Collaborative study into the analysis of total selenium and selenium valence states in glass - a general method by hydride generation atomic absorption spectrometry and photometry : Report of the International Commission on Glass (ICG) Technical Committee 2 "Chemical Durability and Analysis"

Whilst the determination of total selenium in glass is of great relevance to environmental purposes, the measurement of the oxidation states of selenium which may arise under different redox conditions may contribute to clarify the mechanisms of colour formation. In the proposed method, the determination of total selenium is carried out by hydride generation atomic absorption spectrometry (HGAAS) on three different glasses covering the range of selenium content between 10 and 130 mg/kg of glass. To prevent losses during the decomposition step, selenium is converted into a nonvolatile form as selenate (Se6+). When the sample is decomposed, the HCl Solution (c(HC1) = 10 mol/l) is made and selenium is pre-reduced to Se4+ by heating for 2 h at 80 °C. The determination of selenium oxidation states requires a stepwise approach. The measurement of Se0 is based on its insolubility in HF. After the sample is decomposed, Se0 is filtered off, dissolved from the filter with a HBr-Br2 mixture and finally measured by HGAAS. Se4+ and Se6+ are recovered in the filtrate of the same sample and determined together after conversion of Se4+ into Se6+. Se4+ is determined in the same filtrate by photometry with o-phenylenediamine (OPDA)

Determination of coloring elements in the glass raw materials limestone and dolomite by flame atomic absorption spectrometry : Report of the International Commission on Glass (ICG) Technical Committee 2 "Chemical Durability and Analysis"

Α method is described for the determination of impurities such as iron, chromium, manganese, nickel, cobalt and copper in the glass raw materials limestone and dolomite. The proposed method involves the direct measurement technique in aqueous Solution by flame atomic absorption spectrometry. It has been proved to be an accurate and quick method for analyzing concentrations normally found in these raw materials

Handling Constrained Optimization in Factor Graphs for Autonomous Navigation

Factor graphs are graphical models used to represent a wide variety of problems across robotics, such as Structure from Motion (SfM), Simultaneous Localization and Mapping (SLAM) and calibration. Typically, at their core, they have an optimization problem whose terms only depend on a small subset of variables. Factor graph solvers exploit the locality of problems to drastically reduce the computational time of the Iterative Least-Squares (ILS) methodology. Although extremely powerful, their application is usually limited to unconstrained problems. In this letter, we model constraints over variables within factor graphs by introducing a factor graph version of the Augmented Lagrangian (AL) method. We show the potential of our method by presenting a full navigation stack based on factor graphs. Differently from standard navigation stacks, we can model both optimal control for local planning and localization with factor graphs, and solve the two problems using the standard ILS methodology.We validate our approach in real-world autonomous navigation scenarios, comparing it with the de facto standard navigation stack implemented in ROS. Comparative experiments show that for the application at hand our system outperforms the standard nonlinear programming solver Interior-Point Optimizer (IPOPT) in runtime, while achieving similar solutions

Exploring miRNAs as Modulators in Retinal Degeneration: Potential Therapeutic Tools for Inherited Retinal Dystrophies

Inherited retinal dystrophies (IRDs) are a large group of genetic diseases that lead to retinal degeneration and represent a major cause of vision impairment or blindness. The high genetic heterogeneity of IRDs hinders a broad application of gene-specific therapies. There is an unmet need for therapies that can target common pathological mechanisms, regardless of the genetic cause. MicroRNAs (miRNAs) are important players in retinal biology and my group has demonstrated that miR-204 has a pathogenic role in human IRDs. Due to their pleiotropic actions, miRNAs represent promising therapeutic tools. On this basis, I hypothesized that the modulation of miR-204 levels, as well as other miRNAs, could represent a valid approach to tackle retinal degeneration. The first part of my thesis elucidates the potential of miR-204 administration as a therapeutic approach for IRDs. I found that administration of miR-204 by an adeno-associated viral (AAV) vector at patient-relevant stages of disease progression led to a long-term preservation of retinal function in a mouse model for a dominant form of Retinitis Pigmentosa (RP). Interestingly, transcriptome analysis revealed that miR-204 effect is mediated by dampening pathological processes shared by different IRDs (e.g. innate immune response). The second part of my work was focused on identifying additional miRNAs that can exert a protective action in IRDs in a gene-independent manner. Using a High Content Screening (HCS) approach I tested 560 miRNAs in a model of oxidative stress-induced retinal degeneration, the light-damage in cone-like cells (661W). As a result, I found that miR-429 significantly preserved 661W viability during photo-stress. Further analysis revealed that miR-429 overexpression increases the activated form of the pro-survival AMP-activated protein kinase (AMPK), recently demonstrated to be protective during retinal degeneration. Overall, the results of this thesis indicate that modulation of miRNAs can be a promising approach to develop mutation-independent treatments for IRDs

Determination of mercury in glass: A new analytical need in the evaluation of the packaging waste quality

In December 1989 the "Coalition of Northeastern Governors" of the United States of America developed a new toxics legislation with the aim to prohibit the intentional addition of lead, cadmium, chromium(VI), and mercury to packaging and packaging materials, fixing as a sum of these elements a scale of tolerable limit values, up to a final target of 100 ppm to be finally reached in 1994. Testing methods have not been indicated, but it is a fact that analytical procedures for the determination of mercury in glass are not often reported in literature. In this paper a closed-system decomposition procedure is proposed and mercury analyzed by cold vapour atomic absorption spectrometry. Employing this procedure has given excellent results allowing the total recovery of mercury contained in the glass decomposition solutions. Tests of the analytical accuracy have also been performed on a soda-lime-silica glass to which traces of mercury have been incorporated by experimental melting. As final point the possible presence of mercury traces in glass packaging containers of different production, type and colour was investigated, but all tests were negative

MD-SLAM: Multi-cue Direct SLAM

Simultaneous Localization and Mapping (SLAM) systems are fundamental building blocks for any autonomous robot navigating in unknown environments. The SLAM implementation heavily depends on the sensor modality employed on the mobile platform. For this reason, assumptions on the scene's structure are often made to maximize estimation accuracy. This paper presents a novel direct 3D SLAM pipeline that works independently for RGB-D and LiDAR sensors. Building upon prior work on multi-cue photometric frame-to-frame alignment [4], our proposed approach provides an easy-to-extend and generic SLAM system. Our pipeline requires only minor adaptations within the projection model to handle different sensor modalities. We couple a position tracking system with an appearance-based relocalization mechanism that handles large loop closures. Loop closures are validated by the same direct registration algorithm used for odometry estimation. We present comparative experiments with state-of-the-art approaches on publicly available benchmarks using RGB-D cameras and 3D LiDARs. Our system performs well in heterogeneous datasets compared to other sensor-specific methods while making no assumptions about the environment. Finally, we release an open-source C++ implementation of our system

Long-Term Localization using Semantic Cues in Floor Plan Maps

Lifelong localization in a given map is an essential capability for autonomous service robots. In this paper, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost and time of deployment. Furthermore, their detailed nature requires that they are updated when significant changes occur. We address the difficulty of localization when the correspondence between the map and the observations is low due to the sparsity of the CAD map and the changing environment. To overcome both challenges, we propose to exploit semantic cues that are commonly present in human-oriented spaces. These semantic cues can be detected using RGB cameras by utilizing object detection, and are matched against an easy-to-update, abstract semantic map. The semantic information is integrated into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. We provide a long-term localization solution and a semantic map format, for environments that undergo changes to their interior structure and detailed geometric maps are not available. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment, taken weeks apart. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We released the open source implementation of our approach written in C++ together with a ROS wrapper.Comment: Under review for RA-

Delayed diagnosis of extrapulmonary tuberculosis in a 32-year-old man with knee pain

A 32-year-old Bangladeshi male was admitted at our emergency department for trauma of the left knee. The radiographs showed absence of fracture, and presence of an indeterminate oval lucency in the proximal tibia. Further examinations were suggested, but the patient refused. 6 months later, the patient re-presented at our emergency department. A CT scan showed progression of musculoskeletal involvement and spread to the liver. This case underlines the importance of considering tuberculosis in the differential diagnosis of indeterminate bone lesions in immigrant patients

KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way

Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removing a majority of parts and focusing on the core elements, we obtain a surprisingly effective system that is simple to realize and can operate under various environmental conditions using different LiDAR sensors. Our odometry estimation approach relies on point-to-point ICP combined with adaptive thresholding for correspondence matching, a robust kernel, a simple but widely applicable motion compensation approach, and a point cloud subsampling strategy. This yields a system with only a few parameters that in most cases do not even have to be tuned to a specific LiDAR sensor. Our system using the same parameters performs on par with state-of-the-art methods under various operating conditions using different platforms: automotive platforms, UAV-based operation, vehicles like segways, or handheld LiDARs. We do not require integrating IMU information and solely rely on 3D point cloud data obtained from a wide range of 3D LiDAR sensors, thus, enabling a broad spectrum of different applications and operating conditions. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.Comment: 8 page

LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters

Odometry estimation is crucial for every autonomous system requiring navigation in an unknown environment. In modern mobile robots, 3D LiDAR-inertial systems are often used for this task. By fusing LiDAR scans and IMU measurements, these systems can reduce the accumulated drift caused by sequentially registering individual LiDAR scans and provide a robust pose estimate. Although effective, LiDAR-inertial odometry systems require proper parameter tuning to be deployed. In this paper, we propose LIO-EKF, a tightly-coupled LiDAR-inertial odometry system based on point-to-point registration and the classical extended Kalman filter scheme. We propose an adaptive data association that considers the relative pose uncertainty, the map discretization errors, and the LiDAR noise. In this way, we can substantially reduce the parameters to tune for a given type of environment. The experimental evaluation suggests that the proposed system performs on par with the state-of-the-art LiDAR-inertial odometry pipelines but is significantly faster in computing the odometry. The source code of our implementation is publicly available (https://github.com/YibinWu/LIO-EKF).Comment: 7 pages, 2 figure