Air Transportation System Performance: Estimation and Comparative Analysis of Departure Delays

The U.S. National Airspace System (NAS) is inherently highly stochastic. Yet, many existing decision support tools for air traffic flow management take a deterministic approach to problem solving. In this study, we focus on the flight departure delays because such delays serve as inputs to many air traffic congestion prediction systems. Modeling the randomness of the delays will provide a more accurate picture of the airspace traffic situation, improve the prediction of the airspace congestion and advance the level of decision making in aviation systems. We first develop a model to identify the seasonal trend and daily propagation pattern for flight delays, in which we employ nonparametric methods for modeling the trends and mixture distribution for the residual errors estimation. This model demonstrates reasonable goodness of fit, robustness to the choice of the model parameters, and good predictive capabilities. We emphasize that a major objective is to produce not just point estimates but estimates of the entire distribution since the congestion estimation models envisioned require delay distribution functions, e.g. to produce probability of certain delays or expected traffic levels for arbitrary time intervals. Local optima problems are typically associated with mixture distribution estimation. To overcome such problems, we develop a global optimization version of the Expectation Maximization algorithm, borrowing ideas from Genetic Algorithms. This optimization algorithm shows the ability to escape from local traps and robustness to the choice of parameters. Finally, we propose models to estimate the so called "wheels-off delays" for flights within the NAS while incorporating a dynamic update capability. Approaches are evaluated based on their ability to reduce variance and their predictive accuracy. We first show that how a raw histogram can be misleading when a trend is present and how variance can be reduced by trend estimation. Then, various techniques are explored for variance reduction. The multiple seasonal trends method shows great capability for variance reduction while staying parsimonious in parameters. The downstream ripple effect method further enhances the variance reduction capability and makes real-time prediction practical and accurate. A rolling horizon updating procedure is described to accommodate the arrival of new information. Finally different models are compared with the current model adopted by the ETMS systems and the predictive capabilities of all models are shown

The signal pathways and treatment of cytokine storm in COVID-19

The Coronavirus Disease 2019 (COVID-19) pandemic has become a global crisis and is more devastating than any other previous infectious disease. It has affected a significant proportion of the global population both physically and mentally, and destroyed businesses and societies. Current evidence suggested that immunopathology may be responsible for COVID-19 pathogenesis, including lymphopenia, neutrophilia, dysregulation of monocytes and macrophages, reduced or delayed type I interferon (IFN-I) response, antibody-dependent enhancement, and especially, cytokine storm (CS). The CS is characterized by hyperproduction of an array of pro-inflammatory cytokines and is closely associated with poor prognosis. These excessively secreted pro-inflammatory cytokines initiate different inflammatory signaling pathways via their receptors on immune and tissue cells, resulting in complicated medical symptoms including fever, capillary leak syndrome, disseminated intravascular coagulation, acute respiratory distress syndrome, and multiorgan failure, ultimately leading to death in the most severe cases. Therefore, it is clinically important to understand the initiation and signaling pathways of CS to develop more effective treatment strategies for COVID-19. Herein, we discuss the latest developments in the immunopathological characteristics of COVID-19 and focus on CS including the current research status of the different cytokines involved. We also discuss the induction, function, downstream signaling, and existing and potential interventions for targeting these cytokines or related signal pathways. We believe that a comprehensive understanding of CS in COVID-19 will help to develop better strategies to effectively control immunopathology in this disease and other infectious and inflammatory diseases

Investigation of low-dissipation monotonicity-preserving scheme for direct numerical simulation of compressible turbulent flows

© 2014 Elsevier Ltd. The influence of numerical dissipation on direct numerical simulation (DNS) of decaying isotropic turbulence and turbulent channel flow is investigated respectively by using the seventh-order low-dissipation monotonicity-preserving (MP7-LD) scheme with different levels of bandwidth dissipation. It is found that for both benchmark test cases, small-scale turbulence fluctuations can be largely suppressed by high level of scheme dissipation, while the appearance of numerical errors in terms of high-frequency oscillations could destabilize the computation if the dissipation is reduced to a very low level. Numerical studies show that reducing the bandwidth dissipation to 70% of the conventional seventh-order upwind scheme can maximize the efficiency of the MP7-LD scheme in resolving small-scale turbulence fluctuations and, in the meantime preventing the accumulation of non-physical numerical errors. By using the optimized MP7-LD scheme, DNS of an impinging oblique shock-wave interacting with a spatially-developing turbulent boundary layer is conducted at an incoming free-stream Mach number of 2.25 and the shock angle of 33.2°. Simulation results of mean velocity profiles, wall surface pressure, skin friction and Reynolds stresses are validated against available experimental data and other DNS predictions in both the undisturbed equilibrium boundary layer region and the interaction zone, and good agreements are achieved. The turbulence kinetic energy transport equation is also analyzed and the balance of the equation is well preserved in the interaction region. This study demonstrates the capability of the optimized MP7-LD scheme for DNS of complex flow problems of wall-bounded turbulent flow interacting with shock-waves

Integrated supply–demand energy management for optimal design of off-grid hybrid renewable energy systems for residential electrification in arid climates

The growing research interest in hybrid renewable energy systems (HRESs) has been regarded as a natural and yet critical response to address the challenge of rural electrification. Based on a Bibliometric analysis performed by authors, it was concluded that most studies simply adopted supply-side management techniques to perform the design optimization of such a renewable energy system. To further advance those studies, this paper presents a novel approach by integrating demand-supply management (DSM) with particle swarm optimization and applying it to optimally design an off-grid hybrid PV-solar-diesel-battery system for the electrification of residential buildings in arid environments, using a typical dwelling in Adrar, Algeria, as a case study. The proposed HRES is first modelled by an in-house MATLAB code based on a multi-agent system concept and then optimized by minimizing the total net present cost (TNPC), subject to reliability level and renewable energy penetration. After validation against the HOMER software, further techno-economic analyses including sensitivity study are undertaken, considering different battery technologies. By integrating the proposed DSM, the results have shown the following improvements: with RF = 100%, the energy demand and TNPC are reduced by 7% and 18%, respectively, compared to the case of using solely supply-side management. It is found that PV-Li-ion represents the best configuration, with TNPC of $23,427 and cost of energy (COE) of 0.23$/kWh. However, with lower RF values, the following reductions are achieved: energy consumption (19%) and fuel consumption or CO 2 emission (57%), respectively. In contrast, the RF is raised from 15% (without DSM) to 63% (with DSM). It is clear that the optimal configuration consists of wind-diesel, with COE of 0.21 $/kWh, smaller than that obtained with a stand-alone diesel generator system. The outcomes of this work can provide valuable insights into the successful design and deployment of HRES in Algeria and surrounding regions

A New Mobile Sliding Door for Rural Elderly-friendly Suspended Bathroom and Toilet in the Context of Rural Population Aging

Taking the rural left-behind elderly as the research object, this paper made an in-depth analysis of the living needs of the rural "empty-nest elderly". Combined with the current market development situation of the elderly-friendly bathroom sliding door in China, it proposed the design scheme and industrial development strategy of the suspended bathroom sliding door, to provide a certain reference for the elderly-friendly development of home decoration basic materials for elderly homes in rural areas

Facile Synthesis and Herbicidal Evaluation of 4H-3,1-Benzoxazin-4-ones and 3H-Quinazolin-4-ones with 2-Phenoxymethyl Substituents

Series of 4H-3,1-benzoxazin-4-ones and 3H-quinazolin-4-ones with phenoxy-methyl substituents were rationally designed and easily synthesized via one-pot N-acylation/ring closure reactions of anthranilic acids with 2-phenoxyacetyl chlorides to yield the 4H-3,1-benzoxazin-4-ones, and subsequently substituted with amino derivatives to obtain the 3H-quinazolin-4-ones. The herbicidal evaluation was performed on the model plants barnyard grass (a monocotyledon) and rape (a dicotyledon), and most of the title compounds displayed high levels of phytotoxicity. The active substructure and inhibitory phenotype analysis indicated that these compounds could be attributed to the class of plant hormone inhibitors. A docking study of several representative compounds with the hormone receptor TIR1 revealed an appreciable conformational match in the active site, implicating these compounds are potential lead hits targeting this receptor

Investigating the <i>β</i>-Mg₁₇Al₁₂ Alloy under Pressure Using First-Principles Methods: Structure, Elastic Properties, and Mechanical Properties

Calculations of first principles were employed to explore the elastic constants of the β-Mg17Al12 intermetallic complex under pressure, along with several related physical parameters, including the bulk modulus, the shear modulus, Young’s modulus, Poisson’s ratio, and the anisotropy index. The volume of the β-Mg17Al12 crystal in the ground state was V0 = 1180.353 Å3, and the lattice parameter was 10.57 Å. This is in agreement with the available results in the literature, which indicate that the calculations were correct. The three independent elastic constants, C11, C12, and C44, increased with increasing pressure. The bulk modulus B, shear modulus G, and Young’s modulus E increased with increasing pressure, indicating that the bulk deformation resistance, shear deformation resistance, and stiffness of the β-Mg17Al12 phase increased with increasing pressure. The phase had a B/G > 1.75 and a Poisson’s ratio of ν > 0.26 and increased with pressure, indicating that the β-Mg17Al12 crystals were ductile and that the ductility increased with pressure. The Cauchy pressure C12–C44 increased with increasing pressure. The anisotropy coefficients A(100) and A(110) deviated further from 1, and the anisotropy increased. The electronic structure calculations showed that the total density of the states (TDOS) was achieved mainly by the Mg-3p and Al-3p states, and the total density of states moved toward the higher energy regions under pressure, with enhanced interatomic bonding, leading to an increase in the elastic constants and ultimately to an increase in each physical property with increasing pressure