Optimal Path Model Research Articles

Intelligent soaring and path planning for solar-powered unmanned aerial vehicles

PurposeThis study aims to study the thermal identification issue by harvesting both solar energy and atmospheric thermal updraft for a solar-powered unmanned aerial vehicle (SUAV) and its electric energy performance under continuous soaring conditions.Design/methodology/approachThe authors develop a specific dynamic model for SUAVs in both soaring and cruise modes. The support vector machine regression (SVMR) is adopted to estimate the thermal position, and it is combined with feedback control to implement the SUAV soaring in the updraft. Then, the optimal path model is built based on the graph theory considering the existence of several thermals distributed in the environment. The procedure is proposed to estimate the electricity cost of SUAV during flight as well as soaring, and making use of dynamic programming to maximize electric energy.FindingsThe simulation results present the integrated control method could allow SUAV to soar with the updraft. In addition, the proposed approach allows the SUAV to fly to the destination using distributed thermals while reducing the electric energy use.Originality/valueTwo simplified dynamic models are constructed for simulation considering there are different flight mode. Besides, the data-driven-based SVMR method is proposed to support SUAV soaring. Furthermore, instead of using length, the energy cost coefficient in optimization problem is set as electric power, which is more suitable for SUAV because its advantage is to transfer the three-dimensional path planning problem into the two-dimensional.

Behavioural implications of risk–return associations based on the adjusted thermal optimal path method: Large versus small banks

AbstractThis article investigates the impact of bank size and business model on bank risk‐taking within the framework of the prospect theory. To fulfil this objective, we use the adjusted thermal optimal path model. The results suggest that conventional banks adopt the same risk‐taking behaviour for performance measures, regardless of their size. However, the size mainly influences the attitudes of managers towards Islamic banks. On the other hand, small Islamic banks, whether under‐ or over‐performing, take excessive risks. This behaviour is mainly explained by loss aversion. However, large Islamic banks that situated above the target are risk‐averse, since they adopt defensive behaviour. The results reveal that for risk measures and for both small and large banks, a bank's business model does not affect managers' attitude to risk. Therefore, small (large) banks adopt excessive risk‐taking (risk‐averse) behaviour and an offensive (defensive) strategy. The study has important implications for GCC banking regulators, supervisors, and market participants. Thus, our findings imply that understanding the impact of the bank size and business model on the risk‐taking behaviour of Islamic and conventional banks can help them reduce their risk and mitigate moral hazard and regulatory arbitrage behaviour.

Integrated planning of UAV 3D trajectory based on ant colony algorithm

To address the problem of UAV trajectory planning optimality, a dynamic multi-objective optimal path model is established based on satisfying the basic conditions of UAV flight constraints, taking into account the time window restricted area and radio interference threat, etc. comprehensively. Based on the characteristics of the complex environment, an ant colony algorithm with reduced adaptive step size is constructed based on the theory of ant colony algorithm to quickly plan the best safe UAV trajectory in the complex dynamic environment. Simulation results show that the model can meet the complex airspace conditions of UAV missions, while the algorithm can find shorter trajectories and shorten the search time.

Theoretical Analysis and Strategy Research of Desert Travel Model

In this paper, aiming at the decision-making problem of players' walking scheme in the game, according to graph theory, benefit and cost game, the optimal path model is established, which provides the choice scheme for players to complete the game, gives the walking scheme with the most remaining funds under the condition of completing the game, and solves the problem of finding the optimal scheme under the influence of other players' actions.

The impact of COVID-19 on the portfolio optimization

PurposeThis article unveils first the lead–lag structure between the confirmed cases of COVID-19 and financial markets, including the stock (DJI), cryptocurrency (Bitcoin) and commodities (crude oil, gold, copper and brent oil) compared to the financial stress index. Second, this paper assesses the role of Bitcoin as a hedge or diversifier by determining the efficient frontier with and without including Bitcoin before and during the COVID-19 pandemic.Design/methodology/approachThe authors examine the lead–lag relationship between COVID-19 and financial market returns compared to the financial stress index and between all markets returns using the thermal optimal path model. Moreover, the authors estimate the efficient frontier of the portfolio with and without Bitcoin using the Bayesian approach.FindingsEmploying thermal optimal path model, the authors find that COVID-19 confirmed cases are leading returns prices of DJI, Bitcoin and crude oil, gold, copper and brent oil. Moreover, the authors find a strong lead–lag relationship between all financial market returns. By relying on the Bayesian approach, findings show when Bitcoin was included in the portfolio optimization before or during COVID-19 period; the Bayesian efficient frontier shifts to the left giving the investor a better risk return trade-off. Consequently, Bitcoin serves as a safe haven asset for the two sub-periods: pre-COVID-19 period and COVID-19 period.Practical implicationsBased on the above research conclusions, investors can use the number of COVID-19 confirmed cases to predict financial market dynamics. Similarly, the work is helpful for decision-makers who search for portfolio diversification opportunities, especially during health crisis. In addition, the results support the fact that Bitcoin is a safe haven asset that should be combined with commodities and stocks for better performance in portfolio optimization and hedging before and during COVID-19 periods.Originality/valueThis research thus adds value to the existing literature along four directions. First, the novelty of this study lies in the analysis of several financial markets (stock, cryptocurrencies and commodities)’ response to different pandemics and epidemics events, financial crises and natural disasters (Correia et al., 2020; Ma et al., 2020). Second, to the best of the authors' knowledge, this is the first study that examine the lead–lag relationship between COVID-19 and financial markets compared to financial stress index by employing the Thermal Optimal Path method. Third, it is a first endeavor to analyze the lead–lag interplay between the financial markets within a thermal optimal path method that can provide useful insights for the spillover effect studies in all countries and regions around the world. To check the robustness of our findings, the authors have employed financial stress index compared to COVID-19 confirmed cases. Fourth, this study tests whether Bitcoin is a hedge or diversifier given this current pandemic situation using the Bayesian approach.

Exploring the Reciprocal Relationships between Happiness and Life Satisfaction of Working Adults-Evidence from Abu Dhabi.

This paper examines the relationships between a range of well-being factors and two commonly used subjective well-being measures—happiness and life satisfaction. Data from the second cycle of the Quality of Life (QoL) Survey in Abu Dhabi were used, which included 32,087 working adults. The well-being factors included in the analysis covered various aspects of life themes: income and jobs, work–home balance, health and physical activities, social and community services, living environment, and family/friends’ relationships and connections. Using standardized data, path analysis yielded an optimal path model that suggested the presence of a reciprocal relationship between happiness and life satisfaction. In addition, the final model suggested that four variables—job satisfaction, mental health, satisfaction with relationships with people, and the size of the social support network—had direct effects on happiness and life satisfaction. The model also identified three variables—satisfaction with family life, mental health, and job satisfaction—to have the most significant effect on happiness.

Time-Dependent Electric Vehicle Routing Problem with Time Windows and Path Flexibility

With energy and environmental issues becoming increasingly prominent, electric vehicles (EVs) have become the important transportation means in the logistics distribution. In the real-world urban road network, there often exist multiple paths between any two locations (depot, customer, and charging station) since the time-dependent travel times. That is, the travel speed of an EV on each path may be different during different time periods, and thus, this paper explicitly considers path selection between two locations in the time-dependent electric vehicle routing problem with time windows, denoted as path flexibility. Therefore, the integrated decision-making should include not only the routing plan but also the path selection, and the interested problem of this paper is a time-dependent electric vehicle routing problem with time windows and path flexibility (TDEVRP-PF). In order to determine the optimal path between any two locations, an optimization model is established with the goal of minimizing the distance and the battery energy consumption associated with travel speed and cargo load. On the basis of the optimal path model, a 0-1 mixed-integer programming model is then formulated to minimize the total travel distance. Hereinafter, an improved version of the variable neighborhood search (VNS) algorithm is utilized to solve the proposed models, in which multithreading technique is adopted to improve the solution efficiency significantly. Ultimately, several numerical experiments are carried out to test the performance of VNS with a view to the conclusion that the improved VNS is effective in finding high-quality distribution schemes consisted of the distribution routes, traveling paths, and charging plans, which are of practical significance to select and arrange EVs for logistics enterprises.

Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm

This study studies the problem of on-orbit maintenance task planning for space-robot clusters. Aiming at the problem of low maintenance efficiency of space-robot cluster task-planning, this study proposes a cluster-task-planning method based on energy and path optimization. First, by introducing the penalty-function method, the task planning problem of the space-robot cluster under limited energy is analyzed, and the optimal-path model for task planning with comprehensive optimization of revenue and energy consumption are constructed; then, the maintenance task points are clustered to reduce the scale of the problem, thus reducing the difficulty of solving the problem; finally, a modified differential evolution algorithm is proposed to solve the problem of space-robot cluster task-planning, improve the performance of space-robot cluster task-assignment and path planning. Simulation results show that the proposed optimal-path model of space-robot cluster and the modified differential evolution algorithm can effectively solve the task-planning problem of spatial robot clusters.

The thermal optimal path model: Does Google search queries help to predict dynamic relationship between investor’s sentiment and indexes returns?

This paper sheds lights on the predictive power of googling investor sentiment on MENA market indexes returns from 2004 to 2018 by using a novel approach based on the thermal optimal path model, Diebold–Yilmaz Spillover Indexes and the wavelet coherence model. Thermal optimal path reveals that googling investor sentiment exhibits a lead effect for Islamic and conventional indexes returns which is influenced by political, social and economic conditions. Using Diebold–Yilmaz Spillover Indexes, we find that googling investor’s sentiment is the main net transmitter of shocks to Saudi Arabia, Egypt, Qatar, Bahrain, Oman and Jordan Islamic market indexes and the majority of conventional indexes. Further, the wavelet coherence model confirms the thermal optimal path results of the leading effect of googling investor sentiment, especially during crises periods. These results depict the robust and significant predictive power of googling investor’s sentiment to detect the behavior of investor, especially during instability periods and to predict market returns in MENA financial markets. Consequently, investor can exploit googling investor sentiment measure in portfolio investments and trading strategy in MENA financial markets.

Selecting Path Models in SEM: A Comparison of Model Selection Criteria

Model comparison is one useful approach in applications of structural equation modeling. Akaike’s information criterion (AIC) and the Bayesian information criterion (BIC) are commonly used for selecting an optimal model from the alternatives. We conducted a comprehensive evaluation of various model selection criteria, including AIC, BIC, and their extensions, in selecting an optimal path model under a wide range of conditions over different compositions of candidate set, distinct values of misspecified parameters, and diverse sample sizes. The chance of selecting an optimal model rose as the values of misspecified parameters and sample sizes increased. The relative performance of AIC and BIC type criteria depended on the magnitudes of the parameter misspecified. The BIC family in general outperformed AIC counterparts unless under small values of omitted parameters and sample sizes, where AIC performed better. Scaled unit information prior BIC (SPBIC) and Haughton's BIC (HBIC) demonstrated the highest accuracy ratios across most of the conditions investigated in this simulation.

不确定因素下一类物流车最优路径模型的建立与求解

本文以徐州市圆通快递文化宫营业部到中国矿业大学南湖校区为研究对象，建立了不确定因素下最优路径模型，利用正态分布的可加性，得出各条路径的总行驶时间正态分布表达式，并进行标准化，解出行驶时间的表达式。当到达终点的概率确定时，最优路径为行驶时间最少的路径。在此基础上设计了基于深度优先搜索的最优路径算法，运用MATLAB编程，得出7条不同的路径，求出最短行驶时间和最优路径。 This paper has established the optimal path model under uncertain factors as the subject of re-search, i.e., from Culture Palace Business Department of Xuzhou Yuantong Express to Nanhu Cam-pus of China University of Mining and Technology. By using the additive property of the normal distribution, the expression of the normal distribution for travel time of each path has been ob-tained, which is standardized to derive the expression of travel time. When the probability of reaching the end point is determined, the optimal path is the path with the shortest travel time. On this basis, the optimal path algorithm based on depth-first search is designed, and seven different paths are obtained by MATLAB programming. The minimum travel time and the optimal path are obtained.

Mental health literacy as a mediator in use of mental health services among older korean adults

Existing literature suggests that mental health literacy is positively associated with mental health services utilization. Despite an aging population that faces significant mental health concerns in Korea, the role of mental health literacy on mental health services utilization is not known among older adults in Korea. This study aimed to (1) identify whether mental health literacy mediates the association between population characteristics and mental health services utilization and (2) identify an optimal path model for mental health services utilization among Korean older adults. Using a cross-sectional survey with a quota sampling strategy, we collected and analyzed responses from 596 community-dwelling individuals ages 65 years and older. We used structural equation modeling (SEM) to estimate the effect of mental health literacy as a mediator. When controlling for other relevant covariates in the optimal path model, mental health literacy mediated the relationships between three socio-demographic factors (education, general literacy, and health status) and mental health services utilization. The model fit index shows that the SEM fits very well (CFI = 0.92, NFI = 0.90, RMSEA = 0.07). Efforts to improve mental health literacy through community-based education programs may need to particularly target Korean older adults with the relevant socio-demographic characteristics to enhance their utilization of appropriate mental health services.

Cancer literacy as a mediator for cancer screening behaviour in Korean adults.

This study investigates the cancer literacy level in Korean adults and examines whether cancer literacy plays a mediating role in the relationship between population characteristics and cancer screening behaviours. We collected data from 585 community-dwelling adults in Korea using self-administered surveys and face-to-face interviews from October to December in 2009. Guided by Andersen's behavioural model, we used a structural equation model to estimate the effect of cancer literacy as a mediator and found that cancer literacy mediated cancer screening behaviour. In the individual path analysis models, cancer literacy played a significant mediating role for the use of eastern medicine, fatalism, health status and the number of chronic diseases. When controlling for other relevant covariates, we found that in the optimal path model, cancer literacy played a mediating role in the relationship between the use of eastern medicine and self-rated health status as well as cancer screening behaviour. Thus, developing community-based cancer education programmes and training clinical practitioners in eastern medicine clinics about the importance of informing their patients about regular cancer screening may be an option to boost cancer literacy and screening behaviour in Korea.

The relationship, structure and profiles of schizophrenia measurements: a post-hoc analysis of the baseline measures from a randomized clinical trial

BackgroundTo fully assess the various dimensions affected by schizophrenia, clinical trials often include multiple scales measuring various symptom profiles, cognition, quality of life, subjective well-being, and functional impairment. In this exploratory study, we characterized the relationships among six clinical, functional, cognitive, and quality-of-life measures, identifying a parsimonious set of measurements.MethodsWe used baseline data from a randomized, multicenter study of patients diagnosed with schizophrenia, schizoaffective disorder, or schizophreniform disorder who were experiencing an acute symptom exacerbation (n = 628) to examine the relationship among several outcome measures. These measures included the Positive and Negative Syndrome Scale (PANSS), Montgomery-Asberg Depression Rating Scale (MADRS), Brief Assessment of Cognition in Schizophrenia Symbol Coding Test, Subjective Well-being Under Neuroleptics Scale Short Form (SWN-K), Schizophrenia Objective Functioning Instrument (SOFI), and Quality of Life Scale (QLS). Three analytic approaches were used: 1) path analysis; 2) factor analysis; and 3) categorical latent variable analysis. In the optimal path model, the SWN-K was selected as the final outcome, while the SOFI mediated the effect of the exogenous variables (PANSS, MADRS) on the QLS.ResultsThe overall model explained 47% of variance in QLS and 17% of the variance in SOFI, but only 15% in SWN-K. Factor analysis suggested four factors: "Functioning," "Daily Living," "Depression," and "Psychopathology." A strong positive correlation was observed between the SOFI and QLS (r = 0.669), and both the QLS and SOFI loaded on the "Functioning" factor, suggesting redundancy between these scales. The measurement profiles from the categorical latent variable analysis showed significant variation in functioning and quality of life despite similar levels of psychopathology.ConclusionsResearchers should consider collecting PANSS, SOFI, and SWN-K in their trials. This would allow a broad spectrum of assessments that would have the ability to capture a wide range of treatment outcomes and allow for a rich characterization of the subgroups involved. Additional research is needed to identify the critical cognitive measures.Trials registrationClinical trials registration: Predicting Response to Risperidone Treatment Through Identification of Early-onset of Antipsychotic Drug Action in SchizophreniaClinicalTrials.gov identifier: NCT00337662; http://www.clinicaltrials.gov/