Conserved laws and dynamical structure of axions coupled to photons

In this work, we carry out a study of the conserved quantities and dynamical structure of the four-dimensional modified axion electrodynamics theory described by the axion-photon coupling. In the first part of the analysis, we employ the covariant phase space method to construct the conserved currents and to derive the Noether charges associated with the gauge symmetry of the theory. We further derive the improved energy–momentum tensor using the Belinfante–Rosenfeld procedure, which leads us to the expressions for the energy, momentum, and energy flux densities. Thereafter, with the help of Faddeev–Jackiw’s Hamiltonian reduction formalism, we obtain the relevant fundamental brackets structure for the dynamic variables and the functional measure for determining the quantum transition amplitude. We also confirm that modified axion electrodynamics has three physical degrees of freedom per space point. Moreover, using this symplectic framework, we yield the gauge transformations and the structure of the constraints directly from the zero-modes of the corresponding pre-symplectic matrix.

Classification of Dynamic Cartographic Symbols applied to Augmented Reality (AR) Systems

Paper maps were widely used during centuries; however, these maps do not change dynamically regarding its use context, the user behaviour and the change in the representations through time. Considering the research related to digital cartography, maps started to be seen both digitally and in a dynamic way due to the application of static and dynamic visual variables. During the process of navigation supported by maps, the comprehension of certain cartographic symbols can be a complex task for people. When using representations for virtual environment, specifically the Augmented Reality (AR) and Virtual Reality (VR), an advantage is the complementing of the information communication through virtual objects, which reduces the cognitive effort to decode all the representation as in the traditional maps. Until now, several scientific investigations about adjusting the cartographic design aimed to personal and vehicular navigation maps in AR are being developed. However, few studies investigate the application of dynamic symbols in AR built from the dynamic visual variables of Cartography. In this way, the aim on this research is to classify the symbols that use the dynamic variables. In addition, verify the presence of these variables in Augmented Reality systems in mobile devices that use AR to represent spatial information in the context of personal navigation in an outdoor environment.

Slowdown in the Decay of Western North Pacific Tropical Cyclones Making Landfall on the Asian Continent

This study finds an increasing trend in the decay timescale (τ) of western North Pacific (WNP) tropical cyclone (TCs) making landfall on the Asian continent from 1966–2018. Statistical analysis of individual landfalling TCs shows that τ is significantly positively linked to soil wetness, 850-hPa relative vorticity and 200-hPa divergence, whereas it is weakly correlated with 700–500-hPa relative humidity and 850–200-hPa vertical wind shear. For TCs hitting southeastern China, the observed increasing τ is likely caused by enhanced 850-hPa vorticity and 200-hPa divergence. For TCs hitting southern China, increasing τ is likely driven by increased 850-hPa vorticity. By comparison, there are no significant trends in environmental variables over the eastern Indo-China Peninsula, and τ has not significantly changed in this region. Our results imply that the increasing τ of WNP landfalling TCs on the Asian continent are more likely caused by changes in dynamic variables than changes in thermodynamic variables.

Study of a Gas Disturbance Mode Content Based on the Measurement of Atmospheric Parameters at the Heights of the Mesosphere and Lower Thermosphere

The main result of this work is the estimation of the entropy mode accompanying a wave disturbance, observed at the atmosphere heights range of 90–120 km. The study is the direct continuation and development of recent results on diagnosis of the acoustic wave with the separation on direction of propagation. The estimation of the entropy mode contribution relies upon the measurements of the three dynamic variables (the temperature, density, and vertical velocity perturbations) of the neutral atmosphere measured by the method of the resonant scattering of radio waves on the artificial periodic irregularities of the ionospheric plasma. The measurement of the atmosphere dynamic parameters was carried out on the SURA heating facility. The mathematical foundation of the mode separation algorithm is based on the dynamic projection operators technique. The operators are constructed via the eigenvectors of the coordinate evolution operator of the transformed system of balance equations of the hydro-thermodynamics.

A box, a trough and marbles: How the Reed-Frost epidemic theory shaped epidemiological reasoning in the 20th century

The article takes the renewed popularity and interest in epidemiological modelling for Covid-19 as a point of departure to ask how modelling has historically shaped epidemiological reasoning. The focus lies on a particular model, developed in the late 1920s through a collaboration of the former field-epidemiologists and medical officer, Wade Hampton Frost, and the biostatistician and population ecologist Lowell Reed. Other than former approaches to epidemic theory in mathematical formula, the Reed-Frost epidemic theory was materialised in a simple mechanical analogue: a box with coloured marbles and a wooden trough. The article reconstructs how the introduction of this mechanical model has reshaped epidemiological reasoning by shifting the field from purely descriptive to analytical practices. It was not incidental that the history of this model coincided with the foundation of epidemiology as an academic discipline, as it valorised and institutionalised new theoretical contributions to the field. Through its versatility, the model shifted the field’s focus from mono-causal explanations informed by bacteriology, eugenics or sanitary perspectives towards the systematic consideration of epidemics as a set of interdependent and dynamic variables.

Exploration on Research Methods for Exploring Human Response to Luminous Environment with Chromogenic Windows Applied

Chromogenic windows enable both energy-saving and daylighting regulation. However, the human response to the luminous environment affected by them is difficult to test, since their features of dynamic change and tinting automatically. This study explores the research methods, including experimental design and statistical analysis, by literature review and an experiment demonstration. The results show that a proper size of the test room is significant to obtain desired data, and the advanced VR technologies have the potential to be applied for testing these dynamic variables. Bayesian approaches are recommended to be tried and get more accurate interference about the experimental results.

A Review of Recent Spatial Accessibility Studies That Benefitted from Advanced Geospatial Information: Multimodal Transportation and Spatiotemporal Disaggregation

Spatial accessibility provides significant policy implications, describing the spatial disparity of access and supporting the decision-making process for placing additional infrastructure at adequate locations. Several previous reviews have covered spatial accessibility literature, focusing on empirical findings, distance decay functions, and threshold travel times. However, researchers have underexamined how spatial accessibility studies benefitted from the recently enhanced availability of dynamic variables, such as various travel times via different transportation modes and the finer temporal granularity of geospatial data in these studies. Therefore, in our review, we investigated methodological advancements in place-based accessibility measures and scrutinized two recent trends in spatial accessibility studies: multimodal spatial accessibility and temporal changes in spatial accessibility. Based on the critical review, we propose two research agendas: improving the accuracy of measurements with dynamic variable implementation and furnishing policy implications granted from the enhanced accuracy. These agendas particularly call for the action of geographers on the full implementation of dynamic variables and the strong linkage between accessibility and policymaking.

Reservoir Simulation, Modeling of a Complex Offshore Field Uses AI and Machine Learning

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201693, “Subsurface Analytics Case Study: Reservoir Simulation and Modeling of a Highly Complex Offshore Field in Malaysia Using Artificial Intelligence and Machine Learning,” by Rahim Masoudi, SPE, Petronas; Shahab D. Mohaghegh, SPE, West Virginia University; and Daniel Yingling, Intelligent Solutions, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, 5–7 October. The paper has not been peer reviewed. Using commercial numerical reservoir simulators to build a full-field reservoir model and simultaneously history matching multiple dynamic variables for a highly complex offshore mature field in Malaysia had proved challenging. In the complete paper, the authors demonstrate how artificial intelligence (AI) and machine learning can be used to build a purely data-driven reservoir simulation model that successfully history matches all dynamic variables for wells in this field and subsequently can be used for production forecasting. This synopsis concentrates on the process used, while the complete paper provides results of the fully automated history matching. Subsurface Analytics In the presented technique, which the authors call subsurface analytics, data-driven pattern-recognition technologies are used to embed the physics of the fluid flow through porous media and to create a model through discovering the best, most-appropriate relationships between all measured data in each reservoir. This is an alternative to starting with the construction of mathematical equations to model the physics of the fluid flow through porous media, followed by modification of geological models in order to achieve history match. The key characteristics of subsurface analytics are that no interpretations, assumptions, or complex initial geological models (and thus no upscaling) exist. Furthermore, the main series of dynamic variables used to build this model is measured on the surface, while other major static, and sometimes even dynamic, characteristics are based on subsurface measurements, thereby making this approach a combination of reservoir and wellbore-simulation models rather than merely a reservoir model. The history-matching process of the subsurface analytics process is completely automated. Top-Down Modeling (TDM) TDM is a data-driven reservoir modeling approach under the realm of subsurface analytics technology that uses AI and machine learning to develop full-field reservoir models based on measurements rather than solutions of governing equations. TDM integrates all available field measurements into a full-field reservoir model and matches the historical production of all individual wells in a mature field with a single AI-based model. The model is validated through blind history matching. The approach then can forecast a field’s behavior on a well-by-well basis. TDM is a data-driven approach; thus, the quality assurance/quality control (QA/QC) of the data input is para-mount before embarking on the modeling process to ensure that the artificial neural network (ANN) is taught properly with reliable training of the data set. This includes the understanding of data availability and magnitude, analysis of well-by-well production performance trends, and identification of data anomalies.

Conveyance of texture signals along a rat whisker

Neuronal activities underlying a percept are constrained by the physics of sensory signals. In the tactile sense such constraints are frictional stick–slip events, occurring, amongst other vibrotactile features, when tactile sensors are in contact with objects. We reveal new biomechanical phenomena about the transmission of these microNewton forces at the tip of a rat’s whisker, where they occur, to the base where they engage primary afferents. Using high resolution videography and accurate measurement of axial and normal forces at the follicle, we show that the conical and curved rat whisker acts as a sign-converting amplification filter for moment to robustly engage primary afferents. Furthermore, we present a model based on geometrically nonlinear Cosserat rod theory and a friction model that recreates the observed whole-beam whisker dynamics. The model quantifies the relation between kinematics (positions and velocities) and dynamic variables (forces and moments). Thus, only videographic assessment of acceleration is required to estimate forces and moments measured by the primary afferents. Our study highlights how sensory systems deal with complex physical constraints of perceptual targets and sensors.

Analysis of Driving Dynamics Considering Driving Resistances in On-Road Driving

Internal combustion engine emissions are a serious worldwide problem. To combat this, emission regulations have become stricter with the goal of reducing the proportion of transportation emissions in global air pollution. In addition, the European Commission passed the real driving emissions–light-duty vehicles (RDE-LDV) regulation that evaluates vehicle emissions by driving on real roads. The RDE test is significantly dependent on driving conditions such as traffic or drivers. Thus, the RDE regulation has the means to evaluate driving dynamics such as the vehicle speed per acceleration (v·apos) and the relative positive acceleration (RPA) to determine whether the driving during these tests is normal or abnormal. However, this is not an appropriate way to assess the driving dynamics because the v⋅apos and the RPA do not represent engine load, which is directly related to exhaust emissions. Therefore, in the present study, new driving dynamic variables are proposed. These variables use engine acceleration calculated from wheel force instead of the acceleration calculated from the vehicle speed, so they are proportional to the engine load. In addition, a variable of driving dynamics during braking is calculated using the negative wheel force. This variable can be used to improve the accuracy of the emission assessment by analyzing the braking pattern.