Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm

Accurate flight delay prediction is fundamental to establishing an efficient airline business. It is considered one of the most critical intelligent aviation systems components. Recently, flight delay has been a significant cause that deprives airlines of good performance. Hence, airlines must accurately forecast flight delays and comprehend their sources to have excellent passenger experiences, increase income and minimise unwanted revenue loss. In this paper, we developed a novel approach that is an optimisation-driven deep learning model for predicting flight delays by extending a state-of-the-art method, DeepONet. We utilise the Box-Cox transformation for data conversion with a minimal error rate. Also, we employed a deep residual network for the feature fusion before training our model. Furthermore, this research uses flight on-time data for flight delay prediction. To validate our proposed model, we conducted a numerical study using the US Bureau of Transportation of Statistics. Also, we predict the flight delay by selecting the optimum weights using the novel DeepONet with the Gradient Mayfly Optimisation Algorithm (GMOA). Our experiment results show that the proposed GMOA-based DeepONet outperformed the existing methods with a Root Mean Square Error of 0.0765, Mean Square Error of 0.0058, Mean Absolute Error of 0.0049 and Mean Absolute Percent Error of 0.0043, respectively. When we apply 4-fold cross-validation, the proposed GMOA-based DeepONet outperformed the existing methods with minimal standard error. These results also show the importance of optimisation algorithms in deciding the optimal weight to improve the model performance. The efficacy of our proposed approach in predicting flight delays with minimal errors well define from all the evaluation metrics. Also, utilising the prediction outcome of our robust model to release information about the delayed flight in advance from the aviation decision systems can effectively alleviate the passengers’ nervousness.UKRI for the COVID-19 recovery grant under the budget code SA077N. This research was heavily affected by the COVID-19 pandemic during the first authors' PhD studies. This lead to an extension to registration for 3 months, which was funded by the UKRI doctoral extension recovery grant. (PTDF main funder of PhD)

\u3ci\u3eThe Conference Proceedings of the 2001 Air Transport Research Society (ATRS) of the WCTR Society, Volume 2\u3c/i\u3e

UNOAI Report 01-7https://digitalcommons.unomaha.edu/facultybooks/1147/thumbnail.jp

International travel-related control measures to contain the COVID-19 pandemic: a rapid review

BACKGROUND: In late 2019, the first cases of coronavirus disease 2019 (COVID‐19) were reported in Wuhan, China, followed by a worldwide spread. Numerous countries have implemented control measures related to international travel, including border closures, travel restrictions, screening at borders, and quarantine of travellers. OBJECTIVES: To assess the effectiveness of international travel‐related control measures during the COVID‐19 pandemic on infectious disease transmission and screening‐related outcomes. SEARCH METHODS: We searched MEDLINE, Embase and COVID‐19‐specific databases, including the Cochrane COVID‐19 Study Register and the WHO Global Database on COVID‐19 Research to 13 November 2020. SELECTION CRITERIA: We considered experimental, quasi‐experimental, observational and modelling studies assessing the effects of travel‐related control measures affecting human travel across international borders during the COVID‐19 pandemic. In the original review, we also considered evidence on severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In this version we decided to focus on COVID‐19 evidence only. Primary outcome categories were (i) cases avoided, (ii) cases detected, and (iii) a shift in epidemic development. Secondary outcomes were other infectious disease transmission outcomes, healthcare utilisation, resource requirements and adverse effects if identified in studies assessing at least one primary outcome. DATA COLLECTION AND ANALYSIS: Two review authors independently screened titles and abstracts and subsequently full texts. For studies included in the analysis, one review author extracted data and appraised the study. At least one additional review author checked for correctness of data. To assess the risk of bias and quality of included studies, we used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS‐2) tool for observational studies concerned with screening, and a bespoke tool for modelling studies. We synthesised findings narratively. One review author assessed the certainty of evidence with GRADE, and several review authors discussed these GRADE judgements. MAIN RESULTS: Overall, we included 62 unique studies in the analysis; 49 were modelling studies and 13 were observational studies. Studies covered a variety of settings and levels of community transmission. Most studies compared travel‐related control measures against a counterfactual scenario in which the measure was not implemented. However, some modelling studies described additional comparator scenarios, such as different levels of stringency of the measures (including relaxation of restrictions), or a combination of measures. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to the selection of travellers and the reference test, and unclear reporting of certain methodological aspects. Below we outline the results for each intervention category by illustrating the findings from selected outcomes. Travel restrictions reducing or stopping cross‐border travel (31 modelling studies) The studies assessed cases avoided and shift in epidemic development. We found very low‐certainty evidence for a reduction in COVID‐19 cases in the community (13 studies) and cases exported or imported (9 studies). Most studies reported positive effects, with effect sizes varying widely; only a few studies showed no effect. There was very low‐certainty evidence that cross‐border travel controls can slow the spread of COVID‐19. Most studies predicted positive effects, however, results from individual studies varied from a delay of less than one day to a delay of 85 days; very few studies predicted no effect of the measure. Screening at borders (13 modelling studies; 13 observational studies) Screening measures covered symptom/exposure‐based screening or test‐based screening (commonly specifying polymerase chain reaction (PCR) testing), or both, before departure or upon or within a few days of arrival. Studies assessed cases avoided, shift in epidemic development and cases detected. Studies generally predicted or observed some benefit from screening at borders, however these varied widely. For symptom/exposure‐based screening, one modelling study reported that global implementation of screening measures would reduce the number of cases exported per day from another country by 82% (95% confidence interval (CI) 72% to 95%) (moderate‐certainty evidence). Four modelling studies predicted delays in epidemic development, although there was wide variation in the results between the studies (very low‐certainty evidence). Four modelling studies predicted that the proportion of cases detected would range from 1% to 53% (very low‐certainty evidence). Nine observational studies observed the detected proportion to range from 0% to 100% (very low‐certainty evidence), although all but one study observed this proportion to be less than 54%. For test‐based screening, one modelling study provided very low‐certainty evidence for the number of cases avoided. It reported that testing travellers reduced imported or exported cases as well as secondary cases. Five observational studies observed that the proportion of cases detected varied from 58% to 90% (very low‐certainty evidence). Quarantine (12 modelling studies) The studies assessed cases avoided, shift in epidemic development and cases detected. All studies suggested some benefit of quarantine, however the magnitude of the effect ranged from small to large across the different outcomes (very low‐ to low‐certainty evidence). Three modelling studies predicted that the reduction in the number of cases in the community ranged from 450 to over 64,000 fewer cases (very low‐certainty evidence). The variation in effect was possibly related to the duration of quarantine and compliance. Quarantine and screening at borders (7 modelling studies; 4 observational studies) The studies assessed shift in epidemic development and cases detected. Most studies predicted positive effects for the combined measures with varying magnitudes (very low‐ to low‐certainty evidence). Four observational studies observed that the proportion of cases detected for quarantine and screening at borders ranged from 68% to 92% (low‐certainty evidence). The variation may depend on how the measures were combined, including the length of the quarantine period and days when the test was conducted in quarantine. AUTHORS' CONCLUSIONS: With much of the evidence derived from modelling studies, notably for travel restrictions reducing or stopping cross‐border travel and quarantine of travellers, there is a lack of 'real‐world' evidence. The certainty of the evidence for most travel‐related control measures and outcomes is very low and the true effects are likely to be substantially different from those reported here. Broadly, travel restrictions may limit the spread of disease across national borders. Symptom/exposure‐based screening measures at borders on their own are likely not effective; PCR testing at borders as a screening measure likely detects more cases than symptom/exposure‐based screening at borders, although if performed only upon arrival this will likely also miss a meaningful proportion of cases. Quarantine, based on a sufficiently long quarantine period and high compliance is likely to largely avoid further transmission from travellers. Combining quarantine with PCR testing at borders will likely improve effectiveness. Many studies suggest that effects depend on factors, such as levels of community transmission, travel volumes and duration, other public health measures in place, and the exact specification and timing of the measure. Future research should be better reported, employ a range of designs beyond modelling and assess potential benefits and harms of the travel‐related control measures from a societal perspective

The Planning Strategies for Achieving Sustainable Urban Form based on Space Production Theory

13301甲第5411号博士（学術）金沢大学博士論文本文Ful

Occupational health and safety for informal sector workers: the case of street traders in Nigeria

This study examined two important types of occupational hazards in the street trading activities in Nigeria which are (i) injuries sustained from road traffic accident and (ii) harassment of traders through indiscriminate arrest, seizure and confiscation of merchandise and occasional incarceration of sellers in police cells without trials. The data for the study was generated from a 2011 national survey of 3,873 street traders in Nigeria which was made possible through a research grant provided by the Covenant University’s Centre for Research and Development. In addition to the descriptive statistics used in profiling the street traders, the binary logistic regression approach was also used to estimate the log of odds of experiencing occupational hazards in street trading activities. The study found out that 25% of the traders have suffered injury, while 49.1% have experienced harassment from public authority officials. Given these findings, policy measures that are capable of enhancing the safety of street traders, and stem urban-ward migration have been proposed

Sustainable Industrial Engineering along Product-Service Life Cycle/Supply Chain

Sustainable industrial engineering addresses the sustainability issue from economic, environmental, and social points of view. Its application fields are the whole value chain and lifecycle of products/services, from the development to the end-of-life stages. This book aims to address many of the challenges faced by industrial organizations and supply chains to become more sustainable through reinventing their processes and practices, by continuously incorporating sustainability guidelines and practices in their decisions, such as circular economy, collaboration with suppliers and customers, using information technologies and systems, tracking their products’ life-cycle, using optimization methods to reduce resource use, and to apply new management paradigms to help mitigate many of the wastes that exist across organizations and supply chains. This book will be of interest to the fast-growing body of academics studying and researching sustainability, as well as to industry managers involved in sustainability management

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts