Environment-Centric Safety Requirements forAutonomous Unmanned Systems

Autonomous unmanned systems (AUS) emerge to take place of human operators in harsh or dangerous environments. However, such environments are typically dynamic and uncertain, causing unanticipated accidents when autonomous behaviours are no longer safe. Even though safe autonomy has been considered in the literature, little has been done to address the environmental safety requirements of AUS systematically. In this work, we propose a taxonomy of environment-centric safety requirements for AUS, and analyse the neglected issues to suggest several new research directions towards the vision of environment-centric safe autonomy

Privacy-Aware UAV Flights through Self-Configuring Motion Planning

During flights, an unmanned aerial vehicle (UAV) may not be allowed to move across certain areas due to soft constraints such as privacy restrictions. Current methods on self-adaption focus mostly on motion planning such that the trajectory does not trespass predetermined restricted areas. When the environment is cluttered with uncertain obstacles, however, these motion planning algorithms are not flexible enough to find a trajectory that satisfies additional privacy-preserving requirements within a tight time budget during the flights. In this paper, we propose a privacy risk aware motion planning method through the reconfiguration of privacy-sensitive sensors. It minimises environmental impact by re-configuring the sensor during flight, while still guaranteeing the hard safety and energy constraints such as collision avoidance and timeliness. First, we formulate a model for assessing privacy risks of dynamically detected restricted areas. In case the UAV cannot find a feasible solution to satisfy both hard and soft constraints from the current configuration, our decision making method can then produce an optimal reconfiguration of the privacy-sensitive sensor with a more efficient trajectory. We evaluate the proposal through various simulations with different settings in a virtual environment and also validate the approach through real test flights on DJI Matrice 100 UAV

Cathepsin B abundance, activity and microglial localisation in Alzheimer's disease-Down syndrome and early onset Alzheimer's disease; the role of elevated cystatin B

Cathepsin B is a cysteine protease that is implicated in multiple aspects of Alzheimer's disease pathogenesis. The endogenous inhibitor of this enzyme, cystatin B (CSTB) is encoded on chromosome 21. Thus, individuals who have Down syndrome, a genetic condition caused by having an additional copy of chromosome 21, have an extra copy of an endogenous inhibitor of the enzyme. Individuals who have Down syndrome are also at significantly increased risk of developing early-onset Alzheimer's disease (EOAD). The impact of the additional copy of CSTB on Alzheimer's disease development in people who have Down syndrome is not well understood. Here we compared the biology of cathepsin B and CSTB in individuals who had Down syndrome and Alzheimer's disease, with disomic individuals who had Alzheimer's disease or were ageing healthily. We find that the activity of cathepsin B enzyme is decreased in the brain of people who had Down syndrome and Alzheimer's disease compared with disomic individuals who had Alzheimer's disease. This change occurs independently of an alteration in the abundance of the mature enzyme or the number of cathepsin B+ cells. We find that the abundance of CSTB is significantly increased in the brains of individuals who have Down syndrome and Alzheimer's disease compared to disomic individuals both with and without Alzheimer's disease. In mouse and human cellular preclinical models of Down syndrome, three-copies of CSTB increases CSTB protein abundance but this is not sufficient to modulate cathepsin B activity. EOAD and Alzheimer's disease-Down syndrome share many overlapping mechanisms but differences in disease occur in individuals who have trisomy 21. Understanding this biology will ensure that people who have Down syndrome access the most appropriate Alzheimer's disease therapeutics and moreover will provide unique insight into disease pathogenesis more broadly

Application of Extracellular Vesicles in Gynecologic Cancer Treatment

Ovarian, cervical, and endometrial cancer are the three most common gynecological malignancies that seriously threaten women’s health. With the development of molecular biology technology, immunotherapy and targeted therapy for gynecologic tumors are being carried out in clinical treatment. Extracellular vesicles are nanosized; they exist in various body fluids and play an essential role in intercellular communication and in the regulation of various biological process. Several studies have shown that extracellular vesicles are important targets in gynecologic cancer treatment as they promote tumor growth, progression, angiogenesis, metastasis, chemoresistance, and immune system escape. This article reviews the progress of research into extracellular vesicles in common gynecologic tumors and discusses the role of extracellular vesicles in gynecologic tumor treatment

An Agent-Based Occupancy Simulator for Building Performance Simulation:

Traditionally, in building energy modeling (BEM) programs, occupancy inputs are deterministic and less indicative of real world scenarios, contributing to discrepancies between simulated and actual energy use in buildings. This paper presents an agent-based occupancy simulator, which models each occupant as an agent with specified movement events and statistics of space uses. To reduce the amount of data inputs, the simulator allows users to group occupants with similar behaviors as an occupant type, and spaces with similar function as a space type. It is a web-based application with friendly graphical user interface, cloud computing, and data storage. A case study is presented to demonstrate the usage of the occupancy simulator and its integration with EnergyPlus and obFMU. It first shows the required data inputs and the results from the occupancy simulator. Then, the generated occupant schedules are used in the EnergyPlus and obFMU simulation to evaluate the impacts of occupant behavior on building energy performance. The simulation results indicate that the occupancy simulator can capture the diversity of space’s occupancy behavior rather than the static weekly profiles, and can generate realistic occupancy schedules to support building performance simulation

An Image Matching Method for SAR Orthophotos from Adjacent Orbits in Large Area Based on SAR-Moravec

In producing orthophoto mosaic in a large area from spaceborne synthetic aperture radar (SAR) images, SAR image matching from adjacent orbits is a technical difficulty due to the speckle noise and different imaging mechanism between azimuth and range direction. In this paper, an area-based method, SAR-Moravec, is proposed for SAR orthophoto matching from adjacent orbits in a large area. Compared with the classical area-based Moravec, the template of SAR-Moravec is characterized by more directions for speckle noise restraint and a main direction consistent with the azimuth. In order to get evenly distributed matching points with high accuracy, the grid control mechanism and Gaussian pyramid from coarse to fine are introduced in matching. The whole process contains three steps. First, the pyramid images are constructed by the down-sampling process. Second, under grid control, the feature points are evenly extracted by the modified template. Third, the transformation model is iteratively calculated from the first to the last layer of the pyramid. After the matching process layer-by-layer, the final matching points and transformation model can be obtained. In the experiments, we compare the SAR-Moravec with three widely used methods, including the Moravec, the SAR-scale invariant feature transform (SAR-SIFT), and the SAR-features from an accelerated segment test (SAR-FAST). The results indicate that the proposed method has the best global matching accuracy among these methods and the matching efficiency is better than SAR-SIFT and SAR-FAST methods in large area

Modeling City Building Stock for Large-Scale Energy Efficiency Improvements using CityBES

Buildings in San Francisco consumed 52% of total primary energy. Improving building energy efficiency is one of the key strategies cities are adopting towards their energy and climate goals. Urban building energy models (UBEM) can support city managers to evaluate and prioritize energy conservation measures for investment and to design effective incentive and rebate programs. This paper introduces methods to develop a standardized dataset of city building stock, and it demonstrates the use of a UBEM tool, City Building Energy Saver (CityBES), for an urban-scale energy retrofit analysis of building stock in the city of San Francisco. CityBES is an open web-based data and computing platform providing city-scale building energy modeling and performance visualization and benchmarking. CityBES utilizes an international standard CityGML to represent the three-dimensional building stock in cities. As an application example, 940 office and retail buildings in six districts of northeast San Francisco were modeled and analyzed with CityBES to evaluate energy savings for five selected measures. The analysis found that replacing existing lighting with LED and adding an air economizer to HVAC systems are cost-effective measures with combined savings per building between 17% to 31%. The CityBES retrofit analysis feature does not require users to have deep knowledge of building systems or building energy models, which helps overcome barriers for city managers and their consultants to adopt UBEM

Modeling City Building Stock for Large-Scale Energy Efficiency Improvements using CityBES

Buildings in San Francisco consumed 52% of total primary energy. Improving building energy efficiency is one of the key strategies cities are adopting towards their energy and climate goals. Urban building energy models (UBEM) can support city managers to evaluate and prioritize energy conservation measures for investment and to design effective incentive and rebate programs. This paper introduces methods to develop a standardized dataset of city building stock, and it demonstrates the use of a UBEM tool, City Building Energy Saver (CityBES), for an urban-scale energy retrofit analysis of building stock in the city of San Francisco. CityBES is an open web-based data and computing platform providing city-scale building energy modeling and performance visualization and benchmarking. CityBES utilizes an international standard CityGML to represent the three-dimensional building stock in cities. As an application example, 940 office and retail buildings in six districts of northeast San Francisco were modeled and analyzed with CityBES to evaluate energy savings for five selected measures. The analysis found that replacing existing lighting with LED and adding an air economizer to HVAC systems are cost-effective measures with combined savings per building between 17% to 31%. The CityBES retrofit analysis feature does not require users to have deep knowledge of building systems or building energy models, which helps overcome barriers for city managers and their consultants to adopt UBEM