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As an essential component of Islamic governance for ensuring religious compliance, Shari’ah annual reports (SARs) play an important role in providing communication between Shari’ah board (SB) members and stakeholders. This paper aims to determine the ethical disclosure in SARs to identify how close the Shari’ah disclosure to the standards set by AAOIFI and also substantive morality of Islam. The research also aims to examine the factors determining disclosure performance.

Two disclosure indices are developed to generate data from the SARs: the AAOIFI standards for Shari’ah governance index for form related approach, an Islamic ethicality augmented index reflecting on substantive morality approach. The sample consists of 41 Islamic banks from 15 different countries for the period of 2007–2014. Sampled 305 SARs were examined through disclosure analysis in line with the two indices developed for this study. The econometric analysis was run to identify the factors determining disclosure performance.

The findings suggest that AAOIFI guidelines have an influence on the level of disclosure, even if Islamic banks have not adopted them. However, the level of disclosure for the ethically augmented index is found to be very limited with reliance on general statements in most of the cases. As part of determining factors, the popularity of Shari’ah scholars is significant for both indices, while the existence of an internal Shari’ah auditing department holds some explanatory power. The adoption of AAOIFI standards at the country level, the regulatory quality and the duration of Sharīʿah-compliance are particularly deterministic factors in terms of complying with AAOIFI standards for SARs.

Although SB is the most crucial division of corporate governance in Islamic banks in terms of securing the “Islamic” identity of these institutions, their most important communication instrument, namely, SAR, has not been explored sufficiently, alongside an insufficient attempt to constitute Islamic corporate governance. Initially, this study attempted to constitute an Islamic corporate governance framework as a theoretical construct, which provides context for the empirical part of the research and this should be considered a novel approach. Second, the empirical part of the research aims to fill the gap observed in the literature such as small sample size and index construction-related matters. This research is conducted with a larger sample size as compared to the available studies in the literature and it has developed two indices for disclosure analysis along with developing an Islamic morality-based index beside an index based on AAOIFI standards.

In Turkey, dependence on foreign countries for energy is a problem which upsets all economic balances. Turkey’s biggest fossil energy source is lignite coal. Therefore, energy conversion of lignite in thermal plants, causing minimum environmental effect is extremely important. The basic problem in terms of the combustion technology is to improve the combustion technology that can burn the low-qualified fuels that do not have standard fuel features (lignite, peat, schist). The most suitable technology today for the efficient and clean combustion of nonstandard low-qualified fuels is the combustion at fluidized-bed technology. In this study, CO₂ emission that occurs during the combustion of Orhaneli coal that is one of our native low-qualified lignite, has been investigated according to the experimental study.

For this combustion experiment, laboratory-scaled circulating fluidized-bed (CFB) process that exists at TÜBITAK-MAM Energy Institute which has been designed and used before has been used. The effect of excess-air coefficient, combustion type and bed temperature to the greenhouse gas formation and CO₂ emission has been investigated experimentally. In terms of flue gas emissions, it has been detected that the decrease of the amount of CO₂ that has occurred has no positive effects on combustion efficiency, water vapor, SO₂, NO_x, CO and other gases which occur during deficient combustion must be thought as a whole and each reaction affects each other similar to complex reactions.

As a consequence of measuring CO₂ emissions over 10 minute periods, CO₂ emissions are 12.43 percent average, CO₂ decreases at different air coefficient values; Often form undesirable side reactions such as CO, NOx with back and forth reactions.

The importance of aerodynamic structure of the system, and the losses and leakages forming in the system has been observed experimental and affected parameters are evaluated.

A vertically stacked, three layer hybrid Hilbert fractal geometry and serpentine radiator-based patch antenna is proposed and characterized for medical implant applications at the Industrial, Scientific and Medical band (2.4-2.48 GHz). Antenna parameters are optimised to achieve miniaturized, biocompatible and stable transmission characteristics. The paper aims to discuss these issues.

Human tissue effects on the antenna electrical characteristics were simulated with a three-layer (skin, fat and muscle) human tissue model with the dimensions of 180×70×60 mm3 (width×height×thickness mm3). Different stacked substrates are utilized for the satisfactory characteristics. Two identical radiating patches are printed on Roger 3,010 (ε _r=10.2) and Alumina (ε _r=9.4) substrate materials, respectively. In addition, various superstrate materials are considered and simulated to prevent short circuit the antenna while having a direct contact with the metallization, and achieve biocompatibility. Finally, superstrate material of Zirconia (ε _r=29) is used to achieve biocompatibility and long-life. A finite element method is used to simulate the proposed hybrid model with commercially available Ansoft HFSS software.

The antenna is miniaturized, having dimensions of 10×8.4×2 mm3 (width×height×thickness mm3). The resonance frequency of the antenna is 2.4 GHz with a bandwidth of 100 MHz at return loss (S11) of better than −10 dB characteristics. Overall, the proposed antenna have 50 Ω impedance matching, −21 dB far field antenna gain, single-plane omni-directional radiation pattern properties and incident power of 5.3 mW to adhere Specific Absorption Rate regulation limit.

Vertically stacked three layer hybrid design have miniaturized characteristics, wide bandwidth, biocompatible, and stable characteristics in three layer human tissue model make this antenna suitable for implant biomedical monitor systems. The advanced simulation analysis of the proposed design constitutes the main contribution of the paper.

This study aims to examine the current state of the research activities of scholars in the hospitality and tourism field by analyzing the first 20 years of the new millennium.

Longitudinal analyses using 14,229 journal articles as data source were realized by adopting BibExcel, Gephi and VOSviewer network analysis software packages.

This study provides a comprehensive overview of the hospitality and tourism research based on authorship and social network analysis, with patterns of prolific authors compared over four distinct periods.

The hospitality and tourism academic society is clearly illustrated by tracing academic publication activities across 20 years in the new millennium. In addition, this study provides a guide for scholars to search for multidisciplinary collaboration opportunities. Government agencies and non-governmental organisations can also benefit from this study by identifying appropriate review panel members when making decisions about hospitality- and tourism-related proposals.

To the best of authors’ knowledge, this study is the first to use bibliometric analysis in assessing research published in leading hospitality and tourism journals across the four breakout periods in the new millennium.

Availability of accurate and reliable information in financial markets helps investors make well-informed decisions on capital allocations which is beneficial for long-term economic growth. In this regards, the role of auditing firms that inspect the financial statements of the publicly traded companies in sound operation of financial markets has been increasing. The Capital Market Board of Turkey (CMBT) has the task and responsibility of investigating fraudulent information disseminated by the firms whose stocks are traded in Borsa Istanbul. The investigations can lead to monetary penalties if fraud is proven and the results are published by CMBT in its weekly bulletin. The present study aims to examine the effect of announcements of financial irregularities of companies in CMBT Bulletin on the performance of the relevant company stock in the short term.

This study uses abnormal return, cumulative abnormal return and cumulative average abnormal return as metrics and parametric, as well as non-parametric tests to ascertain whether the announcements of financial irregularities in company operations have any statistically significant effect on the return of its stock.

The results indicate that publication of the financial penalty news by CMBT in its bulletin has almost no statistically significant influence on the performance of the relevant companies’ stock in Borsa Istanbul. The findings indicate that either the investors in this particular markets do not consider such news relevant to long-term success of the firm or the announcement does not provide any new information and penalties have been priced into the stock before the announcement in the bulletin.

In literature there is no more research about the effect of the announcements of administrative monetary penalties and crime complaints on the stock returns.

The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times.

Grey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students.

In this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios.

It is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters.

Grey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.

Fluctuations in construction output has an adverse effect on the construction industry and the economy due to its strong linkage. Developing reliable and accurate predictive models is vital to implementing effective response strategies to mitigate the impact of such fluctuations. The purpose of this paper is to compare the accuracy of two univariate forecast models, i.e. Box-Jenkins (autoregressive integrated moving average (ARIMA)) and Neural Network Autoregressive (NNAR).

Four quarterly time-series data on the construction output of Hong Kong were collected (1983Q1-2014Q4). The collected data were divided into two parts. The first part was fitted to the model, while the other was used to evaluate the predictive accuracy of the developed models.

The NNAR model can provide reliable and accurate forecast of total, private and “others” construction output for the medium term. In addition, the NNAR model outperforms the ARIMA model, in terms of accuracy.

The applicability of the NNAR model to the construction industry of other countries could be further explored. The main limitation of artificial intelligence models is the lack of explanatory capability.

The NNAR model could be used as a tool for accurately predicting future patterns in construction output. This is vital for the sustained growth of the construction industry and the economy.

This is the first study to apply the NNAR model to construction output forecasting research.

The purpose of this paper is to analyze the development of a novel approach for automated terrain mapping a robotic vehicles path tracing.

The approach includes stitching of images, obtained from unmanned aerial vehicle, based on ORB descriptors, into an orthomosaic image and the GPS-coordinates are binded to the corresponding pixels of the map. The obtained image is fed to a neural network MASK R-CNN for detection and classification regions, which are potentially dangerous for robotic vehicles motion. To visualize the obtained map and obstacles on it, the authors propose their own application architecture. Users can any time edit the present areas or add new ones, which are not intended for robotic vehicles traffic. Then the GPS-coordinates of these areas are passed to robotic vehicles and the optimal route is traced based on this data

The developed approach allows revealing impassable regions on terrain map and associating them with GPS-coordinates, whereas these regions can be edited by the user.

The total duration of the algorithm, including the step with Mask R-CNN network on the same dataset of 120 items was 7.5 s.

Creating an orthophotomap from 120 images with image resolution of 470 × 425 px requires less than 6 s on a laptop with moderate computing power, what justifies using such algorithms in the field without any powerful and expensive hardware.