Analytical calculation of the parameters of light bullets propagating in the tunnel ionization regime

Analytic estimation of the parameters of light bullets formed in the anomalous group dispersion region of transparent dielectrics under conditions of tunneling photoionization was performed. For this purpose, the system of the ordinary differential equations for the laser pulse’s parameters such as amplitude, temporal duration, chirp parameter, temporal delay, frequency shift, radius and curvature were obtained. The stationary solution of this system and conditions of the quasi-stable regime of propagation were found.

The efficacy of event isotropy as an event shape observable

Event isotropy $$ {\mathcal{I}}^{\mathrm{sph}} $$ I sph , an event shape observable that measures the distance of a final state from a spherically symmetric state, is designed for new physics signals that are far from QCD-like. Using a new technique [1] for producing a wide variety of signals that can range from near-spherical to jetty, we compare event isotropy to other observables. We show that thrust T and the C parameter (and λmax, the largest eigenvalue of the sphericity matrix) are strongly correlated and thus redundant, to a good approximation. By contrast, event isotropy adds considerable information, often serving to break degeneracies between signals that would have almost identical T and C distributions. Signals with broad distributions in T (or λmax) and in $$ {\mathcal{I}}^{\mathrm{sph}} $$ I sph separately often have much narrower distributions, and are more easily distinguished, in the ($$ {\mathcal{I}}^{\mathrm{sph}} $$ I sph , λmax) plane. An intuitive, semi-analytic estimation technique clarifies why this is the case and assists with the interpretation of the distributions.

The small impact of p-hacking marginally significant results on the meta-analytic estimation of effect size

La etiqueta p-hacking (pH) se refiere a un conjunto de prácticas oportunistas destinadas a hacer que sean significativos algunos valores p que deberían ser no significativos. Algunos han argumentado que debemos prevenir y luchar contra el pH por varias razones, especialmente debido a sus posibles efectos nocivos en la evaluación de los resultados de la investigación primaria y su síntesis meta-analítica. Nos focalizamos aquí en el efecto de un tipo específico de pH, centrado en estudios marginalmente significativos, en la estimación combinada del tamaño del efecto en el meta-análisis. Queremos saber cuánto deberíamos preocuparnos por su efecto de sesgo al evaluar los resultados de un meta-análisis. Hemos calculado el sesgo en una variedad de situaciones que parecen realistas en términos de prevalencia y de la definición operativa del pH. Los resultados muestran que en la mayoría de las situaciones analizadas el sesgo es inferior a una centésima (± 0.01), en términos de d o r. Para alcanzar un nivel de sesgo de cinco centésimas (± 0.05), tendría que haber una presencia masiva de este tipo de pH, lo que parece poco realista. Hay muchas buenas razones para luchar contra el pH, pero nuestra conclusión principal es que entre esas razones no se incluye que tenga un gran impacto en la estimación meta-analítica del tamaño del efecto. The label p-hacking (pH) refers to a set of opportunistic practices aimed at making statistically significant p values that should be non-significant. Some have argued that we should prevent and fight against pH for several reasons, especially because of its potential harmful effects on the assessment of both primary research results and their meta-analytical synthesis. We focus here on the effect of a specific type of pH, focused on marginally significant studies, on the combined estimation of effect size in meta-analysis. We want to know how much we should be concerned with its biasing effect when assessing the results of a meta-analysis. We have calculated the bias in a range of situations that seem realistic in terms of the prevalence and the operational definition of pH. The results show that in most of the situations analyzed the bias is less than one hundredth (± 0.01), in terms of d or r. To reach a level of bias of five-hundredths (± 0.05), there would have to be a massive presence of this type of pH, which seems rather unrealistic. There are many good reasons for fighting against pH, but our main conclusion is that among them is not that it has a big impact on the meta-analytical estimation of effect size.

A Study on the Analytic Power Estimation of the Electromagnetic Resonant Energy Harvester for the High-Speed Train

This study is intended to identify the applicability of energy harvesting technologies that are regarded as new electrical power sources for the sensors on high-speed trains. The analytic estimation research is conducted on the amount of electric energy harvested from the high-speed trains, operating at a maximum speed of over 400km/h to verify the applicability of the energy harvesting technology converting the vibration energy of axle and bogie into electric power. Based on the data of the vibration acceleration on the axles and bogies, which were measured by using a 500 Hz analog filter, an analytic estimation on the amount of power harvested by an electromagnetic resonant harvester is conducted through the analysis of the main frequency. The power of the electromagnetic resonant harvester is based on a theoretical model of the mass-spring-damper system, and the harvested power from the axles and bogies in the vertical direction is analytically estimated in this study. The analytic calculations typically give the target value for the final performance of the electromagnetic resonant energy harvester. The targets of the analytic estimations are given to provide the basis for the detailed design and to give a basis for defining the basic design parameters of the electromagnetic resonant energy harvester.

Deep learning for robust meta-analytic estimation

Meta-analysis represents the promise of cumulative science--that each successive study brings us greater understanding of a given phenomenon. As such, meta-analyses are highly influential and gaining in popularity. However, there are well-known threats to the validity of meta-analytic results, such as processes like publication bias and questionable research practices which can cause researchers to massively overestimate the evidence in support of a claim. There are many statistical methods to correct for such bias, but no single method has been found to be robust in all realistic conditions. Here, I describe a method that merges statistical simulation and deep learning to achieve an unprecedented level of robust meta-analytic estimation in the face of numerous forms of bias and other historically problematic conditions. Furthermore, the resulting estimator, called DeepMA, has the unique property that it can easily evolve: As new conditions for which robustness is needed are identified, DeepMA can be re-trained to maintain high performance. Given the weaknesses that have been identified for meta-analysis, the current consensus is that it should serve as simply another data point, rather than residing at the top of the hierarchy of evidence. The novel approach I describe, however, holds the potential to eliminate these weaknesses, possibly solidifying meta-analysis as the platinum standard in scientific debate.

The Spread of Conflict in International Relations

International relations as a subfield in political science has always been fundamentally concerned about the relations between actors and how they lead to conflictual or cooperative outcomes. However, despite this inherent interest in relations between actors, the gap between theoretical conceptualization and empirical estimation considerably widened until spatial econometric and network analytic estimation approaches allowed researchers to address interdependencies in multiactor settings empirically. However, the discipline needs to strengthen the link between theoretical and empirical network analysis by integrating formal theoretical advances and fully embracing inferential statistical network approaches that are available to researchers. Formal theories of network behavior need to be further developed to establish systematic insights into the conditions under which complex network structures arise and how they affect actor behavior. Rigorous theoretically guided research will form the basis of linking network theories of conflict and cooperation and empirical testing.