An artificial neural network application on nuclear charge radii

The artificial neural networks (ANNs) have emerged with successful applications in nuclear physics as well as in many fields of science in recent years. In this paper, by using (ANNs), we have constructed a formula for the nuclear charge radii. Statistical modeling of nuclear charge radii by using ANNs has been seen as to be successful. Also, the charge radii, binding energies and two-neutron separation energies of Sn isotopes have been calculated by implementing of the new formula in Hartree-Fock-Bogoliubov (HFB) calculations. The results of the study shows that the new formula is useful for describing nuclear charge radii.Comment: 7 pages, 3 figure

Critical elements essential for exemplary educational leadership

Education is a chameleon that is ever changing. As educators we must be willing to see where change needs to be made and accept the fact that we as individuals may need to change in order to provide the best education that the students deserve. Change is not an easy thing to accept or accomplish. It is more comfortable to sit in the same old place and not look outside our blinders. However we must realize that this does not help students or their parents. Not only does it not help these people it does not help us become better educators

Structural, Vibrational and Thermodynamic Properties of AgnCu34-n Nanoparticles

We report results of a systematic study of structural, vibrational and thermodynamical properties of 34-atom bimetallic nanoparticles from the AgnCu34-n family using model interaction potentials as derived from the embedded atom method and in the harmonic approximation of lattice dynamics. Systematic trends in the bond length and dynamical properties can be explained largely on arguments based on local coordination and elemental environment. Thus increase in the number of silver atoms in a given neighborhood introduces a monotonic increase in bond length while increase of the copper content does the reverse. Moreover, based on bond lengths of the lowest coordinated (6 and 8) copper atoms with their nearest neighbors (Cu atoms), we find that the nanoparticles divide into two groups with average bond length either close to (~ 2.58 A) or smaller (~ 2.48 A) than that in bulk copper, accompanied by characteristic features in their vibrational density of states. For the entire set of nanoparticles, vibrational modes are found above the bulk bands of copper/silver. Furthermore, a blue shift in the high frequency end with increasing number of copper atoms in the nanoparticles is traced to a shrinkage of bond lengths from bulk values. The vibrational densities of states at the low frequency end of the spectrum scale linearly with frequency as for single element nanoparticles, however, the effect is more pronounced for these nanoalloys. The Debye temperature was found to be about one third of that of the bulk for pure copper and silver nanoparticles with a non-linear increase with increasing number of copper atoms in the nanoalloys.Comment: 37 pages, 12 figure

Self-learning Kinetic Monte-Carlo method: application to Cu(111)

We present a novel way of performing kinetic Monte Carlo simulations which does not require an {\it a priori} list of diffusion processes and their associated energetics and reaction rates. Rather, at any time during the simulation, energetics for all possible (single or multi-atom) processes, within a specific interaction range, are either computed accurately using a saddle point search procedure, or retrieved from a database in which previously encountered processes are stored. This self-learning procedure enhances the speed of the simulations along with a substantial gain in reliability because of the inclusion of many-particle processes. Accompanying results from the application of the method to the case of two-dimensional Cu adatom-cluster diffusion and coalescence on Cu(111) with detailed statistics of involved atomistic processes and contributing diffusion coefficients attest to the suitability of the method for the purpose.Comment: 18 pages, 9 figure

Structure, Dynamics and Themodynamics of a metal chiral surface: Cu(532)

The structure, vibrational dynamics and thermodynamics of a chiral surface, Cu(532), has been calculated using a local approach and the harmonic approximation, with interatomic potentials based on the embedded atom method. The relaxation of atomic positions to the optimum configuration results in a complex relaxation pattern with strong contractions in the bond length of atoms near the kink and the step site and an equivalently large expansion near the least under-coordinated surface atoms. The low coordination of the atoms on the surface affects substantially the vibrational dynamics and thermodynamics of this system. The local vibrational density of states show a deviation from the bulk behavior that persist down to the 10th layer resulting in a substantial contribution of the vibrational entropy to the excess free energy amounting to about 90 meV per unit cell at 300K

Summary of a Crew-Centered Flight Deck Design Philosophy for High-Speed Civil Transport (HSCT) Aircraft

Past flight deck design practices used within the U.S. commercial transport aircraft industry have been highly successful in producing safe and efficient aircraft. However, recent advances in automation have changed the way pilots operate aircraft, and these changes make it necessary to reconsider overall flight deck design. Automated systems have become more complex and numerous, and often their inner functioning is partially or fully opaque to the flight crew. Recent accidents and incidents involving autoflight system mode awareness Dornheim, 1995) are an example. This increase in complexity raises pilot concerns about the trustworthiness of automation, and makes it difficult for the crew to be aware of all the intricacies of operation that may impact safe flight. While pilots remain ultimately responsible for mission success, performance of flight deck tasks has been more widely distributed across human and automated resources. Advances in sensor and data integration technologies now make far more information available than may be prudent to present to the flight crew

What Matters Most in Transportation Demand Model Specifications: A Comparison of Outputs in a Mid-size Network

This paper examines the impact of travel demand modeling (TDM) disaggregation techniques in the context of medium-sized communities. Specific TDM improvement strategies are evaluated for predictive power and flexibility with case studies based on the Tyler, Texas, network. Results suggest that adding time-of-day disaggregation, particularly in conjunction with multi-class assignment, to a basic TDM framework has the most significant impacts on outputs. Other strategies shown to impact outputs include adding a logit mode choice model and incorporating a congestion feedback loop. For resource-constrained communities, these results show how model output and flexibility vary for different settings and scenarios.BACKGROUND Transportation directly provides for the mobility of people and goods, while influencing land use patterns and economic activity, which in turn affect air quality, social equity, and investment decisions. Driven by the need to forecast future transportation demand and system performance, Manheim (1979) and Florian et al. (1988) introduced a transportation analysis framework for traffic forecasting using aggregated data that provide the basis for what is known as the four-step model: a process involving trip generation, then trip distribution and mode choice, followed by route choice. Aggregating demographic data at the zone level, the four-step model generates trip productions based on socioeconomic data (e.g., household counts by income and size) and trip attractions primarily based on jobs counts. The model then proportionally distributes trips between each origin and destination (OD) zone pair based on competing travel attractions and impedances, under the assumption that OD pairings with higher travel costs draw fewer trips. Trips between each OD pair are split among a variety of transportation modes, allocating trips to private vehicle, transit, or othe