Safety in Automated Vehicles

Day by day, automated vehicles are becoming complex whether that is their connection to different networks, to the internet of things, or simply in security and safety for users. The more intricate automation becomes, the more safety needs to mature. There is safety in the software of systems however the bigger concern lies in ensuring the safety of the drivers and passengers. Whether a person is a contributor to the automation industry or a user of automated products and services, it is important to ask some critical questions. Does the industry have enough knowledge or has there been enough research and experimentation done to allow such a complex system to make decisions whether that is as simple as heating the car for a few minutes before going in or something big as changing lanes on a highway where cars are speeding? The purpose of this paper is to explore the different ways in which people can trust such systems and for those who can begin to start trusting

The presidential, parliamentary andlocal elections in Malawi, May 2014

On 20 May 2014, Malawi arranged tripartite elections for president, parliament and local councils. The elections were remarkable for several reasons, seen from both an African and a Malawian perspective. Despite an uneven electoral playing field, the elections were highly competitive, ultimately leading to the country’s second turnover of power when opposition challenger Peter Mutharika defeated the incumbent president, Joyce Banda. The electoral results also show a return to regionalistic voting patterns and a continuing weakening of political parties, as independent candidates emerged as the largest group in parliament. Although the results were generally credible, the election remains controversial. Several stakeholders questioned the general integrity of the process, and significant logistical problems on election day might have harmed public trust in the electoral authorities

Coral bleaching due to increased sea surface temperature in Gulf of Kachchh Region, India, during June 2016

327-332The 2015-2016 E1 Niño Southern Oscillation event was one of the extreme climate events which elevated the sea surface temperature (SST) of tropical oceans, which in turn increased the level of thermal stress on corals. Coral bleaching event is mainly caused due to high positive SST anomaly, i.e., when SST exceeds its normal summer maxima. Corals in the Gulf of Kachchh region of Gujarat earlier experienced coral bleaching events during 1988, 2010 and 2014. For this study, SST was derived from NOAA OISST data set which is available daily at 0.25° global grids from 1982 to present. The climatologically warmest month for the Gulf of Kachchh region is June when the maximum monthly mean temperature is 29.31°C, as observed from NOAA OISST. The present study focuses on monitoring daily SST anomalies during summer 2016 for the Gulf of Kachchh reefs and field observations on early responses of coral bleaching from Laku Point reef, a site known for high coral diversity. It was found that in summer 2016, SST rose to 30.62 °C and recorded a maximum positive anomaly of 1.31°C in the month of June. A total of 72 days out of 122-day monitoring period showed positive SST anomaly, including 28 days of continuous positive thermal stress in June 2016.To validate coral bleaching forecast at the end of the regional warmest quarter, a field visit was carried out at Laku Point reef near Poshitra village in the southern coast of the Gulf of Kachchh. A total of 13 coral species and a sea anemone were found bleached in various proportions during the field sampling after two months of prolonged thermal stress. The field data showed an average of 3.9% bleaching of corals at colony scale. The maximum proportion of colony scale bleaching was observed in Porites lutea species

High prevalence of alpha thalassemia in the tribal community of the western part of India! Reality or myth? Can simple hematology parameters; MCV and MCH act as screening tools at birth?

Background: The majority of adult tribal subjects in the western part of India, show microcytic hypochromic red cells, and borderline anemia with a normal iron profile, suggesting a high prevalence of thalassemia in this population. Methods: The current study was designed to perform qualitative (to screen for Hb Bart’s) and quantitative (to estimate percentage of Hb Bart’s) hemoglobin electrophoresis with modification of the method, to evaluate the prevalence of α thalassemia and to determine gene frequency of α+ thal gene. Furthermore, the present study also aimed to evaluate common hematology parameters like MCV and MCH as screening tools to suspect α thalassemia at birth. Results: Based on hemoglobin electrophoresis, the prevalence of α thalassemia in all its forms was found to be 66.66%. The estimated gene frequency for α+ thal was found to be 0.7453 and based on that, the extrapolated prevalence of α thalassemia was 93.52% (55.55% homozygous and 37.97% heterozygous). MCV<100 fl and MCH<31 pg were found to be reliable screening tools to predict α thalassemia at birth in full-term uncomplicated pregnancy. Conclusions: Tribal community in the western part of India bears a very high prevalence of α thalassemia, it’s a reality and not a myth. Simple hematological parameters like MCV (<100 fl) and MCH (<31 pg) measured at birth can prove to be cost-effective surrogate markers for α thalassemia. Large scale study using confirmatory genetic analysis is required to validate the findings.

Revealing the Unknown: Real-Time Recognition of Galápagos Snake Species Using Deep Learning

Real-time identification of wildlife is an upcoming and promising tool for the preservation of wildlife. In this research project, we aimed to use object detection and image classification for the racer snakes of the Galápagos Islands, Ecuador. The final target of this project was to build an artificial intelligence (AI) platform, in terms of a web or mobile application, which would serve as a real-time decision making and supporting mechanism for the visitors and park rangers of the Galápagos Islands, to correctly identify a snake species from the user’s uploaded image. Using the deep learning and machine learning algorithms and libraries, we modified and successfully implemented four region-based convolutional neural network (R-CNN) architectures (models for image classification): Inception V2, ResNet, MobileNet, and VGG16. Inception V2, ResNet and VGG16 reached an overall accuracy of 75%This article belongs to the Section Wildlif

Revealing the Unknown: Real-Time Recognition of Galápagos Snake Species Using Deep Learning

Real-time identification of wildlife is an upcoming and promising tool for the preservation of wildlife. In this research project, we aimed to use object detection and image classification for the racer snakes of the Galápagos Islands, Ecuador. The final target of this project was to build an artificial intelligence (AI) platform, in terms of a web or mobile application, which would serve as a real-time decision making and supporting mechanism for the visitors and park rangers of the Galápagos Islands, to correctly identify a snake species from the user’s uploaded image. Using the deep learning and machine learning algorithms and libraries, we modified and successfully implemented four region-based convolutional neural network (R-CNN) architectures (models for image classification): Inception V2, ResNet, MobileNet, and VGG16. Inception V2, ResNet and VGG16 reached an overall accuracy of 75%This article belongs to the Section Wildlif