Adaption algorithms for mobile traffic offloading

Der durch Smartphones generierte Datenverkehr in Mobilfunknetzen steigt seit Jahren unablässig an. Dazu trägt sowohl die steigende Verbreitung der Smartphones, als auch die immer größer werdenden Bandbreitenanforderungen z.B. durch Audio- oder Videostreaming bei. Um den Zuwachs an nötiger Bandbreite zu reduzieren, soll entstehender Traffic, von dem Mobilfunknetz auf lokale WLAN Kommunikation verschoben werden. Eine besondere Art dieser Auslagerung bezeichnet man als "Opportunistic Traffic Offloading". Dabei tauschen benachbarte Smartphones beispielsweise per WLAN Daten aus, wenn sie sich begegnen. Im Laufe dieser Arbeit werden Algorithmen vorgestellt, die anhand der Positionsdaten von Smartphones besonders günstige Ziele, das heißt Smartphones mit einer hohen Anzahl an Begegnungen, bestimmen. Diesen Zielen kann per Mobilfunk die entsprechende Nachricht übermittelt werden, woraufhin sie selbstständig die Nachricht per WLAN verbreiten. Eine hohe Zahl an Begegnungen fördert dabei die schnelle Verbreitung von Nachrichten, da nach jeder Begegnung ein weiteres Smartphone in der Lage ist die Nachricht weiter zu verteilen. Weiterhin wird gezeigt, wie mit dem periodischen Versenden einer Nachricht per Mobilfunk und dem Anwenden der beschriebenen Algorithmen, im Vergleich zum reinen Versenden per Mobilfunk, Traffic-Einsparungen von durchschnittlich 40 bis 50% möglich sind

Resisting student labeling in this era of testing

We examined perspectives that prospective elementary teachers (PTs) noticed their mentor teachers held with regards to classifying students by achievement or behavior. Many PTs noticed deficit mathematical discourse and expressed concern about how that could lead students to develop negative views of themselves

How Colonization Impacts Identity Through the Generations: A Closer Look at Historical Trauma and Education

Through the lived experiences of 26 New Mexicans, this inquiry investigates how colonization impacts identity through the generations, particularly looking at historical trauma and education. The findings demonstrate the importance of decolonizing spaces within educational settings. Furthermore, the findings demonstrate the need for viewing educational systems and spaces through a Tribal Critical Race Theory (TribalCrit) and Indigenous Storywork lens that lead Indigenous students in finding face (one’s identity), finding foundation (one’s greatest potential/full expression) and finding heart (one’s flow with the creator). The lens of TribalCrit enables the creation of a space where Indigenous students are empowered by their educational institutions in exploring their cultural and academic identity. That being said, this dissertation needs to engage in aspects of decolonization. In the conceptualization and implementation of the decolonizing approach, an Indigenous and Western academic knowledge was sought and found in the following devices: Indigenous storywork protocol, conversation as method for data collection, data analysis in the usage of boxed writing, and arts-based research in data presentation. By merging Indigenous knowledge with Western concepts, I’m recognizing that a dissertation cannot be fully decolonized, but an attempt should still be made. Thus, this dissertation attempts to move towards decolonization in several ways. First, I utilized Jo-Ann Archibald’s (2008) seven Indigenous Storywork principles. Her seven principles permeated everything I did from beginning to end. The research questions are as follows: Overarching research questions How do the identities of one Indigenous family inform our understanding of colonization? The following sub questions asked: How is knowledge generated within a family context? How does education impact Indigenous self-identity and the identity of family? What are the contributions of Indigenous scholars on curriculum studies? Next, I used conversation as method as described by Kovach (2009), a research method grounded in Indigenous ideas derived from an Aboriginal writer. Conversation as method comprises my data collection procedures. Third, I used several different writers’ ideas as conceptual frameworks to analyze my data. Brayboy’s (2005) Tribal Critical Race Theory was especially important. Finally, I incorporated arts-based research and digital storytelling for my data presentation

Early Childhood Infection of Kaposi’s Sarcoma-associated Herpesvirus in Zambian Households: a Molecular Analysis

Sub-Saharan Africa is endemic for Kaposi’s sarcoma-associated herpesvirus (KSHV) and there is a high rate of early childhood infection; however, the transmission sources are not well characterized. We examined household members as potential KSHV transmission sources to young children in the KSHV-endemic country of Zambia. To this end, we enrolled and followed Zambian households with at least one KSHV-seropositive child and collected longitudinal buccal swab samples. KSHV burden was evaluated and K1 sequences from the children were determined and analyzed for differences to K1 sequences from household members. The K1 sequences were also analyzed for evolution over time. We generated K1 sequences from 31 individuals across 16 households. Nine households contained multiple KSHV-positive members, including at least one child. In 6 of 9 households, the child had 100% sequence identity to all household members. However, in two households the child and mother had distinct K1 sequences. In the remaining household, the children were the only KSHV-infected individuals. Furthermore, we report that 1 of 18 individuals had K1 sequence variation within the timespan analyzed. In the present study, we provide evidence that (1) early childhood KSHV transmission occurs from both within and outside the household, (2) intra-household transmission can occur via non-maternal sources, (3) viral shedding in the buccal cavity is highly variable, and (4) the dominant K1 sequence within an individual did not rapidly evolve over time. These results are important for developing KSHV intervention strategies

Seroprevalence of SARS-CoV-2 among Blood Donors and Changes after Introduction of Public Health and Social Measures, London, UK

We describe results of testing blood donors in London, UK, for severe acute respiratory disease coronavirus 2 (SARS-CoV-2) IgG before and after lockdown measures. Anonymized samples from donors 17–69 years of age were tested using 3 assays: Euroimmun IgG, Abbott IgG, and an immunoglobulin receptor-binding domain assay developed by Public Health England. Seroprevalence increased from 3.0% prelockdown (week 13, beginning March 23, 2020) to 10.4% during lockdown (weeks 15–16) and 12.3% postlockdown (week 18) by the Abbott assay. Estimates were 2.9% prelockdown, 9.9% during lockdown, and 13.0% postlockdown by the Euroimmun assay and 3.5% prelockdown, 11.8% during lockdown, and 14.1% postlockdown by the receptor-binding domain assay. By early May 2020, nearly 1 in 7 donors had evidence of past SARS-CoV-2 infection. Combining results from the Abbott and Euroimmun assays increased seroprevalence by 1.6%, 2.3%, and 0.6% at the 3 timepoints compared with Euroimmun alone, demonstrating the value of using multiple assays

Species‐level image classification with convolutional neural network enables insect identification from habitus images

1. Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground-dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity. 2. We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine-tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade-off between classification accuracy, precision, and recall and taxonomic resolution. 3. The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%. Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species. 4. Fine-tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability. This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species. 5. Together, species-level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. The attached file is the published pdf