Search Results (7,457)

The routine assessment of pilot fatigue is paramount to ensuring aviation safety. However, current designs of pilot fatigue factors often lack the comprehensiveness needed to fully account for the dynamic and cumulative nature of fatigue. To bridge this gap, this study introduces a biomathematical fatigue model (BFM) that leverages system dynamics theory, integrating a dynamic feedback mechanism for fatigue information. The novelty of this approach lies in its capability to continuously capture and model fatigue fluctuations driven by varying operational demands. A comparative analysis with international methodologies for evaluating cumulative fatigue on weekly and monthly scales demonstrates that the proposed BFM effectively reproduces variations in pilot fatigue characteristics. Moreover, the pilot fatigue coefficient derived from the model provides a robust differentiation of fatigue profiles across diverse work types, making it particularly suitable for estimating cumulative fatigue over monthly intervals. This BFM-based approach offers valuable insights for the strategic planning of flight schedules and establishes an innovative framework for utilizing BFMs in fatigue management. By employing a scientifically grounded evaluation method rooted in system dynamics and the BFM, this study rigorously assesses cumulative pilot fatigue, confirming the model's accuracy in replicating fatigue patterns and validating the efficiency and reliability of the derived fatigue coefficient. Full article

We introduce derangetropy, which is a novel functional measure designed to characterize the dynamics of information within probability distributions. Unlike scalar measures such as Shannon entropy, derangetropy offers a functional representation that captures the dispersion of information across the entire support of a distribution. By incorporating self-referential and periodic properties, it provides insights into information dynamics governed by differential equations and equilibrium states. Through combinatorial justifications and empirical analysis, we demonstrate the utility of derangetropy in depicting distribution behavior and evolution, providing a new tool for analyzing complex and hierarchical systems in information theory. Full article

We analyze the generalization properties of batch reinforcement learning (batch RL) with value function approximation from an information-theoretic perspective. We derive generalization bounds for batch RL using (conditional) mutual information. In addition, we demonstrate how to establish a connection between certain structural assumptions on the value function space and conditional mutual information. As a by-product, we derive a high-probability generalization bound via conditional mutual information, which was left open and may be of independent interest. Full article

Project cost forecasting is a complex and critical process, and it is of paramount importance for the successful implementation of engineering projects. Accurately forecasting project costs can help project managers and relevant decision-makers make informed decisions, thereby avoiding unnecessary cost overruns and time delays. Furthermore, accurately forecasting project costs can make important contributions to better controlling engineering costs, optimizing resource allocation, and reducing project risks. To establish a high-precision cost forecasting model for construction projects in Guangdong Province, based on case data of construction projects in Guangdong Province, this paper first uses the Analytic Hierarchy Process (AHP) to obtain the characteristic parameters that affect project costs. Then, a neural network training and testing dataset is constructed, and a genetic algorithm (GA) is used to optimize the initial weights and biases of the neural network. The GA-BP neural network is used to establish a cost forecasting model for construction projects in Guangdong Province. Finally, by using parameter sensitivity analysis theory, the importance of the characteristic values that affect the project cost is ranked, and the optimal direction for controlling the project cost is obtained. The results showed: (1) The determination coefficient between the forecasting and actual values of the project cost forecasting model based on the BP neural network testing set is 0.87. After GA optimization, the determination coefficient between the forecasting and actual values of the GA-BP neural network testing set is 0.94. The accuracy of the intelligent forecast model for construction project cost in Guangdong Province has been significantly improved after optimization through GA. (2) Based on sensitivity analysis of neural network parameters, the most significant factor affecting the cost of construction projects in Guangdong Province is the number of above-ground floors, followed by the main structure type, foundation structure, above-ground building area, total building area, underground building area, fortification intensity, and building height. The results of parameter sensitivity analysis indicate the direction for cost control in construction projects. The research results of this paper provide theoretical guidance for cost control in construction projects. Full article

In the post-COVID-19 era, tourism and cultural industries have begun to bounce back, and their “revenge tourism” desire has forced “Special Forces-style tourism”, which was popularized throughout Chinese social media in 2023. This study explores a current knowledge gap in understanding how social media inspires Generation Z’s engagement in Special Forces tourism and implications for practical sustainable tourism. By leaning on the SOR model, Social Identity Theory, and Social Learning Theory, this study investigates how supportive and interactive social media environments shape the sustainable engagement intentions of Generation Z. We experimentally find that perceived supportive communication about sustainability issues significantly inflates sustainable engagement intentions in high-interactivity settings, by encouraging flow experiences and vicarious reinforcement that facilitate socially responsible travel decisions. Key findings include identification of supportive online interactions that can facilitate sustainable tourism among born-digital travelers, who enable informed, socially and environmentally responsible tourism behaviors. Full article

Contemporary Management Accounting Practices (MAPs) were developed to address the weaknesses of traditional practices and to meet financial managers’ need for accurate and timely information. Consequently, they contribute to optimal decision-making that enhances firms’ efficiency and competitiveness, leading to improved organizational performance. Simultaneously, the success of Accounting Information Systems (AIS) is essential, as they improve the quality of information and reporting. In information- and competition-intensive environments such as the hotel industry, AIS user satisfaction, as an indicator of AIS success, can play a decisive role in the effective use of contemporary MAPs. The purpose of this paper is to explore the relationship between contemporary MAPs usage and hotel performance, and to investigate the moderating role of AIS user satisfaction. Using hierarchical multiple regression analysis, the findings indicate that the interaction of contemporary MAPs usage and AIS user satisfaction results in improved hotel performance. This study contributes to the current knowledge by developing a framework of the relationship of Management Accounting and Information Technology, through the lens of Contingency Theory and the Information Systems Success Model of DeLone and McLean. Additionally, the findings provide managerial implications for financial managers and IS developers. Full article

Youth mental health concerns, including substance abuse, continue to rise. With high co-morbidity rates and a marked lack of representation from diverse groups in study conceptualization, measurement, and implementation, efforts to understand factors impacting youth mental health from a cultural lens are needed. The theory of emotionality stigma posits that many mental health concerns can be understood based on one’s endorsement of emotionality stigma—the experience of stigma around emotions—which manifests within one’s context. Informed by this theory, the current study aimed to adapt and test a measure of emotionality stigma for diverse youth in combined mental health and substance use treatment. Targeted youth focus groups informed the adaptation of the pre-existing Emotionality Stigma Scale for implementation with diverse youth. Using a mixed methods approach, this measure was then tested for relevance, reliability, and validity in an outpatient youth clinic. Patients (N = 58, aged 13 to 21) reported their emotionality stigma, values, and attachment as part of routine monitoring. Based on qualitative feedback and quantitative analysis, our results illustrate the reliability and validity of the adapted Emotionality Stigma Scale and the relevance of this new measure for assessing youth mental health concerns and treatment outcomes. Areas for continued research are identified, and recommendations for implementation in conceptualization and treatment are provided. Full article

DC converter stations have a high voltage level, a long transmission distance, and complex internal equipment, and contain power electronic devices, which seriously endanger the stable operation of the system itself and the active distribution network at the receiving end when faults occur. Accurate fault analysis and diagnosis are critical to the safe and stable operation of power systems. Traditional fault diagnosis methods often rely on a single source of information, leading to issues such as insufficient information utilization and incomplete diagnostic scope when applied to DC transmission systems. To address these problems, a fault diagnosis method for converter stations based on preliminary identification of the fault range and the fusion of evidence information of the switch signal and electrical quantity is proposed. First, the preprocessing of converter station sequential event recording (SER) events and a statistical analysis of event characteristics are completed to initially determine the range of the fault.Then, a fuzzy Petri net model and a BP neural network model are constructed on the basis of the fault data from a real-time digital simulation system (RTDS), and the corresponding evidence information of the switch signal and electrical quantity are obtained via iterative inference and deep learning methods. Finally, on the basis of D-S evidence theory, a comprehensive diagnosis result is obtained by fusing the switch and electric evidence information. Taking the fault data of a DC converter station as an example, the proposed method is analyzed and compared with the traditional method, which is based on single information. The results show that the proposed method can reliably and accurately identify fault points in the protected area of the converter station. Full article

The terms “Industry 5.0” and “smart logistics” have recently emerged as key concepts within the field of logistics. Nevertheless, the interconnection between these two concepts has been less extensively examined in academic literature, particularly in the context of emerging economies. In the contemporary business context, the logistics industry is seeking to advance sustainable development through the implementation of Industry 5.0. However, the industry is still in its nascent stages of realizing the transformation of smart logistics. Accordingly, the objective of this study is to identify the key drivers of Industry 5.0 in relation to the advancement of smart logistics in the logistics industry in emerging economies. In this study, the initial screening and identification of 15 core drivers was conducted using the fuzzy Delphi method. This involved the collation of the relevant literature and the collection of opinions from experts in the field. The identified drivers were then classified into three groups: sustainability, people-centricity, and resilience. Subsequently, the study adopted the Grey-DEMATEL method, which combines grey system theory with the decision making trial and evaluation laboratory (DEMATEL) technology. This approach enables the effective resolution of complex system issues characterized by uncertainty and incomplete information, facilitating the identification of causal relationships between the drivers and the construction of a centrality–causality outcome diagram. The study identified two key drivers: “government support policies” and “logistics standardization and infrastructure development”. This study represents a preliminary investigation into the ways managers, practitioners and policy makers can leverage Industry 5.0 to advance the field of smart logistics within the logistics industry. Full article

This paper discusses the foundation of security theory for the Quantum stream cipher based on the Holevo–Yuen theory, which allows the use of “optical amplifiers”. This type of cipher is a technology that provides information-theoretic security (ITS) to optical data transmission by randomizing ultrafast optical communication signals with quantum noise. In general, the quantitative security of ITS is evaluated in terms of the unicity distance in Shannon theory. However, the quantum version requires modeling beyond the Shannon model of a random cipher to utilize the characteristics of the physical layer. Therefore, as the first step, one has to develop a generalized unicity distance theory and apply it to the evaluation of security. Although a complete theoretical formulation has not yet been established, this paper explains a primitive structure of a generalization of the Shannon random cipher and shows that the realization of this is the generalized quantum stream cipher. In addition, we present several implementation methods of generalized quantum stream ciphers and their security. Full article

Rapid assessment of physiological status is a precondition for addressing biological stress in trees so that they may recover. Environmental stress can cause water deficit in plants, while terahertz (THz) spectroscopy is sensitive to changes in aqueous solutions within organisms. This has given the THz sensor a competitive edge for evaluating plant phenotypes, especially under similar environmental stress, if there are existing differences in the corresponding THz information. In this study, we utilized THz technology in association with traditional weighing methods to explore physiological changes in citrus leaves under different temperature, duration, and stress treatment conditions. It was found that the higher the temperature and the longer the exposure duration, the more severe the reduction in the relative absorption coefficient. There was a positive correlation between the trends and the increase in the ion permeability of cells. In addition, based on the effective medium theory, THz spectral information can be transformed into information on free water and bound water in the leaves. Under different treatment conditions, water content shows different trends and degrees of change on the time scale, and accuracy was verified by traditional weighing methods. These findings revealed that characteristics of THz information can serve as a simple and clear indicator for judging a plant’s physiological status. Full article

This study investigates the impact of enterprise digital transformation through information and communication technology (ICT) on auditing fees. Based on data from publicly listed companies in China and employing information asymmetry theory, the research finds that the adoption of three factors associated with digital transformation—artificial intelligence (AI), cloud computing (CC), and big data technologies (BD)—exhibits a significant inverted U-shaped effect on auditing fees. Further analysis reveals that this effect is moderated by the quality of internal controls, the level of corporate governance, and discretionary accruals. These findings underscore the necessity for a nuanced understanding of the relationship between technology and auditing, as well as the importance for audit organizations to integrate new technologies into their practices to effectively respond to the rapid adoption of digital technologies by enterprises. Full article

Quantum entanglement as a non-local correlation between particles is critical to the transmission of quantum information in quantum networks (QNs); the key challenge lies in establishing long-distance entanglement transmission between distant targets. This issue aligns with percolation theory, and as a result, an entanglement distribution scheme called “Classical Entanglement Percolation” (CEP) has been proposed. While this scheme provides an effective framework, “Quantum Entanglement Percolation” (QEP) indicates a lower percolation threshold through quantum preprocessing strategies, which will modify the network topology. Meanwhile, an emerging statistical theory known as “Concurrence Percolation” reveals the unique advantages of quantum networks, enabling entanglement transmission under lower conditions. It fundamentally belongs to a different universality class from classical percolation. Although these studies have made significant theoretical advancements, most are based on an idealized pure state network model. In practical applications, quantum states are often affected by thermal noise, resulting in mixed states. When these mixed states meet specific conditions, they can be transformed into pure states through quantum operations and further converted into singlets with a certain probability, thereby facilitating entanglement percolation in mixed state networks. This finding greatly broadens the application prospects of quantum networks. This review offers a comprehensive overview of the fundamental theories of quantum percolation and the latest cutting-edge research developments. Full article

The Murciano-Granadina goat (MUG) is a renowned dairy breed, known for its adaptability and resilience, as well as for its exceptional milk traits characterized by high protein and fat content, along with low somatic cell counts. These traits are governed by complex biological processes, crucial in shaping phenotypic diversity. Thus, it is imperative to explore the factors regulating milk production and lactation for this breed. In this study, we investigated the genetic architecture of seven milk traits in MUGs, employing a two-step computational analysis to examine genotype–phenotype associations. Initially, a random forest algorithm identified the relative importance of each single-nucleotide polymorphism (SNP) in determining the traits of interest. The second step applied an information theory-based approach to exploring the complex genetic architecture of quantitative milk traits, focusing on epistatic interactions that may have been overlooked in the first step. These approaches allowed us to identify an almost distinct set of candidate genes for each trait. In contrast, by analyzing the promoter regions of these genes, we revealed common regulatory networks among the milk traits under study. These findings are crucial for understanding the molecular mechanisms underlying gene regulation, and they highlight the pivotal role of transcription factors (TFs) and their preferential interactions in the development of these traits. Notably, TFs such as DBP, HAND1E47, HOXA4, PPARA, and THAP1 were consistently identified for all traits, highlighting their important roles in immunity within the mammary gland and milk production during lactation. Full article

Developing the company’s capacity to deal with changing environments means ceasing to see processes as a traditional and linear model. Therefore, the objective of this research is to apply VSM to HRM to show its complexity. It is qualitative research, which is carried out in two moments. The first consists of a literature review in the WoS, and the second, is the design of the model “MV-HRM”, based on the approach of complex adaptive systems, viable system model, soft system methodology, and holistic theory. The MV-HRM consists of five systems: (S1) HRM processes, (S2) information system (S4) operational control, (S4) strategic planning and (S5) governance. The model emphasizes the relationships and interactions it has with its immediate and future environment. Finally, the contribution of the research is to show another look and understanding of the functioning of HRM, in addition to awakening the interest of strategists to develop best practices that allow them to respond in an agile way to the dynamic and complex environment. Full article