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The purpose of this paper is to present a novel approach in terrain following (TF) flight using fuzzy logic. The fuzzy controller as presented in this work decides where and how the aircraft needs to change its altitude. The fast decision‐making nature of this method promises real‐time applications even for tough terrains in terms of shape and peculiarities. The method could always assist to design trajectories in an off‐line manner.

To achieve the aforementioned goal, the method effectively incorporates the dynamics of the aircraft. Basically, the mathematical method employs special relationships among existing slope of the terrain and its derivative together with aircraft flying speed and height above the ground to construct suitable fuzzy rules. The fuzzification method is based on Sugeno and three rule‐sets are used for fuzzy structure. These rules are implemented using Fuzzy Logic Toolbox in MATLAB.

Different case studies conducted for flights in XZ‐plane show the effectiveness of the method as compared to other existing methods available to the authors. The results illustrate a good tracking based on the fuzzy approach while using both 18 and 27 rules with respect to the optimal approach. Furthermore, it is shown that decreasing number of rules from 27 to 18 rules causes only minor changes in the solution.

The current work offers a new approach in low‐level flights where maintaining a suitable height above the ground is essential. This is especially important for civil aircraft approaching an airport with low or non‐visibility and during aborted landing manoeuvres. The domain of the current work is however confined to only planning of TF manoeuvres. Nevertheless, the work could be expanded into TF/terrain avoidance and three‐dimensional manoeuvres which are not in the scope of the current work.

The current work addresses the problems associated with low‐level flight; such as TF using artificial intelligence and fuzzy logic. The provided intelligence helps the aircraft conduct TF manoeuvres by understanding the general patterns of the existing terrain. The method is fast enough to be applied for real‐time applications.

Managing mega infrastructure projects (MIPs) is more complex because of time, size, social, environmental and financial implications. This study aims to address the management approaches, complexity and risk factors involved in MIPs. The study focuses on project success criteria and their individual effects on the success of MIPs.

To address the challenges and identify the most influencing factor for the success of MIPs, the study deployed a cross-sectional survey approach. Six hundred eighty-two usable samples were collected from the respondents to understand the impact of predetermined factors on the success of MIPs. The structural equation model and artificial neural network approach were used to derive the importance of factors affecting the success of MIPs.

The study's outcome confirms that all three influencing factors: feasibility studies, community engagements and contract selection, have a significant positive impact on the success of MIPs. Community engagement amongst all three has the most influential predictor for the success of MIPs.

The developed model will enable practitioners and policymakers from Indian construction companies and other emerging nations to concentrate on recognized risk reduction variables to enhance project success criteria and project management success, especially for MIPs.

This paper aims to examine the integration of lateral transshipment and road vulnerability into the humanitarian relief chain in light of affected area priority to address equitable distribution and assess the impact of various parameters on the total average inflated distance traveled per relief item.

After identifying comprehensive critical criteria and subcriteria, a hybrid multi-criteria decision-making framework was applied to obtain the demand points’ weight and ranking in a real-life earthquake scenario. Direct shipment and lateral transshipment models were then presented and compared. The developed mathematical models are formulated as mixed-integer programming models, considering facility location, inventory prepositioning, road vulnerability and quantity of lateral transshipment.

The study found that the use of prioritization criteria and subcriteria, in conjunction with lateral transshipment and road vulnerability, resulted in a more equitable distribution of relief items by reducing the total average inflated distance traveled per relief item.

To the best of the authors’ knowledge, this study is one of the first research on equity in humanitarian response through prioritization of demand points. It also bridges the gap between two areas that are typically treated separately: multi-criteria decision-making and humanitarian logistics.

This is the first scholarly work in Shiraz focused on the equitable distribution system by prioritization of demand points and assigning relief items to them after the occurrence of a medium-scale earthquake scenario considering lateral transshipment in the upper echelon.

The paper clarifies how to prioritize demand points to promote equity in humanitarian logistics when the authors have faced multiple factors (i.e. location of relief distribution centers, inventory level, distance, lateral transshipment and road vulnerability) simultaneously.

Evaluating project success within the construction industry presents challenges due to the unique characteristics of the sector, the complexity of projects, and the involvement of diverse stakeholders. Conducting a bibliometric analysis, this paper aims to unravel the major research themes and methodologies utilised by researchers in studying the critical success criteria for construction projects, as well as extracting these success criteria.

The researchers systematically searched and screened 95 papers from Scopus and Web of Science (WoS) databases. This study conducted research focus parallelship network (RFPN) analysis and keywords co-occurrence network (KCON) analysis using BibExcel and Gephi to cluster the papers, illuminate the relationships among keywords within each cluster, and identify the primary research directions.

Using the RFPN analysis, this study classified the papers into three distinct clusters: infrastructure and public projects success, risk and knowledge management, and contractors and procurement management. Statistical techniques such as structural equation modelling (SEM) and multi-criteria decision-making methods such as analytic hierarchy process (AHP) have been used to analyse project success in the construction industry.

Considering the intensified demand for streamlined digital interactions and the increasing emphasis on sustainability and safety performance, construction companies are recommended to allocate greater investments toward the automation and digitisation of their products and processes. Prioritising modular construction and embracing transformative technologies alongside data science is crucial for enabling well-informed decision-making, and enhancing project success.

This study contributes to the existing body of knowledge by conducting a quantitative and systematic evaluation of the literature on project success criteria in the construction industry and uncovering key research areas. It addresses the pressing need to understand the complexities of construction projects amidst evolving industry dynamics and emerging disruptions. Moreover, by highlighting the implications of digital innovations and modular construction, this study urges deeper exploration into their impact on project performance and stakeholder satisfaction. This research sets a comprehensive framework for investigating the interplay between project complexity, technological advancements, and sustainable practices in the construction sector, paving the way for strategic advancements in the field.

This study aims to investigate the monitoring role of ownership structure (OWS) on real earnings management (REM) practices; previous studies primarily examined the effect of OWS on accrual-based earnings management.

The sample of this study is 490 companies listed on the Malaysian Stock Exchange during the period 2013–2016 (1,960 company-year observations). The regression of a feasible generalized least square was used for data analysis. The authors use three regression models ordinary least squares, panel-corrected standard errors and Driscoll–Kraay standard errors to corroborate the findings and also examine alternative REM measures.

Analysis of the data shows that family, foreign and institutional ownership has a positive link with the quality of financial reporting and, to a large extent, is capable of alleviating REM. The findings also indicate that some form of OWS significantly affects REM, corroborating existing theories on corporate governance (CG) and the perspectives of practitioners.

The evidence concerns the significant role played by the OWS in reducing REM activities. The findings are useful in support of regulatory activities, particularly in the design of policies to regulate the OWS. The results may also provide useful insights to inform other policymakers, investors, shareholders and researchers about the active role of family, foreign and institutional investors in monitoring Malaysia's public listed companies (PLCs) to strengthen CG practices. This also leads to less REM and enhances the quality of financial reporting.

To the authors' knowledge, this work is pioneering research from a developing country, specifically from Malaysia, to investigate the manner in which all possible OWSs influence REM. More importantly, the study recommends that regulators and researchers do not envisage OWS as a holistic phenomenon.

The purpose of this paper is to present research into reducing the aircraft design cycle period, by reducing the necessary number of design cycle iterations. The design cycle period is one of the main characteristics of the design process and design cycle iterations play a major role in the design cycle period.

To achieve the above‐mentioned goal, the paper presents a mathematical model of iterations for the aircraft design process. This model describes the design coupled tasks as a discrete‐linear time invariant dynamic system. This model also helps identify tasks which are the most important for generating iterations. This new method basically helps break information cycles that create iterations among important tasks.

Studies conducted on a general aviation (GA) airplane (FAJR‐3) design process show the success of the suggested approach. This procedure eventually leads to an expedited convergence rate for the design iterations. That is, through proper breaking of information cycles, the convergence rate of the most dominant design mode could be increased by up to 31 percent. The process also leads to decoupling of the so‐called “coupled parts of design process,” which in turn leads to a more modular design with relatively easier management.

This method offers a new way of managing aircraft design processes while having to deal with constraints such as time and resources. The approach could be easily implemented as it manages any complex design‐process based on its resemblance to a dynamic system. The method can also be used as a component of an Integrated Airframe Design (IAD), as a tool for “Cycle time reduction”.

The advantage of this new approach, over other existing ones, lies in its ability to distinguish the important information cycles in a systematic manner. This helps to break the design process in a way that guarantees the increase in convergence speed of the whole design process.

Housing is one of the basic necessities of humankind for survival and purchasing a home is often a substantial milestone. Individuals exhibit complex behaviour influenced by various factors while making decisions related to the purchase of residential properties. While most of the earlier studies have focused on understanding the purchasing behaviour of home buyers’ in developed countries, the research is limited on this topic in developing countries such as India. The booming information technology industry has rapidly increased the demand for residential properties among the migrant population in Bengaluru, India’s largest technology hub. Real estate developers strive to meet the requirements of prospective customers through innovative ways but face challenges in a competitive market.

It is essential to understand the factors influencing home buyers’ purchase attitudes for increasing property sales. However, a lack of research on this subject is identified in the emerging city of Bengaluru. This study aims to analyse the determinants of home buying decisions for potential investors in Bengaluru through structural equation modelling to provide insights for the real estate industry to construct houses as per their customers’ needs.

It was found that housing amenities, financial aspects, location conveniences and marketing services significantly influenced home buyers’ purchase decisions, whereas housing features do not.

The findings of this study offer valuable insights for governmental bodies to implement appropriate policies and for builders to design properties with features that cater to the target population, thereby promoting sustainable growth in the real estate industry.

The purpose of this paper is to develop a behavioral mode (as opposed to a numerical one), which considers any parasitic behavior as an indication of a potentially contributing element in the overall system deficiency.

Similar to that associated with general systems theory, this paper concentrates on the organization and functions of natural systems which exhibit a loss of efficiency due to some general parasitic behavior. Based on the philosophy of “Bionics”, it clarifies the functional architecture and root-cause of this behavior in living systems in order to model subsystems characteristics which exhibit the same behavior in a complex system. In addition, a case study is done on the so-called “Eurozone financial difficulties” to examine the idea of this paper. However, limited access to proprietary financial data limits the generalization of the theory and therefore special case studies seem to be necessary.

The study of “parasites” and their associated functions introduces one possible explanation for the shortage of resources in systems. This concept could also help understand “interfaces” in a new perspective, where changing the system boundary could alter the type of interfaces in the system.

This work is applicable where there is a competition for the limited resources. Resource-based systems, such as financial systems, could be the subject of parasitic attack, and therefore, this work could build an appropriate foundation to understand and prevent this kind of danger to the system.

The presented behavioral model would be very useful to predict failures in systems which are rich in the resources. Detailed analysis on the subject, presented in this paper, provides initial knowledge to prevent shortage in system resources, caused by a general parasitic behavior.

Attitude determination and control subsystem (ADCS) is a vital part of earth observation satellites (EO-Satellites) that governs the satellite’s rotational motion and pointing. In designing such a complicated sub-system, many parameters including mission, system and performance requirements (PRs), as well as system design parameters (DPs), should be considered. Design cycles which prolong the time-duration and consequently increase the cost of the design process are due to the dependence of these parameters to each other. This paper aims to describe a rapid-sizing method based on the design for performance strategy, which could minimize the design cycles imposed by conventional methods.

The proposed technique is an adaptation from that used in the aircraft industries for aircraft design and provides a ball-park figure with little engineering man-hours. The authors have shown how such a design technique could be generalized to cover the EO-satellites platform ADCS. The authors divided the system requirements into five categories, including maneuverability, agility, accuracy, stability and durability. These requirements have been formulated as functions of spatial resolution that is the highest level of EO-missions PRs. To size, the ADCS main components, parametric characteristics of the matching diagram were determined by means of the design drivers.

Integrating the design boundaries based on the PRs in critical phases of the mission allowed selecting the best point in the design space as the baseline design with only two iterations. The ADCS of an operational agile EO-satellite is sized using the proposed method. The results show that the proposed method can significantly reduce the complexity and time duration of the performance sizing process of ADCS in EO-satellites with an acceptable level of accuracy.

Rapid performance sizing of EO-satellites ADCS using matching diagram technique and consequently, a drastic reduction in design time via minimization of design cycles makes this study novel and represents a valuable contribution in this field.

The construction of megaprojects often involves substantial risks. While insurance plays an important role as a traditional risk transfer means, owners and insurance companies may still suffer huge losses during the risk management process. Therefore, considering the strong motivation of insurance companies to participate in the on-site risk management of megaprojects, this study aims to propose a collaborative incentive mechanism involving insurance companies, to optimize the risk management effect and reduce the risk of accidents in megaprojects.

Based on principal-agent theory, the research develops the static and dynamic incentive models for risk management in megaprojects, involving both the owner and insurance company. The study examines the primary factors influencing incentive efficiency. The results are numerically simulated with a validation case. Finally, the impact of parameter changes on the stakeholders' benefits is analyzed.

The results indicate that the dynamic incentive model is available to the achievement of a flexible mechanism to ensure the benefits of contractors while protecting the benefits of the owner and insurance company. Adjusting the incentive coefficients for owners and insurance companies within a specified range promotes the growth of benefits for all parties involved. The management cost and economic benefit allocation coefficients have a positive effect on the adjustment range of the incentive coefficient, which helps implement a more flexible dynamic incentive mechanism to motivate contractors to carry out risk management to reduce risk losses.

This study makes up for the absence of important stakeholders in risk management. Different from traditional megaproject risk management, this model uses insurance companies as bridges to break the island effect of risk management among multiple megaprojects. This study contributes to the body of knowledge by designing appropriate dynamic incentive mechanisms in megaproject risk management through insurance company participation, and provides practical implications to both owner and insurance company on incentive contract making, thus achieving better risk governance of megaprojects.