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20 Years of RoboCup
(2016)
Contemporary research appreciates a diverse workforce as a potential source of innovation. Researchers explore the fine details of why diversity management is central for generating innovations in heterogeneous research groups and how it could be effectively implemented into organizations. Complex research associations that discuss topics with a high impact on society increasingly address the necessity of establishing a diverse workforce to confront the challenges of tomorrow. Characterized by complex management structures as well as hierarchies, research associations have not been a subject of investigation until now. For this reason, the presented research project aims to develop a diversity and innovation management strategy with the ultimate goal of inducing change in the corporate culture. The proposed approach consisted of six phases; the first two phases investigated the status quo of diversity in the existing organizational structures of member institutes and the variety of particular working cultures within the research association. The third and the fourth phases utilized qualitative and quantitative studies. The third phase focused on the connection of management level to diversity and innovation, and the need for diversity and innovation management, and tailor-made methods of implementing them. The first three phases have been accomplished successfully; preliminary results are already available. The fourth phase will mainly focus on exploring the mind-set of the employees. The fifth phase will consolidate the findings in the first four phases into an implementable strategy. The final phase will address the implementation of this strategy into the organization. Phases 4 to 6 have not yet been undertaken
This work presents the Multi-Bees-Tracker (MBT3D) algorithm, a Python framework implementing a deep association tracker for Tracking-By-Detection, to address the challenging task of tracking flight paths of bumblebees in a social group. While tracking algorithms for bumblebees exist, they often come with intensive restrictions, such as the need for sufficient lighting, high contrast between the animal and background, absence of occlusion, significant user input, etc. Tracking flight paths of bumblebees in a social group is challenging. They suddenly adjust movements and change their appearance during different wing beat states while exhibiting significant similarities in their individual appearance. The MBT3D tracker, developed in this research, is an adaptation of an existing ant tracking algorithm for bumblebee tracking. It incorporates an offline trained appearance descriptor along with a Kalman Filter for appearance and motion matching. Different detector architectures for upstream detections (You Only Look Once (YOLOv5), Faster Region Proposal Convolutional Neural Network (Faster R-CNN), and RetinaNet) are investigated in a comparative study to optimize performance. The detection models were trained on a dataset containing 11359 labeled bumblebee images. YOLOv5 reaches an Average Precision of AP = 53, 8%, Faster R-CNN achieves AP = 45, 3% and RetinaNet AP = 38, 4% on the bumblebee validation dataset, which consists of 1323 labeled bumblebee images. The tracker’s appearance model is trained on 144 samples. The tracker (with Faster R-CNN detections) reaches a Multiple Object Tracking Accuracy MOTA = 93, 5% and a Multiple Object Tracking Precision MOTP = 75, 6% on a validation dataset containing 2000 images, competing with state-of-the-art computer vision methods. The framework allows reliable tracking of different bumblebees in the same video stream with rarely occurring identity switches (IDS). MBT3D has much lower IDS than other commonly used algorithms, with one of the lowest false positive rates, competing with state-of-the-art animal tracking algorithms. The developed framework reconstructs the 3-dimensional (3D) flight paths of the bumblebees by triangulation. It also handles and compares two alternative stereo camera pairs if desired.
Retinal endothelial function in cardiovascular risk patients: A randomized controlled exercise trial
(2020)
The aim of this study was to investigate, for the first time, the effects of high-intensity interval training (HIIT) on retinal microvascular endothelial function in cardiovascular (CV) risk patients. In the randomized controlled trial, middle-aged and previously sedentary patients with increased CV risk (aged 58 ± 6 years) with ≥ two CV risk factors were randomized into a 12-week HIIT (n = 33) or control group (CG, n = 36) with standard physical activity recommendations. A blinded examiner measured retinal endothelial function by flicker light-induced maximal arteriolar (ADmax) and venular (VDmax) dilatation as well as the area under the arteriolar (AFarea) and venular (VFarea) flicker curve using a retinal vessel analyzer. Standardized assessments of CV risk factors, cardiorespiratory fitness, and retinal endothelial function were performed before and after HIIT. HIIT reduced body mass index, fat mass, and low-density lipoprotein and increased muscle mass and peak oxygen uptake (VO2peak). Both ADmax (pre: 2.7 ± 2.1%, post: 3.0 ± 2.2%, P = .018) and AFarea (pre: 32.6 ± 28.4%*s, post: 37.7 ± 30.6%*s, P = .016) increased after HIIT compared with CG (ADmax, pre: 3.2 ± 1.8%, post: 2.9 ± 1.8%, P = .254; AFarea, pre: 41.6 ± 28.5%*s, post: 37.8 ± 27.0%*s, P = .186). Venular function remained unchanged after HIIT. There was a significant association between ∆-change VO2peak and ∆-changes ADmax and AFarea (P = .026, R² = 0.073; P = .019, R² = 0.081, respectively). 12-weeks of HIIT improved retinal endothelial function in middle-aged patients with increased CV risk independent of the reduction in classical CV risk factors. Exercise has the potential to reverse or at least postpone progression of small vessel disease in older adults with increased CV risk under standard medication. Dynamic retinal vessel analysis seems to be a sensitive tool to detect treatment effects of exercise interventions on retinal microvascular endothelial function in middle-aged individuals with increased CV risk.
Plant growth and transport processes are highly dynamic. They are characterized by plant-internal control processes and by strong interactions with the spatially and temporally varying environment. Analysis of structure- function relations of growth and transport in plants will strongly benefit from the development of non-invasive techniques. PlanTIS (Plant Tomographic Imaging System) is designed for non-destructive 3D-imaging of positron emitting radiotracers. It will permit functional analysis of the dynamics of carbon distribution in plants including bulky organs. It will be applicable for screening transport properties of plants to evaluate e.g. temperature adaptation of genetically modified plants. PlanTIS is a PET scanner dedicated to monitor the dynamics of the 11C distribution within a plant while or after assimilation of 11CO2. Front end electronics and data acquisition architecture of the scanner are based on the ClearPETTM system [1]. Four detector modules form one of two opposing detector blocks. Optionally, a hardware coincidence detection between the blocks can be applied. In general the scan duration is rather long (~ 1 hour) compared to the decay time of 11C (20 min). As a result the count rates can vary over a wide range and accurate dead time correction is necessary.
Within the Crystal Clear Collaboration a modular system for a small animal PET scanner (ClearPET™) has been developed. The modularity allows the assembly of scanners of different sizes and characteristics in order to fit the specific needs of the individual member institutions. Now a first demonstrator is being completed in Julich. The system performs depth of interaction detection by using a phoswich arrangement combining LSO and LuYAP scintillators which are coupled to multi-channel photomultipliers (PMTs). A free-running ADC digitizes the signal from the PMT and the complete scintillation pulses are sampled by an FPGA and sent with 20 MB/S to a PC for preprocessing. The pulse provides information about the gamma energy and the scintillator material which identifies the interaction layer. Furthermore, the exact pulse starting time is obtained from the sampled data. This is important as no hardware coincidence detection is implemented. All single events are recorded and coincidences are identified by software. An advantage of that is that the coincidence window and the dimensions of the field of view can be adjusted easily. The ClearPET™ demonstrator is equipped with 10240 crystals on 80 PMTs. This paper presents an overview of the data acquisition system.
The small animal PET scanners developed by the Crystal Clear Collaboration (ClearPETtrade) detect coincidences by analyzing timemarks which are attached to each event. The scanners are able to save complete single list mode data which allows analysis and modification of the timemarks after data acquisition. The timemarks are obtained from the digitally sampled detector pulses by calculating the baseline crossing of the rising edge of the pulse which is approximated as a straight line. But the limited sampling frequency causes a systematic error in the determination of the timemark. This error depends on the phase of the sampling clock at the time of the event. A statistical method that corrects these errors will be presented
The Crystal Clear Collaboration has developed a modular system for a small animal PET scanner (ClearPET). The modularity allows the assembly of scanners of different sizes and characteristics in order to satisfy the specific needs of the individual member institutions. The system performs depth of interaction detection by using a phoswich arrangement combining LSO and LuYAP scintillators which are coupled to Multichannel Photomultipliers (PMTs). For each PMT a free running 40 MHz ADC digitizes the signal and the complete scintillation pulse is sampled by an FPGA and sent with 20 MB/s to a PC for preprocessing. The pulse provides information about the gamma energy and the scintillator material which identifies the interaction layer. Furthermore, the exact pulse starting time is obtained from the sampled data. This is important as no hardware coincidence detection is implemented. All single events are recorded and coincidences are identified by software. The system in Jülich (ClearPET Neuro) is equipped with 10240 crystals on 80 PMTs. The paper will present an overview of the data acquisition system.
Pulse shape discrimination of LSO and LuYAP scintillators for depth of interaction detection in PET
(2003)
A feasible way to gain the depth of interaction information in a PET scanner is the use of phoswich detectors. In general the layer of interaction is identified front the pulse shape of the corresponding scintillator material. In this work pulses from LSO and LuYAP crystals were investigated in order to find a practical method of distinguishing. It turned out that such a pulse processing could he kept simple due to an additional slow component in the light decay of the LuYAP pulse. At the same time the short decay time guarantees that the major amount of the light output is still collected within a short pulse recording time.