The Greenest Hungarian University for the Greenest Hungarian City – the University of Pécs in the light of sustainability

Five years ago, the University of Pécs (UP) stated in its Green University Program mission, that every useful practice, innovation, management in the environment tend to help sustainability and sustainable administration has to be encouraged, with the aid of education and science aiming to strengthen ecological consciousness.In 2020 the University of Pécs participated for the fifth time in the international “UI GreenMetric World University Rankings”. Thanks to its conscious, disciplined environmental ambitions and related efforts, UP has been able to move forward year after year in ever-expanding rankings. 2020 UP made it to the 59th place worldwide and reaching the 1st place in Hungary, earning the Greenest Hungarian University title. The university started to build a network both on the national and international level. The University of Pécs, together with the University of Szeged, plays the role of national coordinator in the UI GreenMetric World University Rankings Network.The Green University Program is playing a more and more important role in the university life and helps to further develop the visibility of the UP brand both nationally and internationally contexts. Complex sustainability efforts provide an opportunity to address responses to challenges holistically. UP is involved in a harmonic and sustainable relationship with the environment, balancing the university and environmental interests. Besides the traditional roles of teaching and research UP is focusing on its third mission together with its stakeholders.UP as a green university is a catalyst of innovation aiming at reaching sustainable development goals. “Green thinking” is present in all aspects of life, so it is important to exploit the synergies that reinforce and shape the thinking of individuals to make “green acting” part of their everyday lives. The existence and life quality of future generations largely depend on our success in transforming our views on the world, environment, and attitude towards life. Are we ready to change our habits and save the environmental resources, to protect our environment and maintain biodiversity, to live in a sustainable way? The University of Pécs is a leader in Hungary in this mission. Due to its collaborative work together with its partners Pécs won the Greenest City of Hungary title in 2020. Keyword: sustainability, eco awareness, partnering, innovation, green university, responsibility, third mission, University of Pécs (Hungary

Implicit 3D Orientation Learning for 6D Object Detection from RGB Images

We propose a real-time RGB-based pipeline for object detection and 6D pose estimation. Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization. This so-called Augmented Autoencoder has several advantages over existing methods: It does not require real, pose-annotated training data, generalizes to various test sensors and inherently handles object and view symmetries. Instead of learning an explicit mapping from input images to object poses, it provides an implicit representation of object orientations defined by samples in a latent space. Our pipeline achieves state-of-the-art performance on the T-LESS dataset both in the RGB and RGB-D domain. We also evaluate on the LineMOD dataset where we can compete with other synthetically trained approaches. We further increase performance by correcting 3D orientation estimates to account for perspective errors when the object deviates from the image center and show extended results.Comment: Code available at: https://github.com/DLR-RM/AugmentedAutoencode

Rapid Induction of P/C-type Inactivation Is the Mechanism for Acid-induced K+ Current Inhibition

Extracellular acidification is known to decrease the conductance of many voltage-gated potassium channels. In the present study, we investigated the mechanism of H+o-induced current inhibition by taking advantage of Na+ permeation through inactivated channels. In hKv1.5, H+o inhibited open-state Na+ current with a similar potency to K+ current, but had little effect on the amplitude of inactivated-state Na+ current. In support of inactivation as the mechanism for the current reduction, Na+ current through noninactivating hKv1.5-R487V channels was not affected by [H+o]. At pH 6.4, channels were maximally inactivated as soon as sufficient time was given to allow activation, which suggested two possibilities for the mechanism of action of H+o. These were that inactivation of channels in early closed states occurred while hyperpolarized during exposure to acid pH (closed-state inactivation) and/or inactivation from the open state was greatly accelerated at low pH. The absence of outward Na+ currents but the maintained presence of slow Na+ tail currents, combined with changes in the Na+ tail current time course at pH 6.4, led us to favor the hypothesis that a reduction in the activation energy for the inactivation transition from the open state underlies the inhibition of hKv1.5 Na+ current at low pH

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

Performance Evaluation of Whole Body Counting Facilities in the Marshall Islands (2002-2005)

The United States Department of Energy (U.S. DOE) has recently implemented a series of strategic initiatives to address long-term radiological surveillance needs at former U.S. nuclear test sites in the Marshall Islands (https://eed.llnl.gov/mi/). Local atoll governments have been actively engaged in developing shared responsibilities for protecting the health and safety of resettled and resettling population at risk from exposure to elevated levels of residual fallout contamination in the environment. Under the program, whole body counting facilities have been established at three locations in the Marshall Islands. These facilities are operated and maintained by Marshallese technicians with scientists from the Lawrence Livermore National Laboratory (LLNL) providing technical support services including data quality assurance and performance testing. We have also established a mirror whole body counting facility at the Lawrence Livermore National Laboratory as a technician training center. The LLNL facility also allows program managers to develop quality assurance and operational procedures, and test equipment and corrective actions prior to deployment at remote stations in the Marshall Islands. This document summarizes the results of external performance evaluation exercises conducted at each of the facilities (2002-2005) under the umbrella of the Oak Ridge National Laboratory Intercomparison Studies Program (ISP). The ISP was specifically designed to meet intercomparison requirements of the United States (U.S.) Department of Energy Laboratory Accreditation Program (DOELAP). In this way, the Marshall Islands Radiological Surveillance Program has attempted to establish quality assurance measures in whole body counting that are consistent with standard requirements used to monitor DOE workers in the United States. Based on ANSI N13.30, the acceptable performance criteria for relative measurement bias and precision for radiobioassay service laboratory quality control, performance evaluation, and accreditation is -25% to +50% and less than or equal to 40%, respectively

Uptake of 137Cs by Leafy Vegetables and Grains from Calcareous Soils

Cesium-137 was deposited on Bikini Island at Bikini Atoll in 1954 as a result of nuclear testing and has been transported and cycled in the ecosystem ever since. Atoll soils are of marine origin and are almost pure CaCO{sub 3} with high concentrations of organic matter in the top 40 cm. Data from previous experiments with mature fruit trees show very high transfer factors (TF's), [Bq g{sup -1} plant/ Bq g{sup -1} soil, both in dry weight] into fruits from atoll calcareous soil. These TF's are much higher than reported for continental, silica-based soils. In this report TF's for 5 types of leafy vegetable crops and 2 types of grain crops are provided for use in predictive dose assessments and for comparison with other data from other investigators working with other types of soil in the IAEA CRP ''The Classification of Soil Systems on the Basis of Transfer Factors of Radionuclides from Soil to Reference Plants''. Transfer factors for plants grown on calcareous soil are again very high relative to clay-containing soils and range from 23 to 39 for grain crops and 21 to 113 for leafy vegetables. Results from these experiments, in this unique, high pH, high organic content, low potassium (K) soil, provide a boundary condition for models relating soil properties to TF

In vitro metabolic fate of nine LSD-based new psychoactive substances and their analytical detectability in different urinary screening procedures

The market of new psychoactive substances (NPS) is characterized by a high turnover and thus provides several challenges for analytical toxicology. The analysis of urine samples often requires detailed knowledge about metabolism given that parent compounds may either be present only in small amounts or may not even be excreted. Hence, knowledge of the metabolism of NPS is a prerequisite for the development of reliable analytical methods. The main aim of this work was to elucidate for the first time the pooled human liver S9 fraction metabolism of the nine d-lysergic acid diethylamide (LSD) derivatives 1-acetyl-LSD (ALD-52), 1-propionyl-LSD (1P-LSD), 1-butyryl-LSD (1B-LSD), N6-ethyl-nor-LSD (ETH-LAD), 1-propionyl-N6-ethyl-nor-LSD (1P-ETH-LAD), N6-allyl-nor-LSD (AL-LAD), N-ethyl-N-cyclopropyl lysergamide (ECPLA), (2’S,4’S)-lysergic acid 2,4-dimethylazetidide (LSZ), and lysergic acid morpholide (LSM-775) by means of liquid chromatography coupled to high resolution tandem mass spectrometry. Identification of the monooxygenase enzymes involved in the initial metabolic steps was performed using recombinant human enzymes and their contribution confirmed by inhibition experiments. Overall, N-dealkylation, hydroxylation, as well as combinations of these steps predominantly catalyzed by CYP1A2 and CYP3A4 were found. For ALD-52, 1P-LSD, and 1B-LSD deacylation to LSD was observed. The obtained mass spectral data of all metabolites is essential for reliable analytical detection particularly in urinalysis and for differentiation of the LSD-like compounds as biotransformations also led to structurally identical metabolites. However, in urine of rats after the administration of expected recreational doses and using standard urine screening approaches, parent drugs or metabolites could not be detected

An improved pipeline to search for gravitational waves from compact binary coalescence

The second generation of ground-based gravitational-wave detectors will begin taking data in September 2015. Sensitive and computationally-efficient data analysis methods will be required to maximize what we learn from their observations. We describe improvements made to the offline analysis pipeline searching for gravitational waves from stellar-mass compact binary coalescences, and assess how these improvements affect search sensitivity. Starting with the two-stage ihope pipeline used in S5, S6 and VSR1-3 and using two weeks of S6/VSR3 data as test periods, we first demonstrate a pipeline with a simpler workflow. This single-stage pipeline performs matched filtering and coincidence testing only once. This simplification allows us to reach much lower false-alarm rates for loud candidate events. We then describe an optimized chi-squared test which minimizes computational cost. Next, we compare methods of generating template banks, demonstrating that a fixed bank may be used for extended stretches of time. Fixing the bank reduces the cost and complexity, compared to the previous method of regenerating a template bank every 2048 s of analyzed data. Creating a fixed bank shared by all detectors also allows us to apply a more stringent coincidence test, whose performance we quantify. With these improvements, we find a 10% increase in sensitive volume with a negligible change in computational cost