Deep Learning for En-Route Aircraft Conflict Resolution : two complementary approaches

Une situation est considérée comme un conflit lorsque deux ou plusieurs aéronefs ne parviennent pas à maintenir une certaine distance entre eux pendant leur trajet. Les modèles antérieurs destinés à aider les contrôleurs aériens à résoudre les conflits étaient basés sur des modèles mathématiques et statistiques. Les récents succès des modèles de réseaux de neurones profonds dans divers domaines ont relancé l’intérêt de la recherche sur la résolution automatique des conflits entre avions. Les conflits sont résolus par les contrôleurs en donnant des ordres aux pilotes pour modifier la trajectoire de l’avion, en fonction des différentes positions et trajectoires de l’avion. Dans cette thèse, nous proposons deux façons différentes d’exploiter ces données, en considérant soit les données de trajectoire, soit les images correspondantes des trajectoires. Le premier modèle, CRMLnet, est un modèle de neural network dont la sortie est une classification multi-label. Ce modèle prend en entrée la trajectoire de positionnement (latitude, longitude, altitude, vitesse, cap, etc.) de tous les avions impliqués dans le conflit et fournit en sortie les changements de cap des avions à différents angles. Comparé à d’autres modèles d’apprentissage automatique qui utilisent plusieurs classificateurs à étiquette unique, tels que SVM, KNC et LR, notre CRMLnet obtient les meilleurs résultats avec une précision de 98,72% et une ROC de 0,999. Ce modèle n’est pas approprié pour traiter un nombre variable d’avions impliqués. Le deuxième modèle ne dépend pas du nombre d’avions concernés. Pour ce modèle, nous avons transformé la scène de conflit en une image. Notre deuxième modèle de résolution de conflit multi-label, ACRnet, est conçu comme un convolutional neural network. Le modèle ACRnet atteint une précision de 99,16% sur les données d’apprentissage et de 98,97% sur l’ensemble des données de test pour deux avions. Pour les deux et trois avions, la précision est de 99,05% (resp. 98,96%) sur l’ensemble de données d’entraînement (resp. de test).A situation is identified as a conflict when two or more aircraft fail to maintain a certain distance between them on their way. Earlier models to support air traffic controllers in solving conflicts were based on mathematical and statistical models. The recent successes of deep neuron network models in various domains have rekindled the research interest on automatic aircraft conflict resolution. Conflicts are solved by controllers by giving orders to pilots to change the aircraft trajectory, based on the various aircraft positions and trajectories. In this thesis we propose two different ways of exploiting these data, considering either the trajectory data or the corresponding images of the trajectories. The first model, CRMLnet, is a neural network model which output is a multi-label classification. This model takes the positioning trajectory (latitude, longitude, altitude, speed, heading, etc.) of all the aircraft involved in the conflict as input and provides the heading changes for the aircraft at different angles as output. When compared to other machine learning models that use multiple single-label classifiers such as SVM, KNC, and LR, our CRMLnet achieves the best results with an accuracy of 98.72% and ROC of 0.999. This model is not appropriate to handle with a variable number of aircraft involved. The second model does not depend on the number of planes involved. For that model, we transformed the conflict scene into an image. Our second multi-label conflict resolution model, ACRnet 5, is designed as a convolutional neural network. ACRnet model achieves an accuracy of 99.16% on the training data and of 98.97% on the test data set for two aircraft. For both two and three aircraft, the accuracy is 99.05% (resp. 98.96%) on the training (resp. test) data set

Influences of Cultural Dimensions on Obstetric Health in Northern Bangladesh

The present study attempts to explore the cultural dimensions that influence obstetric health in suburban areas in northern Bangladesh. Mixed methods were used to conduct the present study. One hundred and twenty-five (125) respondents were selected through purposive sampling; all the female respondents had given birth within the last two years. Additionally, twenty (20) case studies and five (5) key informants’ analysis were conducted in this regard. The research findings showed that some sociocultural factors such as patriarchy, conservative attitudes towards female gender, food taboos, high workload, shyness and taboo around gynecological and obstetric health issues, and perception of women being a family burden influenced obstetric health adversely. Also, many other cultural practices regarding obstetric health were noticed to have influenced obstetric health in the area in various other ways

Android Assistant EyeMate for Blind and Blind Tracker

At present many blind assistive systems have been implemented but there is no such kind of good system to navigate a blind person and also to track the movement of a blind person and rescue him/her if he/she is lost. In this paper, we have presented a blind assistive and tracking embedded system. In this system the blind person is navigated through a spectacle interfaced with an android application. The blind person is guided through Bengali/English voice commands generated by the application according to the obstacle position. Using voice command a blind person can establish voice call to a predefined number without touching the phone just by pressing the headset button. The blind assistive application gets the latitude and longitude using GPS and then sends them to a server. The movement of the blind person is tracked through another android application that points out the current position in Google map. We took distances from several surfaces like concrete and tiles floor in our experiment where the error rate is 5%.Comment: arXiv admin note: text overlap with arXiv:1611.09480 by other autho

How air quality and COVID-19 transmission change under different lockdown scenarios? A case from Dhaka city, Bangladesh

The transmission of novel coronavirus (COVID-19) can be reduced by implementing a lockdown policy, which has also been proven as an effective control measure for air pollution in the urban cities. In this study, we applied ground- and satellite-based data of five criteria air pollutants (PM2.5, NO2, SO2, O3, and CO) and meteorological factors from March 8 to May 15, 2020 (before, partial-, and full-lockdown). The generalized additive models (GAMs), wavelet coherence, and random forest (RF) model were employed to explore the relationship between air quality indicators and COVID-19 transmission in Dhaka city. Results show that overall, 26, 20.4, 17.5, 9.7 and 8.8% declined in PM 2.5, NO2, SO2, O3, and CO concentrations, respectively, in Dhaka City during the partial and full lockdown compared to the period before the lockdown. The implementation of lockdown policy for containing COVID-19 transmission played a crucial role in reducing air pollution. The findings of wavelet coherence and partial wavelet coherence demonstrate no standalone coherence, but interestingly, multiple wavelet coherence indicated a strong short-term coherence among air pollutants and meteorological factors with the COVID-19 outbreak. Outcomes of GAMs indicated that an increase of 1-unit in long-term exposure to O3 and CO (lag1) was associated with a 2.9% (95% CI: −0.3%, −5.6%), and 53.9% (95% CI: 0.2%, −107.9%) decreased risk of COVID-19 infection rate during the full-lockdown period. Whereas, COVID-19 infection and MT (mean temperature) are modulated by a peak during full-lockdown, which is mostly attributed to contact transmission in Dhaka city. RF model revealed among the parameters being studied, MT, RH (relative humidity), and O3 were the dominant factors that could be associated with COVID-19 cases during the study period. The outcomes reported here could elucidate the effectiveness of lockdown scenarios for COVID-19 containment and air pollution control in Dhaka city

Brucellosis among ruminants in some districts of Bangladesh using four conventional serological assays

Brucellosis causes a great economic loss to the livestock industries through abortion, infertility, birth of weak and dead offspring, increased calving interval and reduction of milk yield and it is endemic in Bangladesh. The present study was performed to know the seroprevalence of brucellosis for 1000 ruminants (135 Buffaloes, 465 cattle, 230 goats and 170 sheep) in five different districts of Bangladesh by four conventional serological tests such as: Rose Bengal Plate Test (RBT), tube agglutination test (TAT), competitive enzyme-linked immunosorbent assay (C-ELISA), and Fluorescent polarization assay (FPA). Sheep has the highest prevalence (8.24%) of brucellosis. The seroprevalence of brucellosis was significantly higher in animals with previous abortion record in case of buffaloes, cattle, goats and sheep than that with no abortion record. C-ELISA can be the most suitable choice for extensive use in many kinds of livestocks and accurate estimation of Brucella antibodies in ruminants in Bangladesh

Microbial Contamination and Antibiotic Resistance in Marketed Food in Bangladesh: Current Situation and Possible Improvements

Antimicrobial resistance (AMR) is a public health problem worldwide. Bangladesh, like its neighboring countries, faces many public health challenges, including access to safe food, inadequate food surveillance, as well as increasing AMR. This study investigated bacterial contamination and the AMR profile of pathogens in marketed food in Bangladesh and explored barriers to reducing AMR in the country. We collected 366 tomatoes, 359 chicken and 249 fish samples from 732 vendors in traditional markets in urban, peri-urban and rural areas in Bangladesh, as well as from 121 modern retails in Dhaka capital to analyse Vibrio cholerae and Escherichia coli in fish, Salmonella in chicken, and Salmonella and E. coli in tomatoes. Antibiotic susceptibility against 11 antibiotics was tested using a disc diffusion test and interpreted by an automated zone inhibition reader. In addition, a qualitative study using key informant interviews was conducted to explore antimicrobial use and AMR reduction potential in Bangladesh. We found E. coli in 14.21% of tomatoes and 26.91% of fish samples, while 7.38% of tomatoes and 17.27% of chicken were positive for Salmonella, and 44.98% of fish were positive for Vibrio cholerae. In total 231/319 (72.4%) of all pathogens isolated were multidrug-resistant (MDR) (resistant to three or more antibiotic groups). Qualitative interviews revealed an inadequate surveillance system for antibiotic use and AMR in Bangladesh, especially in the agriculture sector. To be able to fully understand the human health risks from bacterial hazards in the food and the AMR situation in Bangladesh, a nationwide study with a one health approach should be conducted, within all sectors, including AMR testing as well as assessment of the antimicrobial use and its drivers

Microbial Contamination and Antibiotic Resistance in Marketed Food in Bangladesh: Current Situation and Possible Improvements

Antimicrobial resistance (AMR) is a public health problem worldwide. Bangladesh, like its neighboring countries, faces many public health challenges, including access to safe food, inadequate food surveillance, as well as increasing AMR. This study investigated bacterial contamination and the AMR profile of pathogens in marketed food in Bangladesh and explored barriers to reducing AMR in the country. We collected 366 tomatoes, 359 chicken and 249 fish samples from 732 vendors in traditional markets in urban, peri-urban and rural areas in Bangladesh, as well as from 121 modern retails in Dhaka capital to analyse Vibrio cholerae and Escherichia coli in fish, Salmonella in chicken, and Salmonella and E. coli in tomatoes. Antibiotic susceptibility against 11 antibiotics was tested using a disc diffusion test and interpreted by an automated zone inhibition reader. In addition, a qualitative study using key informant interviews was conducted to explore antimicrobial use and AMR reduction potential in Bangladesh. We found E. coli in 14.21% of tomatoes and 26.91% of fish samples, while 7.38% of tomatoes and 17.27% of chicken were positive for Salmonella, and 44.98% of fish were positive for Vibrio cholerae. In total 231/319 (72.4%) of all pathogens isolated were multidrug-resistant (MDR) (resistant to three or more antibiotic groups). Qualitative interviews revealed an inadequate surveillance system for antibiotic use and AMR in Bangladesh, especially in the agriculture sector. To be able to fully understand the human health risks from bacterial hazards in the food and the AMR situation in Bangladesh, a nationwide study with a one health approach should be conducted, within all sectors, including AMR testing as well as assessment of the antimicrobial use and its drivers

Apprentissage profond pour la résolution des conflits entre aéronefs en route : deux approches complémentaires

Une situation est considérée comme un conflit lorsque deux ou plusieurs aéronefs ne parviennent pas à maintenir une certaine distance entre eux pendant leur trajet. Les modèles antérieurs destinés à aider les contrôleurs aériens à résoudre les conflits étaient basés sur des modèles mathématiques et statistiques. Les récents succès des modèles de réseaux de neurones profonds dans divers domaines ont relancé l’intérêt de la recherche sur la résolution automatique des conflits entre avions. Les conflits sont résolus par les contrôleurs en donnant des ordres aux pilotes pour modifier la trajectoire de l’avion, en fonction des différentes positions et trajectoires de l’avion. Dans cette thèse, nous proposons deux façons différentes d’exploiter ces données, en considérant soit les données de trajectoire, soit les images correspondantes des trajectoires. Le premier modèle, CRMLnet, est un modèle de neural network dont la sortie est une classification multi-label. Ce modèle prend en entrée la trajectoire de positionnement (latitude, longitude, altitude, vitesse, cap, etc.) de tous les avions impliqués dans le conflit et fournit en sortie les changements de cap des avions à différents angles. Comparé à d’autres modèles d’apprentissage automatique qui utilisent plusieurs classificateurs à étiquette unique, tels que SVM, KNC et LR, notre CRMLnet obtient les meilleurs résultats avec une précision de 98,72% et une ROC de 0,999. Ce modèle n’est pas approprié pour traiter un nombre variable d’avions impliqués. Le deuxième modèle ne dépend pas du nombre d’avions concernés. Pour ce modèle, nous avons transformé la scène de conflit en une image. Notre deuxième modèle de résolution de conflit multi-label, ACRnet, est conçu comme un convolutional neural network. Le modèle ACRnet atteint une précision de 99,16% sur les données d’apprentissage et de 98,97% sur l’ensemble des données de test pour deux avions. Pour les deux et trois avions, la précision est de 99,05% (resp. 98,96%) sur l’ensemble de données d’entraînement (resp. de test).A situation is identified as a conflict when two or more aircraft fail to maintain a certain distance between them on their way. Earlier models to support air traffic controllers in solving conflicts were based on mathematical and statistical models. The recent successes of deep neuron network models in various domains have rekindled the research interest on automatic aircraft conflict resolution. Conflicts are solved by controllers by giving orders to pilots to change the aircraft trajectory, based on the various aircraft positions and trajectories. In this thesis we propose two different ways of exploiting these data, considering either the trajectory data or the corresponding images of the trajectories. The first model, CRMLnet, is a neural network model which output is a multi-label classification. This model takes the positioning trajectory (latitude, longitude, altitude, speed, heading, etc.) of all the aircraft involved in the conflict as input and provides the heading changes for the aircraft at different angles as output. When compared to other machine learning models that use multiple single-label classifiers such as SVM, KNC, and LR, our CRMLnet achieves the best results with an accuracy of 98.72% and ROC of 0.999. This model is not appropriate to handle with a variable number of aircraft involved. The second model does not depend on the number of planes involved. For that model, we transformed the conflict scene into an image. Our second multi-label conflict resolution model, ACRnet 5, is designed as a convolutional neural network. ACRnet model achieves an accuracy of 99.16% on the training data and of 98.97% on the test data set for two aircraft. For both two and three aircraft, the accuracy is 99.05% (resp. 98.96%) on the training (resp. test) data set