extfieldsofstudy: Physics Sociology

text:
The Impact of Cultural Context on Innovative Iraqi Architecture: Houses Façades as a Case Study

This research aimed to discuss the notions of innovation and cultural context, common terms in architecture, with regard to their various applications in attempts to achieve coherent results related to context. As a case study, the research analysed the practice of private house design at façade level in Baghdad city in the 1950s and 1960s, which act as a benchmark for modern Iraqi architecture. The results suggest that changes in cultural context enhance architectural practice by inspiring designers, leading to the introduction of innovative models that in turn elevate practice and improve cultural contextual values with regard to architecture.

Preface
Many researchers have interpreted innovation as a global phenomenon; however, each culture approaches it in a different way according to its values, resources, technologies and the nature of individual and collective roles within that culture. Innovation is also interpreted in many studies as the product of cultural contexts that encourage or discourage individuals' innovative actions, and as innovation is a complex concept that includes both learning and developing new ideas, it is certainly the case that culture is integral to innovation.

Innovation
Innovation is defined as producing something new, perhaps product or tool, in a manner different from that previously known, which may involve the use of process, strategy, or technology [1]. O'Brien and Shenna define innovation as the state of change that arises in the move from an established balanced system towards a newer, more uncertain state that could never be achieved taking only known steps [2]. Others have opined that for innovation to happen, something that has never been done before must occur, or a thing must be done in a totally new way. The concept of "new" is hard to define in most cases, however, and a chain of incremental innovation may lead eventually to a radical change [3]. Innovation therefore works on two levels: the incremental level that deals with the act of developing and improving things, and the radical level, which deals with the act of change and initiation. Garcia [4] defined four levels of innovation, based on the nature of change such innovation achieves within its context: 1) the technological level, in which new technologies replace old ones and thus open IOP Publishing doi: 10.1088/1757-899X/1067/1/012079 2 up new potential in the construction market; 2) the organisational level, which refers to the changes in strategies and frameworks caused by the accrual of radical innovations; 3) the market level, which refers to the development of new markets that use new technologies; and 4) the cultural level, in which radical innovations demand that users who adopt them must abandon their existing knowledge base. This final aspect can cause users to resist these changes due to the risk of damaging their certainty, increasing conservatism. Innovation may thus be defined as the introduction of a novel product, act, or problem solution by innovators that impacts users' everyday lives. Such actions thus affect the cultural context and thus are converted into commonplace knowledge. The question that arises is thus how designers, as individuals in particular societies, can innovate according to the changes in their cultural context, and how this impacts on their practice. This research thus aimed to investigate the relationship between designers as individuals within society and their cultural contexts by extracting indicators and applying these to Iraqi architectural practice as seen in the designs of the façades of houses in the 1950s and 1960s.

Culture
Culture is defined by all components of a society, including knowledge, beliefs, art, ethics, habits and human preferences [5]. Herbig and Dunphy defined culture as a coherent system of communications that includes both biological and artistic human behaviours and their verbal and non-verbal expressions; it can also be defined as life patterns, including the expected behaviours, values, language, and practices shared by individuals [6]. Others define culture as a system shared by members of entities such as societies, nations, or governments. It thus indicates a set of behavioural criteria that are learned and transmitted socially, being expressed, however, by individuals with their personal values, preferences for activities, and intellectual processes. Culture thus has three attributes that support its diffusion within societies: 1) shareability; 2) transferability; and 3) a contextual nature [7].

Cultural Context
Some researchers believe that in order for people to accept an innovative idea, any mental gaps they have should be filled by it being presented in an explicit way, which supports the assertion that an implicit level of understanding among the audience is needed for them to accept such ideas [8]. Others see innovation more as dealing with context, with reference to pieces of scripts created between the product of an innovative idea and its receptors; the idea's success is related to the degree of acceptance to the changes engendered by the idea; it may thus not be welcomed if it portends drastic changes and increases risk levels [9]. Jean Nouvel categorised context in architectural practice into four levels: 1) Historic context, with reference to the place and time of building; 2) Anthropological context, referring to the need to work within society and to consider interactions with the others; 3) Geographical and economic context, which deals with the work environment and logistics available; and 4) Cultural context. This last segment was important to Nouvel's theory, as he considered architecture to be not an autonomous profession but rather to be directly to the nature of the culture surrounding it [10]. In his book "The myth of innovation", Burken defined several contextual factors throughout history that have impeded innovative practices: 1) Culture, whereby even innovative products must fit with a given society's cultural values; 2) Dominant paradigm, which may only be changed by the imposition of a later successful paradigm; 3) Inheritance and tradition, due to the fact that inherited ideas are heavily protected by those who respect traditions and heritages, increasing the impact of tradition as a cultural factor; and 4) The subjective nature of goodness based on differences between individuals in terms of tastes and preferences, which plays role in determining wat they really need in certain contexts [11]. This highlights the dynamic and high vitality nature of cultural context as an intellectual environment that impacts individuals within societies. The democratic open context tends to strengthen individual practices and approaches, allowing the achievement of radical innovations. The development of culture values within context may also lead to innovative practices and solutions emerging, and vice-versa, however. Cultural context must also take into account individuals' roles and the implications of these within societies, with innovators studying how to communicate with the others, and how to learn and apply new meanings in a given context. Previous research thus suggests that there are four aspects to cultural context: individuals' roles, societal stance, communication, and social learning.

4-1 Individuals' roles
Some researchers believe that the effect an individual may have related to the variation level between society members and their point of views, and how each individual determines what is proper in a given cultural context [7]. Other researchers, however, have assumed that the nature of context plays a role in shaping these individual roles: western and similar open cultures emphasis on individuals' roles seem to cause more novel ideas to arise, while other cultural contexts emphasise personal preferences and attributes conforming to contextual values, enhancing the role of traditional and known approaches. Individuals require at least some personal cultural attributes to practice innovation, including a readiness to face doubts, an ability to balance risks, insistence, good timing, the ability to accept change, and a long-term dynamic orientation [6]. Csikszentmihalyi notes that an individual can reach a level of cultural influence when they have a critical number of followers who adopts their ideology and practice. He describes such individuals as initiative persons, and suggests that their actions play major role in shaping the context of their cultures [13].The nature of the individual's complexity with regard to personality and preferences plays a role in forming integrated relationships with the cultural context. Both play a role in enhancing each other, with context and the nature of its values and practices affecting the individual, and the latter's aspiration to achieve change influencing the relevant context.

4-2 Societal Stance
Certain cultures may encourage or suppress the spread of new ideas among their members. Some ideas look ambitious yet achievable in some cultures while being denounced as risky and inappropriate in more conservative ones. It is the readiness of the society members to accept novel ideas that defines whether a culture is open or conservative [7]. Arieti, among others, thus developed term "Creativogenic", referencing societies that encourage creativity by offering the affordance of cultural means, usually an elite group with access to the required logistics; being open to cultural changes; offering freedom in the expression of new ideas; facilitating interaction with different and contradictory cultural stimulators; offering tolerance of new viewpoints, encouraging the interaction of influencing members in society; and enhancing the concept of awards and recognition [14]. However, most societies restrict their practices to the dominant and common paradigms, making them miss the opportunities to develop radical change as these do not meet "safe" expectations. It is thus the role of complex integration between effective members of societies to achieve change by introducing radical or novel innovations to the challenges of everyday problems [15]. Scarcities also play a role in enhancing the individual role in terms of the adoption and acceptance of new and different ideas, and the way these ideas are transferred into a wider context by societies' prominent or more effective members as they seek to improve their cultural context in new directions.

4-3 Effective communication and learning
Cropley highlighted both the importance of the communicative ability of the individual as an innovator and the need for innovative products to enhance the act of change and to diffuse in certain contexts; IOP Publishing doi:10.1088/1757-899X/1067/1/012079 4 successful communication is, however, related to validation and verification by both clients and users even after a successful product is introduced [16]. Csikszentmihalyi noted the interchanging relationships between three integral factors of innovation in communication, which he defined as person, field, and domain: the person (user, critic, expert) acts as the gatekeeper in a domain, which is itself a system of rules and signals [17]. For Peruta and others, it is important to reach a level of understanding that transforms into implicit knowledge in order to develop the translation from old to new in a certain context; such meaning is broader than literal explanation, and may refer to invention or to divergent new agents constructing a bond that has never before been create [18]. Communicating innovation requires, according to Burken, the act of making things easy to learn and adopt as users interact with them. Great ideas must be simplified a when presented to others [11]. This requires the development if mechanisms for exchanging thoughts and practices in conversations, meetings, etc. that recognise the need for a learning process within the context, identifying the experience and tools necessary to strength both formal and explicit knowledge [18]. The importance of learning lies in the means to pass knowledge from individuals to groups to society to later generations; this includes social values and traditions and certain attributes that come with these. In more collaborative societies, cultural contexts thus focus on bonding, cooperation and public interest [19]. However, such effective communication plays a role in developing new interpretations that are impeded within apparent knowledge and offer opportunities to shape new innovative values in a cultural context. The importance of learning in terms of enhancing cultural context and accruing innovations derives from the experience and knowledge gained from the interaction of individuals within that context.

Architectural Practice and Cultural Context
In relation to architecture, the designer's role in innovation is related to initiating change within a given context by introducing new and improved innovative ideas and methods into various projects. The existing research shows, however, that the role of culture and its components as a resource in extracting new and unique meanings from classical, historical or traditional meanings remains vital. Individual aspects such as competitiveness also play a role in stimulating designers to develop new and different methods and paths to facilitate innovation, especially in the more open cultural contexts discussed previously. Many changes accrue as designers work towards initiating an innovation culture, a culture that adopts change and improvement as the main discourse in the thought and practice of architecture, and diffuses this by communication and shared learning between practitioners and receptors, improving and enriching the meanings and values of the cultural context and its impact on architectural practice. Culture may thus be enhanced by the role of architectural pioneers and mentors whose works become a source of influence for architectural practitioners, and by societies being open to adopting and applying new innovative designs.

Method
The research utilised a descriptive analysis approach to examine the role of pioneering Iraqi designers in terms of innovating new styles at façade level in house design.

Case Study
This research studies the practice of house design in Baghdad in the 1950s and 1960s. This practice at the time was considered a benchmark for the design of private houses in modern Iraqi architecture, as these houses presented a drastic change from the house designs based on the traditional approach used in the previous decades of the twentieth century. The choice of houses for the case study was influenced by the following inclusion factors: 1) having been designed by a consultant architect or office; 2) being in a good neighbourhood, 3) being within Baghdad city; and 4) belonging to a middle or high status client.

7-1 Characteristics of the cultural context:
The Iraqi cultural context witnessed drastic and tangible changes at the time in question that significantly impacted cultural openness to innovation. The characteristics of these changes were • The need to find a new national identity outside of the colonial Britain mandate and its influences on policy; • The flourishing economy, which was reflected at the residential level in an increase in both middle class society and the plot areas selected for private houses; • An enhanced individualism, which saw the rise of new modern and individual styles and techniques in fine arts, as represented by the products of elite artists such as Jawad Salim and Faiq Hasan. • The influence of western modernism created by the return of many Iraqi graduate architects from the British and American schools of architecture. • Factors such as competitiveness and leadership increasing significantly within the cultural context, leading to the expansion of consulting offices, and the establishment of the first school of architecture.

7-2 Characteristics of Innovation in Private Houses (sample analysis):
The major characteristics of the houses at time were their large plot areas, their direct relationships with the streets, and the well-educated nature of their owners. The main approach adopted in designing for these large house plots was to surround the houses with gardens, and this approach offered designers the  The individual designers in Iraqi practice in the 1950s and 1960s reflected the ongoing innovation in culture in their practice by initiating novel models and applying individual flexibility in combining traditional and modern aspects; this confirms the importance of individuality in shaping practice at the time, and the use of radical or experimental approaches to achieve novel results.       Realising context: identifying the importance of incorporating local meanings to create a modern identity for Iraqi architecture Improving techniques: employing local materials in new ways and patterns.

Conclusions
• Changes in cultural context lead to enhancements in architectural practice based on encouraging individual as designers to introduce innovative models that in turn elevate the general practice level and improve cultural contextual values with regard to architecture. • Aspects related to societal cultural values, such as the openness to try new ideas and high intellectual and economic levels, play a major role in the adoption of innovative practice by aspirational architects. To successfully innovate in the context of architectural practice is to deal and communicate with a society that also seeks to improve the status of its cultural context. • Achieving innovation at the individual level is about extracting new meaning and techniques from the cultural context and dominant paradigms and values to generate legitimate and known sources for designers to use to facilitate communication with the others. This also requires the exploitation of non-architectural resources such as economics, logistics, and technology.


s2fieldsofstudy:
Business
Computer Science

text:
RFID E-plate Data based Travel Time Reliability Estimation of Urban Expressway

Travel time distribution shows the aggregation degree of vehicle travel time in road network, and travel time reliability is the key index to measure the aggregation degree of travel time. In order to quantitatively evaluate the fluctuation of travel time of expressway vehicles, it is necessary to establish an estimation method of travel time reliability of expressway. Travel time reliability estimation needs to obtain travel time data of vehicles. Traditional Global Position System (GPS) and video detection methods are easily affected by external factors such as weather and environment, and the description of travel time distribution is not comprehensive under the condition of low permeability. On the other hand, in the evaluation of travel time reliability, although there are lots of travel time probability distribution models, the correlation of vehicle travel time between links is ignored when calculating travel time reliability. In order to estimate travel time reliability more accurately, based on Radio Frequency Identification (RFID) electronic license plate (E-plate) data, this paper proposes a path travel time reliability evaluation model considering travel time correlation between links. First, the RFID readers installed on the gantry of expressway acquires data from the electronic tags deployed on vehicles. Secondly, a percentile travel time estimation method is proposed based on Cornish-Fisher expansion and the skewness of travel time distribution. Finally, considering the reliability estimation of travel time under the correlation of links, the estimated value is closer to the true value. The experimental results show that the travel time reliability estimation method considering the correlation between links improves the accuracy of estimation in the expressway network environment.

. However, most of the research methods on travel time reliability need to fit the travel time distribution first, then calculate the percentile function of travel time through the parameters of the fitting function, so as to finish the calculation of travel time reliability [5][6][7][8]. However, these methods based on travel time fitting have three problems that are hard to solve. One is that all the above methods rely on the full sample of single vehicle information. Second, the fitting function changes with the change of distribution, and the portability of the method is low and the calculation is cumbersome. Third, related studies often assume that the travel time of links is independent of each other. On the other hand, it is found that the fourth-order moment method is suitable for scenarios where the distribution is similar to normal distribution and there is no need to fit the distribution. However, there are few literatures used in travel time reliability because of the quality of traffic data [9].
With the development of RFID technology, compared with the traditional detection techniques, RFID has a unique advantage, because it mainly communicates through radio, and has the characteristics of high identification accuracy, strong anti-interference ability, waterproof, dustproof, shockproof, antimagnetic, heat resistance and so on [10].
The motivation of this paper is to improve the estimation accuracy of travel time reliability based on the first four moments of travel time and considering the correlation of travel time between links. The main contributions of this paper are as follows:  We propose an expressway travel time collection scheme based on RFID E-Plate data, which can obtain complete travel time information and improve the accuracy of reliability estimation.  We propose a method to estimate the percentile travel time based on the Cornish-Fisher expansion method.  We propose a travel time reliability estimation method considering the correlation of travel time. The remainder of this paper is organized as follows. Section 2 introduces the RFID E-plate data under the expressway environment. Section 3 introduces the travel time reliability estimation method based on the first four moments of travel time. Then a large number of experimental results are provided to prove the effectiveness of the model. Finally, we make a summary of this paper and put forward expectations for the future work.

2.RFID E-plate data in urban expressway environment
There are many similarities between urban expressway and highway, such as road structure, traffic conditions, road infrastructure and so on. However, some types of vehicles (such as motorcycles and buses) which are prohibited from running on the highway can still drive on the urban expressway, which leads to more types of vehicles on the highway. On the other hand, in terms of road structure, the urban expressway is built around the city, the traffic density on the urban expressway is also higher, the ramp entrance is denser, and the distance between the basic sections of the urban expressway between the adjacent ramps is shorter. Frequent lane-changing behavior and up-and-down behavior of vehicles are more likely to cause fluctuations in the state of road traffic.
As a new sensor detection technology, RFID technology has been widely used in daily life because of its low cost and convenience [12]. The communication equipment of RFID mainly includes RFID reader, RFID tag and antenna. Because of wireless communication, RFID technology has the advantages of over-the-horizon, long distance, large capacity storage and fast identification, and the data collected is the information of each item with RFID tag [13]. When used in traffic, all-weather, full sample and single vehicle data can be collected, which makes RFID technology for traffic data collection become more detailed. On the other hand, although the amount of RFID readers at each intersection in the city is over 800, its distribution is still relatively sparse. However, it can be determined in expressway environment that there is almost and only one path between adjacent RFID readers to avoid the difference in travel time caused by vehicle detour.  Figure 1. Typical RFID sensor system mounted in Chongqing, China [14] A RFID E-plate data record read by RFID readers roughly contain basic field information such as the E-plate number of the vehicle, the time the vehicle passed through the RFID reader, the IP address of the RFID reader and the type of vehicle, as shown in the example in Table 1.

The first four moments and percentile travel time of travel time
The first four moments refer to the first-order origin moment, the second-order central moment, the third-order central moment and the fourth-order central moment of a variable. The mean, variance, skewness and kurtosis of travel time are the first four moments of travel time distribution. When the first four moments of the distribution are known, Cornish-Fisher proposed an expansion method, which establishes the relationship between the arbitrary distribution and the standard normal distribution, so the percentile under arbitrary confidence p can be approximately obtained by using the first four moments of random variables. the estimated percentile travel time after using this expansion is as follows: is a random variable related to skewness and kurtosis: Where 1 ( )    is the inverse function of the cumulative distribution function.

Logarithmic processing of travel time
In order to calculate the travel time reliability, the concept of valid domain is proposed by Zang [15]. It is found that only the data within the valid domain can use Cornish-Fisher expansion, otherwise it is invalid.
Since the accuracy of calculating percentile travel time by using Cornish-Fisher expansion depends on the similarity between the distribution and the standard normal distribution, and the expansion requires that the derivative of percentile travel time is non-negative, considering that the original travel time distribution has certain skewness and kurtosis characteristics, it is necessary to logarithm the original data.
Accordingly, the Cornish-Fisher expansion for calculating percentile travel time is also changed to: Where   Similarly, in order to ensure the necessary requirements for the increase of the inverse cumulative distribution function, there are the following formulas.
For the valid domain, whether it has been logarithmized and which logarithmic method (such as taking the natural base e or 10 as the base) has no change for the interval of the valid field.
Therefore, the formula can be regarded as a quadratic function with respect to K. According to the discriminant, when the travel time skewness is little, the valid domain is: However, when the travel time skewness is large, the skewness can reach infinity in theory. From the actual data, the skewness is less than 20 and the negative value is not less than 6( 2 1)   , so the valid domain when the skewness is large is defined as: In the logarithmic processing of the original data, although the natural base e can make more data fall into the valid domain, it can not satisfy all the cases, so the logarithmic processing method with base 10 is adopted here.

Link travel time reliability
In the existing research, in order to quantify the travel time reliability, there are many kinds of related indicators, and there are some differences in the contents described. At present, the calculation expressions of the commonly used evaluation indicators are shown in Table 2.

Path travel time reliability
First of all, the path model is simplified, and it is considered that the path is composed of multiple units, because there is no intersection on the urban expressway and there is no influence of traffic lights. At the same time, due to the layout characteristics of RFID readers, there is an upper and lower entrance between adjacent RFID readers. Based on the above situation, this paper assumes that the path is only composed of multiple links, so the travel time of the path can be regarded as the sum of the travel time of all the links on that section.
Where path T is path travel time， 1 m i T  is the travel time of all links. According to the relationship between random variables in statistics, similarly, the first-order moment of travel time between two adjacent links can be expressed as follows:

E T T ET ET
(12) It can be seen that the first moment or mean value of travel time is not directly affected by the correlation of links.The second-order moment of travel time between two adjacent links can be expressed as follows： The covariance can quantitatively express the relationship between pairwise random variables, and its calculation formula is：

Cov T T E TT T T DT DT
Where  i j T T is the travel time correlation coefficient between link i and link j. The existing research on travel time reliability almost only analyzes the stage of the first two moments. The derivation and calculation formula of the third-order moment of travel time, that is, the skewness of path travel time.

E T T E T T D T T E T T S T T D T T
(15) Similarly, the formula for calculating the fourth moment, that is, the kurtosis of path travel time.

K T T E T T E T T E T T E T T E T T E T T E T T D T T
(16) In the case that the high-order moment of the path travel time is known, combined with the expansion of Cornish-Fisher, the percentile travel time on the path can be obtained. Considering METT as the travel time reliability evaluation indicator, the calculation process is as follows:  , ,... n T t t t of link j, this paper refers to the research status of Seshadri, Chen and others [16], in which Seshadri proposed that the correlation coefficient between adjacent links is 0.75, while that of non-adjacent links is 0. Therefore, when the correlation coefficient between links is considered in this model, the calculation formula is the inverse of the above formula for the travel time correlation coefficient between two links:

DT DT
(19) For the two non-adjacent links, the average is obtained according to the correlation coefficient between the adjacent links, and the calculation formula is as follows:

Data description
In order to verify the effectiveness of the above model, this paper uses the measured traffic flow data based on RFID E-plate data in Chongqing. Three long-distance connected expressways from Shimahe to Gaotanyan Interchange in Shapingba inner ring urban expressway of Chongqing are selected. The total length of the expressway is 5.5 km, including the 7-day traffic flow data collected by 4 RFID readers from February 29, 2016 to March 06, 2016, with a total of 777017 data. Figure 2 shows how the first four moments of the travel time of the original data fall around the valid domain and after logarithmic processing for three links in the past week.  Figure 2. Valid domain and skewness, kurtosis scatter distribution It can be seen that a large number of travel time distribution in the original data will fall outside the valid domain, so that the estimated value of percentile travel time may not be accurate, and after logarithmic processing of the data, the scattered points that originally fell outside the valid domain can fall within the valid domain. On the other hand, it also shows that the valid domain with large skewness in Section 3.2 is not suitable for this study as a basis for discrimination. Table 3

Logarithmic processing result
It can be seen that when calculating the adjacent links, the calculation methods and results of the two methods are actually exactly the same, and the difference lies in the processing of the travel time correlation coefficient between the non-adjacent links. Using the formula (20), the MAPE of the travel time correlation coefficient of the non-adjacent links is 2.63% with the RMSE is 0.0152, and the error is little. This also shows the effectiveness of this method. Later, this method will be used to calculate the path percentile travel time and path travel time reliability.

Path travel time reliability
The comparison is made by using the travel time distribution fitting method, which is obtained by considering the convolution of the distribution when considering the path travel time reliability, and the results are shown in Table 4. It can be seen that after considering the section correlation, the accuracy of the path travel time reliability based on the first four moments are even higher than that of the travel time reliability estimation on the links.  (17), the METT indicator of path travel time reliability at different confidence levels is shown in Table 5. 48 It can be seen that with the increase of the confidence level, the METT gradually decreases. In other words, the lower the confidence level is, the higher risk will driver take; on the contrary, if the driver has lower requirements for "arriving on-time", the corresponding possible buffer time is less.

5.Conclusions
In this study, a travel time reliability estimation scheme based on RFID E-plate data is proposed, and the effectiveness and superiority of this method are verified by using the real traffic big data with 7-day coverage in Chongqing, China. In this paper, the emerging radio frequency identification technology is used to identify vehicles. In the third section, we first introduce the potential problems of travel time reliability estimation, and put forward some solutions. In the fourth section, we find that the accuracy of our proposed model is superior to the traditional travel time fitting method in almost every indicator, and will not overestimate the reliability of travel time. Therefore, the proposed model is sufficient to evaluate the reliability of vehicle travel time in expressway environment. In addition, the model can provide corresponding travel time reliability data according to the different needs of travelers for "arriving on-time", which is of guiding significance for inducing travelers to plan a reasonable departure time and urban traffic managers.


s2fieldsofstudy: Business Economics

text:
DETERMINANTS OF CAPITAL STRUCTURE USING PROFITABILITY AS MODERATING IN INDONESIA’S PROPERTY AND REAL ESTATE FIRMS

The research aim is to examine determinant factors of Indonesia's property and real estate firm’s capital structure listed in the Indonesia Stock Exchange. This is a quantitative research which taken 72 observation data from 12 companies audited financial statement and fulfilled certain criteria. Processing through classical assumption tests and multivariate analysis with the help of the EViews 10 software instrument. The results show that tangibility assets, business risk, and firm size have a significant influence on capital structure partially, but sales growth and liquidity have insignificant. The determination coefficient is 42.83% and the proportion is included in the strong criteria. Profitability as a moderating variable weakens the effect of business risk but strengthens the effect of firm size on the debt to equity ratio, further determining the company's ability to pay off its debt which is of great concern to investors and creditors. Furthermore, as a consideration for choosing the composition of a good funding decision in Indonesia.

Introduction
Property is related to land, occupancy, type of office buildings, and types of buildings for trade. The property business can be interpreted as an activity of exchanging goods, services, or money related to land, housing, office buildings, and commercial buildings. Generally, the market of property is partitioned into three sections; in particular of commercial property, basically property is designed for business purposes, for instance, a stockpile of goods building and parking lot. The second is residential property, which incorporates housing, condos and multi-unit building and industrial property, for instance, property intended for industrial purposes, such as factory buildings (Henneberry, 2009).
Property and real estate industry are the sector with attributes that are difficult to make a future forecast and risky profoundly, in practically all nations inclu-ding Indonesia. The difficulty to forecast implies that the tides of this sector have a tremendous signal (Liang et al., 2014). At the point when a high economic growth occurs and macroeconomic conditions are in great condition, the property and real estate industry will boom and generally will be oversupplied, yet on the other hand, when economic growth diminishes or in a down position and macroeconomic conditions are in a recession, this property and real estate segment will rapidly experience in a drastic decline (Chadha & Sharma, 2015).
In 2013 and the first half of 2014, the property and real estate sector grew rapidly and the profits of Indonesian property developers was increasing sharply, a sum of 26 out of 45 property and real estate companies in 2014, which listed on the Indonesia Stock Exchange, recorded a huge growth in net income (Central Bureau of Statistics, 2019). According to Table 1, the contribution of this industry to GDP in 2018 was 3.97%. In total, the mean contribution as a whole in 2013 until 2018 amounted to 4.23% to GDP, respectively. Economic growth is a major problem for every country. High GDP growth rates, structural change and social reform are activities that every country in transition to a market economy and democratization must undertake (Popovic, Eric & Stanic, 2020). This showed that one of the infrastructures still as an indicator of national economic development. In today's era, the development of the world property and real estate business is increasing rapidly leading to the intense competition among the firms in similar sectors. This encourages every firm to develop to increase its business capacity through gradual business development and the expansion of the business on a large scale in aim to maintain every firm's existence. According to Atherton et. al. (2008), the property DETERMINANTS OF CAPITAL STRUCTURE USING Tamba and Purwanto PROFITABILITY AS MODERATING IN INDONESIA'S PROPERTY AND REAL ESTATE FIRMS business is one of the sectors that assign a going down signals or the development of a country's economy.
This factor has led the firm to increase the funding needs of the firm to expand the operational activities of each firm. In general, the owner of every firm hands over the authority and duties to carry out the firm's operational activities to a manager of the firm. Managers have full duties and also responsibilities for the firm's operations activity to perform through appropriate and prudent decisions to maintain the firm's survival (Boateng, 2017). In the context of the financial matter of a firm, the manager of financial should be able to determine the firm's financial decisions through a mature and well-developed plan of financial management (Jeff & Roland, 2010).
In financial management, the activities of it should undertake several decisions, which are known as the financial management functions, specifically; the use of funds or the investment decisions, funding decisions or obtaining funds, and profit-sharing or the dividend policy. Investment decisions are the decisions which are allocating several funds to obtain an increase in the amount of investment profits gained in the future (Brown & Reilly, 2012). The investment decision who has made by the firm has the goal of obtaining the optimal profits, which the firm invests in a business that has a promising prospect in the future, not to obliterate by observing the development of that business from the previous performance, so that liquidity of the firm will be at the optimal level (Chen, 2014). On the other hand, funding decisions and dividend policies will be reflected on the liability side of the firm (Jeff & Roland, 2010). Funding decisions made by every firm in obtaining funds related to the composition of the debts that are being held, preferred shares and ordinary shares, through the cost and period of the fund's consideration.
One of the important element decisions faced by financial managers related to the firm's operations is a funding decision. A good funding decision from a firm can be indicated from its capital structure, specifically, the financial decisions that are related to the composition of debt and equity of the firm (Liang et al., 2014). Capital structure plays a core in a business regarding the funding making decisions of a firm that every financial manager has to make sure a company can obtain the necessary funds to prevent financial distress by the ratio or balance of long-term funding shown by the ratio of debt to equity (Hasliyawani & Othman, 2016). This funding in the capital structure is indicated by debt, equity, preferred shares and ordinary shares. Capital structure must be executed both from the internal of a firm and from the external effectively and efficiently to improve the prosperity of shareholders as well as the owner of firms. The prosperity of shareholders can be seen from the manifestation of high share prices which are reflected in funding decisions in the capital structure (Modugu, 2013).
As important of factors that influence capital structure, several researchers have done a study which resulted from tangibility asset influenced capital structure positively and profitability influenced capital structure negatively (Gómez & Mena, 2014). Liquidity ratio is empirically found to be small impact to the capital structure (Chadha & Sharma, 2015). Thippayana (2014) found that there are no significant relationships between variables of tangibility, growth opportunity, business risk, and the leverage ratios. On the other hand, tangibility and profitability are statistically significant to the capital structure (Anarfo, 2015). The negative relationship between tangibility asset and capital structure (Alipour et. al., 2015) and resulted that assets tangibility, firm size, total liquidity, and business risk are the main element impacting the capital structure of the hospitality sector (Pacheco  , 2017). Therefore, what factors influence the capital structure or leverage level is necessary to understand more, to find another empirical result despite the different results of research done before.
The decision of capital structure directly influences the huge of the risk that is being borne by shareholders and the magnitude of the expected rate level of the firm's return (Modugu, 2013). The inappropriate decisions can drive to the risky financial positions and ultimately lead to bankruptcy or financial distress to repay its debt (Hasliyawani & Othman, 2016). Therefore, the measurement of what factors affect the capital structure is needed to be evaluated. This research would fortify the capital structure theory captured in the property and real estate market in an emerging economy and might provide information for investors also managers of a firm. The structure and systematics of the order of the papers begin with phenomena. The second part describes the concept of capital structure, firm size, profitability, while the third part states the method, and the fourth part is a discussion before the conclusion.

Literature Review and Hypothesis
According to the trade-off theory revealed by (Myers, 1984), companies will owe up to a certain level of a debt, where the tax savings or tax shield from the additional debt is equal to the cost of financial difficulties or known as financial distress. This arises from increased bankruptcy and agency costs due to decreased credibility of a company. Several factors that affect the capital structure include tangible assets, sales growth, business risk and liquidity (Liang et al., 2014). Erlina and Rasdianto (2013) defined that fixed assets are the assets which often used as the collateral that is used by the company to obtain a loan. The bigger amount of the number of fixed assets of a company, then the easier it will be to obtain a loan for a company that needs it. Certainly, the company has a guarantee which makes creditors more confident and have faith in investing in the company (Brigham & Houston, 2014). Most of the companies that are mostly invested in fixed assets, which prioritize the fulfillment of their capital from a permanent capital, which described as their capital, while the debt is complementary (Liang et al., 2014). In measuring the tangibility asset, fixed assets ratio is used, and this ratio is also known as asset structure.
Growth reflects one indicator of progress or failure of a company. Companies that are included in the industry category with high growth rates must provide sufficient capital to finance their business operations (Ulzanah & Murtaqi, 2009). Companies with rapid growth tend to use debt more rather than companies with slow growth. Sales growth has a strategic influence on a company because sales operation must be supported by the assets and if sales are increased, then as well as the assets should be added by a company (Eldomiaty et al., 2017). In this context, analyzing the sales from the previous year, companies can optimize existing resources. Companies with relatively stable sales can be more protected to gain a lot of loans and bear a higher fixed expense compared to the companies whose sales are not stable (Karyotis & Onochie, 2016).
One of the risks faced by the company when undergoing the operational activity is business risk (Nejad & Wasiuzzaman, 2015). The level of business risk of a company is influenced by the stability of income and the structure of operational costs. Additionally, business risks can occur if there is a huge usage of a debt in a company. In a company, business risk will surely increase if you use too high of debt. This factor will lead the company to the increasing possibility of bankruptcy as well (Keegan, 2004). Based on a business point of view, risk is sometimes associated with a possible negative effect on firm value, and DETERMINANTS OF CAPITAL STRUCTURE USING Tamba and Purwanto PROFITABILITY AS MODERATING IN INDONESIA'S PROPERTY AND REAL ESTATE FIRMS most monetary coursebooks and experimental research estimate an inverse relationship between business risk and the amount of leverage a firm can exploit. The opposite condition shows that the expansion of business risk has an impact on decreasing the amount of leverage that can be used by a company (Ennouri, 2013). The high risk that bears by companies should use less of debt to avoid the possibility of bankruptcy. Brigham & Houston (2014) explained that the measurement of business risk can be measured by using the coefficient of variation from profit.
The mandatory factor in the form of capital structure is liquidity, if a company faces the threat of bankruptcy, which will be better able to take advantage of its liabilities, for example having sufficient liquid assets (with the threat of bankruptcy, the company can more effectively transform its liquid assets into asset needs. Durrah & Aziz (2016) stated that the conventional view is this liquidity increases the capacity of the debt because the higher liquidity might increase the value of a company in liquidation and then the liquidity could diminish the capability of a company to issue the securities of debt.
The size of the company describes a company in which the larger companies will more easily get loans from outside parties in form of debt and equity because a large company is accompanied by a fairly good public reputation. Chakrabarti (2018) stated that the larger the size of the company indicates the level of risk for investors to invest their capital so that if the financial performance is good, it is believed that the company will be able to fulfill all obligetions and provide adequate benefits for investors. The larger the size of a company, the greater capital needed for the company operations, the greater the total assets owned by the company, and the greater as well for the tendency to use external funds (Nugrahani & Sampurno, 2012).
Profitability can be translated for a company to gain profits within a certain time. Meanwhile, the profitability ratio is defined as a measure of the company's ability to obtain profits in the form of capabilities such as sales revenue, assets, and capital (Riswan & Kesuma, 2014). According to Velnampy & Aloy Niresh (2012), profitability is concerned with the effectiveness of the business in generating profits. A very familiar definition of assessing business is to assess the amount of wealth that is invested. Based on Ahmad & Etudaiye (2017), several ratios are used to measure profitability. Each type of profitability ratio is used to measure and assess the company's financial position in a certain period or for several periods. The function of all or part of the profitability ratio depends on the policy on the company's management.
In this research, profitability is the moderating variable which moderate the influence between firm size and capital structure; also, business risk and capital structure. High profit but high-risk firms surely reduce the business risk of the firms. Supported by Trade-Off Theory, firms will therefore use more debt because if a firm is profitable, it will give creditors a positive signal which means the firm has good prospects in the future, so creditors will give the company their trust by the given funds. This is backed by research done by Anarfo (2015) and Purohit & Khanna (2012), which showed that profitability has a high impact on capital structure.
The firm's size which is getting bigger will signal investors that the company can develop well in amid business rivalry. The larger size of the firm also gives a creditor a trust in form of the company's success in the future and is expected to be able to return the loan of a creditor (Ahmad & Etudaiye, 2017). Furthermore, a high profitability will also provide a positive signal for prospects for external parties. This is supported by the research done by Gómez et al. (2014) and Chadha & Sharma (2015), which showed a result that profitability has a significant Referring to this definition, it can be understood that the influence of firm size and business risk on capital structure can be moderated by profitability. Company's profitability in this research can be measured using the ratio of return on asset (ROA). The theoretical framework of this research can be generated into the scheme as follows: Regarding the problem and theoretical framework above, the hypotheses are H 1 : There is an influence of sales growth on capital structure H 2: There is an influence of tangibility asset ratio on capital structure H 3 : There is an influence of business risk ratio on capital structure H 4 : There is an influence of liquidity ratio on capital structure H 5 : There is an influence of firm size on capital structure H 6 : Profitability ratio can moderate the influence of business risk on capital structure H 7 : Profitability ratio can moderate the influence of firm size on capital structure Through these hypotheses stated, this study will find out the influence of financial ratio determinants towards capital structure.

Methodology
This study is using the approach of the quantitative method and adopts secondary data. The data can be counted mathematically and is in the form of numbers. Population is the whole observation consisting of objects with certain qualities and characteristics categorized by the researcher for investigation before making conclusions (Sugiyono, 2013). All property and real estate companies listed on the Indonesia Stock Exchange were made population. A total of which is 48 companies. The sample is part of the population which is being used as the object of research. Sampling in this study uses the technique of the target population. The target population technique in this study is based on certain criteria that are fitting to the objectives and research problems (Erlina, 2011 then H0 is rejected and H1 is accepted, which means there is no problem with heteroscedasticity. This test shows a correlation between members of a series of observations sorted by time or space or to recognize the correlation towards each variable in a period of t with variables in prior period (t-1) (Santoso, 2014). One of the things that can be done to detect the autocorrelation occurrence is the Durbin Watson (DW) values.
Multicollinearity described that there is a perfect linear relationship among several or all variables that explain the regression model. The presence or not of the multicollinearity can be seen from the correlation coefficient of each independent variable. If the correlation coefficient between each variable is greater than 0.8, the multicollinearity problem occurs and shows there is bias from multicollinearity (Gujarati, 2012).
Panel data is the data collected in a crosssection and in a certain period. Due to panel data is a combination of cross-section data and time series, the number of observations becomes greater (Hikmah, 2017). Testing of the hypothesis in this study uses multiple regression analysis methods. Regression analysis is used to find out how the dependent variable can be predicted through each variable (Gujarati, 2012). The regression equation used in this study is: where Y = capital structure (DER), β0 = constant, β1, β2,..., β5 = regression coefficient, X1 = sales growth, X2 = tangibility asset (FAR), X3 = business risk, X4 = liquidity (CR), X5 = firm size, and Ꜫ = error. If the value of regression shows a positive coefficient (+) in the result, indicates the positive relationship towards independence such as; sales growth, tangibility asset, business risk, liquidity and firm size, and dependent variable which is debt to equity ratio. This means the increase in coefficient value will increase the Y. On the other hand, if the value is negative (-), that indicates the negative relationship between the independent and dependent variables, which means the increasing regression coefficient value resulting in the decreasing of the value Y. where Z = profitability (moderator), X1 = business risk, X2 = firm size, β1, β2…. β6 = regression coefficient and Y = capital structure (DER). The coefficient of determination is the value coefficient that shows the amount of dependent variable which is influenced by the variance of independent variables. If the adjusted R squared value of regression is close to one, then the better the regression and the closer it is to zero, means the overall independent variable has a weaker capability in explaining the dependent variable (Cameron & Windmeijer, 2015).

Normality
The normality test towards residual values uses the Jarque-Bera test, with a significance level used is 0.05 in aim to figure out whether the data is normally distributed (Hikmah, 2017). The result of probability in this research based on Figure 2 is 0.608168 which greater than the significance level α = 0.05. Thus, the data is proven normally distributed and indicates that the assumption of the normality test is fulfilled.

Multicollinearity
In this research, a multicollinearity symptom can be seen from the correlation values between variables contained in the correlation matrix. If there is a correlation above 0.8 between independent variables, interpreted as the multicollinearity problem (Gujarati, 2012).
On other words, the suggested correlation values in observed data should be less than 0.8 and for the variance inflation factor as the other recommended selection criteria with the values of less than 5. The result of this test is shown in Table 2  The correlation between sales growth and FAR is -0.013555, sales growth and business risk is 0.111004, FAR and firm size is 0.158530, CR and sales growth is 0.018324, last but not least firm size and CR is 0.05698.

Heteroscedasticity
The test of heteroscedasticity is used to assess whether there is a deviation in residual variants for one variable to another variable in the linear regression model. One of the tests used in this research is the white test which showed in the table below: The value of prob. Chi-Square of Obs*Rsquared based on the table showed above is equal to 0.0896, which more than 0.05, implied the assumption of homoscedasticity is fulfilled. In other words, there is no problem in heteroscedasticity in residues of this research.

Autocorrelation
A good regression model is a model that does not contain an autocorrelation problem. This test can be done by using Durbin Watson. The data can be proven free from autocorrelation symptom, when the result is implying between -2 and +2 (Santoso, 2014). The result of DW of this research is 0.426035 which shows a value of more than -2 and less than +2.

Panel Data Model Selection
To select the panel data equation model, a test which has been acquired based on the steps in this research is Hausman Test. The hypothesis criteria as follows: a. H0: REM is better than FEM b. H1: FEM is better than REM If the probability value < 0.05 (significance level), H0 is rejected and H1 is accepted. Based on the observed data, this research rejected the H0 and accept the H1.  Referring to these results, the following interpretations can be made:

The Influence of Sales Growth toward
Capital Structure Sales growth has a positive and small contribution on the debt to equity ratio. The result of sales growth influence is contrary to trade-off theory which states a high sales growth company will depend on external funds, in this case of research the funding is debt-based. Besides, the cost of issuing shares will usually be higher than the cost of issuing bonds. As a result, companies with high growth will have higher debt than companies with low sales growth. According to Karyotis & Onochie (2016), companies that have relatively stable sales growth and rapid growth would be more secured within more debts. However, the result of this research showed that sales growth did not significantly influence the capital structure. This is similar with the previous study by Thippayana (2014) and Harjito (2011). Conversely, research conducted by Suweta & Dewi (2016) and Chen (2014) found a significant influence of sales growth on capital structure.

The Influence of Tangibility Asset toward
Capital Structure Tangibility asset has a positive and significant influence towards debt to equity ratio. According to Brigham & Houston (2014), if a company has a large amount of fixed asset or show its tangibility asset, thus the company can acquire more funding through debt, with the expectation that this fixed asset can cover its debt and fixed asset owned by the firm reflects the company's debt collateral to the source of fund or investor, then, the agency cost of debt might be lesser due to the greater number of fixed asset or collateral had by a company and found to be in line with MM Theory. For instance; it is implemented by BIPP in 2013 which its fixed asset could cover its debt. Additionally, the greater debt collateral offered by a company, the more trust is given by the source of capital to the company because the creditor believes the company will be able to repay-off the debt on time, which has the consistency towards Trade-Off Theory. This is in line with previous research by Pacheco & Tavares (2017) and Cortez & Susanto (2012). As the opposite, research done by Thippayana (2014) and Nejad & Wasiuzzaman (2015) found that there is a small contribution between tangibility assets and capital structure.

The Influence of Business Risk toward
Capital Structure Business risk has a negative and big impact on the debt to equity ratio. The negative result of business risk influence explains that the higher business risk, the utilization of debt is decreasing in capital structure. This is due to companies that have an expansion in business risk should utilize a smaller debt in the financing, because the company who has a greater business risk, the use of greater debt as well, will lead to difficulties in repay-off or withdraw their external funding in form of debts (Ennouri, 2013). The result of this research is in line with Pacheco & Tavares (2017), which stated that business risk has a high impact. As the opposite, a study related by Thippayana (2014).

The Influence of Liquidity toward Capital
Structure Liquidity has a positive and insignificant influence on the debt to equity ratio. Property and real estate tend to have a good liquidity ratio, so managers are more concerned with the need for financing and do not consider liquidity directly in determining their capital structure. According to Eriotis (2007), a higher liquidity of a firm can indicate the company has a healthy financial. Thus, will make it easier for companies to obtain long-term liabilities that come from external parties. However, the result showed that liquidity did not significantly influence the capital structure. The result of this research is in line with research by Chadha & Sharma (2015) and Handayani & Seftianne (2011) were found a positive and insignificant influence of liquidity toward capital structure. Conversely, a research done by Guna & Sampurno (2018) and Pacheco & Tavares (2017) found a significant influence.

The Influence of Firm Size toward
Capital Structure Firm size has a negative and high effect on the debt to equity ratio. This is due to small companies may not have a lot of choice in financing their companies, but they may have to rely on utilize debt. Nevertheless, this result is in line with the Pecking order theory. In other words, large companies are less likely to experience bankruptcy due to has abundant internal sources of funding and therefore tend to use less debt in the firm to finance their companies and the investment opportunities as well (Toumi & Louhichi, 2012). The evidence is not contradictive with investigate by Nejad & Wasiuzzaman (2015), Alipour et al. (2015), and Modugu (2013) and conversely with research by Cortez & Susanto (2012).

Simultaneous Influence of Sales Growth, Tangibility Asset, Business Risk, Liquidity and Firm Size towards
Capital Structure The f-statistic on Table 4 shows that sales growth, FAR, business risk, CR, and firm size that are used to define the influence toward debt to equity ratio found to be significant all together. The proportion of determination has a value of 0.4283. It indicates the capital structure proxied by the debt to equity ratio is being explained by the chosen independent variables in the amount of 42.83% and strong criteria.

The Most Influence Significant Influence
Factor The most significant variable influences debt to equity ratio in this sector is firm size. The probability value is the closest to 0 compared to others with value by 0.0000. Therefore, it concludes that the size of a firm in this sector is determining the usage of debt conducted in the firm's capital structure due to it determines the capability in doing a repayment of its debt which will be tightly noticed by investors to invest and creditor to lend.

Profitability as Moderating Variables
Profitability as a moderator in the form of the influence business risk on debt to equity ratio shows a result of positive and insignificant. If it is without ROA as moderating, the influence of business risk on the debt to equity ratio is negative and significant. The adjusted R square shows a decreasing note from 0.4283 to 0.044 as moderating by ROA. This indicates that ROA is not capable to moderate the influence of business risk variable towards DER or ROA is weakening the influence of business risk towards debt to equity ratio. However, this result is not similar to Tradeoff theory which states that firms with high business risk and profits tend to use less debt to prevent the increase in business risk. Also, this is in line with the Signaling Theory which explains the action taken by the management of the firm, which gives the creditors a guidance as to how the management sees the prospects of the firm business. A high level of profitability would send a signal to creditors shows a company has better prospects in the future, thus creditors put confidence to lend funds to the firm (Connelly & Ireland, 2011). Eventually, this will increase the business risk of the firm. Therefore, Trade-off theory cannot be used as a principle in describe profitability as moderating the influence of business risk on capital structure. This result is not in line with the research done by Anarfo (2015) and Purohit & Khanna (2012) resulted in profitability has a significant influence on capital structure.
Profitability as a moderator in the form of the influence firm size on debt to equity ratio shows a result of negative and significant. The coefficient within ROA as moderator resulted -0.174. If it is without ROA as a moderator, the coefficient is -0.1873. This result indicates that ROA is capable to moderate the influence of firm size variable towards debt to equity ratio or ROA is strengthening the influence of firm size towards leverage. According to Nenu & Vintilă (2018), the higher size of the firm, the firm has a less probability of bankruptcy, means have a lower financial distress cost as well, which shows the larger firms are instead to use an internal funding rather than use debt in financing their firms, as well as their investment opportunities. This is also in line with the research by Nejad

Conclusions
Sales Growth has an insignificant positive influence on property and real estate capital structure. The result denotes that companies with high sales growth will have higher debt than companies with low sales growth which contrary to trade-off theory.
Tangibility asset has a significant positive influence on property and real estate capital structure. The result explains that higher tangibility assets or fixed assets owned by a company, the utilization of funding in the form of debt is increasing as well. A large amount of fixed asset is expected can cover its debt and fixed asset owned by the company as a reflection of company's debt collateral to the source of fund or investor and for agency cost of debt might be lesser due to the greater number of fixed asset or collateral had by a company.
Business risk has a negative significant influence on property and real estate capital structure. The result denotes that the higher level of business risk measured, the use of debt is decreasing in capital structure because a company with high business risk followed by a greater amount of external funding in form of debt will lead those companies to difficulties in repay-off or withdraw their debt issued.
Liquidity has a positive insignificant influence on property and real estate capital structure. The result explains that a good level of liquidity leads managers more concerned with the need for financing and does not consider liquidity directly in determining a firm's capital structure. Firm size has a negative significant influence on property and real estate capital structure. The result denotes that if the firm size is greater, the use of debt is decreasing in capital structure because a larger company is less likely to experience in bankruptcy due to has abundant internal sources of funding and therefore tends to utilize less debt. This result is found to be consistent with the pecking order theory.
Profitability proxied by ROA is not capable to moderate the influence of business risk variable towards capital structure significantly or ROA is weakening the influence of business risk towards the capital structure. This result is not in line with trade-off theory which states that firms with high business risk and profits tend to use less debt to prevent the increase in business risk.
Profitability is strengthening the influence of firm size towards the capital structure. The result is the higher size of the firm, the firm has a less probability of bankruptcy, means have a lower financial distress cost as well, which shows the larger firms are instead to use an internal funding rather than use debt in financing their firms, as well as their investment opportunities.
Sales growth, tangibility asset, business risk, liquidity, and firm size that used to define the influence toward capital structure determinants on property and real estate firms, found to be significant simultaneously. This is evidenced by the f-test resulted in a significance value of 0.0000. Therefore, sales growth, tangibility asset, business risk, liquidity, and firm size could explain the variation of debt to equity ratio by 42.83%, and the remaining percentage which is 57.17% is influenced by other factors besides the variables conducted in this research.
The most significant variable that influences the debt to equity ratio of property and real estate firms is firm size. Therefore, it concludes that the size of a firm in property and real estate is determining the usage of debt conducted in the firm's capital structure due to it determines the capability to repayoff its debt which will be tightly noticed by investors to invest and creditor to lend.

Recommendation
In this research, variables that influence capital structure in property and real estate firms which have significant results are tangibility asset, DETERMINANTS OF CAPITAL STRUCTURE USING Tamba and Purwanto PROFITABILITY AS MODERATING IN INDONESIA'S PROPERTY AND REAL ESTATE FIRMS business risk and firm size. All of them can be used as a concern for managers in every firm to compose decisions regarding the use of capital structure optimally and all of them is significant, in order to accomplish company goals as well as to enhance the investors prosperity. It is expected from investors to be more selective in determining a firm where will be a place to invest. The consideration based on this research are through the tangibility asset, business risk and the firm size element of a firm, due to it influences the optimization of the capital structure which certainly will impact the value of a firm has. It is expected for further researchers to add other variables besides the independent variables in this research that influence capital structure which might be considered can increase the result of adjusted Rsquared in explaining the dependent variable. It is expected for further researchers to conduct a research within the newest period of the samples so that the result will be more accurate as well as intended to use more diverse characteristics, in aim to obtain a representative conclusion by comparing each sector of company listed on Indonesia Stock Exchange. Addition of recommendation, future researcher could have a deeper analysis related the capital structure determinants. Durrah, O., & Aziz, A. (2016). Exploring the Relationship between liquidity ratios and indicators of financial performance: an analytical study on food industrial companies listed in Amman Bursa, International Journal of Economics and Financial Issues, 6(2), pp. 435-441.


OFDM-IM for Joint Communication and Radar-Sensing: A Promising Waveform for Dual Functionality

The demand for dual-functional wireless systems is on the rise as certain resources become more congested and scarce. Joint communication-radar (JCR) is a promising technology that is becoming very critical and growing in popularity, where communication and radar applications are serviced simultaneously sharing the same hardware/software and the frequency band resources. JCR and its alternatives need to be cleverly integrated into certain waveforms such as orthogonal frequency division multiplexing (OFDM) to function properly without degradation in the performance. With the aid of the promising concepts of index modulation (IM) and Golay complementary sequences, a novel JCR waveform is proposed to serve both communication and radar applications with the same resources. It has been shown by extensive computer simulations that the proposed OFDM with an index modulation (OFDM-IM) waveform outperforms the classical OFDM with fixed pilot design both in bit error rate (BER) performance and radar-based applications by introducing diversity among subcarriers and frequency agility over the whole frequency band.

parameter is the communication capability of a DFRC system. Last but not least, a unified architecture proposes a codesign system that is designed for the application requirements from the scratch.
Moreover, DFRC provides hardware and software convergence of radar-sensing and communication applications to utilize scarce resources more efficiently and effectively. With these and upcoming considerable contributions, DFRC is envisioned to be implemented and provide service to many applications, some of which include intelligent vehicular transportation, aircraft traffic, and military applications whether it is in the air, sea, or space. JCR, which is a form of the DFRC system, creates a fusion of both functionalities in a unified platform, which is feasible since the gap between radar and communication systems is small. This unification not only grants a cost reduction but also enables an efficient spectrum usage, efficient power consumption, and a more compact system size. One major challenge of designing a JCR system is the performance criteria (Zheng et al., 2019); that is, both functionalities are desired to be designed cooperatively without performance degradation. Additional challenges include dedicated receiver algorithms and complication of the transmitter design.
JCR research has pushed significant efforts to share the spectrum between radar and communication without interference.
Fundamental concepts and important performance metrics have been discussed, and resource management approaches have been analyzed for JCR in the study by Luong et al. (2021). Waveform design plays a critical role in the JCR concept, and a single novel waveform for both radar and communication functionalities, without a reduction in performance of either, is yet to exist. Orthogonal frequency division multiplexing (OFDM) is a very popular waveform integrated to JCR (Ozkaptan et al., 2018). Also, a JCR technique on the wireless system standard IEEE 802.11 employing OFDM is proposed in the study by Nguyen and Heath (2017) with dedicated OFDM-radar algorithms. However, for the dual-functionality of the system, it is not mandatory to have a single waveform. Different waveforms may be superimposed and transmitted simultaneously as in the study by Şahin and Arslan (2020), Şahin and Arslan (2021), where frequency-modulated continuous wave (FMCW) and OFDM are used. Additionally, millimeter wave (mmWave) communication provides large transmission bandwidth enabling large data rates and has a potential to collaborate with JCR. From the signal processing perspective, Mishra et al. (2019) dived in to mm-Wave JCR systems and waveform design. As a further note, Feng et al. (2020) explored state-of-the-art JCR evolution with a comprehensive survey on the DFRC technology.
On the other hand, index modulation (IM) is a simple, spectral, and energy efficient scheme that can be implemented by many waveforms and techniques, such as OFDM. OFDM with index modulation (OFDM-IM) is a very popular scheme that conveys information not only through quadrature amplitude modulated symbols but also through the indices of active subcarriers in the frequency domain Basar et al. (2013). It has been utilized in various ways to transmit additional bits, provide flexibility, improve reliability, and more, such as in the study by Basar (2015), Arslan et al. (2020a), Dogukan and Basar (2020), Arslan et al. (2020b). Superiorities of OFDM-IM over classical OFDM include efficient implementation and flexibility of active subcarriers, energy efficiency, a reduced peak-to-average power ratio (PAPR), robustness to inter-carrier interference (ICI), and improved bit error performance Basar et al. (2017). Recently, IM has even been applied to DFRC. In the study by Hoque and Şahin (2020), IM-aided circularly shifted chirps are proposed for DFRC systems. In the study by Huang et al. (2020), information is embedded on the DFRC system in an IM manner, where different frequency and antenna selections construct possible constellation space. However, to the best of our knowledge, there is no study focusing on the implementation and analysis of OFDM-IM in JCR systems.
This study sheds lights on the drawbacks of pilot-based OFDM in the JCR system such as a limited unambiguous radar range and channel estimation errors, and proposes a novel structure using OFDM-IM to overcome these problems. To perform radar functionality on the proposed structure, Golay complementary sequences (GCS) are inserted into the OFDM-IM waveform, which are explained in Section 2. Since GCS have suitable ambiguity function properties for radar functionality (Kumari et al., 2018), they are used as preknown radar symbols in our proposed scheme.
The contributions of this study can be summarized and highlighted as follows: • A novel JCR waveform scheme is proposed using the OFDM-IM structure and GCS as radar symbols. • The transmitter and receiver structures are introduced to improve both the communication and radar performance of the proposed waveform scheme. • A novel channel estimation scheme is proposed for OFDM-IM utilizing its radar functionality. • Superiority of the proposed scheme is analyzed compared to the conventional OFDM structure with comb-type pilots for JCR systems proposed in the study by Ozkaptan et al. (2018).
The rest of the article can be summarized as follows. In Section 2, the system model of the proposed JCR scheme is presented. In Sections 3 and 4, radar and communication functionalities of the proposed scheme are explained. Computer simulation results are presented in Section 5. Finally, Section 6 concludes the article.

SYSTEM MODEL
In this section, the system model of the proposed OFDM-IM JCR scheme is provided. First, the transmitter architecture is presented, and then the channel model is explained.

Transmitter Design
The block diagram of the considered OFDM-IM JCR system is presented in Figure 1. As in classical OFDM-IM, the entire OFDM-IM symbol consisting of N subcarriers is split into total G subblocks. Since each subblock undergoes the same procedures, it is sufficient to consider the ξ th subblock x(ξ) ∈ C g×1 , where ξ ∈ {1, . . . , G}. Each subblock has a length of g N/G subcarriers with indices (i 1 , . . . , i n , . . . , i g ), where i n is the n th subcarrier index of the ξ th subblock.
For the ξ th subblock, incoming b bits are split into b 1 and b 2 bits. The b 1 log 2 ( g k ) bits determine the indices of the k communication, r radar, and l null subcarriers of a subblock, via binary-to-decimal conversion, using a predefined look-up table. Then the remaining b 2 klog 2 (Q) bits assign quadrature amplitude modulation (QAM) symbols, which are chosen from the complex set S, to the communication subcarriers of the subblock. Consequently, a total of  Figure 2A. In this example, there is one radar (r 1) and one null subcarrier (l 1) in a subblock. The number of total radar symbols in one OFDM symbol is denoted as R rN/g. Figure 2B is presented to visualize the considered time-frequency lattice, according to Figure 2A. The flexible design of OFDM-IM allows the proposed scheme to utilize the whole bandwidth for radar processing such as frequency-agile radars. At the receiver side, the location of the radar symbol is found via estimation of the null subcarrier location, which will be explained in Section 3. Therefore, null subcarriers are distributed intelligently to find the locations of radar symbols.
Next, two complementary Golay sequences G a and G b , both with length R/2, are generated. In succession, the elements of G a are placed into the first half of the OFDM-IM JCR block with N/2 subcarriers. Subsequently, the aforementioned procedure is repeated for the remaining N/2 subcarriers using G b . After this point, the procedures as in OFDM are applied. The time domain m th OFDM symbol x m is generated by applying the inverse fast Fourier (IFFT) transformation on frequency domain samples X m , as given below: Let the matrix X ∈ C M×N be the frame of frequency domain-transmitted symbols, where the element X m,n is the mth symbol transmitted on subcarrier n, for m 1, . . . , M and n 1, . . . , N. After the IFFT operation, a cyclic prefix (CP) of T is added to the beginning of the OFDM symbol. Then, the OFDM-IM frame x ∈ C M(N+C)×1 including M OFDM symbols is generated by appending each OFDM symbol back to back. Afterward, the baseband discrete signal x is processed with a digital-to-analog converter (DAC) and upconverted to the desired radio frequency (RF) band, where the transmitted passband analog signal becomes where R{.} denotes the real part of a complex quantity. Here, f c and θ are the carrier frequency and the initial phase of the transmitted signal, respectively, where f c is selected according to the 5G mm wave band.

Channel Model
Throughout the study, the communication receiver is also exploited as a radar receiver. Hence, the bistatic radar model is used, where the transmitter and the radar receiver are not colocated. The wireless channel is dedicated for both communication and radar operation, where the transmitted OFDM-IM JCR waveform propagates through. The channel model includes complex channel gain, Doppler shift, and delay for every path. It is also assumed that the transmission of the frame is long enough to be affected from time selectivity, but short enough such that the time variation can be accurately modeled as depending only on per-tap linear phase variations due to Doppler effects (Letzepis et al., 2011). Therefore, the linear time-variant channel in the time-delay domain, c(t, τ), can be modeled as follows (Hlawatsch and Matz, 2011): where α p , τ p , and ] p denote the complex attenuation factor, bistatic time delay, and the Doppler frequency shift associated with the p th target, respectively, where there are a total of P targets. The estimation of the complex gain, delay, and the Doppler shift from each tap of the multipath channel is performed via radar signal processing techniques as explained in Section 3. It is known that the frequency domain channel coefficients for an entire frame can be expressed as the superimposition of two-dimensional complex sinusoids, which allow us to use a periodogram-based delay and Doppler shift estimation. Down-converting the received passband signal into baseband and sampling with the frequency of F s NΔf, the discrete-time signal becomes where w[n] ∼ CN (0, σ 2 ) denotes the sampled additive white Gaussian noise (AWGN). With the assumption that the channel components remains the same during transmission of an OFDM frame and the CP length (C) is larger than maximum bistatic delay eliminating inter-symbol interference (ISI), the received vector is given by where H t ∈ C M(N+C)×M(N+C) is the time domain channel matrix and w is the noise vector. The signal-to-noise ratio (SNR) is defined as 1/σ 2 . The relation of Eq. 4 with the time domain channel matrix H t can be represented as follows: where the delay matrix Π n τp ∈ C M(N+C)×M(N+C) is the forward cyclic shifted permutation matrix according to the delay of the pth path. It can be expressed as follows: The Doppler shift matrix for the pth path, Δ(] p ) ∈ C M(N+C)×M(N+C) , can be written as follows: where there are total P paths with different delays and Doppler shifts.
The channel estimation and decoding processes are explained in the next sections.

BISTATIC RADAR AND CHANNEL ESTIMATION
In this section, the bistatic radar signal processing scheme is introduced using the known Golay sequence on the transmitted OFDM-IM frame. The general structure of the steps followed both in this section and the next section can be seen in Figure 3. Revisiting (Eq. 6), the received signal can be written in frequency domain as follows: where ʘ denotes the Hadamard product, Y ∈ C M×N is the frequency domain received matrix, and Z ∈ C M×N is the frequency domain representation of noise w. The elements of frequency domain channel matrix H f ∈ C M×N can be expressed as follows: where T d is the OFDM symbol duration after cyclic prefix (CP) in addition to the original duration T 1 Δf .

Null Subcarrier Estimation
In this step, the entire Y matrix is processed to estimate the null subcarrier locations. Once the null subcarrier location is found for each subblock, the location of radar and communication subcarrier can also be found as it can be seen in Figure 2A.
For each ξ subblock in the m th OFDM symbol, the null subcarrier estimation (n ξ m ) + is performed as follows: (n ξ m ) + min n: n∈{1,...,N/g} |Y ξ m,n |. 2 Afterward, the vector n + ∈ C 1×M·R indicating null subcarriers is constructed by gathering all estimated (n ξ m ) + . Once the null subcarrier location is estimated, locations of radar and communication subcarriers are also known due to the mapping scheme shown in Figure 2A.

De-Chirping
After the null subcarrier estimation, de-chirping of the received sequence is done. Also, it is followed by filling estimated communication subcarriers with zeros. In this step, the fast-time/ slow-time coherent processing interval matrix K ∈ C M×N is formed. Let the matrixX ∈ {−1, 1} M×R denote the known Golay sequences for all OFDM frames. Then, the elements K m,n of matrix K become K m,n Y m,n /X m,β , when n ∈ n + , 0, otherwise, where the index in the Golay sequence matrix is β n·g N .

Delay and Doppler Shift Estimation
Delay and Doppler shift estimation of the proposed method is done via the 2D-periodogram method. The matrix K is utilized to perform periodogram-based radar processing. The output power of the periodogram at the ι th Doppler and ς th range bin is as follows: where sinusoids in K related to object's distance and velocity lead to peaks in P (ι, ς). Then certain distance and velocity values can be found from the related range and the Doppler bin value of peaks. The threshold value is set according to the SNR of the system. Distance d and velocity ] of the paths are found as follows: where c denotes the speed of light.

Complex Attenuation Estimation
In this step, the complex attenuation factor of the channel is estimated. It is assumed that the total number of targets is known in advance. The number of targets can be obtained by simply putting a threshold in Eq. 14. Let the vectorỸ ∈ C M·R×1 denote the complex symbols extracted from the estimated radar subcarrier locations, which are also affected by the noisẽ Z ∈ C M·R×1 . Considering the frequency domain channel shown in Eq. 11,Ỹ can be written as follows: Y Ω(τ, ν,X)α +Z, and the MR × P matrix Ω as a function of τ, ν,X becomes Ω(τ, ν,X) where Ψ(] p ) e −j2π]pT d (m−1) and Φ(τ p ) e −j2π(n−1− N/2 )τp/T . Then the estimation of complex attenuation factor vector α ∈ C P×1 for all targets can be found as follows: where (·) H denotes the Hermitian operator.

COMMUNICATION FUNCTIONALITY AND DECODING ALGORITHMS
In this section, the decoding process of transmitted information is explained with two different options. Both options are numerically evaluated in Section 5.

Null Subcarrier Estimation-Based Decoding
Here, the null subcarrier estimation in the first step of radar processing is utilized to determine the location of communication symbols and the index bits b 1 . The QAM symbols on estimated communication subcarrier locations are demodulated via wellknown maximum likelihood (ML) QAM demodulation, which calculates the minimum Euclidean distance with the possible symbols in a complex symbol set.

Maximum-Likelihood-Based Decoding
The full-ML detector considers all possible subblock realizations by searching for all possible subcarrier index combinations, the signal constellation points, and corresponding radar symbol for each subblock in order to make a joint decision on the active indices and the constellation symbols. For the m th OFDM symbol, the full-ML detector is employed in each subblock by minimizing the following metric: where c denotes all possible subblock realizations, including radar symbol, null subcarrier, and QAM symbols.

Soft Decoding With a Channel Decoder
This section includes the log-likelihood ratio (LLR) calculations of the proposed OFDM-IM structure. Calculated LLRs are sent to the channel decoder as input. For the sake of fair comparison, we have used the log-sum approximation technique (Caire et al., 1998) to calculate approximate LLR of the proposed OFDM-IM scheme and conventional OFDM with a comb-type pilot scheme (Ozkaptan et al., 2018). The LLR of the bit i (b m ξ (i)) of the proposed OFDM-IM JCR scheme at the ξ th subblock of the m th OFDM symbol Λ m ξ (i) is as follows: where h m ξ ∈ C 1×g denotes the channel state information (CSI) through ξ th subblock of the m th OFDM symbol. Also, b m ξ (i) ∈ S ′ {0, G a , G b , S} is the all possible symbol set that is inserted into the proposed OFDM-IM JCR structure.

Complexity Analysis
Here, the complexity analysis of the proposed decoding methods is performed to compare decoding schemes. The complexity of null subcarrier-based decoding becomes, in terms of complex multiplications, ∼ O(k|S|), whereas the complexity of ML-based decoding is ∼ O(2 b 1 |S| k ). The complexity of LLR calculation, in terms of complex multiplications, is also ∼ O(2 b 1 |S| k ); however, this calculation is followed by the complex channel decoding process. Therefore, the overall complexity is higher than that of the previous two methods.

SIMULATION RESULTS
This section demonstrates the applicability of the proposed OFDM-IM JCR waveform structure in the presence of multiple and mobile targets. Mean-square error (MSE) and the bit error rate (BER) metrics are used to demonstrate the radarsensing and communication performance of the proposed scheme. Simulation parameters depending on radar and communication requirements are shown in Table 1. It is assumed that the maximum delay τ max caused by targets is smaller than the CP length of each OFDM symbol. The total symbol energy is set the same for the proposed scheme and the conventional OFDM JCR scheme. For LDPC codes, NR LDPC codes with code rates 0.5 and 0.75 are implemented, where the code length is set to be 384, and the iteration number of decoding is 20. Interleaving after LDPC encoding is not implemented to have fair comparison between conventional OFDM JCR and the proposed OFDM-IM JRC scheme. The number of subcarriers in one OFDM symbol is 256 due to the code length and SE, and 180 OFDM symbols are transmitted with one OFDM frame. BER and MSE results are obtained with repeated 1,000 Monte Carlo simulations to get ensemble averages. To compare the proposed OFDM-IM JCR scheme, the conventional OFDM JCR scheme with comb-type pilot arrangement is implemented, which is proposed in Ozkaptan et al. (2018); one out of four subcarriers is assigned with a pilot symbol. For communication symbols, QPSK modulation is utilized. Therefore, the spectral efficiency of both schemes becomes 1.5 bit per subcarrier. The power delay profile (PDP) of the channel is determined as an exponentially decaying function where the power of complex channel coefficients is set as E[α p (c)] 2 ηe −p , where η denotes the normalization factor and p is the target index. Each tap amplitude follows the Rayleigh distribution.
After evaluating the complex attenuation factor α p in Eq. 5, the estimation of channel matrixĤ f is performed by using the obtained values of Doppler shifts and delays which is done previously via radar symbols on OFDM-IM. Finally, these estimates are used to demodulate communication symbols in the proposed OFDM-IM waveform structure. MSE is calculated withĤ f and H f matrices, , where E [·] denotes the expected value.
The proposed scheme is compared with the conventional OFDM scheme where the channel estimation is done with comb-type pilot arrangements explained in the study by Ozdemir and Arslan (2007). It is well known that the minimum MSE is obtained when the pilots are equi-spaced (comb-type arrangement), when the MSE of the least-square (LS) estimation is utilized. Also, the use of the same subcarriers for pilot allocation among subsequent OFDM symbols is a widely used pilot arrangement scheme Ozdemir and Arslan (2007). Simple LS estimate is employed to find initial channel estimates on pilot subcarriers, where the correlation across the OFDM carriers and OFDM symbols is not exploited. The channel frequency response (CFR) vector is obtained by implementing linear interpolation over all pilots. This is followed by replacing the samples after the length of the CP with zeros in the time domain by taking IFFT. The noise reduced signal is then transformed back into the frequency domain via FFT operation to obtain the estimated value of the channel over each OFDM symbol. The number of pilots, symbols, and subcarrier spacing (Δf) used in conventional OFDM for comparison are the same as those of the proposed OFDM-IM JCR structure. Figure 4 illustrates the radar functionality of the proposed scheme compared to conventional OFDM when SNR is 10 dB. It is obviously seen in Figure 4A that a fixed pilot design limits the unambiguous radar range, whereas it is not the case in the proposed scheme as seen in Figure 4B. Figure 5 shows the performance analysis for two stationary and one moving targets and their respective distances. In Figure 5A, conventional OFDM JCR with a comb-type pilot arrangement, OFDM-IM with interpolation of irregularly spaced pilots, and the proposed radar-based OFDM-IM waveform are compared considering their MSE of channel estimation. It can be observed that the proposed scheme significantly outperforms the two existing conventional schemes. As a channel decoder to perform BER simulations, LDPC (low-density parity check) is used with two different rates, which are 0.5 and 0.75. Figure 5B shows the benefit of the proposed scheme in terms of error performance compared to conventional OFDM with pilots. It also compares the proposed OFDM-IM JCR scheme with two different decoding types such as the null subcarrier-based and ML-based decoding explained in Section 4. It can be observed that our scheme outperforms the conventional schemes in both detection methods. The ML-based scheme provides better error performance than the proposed nullbased decoding; however, it has a much higher complexity. Figure 6 shows the performance analysis for one stationary and three moving targets and their respective distances. From Figures 6A,B, the conventional OFDM scheme reaches error floor at ∼ 10 -3 BER, whereas proposed scheme exhibits promising performance reaching beyond 10 -4 BER. There exists a trade-off between complexity and performance for ML detection over null subcarrier-based detection. It is observed that since there are more active targets in Figure 5, due to multiple Doppler effects, the performance of the conventional OFDM scheme degrades. However, the performance of the proposed OFDM-IM JCR scheme is maintained the same as in Figure 5B, claiming the superiority of the proposed scheme against high Doppler effect. Also, it should be noted that the conventional OFDM with 0.75 code rate performs worse than the uncoded system in Figures  5B, 6B. It is the case because we do not implement interleaving after encoding the bit sequence for both the conventional OFDM JCR scheme and the proposed OFDM-IM JRC scheme. It is can be seen that the proposed OFDM-IM performs better and index bits inherently introduce diversity over one OFDM-IM symbol.

CONCLUSION
In this study, a novel JCR waveform utilizing OFDM-IM and its promising properties has been proposed. The system model description along with numerical and computational evaluations has been provided. Computer simulations show that the proposed scheme provides not only improvement in error performance for communication but also enhanced radar performance when compared to OFDM-JCR schemes in terms of BER and MSE performance. Also, it is shown that the proposed scheme increases the maximum unambiguous radar range compared to conventional OFDM with a comb-type pilot design. For future research direction, generalization of the proposed OFDM-IM scheme for different subblocks and active subcarrier numbers will be studied.

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.