Roadway construction workers have to work in close proximity to construction equipment as well as high-speed traffic, exposing them to an elevated risk of collisions. This research aims to develop an innovative holistic solution to reduce the risk of collisions at roadway work zones. To this end, a connected hazard detection and prevention system is developed to detect potential unsafe proximities in highway work zones and provide warning and instructions of imminent threats. This connected system collects real-time information from all the actors inside and outside of the work zone and communicates it with a cloud server. A hazard detection algorithm is developed to identify potential proximity hazards between workers and connected/automated vehicles (CAV) and/or construction equipment. Detected imminent threats are communicated to in-danger workers and/or drivers. The trajectories and safety status of each actor is visualized on Virginia Connected Corridors (VCC) Monitor, a custom web-based situational awareness tool, in real-time. To assure the accuracy of hazard detection, the algorithm accommodates various parameters including variant threat zones for workers-on-foot, vehicles, and equipment, the direction of movement, workers' distance to the work zone border, shape of road, etc. The designed system is developed and evaluated through various experiments on the Virginia's Smart Roads located at Virginia Tech. Data regarding activities of workers-on-foot was collected during experiments and was used and classified for activity recognition using supervised machine learning methods. A demonstration was held to evaluate the usability of the developed system, and the results proved the efficacy of the algorithm in successfully detecting potential collisions and provide prompt warnings and instructions. The developed holistic system elevates safety of highway construction and maintenance workers at work sites. It also helps managers and inspectors to keep track of the real-time safety status of their work zone actors as well as the accidents occurrences. As such, with the connected work zone hazard detection system, the safety level and productivity of the workers is expected to be greatly enhanced. / Master of Science / In order to reduce the risk of collisions for roadway construction workers, this research aims to develop an innovative holistic solution at roadway work zones. In this research, a connected hazard detection and prevention system is developed to detect potential collision hazards in highway work zones and generate warning and instructions of imminent threats. This system collects real-time information from all the workers, construction equipment and connected/automated vehicles (CAV) of the work. A hazard detection algorithm is developed to identify potential proximity hazards between them as well as to recognize the activities of workers. The trajectories and safety status of each worker, equipment or vehicle is visualized on Virginia Connected Corridors (VCC) Monitor, a custom web-based tool, in real-time. A demonstration was held to evaluate the developed system, and the results proved the efficacy of the algorithm in successfully detecting potential collisions and provide prompt warnings and instructions. The developed holistic system helps managers and inspectors to keep track of the real-time safety status of their work zone worker, equipment and vehicles as well as the accidents occurrences. As such, with the connected work zone hazard detection system, the safety level and productivity of the workers is expected to be greatly enhanced.

[ES] Los sistemas de alerta temprana en tiempo real son instrumentos claves para vigilar posibles desastres geológicos como terremotos, tsunamis, actividades volcánicas, hundimiento del terreno o deslizamientos de ladera. Durante las últimas décadas, el número de personas afectadas por los diversos desastres geológicos ha aumentado de forma sustancial. Las consecuencias negativas de estos desastres afectan a la población y a las infraestructuras con diferentes niveles de gravedad, pudiendo llegar a tener un impacto elevado en pérdidas humanas y económicas. Sin embargo, los sistemas de alerta temprana tienen la capacidad de proporcionar avisos adecuados y confiables, lo que puede llevar a minimizar las pérdidas humanas y económicas. El método de Posicionamiento Puntual Preciso en tiempo real (RT-PPP) desempeña un papel importante como parte de los sistemas de alerta temprana; debido a su capacidad para proporcionar seguimiento en tiempo real, cobertura global y su capacidad de obtención de mediciones precisas en tiempo real adquiridas por un solo receptor. A pesar de esto, el método (RT-PPP) utiliza productos para la corrección de la órbita y los relojes de los satélites (productos SSR) que son sensibles de los errores de la tecnología GNSS. Como consecuencia, estos errores pueden afectar la disponibilidad y fiabilidad de los sistemas de alerta temprana basados en la técnica RT-PPP. Debido a estos errores, se pueden llegar a generar avisos falsos, algunos de estos errores son: largos tiempos de inicialización, falta de continuidad y exactitud en los resultados, mala calidad de corrección de órbita y reloj de los satélites, mala resolución de la ambigüedad, etc. Además, la mala geometría de los satélites y la latencia de los productos SSR afectan gravemente el rendimiento del posicionamiento PPP en tiempo real. Este trabajo de investigación, se enfoca, en una primera parte, en el análisis de los efectos y los métodos de mitigación de la latencia de los productos de corrección en tiempo real. El International GNSS Service (IGS) proporciona productos oficiales para materializar la técnica de PPP en tiempo real, estos productos contienen correcciones para las órbitas y los relojes de los satélites que se generan como combinación de los calculados en los diferentes centros de cálculo repartidos por el mundo. Este proceso de combinación aumenta la latencia y, por tanto, su impacto en la solución RT-PPP, afectando el desempeño de cualquier sistema de alerta temprana basada en RT-PPP. Así, en esta tesis, se usará el enfoque de Aprendizaje Automático para resolver el problema de la latencia, intentando predecir los valores de las correcciones en los productos SSR para el tiempo de la latencia. Se han utilizado los modelos de Support Vector Regression (SVR) y de media móvil integrada autorregresiva (ARIMA) para la predicción, necesitando, en el proceso, la implantación de ventanas deslizantes para entrenar y parametrizar los modelos de aprendizaje automático. En cuanto al desempeño del sistema RT-PPP de alerta temprana, este trabajo de investigación ha evaluado, estadísticamente, varios modelos de aprendizaje automático, entre ellos los métodos de Árbol de decisión, Random Forest, Máquina de vectores de soporte (SVM), K vecinos más cercanos, Regresión logística, y el modelo de boosting extremo por gradientes (XGB). El análisis de los resultados indica que los modelo de XGB y Random Forest muestran los resultados más coherentes y precisos con 97 y 99 porciento de precisión. Asimismo, el modelo XGB muestra menos tendencia a iniciar falsas alarmas con un 2,48 por ciento en comparación con el 16,28 por ciento del modelo Random Forest.A partir de los resultados de la investigación, se derivan un conjunto de pruebas estadísticas para evaluar el desempeño de los sistemas de alerta temprana establecidos. Estas pruebas estadísticas pueden evaluar la capacidad de los modelos de aprendizaje automático utilizados con a la detecciónde deformaciones. / [CA] Els sistemes d'alerta primerenca en temps real són instruments claus per vigilar possibles desastres geològics com ara terratrèmols, tsunamis, activitats volcàniques, enfonsament del terreny o lliscaments de vessant. Durant les darreres dècades, el nombre de persones afectades pels diversos desastres geològics ha augmentat de manera substancial. Les conseqüències negatives d'aquests desastres afecten la població i les infraestructures amb diferents nivells de gravetat i poden arribar a tenir un impacte elevat en pèrdues humanes i econòmiques. Tot i això, els sistemes d'alerta primerenca tenen la capacitat de proporcionar avisos adequats i fiables, la qual cosa pot portar a minimitzar les pèrdues humanes i econòmiques. El mètode de Posicionament Puntual Precís en temps real (RT-PPP) té un paper important com a part dels sistemes d'alerta primerenca; a causa de la seva capacitat per proporcionar seguiment en temps real, cobertura global i la seva capacitat d'obtenció de mesuraments precisos en temps real adquirits per un sol receptor.Tot i això, el mètode RT-PPP utilitza productes per a la correcció de l'òrbita i els rellotges dels satèl·lits (productes SSR) que són sensibles als errors de la tecnologia GNSS. Com a conseqüència, aquests errors poden afectar la disponibilitat i la fiabilitat dels sistemes d'alerta primerenca basats en la tècnica RT-PPP. A causa d'aquests errors, es poden arribar a generar avisos falsos, alguns d'aquests errors són: llargs temps d'inicialització, manca de continuïtat i exactitud als resultats, mala qualitat de correcció d'òrbita i rellotge dels satèl·lits, mala resolució de l'ambigüitat, etc. A més, la mala geometria dels satèl·lits i la latència dels productes SSR afecten greument el rendiment del posicionament PPP en temps real. Aquest treball de recerca s'enfoca, en una primera part, a l'anàlisi dels efectes i els mètodes de mitigació de la latència dels productes de correcció en temps real. L'International GNSS Service (IGS) proporciona productes oficials per materialitzar la tècnica de PPP en temps real, aquests productes contenen correccions per a les òrbites i els rellotges dels satèl·lits que es generen com a combinació dels calculats als diferents centres de càlcul repartits pel món. Aquest procés de combinació augmenta la latència i, per tant, el seu impacte en la solució RT-PPP, afectant l'exercici de qualsevol sistema d'alerta primerenca basada en RT-PPP. Així, en aquesta tesi, s'usarà l'enfocament d'aprenentatge automàtic (Machine Learning) per resoldre el problema de la latència, intentant predir els valors de les correccions en els productes SSR per al temps de la latència. S'han utilitzat els models de Support Vector Regression (SVR) i de mitjana mòbil integrada autoregressiva (ARIMA) per a la predicció, necessitant, en el procés, la implantació de finestres lliscants per entrenar i parametritzar els models d'aprenentatge automàtic. Els resultats de la investigació de la part de la latència han indicat que els models SVR i ARIMA podran mitigar la influència de la latència per als principals sistemes de navegació per satèl·lit (GPS i GLONASS) al voltant d'un vint per cent. El model SVR va mostrar una lleugera tendència a predir valors atípics; tot i això, el temps d'execució del SVR és significativament menor que el temps de processament del model ARIMA. Pel que fa a desenvolupament del sistema RT-PPP d'alerta primerenca, aquest treball de recerca ha avaluat, estadísticament, diversos models d'aprenentatge automàtic, entre ells els mètodes d'Arbre de Decisió, Random Forest, Màquina de Vectors de Suport (SVM), K veïns més propers, Regressió Logística, i el model de Boosting Extrem per gradients (XGB).L'anàlisi dels resultats indica que els models de XGB i Random Forest mostren els resultats més coherents i precisos amb 97i99 porcent de precisió respectivament. Així mateix, el model XGB mostra menys tendència a iniciar falses alarmes amb un 2,48% en comparació del 16,28% del model RF. / [EN] Real-Time Early Warning Systems are a critical approach implemented for monitoring geo-hazard disasters such as earthquakes, tsunamis, volcanic activities, and land subsidence. The Earth's population has experienced a substantial increasement, consequently exposing a growing number of people to the effects of various geo-hazard disasters. These influences could impact citizens and countries at different severity levels, reaching high costs in terms of human beings and economic losses. However, the early warning system's ability to initiate proper and reliable warnings significantly impacts in disaster cost reductions in terms of saving lives, reducing home and infrastructure damages, and mitigating economic losses. Real-Time Precise Point Positioning (RT-PPP) plays a significant role as part of the Early Warning Systems, due to its potential to provide real-time tracking and global coverage and its reliance on precise real-time measurements acquired from only one receiver. However, the RT-PPP approach applies State Space Representation (SSR) products that are highly sensitive to several GNSS error sources. As a result, the warning system's availability and reliability are negatively impacted. It may even be triggered to issue false warnings by factors such as long initialization times, convergence losses, due to poor quality of orbital and clock corrections, ambiguity resolutions, or/and multipath error. Furthermore, poor satellite geometry and the latency of SSR products severely affect the performance of real-time PPP positioning. In this research, we investigated the effect and mitigation of latency on real-time correction products. The International GNSS Services (IGS) provides official real-time products for RT-PPP; these products contain clock and orbit corrections, among others, and they are the main research concerns as the combination process increases the latency impact on both RT-PPP results and influences the early warning systems performance based on this positioning technique. In this research, investigations into the potentiality of using machine learning approaches to overcome latency problems were carried out. The research examines the Support Vector Regression (SVR) and Autoregressive Integrated Moving Average (ARIMA) machine learning models to predict the corrections broadcasted in SSR products that have a big capability in order to be used instead of the corrections impacted with latency The research results regarding latency showed that the SVR and ARIMA models could mitigate the latency influences for the primary navigation satellite systems GPS and GLONASS by around twenty percent. The SVR model showed a tendency to predict outliers; however, the execution time for the SVR is significantly faster than the ARIMA model processing time. Regarding the performance of the RT-PPP early warning system, the research statistically evaluates several machine learning models, including decision tree, random forest, support vector classifier, K nearest neighbors, logistic regression, and extreme gradient boosting models as machine learning approaches for establishing an early warning system. The extreme gradient boosting and random forest models were more accurate than the other utilized models, with 97 and 99 percent overall accuracy. At the same time, the extreme gradient boosting showed less tendency to initiate false alarms, with 2.48 percent compared to 16.28 percent for the random forest model. From the research findings, we derived a set of statistical assessments to evaluate the performance of the established early warning systems. These statistical assessments can evaluate the ability of the utilized machine learning models regarding deformation detections and the model's tendency to initiate false warnings. The study's results confirmed that extreme gradient boosting is the most effective machine learning technique for creating an early warning system. / Qafisheh, MWA. (2024). Establishing a Real-time Precise Point Positioning Early Warning System [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/206740

Ziel dieses Beitrags ist die Benennung von Anforderungen an eine IT-basierte Forschungs-und Betriebsplattform zur Unterstützung eines Frühwarnsystems, welches Defizite bei der Zusammenarbeit und Kommunikation von Kooperationspartnern in virtuellen Unternehmen (VU) frühzeitig erkennen und Optionen zur Beseitigung dieser Defizite anbieten soll. Bestandteile dieser Plattform sind web-basierte adaptive Fragebögen und ein elektronisches Kommunikationstagebuch. Neben der Darstellung von Anforderungen an diese Softwarewerkzeuge erfolgt eine konzeptionelle Beschreibung ihrer Funktionsweise. Die Entwicklung des Frühwarnsystems erfolgt im Rahmen des interdisziplinären Projekts @VirtU [1]. Der Fokus der Forschungsarbeiten liegt dabei u. a. auf der Betrachtung von Motivationsfaktoren für die Teamarbeit, der Teamkommunikation und dem Informationsaustausch zwischen den Partnern in einem VU. Im Rahmen von @VirtU werden VU als eine Kooperationsform voneinander unabhängiger Wertschöpfungseinheiten angesehen, in welcher das Managementprinzip der „virtual organization“ (vgl. Mowshowitz [2]) umgesetzt wird. Gegenstand des zu entwickelnden Frühwarnsystems sind VU im engeren Sinne, d. h. die Missionsnetzwerke, in denen der Wertschöpfungsprozess stattfindet (vgl. Neumann, Meyer [3] in diesem Band).

A citricultura brasileira se destaca no mercado global de citros, apresentando altos valores de produção e produtividade. Dentre as diversas doenças que afetam a cultura, a podridão floral dos citros (PFC) vem ganhando destaque com o deslocamento de áreas produtoras para regiões mais favoráveis à ocorrência desta doença. A PFC, causada por espécies dos complexos Colletotrichum acutatum e C. gloeosporioides, é especialmente problemática em anos de temperaturas amenas e alta umidade. Os citricultores realizam pulverizações preventivas para o controle da PFC todos os anos, embora condições climáticas favoráveis à doença ocorram apenas ocasionalmente. Além do impacto econômico, as frequentes pulverizações com fungicidas aumentam a pressão de seleção sobre isolados resistentes, o que pode interferir na eficiência do controle químico. A utilização de sistemas de previsão de epidemias pode evitar que pulverizações desnecessárias de fungicidas sejam realizadas caso não haja condições favoráveis à ocorrência da doença. Para o desenvolvimento destes sistemas, é imprescindível determinar um limiar de ação para a aplicação de fungicidas nos pomares. Dessa forma, o presente trabalho teve por objetivos: (i) estabelecer um limiar de ação para a aplicação de fungicidas com base na germinação de conídios de C. acutatum que permita o controle a doença igualmente ou de forma mais eficiente do que o sistema de pulverização adotado pelos citricultores do sudoeste paulista; (ii) caracterizar a sensibilidade de isolados dos complexos C. gloeosporioides e C. acutatum à trifloxistrobina e ao tebuconazol in vitro e molecularmente, para identificar possíveis mudanças de sensibilidade das espécies a estes fungicidas. Para a definição do limiar de ação, tratamentos baseados em índices de risco foram comparados ao tratamento testemunha e ao calendário fixo de aplicações, usualmente adotado pelos produtores. A aplicação de fungicidas quando limiar de 15% de germinação de conídios era atingido, foi eficiente em controlar a doença e reduzir o número de aplicações de fungicidas. Nos ensaios de sensibilidade a fungicidas dois métodos foram utilizados para a determinação da CE50: o da diluição em gradiente espiral para trifloxistrobina e tebuconazol e o da inibição da germinação de conídios para a trifloxistrobina. Foram utilizados isolados coletados entre 1999 e 2016. A CE50 média da coleção de isolados de acordo com o método da diluição em gradiente espiral variou de 0,158 a 0,297 μg/ml e 0,1 a 0,182 μg/ml para trifloxistrobina e tebuconazol, respectivamente. Para a trifloxistrobina, de acordo com o método da inibição da germinação, a CE50 média foi de 0,002 μg/ml. Não foram verificadas características moleculares nem valores de CE50 atrelados à mudança de sensibilidade dos isolados. / Brazilian citrus industry represents a significant share in the global citrus market. Amongst several diseases that affect the crop, postbloom fruit drop (PFD) has been gaining prominence in Sao Paulo with the displacement of citrus areas to regions in which weather conditions are more favorable to the occurrence of this disease. PFD, caused by species of the complexes Colletotrichum acutatum and C. gloeosporioides, is especially problematic in years of mild temperatures and high humidity. Citrus growers spray the orchards preventively for PFD control every year, although favorable climatic conditions do not occur regularly. In addition to the economic impact, this practice increases the selection pressure of fungicide resistant isolates, and may decrease the efficiency of chemical control in a long term. The use of disease forecasting systems is able to prevent unnecessary fungicide sprays. For the development of such systems, it is essential to determine an action threshold for the application of fungicides in the orchards. The objectives of this work were: (i) to establish an action threshold for fungicide sprays based on the germination of C. acutatum conidia; (ii) to characterize the sensitivity of C. gloeosporioides and C. acutatum isolates to trifloxystrobin and tebuconazole in vitro and molecularly. Regarding the definition of the action threshold, treatments based on risk indices were compared to the control treatments and calendar based sprays, usually adopted by growers. The 15% conidia germination threshold was efficient in controlling the disease and reducing the number of fungicide applications. In the fungicide sensitivity tests, two methods were used to determine the EC50, the spiral gradient dilution for trifloxystrobin and tebuconazole, and the method of conidial germination inhibition for trifloxystrobin. Isolates collected between 1999 and 2016 were used. The mean EC50 of the isolate collection determined by the spiral gradient dilution method ranged from 0.158 to 0.297 μg/ml and from 0.1 to 0.182 μg/ml for trifloxystrobin and tebuconazole, respectively. Mean EC50 of trifloxystrobin estimated by the conidial germination inhibition method was 0.002 μg/ml. No mutations or EC50 values indicated shifts of fungicide sensitivity on the isolates.

Wider awareness, proper preparedness and effective mitigation strategies need better understanding of tsunamis and tsunami hazard assessment. Tsunami assessment study covers the exchange and enhancement of available earthquake and tsunami data, development of bathymetric and topographic data in sufficient resolution, selection of possible or credible tsunami scenarios, selection and application of the valid and verified numerical tools for tsunami generation, propagation, coastal amplification, inundation and visualization. From this point of view, this thesis deals with all these components of tsunami hazards assessment. The database of 38 different seismic sources is generated and applied to Eastern Mediterranean Basin by using numerical code called NAMI DANCE. Furthermore, the simulation results are compared and discussed. In the thesis, the difficulties in defining seismic source parameters, the effect of dip and rake (slip) angle on seismic generated tsunamis are evaluated. Moreover, the performance of the numerical code, the accuracy of results, the efficiency of the numerical methods in the application to Mediterranean Basin Tsunamis and the comparisons of simulations in nested domains for Bodrum, Kas and Iskenderun are given as case studies. According to the study, north-west and south-west of Turkey may have tsunami risk more than other regions. The maximum wave amplitudes, which may be expected to occur near the shore, are found more than 4 m. However, maximum positive wave amplitude observed in history is approximately 8 m. The arrival time of first wave to hit the coasts vary in a range of 15 to 60 minutes depending on the closeness of the location to the sources&rsquo / epicenter.

A citricultura brasileira se destaca no mercado global de citros, apresentando altos valores de produção e produtividade. Dentre as diversas doenças que afetam a cultura, a podridão floral dos citros (PFC) vem ganhando destaque com o deslocamento de áreas produtoras para regiões mais favoráveis à ocorrência desta doença. A PFC, causada por espécies dos complexos Colletotrichum acutatum e C. gloeosporioides, é especialmente problemática em anos de temperaturas amenas e alta umidade. Os citricultores realizam pulverizações preventivas para o controle da PFC todos os anos, embora condições climáticas favoráveis à doença ocorram apenas ocasionalmente. Além do impacto econômico, as frequentes pulverizações com fungicidas aumentam a pressão de seleção sobre isolados resistentes, o que pode interferir na eficiência do controle químico. A utilização de sistemas de previsão de epidemias pode evitar que pulverizações desnecessárias de fungicidas sejam realizadas caso não haja condições favoráveis à ocorrência da doença. Para o desenvolvimento destes sistemas, é imprescindível determinar um limiar de ação para a aplicação de fungicidas nos pomares. Dessa forma, o presente trabalho teve por objetivos: (i) estabelecer um limiar de ação para a aplicação de fungicidas com base na germinação de conídios de C. acutatum que permita o controle a doença igualmente ou de forma mais eficiente do que o sistema de pulverização adotado pelos citricultores do sudoeste paulista; (ii) caracterizar a sensibilidade de isolados dos complexos C. gloeosporioides e C. acutatum à trifloxistrobina e ao tebuconazol in vitro e molecularmente, para identificar possíveis mudanças de sensibilidade das espécies a estes fungicidas. Para a definição do limiar de ação, tratamentos baseados em índices de risco foram comparados ao tratamento testemunha e ao calendário fixo de aplicações, usualmente adotado pelos produtores. A aplicação de fungicidas quando limiar de 15% de germinação de conídios era atingido, foi eficiente em controlar a doença e reduzir o número de aplicações de fungicidas. Nos ensaios de sensibilidade a fungicidas dois métodos foram utilizados para a determinação da CE50: o da diluição em gradiente espiral para trifloxistrobina e tebuconazol e o da inibição da germinação de conídios para a trifloxistrobina. Foram utilizados isolados coletados entre 1999 e 2016. A CE50 média da coleção de isolados de acordo com o método da diluição em gradiente espiral variou de 0,158 a 0,297 μg/ml e 0,1 a 0,182 μg/ml para trifloxistrobina e tebuconazol, respectivamente. Para a trifloxistrobina, de acordo com o método da inibição da germinação, a CE50 média foi de 0,002 μg/ml. Não foram verificadas características moleculares nem valores de CE50 atrelados à mudança de sensibilidade dos isolados. / Brazilian citrus industry represents a significant share in the global citrus market. Amongst several diseases that affect the crop, postbloom fruit drop (PFD) has been gaining prominence in Sao Paulo with the displacement of citrus areas to regions in which weather conditions are more favorable to the occurrence of this disease. PFD, caused by species of the complexes Colletotrichum acutatum and C. gloeosporioides, is especially problematic in years of mild temperatures and high humidity. Citrus growers spray the orchards preventively for PFD control every year, although favorable climatic conditions do not occur regularly. In addition to the economic impact, this practice increases the selection pressure of fungicide resistant isolates, and may decrease the efficiency of chemical control in a long term. The use of disease forecasting systems is able to prevent unnecessary fungicide sprays. For the development of such systems, it is essential to determine an action threshold for the application of fungicides in the orchards. The objectives of this work were: (i) to establish an action threshold for fungicide sprays based on the germination of C. acutatum conidia; (ii) to characterize the sensitivity of C. gloeosporioides and C. acutatum isolates to trifloxystrobin and tebuconazole in vitro and molecularly. Regarding the definition of the action threshold, treatments based on risk indices were compared to the control treatments and calendar based sprays, usually adopted by growers. The 15% conidia germination threshold was efficient in controlling the disease and reducing the number of fungicide applications. In the fungicide sensitivity tests, two methods were used to determine the EC50, the spiral gradient dilution for trifloxystrobin and tebuconazole, and the method of conidial germination inhibition for trifloxystrobin. Isolates collected between 1999 and 2016 were used. The mean EC50 of the isolate collection determined by the spiral gradient dilution method ranged from 0.158 to 0.297 μg/ml and from 0.1 to 0.182 μg/ml for trifloxystrobin and tebuconazole, respectively. Mean EC50 of trifloxystrobin estimated by the conidial germination inhibition method was 0.002 μg/ml. No mutations or EC50 values indicated shifts of fungicide sensitivity on the isolates.

Ziel dieses Beitrags ist die Benennung von Anforderungen an eine IT-basierte Forschungs-und Betriebsplattform zur Unterstützung eines Frühwarnsystems, welches Defizite bei der Zusammenarbeit und Kommunikation von Kooperationspartnern in virtuellen Unternehmen (VU) frühzeitig erkennen und Optionen zur Beseitigung dieser Defizite anbieten soll. Bestandteile dieser Plattform sind web-basierte adaptive Fragebögen und ein elektronisches Kommunikationstagebuch. Neben der Darstellung von Anforderungen an diese Softwarewerkzeuge erfolgt eine konzeptionelle Beschreibung ihrer Funktionsweise. Die Entwicklung des Frühwarnsystems erfolgt im Rahmen des interdisziplinären Projekts @VirtU [1]. Der Fokus der Forschungsarbeiten liegt dabei u. a. auf der Betrachtung von Motivationsfaktoren für die Teamarbeit, der Teamkommunikation und dem Informationsaustausch zwischen den Partnern in einem VU. Im Rahmen von @VirtU werden VU als eine Kooperationsform voneinander unabhängiger Wertschöpfungseinheiten angesehen, in welcher das Managementprinzip der „virtual organization“ (vgl. Mowshowitz [2]) umgesetzt wird. Gegenstand des zu entwickelnden Frühwarnsystems sind VU im engeren Sinne, d. h. die Missionsnetzwerke, in denen der Wertschöpfungsprozess stattfindet (vgl. Neumann, Meyer [3] in diesem Band).

Climate change affects Sweden by causing mild winters and increased precipitation during the winter season. This will affect the risk for flooding in areas close to water. Flood warnings mean to mitigate and prevent damages that may occur due to flooding.  The purpose of this study is to research how Swedish municipalities and county administrations communicate flood risks and risk preparedness with each other and the Swedish meteorological and hydrological institute (SMHI) to avoid damages. The study also researches how a consequence based early warning system affects the communication and preparedness for flood risks between municipalities, county administrations and SMHI. The subjects of the study are four municipalities and two county administrations that have a significant risk of flooding around the lake Vänern. The methods used are semi-structured interviews for data collection and qualitative data analysis. The result is discussed in relationship to the theoretical frameworks risk governance, risk society and risk communication.  The results of the study show that there is a difference between how municipalities and county administrations perceive the communication between them and SMHI which is the official source of weather-related warnings in Sweden. The relationship between the municipalities and county administrations is mostly good while the relationship between municipalities and SMHI is less so. The communication between county administrations and SMHI is better than that between SMHI and the municipalities. The county administrations believe a new, consequence based early warning system may have a positive impact on the communication between the stakeholders while the municipalities believe the impact will be small or none. The municipalities and county administrations agree that a consequence based early warning system will have a positive effect on the preparedness for floods. / Klimatförändringarna påverkar Sverige med mildare vintrar och en ökad nederbörd under vinterhalvåret som kommer påverka översvämningsrisken i vattennära områden. Översvämningsvarningar är en åtgärd för att mildra och förebygga skador som kan uppstå till följd av höga vattenflöden, höga vattennivåer och skyfall. Studiens syfte är att undersöka hur kommuner och länsstyrelser kommunicerar med varandra och med Sveriges meteorologiska och hydrologiska institut (SMHI) om översvämningsrisker och beredskap för att anpassa och undvika skador. Studien undersöker även hur ett konsekvensbaserat varningssystem påverkar översvämningsberedskap och kommunikation mellan kommuner, länsstyrelser och SMHI. Undersökningens urval består av fyra kommuner och två länsstyrelser med utpekad översvämningsrisk i området kring Vänern. I studien används metoderna semi-strukturerad intervju och kvalitativ innehållsanalys som datainsamlings- och analysmetoder och dess resultat diskuteras utifrån de teoretiska ramverken om riskstyrning, risksamhället och riskkommunikation.  Resultatet pekar mot att det finns en dissonans mellan kommuner och länsstyrelsers uppfattning av den kommunikation som bedrivs mellan dem och SMHI som utfärdar vädervarningar i Sverige. Kommunernas relation till länsstyrelserna är mestadels god medan deras relation till SMHI är mindre god. Kommunikationen mellan Länsstyrelse och SMHI är bättre än den mellan kommun och SMHI. Kommunerna upplever inte att ett konsekvensbaserat varningssystem kommer påverka kommunikationen mellan dem, länsstyrelserna och SMHI. Länsstyrelserna tror däremot att den nya kommunikationskedja som följer med det konsekvensbaserade varningssystemet kan ha en positiv effekt på kommunikationen. Både kommuner och länsstyrelser anser att ett konsekvensbaserat varningssystem kan påverka översvämningsberedskapen positivt.

With the rapid improvement of the Advanced Driver Assistant System (ADAS), autonomous driving has become one of the most common hot topics in recent years. While driving, many technologies related to autonomous driving choose to use the sensors installed on the vehicle to collect the information of road status and the environment outside. This aims to warn the driver to perceive the potential danger in the fastest time, which has become the focus of autonomous driving in recent years. Although autonomous driving brings plenty of conveniences to people, the safety of it is still facing difficulties. During driving, even the experienced driver can not guarantee focus on the status of the road all the time. Thus, lane departure warning system (LDWS) becomes developed. The purpose of LDWS is to determine whether the vehicle is in the safe driving area. If the vehicle is out of this area, LDWS will detect it and alert the driver by the sensors, such as sound and vibration, in order to make the driver back to the safe driving area. This thesis proposes a novel lightweight LDWS model LEHA, which divides the entire LDWS into three stages: image preprocessing, lane detection, and lane departure recognition. Different from the deep learning methods of LDWS, our LDWS model LEHA can achieve high accuracy and efficiency by relying only on simple hardware. The image preprocessing stage aims to process the original road image to remove the noise which is irrelevant to the detection result. In this stage, we apply a novel algorithm of grayscale preprocessing to convert the road image to a grayscale image, which removes the color of it. Then, we design a binarization method to greatly extract the lane lines from the background. A newly-designed image smoothing is added to this stage to reduce most of the noise, which improves the accuracy of the following lane detection stage. After obtaining the processed image, the lane detection stage is applied to detect and mark the lane lines. We use region of interest (ROI) to remove the irrelevant parts of the road image to reduce the detection time. After that, we introduce the Canny edge detection method, which aims to extract the edges of the lane lines. The last step of LDWS in the lane detection stage is a novel Hough transform method, the purpose of it is to detect the position of the lane and mark it. Finally, the lane departure recognition stage is used to calculate the deviation distance between the vehicle and the centerline of the lane to determine whether the warning needs to turn on. In the last part of this paper, we present the experiment results which show the comparison results of different lane conditions. We do the statistic of the proposed LDWS accuracy in terms of detection and departure. The detection rate of our proposed LDWS is 98.2% and the departure rate of it is 99.1%. The average processing time of our proposed LDWS is 20.01 x 10⁻³s per image.

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