of people around the world. This moment, according to Cisco, is the actual birth of the “Internet of Things”, referred to more often as the “Internet of Everything”. A system consists of people and objects, as well as processes, animals and data, including atmospheric phenomena—all elements that can be used as variables [12].

The Internet of Things has three distinguishing features. These are context, omnipresence and optimisation. The Internet of Things creates the possibility of an advanced interaction between an object and its environment and enables immediate response to change. The feature of context manifests itself in providing location, atmospheric conditions and other information necessary for an object. Omnipresence illustrates that connected objects are not only human users of the network. Objects will communicate with each other without human interference. Optimisation is the expression of the functionality of any object [12]. Tun et al. [57] argue that the Internet of Things can be used in healthcare systems globally. It may improve the quality of life of elderly populations while reducing costs on healthcare systems. However, one should point out the threat posed by the unauthorised usage of this technology and the possibility of tracking people without their permission. The “Big Brother” case is not fictitious. Quite recently, the media circulated information about the illegal tapping of private conversations of the President of France with the use of spyware [58]. Therefore, it is already necessary to consider preventive measures.

  Methods of Augmenting CBT With Cognitive Enhancement

CBT augmented with cognitive enhancement can be completed through two avenues that can be conducted on their own or in conjunction with each other. The first method is to utilize specific cognitive strategies to enhance understanding and memory for and implementation of CBT techniques. For example, a therapist may utilize repetition to increase patient recall for important components of treatment. Specific memory support strategies have been used in targeting depression in younger (Dong, Lee, & Harvey, 2017; Harvey et al., 2016) and older adults (Scogin et al., 2013) and late-life GAD (Mohlman et al., 2003). See Table 1 for specific strategies that may be implemented to enhance treatment.

The second method is to apply cognitive enhancement more generally by aiming to improve cognitive skills themselves. Therapists can augment CBT with cognitive skills training as poor cognitive skills have been shown to impact CBT outcomes (Mohlman & Gorman, 2005). This nonspecific strategy has been tested in younger adults with unipolar depression (Siegle, Ghinassi, & Thase, 2007), bipolar disorder (BD; Deckersbach et al., 2010), and social anxiety disorder (SAD; McEvoy & Perini, 2009 ) , and in older adults with GAD (Mohlman, 2008; Mohlman & Gorman, 2005), anxiety co -occurring with Parkinson ’s disease (PD; Mohlman, DeVito, Lauderdale, & Dobkin, 2017), and hoarding disorder (HD; Ayers, Wetherell, Golshan, & Saxena, 2011 ; Ayers et al., 2009).

  IV. AN EXPERIMENTAL STUDY: PADOVA SMART CITY

The framework discussed in this paper has already been successfully applied to a number of different use cases in the context of IoT systems. For instance, the experimental wireless sensor network testbed, with more than 300 nodes, deployed at the University of Padova [31], [32] has been designed according to these guidelines, and successfully used to realize proof-ofconcept demonstrations of smart grid [33] and health care [34] services.

In this section, we describe a practical implementation of an urban IoT, named “Padova Smart City,” that has been realized in the city of Padova; thanks to the collaboration between public and private parties, such as the municipality of Padova, which has sponsored the project, the Department of Information Engineering of the University of Padova, which has provided the theoretical background and the feasibility analysis of the project, and Patavina Technologies s.r.l.,9 a spin-off of the University of Padova specialized in the development of innovative IoT solutions, which has developed the IoT nodes and the control software.

The primary goal of Padova Smart City is to promote the early adoption of open data and ICT solutions in the public administration. The target application consists of a system for collecting environmental data and monitoring the public street lighting by means of wireless nodes, equipped with different kinds of sensors, placed on street light poles and connected to the Internet through a gateway unit. This system shall make it possible to collect interesting environmental parameters, such as CO level, air temperature and humidity, vibrations, noise, and so on, while providing a simple but accurate mechanism to check the correct operation of the public lighting system by measuring the light intensity at each post. Even if this system is a simple application of the IoT concept, it still involves a number of different devices and link layer technologies, thus being representative of most of the critical issues that need to be taken care of when designing an urban IoT.

  4. HYPOTHESES DEVELOPMENTS

   4.1 Brand Trust and Brand Loyalty

Brand trust and brand loyalty have been classified as central aspects of brand management (Chaudhuri & Holbrook, 2001). Brand trust has been operationalized severally in the extant literature. For instance, Delgadoballester et al. [10] view brand trust as a state of feeling secure while interacting with a brand based on the discernment that the brand will remain dependable and responsible to satisfy the customer. Brand trust is also viewed as consumers’ inclination to rely on a brand (Chaudhri & Holbrook, 2001; Mobiman et al., 1993). Additionally, brand trust is perceived as expectations that are based on beliefs that a brand has specific features and traits that are consistent, competent and credible [13,12].

From the view of Garbine and Johnson [11], brand trust is an outcome of previous experiences and interactions and it mirrors the process of learning over time. This position is a corroboration of Keller and Krishnan's [22] position that brand involvement is an essential source of brand trust. Additionally, Morgan and Hunt [18] note that trust is an important factor in building loyalty as it establishes relationships that are considered with high value. In line with the foregoing, Kumar and Advani [25] intimate that high levels of trust in a brand enable customers to reduce perceived risk and facilitate repeat patronage of the brand which leads to loyalty. Consequent to these arguments, it stands to reason that consumers’ trust in a brand has a positive effect on consumer’s loyalty to the brand and therefore we argue that brand trust will positively and significantly predict brand loyalty.

  3. Research design

   3.1 Research aim

Smart city applications address various environmental problems and challenges that cities with growing urbanization are facing today. These include traffic congestion, air pollution and waste disposal (Bhatta, 2010; Broere, 2012; Ferguson and Dickens, 1999). Moreover, software applications are a core component of the multi-layer architecture of the technology stack for smart cities (Komninos et al., 2014), as they act as integrators of heterogeneous ICT hardware, data, user engagement and urban processes (Tsarchopoulos et al., 2017). However, these applications vary significantly both in terms of purpose and in terms of function. Considering digital technologies as an enabling feature of smart cities, the questions we wish to discuss in this paper concern what kind of digital applications cities can currently use to tackle environmental issues, identify the main trends and detect gaps on technical and policy levels. Moreover, we examine whether these applications could attain the “zero vision” objective.

   3.2 Research approach

To address these questions, we analyzed 32 applications addressing environmental sustainability issues that can be found in the Intelligent/Smart Cities Open Source (ICOS) community repository (ICOS, 2017). ICOS is a meta-repository for intelligent/ smart cities applications and solutions, addressed to city authorities and application developers with the aim of facilitating the uptake and implementation of smart city solutions (Komninos et al., 2016). Each application is categorized by the domain it serves (“Innovation Economy”, “Living in the city – Quality of life”, “City Infrastructure and utilities”, “City Governance” and “Generic”) (Table I), the type of software it uses, its technical characteristics and license type. These four domains correspond to the classic structuring elements of cities, the production and consumption subsystems, their network system and the pertinent government system (Castells, 1974). Based on their relevance with the topic of this paper, the applications selected for this research are included in the domains of “City infrastructure and Utilities” and “Living in the city – Quality of life” and they are listed in (Digital applications assessed [authors’ elaboration]).

  2. Method

   2.1. Research design

This study employed a cross-sectional correlational design.

   2.2. Participants

The population of interest was Facebook users aged between 18 and 25 years as emerging adulthood is a key period for self-identity exploration (Arnett, 2000), inviting social comparisons. An a-priori analysis determined that to capture the small to moderate effect sizes reported in previous research (Feinstein et al., 2013; Hanna et al., 2017) at a statistical power of 0.08 and an alpha level of 0.05, a sample size of 167 participants would be required. The final sample recruited was 181 participants aged 18 to 25 years (M age = 21.90 years; SD = 2.14; 51 males, 130 females). Employment status of the sample was 71.2% employed, 43.6% students, 8.9% unemployed, and 1.1% homemakers. The median number of Facebook friends was 520 (SD = 496.51; range = 1–2500), and the median number of minutes spent on Facebook per day was 60 (SD = 71.47; range = 1–300).

  3. Results

We report analyses of the pretest results for both studies at first. Results of the posttest are reported for study 1 and study 2 separately before differences between high-achieving and low-achieving students are given.

   3.1. Prior knowledge of fractions

First, we checked whether high-achieving and low-achieving students, girls and boys, and students from the three different intervention groups differed in terms of prior knowledge of fractions before the intervention. We used the pretest as a measure of this prior knowledge. Table 4 summarizes the results from the pretest for high-achieving students (left) and low-achieving students (right), respectively. Here, the mean number of items solved in the pretest are reported separately for the total sample, female and male students, and different intervention groups.

For our a priori appraisal of high-achieving and low-achieving students to be eligible, we expected the high-achieving students to have more prior knowledge of fractions than the low-achieving students before the intervention. As can be seen in Table 4, the data is in line with our expectation: high-achieving students solved on average 4.84 of the 10 items in the pretest, whereas low-achieving students only got a mean number of 2.28 items correct. Thus, there was a significant and large effect of the a priori achievement grouping—operationalized through different school tracks of the German three-track public school system—on the pretest scores, t(645.45) = 15.66, p < .001, d = 0.95, 95%CI [0.81, 1.10], in favor of the high-achieving students. Therefore, we concluded that our operationalization of high-achieving and lowachieving students is appropriatesystem—on the pretest scores, t(645.45) = 15.66, p < .001, d = 0.95, 95%CI [0.81, 1.10], in favor of the high-achieving students. Therefore, we concluded that our operationalization of high-achieving and lowachieving students is appropriate.

3. Study design

In this section, we present the protocol of the systematic mapping study based on the guidelines described by Petersen et al. (2008).

   3.1 Objectives and Research Questions

The goal of this study, expressed in the Goal-Question-Metrics (GQM) format (Basili et al., 1994), is to analyze the development of scientific software applications for the purpose of characterization and evaluation with respect to the software engineering practices employed and the quality attributes of interest from the point of view of researchers and practitioners. Based on this goal, we define the following research questions. To address the cross-cutting G3 (assess the level of empirical evidence that supports the use of SE practices in scientific software development), we have added sub-research questions in RQ2.

RQ1: Which SE practices used in the development of scientific software have researchers studied the most?

  2.5. Quad-qualification: composite of (a) independence, (b) directorship experience and financial expertise, (c) bandwidth, and (d) stock ownership

As described earlier, monitoring effectiveness is contingent on the combined presence of three aspects of ability plus motivation. These four requisite qualifications collectively enhance the likelihood that a given audit committee director will be a superior monitor. Such a person is able to monitor vigilantly on behalf of shareholders and also be willing to do so.

These qualifications equip an audit committee director to withstand the prevalent social influence that suppresses effective monitoring. Prior research has demonstrated that over time CEOs attempt to persuade, ingratiate, threaten, and socialize independent directors to enlist into corrupt cultures (Khanna et al., 2015; Westphal and Khanna, 2003; Zyglidopoulos and Fleming, 2008). Such influence is essential to sustaining corrupt practices (Ashforth and Anand, 2003). By virtue of the ability to (a) be dispassionate, (b) be cognizant of such influence and stand up to executives in financial matters, (c) devote the requisite time and energy to keep a close watch, together with the drive to govern vigilantly, the quad-qualified audit committee director can effectively nullify such corrupting influence. If this director identifies a problem, he or she is likely to raise the alarm when others would not be able or willing to do so (Hambrick et al., 2015). Correspondingly, the quad-qualified audit committee director is effective in deterring financial corruption. Thus, we posited the following hypothesis:

Hypothesis 1. The presence of a quad-qualified audit committee director will reduce the likelihood of financial corruption.

  4. Results and discussion

The LPI values of 25 SPT-N profles and other information are provided in Table 4. The liquefaction hazard map for Moulvibazar town has been prepared based on the LPI values and cumulative frequency distribution of the LPI values for each unit Fig. 6. The contour lines of equal LPI values have been drawn at an interval of 5 LPI value to predict the LPI values for the areas where the SPT-N profles are not available. The liquefaction hazard map also represents the likelihood of surface manifestation of liquefaction of each surface geological unit as percentage, where the LPI value is equal to or greater than 5. Thus, the study area is divided into two liquefaction hazard zones based on the surface geological units: Zone 1 (Holo–Pleistocene low elevated terrace deposits) and Zone 2 (Holocene food plain deposits). Zone 1 covers southeastern corner of the mapped area that contains fve LPI values, and Zone 2 covers rest of the mapped area that contains twenty LPI values.

Cumulative frequency distribution of LPI is a quantitative approach to estimate how much area of a surface geological unit will show surface manifestation of liquefaction. Large and small percentage of area predicted to liquefy by cumulative frequency distribution of LPI is consistent with historical earthquake [12]. However, in Zone 1, 42% of the total area will show surface manifestation of liquefaction, whereas in Zone 2, 78% of total area will show surface manifestation of liquefaction

  Smart urban analytics and policy: editorial introduction

In the ‘century of the city’, with an increasing share of the world population living in urban agglomerations, cities have not only increased in number and size, but have also turned into complex and multi-faceted organisms. A modern city is no longer a simple settlement system with buildings, infrastructure and people, but displays nonlinear evolutionary structures and trajectories as a result of an underlying complex force field comprising a multiplicity of (internal and external) actors and of (tangible and immaterial) constituents that altogether shape contemporaneous city life. Urbanity has become a “modus vivendi” of the 21st century, in which urban agglomerations are the geographic projections of an emerging new society characterized by connectivity, mobility and flexibility.

The complexity of ever growing cities in this world prompts serious policy concerns regarding environmental quality, energy use, transport accessibility, social cohesion, labour and housing markets, public amenities, safety, effective governance, local well-being, and so forth. To cope with all these challenges – and many more – in the harsh reality of urban policy and management, city authorities have over the years often resorted to sectoral responses, without sufficient regard of the interwovenness of a complex urban system and without carefully basing necessary urban decisions and adjustments on a solid and verified information base that maps out the multidimensional complexity of the urban area concerned. Consequently, urban policy tends to become fragmented and not supported by quantitative accountability and solid test principles.

  1. Events in destination branding

Destinations often bid for and host large international events with the intention to help shape the image and profile of the city (Xing & Chalip, 2006). Destination image is, ‘‘the sum of beliefs, ideas, and impressions that a person has of a destination’’ (Crompton, 1979, p. 18). How visitors perceive the destination contributes to the location’s ability to attract tourists and maintain competitiveness. Hosting large international sport events such as the Commonwealth Games provides increased media exposure and attention to the host city and country (Green, 2002). Destinations often utilize events in their destination marketing efforts to increase both direct, which includes visitor expenditure during the actual event, and indirect tourism that focuses on the effects the media coverage of the event has on brand awareness and recognition of the destination as an attractive location to tourists (Chalip, Green, & Hill, 2003; Getz & Fairley, 2004).

Likewise, the event also seeks to create a strong brand image. Keller (1993) defined brand image as consumer-held perceptions as determined by brand associations. These associations are linked in memory and create meaning of the brand for the consumer (Keller, 1993). Events provide these linkages between the brand image of the host community and the events it hosts (Chalip, 2004). Due to these linkages, potentially reciprocal effects and transfer of image of event to destination and destination to event through strategic marketing initiatives could possibly be expected (Brown, Chalip, Jago, & Mules, 2004). Xing and Chalip (2006) suggested that events seem to have a bigger impact on the destination’s image than vice versa, with size and status of the event as key determinants of sport event image (Gwinner, 1997). Thus, it is useful for students to consider the role of events in destination branding.

  3. Results and discussion

    3.1. Profile of DM volatile compounds

The profile of volatile compounds was determined on DM samples A (fresh milk), A2, A3 (milk stored at 20 °C for 48 and 72 h, respectively), B (freeze-dried milk) and C (spray-dried milk) in order to determine for the first time, the volatile fingerprint of fresh donkey milk and of DM subjected to the accelerated shelf-life test (samples A2 and A3) and to the freeze-drying and spray-drying processes (samples B and C, respectively).

It is well known that the flavor of fresh milk is due to the presence of sulphur and carbonyl compounds and other molecules such as alcohols and free fatty acids. A large contribution to milk flavor is given by volatile compounds which derive largely from milk fats. However, the composition of these volatile molecules that characterizes the flavor of fresh milk, changes after heat treatments or light exposures (Skibsted, 2000). For this reason, some particular volatile compounds have been used to identify milk freshness and to predict its shelf-life (Valero, Villamiel, Miralles, Sanz, & Martınez-Castro, 2001).

Regarding the composition of the aroma of DM samples, the averaged results of HS-SPME-GC-MS analysis are detailed in Table 1 that outlines also the aroma molecular fingerprint of each analyzed samples and the odor type of each molecule (Dunkel et al., 2009).

  I. INTRODUCTION

Deep learning (DL) [1], [2] has recently become dominant in a wide variety of biometrics problems and many other artificial intelligence (AI) areas. One of the greatest successes of DL has been in face recognition (FR) where the accuracies have been improved greatly over the traditional methods [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]. We raise a question: Can deep learning really solve the face recognition problem? Or, can we say that, given the great success of DL, the face recognition problem has been solved or almost solved?

To answer this question, and get a better understanding of the performance of DL methods in FR, we perform an empirical study with designed experiments accordingly.

In face recognition, it is well-known that the hard problem is unconstrained face matching, in which we believe that the face image quality variations are probably the biggest issue that makes the problem hard. Based on this view, our conjecture is that the face image quality issue may still be a grand challenge even for the recently developed DL methods.

In FR with traditional features, it is well-known that the face image quality has a big influence on recognition accuracy; In DL features, however, a large dataset with face images of different quality for each subject, is used to train the deep models. Will the quality still be an issue?

In our empirical study, we design the face recognition experiments with matching across different face image qualities, which is seldom done in an explicit way in previous face recognition approaches. In practice, however, one can meet the cross-quality face matching problem frequently. For example, in the FBI’s interstate photo system (IPS), millions This was partly supported by a NSF-CITeR grant and a WV HEPC grant. of mugshot photos could be matched to face images collected from social media web sites where the wild photos may have a wide variety of qualities.

   3. Methods

     3.1. Research setting

 This study has a global setting, so it is not attached to any particular region. It considers events, incidences, and decisions associated with the Covid-19 in the global SE sector. This study is explorative with a qualitative approach. Our data collection focuses on capturing the state of the SE amid the pandemic based on secondary data sources, which are widely used for high-quality research (Johnston, 2017). Indeed, secondary data is perhaps more appropriate for this study, because first-hand data could not provide comprehensive knowledge about the Covid-19’s effect on the SE. Secondary data has been widely used in a range of disciplines, including strategic orientation (Shortell and Zajac, 1990), microfinancing (Zhao and Wry, 2016), tourism injury problems (Bentley and Page, 2008), sustainable tourism (Pereira and Mykletun, 2012), and management reaction to hotel performance (Xie et al., 2014), and it has several benefits for academic studies (Houston, 2004). It shows the real decisions being made by real decision-makers, having been collected in a less obstructive manner and not influenced by the biases of self-reporting. The biases related to the key informant sampling approach can therefore be avoided. Recent studies based on online comments to a newspaper article about the SE present a vivid exemplification of the importance of such data for studies (Cheng and Foley, 2018; Cheng et al., 2019).

   5. Discussion 

The liquidity of an asset or a financial market is a concept that encompasses different facets or aspects and is difficult to measure with a single measure. The literature emphasizes that there are five different aspects that characterize liquidity, namely, breadth, depth, immediacy, resilience, and tightness. Some authors point out the pitfalls of using one single measure or proxy when it comes to measuring liquidity (Bernstein, 1987). There are multiple measures proposed from high-frequency data, called liquidity measures, and others that only require low-frequency data, called liquidity proxies. There is no consensus on which measure is best suited to measure the liquidity of an asset or market, or even to decide which is best when it comes to showing one of the facets of liquidity. In fact, new liquidity measures are appearing in recent papers.

In any case, liquidity is a crucial issue for researchers, practitioners and policymakers. For researchers, liquidity measures are a topic of study or simply a necessary tool for financial asset valuation proposals. The choice between different liquidity measures is often made arbitrarily. It is difficult to propose a measure among the existing ones as the most appropriate for any proposal globally. Certainly, the selection of the liquidity measures depends on the research question. Existing papers obtain different results depending on the asset analyzed, the database available and even the frequency of observations. For example, recent empirical evidence shows that there are many liquidity proxies that are able to measure liquidity as accurately as liquidity measures in the US corporate bond market (Schestag et al., 2016) and in the US stock market (Fong et al., 2017). Therefore, the lack of high-frequency data is not a drawback in order to assess the degree of liquidity. These results are of interest to researchers, in the sense that they can avoid intensive data processing and cleansing procedures that many large databases require for being processing

   3. Model verification 

      3.1. Refrigerant supply loop

The refrigerant loop consists of a compressor, an oil separator, a shell and tube type condenser, a receiver, a flow meter, capillary tubes, an evaporator, and an accumulator, as shown in Fig. 5. Discharged oil and refrigerant from the compressor passes through the oil separator, and only high pressure and temperature refrigerant flows into the shell and tube condenser. The cooling water loops consist of a constant water bath, a pump, a by-pass loop, and a motor driven micro-valve.

These are used to control the sub-cooling of the refrigerant at the inlet of the distributor. A mass flow meter measures the mass flow rate of the refrigerant liquid. A receiver and sight glass are used to reserve and check the liquid refrigerant. The supplementary temperature control loop controls the refrigerant temperature at the distributor inlet. The temperature and pressure are measured at the distributor inlet to determine the amount of sub-cooling and the properties of the refrigerant. The temperature is measured using a Pt 100 RTD inserted through the copper tube wall. The pressure is detected by a digital pressure gauge with a range of 0–5 MPa. The thermodynamic quality at the inlet of test evaporator is calculated, including the amount of sub-cooling and assuming an adiabatic process in the capillary tubes. After the test section, the refrigerant is returned to the compressor through a supplementary heater and an accumulator. The refrigerant temperature and pressure are measured at the outlet of the evaporator in the same manner as at the inlet of distributor, except that the digital pressure gauge has a range of 0–1.5 MPa.

   4.4. Evaluation of a Microcapsule Based Self-Resilience System 

The behavior and performance of the microcapsule based self-resilience system have to be evaluated through effective and meaningful ways (either experimentally or numerically). Up to now, various techniques have been applied to appraise the obtained system with success to some extent. In addition to the traditional macro test methods (for determination of mechanical and transport properties of concrete) [26,46–48], the more advanced technology, such as the X-ray computed tomography (XCT) and the image analysis technique, has been adopted in the evaluation process [49].

The whole healing process and healing effect of a microcapsule based self-healing system in concrete was monitored and qualitatively as well as quantitatively evaluated by means of XCT and an image analysis technique. The experimental procedures are illustrated in Figure 21A. A microscopic compression testing instrument was integrated into the XCT system to obtain a real-time compressive loading. Sample 1 and Sample 2 were put in the built-in loading device and pre-loaded with 1100 N and 900 N, respectively. As shown in Figure 22A, microcapsules are broken by the formation of cracks (working mechanism of a physical trigger) and healing materials are released from microcapsules. Then, the samples with initial cracks were cured in different curing conditions, in which Sample 1 was submerged and Sample 2 was cured in a standard curing condition (95% RH, ±20). XCT was applied to monitor the variation of inner microstructures at different healing times (see Figure 21B). Based on the raw data measured by XCT, the reconstruction of the inner microstructure was carried out by means of an image analysis technique.

  3.1. Energy subsystem 

Grain is not only an important component of energy subsystem but also the most fundamental expression of man-earth relationship. It includes two parts, grain production and grain utilization. In energy subsystem, we take grain per-unit-area yield as core variable and grain total yield is decided by both grain per-unit-area yield and sowing area of grain crop. Total population and grain consumption level per capita decide the total grain demand; total yield and demand of final grain together decide the supply and demand ratio of grain. Fig. 2 is a causal graph that only qualitatively expresses relationships among variables and reflects influences of positive and negative feedback. Only after establishing causal graphs of every subsystem can numerical relationship among variables be effectively and quantitatively analyzed.

  3.2. Economy subsystem

In economy subsystem, total industrial output value and effective irrigation area of farmland are taken as core variables. Judging from industrial level, the higher the total industrial output value, the better the economic development and the more the industrial water consumption. Similarly, effective irrigation area of farmland decides the agricultural water consumption. Changes of industrial water consumption and agricultural water consumption will make the total water demand change and finally affect the supply and demand ratio of water resource. Causal relationship is showed in Fig. 3.

 3.3. Technology subsystem

In agricultural production and utilization, technological progress is mainly reflected in the improvement of efficiency of labor force to land use. Therefore, to land green supply chain, population will directly influence the degree of technological content of land. This shows that we can take population as the substitute index for technological factor and combine it with other subsystems. Here we take total population as a core variable and divide it into urban population and rural population. Suppose that urban and rural household domestic water consumption is a fixed value, number of both urban and rural population will decide the total domestic water consumption together and further influence the water demand and water supply-demand ratio in the whole system.

   1. Introduction 

Over the past decades, considerable effort has been devoted to enhancing our understanding of the complexity of corporate financing decisions. To date, studies on corporate capital structure and debt maturity choices have mainly focused on firm characteristics and industry determinants (De Jong, Kabir, & Nguyen, 2008; Titman & Wessels, 1988), as well as on the influence of the national culture, legislation and other country characteristics (Demirgüç-Kunt & Maksimovic, 1999; Fan, Titman, & Twite, 2012). Studies investigating the relationship between internationalization and corporate financing policy, however, are much more limited and are mostly confined to large, stock exchange quoted firms. One of the main insights of this literature is that multinational corporations (MNCs) have lower longterm debt ratios and higher short-term debt ratios than those of comparable domestic corporations (DCs) (Burgman, 1996; Doukas & Pantzalis, 2003; Fatemi, 1988). This leverage differential between MNCs and DCs is explained by the fact that the positive effect of geographic sales diversification on long-term debt financing is offset by the increased risk stemming from exchange rate exposure and unforeseen political events. Furthermore, due to their operational complexity, MNCs are more informationally opaque, which increases the agency costs of debt.

To mitigate the problems associated with a riskier borrower profile and agency conflicts, loan maturities are shortened (Barclay & Smith, 1995; Myers, 1977). Building on these studies, the aim of this article is to advance the current literature by empirically investigating the impact of exporting on the corporate financing decisions of another important class of exporters, viz., small and mediumsized enterprises (SMEs). Since SMEs cannot substitute short-term and long-term debt financing as easily as large companies - due to difficulties in obtaining long-term debt financing from financial institutions (Ortiz-Molina & Penas, 2008) - the mechanism through which export activities affect SME financing policies may very well be different from what is demonstrated in the MNC literature. According to the World Trade Organization (WTO), access to financial resources to support export activities is a key concern for SMEs since, besides the one-time upfront sunk costs (e.g., costs related to compliance with foreign market regulations and preparatory market research), exporting requires substantial ongoing investment in working capital, as export activities considerably lengthen the cash conversion cycle of the firm (e.g., through longer shipment periods and the administrative burden associated with trading internationally) (WTO, 2016). Hence, understanding how exporting SMEs cope with these financing needs may yield useful insights for exporters, banks and policy makers.

  Data collection 

The data were collected between March 2017 and November 2017. The data were collected by the research through face-to-face interview with the married women, registered at Family Health Centers, during home visits. It took approximately 15-20 minutes to apply the data collection form. Questions that women do not understand are explained without adding any comment.

  The ethical principles of the study 

In order to conduct the study, approval was obtained from the Ethics Committee of the Inönü University Faculty of Health Sciences (2017 / 19-7) and legal permissions were obtained from the related institutions. The women included in the study were informed about the purpose of the study and their questions were answered. The women were informed about that the information they provide would be confidential, not be used elsewhere and that they have the right to withdraw from the study anytime they want. 

  Assessment of the Study data 

The data obtained from the study were evaluated by using SPSS 18 statistical package program. Percentage distribution, arithmetic mean, t-test in independent groups and Correlation were used to assess the data.

   Results

It was found that 39.3% of the women who participated in the study were in the age range of 29-39 years, 26.7% were married for 18-23 years, 40.1% had 3 or more children, 56.7% ACCEPTED MANUSCRIPT were married with arranged marriage, 57.9% had middle income level and husbands of 38.9% of them were in the age range of 29-39 years (Table 1).

  Abstract

The concept of sustainable development is gaining popularity and hydroelectric projects designed and operated on this concept require sediment management as prime design criteria. Drawdown flushing is being practised in such projects for sediment management. Investigations using hydraulic models are required for the projects to address the site-specific design concerns. In the present study, simulations conducted using hydraulic model for sediment removal by drawdown flushing of the reservoir of Punatsangchhu hydroelectric project, Bhutan, is presented. The experiments on 1:100 geometrically similar scale model indicated that flushing is effective in maintaining the power intake area clear of sediment deposition. Deposition from the upstream reaches could not be flushed hydraulically. Furthermore, based on wide range of experimental data from hydraulic model studies, empirical equations have been developed for predicting the quantity of sediment that can be flushed from the reservoirs. The present equations have been developed including more parameters than those used in equations already available in literature. Two equations have been developed for different riverbed slope ranges with steep slope (0.005–0.04) and moderate slope (0.001–0.005). The equations developed were validated against different sets of data and it indicated that the predictions could be made within reasonable accuracy. These equations can be effectively used for hydraulic design of sediment removal from run-of-the-river hydropower reservoirs.

2 | Literature Review and development of hypothesis

2.1 | Audit pressures

A significant body of evidence suggests that audit pressures can lead to dysfunctional behavior. According to DeZoort and Lord (1997), pressure serves as an antecedent to individual stress responses and influences outcomes by providing situational incentives for a specific judgment or decision. Thus, pressure is a major dimension of occupational stress (Larson, 2004). This study investigated the effects of two types of pressure: time budget pressure and client pressure.

2.1.1 | Time budget pressure

Time budget pressure is “a chronic, pervasive type of pressure that arises from limitations on the resources allocable to perform a task” (DeZoort & Lord, 1997, p. 53). It occurs when auditors are assigned limited hours to complete audit procedures. As meeting time budgets is a critical element of auditors' performance evaluations, time budget pressure is a major factor affecting auditors' behavior in both developed and developing countries (Shapeero, Koh, & Killough, 2003). Participants in an Australian auditor experiment in Coram et al. (2003) admitted accepting questionable audit evidence to speed audit testing under time budget pressure. In a subsequent study, Coram et al. (2004) indicated that, in addition to accepting questionable evidence, auditors under high time budget pressure failed to test all items in a selected sample when the level of misstatement risk was low.

Introduction

Globally, small and medium-sized enterprises (SMEs) are recognized for their substantive role in promoting economic development and growth for sustainable development. SMEs contribute to the economic development through jobs creation, increasing income levels and equalizing income distribution. According to The World Bank (2015), SMEs contribute approximately 45 percent of total employment and up to 33 percent of national gross domestic product in emerging economies. A recent study conducted in Malaysia revealed that SMEs dominate Malaysian economy in a similar manner. In 2015, SMECorpration reported that SMEs contributed 65.5 percent of total employment and 17.6 percent of total exports of Malaysia. Despite its significant importance in Malaysia economy, SMEs face multiple challenges. Ahmad and Seet (2009) mentioned that the estimated failure rate of Malaysian SMEs is as high as 60 percent. This indicates that SMEs in Malaysia are in the critical stage and face serious obstacles to stay competitive. To mitigate these challenges, several suggestions are put forth including the incorporation of ethics and social responsibility to increase competitiveness and sustainability.

This research paper is organized into six parts. After this introduction section, relevance literature reviews are presented, followed by research model and hypotheses development. The fourth section discusses the research methodology operationalized to carry out the study. The fifth section shows the research findings before the ending conclusion and implication.

Elements in the environment

What exists within the simulation environment? This is a fundamental choice that should be driven by theory. Because organizations are goal-driven entities, organizational learning models usually include some representation of agents, knowledge, and tasks. Although an organization, as a whole, may be said to have some particular piece of information, that information may not be where it can be usefully applied (as defined by tasks). Thus, one common method of evaluating simulated organizational performance is through examining the agreement between three networks (although they may not be represented, explicitly, as networks by the modelers). These three networks are the knowledge network (who knows what), the assignment network (who should do what), and the requirements network (what knowledge is needed for what), as shown in Figure 3.

Not all organizational models include explicit representations of these three objects, but their presence is often implied in the theoretical descriptions of the model.

Agents

Usually, agents are representations of humans, but some simulations allow the set of entities to include nonhuman objects, such as IT systems, and may also include entities at different levels of granularity, an agent representing a single individual may interact with an agent representing a team of individuals. Entities that can affect their own state or the state of others have agency. We refer to these entities as agents. Entities without agency will usually be referred to as objects. Agents may act on objects, but objects do not act directly on agents, although their presence may alter the actions chosen by an agent.

Figure 3 Three common objects within an organizational model are agents, knowledge, and tasks.

Agents have bounded rationality, limited access to the state of the larger world in which they inhabit. There are various limitations that can be prescribed to these agents, usually based on the larger theory of interest and drawn from human limitations. Agents may be only able to access state for things they can directly observe. They may forget knowledge they acquired earlier. They may have a limited number of refinement cycles before a decision must be made. Their perception of state may be error prone. Agents should have one or more of these limitations to be useful in a multiagent simulation.