Classification of Micromobility Vehicles in Thermal-Infrared Images Based on Combined Image and Contour Features Using Neuromorphic Processing

Trends of environmental awareness, combined with a focus on personal fitness and health, motivate many people to switch from cars and public transport to micromobility solutions, namely bicycles, electric bicycles, cargo bikes, or scooters. To accommodate urban planning for these changes, cities and communities need to know how many micromobility vehicles are on the road. In a previous work, we proposed a concept for a compact, mobile, and energy-efficient system to classify and count micromobility vehicles utilizing uncooled long-wave infrared (LWIR) image sensors and a neuromorphic co-processor. In this work, we elaborate on this concept by focusing on the feature extraction process with the goal to increase the classification accuracy. We demonstrate that even with a reduced feature list compared with our early concept, we manage to increase the detection precision to more than 90%. This is achieved by reducing the images of 160 × 120 pixels to only 12 × 18 pixels and combining them with contour moments to a feature vector of only 247 bytes

HOW AGILE IS YOUR IT DEPARTMENT? – DEVELOPMENT AND APPLICATION OF AN FRAMEWORK-INDEPENDENT AGILE SCALING MATURITY MODEL

Many IT departments seek to capitalize on the benefits of agile development by scaling agile practices. To manage the complex scaling, established approaches and frameworks promise guidance. However, although existing works envision a clear target state, they lack relevant capabilities along the scaling process, especially for vertical agile scaling. Managers need these capabilities to assess their company’s status quo and develop a clear scaling roadmap. Thus, within this work, we use the Design Science Research paradigm to build and evaluate a framework-independent agile scaling maturity model that provides management with a tool for ex-ante identification and evaluation of agile scaling capabilities in five maturity stages. To evaluate our model, we applied it at KUKA IT, the IT department of an international provider of automation solutions. As a result, this work provides insights into the application and outlines how IT departments can operationalize and utilize our model to guide agile scaling

Long-term spectroscopic monitoring of the Luminous Blue Variable HD160529

We have spectroscopically monitored the galactic Luminous Blue Variable HD 160529 and obtained an extensive high-resolution data set that covers the years 1991 to 2002. During this period, the star evolved from an extended photometric minimum phase towards a new visual maximum. In several observing seasons, we covered up to four months with almost daily spectra. Our spectra typically cover most of the visual spectral range with a high spectral resolution (about 20,000 or more). This allows us to investigate the variability in many lines and on many time scales from days to years. We find a correlation between the photospheric HeI lines and the brightness of the star, both on a time scale of months and on a time scale of years. The short-term variations are smaller and do not follow the long-term trend, strongly suggesting different physical mechanisms. Metal lines also show both short-term and long-term variations in strength and also a long-term trend in radial velocity. Most of the line-profile variations can be attributed to changing strengths of lines. Propagating features in the line profiles are rarely observed. We find that the mass-loss rate of HD 160529 is almost independent of temperature, i.e. visual brightness.Comment: 14 pages, 15 figures, accepted for publication in Astronomy & Astrophysic

Long-Term Stability of Polymer-Coated Surface Transverse Wave Sensors for the Detection of Organic Solvent Vapors

Arrays with polymer-coated acoustic sensors, such as surface acoustic wave (SAW) and surface transverse wave (STW) sensors, have successfully been applied for a variety of gas sensing applications. However, the stability of the sensors’ polymer coatings over a longer period of use has hardly been investigated. We used an array of eight STW resonator sensors coated with different polymers. This sensor array was used at semi-annual intervals for a three-year period to detect organic solvent vapors of three different chemical classes: a halogenated hydrocarbon (chloroform), an aliphatic hydrocarbon (octane), and an aromatic hydrocarbon (xylene). The sensor signals were evaluated with regard to absolute signal shifts and normalized signal shifts leading to signal patterns characteristic of the respective solvent vapors. No significant time-related changes of sensor signals or signal patterns were observed, i.e., the polymer coatings kept their performance during the course of the study. Therefore, the polymer-coated STW sensors proved to be robust devices which can be used for detecting organic solvent vapors both qualitatively and quantitatively for several years

Toward Understanding Massive Star Formation

Although fundamental for astrophysics, the processes that produce massive stars are not well understood. Large distances, high extinction, and short timescales of critical evolutionary phases make observations of these processes challenging. Lacking good observational guidance, theoretical models have remained controversial. This review offers a basic description of the collapse of a massive molecular core and a critical discussion of the three competing concepts of massive star formation: - monolithic collapse in isolated cores - competitive accretion in a protocluster environment - stellar collisions and mergers in very dense systems We also review the observed outflows, multiplicity, and clustering properties of massive stars, the upper initial mass function and the upper mass limit. We conclude that high-mass star formation is not merely a scaled-up version of low-mass star formation with higher accretion rates, but partly a mechanism of its own, primarily owing to the role of stellar mass and radiation pressure in controlling the dynamics.Comment: 139 pages, 18 figures, 5 tables, glossar

How diverse is your team? Investigating gender and nationality diversity in GitHub teams

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Background Building an effective team of developers is a complex task faced by both software companies and open source communities. The problem of forming a “dream” team involves many variables, including consideration of human factors and it is not a dilemma solvable in a mathematical way. Empirical studies might provide interesting insights to explain which factors need to be taken into account in building a team of developers and which levers act to optimise productivity among developers. Aim In this paper, we present the results of an empirical study aimed at investigating the link between team diversity (i.e., gender, nationality) and productivity (issue fixing time). Method We consider issues solved from the GHTorrent dataset inferring gender and nationality of each team’s members. We also evaluate the politeness of all comments involved in issue resolution. Results Results show that higher gender diversity is linked with a lower team average issue fixing time (higher productivity), that nationality diversity is linked with lower team politeness and that gender diversity is linked with higher sentiment.Peer reviewedFinal Published versio

A novel AKT3 mutation in melanoma tumours and cell lines

Recently, a rare activating mutation of AKT1 (E17K) has been reported in breast, ovarian, and colorectal cancers. However, analogous activating mutations in AKT2 or AKT3 have not been identified in any cancer lineage. To determine the prevalence of AKT E17K mutations in melanoma, the most aggressive form of skin cancer, we analysed 137 human melanoma specimens and 65 human melanoma cell lines for the previously described activating mutation of AKT1, and for analogous mutations in AKT2 and AKT3. We identified a single AKT1 E17K mutation. Remarkably, a previously unidentified AKT3 E17K mutation was detected in two melanomas (from one patient) as well as two cell lines. The AKT3 E17K mutation results in activation of AKT when expressed in human melanoma cells. This represents the first report of AKT mutations in melanoma, and the initial identification of an AKT3 mutation in any human cancer lineage. We have also identified the first known human cell lines with naturally occurring AKT E17K mutations