Integrating Artificial Intelligence (AI) into Unmanned Aerial Vehicle (UAV) operations has advanced efficiency, safety, and decision-making. This study addresses critical gaps in UAV methods, including insufficient integration of human factors, operator variability, and the lack of systematic error analysis. To overcome these challenges, a novel approach combines the Analytic Hierarchy Process (AHP) with three core human factors models: the Observe-Orient-Decide-Act (OODA) loop, the Human Factors Analysis and Classification System (HFACS), and the SHELL model. An online survey was conducted across diverse UAV operator groups to prioritize critical factors within each model. Additionally, real-time monitoring of heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) was conducted during UAV operations at various automation levels with different experience levels. Visualization through boxplots and percentage change matrices provided insights into operator stress and workload across automation levels. Integrating AHP findings and physiological data revealed significant differences in operator prioritization, highlighting the need for tailored AI-UAV strategies. This research combines survey data with real-time physiological monitoring, offering visions into optimizing human-AI interaction in UAV operations and providing a foundation for improving AI integration and operator strategies.

Purpose: Ineffective communication consequences can be life-threatening and drastic. Communication misunderstandings are frequently reported in incidents, accidents and occurrences. This research paper aims to evaluate operator communication load in highly automated systems; distinguish and highlight the communication error factors during flight operations from different perspectives; and provide suggestions to operators to decrease the rate of misunderstandings in aviation communication. Design/methodology/approach: This study is based on a questionnaire that investigated the critical communication load, including aviation training, standard phraseology, operators’ native language and cultural background. In addition to the effect of using controller–pilot data link communications will be discussed widely. In this research, 110 responses were obtained from pilots and air traffic controller (ATCOs) that vary in 44 countries; approximately 20% were ATCOs, and 75% were pilots. Findings: This study was designed to assess the level of aviation operators communication load in highly automated systems, identify and illustrate the factors that contribute to communication errors during flight operations from multiple viewpoints, and offer recommendations to operators to minimize the rate of misunderstandings in aviation communication. Originality/value: This research deals with evaluating the operators’ communication load, which is crucial for the air traffic safety and efficiency.

This research reviews the current literature on the impact of Artificial Intelligence (AI) in the operation of autonomous Unmanned Aerial Vehicles (UAVs). This paper examines three key aspects in developing the future of Unmanned Aircraft Systems (UAS) and UAV operations: (i) design, (ii) human factors, and (iii) operation process. The use of widely accepted frameworks such as the "Human Factors Analysis and Classification System (HFACS)" and "Observe– Orient–Decide–Act (OODA)" loops are discussed. The comprehensive review of this research found that as autonomy increases, operator cognitive workload decreases and situation awareness improves, but also found a corresponding decline in operator vigilance and an increase in trust in the AI system. These results provide valuable insights and opportunities for improving the safety and efficiency of autonomous UAVs in the future and suggest the need to include human factors in the development process.

In the dynamic sphere of building energy systems, this study explores advancements in energy integration, storage technologies, management practices, and occupant behavior, assessing sustainable energy practices, including emerging technologies like fuel cells and energy storage systems. It underscores the significance of efficient energy management, considering both renewable and conventional energy mechanisms. The study comprises four key strata: (i) a thorough literature review of recent energy trends, (ii) a comparative study of global energy patents using the World Intellectual Property Organization (WIPO) database, (iii) a comprehensive analysis of building-energy patents, and (iv) expert-guided Analytic Hierarchy Process (AHP) evaluation. These realms encompass five primary sources: (i) energy-efficient building design, (ii) intelligent building automation, (iii) optimizing energy systems integration, (iv) energy storage, and (v) energy management and optimization. Findings reveal energy storage's dominance, with water energy storage and emerging hydrogen technology leading the trajectory. Global energy patent scrutiny underscores China, the United States, and Japan as influential players in optimizing energy markets. The research shapes energy futures, identifies gaps, and drives sustainable energy practices within the built environment, serving as a compass for policymakers and researchers.

Purpose: The purpose of this paper is to investigate and evaluate the subjective decision-making of pilots during final approach with varying degrees of experience for landing and go-around. Design/methodology/approach: In this research, the “Lorenz Attractor” was modified and used to model the subjective decision-making of pilots during the final approach. For landing and go-around situations, “hesitation frequency” and “decision-making time” were calculated for the subjective decision-making of pilots. Findings: In this research, the modified Chaotic Lorenz Model was used on MATLAB with varying degrees of experience, namely, student pilots, less-skilled pilots, experienced pilots and well-experienced pilots. Based on the outcomes, the less-skilled pilot needs nearly four times more decision-making time on landing or go-around compared to the well-experienced pilot during the final approach. Practical implications: Operators (pilots, air traffic controllers) need to make critical and timely decisions in a highly complex work environment, which is influenced by several external elements such as experience level and human factors. According to NASA, 80% of aviation accidents occur due to human errors specifically over the course of the aviation decision-making process in dynamic circumstances. Due to the consequences of this research the operators' training should be redesigned by assisting flight instructors on the weaknesses of pilots. Originality/value: This research explores the endogenous dynamics of the pilot decision-making process by applying a novel “Chaotic Lorenz Model” on MATLAB. In addition, the operator's total decision time formula was improved by including the decision reviewing time and external factors. Moreover, subjective decision-making model created by the current authors and Wicken's information model were modified to the highly automated systems.