Miracle-dz/UniFAR-FTUs huggingface dataset:
OTANTİK DEĞERLENDİRME YAKLAŞIMLARININ ORTAÖĞRETİM ÖĞRENCİLERİNİN GÖRSEL SANATLAR DERSİNE YÖNELİK TUTUMLARINA ETKİSİ

Bu arastirmanin amaci, ortaogretim gorsel sanatlar dersinde uygulanan otantik degerlendirme yaklasimlarinin ogrencilerin derse yonelik tutum ve bu surecin algilari uzerindeki etkisini arastirmaktir. Arastirma 2011-2012 ogretim yilinin bahar yariyilinda Erzurum Ili Yakutiye Ilcesi’ndeki Erzurum Lisesi’nde yurutulmustur. Arastirma deneysel yontemin on test-son test kontrol gruplu deseni kullanilmistir. Gruplar cinsiyet, ilkogretim mezuniyet puanlari, ortaogretime giris sinav puanlarina gore esitlenmistir. Arastirmada verilerin elde edilmesinde, gorsel sanatlar dersi tutum olcegi, otantik degerlendirme anketi, oz degerlendirme, grup degerlendirme formu ve otantik degerlendirme formlari kullanilmistir. Dersler deney grubunda otantik degerlendirme yaklasimlarina, kontrol gruplarinda ise geleneksel ogretime dayali olarak islenmistir. Arastirmada, gorsel sanatlar tutum puanlarinin ortalama ve standart sapmalari hesaplanmis ve sonuclar analizlerde kullanilmistir. Verilerin analizinde ortalama, standart sapma ve “t” testi kullanilmistir. Analizlerde anlamlilik duzeyi olarak p< . 05 olarak alinmistir. Arastirma verilerinin analizi SPSS istatistik programi kullanilarak gerceklestirilmistir. Deney ve kontrol gruplari yukarida belirtilen istendik degiskenler acisindan denklestirildigi icin, kovaryans analizi yapilmamistir. Nitel veriler ise, nitel veri analizi yonteminin icerik analizi teknigi ile degerlendirilmistir. Otantik degerlendirme yaklasimlarina dayali ogretim uygulanan deney grubu ile geleneksel ogretim uygulanan kontrol gruplarinin gorsel sanatlar dersi tutum olceginden aldiklari on test puanlari kontrol altina alindiginda, son test puanlari arasinda deney grubu lehine anlamli fark bulunmustur. Otantik degerlendirme yaklasimlarina dayali ogretim uygulanan deney grubu ogrencilerinin sureci degerlendirmeye yonelik formlara verdikleri yanitlar uzerinde yapilan icerik analizi sonuclari, deney grubu ogrencilerinin otantik degerlendirme surecini algilari uzerinde etkili oldugu sonucunu gostermistir.
Effect of Directional Spreading and Spectral Bandwidth on the Nonlinearity of the Irregular Waves

Wherever one stands, deep water, intermediate or shallow water, in extreme conditions the nonlinearity in the wave kinematics is large and has a strong influence on the design parameters. Simple models of the wave kinematics have been studied based on hypotheses of narrowband and unidirectionality. Obviously a real sea is neither narrowbanded nor unidirectional and the width of the spectral density and the directional spreading influence the nonlinear characteristics of the waves (skewness, asymmetries,.). A second order directional irregular wave model is used to simulate time series of the free surface elevation. Based on a large simulated data base, a parametric study of the influence of the spectral width and directional spreading is led on several wave characteristics sensitive to nonlinearities (skewness, wave and crest heights distributions, steepness of maximum crest). Three typical situations are analysed which correspond to extreme situations in long and short fetch wind sea. The validity of the simplified assumptions of narrowband and unidirectionality is then discussed.
Health-enhancing physical activity during leisure time among adults in Germany

Self-reported data from the GEDA 2014/2015-EHIS study was used to calculate the level of compliance among adults in Germany with the World Health Organization’s (WHO) recommendations on physical activity. The WHO’s recommendations distinguish between ‘aerobic activity’ and ‘muscle-strengthening activity’. In Germany, 42.6% of women and 48.0% of men reported that they conduct at least 2.5 hours of aerobic physical activity per week, and therefore meet the WHO’s recommendation on this form of activity. A higher level of education among women and men of all ages is associated with a higher frequency of meeting the WHO’s recommendations on aerobic activity. In addition, 27.6% of women and 31.2% of men conduct muscle-strengthening activity at least twice a week, thereby meeting the WHO’s recommendations on this form of activity. About one fifth of women (20.5%) and one quarter of men in Germany (24.7%) meet both of these recommendations. In summary, the results point to the value of encouraging people to conduct more physical activity during their leisure time. In fact, inactive people who begin to follow the WHO’s recommendations can significantly reduce their long-term risk of premature mortality.
Autonomous Exploration and Navigationwith the Pepper robot

Humanoid robots, such as Softbank’s Pepper, are mainly used as eye-catcher during events. Businesses would like to see such robots being used more functionally. They could be used as receptionist or guide. To accomplish these tasks, the robots need to navigate within an environment robustly. This thesis aims to determine whether Pepper, in combination of existing algorithms, is suitable to create maps, localize and navigate successfully. The research question is as follows: Is Pepper suitable for autonomous exploration, Simultatenous Localization And Mapping (SLAM) and navigation using Robot Operating System (ROS) without the usage of external sensors? This research question is answered by three experiments. The SLAM experiment has shown that Gmapping can use the 3D sensor to create accurate maps. The second experiment has shown that Pepper can have difficulty localizing itself, causing undesired navigational behaviour. This is caused by the narrow field of view and the relative low quality of the 3D sensor of Pepper. The last experiment has shown that, mainly due to the failure of localization, Pepper cannot autonomously and completely explore an unknown environment. The results of the experiments indicate that, using the algorithms as defined in this thesis, Pepper is not suitable for autonomous exploration, SLAM and navigation. Further steps could be undertaken to improve these results. Better sensor hardware or adaptations in the algorithms could make the difference in localization and make Pepper suitable for these tasks. Improving the maps manually or automatically, or using landmark detection are also interesting methods that could improve the behaviour of Pepper. v Acknowledgements I would like to thank Joost Bosman for the introduction of this subject and allowing me to work on it. I am also grateful for the help at ING from Guido van Bommel, Annika Schijf and Ralf Wolter, who made sure I had access to a Pepper robot and helped me within ING. Last but not the least I would like to thank my supervisor at Utrecht University Remco Veltkamp, who introduced me to this subject and guided me along the way.
Application of Computer Navigation-assisted Thoracoscopic Surgery in Resection of Rib Tumour

Objective: Excision of rib tumors by conventional surgical method often results in poor prognosis due to trauma and heavily bleeding. In the present study, we evaluated the advantages of applying computer navigation-assisted thoracoscope in the resection of rib tumors. Methods: Prior to surgery, the pathology bearing regions of rib tumors in 4 patients were scanned by computer tomography (CT) and the surgical margin was predetermined. During operation, the fusion image of 3-dimensional navigation scanning and CT clearly provided the guidance for the resection of rib tumor by minimally invasive thoracoscopy techniques. Results: Postoperative specimens demenstruated that a safe border was observed and the tumor specimen margin was negative based on histological study. Combination of computer navigation technique and thoracoscopy could assist surgeons to more accurately judge the tumor margin, implement minimal invasive resection, and minimize the trauma of patients. Conclusion: Based on our clinical experiences, the combined surgical method is especially feasible for the tiny tumor or some tumors that are unreachable or resided in scapula and deep face of breast.
Neue Trematoden aus Vietnamesischen Vögeln des Berliner Tierparks (Mit einer revision der familie Renicolidae)

Mit einem Tiertransport aus der Demokratischen Republik Vietnam waren am 14.10. 1961 unter anderem Nachtreiher (Nycticorax n. nycticorax), Schwarze Zwergscharben oder Mohrenscharben (Phalacrocorax niger), Dunkle Rallenreiher (Ardeola bacchus) und Seidenreiher (Egretta g. garzetta) direkt auf dem See- Wege uber Rostock nach Berlin gelangt. Zum Vorkommen dieser Vogel in Vietnam siehe FISCHER (1961). Einige Vogel gingen ein; sie wurden helminthologisch untersucht. Zur Dauer des Aufenthalts im Berliner Tierpark muss die Dauer der Reise (etwa 2 Monate) sowie ein mehr oder weniger langer Aufenthalt in einem Tierfanglager bei Hanoi hinzugezahlt werden, um die Differenz zwischen dem Zeitpunkt der Sektion und dem Datum des Fanges der Vogel in ihrem Heimatgebiet zu erhalten. Dieser Umstand ist besonders bei der Beurteilung des Metorchis- Fundes zu berucksichtigen. Fur die Ermoglichung und Forderung der Untersuchungen mochte ich den Herren Prof. Dr. H. DATHE, W. GRUMMT und W. FISCHER bestens danken.
Epidemiology of Diabetes and Prediabetes in Uzbekistan: Screening Results

Background: Actual prevalence of type 2 diabetes mellitus (DM) and prediabetes is essential for health care organization, but it significantly differs from data obtained through the registration by endocrinologists. Therefore, active screening for DM and prediabetes at least every 5 years will obtain data on prevalence of these disorders and will help in planning of Diabetes management in the country. Materials and methods: 711 men and 1464 women (2521 persons) aged 35 years or older living in Tashkent, Qashqadarja and Khorezm regions were screened for diabetes according to the two-stage strategy: filling in the FINDRISK-based questionnaire and oral glucose tolerance test. Results: Prevalence of type 2 diabetes was 7.9%, prevalence of IGT was 4.4% and prevalence of IFG was 1.4%. Actual prevalence of type 2 DM 10 times exceeds the data obtained through registration at endocrinological dispensaries. In 74% of patients with diabetes, disease was diagnosed during the screening. There is a tendency to increase in prevalence of all revealed carbohydrate metabolism disorders with age. Over 35% of the Republic population has obesity of various degree and about 35% are overweight. Among persons without any carbohydrate metabolism disorders only 31% had normal weight. Conclusion: So, in our study we obtained data on actual prevalence of type 2 DM, IGT and IFG in Uzbekistan depending on age, sex and urbanization. Data on prevalence of overweight and obesity are deplorable and testify the necessity of preventive work with population concerning healthy lifestyle starting at the early childhood.
Social Media Content About Children’s Pain and Sleep: Content and Network Analysis

Background Social media is often used for health communication and can facilitate fast information exchange. Despite its increasing use, little is known about child health information sharing and engagement over social media. Objective The primary objectives of this study are to systematically describe the content of social media posts about child pain and sleep and identify the level of research evidence in these posts. The secondary objective is to examine user engagement with information shared over social media. Methods Twitter, Instagram, and Facebook were searched by members of the research team over a 2-week period using a comprehensive search strategy. Codes were used to categorize the content of posts to identify the frequency of content categories shared over social media platforms. Posts were evaluated by content experts to determine the frequency of posts consistent with existing research evidence. User engagement was analyzed using Netlytic, a social network analysis program, to examine visual networks illustrating the level of user engagement. Results From the 2-week period, nearly 1500 pain-related and 3800 sleep-related posts were identified and analyzed. Twitter was used most often to share knowledge about child pain (639/1133, 56.40% of posts), and personal experiences for child sleep (2255/3008, 75.00% of posts). For both topics, Instagram posts shared personal experiences (53/68, 78% pain; 413/478, 86.4% sleep), Facebook group posts shared personal experiences (30/49, 61% pain; 230/345, 66.7% sleep) and Facebook pages shared knowledge (68/198, 34.3% pain; 452/1026, 44.05% sleep). Across platforms, research evidence was shared in 21.96% (318/1448) of pain- and 9.16% (445/4857) of sleep-related posts; 5.38% (61/1133) of all pain posts and 2.82% (85/3008) of all sleep posts shared information inconsistent with the evidence, while the rest were absent of evidence. User interactions were indirect, with mostly one-way, rather than reciprocal conversations. Conclusions Social media is commonly used to discuss child health, yet the majority of posts do not contain research evidence, and user engagement is primarily one-way. These findings represent an opportunity to expand engagement through open conversations with credible sources. Research and health care communities can benefit from incorporating specific information about evidence within social media posts to improve communication with the public and empower users to distinguish evidence-based content better. Together, these findings have identified potential gaps in social media communication that may be informative targets to guide future strategies for improving the translation of child health evidence over social media.
Design and Analysis of Approximate Redundant Binary Multipliers

As technology scaling is reaching its limits, new approaches have been proposed for computional efficiency. Approximate computing is a promising technique for high performance and low power circuits as used in error-tolerant applications. Among approximate circuits, approximate arithmetic designs have attracted significant research interest. In this paper, the design of approximate redundant binary (RB) multipliers is studied. Two approximate Booth encoders and two RB 4:2 compressors based on RB (full and half) adders are proposed for the RB multipliers. The approximate design of the RB-Normal Binary (NB) converter in the RB multiplier is also studied by considering the error characteristics of both the approximate Booth encoders and the RB compressors. Both approximate and exact regular partial product arrays are used in the approximate RB multipliers to meet different accuracy requirements. Error analysis and hardware simulation results are provided. The proposed approximate RB multipliers are compared with previous approximate Booth multipliers; the results show that the approximate RB multipliers are better than approximate NB Booth multipliers especially when the word size is large. Case studies of error-resilient applications are also presented to show the validity of the proposed designs.
Electron temperature gradient driven electromagnetic-drift wave instability and associated electron thermal diffusion in a magnetized plasma

The electron temperature gradient driven electromagnetic-drift waves and associated instabilities and electron thermal diffusion in a magnetized plasma have been investigated by employing kinetic ion and fluid electron responses. A new dispersion relation has been derived analytically and studied numerically. Two broad classes of unstable fluctuations, namely 'low' and 'high' frequency unstable modes, have been identified and studied in detail. The instabilities of these modes and associated electron thermal diffusion coefficients are also estimated. The results of our theoretical model can be considered as a plausible explanation for experimentally observed fluctuations in Burke et al. (2000a Phys. Plasmas 7, 1397).
CANÉPHORE : un corpus français pour la fouille d’opinion ciblée

La fouille d’opinion ciblée (aspect-based sentiment analysis) fait l’objet ces dernières années d’un intérêt particulier, visible dans les sujets des récentes campagnes d’évaluation comme SemEval 2014 et 2015 ou bien DEFT 2015. Cependant les corpus annotés et publiquement disponibles permettant l’évaluation de cette tâche sont rares. Dans ce travail nous présentons en premier lieu un corpus français librement accessible de 10 000 tweets manuellement annotés. Nous accompagnons ce corpus de résultats de référence pour l’extraction de marqueurs d’opinion non supervisée. Nous présentons ensuite une méthode améliorant les résultats de cette extraction, en suivant une approche semi-supervisée.
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions -- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective -- giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions.
Deep ground fires cause massive above- and below-ground biomass losses in tropical montane cloud forests in Oaxaca, Mexico

Although fire is occurring at greater frequencies and spatial scales in the moist tropics, few studies have examined the ecological impacts of fire in tropical montane cloud forest (TMCF). This study, conducted in the Chimalapas region of Oaxaca, Mexico, documents changes in live tree biomass, live fine-root biomass, and fallen and standing dead wood 4 y following deep ground fires occurring in TMCF during the 1997–98 El Niño Southern Oscillation event. Forests growing on two different substrates (metamorphic and sedimentary) and having three different statures (mean canopy heights: 20–30 m, 15–20 m and 4–6 m) were assessed within six paired plots established on adjacent burned and unburned forest sites. Total live tree biomass was 82% and 88% lower for burned TMCF growing on metamorphic and sedimentary substrates, respectively, compared with unburned TMCF. Nearly 100% of the living biomass was killed in elfin TMCF located on exposed sedimentary limestone at the highest elevations. Live fine-root biomass in the upper organic soil horizon of burned TMCF sites was 49% lower on metamorphic substrates and 77% lower on sedimentary substrates compared with unburned sites. The amount of total dead wood was 3- to 14-fold greater in burned forests compared with unburned forests. These results suggest that first-time fires in relatively undisturbed TMCF can cause dramatic changes in live above- and below-ground biomass at levels greatly exceeding values reported for most lowland tropical rain forests. These patterns may be attributed to the slower decomposition rates and thick organic soils typical of TMCF, combined with the relatively fast drainage associated with steep topography and, in some locations, sedimentary limestone-derived substrates.
Performance Analysis of Classifier Models to Predict Thyroid Disease

Machine Learning Algorithm aims at providing computational method for accumulating, changing and updating knowledge in health care systems. In particular learning mechanism will assist us to procure knowledge from the data set. The classification of machine learning algorithm is used not only to detect diseases, but also measure better fidelity. This article emphasizes on codification of disease symptoms on thyroid disease among the public. Thyroid disease is rampant worldwide. There are feasibility of thyroid disease and disorder including thyroiditis and thyroid cancer. We used 7200 sample thyroid dataset from the University of California Irvine Machine Learning Repository, a large and highly imbalanced dataset that comprises both discrete and continuous attributes. In this work, we collate machine learning classifiers such as Logistic Regression, Linear Discriminant Analysis, Naive Bayes, k-Nearest Neighbours, Classification and Regression Tree, Support Vector Machine using python to classify the disease symptoms. This work is carried out using different classifiers to achieve more verisimilitude. The selected algorithms are evaluated using five performance metrics namely accuracy, sensitivity, specificity, F1-score and kappa, and also estimated from the confusion matrix produced by the selected classifier. KeywordsCART Decision Tree; KNN algorithm; Support Vector Machine; Thyroid Disease Diagnosis; Linear Regression; Linear Discriminant Analysis.
Self-adaptive containers: a novel framework for building scalable QoS-aware software with low programmer overhead

As the number of execution environments increases dramatically, ever-changing non-functional requirements often lead to the challenge of frequent code refactoring. Despite help of traditional software engineering techniques, adapting software to meet each execution environment and application context remains a non-trivial endeavour. Manually reimplementing software possibly takes months or years of programmer effort and requires high levels of expertise. Furthermore, to build software for different execution environments often results in either a small code base which cannot guarantee Quality of Service or a large manually-optimised code base which is difficult to maintain. This thesis presents a novel self-adaptive container framework which can dynamically adjust its resource usage in an effort to meet resource constraints and scalability requirements. Each container instance is associated with programmer-specified Service Level Objectives with respect to performance, reliability, and primary memory use. To prevent ambiguity among multiple Service Level Objectives, each of them is specified in the format of standard Web Service Level Agreement. This framework features tighter functionality specification than that of standard container frameworks, which enables greater scope for efficiency optimisations, including the exploitation of probabilistic data structures, out-of-core storage, parallelism, and cloud storage. These techniques are utilised in a low-cost way through the integration of third-party libraries, which also enable our framework to provide a wider class of Service Level Objectives. In addition, to reduce the time of learning how to use the framework, its interfaces are designed to be close to those of standardised libraries. The framework has been implemented in C++ and utilised in two case studies centred on explicit state-space exploration adopting a breadth-first search algorithm, and route planning adopting a Dijkstra’s shortest path algorithm. In order to illustrate the framework’s viability and capability, various Service Level Objectives are assigned. Furthermore, the implementation of our framework is utilised to explore approximately 240 million states in the first case study and to find the shortest path of a graph representing the USA road network, containing approximately 24 million nodes and 58 million arcs. The experimental results show that the framework is capable of dynamically adjusting its resource usage according to assigned Service Level Objectives and dealing with large-scale data. At the same time, the programmer overhead is kept low in terms of the degree to which code is modified.