A Complexity Indicator for 4D Flight Trajectories Based on Conflict Probability

In this paper, a complexity indicator for 4D flight trajectories is developed based on conflict probability. A 4D trajectory is modeled as piecewise linear segments connected by waypoints. The position of each aircraft is modeled as a 2D Gaussian random variable and an approximation of the conflict probability between two aircraft is deduced analytically over each segment. Based on such conflict probability, a complexity indicator is constructed for the whole trajectory. Numerical examples show that the proposed complexity indicator is able to reflect the complexity of 4D trajectories perceived by air traffic controllers

Insulin inhibits cardiac contractility by inducing a Gi-biased β2-adrenergic signaling in hearts.

Insulin and adrenergic stimulation are two divergent regulatory systems that may interact under certain pathophysiological circumstances. Here, we characterized a complex consisting of insulin receptor (IR) and β2-adrenergic receptor (β2AR) in the heart. The IR/β2AR complex undergoes dynamic dissociation under diverse conditions such as Langendorff perfusions of hearts with insulin or after euglycemic-hyperinsulinemic clamps in vivo. Activation of IR with insulin induces protein kinase A (PKA) and G-protein receptor kinase 2 (GRK2) phosphorylation of the β2AR, which promotes β2AR coupling to the inhibitory G-protein, Gi. The insulin-induced phosphorylation of β2AR is dependent on IRS1 and IRS2. After insulin pretreatment, the activated β2AR-Gi signaling effectively attenuates cAMP/PKA activity after β-adrenergic stimulation in cardiomyocytes and consequently inhibits PKA phosphorylation of phospholamban and contractile responses in myocytes in vitro and in Langendorff perfused hearts. These data indicate that increased IR signaling, as occurs in hyperinsulinemic states, may directly impair βAR-regulated cardiac contractility. This β2AR-dependent IR and βAR signaling cross-talk offers a molecular basis for the broad interaction between these signaling cascades in the heart and other tissues or organs that may contribute to the pathophysiology of metabolic and cardiovascular dysfunction in insulin-resistant states

Route generation for optimization in the Air Transport System : aircraft recovery problem and 4D trajectory planning

The punctuality and efficiency of the Air Transport System (ATS) has a significant impact on the economy. In 2016, the US Passenger Carrier Delay Costs were estimated to be USD 62.55 per minute and the total US flight arrival delays amounted to over 59 million minutes. Furthermore, air traffic volume is growing rapidly. The International Civil Aviation Organization (ICAO) estimates that air traffic in the Asia Pacific region will triple by 2030. With the rapid growth of air traffic, the inefficiency of the ATS has become a salient problem. Flight delays, cancellations, and air traffic congestion cost customers and airlines a considerable amount of money. In this dissertation, we tackle the inefficiency of the ATS from two perspectives: airspace users (airlines) and Air Traffic Management (ATM) service providers (air traffic controllers). Correspondingly, two critical problems are studied on the operational level, the aircraft recovery problem (ARP) and 4D (three dimensions plus time) trajectory planning. Both problems are formulated as special routing problems, and because of the large computational scale of the two problems encountered in real life, they are solved by carefully designed route generation algorithms employing ad hoc decomposition techniques. In the first part of the dissertation, the aircraft recovery problem with airport capacity constraints and maintenance flexibility is investigated. The problem is to re-schedule flights and re-assign aircraft in real time with minimized recovery cost for airlines after disruptions occur. In most published studies, airport capacity and flexible maintenance are not considered simultaneously via an optimization approach. To bridge this gap, we propose a column generation framework to solve the problem. The framework consists of a master problem for selecting routes for aircraft and subproblems for generating routes. Airport capacity is explicitly considered in the master problem and swappable planned maintenances can be incorporated in the subproblem. Instead of the discrete delay models that are widely adopted in much of the existing literature, in this work flight delays are continuous and optimized accurately in the subproblems. The continuous-delay model improves the accuracy of the optimized recovery cost. In one test scenario, the accuracy is improved by up to 37.74%. The computational study based on real-world problems shows that the master problem gives a very tight linear relaxation with small, often zero, optimality gap. Large-scale problems can be solved within 5 minutes and the run time could be further shortened by parallelizing subproblems on more powerful hardware. In addition, from a managerial point of view, computational experiments reveal that swapping planned maintenances may bring a considerable reduction in recovery cost from an estimated 20% to 60%, depending on specific problem instances. Furthermore, the decreasing marginal value of airport slot quotas is found by computational experiments. In the second part of the dissertation, we consider a coordinated multi-aircraft 4D trajectory planning problem which is illustrated by planning 4D trajectories for aircraft traversing an Air Traffic Control (ATC) sector. The planned 4D trajectories need to specify each aircraft's position at any time, ensuring conflict-free trajectories and reducing fuel and delay costs, with possible aircraft maneuvers such as speed adjustments and flight level changes. In contrast to most existing literature, the impact of buffer safety distance is also under consideration, and freedom from conflict is guaranteed at any given time (not only at discrete time instances). The problem is formulated as a pure-strategy game with aircraft as players and all possible 4D trajectories as strategies. An efficient maximum improvement distributed algorithm decomposing multi-aircraft problems into single-aircraft problems is developed to find an equilibrium at which every aircraft cannot unilaterally improve further without the need to enumerate all possible 4D trajectories in advance. Proofs of the existence of the equilibrium and the convergence of the algorithm are given. A case study based on real air traffic data shows that the algorithm is able to solve 4D trajectories for online applications with an estimated 16.7% reduction in operating costs while allocating an abundant buffer safety distance at the minimum separation point. Computational experiments verify the scalability of the algorithm. Airlines and ATM service providers are the two vital stakeholders of the ATS; the methodologies and techniques developed in this dissertation make enlightening contributions to improving the operation of the ATS from an optimization point of view. Because decomposition methods are highly problem-dependent, the design of the algorithms requires both domain knowledge and mathematical intuition. However, in general, the proposed route generation algorithms in the dissertation also provide hints to many other real-life large-scale problems in logistics systems, such as railway transportation, maritime transportation, and unmanned aerial vehicle (UAV) transportation.Doctor of Philosophy (MAE

Strongly localized states and giant optical absorption induced by multiple flat-bands in AA-stacked multilayer armchair graphene nanoribbons

We propose an AA-stacked multilayer graphene nanoribbon with two symmetrical armchair edges as a multiple flat-band (FB) material. Using the tight-binding Hamiltonian and Green’s function method, we find that the FBs are complete and merged into many dispersive bands. The FBs cause multiple strongly localized states (SLSs) at the sites of the odd lines in every sublayer and a giant optical absorption (GOA) at energy point 2 t , where t is the electronic intralayer hopping energy between two nearest-neighbor sites. By driving an electric field perpendicular to the ribbon plane, the bandgaps of the FBs are tunable. Accordingly, the positions of the SLSs in the energy regime can be shifted. However, the position of the GOA is robust against such field, but its strength exhibits a collapse behavior with a fixed quantization step. On the contrary, by driving an electric field parallel to the ribbon plane, the completeness of FBs is destroyed. Resultantly, the SLSs and GOA are suppressed and even quenched. Therefore, such ribbons may be excellent candidates for the design of the controllable information-transmission and optical-electric nanodevices

Comprehensive genomic signature of pyroptosis-related genes and relevant characterization in hepatocellular carcinoma

Background Currently, the most predominant type of liver cancer is hepatocellular carcinoma (HCC), which is also the fourth leading cause of cancer-related death in the global population. Pyroptosis is an emerging form of cell death that affects the prognosis of cancer patients by modulating tumor cell migration, proliferation and invasion. However, the evaluation of pyroptosis in the prognosis of HCC is still insufficient. Methods A total of 365 HCC patients from the TCGA-LIHC cohort were classified into two distinct subtypes using consensus clustering of pyroptosis-related genes (PRGs). Following univariate Cox analysis of differentially expressed genes between subtypes, we established a prognostic model (PRGs-score, PRGS) by LASSO Cox analysis. We further tested the predictive power of the prognostic model in the ICGC (LIRI-JP) and GEO (GSE14520) cohorts. The tumor microenvironment (TME) was studied using the CIBERSORT. The enrichment scores for immune cells and immune functions in low- and high-PRGS groups were assessed using ssGSEA. The IMvigor210 cohort was used to investigate the immunotherapy efficacy. Furthermore, we validated the expression of prognostic genes in PRGS by RT-qPCR in vitro. Results The subtyping of HCC based on PRGs exhibited distinct clinical characteristics. We developed a prognostic model PRGS by differentially expressed genes between different subtypes. The results showed that PRGS could well forecast the survival of HCC patients in different cohorts and was associated with the immune microenvironment. Moreover, PRGS was considered to be an independent prognostic risk factor and superior to other pyroptosis-related signatures. Low-PRGS implied greater immune cell infiltration and better overall survival with immunotherapy. The results of RT-qPCR also showed that prognostic genes were significantly dysregulated in HCC. Conclusions PRGS has promising application in forecasting the prognosis of HCC patients, and its relationship with the immune microenvironment provides a basis for the subsequent treatment and research of HCC

A column generation-based heuristic for aircraft recovery problem with airport capacity constraints and maintenance flexibility

We consider the aircraft recovery problem (ARP) with airport capacity constraints and maintenance flexibility. The problem is to re-schedule flights and re-assign aircraft in real time with minimized recovery cost for airlines after disruptions occur. In most published studies, airport capacity and flexible maintenance are not considered simultaneously via an optimization approach. To bridge this gap, we propose a column generation heuristic to solve the problem. The framework consists of a master problem for selecting routes for aircraft and subproblems for generating routes. Airport capacity is explicitly considered in the master problem and swappable planned maintenances can be incorporated in the subproblem. Instead of discrete delay models which are widely adopted in much of the existing literature, in this work flight delays are continuous and optimized accurately in the subproblems. The continuous-delay model can improve the accuracy of the optimized recovery cost by up to 37.74%. The computational study based on real-world problems shows that the master problem gives very tight linear relaxation with small, often zero, optimality gaps. Large-scale problems can be solved within 6 min and the run time can be further shortened by parallelizing subproblems on more powerful hardware. In addition, from a managerial point of view, computational experiments reveal that swapping planned maintenances may bring a considerable reduction in recovery cost by about 20% and 60%, depending on specific problem instances. Furthermore, the decreasing marginal value of airport slot quota is found by computational experiments

A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future