A Smartphone Crowdsensing System Enabling Environmental Crowdsourcing for Municipality Resource Allocation with LSTM Stochastic Prediction.

Resource allocation of the availability of certain departments for dealing with emergency recovery is of high importance in municipalities. Efficient planning for facing possible disasters in the coverage area of a municipality provides reassurance for citizens. Citizens can assist with such malfunctions by acting as human sensors at the edge of an infrastructure to provide instant feedback to the appropriate departments fixing the problems.

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes.

Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II.

A stochastic optimization framework for planning of waste collection and value recovery operations in smart and sustainable cities.

The concept of City 2.0 or smart city is offering new opportunities for handling waste management practices. The existing studies have started addressing waste management problems in smart cities mainly by focusing on the design of new sensor-based Internet of Things (IoT) technologies, and optimizing the routes for waste collection trucks with the aim of minimizing operational costs, energy consumption and transportation pollution emissions.

A stochastic multi-agent system for Internet of Things-enabled waste management in smart cities.

The population of the Earth is moving towards urban areas forming smart cities (SCs). Waste management is a component of SCs. We consider a SC which contains a distribution of waste bins and a distribution of waste trucks located in the SC sectors.

G1738R is a BRCA1 founder mutation in Greek breast/ovarian cancer patients: evaluation of its pathogenicity and inferences on its genealogical history.

We have performed screening in 287 breast/ovarian cancer families in Greece which has revealed that approximately 12% (8/65) of all index patients-carriers of a deleterious mutation in BRCA1 and BRCA2 genes, contain the base substitution G to A at position 5331 of BRCA1 gene. This generates the amino acid change G1738R for which based on a combination of genetic, in silico and histopathological analysis there are strong suggestions that it is a causative mutation. In this paper, we present further evidence suggesting the pathogenicity of this variant.