The long-term trends of PM10, O3 and NOx concentrations were analyzed using Holland model (without meteorological-adjusted) and MM-Regression model (with meteorological-adjusted) based on the data of ten EPA air quality stations from 1997 to 2006 in Kao-Ping area. The aim of this study was to determine the impact of meteorological factors on the trends of these pollutants in Kao-Ping. The annual variations (AV) of O3 was −0.496 % in Kaohsiung county, −0.200 in Pingtung county, and 0.277 % in Kaohsiung city, showing different characteristics in Kao-Pin area. On average, the annual variations (AV) influenced by meteorological factors were: PM10: 0.205 %, O3: −0.127 %, and NOx: 0.338 %. After being adjusted by meteorological factors, the seasonal variations (SV) were about 1, indicating little seasonal change. In Kao-Ping region, the influence by meteorological factors was 9.566 %, 8.026 % and 7.351 % in PM10, O3, NOx, respectively. In total, the average influence was 8.314% in Kao-Ping region, with 7.791% in Kaohsiung city (8.481% at Cianjin, the most influenced area), 9.439% in Kaohsiung County (10.368% at Linyuan, the most influenced area), and 7.110% in Pingtung County (7.516% at Chaojhou, the most influenced area). PM10 was influenced most by meteorological factors (PM10: 9.566 %, O3: 8.026 %, NOx: 7.351 %) in Kao-Ping area.. In Kao-Ping region, the contributions by individual meteorological factors were 70.78% in wind speed, 38.23% in total cloudiness, 36.56% in sunshine hour, 19.86% in temperature, 12.40% in atmospheric pressure, 5.96% in relative humidity and 1.27% in wind direction. The influences by the wind speed were 66.62 %, 72.35 % and 72.31 % on the concentrations of PM10, O3, NOx, respectively. Wind speed was the most important factor controlling concentration trends in Kao-Ping area.

The trends of PM10, O3, NOX and NMHC concentrations were analyzed by the Holland model (without meteorological-adjusted) and the MM-Regression model (with meteorological-adjusted) base on the data of eight EPA air quality stations from 1997 to 2006 in Kaohsiung. The aim of this study was to evaluate the influence of meteorological factors on the pollutants (PM10 and O3) trends. The trends of PM10 concentrations in Kaohsiung city analyzed without meteorological-adjusted were 7.18 % at Tzuo-Yin, 3.20 % at Chien-Chin and 9.72 % at Nan-Chie. After eliminating the meteorological factors, the percent of gradual trends were 1.91 % at Tzuo-Yin, 2.92 % at Chien-Chin and 2.02 % at Nan-Chie. The trends of O3 concentrations without meteorological-adjusted were 11.42 % at Tzuo-Yin, 20.92 % at Hsiung-Kong, 42.08 % at Chien-Chin and 13.69 % at Nan-Chie. The trends of PM10 concentrations in Kaohsiung County analyzed without meteorological-adjusted were 14.96 % at Lin-yuan and 3.24 % at Jen-wu. After meteorological factors eliminating, the trend was 3.15 % at Jen-wu but the trend was -2.53 % at Lin-yuan. Meteorological factor was a primary reason that influences the PM10 concentration in recent years. The trends of O3 in Kaohsiung County without meteorological-adjusted were 18.89 % at Da-liao, 4.40 % at Jen-wu, 35.16 % at Lin-yuan and 29.98 % at Mei-nung. After meteorological factors eliminating, the trends were 1.99 % at Da-liao, 2.23 % at Jen-wu, 1.16 % at Lin-yuan and -1.16 % at Mei-nung. The results show that the influence of meteorological factors for O3 trends was more sensitive in Kaohsiung county than in Kaohsiung city. The concentration of PM10 has no significant difference (64.8 ¡V 92.3 %) in Kaohsiung city. For the concentration of O3, the similarity (78 ¡V 100 %) was extensive in Kaohsiung city because O3 could diffuse easily. O3 episodes has no significant difference as PM10 episodes in Kaohsiung city. As above-mentioned, the results show that the contributions of ambient PM10 were individually but the contributions of ambient O3 were uniform extensively.

Générer du gameplay est un incontournable objectif de la réalisation d’un jeu. Nous le recherchons lorsque nous nous rentrons dans la bulle ludique. Pourtant, les éléments produits par les game designer sont des systèmes de jeu, des règles, une simulation, etc. Pour que ce système permette la naissance du gameplay, les game designers doivent nécessairement tenter de modéliser le joueur. Empiriquement ils manipulent des modèles psychologiques et sociologiques du joueur : Courbe d’apprentissage, gestion de la difficulté, degré d’efficience (etc.). Au cœur de la session de jeu industriel et chercheur ont besoin de moyens pour mieux cerner le joueur. La question que nous nous posons est celle de la détection des traits psychologiques, d’éléments caractérisant du joueur, au travers du gameplay ou, pour le moins, engagé dans une expérience ludique. Nous proposons une méthode de game design dédiée à la création d’un profil psychologique du joueur. Nous avons pu l’expérimenter lors d’un travail de collaboration avec INETOP et Paris Ouest sur la question des tests d’orientation professionnelle. Il s’agit du serious game JEU SERAI, développé en partenariat avec l’industriel Wizarbox. Cette première expérimentation nous permet d’envisager un développement de ce champ de recherche à la croisée des sciences de l’informatique, de la psychologie et des sciences cognitives. / Can we track psychological player’s traits or profile through gameplay or, at least, when the player is engaged in a ludic experience? We propose a game design methodology dedicated to the generation of psychological profile of the player. The main experimentation, a vocational guidance game, was created with academic experts and industrial game developpers. The first results set the basis of the exploration of a field at the crossover of computer sciences, in particular game design, psychology and cognitive sciences.

Understanding thermal transport across length scales lays the foundation to developing high-performance electronic devices. Although many experiments and models of the past few decades have explored the physics of heat transfer at nanoscale, there are still open questions regarding the impact of periodic nanostructuring and coherent phonon effects, as well as the interaction of strain and thermal transport. Thermomechanical effects, as well as strains applied in flexible electronic devices, impact the thermal transport. In the simplest kinetic theory models, thermal conductivity is proportional to the phonon group velocity, heat capacity, and scattering times. Periodic porous nanostructures impact the phonon dispersion relationship (group velocity) and the boundaries of the pores increase the scattering times. Strain, on the other hand, affects the crystal structure of the lattice and slightly increases the thermal conductivity of the material under compression. Intriguingly, applying strain combined with the periodic porous structures is expected to influence both the dispersion relation and scattering rates and yield the ability to tune thermal transport actively. But often these interrelated effects are simplified in models.<br><br>This work evaluates the combination of structure and strain on thermal conductivity by revisiting some of the essential methods used to predict thermal transport for a single layer of graphene with a periodic porous lattice structure with and without applied strain. First, we use the highest fidelity method of Non-Equilibrium Molecular Dynamics (NEMD) simulations to estimate the thermal conductivity which considers the impact of the lattice structure, strain state, and phononic band structure together. Next, the impact of the geometry of the slots within the lattice is interrogated with Boltzmann Transport Equation (BTE) models under a Relaxation Time Approximation. A Monte Carlo based Boltzmann Transport Equation (BTE) solver is also used to estimate the thermal conductivity of phononic crystals with varying pore geometry. Dispersion relations calculated from continuum mechanics are used as input here. This method which utilizes a simplified pore geometry only partially accounts for the effects of scattering on the pore boundaries. Finally, a continuum level model is also used to predict the thermal conductivity and its variations under applied strain. As acoustic phonon branches tend to carry the most heat within the lattice, these continuum models and other simple kinetic theories only consider their group velocities to estimate their impact on phonon thermal conductivity. As such, they do not take into account the details of phonon transport across all wavelengths.<br><br>By comparing the results from these different methods, each of which has different assumptions and simplifications, the current work aims to understand the effects of changes to the dispersion relationship based on strain and the periodic nanostructures on the thermal conductivity. We evaluate the accuracy of the kinetic theory, ray tracing, and BTE models in comparison to the MD results to offer a perspective of the reliability of each method of thermal conductivity estimation. In addition, the effect of strain on each phononic crystal with different pore geometry is also predicted in terms of change to their in-plane thermal anisotropy values. To summarize, this deeper understanding of the nanoscale thermal transport and the interrelated effects of geometry, strain, and phonon band structure on thermal conductivity can aid in developing lattices specifically designed to achieve the required dynamic thermal response for future nano-scale thermoelectric applications.