Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning

Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos' workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller's control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at \href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{$\mathsf{Link}$}

Pan-cancer analysis reveals that G6PD is a prognostic biomarker and therapeutic target for a variety of cancers

BackgroundDespite accumulating evidence revealing that Glucose-6-phosphate dehydrogenase (G6PD) is highly expressed in many tumor tissues and plays a remarkable role in cancer tumorigenesis and progression, there is still a lack of G6PD pan-cancer analysis. This study was designed to analyze the expression status and prognostic significance of G6PD in pan-cancer.MethodsG6PD expression data were obtained from multiple data resources including the Genotype-Tissue Expression, the Cancer Genome Atlas, and the Tumor Immunity Estimation Resource. These data were used to assess the G6PD expression, prognostic value, and clinical characteristics. The ESTIMATE algorithms were used to analyze the association between G6PD expression and immune-infiltrating cells and the tumor microenvironment. The functional enrichment analysis was also performed across pan-cancer. In addition, the GDSC1 database containing 403 drugs was utilized to explore the relationship between drug sensitivity and G6PD expression levels. Furthermore, we also performed clinical validation and in vitro experiments to further validate the role of G6PD in hepatocellular carcinoma (HCC) cells and its correlation with prognosis. The R software was used for statistical analysis and data visualization.ResultsG6PD expression was upregulated in most cancers compared to their normal counterparts. The study also revealed that G6PD expression was a prognostic indicator and high levels of G6PD expression were correlated with worse clinical prognosis including overall survival, disease-specific survival, and progression-free interval in multiple cancers. Furthermore, the G6PD level was also related to cancer immunity infiltration in most of the cancers, especially in KIRC, LGG, and LIHC. In addition to this, G6PD expression was positively related to pathological stages of KIRP, BRCA, KIRC, and LIHC. Functional analysis and protein-protein interactions network results revealed that G6PD was involved in metabolism-related activities, immune responses, proliferation, and apoptosis. Drug sensitivity analysis showed that IC50 values of most identified anti-cancer drugs were positively correlated with the G6PD expression. Notably, in vitro functional validation showed that G6PD knockdown attenuated the phenotypes of proliferation in HCC.ConclusionG6PD may serve as a potential prognostic biomarker for cancers and may be a potential therapeutic target gene for tumor therapy

Multimodal Food Discourse and Narrative Analysis: Culinary Persona, Ingredients, and Environment

The study mainly investigates multimodal food discourse, such as socially constructed food videos rich in modes. It took Li Ziqi's "Life Series" food video as an example for analysis, aiming to find out three semiotic resources, namely "culinary persona", "culinary ingredients", and "culinary environment". The research methods are: First, it combined the visual narrative at the level of experiential meaning in Painter et al. (2013) with the auditory one. Second, it slightly adjusted the narrative structure of Labov (1972) to suit the analysis of the narrative structure of food discourse. Next, combined with the given case, this article analyzed the experiential meaning of food discourse from the situational context of the case. Finally, it generalized several cultural keywords based on the case study and discussed their cultural insights. Research has found that the culinary persona, ingredients, and the environment interact with each other, and the cultural connotation behind them is a manifestation of the way of food

Atmospheric Effects of Strange White Flowers: A Corpus Stylistic Approach to the Text World in The Time Machine

Previous research on plants in The Time Machine has shown the importance of putting these peripheral objects to the core. Studies have indicated potential associations between corpus stylistics and cognitive stylistics, but investigations of The Time Machine have not dealt with the “strange white flowers” in much detail, especially observing from a corpus perspective and interpreting from the cognitive one. The principal objective of this study is to explore the atmospheric effects of these flowers with a combined corpus and cognitive methodological approach. Corpus techniques rely on the tools of Sketch Engine and CQPweb, while cognitive methods lie in the theories of Atmosphere and the Text World. The results showed that “strange white flowers” at the macro-level create elusive and diffusive atmospheric effects of transiency and strangeness, which could be represented at the meso-level by the text-world presentation and explored at the micro-level by collocation and colligation. The findings can contribute to a better understanding of peripheral objects in The Time Machine and add new insight into the study of science fiction

A Voice Communication-Augmented Simulation Framework for Aircraft Trajectory Simulation

International audienceAircraft operations in the terminal area rely heavily on voice communications between pilots and air traffic controllers. This paper proposes a novel aircraft trajectory simulation framework by guiding the trajectory simulation following the voice command from controllers. Bayesian model selection is used for checking pilot compliances to controller commands with observed trajectories. This framework is named as Voice Communication-Augmented Simulation. The goal of the proposed study is to enable accurate trajectory predictions. The framework can act as a computer assistant for controllers to monitor pilot compliances and ensure safe operations. The proposed method is tested and validated with actual trajectory data from Sherlock Data Warehouse. The tests showed that the proposed framework can accurately simulate and monitor the flight level change of aircraft and update the approach procedure

A novel nomogram based on routine clinical indicators for screening for Wilson's disease

Background and aims: There is currently no single model for predicting Wilson's disease (WD). We aimed to create a nomogram using daily clinical parameters to improve the accuracy of WD diagnosis in patients with abnormal liver function. Methods: Between July 2016 and December 2020, we identified 90 WD patients with abnormal liver function who had homozygous or compound heterozygous mutations in the ATP7B gene. The control group included 128 patients with similar liver function but no WD during the same time period. To create a nomogram, we screened potential predictive variables using the least absolute shrinkage and selection operator model and multivariate logistic regression. Results: We developed a nomogram for screening for WD based on six predictive factors: serum copper, direct bilirubin, uric acid, cholinesterase, prealbumin, and reticulocyte percentage. In the training cohort, the area under curve (AUC) of the nomogram reached 0.967 (95% confidence interval (CI) 0.946–0.988), while the area under the precision-recall curve was 0.961. Based on the optimal cutpoint of 213.55, our nomogram performed well, with a sensitivity of 96% and a specificity of 87%. In the validation cohort, the AUC of the nomogram was as high as 0.991 (95% CI 0.970–1.000). Conclusions: We developed a nomogram that can predict the risk of WD prior to the detection of serum ceruloplasmin or urinary copper, greatly increasing screening efficiency for patients with abnormal liver function

A systematic review and meta-analysis of digital application use in clinical research in pain medicine.

ImportancePain is a silent global epidemic impacting approximately a third of the population. Pharmacological and surgical interventions are primary modes of treatment. Cognitive/behavioural management approaches and interventional pain management strategies are approaches that have been used to assist with the management of chronic pain. Accurate data collection and reporting treatment outcomes are vital to addressing the challenges faced. In light of this, we conducted a systematic evaluation of the current digital application landscape within chronic pain medicine.ObjectiveThe primary objective was to consider the prevalence of digital application usage for chronic pain management. These digital applications included mobile apps, web apps, and chatbots.Data sourcesWe conducted searches on PubMed and ScienceDirect for studies that were published between 1st January 1990 and 1st January 2021.Study selectionOur review included studies that involved the use of digital applications for chronic pain conditions. There were no restrictions on the country in which the study was conducted. Only studies that were peer-reviewed and published in English were included. Four reviewers had assessed the eligibility of each study against the inclusion/exclusion criteria. Out of the 84 studies that were initially identified, 38 were included in the systematic review.Data extraction and synthesisThe AMSTAR guidelines were used to assess data quality. This assessment was carried out by 3 reviewers. The data were pooled using a random-effects model.Main outcomes and measuresBefore data collection began, the primary outcome was to report on the standard mean difference of digital application usage for chronic pain conditions. We also recorded the type of digital application studied (e.g., mobile application, web application) and, where the data was available, the standard mean difference of pain intensity, pain inferences, depression, anxiety, and fatigue.Results38 studies were included in the systematic review and 22 studies were included in the meta-analysis. The digital interventions were categorised to web and mobile applications and chatbots, with pooled standard mean difference of 0.22 (95% CI: -0.16, 0.60), 0.30 (95% CI: 0.00, 0.60) and -0.02 (95% CI: -0.47, 0.42) respectively. Pooled standard mean differences for symptomatologies of pain intensity, depression, and anxiety symptoms were 0.25 (95% CI: 0.03, 0.46), 0.30 (95% CI: 0.17, 0.43) and 0.37 (95% CI: 0.05, 0.69), respectively. A sub-group analysis was conducted on pain intensity due to the heterogeneity of the results (I 2 = 82.86%; p = 0.02). After stratifying by country, we found that digital applications were more likely to be effective in some countries (e.g., United States, China) than others (e.g., Ireland, Norway).Conclusions and relevanceThe use of digital applications in improving pain-related symptoms shows promise, but further clinical studies would be needed to develop more robust applications.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42021228343