Recommender Thermometer for Measuring the Preparedness for Flood Resilience Management

A range of various thermometers and similar scales are employed in different human and resilience management activities: Distress Thermometer, Panic Thermometer, Fear Thermometer, fire danger rating, hurricane scales, earthquake scales (Richter Magnitude Scale, Mercalli Scale), Anxiety Thermometer, Help Thermometer, Problem Thermometer, Emotion Thermometer, Depression Thermometer, the Torino scale (assessing asteroid/comet impact prediction), Excessive Heat Watch, etc. Extensive financing of the preparedness for flood resilience management with overheated full-scale resilience management might be compared to someone ill running a fever of 41°C. As the financial crisis hits and resilience management financing cools down it reminds a sick person whose body temperature is too low. The degree indicated by the Recommender Thermometer for Measuring the Preparedness for Flood Resilience Management with a scale between Tmin=34,0° and Tmax=42,0° shows either cool or overheated preparedness for flood resilience management. The formalized presentation of this research shows how changes in the micro, meso and macro environment of resilience management and the extent to which the goals pursued by various interested parties are met cause corresponding changes in the “temperature” of the preparedness for resilience management. Global innovative aspects of the Recommender Thermometer developed by the authors of this paper are, primarily, its capacity to measure the “temperature” of the preparedness for flood resilience management automatically, to compile multiple alternative recommendations (preparedness for floods, including preparing your home for floods, taking precautions against a threat of floods, retrofitting for flood-prone areas, checking your house insurance; preparedness for bushfires, preparedness for cyclones, preparedness for severe storms, preparedness for heat waves, etc.) customised for a specific user, to perform multiple criteria analysis of the recommendations, and to select the ten most rational ones for that user. Across the world, no other system offers these functions yet. The Recommender Thermometer was developed and fine-tuned in the course of the Android (Academic Network for Disaster Resilience to Optimise educational Development) project

Monitoring of heart failure: comparison of left atrial pressure with intrathoracic impedance and natriuretic peptide measurements in an experimental model of ovine heart failure

Monitoring of HF (heart failure) with intracardiac pressure, intrathoracic impedance and/or natriuretic peptide levels has been advocated. We aimed to investigate possible differences in the response patterns of each of these monitoring modalities during HF decompensation that may have an impact on the potential for early therapeutic intervention. Six sheep were implanted with a LAP (left atrial pressure) sensor and a CRT-D (cardiac resynchronization therapy defibrillator) capable of monitoring impedance along six lead configuration vectors. An estimate of ALAP (LAP from admittance) was determined by linear regression. HF was induced by rapid ventricular pacing at 180 and 220 bpm (beats/min) for a week each, followed by a third week with daily pacing suspensions for increasing durations (1–5 h). Incremental pacing induced progressively severe HF reflected in increases in LAP (5.9 ± 0.4 to 24.5 ± 1.6 mmHg) and plasma atrial (20 ± 3 to 197 ± 36 pmol/l) and B-type natriuretic peptide (3.7 ± 0.7 to 32.7 ± 5.4 pmol/l) (all P<0.001) levels. All impedance vectors decreased in proportion to HF severity (all P<0.001), with the LVring (left ventricular)-case vector correlating best with LAP (r2=0.63, P<0.001). Natriuretic peptides closely paralleled rapid acute changes in LAP during alterations in pacing (P<0.001), whereas impedance changes were delayed relative to LAP. ALAP exhibited good agreement with LAP. In summary, impedance measured with an LV lead correlates significantly with changes in LAP, but exhibits a delayed response to acute alterations. Natriuretic peptides respond rapidly to acute LAP changes. Direct LAP, impedance and natriuretic peptide measurements all show promise as early indicators of worsening HF. ALAP provides an estimate of LAP that may be clinically useful

Novel Nanohybrids of Silver Particles on Clay Platelets for Inhibiting Silver-Resistant Bacteria

We develop a novel nanohybrid showing a strong antibacterial activity on all of the tested pathogens, including methicillin-resistant Staphylococcus auerus and silver-resistant E. coli. The nanohybrid consists of silver nanoparticles (AgNPs) supported on 1 nm-thick silicate platelets (NSPs). The AgNP/NSP nanohybrid enables to encapsulate bacteria and triggers death signals from the cell membrane. The geographic shape of the NSPs concentrates AgNPs but impedes their penetration into attached cells, mitigating the detrimental effect of silver ion deposition in applied tissues. Moreover, the tightly tethered AgNPs on NSP surface achieve a stronger biocidal effect than silver nitrate, but bypassing Ag+ mechanism, on silver-resistant bacteria. This nanohybrid presents an effective and safe antimicrobial agent in a new perspective

Modélisation des crues de bassins karstiques par réseaux de neurones. Cas du bassin du Lez (France)

Karst is one of the most widespread aquifer formations in the world. Their exploitation provides fresh water to practically 25% of the global population. The high level of structure heterogeneity in these aquifers however makes them complex and their behavior is difficult to study, simulate and forecast. Artificial neural networks are machine learning models widely used in surface hydrology since the 90's thanks to their properties of parsimony and universal approximation. In this thesis, artificial neural networks are used to study karst aquifer behavior. Application is done on the Lez. This aquifer situated near Montpellier conurbation (400 000 inhabitants) provides fresh water for a large part of this population. First, a "classical" black box neural network is applied to simulate and forecast Lez spring discharge. A rainfall input selection method is proposed, using cross correlation analysis and cross validation method at the same time. Results show neural model efficiency in order to simulate and forecast the spring discharge of a complex karstic aquifer. The model was tested using two hydrologic cycles including the two most intense floods of the database. Hydrographs shows that neural model was able to forecast correctly the maximum flood discharge of these intense floods when they are higher than all discharges of the learning database. Forecasting is satisfactory until a one-day horizon. In a second time, extraction of the knowledge included in the black box is proposed. In order to constrain the model to provide physically plausible solution, a priori knowledge about aquifer geology is included into the network architecture. KnoX (Knowledge eXtraction) method proposed in this study aims at extract geological zone contributions to the Lez spring and corresponding response times. The KnoX methodology was applied to a fictitious hydrosystem built using a model with controlled parameters, in particular contributions of subbasin to the outlet and lag time of each subbasin. This application permitted to validate the KnoX methodology. Results obtained on the Lez basin are satisfactory and agree with current knowledge about this hydrosystem. In addition, the KnoX methodology allows refining this knowledge, in particular concerning delayed infiltration because of infiltration in perched aquifer and concerning Lez spring alimentation basin boundaries. Lastly the KnoX methodology is a generic methodology that can be applied on any basin with available discharge and rainfall data.Les karsts sont l'une des formations aquifères les plus présentes au monde. Exploités, ils fournissent de l'eau potable pour près de 25% de la population mondiale. Cependant la forte hétérogénéité de leur structure implique un comportement non-linéaire particulièrement difficile à simuler et à prévoir. Les réseaux de neurones formels sont des modèles d'apprentissage statistique qui ont été largement utilisés en hydrologie de surface depuis les années 1990, grâce à leurs propriétés de parcimonie et d'approximation universelle. Dans cette thèse, il est proposé d'utiliser les réseaux de neurones pour étudier le comportement des aquifères karstiques. L'aquifère du Lez est choisi pour appliquer le modèle par réseaux de neurones. Cet aquifère, situé près de l'agglomération de Montpellier (400 000 habitants), est exploité pour fournir de l'eau potable à une grande partie de l'agglomération. Dans un premier temps, un réseau de neurones " classique ", de type boîte noire, est appliqué à la simulation et à la prévision des débits de la source du Lez. Une méthode de sélection des entrées de pluie est proposée, couplant analyse par corrélations croisées et méthode de validation croisée. Les résultats montrent l'adéquation du modèle neuronal pour la simulation et la prévision du débit de la source d'un aquifère karstique complexe. Le test du modèle est effectué sur les deux cycles hydrologiques comportant les crues les plus intenses de la base de données. Les hydrogrammes montrent que le modèle neuronal a été capable de correctement prévoir les débits des crues majeures en test, qui sont supérieurs aux débits présents dans la base d'apprentissage du modèle. La prévision est acceptable jusqu'à un horizon de prévision de un jour. Dans un second temps, une méthode d'extraction des données contenues dans la boîte noire est proposée. Afin de contraindre le modèle neuronal à donner des valeurs physiquement interprétables, des connaissances a priori sur la géologie de l'aquifère sont incluses dans l'architecture du réseau de neurones. La méthode KnoX (Knowledge eXtraction) proposée dans cette étude permet d'extraire du modèle les contributions des différentes zones géologiques à la source du Lez ainsi que les temps de réponse correspondants. L'application de la méthode KnoX à un hydrosystème fictif dont on contrôle en particulier les temps de réponse et contributions des différents sous-hydrosystèmes fictifs a permis de valider cette méthode. Les résultats obtenus sur le bassin du Lez sont très satisfaisants et en adéquation avec les connaissances actuelles que l'on a sur ce système. De plus la méthode a permis d'affiner ces connaissances, notamment l'infiltration retardée par des aquifères perchés et les limites du bassin d'alimentation de la source du Lez. Enfin, la méthode KnoX est générique et applicable à tout hydrosystème pour lequel on dispose de mesures de pluie et de débit

Building an Interactive Multimedia Learning System Based on Mastery Learning Theory - A Case Study of Accounting Certification Program

[[abstract]]“Introduction to Accounting” is one of the basic professional courses for business students. This is the course that has the most direct influence on business students' academic performance, and the certification for accounting technicians, provides the most immediate evidence of good academic performance. By obtaining a license or a certificate, the students are taking their first step towards demonstrating their professional knowledge, skills, and attitudes. Taiwan, a country that has experienced rapid economic development, relies heavily on commercial activities and the service industry to sustain its domestic economic growth. Professionalism and good customer service are both basic requirements for professionals in these fields. The acquisition of professional licenses is the only way to signify that one has taken the first step towards demonstrating one’s professional capabilities in these fields. The objective of this research is to construct an interactive multi-media learning system using the Top Learn e-learning platform, which will be based on the theories of Mastering Learning developed by Bloom. The hope is that by offering an adaptive learning environment and diversified learning paths, the system will help users prepare for the Level C Certification for Accounting Technicians. The learning activities are designed to be lively and interesting, and all the activities are based on the contents of the “Introduction to Accounting” course, as the topics covered in the course are included in the written exam. To achieve the goal of creating an interactive learning system, the designer has highlighted the interactive learning applications of Web 2.0 and introduced some networking technologies into the e-learning system. Key Words: Mastering Learning, Digital Learning, Technician Certification, Occupational Certificates and Licenses, Adaptive Learnin

Karst flood modeling by artificial neural networks. Case study on the Lez catchment (Hérault, southern France)

Les karsts sont l'une des formations aquifères les plus présentes au monde. Exploités, ils fournissent de l'eau potable pour près de 25% de la population mondiale. Cependant la forte hétérogénéité de leur structure implique un comportement non-linéaire et les rendent particulièrement difficiles à étudier, à simuler et à prévoir. Les réseaux de neurones formels sont des modèles d'apprentissage statistiques qui ont été largement utilisés en hydrologie de surface depuis les années 1990, grâce à leurs propriétés de parcimonie et d'approximation universelle.Dans cette thèse, il est proposé d'utiliser les réseaux de neurones pour étudier le comportement des aquifères karstiques. L'aquifère du Lez est choisi pour appliquer le modèle par réseaux de neurones. Cet aquifère, situé près de l'agglomération de Montpellier (400 000 habitant), est exploité pour fournir de l'eau potable à une grande partie de l'agglomération.Dans un premier temps, un réseau de neurones « classique », de type boîte noire, est appliqué à la simulation et à la prévision des débits de la source du Lez. Une méthode de sélection des entrées de pluie est proposée, couplant analyse par corrélations croisées et méthode de validation croisée. Les résultats montrent l'adéquation du modèle neuronal pour la simulation et la prévision du débit de la source d'un aquifère karstique complexe. Le test du modèle est effectué sur les deux cycles hydrologiques comportant les crues les plus intenses de la base de données. Les hydrogrammes montrent que le modèle neuronal a été capable d'extrapoler puisque les débits prévus pour les crues majeures en test sont corrects et supérieurs aux débits présents dans la base d'apprentissage du modèle. La prévision est acceptable jusqu'à un horizon de prévision de un jour. Dans un second temps, une méthode d'extraction des données contenues dans la boîte noire est proposée. Afin de contraindre le modèle neuronal à donner des valeurs physiquement interprétables, des connaissances a priori sur la géologie de l'aquifère sont incluses dans l'architecture du réseau de neurones. La méthode KnoX (Knowledge eXtraction) proposée dans cette étude permet d'extraire du modèle les contributions des différentes zones géologiques à la source du lez ainsi que les temps de réponse correspondants. L'application de la méthode KnoX a un hydrosystème fictif dont on contrôle en particulier les temps de réponse et les contributions des différents sous-hydrosystèmes fictifs a permis de valider cette méthode. Les résultats obtenus sur le bassin du Lez sont très satisfaisants et en adéquation avec les connaissances actuelles que l'on a sur ce système. De plus la méthode a permis d'affiner ces connaissances, notamment l'infiltration retardée par des aquifères perchés et concernant la limite du bassin d'alimentation de la source du Lez. Enfin, la méthode KnoX est générique et applicable à tout hydrosystème pour lequel on dispose de mesures de pluie et de débit.Karst is one of the most widespread aquifer formations in the worlds. Their exploitation provides fresh water to practically 25% of the global population. The high level of structure heterogeneity in these aquifers however makes them complex and their behavior is difficult to study, simulate and forecast.Artificial neural networks are machine learning models widely used in surface hydrology since the 90's thanks to their properties of parsimony and universal approximation.In this thesis, artificial neural networks are used to study karst aquifer behavior. Application is done in the Lez. This aquifer situated near Montpellier conurbation (400 000 inhabitants) provides fresh water for a large part of this population.First, a “classical” black box neural network is applied to simulate and forecast Lez spring discharge. A rainfall input selection method is proposed, using cross correlation analysis and cross validation method at the same time. Results show neural model efficiency in order to simulate and forecast the spring discharge of a complex karstic aquifer. The model was tested using two hydrologic cycles including the two most intense floods of the database. Hydrographs shows that neural model was able to extrapolate the maximum flood discharge of the learning database. Forecasting is satisfactory until a one-day horizon.In a second time, extraction of the knowledge data included in the black box is proposed. In order to constrain the model to give physically plausible solution, a priori knowledge about aquifer geology is included into the network architecture. KnoX (Knowledge eXtraction) method proposed in this study aims at extract geological zone contributions to the Lez spring and corresponding response times. The KnoX methodology was applied to a fictitious hydrosystem built using a model with controlled parameters, in particular contributions of subbasin to the outlet and lag time of each subbasin. This application permitted to validate the KnoX methodology. Results obtained on the Lez basin are satisfactory and agree with current knowledge about this hydrosystem. In addition, the KnoX methodology allows to refine this knowledge, in particular concerning delayed infiltration because of infiltration in perched aquifer and concerning Lez spring alimentation basin boundaries. Lastly the KnoX methodology is a generic methodology that can be applied on any basin with available discharge and rainfall data