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Three dimensional space is said to be spherically symmetric if it admits SO(3) as the group of isometries. Under this symmetry condition, the Einstein’s Field equations for vacuum, yields the Schwarzschild Metric as the unique solution, which essentially is the statement of the well known Birkhoff’s Theorem. Geometrically speaking this theorem claims that the pseudo-Riemanian space-times provide more isometries than expected from the original metric holonomy/ansatz. In this paper we use the method of Lie Symmetry Analysis to analyze the Einstein’s Vacuum Field Equations so as to obtain the Symmetry Generators of the corresponding Differential Equation. Additionally, applying the Noether Point Symmetry method we have obtained the conserved quantities corresponding to the generators of the Schwarzschild Lagrangian and paving way to reformulate the Birkhoff’s Theorem from a different approach.

Background/Objective: Root-knot nematodes and cyst nematodes are two important plant-parasitic nematodes that cause severe plant diseases in various plant species around the world. They act as obligate and biotrophic parasite within the plant body. The objective of the study is to review for suitable management to keep the nematode population density below the threshold level. Methods: In spite of several nematode control practices such as crop rotation, use of biopesticides or nematicides, each has some limitations of their use but biotechnological applications including RNAi or miRNA represent a potential breakthrough in the application of functional genomics for plant nematode control. Here, a comparison is made between some old and modern nematode management practices but recent data shows that application of RNAi or miRNA has a better option of nematode control in some crop plants. Findings: Efficacy and biotechnological success can be maintained by holistic grasping of several soil biological and ecological factors. Therefore, modern approaches those reviewed herein due to their usefulness in minimizing plant nematode populations and increasing crop yield should be incorporated into management systems. The scientific community has entered into a new era that shows the tools to actually unravel the underlying molecular mechanisms, making this an opportunity for a review of our current knowledge and better understanding. Application: These modern eco-friendly practices may not quickly perform as synthetic chemicals, but they are pest specific, non-toxic to humans or environment, and also serves as a sustainable tool for disease management. Novelty: The present communication identifies plant nematode control approaches with emphasis on modern research. This review article emphasized the importance of modern biotechnological approaches for better crop yield than the common older practices. Keywords: Root-Knot nematode; biotrophic Parasite; threshold level; nematicides; biopesticide; micro RNA

Background/Objective: Present study was carried out to identify zooplankton density at Baburpukur pond along with physicochemical properties. In India. Planktonic richness reflects the biodiversity stock. The present study assumes greater importance for biodiversity conservation/ pollution indicator and aquaculture of fish and prawns. Methods/Statistical analysis: Statistical analysis in this experiment was performed by Student\'s t-test. In this test, 0.05 probability, degree of freedom, critical t-value, and calculated t-values were recorded. Here zooplankton number along with physico-chemical parameters was recorded. From these t-values, significant seasonal variations were found in the respective water body. The zooplankton density and physiochemical parameters were recorded during the period of Jun 2018 to February 2020. Diversity of zooplankton has been counted using Sedgwick-Rafter counting chamber. Findings: Four different species of zooplankton were studied such as Daphnia, Cyclops, Cypris and Brachionus. Zooplankton community structure generally changes with temperature, pH of water, free CO2 level, dissolved O2. This study also reveals that zooplanktons have their own peak periods of density which is influenced by the above environmental conditions. Application: At present, this water reservoir is suitable for fish culturing. So several management practices are necessary to conserve this zooplankton density for proper healthy situation of water body. This study is also helpful in understanding the zooplankton diversity of Baburpukur Pond with proper maintaining of aquaculture. Keywords: Zooplankton; biodiversity conservation; pollution indicator; aquaculture

On any given day, constraints in the National Airspace System (for instance, weather) necessitate the implementation of traffic flow management initiatives, such as ground delay programs. The goal ...

There are several practices in the management of root-knot nematode (Meloidogyne incognita) in crop fields such as chemical pesticides, nematicides, crop rotation etc. But due to the environmental problems caused by the nematicides or pesticides, alternative tactics such as natural products are getting attention. Among the natural products, plant essential oils provide an important role in the protection of plants from insects, nematodes and microorganisms. In the present experiment in a split-root trial with okra (Abelmoschus esculentus) cv. Purbani Kranti as a host plant and M. incognita as challenging parasite, citral and menthol induced the increase of Phenylalanine ammonia lyase activity in okra plants. Citral and menthol at a rate of 2 mg/plant reduced M. incognita infection without adversely affecting plant growth or yield. This is an important finding with the application of these two essential oils for nematode control and also the reduction of chemical nematicides for environmental benefits.

The reaction [Formula: see text]C[Formula: see text]O is an important astrophysical process producing neutrons for [Formula: see text]-process nucleosynthesis of nuclei heavier than iron in low-mass AGB stars. The reaction rate of [Formula: see text]C[Formula: see text]O at relevant energies is dominated by a [Formula: see text] state in [Formula: see text]O near the [Formula: see text]-threshold. The adopted value for the excitation energy of the state is −3[Formula: see text]keV below the threshold at 6359[Formula: see text]keV. However, recent investigations have indicated that the state is, instead of a sub-threshold state, an above threshold resonance state. The observation is also corroborated by neutron scattering studies from [Formula: see text]O. The location of the state and its implication on the low energy behavior of the astrophysical [Formula: see text]-factor are of definite interest. The aim of this work is to ascertain the energy location of [Formula: see text] state in the excitation spectrum of [Formula: see text]O and to estimate its neutron and [Formula: see text]-partial widths. Subsequently, we look into the effect of the resultant set of parameters of the state on the [Formula: see text]-factor and reaction rate at important astrophysical energies. A multilevel, multichannel [Formula: see text]-matrix analysis has been performed for the existing [Formula: see text]O[Formula: see text], [Formula: see text]C[Formula: see text], [Formula: see text]C[Formula: see text] and [Formula: see text]O[Formula: see text] data. The excitation energy of the [Formula: see text] state in [Formula: see text]O is found to be at 6371.9[Formula: see text]keV, about 12.9[Formula: see text]keV above the threshold. The resulting [Formula: see text]-factor around the Gamow energy of 190[Formula: see text]keV [Formula: see text] is [Formula: see text][Formula: see text][Formula: see text]. Relatively, higher [Formula: see text]-factor value yields a larger reaction rate for [Formula: see text]C([Formula: see text])[Formula: see text]O at the required temperature window. Consequently, larger number of neutrons relevant to [Formula: see text]-process nucleosynthesis will be produced from [Formula: see text]C[Formula: see text]O reaction.

The radiative capture reaction [Formula: see text] is analyzed using a hybrid model approach where the non-resonant component has been constructed employing the potential model with a folded M3Y potential. The one-level Breit–Wigner formula has been used to estimate the cross-sections of the resonant decays of dominant ([Formula: see text]) state of [Formula: see text]N at 8.06[Formula: see text]MeV. The contribution of the broad resonance at 8.77[Formula: see text]MeV ([Formula: see text]) has been dealt with differently. While Breit–Wigner formula has been used where the excitation function data exist, the [Formula: see text]-matrix prediction for the cross-section of decay to a bound state of [Formula: see text]N from the broad resonance has been used where excitation function data are not available. The single particle spectroscopic factors for ground and six excited states of [Formula: see text]N have been obtained from the fits. The resulting astrophysical [Formula: see text]-factor at zero relative energy is [Formula: see text][Formula: see text]keV b. The value is in good agreement with the previously reported [Formula: see text]-matrix result and also consistent within error bars with the published values.

Given an irreducible unitary representation of a cocompact lattice of \({{\rm SL}(2, \mathbb{C})}\), we explicitly write down a solution of the Strominger system of equations. These solutions satisfy the equation of motion, and the underlying holomorphic vector bundles are stable.

We prove the following: let $\Gamma\, \subset\, \text{SL}(2,{\mathbb C})$ be a cocompact lattice and let $\rho\,:\, \Gamma\, \longrightarrow\, \text{GL}(r,{\mathbb C})$ be an irreducible representation. Then the holomorphic vector bundle $E_\rho\, \longrightarrow\, \text{SL}(2,{\mathbb C})/ \Gamma$ associated to $\rho$ is polystable. The compact complex manifold $\text{SL}(2,{\mathbb C})/ \Gamma$ has natural Hermitian structures; the polystability of $E_\rho$ is with respect to these natural Hermitian structures. We show that the polystable vector bundle $E_\rho$ is not stable in general.

Plants have evolved several types of sophisticated defence mechanisms to protect themselves from enemies in which the signalling molecules such as salicylic acid (SA), jasmonic acid (JA) and ethylene (ET) often play crucial roles. An experiment was conducted to investigate the water induced stress in young tomato plants (Lycopersicon esculentum var. Pusa Ruby) during water deprivation and after relief from stress in association with salicylic acid. Application of SA or certain analogues such as aspirin induces expression of pathogenesis-related proteins (PR) in plants which serve as molecular markers for the systemic acquired response (SAR). In plants, the production of reactive oxygen species (ROS), such as superoxide radical (O2), hydroxyl radical (OH) and hydrogen peroxide (H2O2), is also common in metabolic and physiological processes. Similar to the defence-regulating compounds such as SA and JA, plant-derived ET is also known to be involved in disease resistance. The role of ROS or ET could be inves...

In 2010, the FAA investigated the use of a capability called a Traffic Management Advisor Flow Programs to control flights destined for capacity-limited airports. This study explores through fast-t...

*† ‡ This paper presents a linear Integer Programming model for managing air traffic flow in the United States. The decision variables in the model are departure delays and predeparture reroutes of aircraft whose trajectories are predicted to cross weather-impacted regions of the National Airspace System. The model assigns delays to a set of flights while ensuring their trajectories are free of any conflicts with weather. In a deterministic setting, there is no airborne holding due to unexpected weather incursion in a flight’s path. The model is applied to solve a large-scale traffic flow management problem with realistic weather data and flight schedules. Experimental results indicate that allowing rerouting can reduce departure delays by nearly 57%, but it is associated with an increase in total airborne time due to longer routes flown by aircraft. The computation times to solve this problem were significantly lower than those reported in the earlier studies.

Traffic flow management in the United States is currently accomplished through a varied collection of controls such as miles-in-trail restrictions, playbook reroutes, air-space flow programs, and ground delay programs that are implemented independently of one another. An integrated traffic flow management decision-making approach that sequentially schedules and reroutes flights according to actual and forecast weather-induced airspace capacity constraints is described. The study tests the integrated system under 80 six-hour, fast-time simulation experiments. The authors describe the overall decision-making approach, discuss the implemented models, flight schedules, weather scenarios, and airspace capacity constraints, and present results of fast-time simulations used to explore trade-offs between optimization and heuristic-based models, strategic versus tactical flight controls, and system versus fleet preferences.

This paper presents a linear integer programming model for assigning departure delays and pre-departure reroutes to flights under airspace capacity restrictions. Experiments are performed in which ...

‡A sequential optimization method is applied to manage air traffic flow under uncertainty in airspace capacity and demand. To support its testing, a decision support system is developed by integrating a deterministic integer programming model for assigning delays to aircraft under en route capacity constraints to reactively account for system uncertainties. To reduce computational complexity, the model assigns only departure controls, while a tactical control loop consisting of a shortest path routing algorithm and an airborne holding algorithm refines the strategic plan to keep flights from deviating into capacity constrained airspace. This integrated approach is used to conduct thirty-two, 6-hour fast-time simulation experiments to explore variations in the number and severity of departure controls, tactical reroutes, and airborne holding controls. Three feasible types of traffic flow controls emerged. The first type relied primarily on departure controls and strategic reroutes on the 300 to 400 nmi look-ahead horizon and worked best when rerouting occurred at a frequency of 10 to 15 minutes. The second type generated more tactical reroutes on the 200 ‐ 300 nmi look-ahead horizon and required little airborne holding or pre-departure control when rerouting occurred at a frequency of 5 minutes. The last type relied heavily on airborne holding controls and infrequent updates to the weather avoidance reroutes. This last type was the least desirable solution due to the impact of its airborne holding on airspace complexity and airspace users.

In this paper, the authors examine the impact of deterministic en route capacity constraints on air traffic flows in the New York metropolitan area and the Eastern U.S. In order to mitigate demand and capacity imbalances induced by the constraints, a binary integer programming model is used to calculate optimal departure controls. By varying cost function parameters in the model, three distinct solutions are created, allowing for the exploration of the differences between how traffic flows are managed under current day operations and possible future operations. In order to test the models, 120 different scenarios are explored. The scenarios allowed for variations in the intensity and geographical location of the constraints, as well as the prioritization of the traffic flows through the constraints. Results are reported.

In this paper, we present a stochastic integer programming model for managing air traffic inbound to an airport when both the airport itself and its approach routes are subject to adverse weather. In the model, ground delay decisions are static, while those on rerouting are dynamic. The decision variables in the model are aggregate number of flights planned to arrive at various capacity constrained resources. The model does not directly assign arrival times to individual flights. Therefore, in context of Collaborative Decision Making, which is the governing philosophy of the air traffic management system of the United States, the solutions from the dynamic rerouting model can be directly fed to some resource allocation algorithm that assigns routes and release times to individual flights or to the airlines who operate them. When adverse weather blocks or severely limits capacity of terminal approach routes, rerouting flights onto other approaches yields substantial benefits by alleviating high ground delays. Our experimental results indicate that making rerouting decisions dynamically results in 10–15% delay cost reduction compared to static rerouting, and about 50% less delay cost compared to a “pure” ground holding strategy (i.e., no rerouting). In contrast to static rerouting, the dynamic rerouting capability results in making rerouting decisions that are better matched to realized weather conditions. Lower total expected delay cost is achieved by delaying the rerouting decisions for flights until they reach the divergence point between alternative routes, and hence exploiting updated information on weather while making those decisions. In cases where the airport is the main, but not the only, bottleneck, the dynamic rerouting model may assign higher ground delays so that the rerouting decisions can be deferred until more information on en route weather becomes available.

This study addresses one of the issues being raised as a Concept of Operation (CONOPS) is being developed for the Next Generation Air Transportation System (NextGen), systems to maximize the use of the oceanic domain airspace through new ways of enhancing situational awareness by pilots and new routes that take advantage of wind conditions. Some of these efforts are creating concern about the significant number of new four-dimensional (4-D) crossing points under user-generated routing. These could significantly increase air traffic controllers’ workloads. In this study, new single and multi-objective binary integer programming models that are derivatives of a model originally developed to solve a deterministic traffic flow management problem are proposed. They minimize the total scheduling delay and the weighted sum of the scheduling delay and the number of residual trajectory merge points that are allowed. The study also leverages an innovative technique for transforming a flight scheduling problem into a job scheduling problem. After routing all flights in the Central East Pacific (CEP) on wind-optimal routes and strategically scheduling the flights to reduce the number of trajectory merge points, the delays are calculated. A multi-objective binary integer program is developed to allow trade-offs between the number of trajectory merge points and delays. A trade-off frontier was calculated for a 24-hour scheduling problem. Future trade-offs can be calculated based on what is tolerable in the CEP to implement strategic scheduling.

Background: The proton capture reaction $^{13}\mathrm{C}(p,\ensuremath{\gamma})^{14}\mathrm{N}$ is an important reaction in the CNO cycle during hydrogen burning in stars with mass greater than the mass of the Sun. It also occurs in astrophysical sites such as red giant stars: the asymptotic giant branch (AGB) stars. The low energy astrophysical $S$ factor of this reaction is dominated by a resonance state at an excitation energy of around 8.06 MeV $({J}^{\ensuremath{\pi}}={1}^{\ensuremath{-}},T=1)$ in $^{14}\mathrm{N}$. The other significant contributions come from the low energy tail of the broad resonance with ${J}^{\ensuremath{\pi}}={0}^{\ensuremath{-}},T=1$ at an excitation of 8.78 MeV and the direct capture process.Purpose: Measurements of the low energy astrophysical $S$ factor of the radiative capture reaction $^{13}\mathrm{C}(p,\ensuremath{\gamma})^{14}\mathrm{N}$ reported extrapolated values of $S(0)$ that differ by about $30%$. Subsequent $R$-matrix analysis and potential model calculations also yielded significantly different values for $S(0)$. The present work intends to look into the discrepancy through a detailed $R$-matrix analysis with emphasis on the associated uncertainties.Method: A systematic reanalysis of the available decay data following the capture to the ${J}^{\ensuremath{\pi}}={1}^{\ensuremath{-}},T=1$ resonance state of $^{14}\mathrm{N}$ around 8.06 MeV excitation had been performed within the framework of the $R$-matrix method. A simultaneous analysis of the $^{13}\mathrm{C}(p,{p}_{0})$ data, measured over a similar energy range, was carried out with the capture data. The data for the ground state decay of the broad resonance state $({J}^{\ensuremath{\pi}}={0}^{\ensuremath{-}},T=1)$ around 8.78 MeV excitations was included as well. The external capture model along with the background poles to simulate the internal capture contribution were used to estimate the direct capture contribution. The asymptotic normalization constants (ANCs) for all states were extracted from the capture data. The multichannel, multilevel $R$-matrix code azure2 was used for the calculation.Results: The values of the astrophysical $S$ factor at zero relative energy, resulting from the present analysis, are found to be consistent within the error bars for the two sets of capture data used. However, it is found from the fits to the elastic scattering data that the position of the ${J}^{\ensuremath{\pi}}={1}^{\ensuremath{-}},T=1$ resonance state is uncertain by about 0.6 keV, preferring an excitation energy value of 8.062 MeV. Also the extracted ANC values for the states of $^{14}\mathrm{N}$ corroborate the values from the transfer reaction studies. The reaction rates from the present calculation are about $10--15%$ lower than the values of the NACRE II compilation but compare well with those from NACRE I.Conclusion: The precise energy of the ${J}^{\ensuremath{\pi}}={1}^{\ensuremath{-}},T=1$ resonance level around 8.06 MeV in $^{14}\mathrm{N}$ must be determined. Further measurements around and below 100 keV with precision are necessary to reduce the uncertainty in the $S$-factor value at zero relative energy.

Recent studies of the single-airport ground-holding problem use static or dynamic optimization to manage uncertainty about future airport capacities. Scenario trees of airport capacity profiles provide the basis for formulating multistage recourse problems. In this paper, methodologies are presented for generating scenario trees from empirical data, and the performance of scenario-based models is examined with the scenario trees. Most U.S. airports have capacity profiles that can be classified into some nominal scenarios, and for many airports, these scenarios can be naturally combined into scenario trees. The delay costs yielded from using dynamic optimization are consistently and considerably lower than from static optimization. The results illustrate the benefit of the wait-and-see strategy in a real-world setting and suggest the need for further research on implementing scenario-based dynamic strategies.

Let [Formula: see text] be a moduli space of stable parabolic Higgs bundles of rank two over a Riemann surface X. It is a smooth variety defined over [Formula: see text] equipped with a holomorphic symplectic form. Fix a projective structure [Formula: see text] on X. Using [Formula: see text], we construct a quantization of a certain Zariski open dense subset of the symplectic variety [Formula: see text].

Parabolic triples of the form $(E_*,\theta,\sigma)$ are considered, where $(E_*,\theta)$ is a parabolic Higgs bundle on a given compact Riemann surface $X$ with parabolic structure on a fixed divisor $S$, and $\sigma$ is a nonzero section of the underlying vector bundle. Sending such a triple to the Higgs bundle $(E_*,\theta)$ a map from the moduli space of stable parabolic triples to the moduli space of stable parabolic Higgs bundles is obtained. The pull back, by this map, of the symplectic form on the moduli space of stable parabolic Higgs bundles will be denoted by $\text{d}\Omega'$. On the other hand, there is a map from the moduli space of stable parabolic triples to a Hilbert scheme $\text{Hilb}^\delta(Z)$, where $Z$ denotes the total space of the line bundle $K_X\otimes{\mathcal O}_X(S)$, that sends a triple $(E_*,\theta,\sigma)$ to the divisor defined by the section $\sigma$ on the spectral curve corresponding to the parabolic Higgs bundle $(E_*,\theta)$. Using this map and a meromorphic one--form on $\text{Hilb}^\delta(Z)$, a natural two--form on the moduli space of stable parabolic triples is constructed. It is shown here that this form coincides with the above mentioned form $\text{d}\Omega'$., Comment: LaTex file; 11 pages

Fix a smooth very ample curve C on a K3 or abelian surface X. Let $ \mathcal{M} $ denote the moduli space of pairs of the form (F, s), where F is a stable sheaf over X whose Hilbert polynomial coincides with that of the direct image, by the inclusion map of C in X, of a line bundle of degree d over C, and s is a nonzero section of F. Assume d to be sufficiently large such that F has a nonzero section. The pullback of the Mukai symplectic form on moduli spaces of stable sheaves over X is a holomorphic 2-form on $ \mathcal{M} $. On the other hand, $ \mathcal{M} $ has a map to a Hilbert scheme parametrizing 0-dimensional subschemes of X that sends (F, s) to the divisor, defined by s, on the curve defined by the support of F. We prove that the above 2-form on $ \mathcal{M} $ coincides with the pullback of the symplectic form on the Hilbert scheme.

The perceived risk regarding any vector borne disease depends upon the level of knowledge of the community people living there and based on the general perception of them, effective control strategies can be framed in the applied area. In the present study, a community-based cross-sectional study covering 400 peoples between an urban area (Bankura Sadar) and a rural area (Village Beliatore, Bankura) was undertaken to evaluate the level of knowledge of the community people regarding mosquito-borne diseases and locally used preventive measures. From the result of the present survey differences in the community perception between rural and urban people of Bankura were established.

Free Flight Phase 1 (FFP1) is an FAA program for improving the performance of the National Airspace System (NAS) through the deployment of advanced technologies for air traffic management. In addition to the deployment activities, FFP1 includes a significant evaluation component, which faces a significant hurdle. A plethora of factors—weather, demand, enhancements to the NAS infrastructure not related to FFP1, facility outages, and so on—may also cause changes in NAS performance. It is necessary to normalize for these factors in order to determine the effect of FFP1. Normalization procedures to isolate the impact of the implementation of an FFP1 technology—the Terminal Area Traffic Management Advisor (T-TMA)—are documented at the Southern California TRACON, where it is used for controlling traffic into Los Angeles International Airport. Two examples of normalization are presented. One examines the effect of T-TMA on airport arrival capacity, and the other looks at arrival delay. The results, although preliminary given the short time since implementation, are consistent: it appears that capacities have increased and delays decreased as a result of the deployment of the tool. Moreover, the magnitudes of the delay reductions and capacity increases are consistent.

The aim of this study was to determine the diversity of zooplankton in a local pond ecosystem. Zooplankton plays a very important role in the food chain of pond ecosystem and its diversity is one of the determinant of shaping the food chain. Its diversity will ultimately decide the nature of the pond community. During our investigation in local pond, we have gathered various types of zooplanktons like Cyclops, Daphnia, Cypris , and Moina . Their number was influenced by the effect of pH (measured by digital pH meter), temperature (measured by thermometer), free CO 2 level (estimated by APHA method), and dissolved O 2 (estimated by Winkler’s method). The season wise variation of zooplankton in pond indicate the different environmental condition in water bodies.

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

In this paper, we present two supervised-learning models, logistic regression and decision tree, to predict occurrence of ground delay program at an airport based on meteorological conditions and scheduled traffic demand. Such predictive capabilities can help the Federal Aviation Administration traffic managers and airline dispatchers to prepare mitigation strategies to reduce the impact of adverse weather. The models are applied to predict ground delay program occurrence at two major U.S. airports: Newark Liberty Intl. and San Francisco Intl. airports. The logistic regression model estimates the probability that a ground delay program will occur during a given hour. Decision tree, on the other hand, classifies an hour as a ground delay program or not based on the input variables. Results indicate that both models perform significantly better than a purely random prediction of ground delay program occurrence at the two airports. The logistic regression model performs better than the decision tree model. The degree to which various input variables impact the probability of ground delay program vary between the two airports. While the enroute convective weather is a dominant factor causing ground delay programs at New York airports, poor visibility and low cloud ceiling caused by marine stratus are major drivers of ground delay programs at San Francisco Intl. airport.

A generalization of the notion of almost complex structure is defined on a nonorientable smooth manifold M of even dimension. It is defined by giving an isomorphism J from the tangent bundle TM to the tensor product of the tangent bundle with the orientation bundle such that J○J=−Id TM . The composition J○J is realized as an automorphism of TM using the fact that the orientation bundle is of order two. A notion of integrability of this almost complex structure is defined; also the Kahler condition has been extended. The usual notion of a complex vector bundle is generalized to the nonorientable context. It is a real vector bundle of even rank such that the almost complex structure of a fiber is given up to the sign. Such bundles have generalized Chern classes. These classes take value in the cohomology of the tensor power of the local system defined by the orientation bundle. The notion of a holomorphic vector bundle is extended to the context under consideration. Stable vector bundles and Einstein–Hermitian connections are also generalized. It is shown that a generalized holomorphic vector bundle on a compact nonorientable Kahler manifold admits an Einstein–Hermitian connection if and only if it is polystable.

A new method to obtain the Picard-Fuchs equations of effective, N=2 supersymmetric gauge theories with massive matter hypermultiplets in the fundamental representation is presented. It generalises a previously described method to derive the Picard-Fuchs equations of both pure super Yang-Mills and supersymmetric gauge theories with massless matter hypermultiplets. The techniques developed are well suited to symbolic computer calculations., Comment: 29 pages, uses phyzzx.tex

A new method to obtain the Picard-Fuchs equations of effective $N = 2$ supersymmetric gauge theories in 4 dimensions is developed. It includes both pure super Yang-Mills and supersymmetric gauge theories with massless matter hypermultiplets. It applies to all classical gauge groups, and directly produces a decoupled set of second-order, partial differential equations satisfied by the period integrals of the Seiberg-Witten differential along the 1-cycles of the algebraic curves describing the vacuum structure of the corresponding $N = 2$ theory., Latex version, 43 pages, a few cosmetic changes and some references added

‡On any given day, constraints in the National Airspace System, for instance weather, necessitate the implementation of Traffic Flow Management initiatives, such as Ground Delay Programs. The parameters associated with these initiatives, for example the location, scope, duration, etc., are typically left to human decision makers, who must rely on intuition, past experience, and weather and traffic forecasts. Although the decisions of these traffic flow specialists are recorded on a daily basis, few studies have attempted to apply data mining techniques to these archives in an attempt to identify patterns and past decisions that could ultimately be used to influence future decision-making. The goal of this study is to take a preliminary step towards informing future decision-making by proposing a technique for identifying similar days in the National Airspace System in terms of the Ground Delay Programs that were operationally implemented. Hence an airport perspective is being taken to identify these similar days, as opposed to considering possible airspace features. A modified k-means clustering algorithm is applied to all days in 2011, resulting in the identification of 18 clusters that represent unique combinations of Ground Delay Program that were historically implemented. A given day was described in terms of the presence or absence of 33 features that were a combination of Ground Delay Program locations and causes. By far the largest cluster that was identified consisted of 73 days on which low ceiling related Ground Delay Programs impacted San Francisco International Airport. In an attempt to verify the stated cause of the Ground Delay Programs, an Expectation Maximization clustering algorithm was applied to the 8,760 hourly Meteorological Aerodrome Reports, scheduled arrival rate and Ground Delay Program start and end time records for 2011. In general, clusters were identified that corroborated the stated causes of the Ground Delay Programs. However, these clusters often contained a significant number of members for which a Ground Delay Program did not occur. Findings from this initial study indicate that it is possible to identify similar days under which the National Airspace System operates, and clustering techniques appear to be promising methods for identifying the major causes of Ground Delay Programs.

Classification of days based on weather impact on the National Airspace System is essential to evaluate the effectiveness of traffic management decisions in the past, which ultimately can improve the operational readiness when similar events occur in the future. To achieve this goal, this paper presents a methodology to classify days based on severe weather impact on traffic. A daily index of the impact of severe weather on scheduled traffic flow, termed as the Weather Impacted Traffic Index, is used as an input to perform the classification. First, a factor analysis is performed to identify the dominant weather patterns that occur on various days. Six major weather factors are identified based on this analysis. Factor scores are obtained for each day based on the day’s weather location and severity. Days are clustered using Ward’s minimum-variance method applied to the daily factor scores. The outcome of the analysis is a set of 21 clusters and days within each cluster. While the weather and traffic in the days belonging to a common cluster are similar, they are not completely identical. Following the classification of days, the reroute advisories are analyzed to identify the frequently used routes on days belonging to various clusters. It is observed that the most frequently used reroutes on days that belong to a particular cluster exhibit similarity to the National Playbook routes designed to mitigate weather impact on those days, which is an intuitive result that is supported by data analysis. I. INTRODUCTION dverse weather reduces the capacity of the National Airspace System (NAS) by partially or completely blocking routes, waypoints, and airports. During such conditions, traffic managers at the FAA’s Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines’ Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. The performance of the TFM control actions is measured using a set of metrics such as total delay, cancellations, diversions, additional flying time, airborne holding time, loss of predictability of operations, etc. These performance metrics vary from day-to-day based on the severity, location, and characteristics of weather as well as the effectiveness of TFM control actions. If a particular day can be characterized as being similar, in terms of weather and traffic, to a few days in the past, then the TFM control actions from those days could serve as a basis for strategizing TFM on the current day of operation. A thorough post-operational evaluation of TFM actions in the past can reveal the potential areas of improvement, if possible. Doing so will better equip the NAS users (i.e., airlines) and the service provider (i.e., ATCSCC) with information to mitigate weather impact, and hence, improve the operational readiness. It will also improve the predictability of TFM control actions if the weather forecasts are reasonably accurate on a given day. A successful classification of days is necessary to evaluate the effectiveness of TFM actions on days that are similar. This paper presents a methodology to classify days based on weather and traffic pattern and to cluster them into groups. Days belonging to the same cluster may not be identical, but are statistically close enough. The Weather Impacted Traffic Index (WITI) measures the location and severity of weather and its impact on traffic.

We discuss the properties of the large phase space of the genus-0 topological minimal $A_{k + 1}$ model coupled to 2d topological gravity. The minimal action is perturbed by adding all possible gravitational descendants with non-trivial couplings which form the infinite-dimensional large phase space. Some general identities (valid on the large phase space) are derived and the puncture and the dilaton equations are obtained as special cases of these results. Finally we show how the couplings to the gravitational descendants can actually be expressed in terms of the flat coordinates defining the small phase space of the theory. Thus eventually the large phase space becomes determined solely by the LG superpotential characterising the matter sector of the coupled model., 29 pages, LATEX file, requires mssymb fonts

Weather can cause flight diversions, passenger delays, additional fuel consumption and schedule disruptions at any high volume airport. The impacts are particularly acute at the Ted Stevens Anchorage International Airport in Anchorage, Alaska due to its importance as a major international portal. To minimize the impacts due to weather, a multi-stage scheduling process is employed that is iteratively executed, as updated aircraft demand and/or airport capacity data become available. The strategic scheduling algorithm assigns speed adjustments for flights that originate outside of Anchorage Center to achieve the proper demand and capacity balance. Similarly, an internal departure-scheduling algorithm assigns ground holds for pre-departure flights that originate from within Anchorage Center. Tactical flight controls in the form of airborne holding are employed to reactively account for system uncertainties. Real-world scenarios that were derived from the January 16, 2012 Anchorage visibility observations and the January 12, 2012 Anchorage arrival schedule were used to test the initial implementation of the scheduling algorithm in fast-time simulation experiments. Although over 90% of the flights in the scenarios arrived at Anchorage without requiring any delay, pre-departure scheduling was the dominant form of control for Anchorage arrivals. Additionally, tactical scheduling was used extensively in conjunction with the pre-departure scheduling to reactively compensate for uncertainties in the arrival demand. For long-haul flights, the strategic scheduling algorithm performed best when the scheduling horizon was greater than 1,000 nmi. With these long scheduling horizons, it was possible to absorb between ten and 12 minutes of delay through speed control alone. Unfortunately, the use of tactical scheduling, which resulted in airborne holding, was found to increase as the strategic scheduling horizon increased because of the additional uncertainty in the arrival times of the aircraft. Findings from these initial experiments indicate that it is possible to schedule arrivals into Anchorage with minimal delays under low-visibility conditions with less disruption to high-cost, international flights.

Air transportation systems are some of the most complex logistical systems imaginable. The world’s airlines transported over 2.2 billion passengers in 2008, and transported approximately 40% of world trade (measured by value). There are nearly 2,000 airlines worldwide, which have a total fleet of nearly 23,000 aircraft and serve some 3,750 airports through a route network of several million miles managed by around 160 air navigation service providers.

Delays caused by uncertainty in weather forecasts can be reduced by improving traffic flow management decisions. This paper presents a methodology for traffic flow management under uncertainty in convective weather forecasts. An algorithm for assigning departure delays and reroutes to aircraft is presented. Departure delay and route assignment are executed at multiple stages, during which, updated weather forecasts and flight schedules are used. At each stage, weather forecasts up to a certain look-ahead time are treated as deterministic and flight scheduling is done to mitigate the impact of weather on fourdimensional flight trajectories. Uncertainty in weather forecasts during departure scheduling results in tactical airborne holding of flights. The amount of airborne holding depends on the accuracy of forecasts as well as the look-ahead time included in the departure scheduling. The weather forecast look-ahead time is varied systematically within the experiments perfomed in this paper to analyze its effect on flight delays. Based on the results, longer look-ahead times cause higher departure delays and additional flying time due to reroutes. However, the amount of airborne holding necessary to prevent weather incursions reduces when the forecast look-ahead times are higher. For the chosen day of traffic and weather, setting the look-ahead time to 90 minutes yields the lowest total delay cost.

Starting in 2010, the FAA began using new traffic flow management initiatives called Traffic Management Advisor Flow Programs to control flights destined for capacity-limited airports. This study explores through fast-time simulations the impact of these new traffic management initiatives on Hartsfield-Jackson Atlanta International Airport arrival operations. The emphasis of this study is on examining the distribution of delays for flights included and exempted from this new initiative. Of the 476 flights included in the Traffic Management Advisor Flow Programs, the results showed that when Atlanta was operating near maximum capacity, the mean total delay assigned to these flights was 31.6 minutes. However, the mean total delay increased to over 49.3 minutes when the airport capacity was reduced by 25%. In contrast, for the 408 arrivals that were exempt from the Traffic Management Advisor Flow Program the mean delay assigned to these flights by the Traffic Management Advisor was found to increase from 6.5 minutes when the airport was operating near maximum capacity to 24.8 minutes when the capacity was reduced by 25%. The experiments clearly illustrated the potential for imbalance in the distribution of delays assigned to the flights included within and exempt from the Traffic Management Advisor Flow Program. The mean total delay experienced by the included flights was between 85% and 99% higher than the delays experienced by the exempt flights. To alleviate this imbalance, a simulation-based approach is presented for selecting the "best" flow rate to reduce this imbalance, when developing a Traffic Management Advisor Flow Program. This approach depends on the arrival demand, airport configuration, and acceptance rate.

The objective of air traffic management is to mitigate imbalances between the demand for air traffic services and the capacity of the air transportation system, so as to ensure that aircraft can flow through the airspace safely and efficiently. This article outlines the principal optimization models that have been proposed to provide decision support within air traffic management, and describes the trade-offs and challenges that arise in this environment. Keywords: air traffic flow management; collaborative decision making; integer programming; stochastic optimization

The loosely coordinated sets of traffic flow management initiatives that are operationally implemented at the national- and local-levels have the potential to under, over, and inconsistently control flights. This study is designed to explore these interactions through fast-time simulations with an emphasis on identifying inequitable situations in which flights receive multiple uncoordinated delays. Two operationally derived scenarios were considered in which flights arriving into the Dallas/Fort Worth International Airport were first controlled at the national-level, either with a Ground Delay Program or a playbook reroute. These flights were subsequently controlled at the local level. The Traffic Management Advisor assigned them arrival scheduling delays. For the Ground Delay Program scenarios, between 51% and 53% of all arrivals experience both pre-departure delays from the Ground Delay Program and arrival scheduling delays from the Traffic Management Advisor. Of the subset of flights that received multiple delays, between 5.7% and 6.4% of the internal departures were first assigned a pre-departure delay by the Ground Delay Program, followed by a second pre-departure delay as a result of the arrival scheduling. For the playbook reroute scenario, Dallas/Fort Worth International Airport arrivals were first assigned pre-departure reroutes based on the MW_2_DALLAS playbook plan, and were subsequently assigned arrival scheduling delays by the Traffic Management Advisor. Since the airport was operating well below capacity when the playbook reroute was in effect, only 7% of the arrivals were observed to receive both rerouting and arrival scheduling delays. Findings from these initial experiments confirm field observations that Ground Delay Programs operated in conjunction with arrival scheduling can result in inequitable situations in which flights receive multiple uncoordinated delays.