Miracle-dz/UniFAR-FTUs huggingface dataset:
Annotated catalogue of the Anthomyiidae, Fanniidae, Muscidae and Scathophagidae (Diptera: Muscoidea) of Greenland.

This is a critical compilation of the species of Anthomyiidae, Fanniidae, Muscidae and Scathophagidae so far found in Greenland. Existing literature records from Greenland have been critically assessed for each species in order to disentangle an often very confused taxonomic and nomenclatural history. The list is based on an examination and identification of some 12,200 specimens of muscoid Diptera from Greenland deposited in the Zoological Museum, University of Copenhagen (ZMUC). The Muscoidea are represented by 76 species in Greenland (Anthomyiidae 32; Fanniidae 3; Muscidae 35; Scathophagidae 6). The following 6 species are recorded from Greenland for the first time: Anthomyiidae: Fucellia vibei Collin, 1951; Myopina crassipalpis Ringdahl, 1937. Fanniidae: Fannia scalaris (Fabricius, 1794). Muscidae: Mydaea palpalis Stein, 1916; Spilogona arcticola Huckett, 1965; Spilogona puberula (Ringdahl, 1918). Two new synonymies are established in Muscidae: Limnophora groenlandica Malloch, 1920 (syn.: L. sinuata Collin, 1930); Spilogona megastoma (Boheman, 1866) (syn.: S. megastoma digressa Huckett, 1965). The unnoticed synonymy Spilogona zaitzevi (Schnabl in Becker & al., 1915) (syn.: S. latilamina (Collin, 1930)) is accepted. Finally, the taxonomic status of Limnophora rotundata Collin and L. nigripes Robineau-Desvoidy is examined.
Feeding ecology of the Tasmanian spotted skink, Niveoscincus ocellatus (Squamata : Scincidae)

The feeding ecology of the spotted skink, Niveoscincus ocellatus, was examined over a 6-month period, from October to March, with additional data from one sample in midwinter. The investigation considered feeding activity, dietary composition and foraging strategy. Feeding activity,measured by stomach fullness, was unrelated to age, sex or reproductive status and showed little variation during the warmer months; in winter feeding was greatly reduced. N. ocellatus exhibits flexible foraging behaviour; a wide variety of terrestrial arthropods is taken opportunistically in accordance with a wide ranging foraging strategy. However, the consistent occurrence of cryptic and hidden prey indicates that active search foraging is also utilised. No evidence for ambush predation was found and, unlike many other small skinks, few arboreal or aerial prey are included in the diet, even though the habitat is rock scree and woodland where such prey abound. No evidence for any in a specific partitioning of diet based on kind or size of prey was obtained although a slight, but significant, correlation exists between maximum prey size and lizard size.
Molecular genetic investigations and reclassification of Fusarium species in sections Fusarium and Roseum

The genetic relationships of 56 isolates from the taxa traditionally grouped in Fusarium sections Fusarium (syn. Discolor ) and Roseum were studied using the Restriction Fragment Length Polymorphism (RFLP) technique, by Southern hybridisation with random genomic and mitochondrial DNA probes originating from Fusarium species. Pairwise distances between taxa were calculated from the 957 RFLP bands scored on autoradiograms, using Dice's coefficient in the RAPDistance computer program. A strong genetic relationship was observed between F. graminearum, F. culmorum and F. crookwellense . However, the morphologically similar taxon, F. pseudograminearum shared only approximately 40 % of genomic DNA RFLP bands with these taxa, demonstrating affinity, but less direct genetic similarity. Of all taxa examined F. pseudograminearum and F. graminearum shared the greatest similarity in mitochondrial DNA RFLP patterns. F. avenaceum subsp. avenaceum , subsp. aywerte and subsp. nurragi displayed very little genetic resemblance with each other, or with F. heterosporum and the cereal infecting pathogens. F. torulosum was closest genetically to subsp. avenaceum , with approximately 22 % of genomic DNA RFLP bands in common. On the basis of these results F. avenaceum subsp. aywerte and subsp. nurragi are elevated to species rank, as F. aywerte comb. nov. and F. nurragi comb. nov., and F. avenaceum subsp. avenaceum is returned to its former species status.
Climate change and water resources in northern Mongolia

 Malys, M. , aus der Beek, T. & Flörke, M. (2014): Evaluation of large-scale precipitation data sets for water resources modelling in Central Asia. Environ Earth Sci., doi: 10.1007/s12665-014-3107-y  Menzel, L. , Hofmann, J. & Ibisch, R. (2011): Studies of water and mass fluxes to provide a basis for an Integrated Water Resources Management (IWRM) in the catchment of the River Kharaa in Mongolia. Hydrologie und Wasserbewirtschaftung, 55(2): 88–103  Törnros, T. & Menzel, L. (2010): Heading for knowledge in a data scarce river basin: Kharaa, Mongolia. Status and Perspectives of Hydrology in Small Basins. IAHS Publ. 336: 270–275  Weedon, G. P., Gomes, S. , Viterbo, P., Shuttleworth, W. J., Blyth, E. , Österle, H. , Adam, J. C. , Bellouin, N. , Broucher, O. & Best, M. (2011): Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the 20th century. J. Hydromet. , 12: 823–848 Lucas Menzel: lucas.menzel@geog.uni-heidelberg.de Tobias Törnros: tobias.toernros@geog.uni-heidelberg.de Introduction In Mongolia, high climatic variability and scarce environmental information complicates the implementation of a reliable water management system. Our study aims at better understanding the possible range of historic and possible future streamflow variability and to place the recent severe droughts into a wider perspective. A globally available reanalysis and scenario product was applied to the Kharaa basin in the semi-arid and cold north of the country (Fig. 1). This basin incorporates some of the most typical problems with regard to water availability and water management in Mongolia (Menzel et al., 2011).
Standards with cybersecurity controls for smart grid—A systematic analysis

In recent years, numerous standards related to the cybersecurity of smart grids have been published, which led to the challenge for operators in obtaining indications that match their specific objectives and contexts. Although several studies approached this problem by providing more or less comprehensive surveys and overviews of smart grid cybersecurity standards, none of them was dedicated to the actual and important subject of cybersecurity controls. This paper aims at filling this gap. A systematic literature analysis was conducted which resulted in the identification of 19 broadly recognised standards that specify cybersecurity controls applicable to the smart grid infrastructure. The publications are described in respect to the controls they define and referred to evaluation criteria. In result, this paper constitutes a guideline on standardised cybersecurity controls for smart grids, where (criteria‐based) indications help to select standards for a particular smart grid area or specific goals. The method of the research as well as the standards' selection and evaluation criteria are presented.
Thin layer chromatography (TLC) technique in the investigation of artemisinin production in Artemisia annua L. medicinal plant hairy roots

Artemisia annua L. is an important medicinal plant, which is used for the treatment of malaria. Artemisinin is the active secondary metabolite in this plant and it is a sesquiterpen. This plant was studied for artemisinin production in hairy root induced by Agrobacterium rhizogenes. Polymerase chain reaction (PCR) for rolB gene confirmed the transformed hairy roots induced by A. rhizogenes. Then, thin layer chromatography (TLC) was employed for investigating the characteristics and quality of artemisinin in extracts of hairy roots and control roots of A. annua. Result of TLC showed the presence of artemisinin in the induced hairy roots. Key words: Artemisia annua, Agrobacterium rhizogenes, hairy root, thin layer chromatography (TLC) technique and artemisinin.
A Method for Determining Face Recognition Suitability of Face Image

Face recognition (FR) has been widely used in various applications, such as smart surveillance systems, immigration control in airports, user authentication in smart devices, and so on. FR in well-controlled conditions has been extensively studied and is relatively mature. However, in unconstrained conditions, FR performance could degrade due to undesired characteristics of the input face image (such as irregular facial pose variations). To overcome this problem, this paper proposes a new method for determining if an input image is suitable for FR. In the proposed method, for an input face image, reconstruction error is computed by using a predefined set of reference face images. Then, suitability can be determined by comparing the reconstruction error with a threshold value. In order to reduce the effect of illumination changes on the determination of suitability, a preprocessing algorithm is applied to the input and reference face images before the reconstruction. Experimental results show that the proposed method is able to accurately discriminate non-frontal and/or incorrectly aligned face images from correctly aligned frontal face images. In addition, only 3 ms is required to process a face image of 64 x 64 pixels, which further demonstrates the efficiency of the proposed method.
Missing pins detection for power equipment firmware using unmanned aerial vehicle images

Maintenance of power equipment is of great significance to ensure the safety and reliability of power equipment. This paper focuses on detecting missing pins of power equipment using Unmanned Aerial Vehicle （UAV） acquired images. We proposed a detection method based on image color histogram and scale invariant feature transform (SIFT). The first step calculates the H-S color histogram of screw image, utilizing histogram back projection method to obtain candidate regions of screw image in the to-be-matched image, applied Bhattacharyya distance as a measurement to compare the similarity of two histograms. Then, the SIFT feature is extracted from the screw image and the key points are matched with the SIFT feature of the candidate regions to detect the screws. Finally, this paper designs a method which uses convolutional neural network to judge whether the screw misses the pin. Experiments show that the proposed algorithm of missing pins detection based on UAV image can achieve competitive results to detect the defects in special scenes, and has good robustness, which satisfies the real-time and accuracy requirements.
A Non-Volatile Threshold Sensing System Using a Ferroelectric Hf0.5Zr0.5O2 Device and a LiNbO3 Microacoustic Resonator

This work reports a novel threshold sensor system that is able to detect and memorize the occurrence of temperature violations by relying on a 20 nm-thick ferroelectric Hafnium Zirconium Oxide (HZO) varactor and a LiNbO3 shear-horizontal (SH0) Lamb wave microacoustic resonator, both microfabricated in-house. The reported sensor system is driven by a continuous-wave signal at a frequency (fin) slightly detuned from the LiNbO3 device’s resonance frequency (fres~33.3 MHz). When the ambient temperature changes, the voltage at fin across the varactor increases proportionally to the resonator’s figure-of-merit (FoM), ultimately causing a partial ferroelectric polarization switching of the HZO varactor for a temperature exceeding a certain programmable threshold (Tth). Following such a switching event, the capacitance (CT) of the HZO varactor experiences a sudden change, causing a non-volatile 0.75-1 MHz shift of the read-out resonance frequency (fread ~260 MHz) that is equal to the resonance frequency of an LC-tank formed by a lumped inductor and by the series of CT with the LiNbO3 device’s capacitance (C0). The ability to generate temperature-induced non-volatile changes of fread through HZO ferroelectric varactors and microacoustic resonators is demonstrated for the first time in this work, and represents the key to implement a threshold sensing functionality and to memorize the occurrence of any temperature violations.
Fluxes of carbon dioxide and water vapour over an undisturbed tropical forest in south‐west Amazonia

1 Carbon dioxide and water vapour fluxes were measured for 55 days by eddy covariance over an undisturbed tropical rain forest in Rondonia, Brazil. Profiles of CO2 inside the canopy were also measured. 2 During the night, CO2 concentration frequently built up to 500 ppm throughout the canopy as a result of low rates of exchange with the atmosphere. In the early morning hours, ventilation of the canopy occurred. 3 Ecosystem gas exchange was calculated from a knowledge of fluxes above the canopy and changes of CO2 stored inside the canopy. Typically, uptake by the canopy was 15 μmol m−2 s−1 in bright sunlight and dark respiration was 6-7 μmol m−2 s−1 The quantum requirement at low irradiance was: 40 mol photons per mol of CO2. 4 Bulk stomatal conductance of the ecosystem was maximal in the early morning (0.4-1.0 mol m−2 s−1) and declined over the course of the day as leaf-to-air vapour pressure difference increased.
Genetic diversity and multiple origins of polyploid Atriplex nummularia Lindl. (Chenopodiaceae)

Few studies have described the genetic diversity within and between populations of polyploid plant species despite the general acceptance of the importance of polyploidy in plant diversification and speciation. The genus Atriplex has a complex evolutionary history in Australia that has included polyploidy and hybridization among perennial forms. The octoploid, dioecious species Atriplex nummularia is proposed to have evolved from an octoploid ancestor in the coastal semi-arid fringe of south-western Australia, and to have spread east and diversified into taxa which occupy edaphically different habitats. Despite interest in the diversification of the genus, and the ecological and economic importance of A. nummularia, there are no descriptions of the genetic structure of the species. Nuclear microsatellite markers and principal coordinate analysis, analysis of molecular variance, Bayesian and phenetic analyses were used to investigate the diversity and taxonomic relationships of two common subspecies of A. nummularia. Genetic diversity was high overall (A = 509, A′ = 42.4, Ho = 0.824, H′ = 2.8), but values were significantly lower in the western subspecies, A. nummularia ssp. spathulata. As in other outbreeding, perennial species, most of the genetic diversity was within populations (FST = 0.125). Clear divergence of subspecies was evident in principal coordinate analysis, neighbor-joining and Bayesian clustering analyses and the differentiation of populations was very low within subspecies (FSC = 0.048). These findings support the taxonomic separation of the two subspecies. Clustering patterns based on Bayesian analyses suggested that the polyploid subspecies of A. nummularia have multiple origins. © 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 105, 218–230.
Intelligent sentiment analysis approach using edge computing‐based deep learning technique

Sentiment analysis and opinion mining has become a major tool for collecting information from customer reviews on user sentiments and emotions, especially for online video streaming services and social networks. The increasing use of smartphones has popularized subscription to various streaming services that provide streaming media and video‐on‐demand. These applications offer a gateway to analyze user reviews by introducing sentiment analysis in the mobile environment. Online user reviews can hold a lot of useful information and help predict user interests. Analysis of user reviews can provide substantive information for business processing. Sentiment classification of these reviews is a commonly used analysis technique. Usually, these reviews are given in a text format, with every word in each considered a feature, so selection should focus on optimal features from all available features present in the reviews. This study employs machine learning algorithms to extract the best features from the training review data set. Then, the selected features are fed into the convolutional neural network and other fully connected layers for further processing. The proposed approach is evaluated with the standard evaluation metrics, such as precision, accuracy, recall, and f‐measure, using three distinct benchmark data sets: polarity, Rotten Tomatoes, and IMDb. This work has also employed a pretrained sentiment analysis model over an Android application framework to classify reviews on a Smartphone without the need for any cloud or server‐side API.
The company you keep: mobile malware infection rates and inexpensive risk indicators

There is little information from independent sources in the public domain about mobile malware infection rates. The only previous independent estimate (0.0009%) [11], was based on indirect measurements obtained from domain-name resolution traces. In this paper, we present the first independent study of malware infection rates and associated risk factors using data collected directly from over 55,000 Android devices. We find that the malware infection rates in Android devices estimated using two malware datasets (0.28% and 0.26%), though small, are significantly higher than the previous independent estimate. Based on the hypothesis that some application stores have a greater density of malicious applications and that advertising within applications and cross-promotional deals may act as infection vectors, we investigate whether the set of applications used on a device can serve as an indicator for infection of that device. Our analysis indicates that, while not an accurate indicator of infection by itself, the application set does serve as an inexpensive method for identifying the pool of devices on which more expensive monitoring and analysis mechanisms should be deployed. Using our two malware datasets we show that this indicator performs up to about five times better at identifying infected devices than the baseline of random checks. Such indicators can be used, for example, in the search for new or previously undetected malware. It is therefore a technique that can complement standard malware scanning. Our analysis also demonstrates a marginally significant difference in battery use between infected and clean devices.
Performance of Convolutional Neural Networks and Transfer Learning for Skeletal Bone Age Assessment

OBJECTIVES: Bone age assessment is used by clinicians for estimating the maturity of a child’s skeletal system. Traditionally, physicians use template matching methods (GP and/or TW2). Time and accuracy of the evaluation rely on a physician’s experience. Therefore, this research proposes a fully automatic system for bone age assessment with cutting edge artificial Intelligence (AI) technology. MATERIAL AND METHODS: Convolutional Neural Network (CNNs), a Deep Learning (DL) technique is applied to skeletal bone age prediction combined with transfer learning algorithm. Hence, various kinds of transfer learning algorithms (ResNet-50, Inception-V3, and VGG-16) are investigated in training in the proposed model fed by a number of x-ray images (12,000 image approximately—imbalanced data). RESULT: VGG-16 shows significant accuracy compared to ResNet-50 and Inception-V3 (mae = 6.53, 20.52 and 43.11 months respectively) CONCLUSION: The most effective pre-trained layer for CNNs in bone age assessment is VGG-16 according to the accuracy of its prediction. Keywords: deep learning, convolutional neural network, bone age, growth disorder, maturity estimation, transfer learning DOI: 10.31524/bkkmedj.2019. 02.001
Auction-Based Data Transaction in Mobile Networks: Data Allocation Design and Performance Analysis

Mobile data traffic is experiencing unprecedented increases due to the proliferation of highly capable smartphones, laptops and tablets, and mobile data offloading can be used to move traffic from cellular networks to other wireless infrastructures such as small-cell base stations. This work addresses the related issue of data allocation, by proposing a novel infrastructure independent method based on the hotspot function of smartphones. In the proposed scheme, smartphones transfer data allowances among mobile users, so that users with excess data allowances act as accessible Wi-Fi hotspots, selling their data allowance to other users who need extra data allowances. To achieve this objective, we propose to use auctions with single and multiple data sellers. Efficient schemes based on auction models are discussed to sell the data allowances over successive days in a month, and over different time slots during a single day. Overall system performance is considered based on the behavior of mobile users, such as changing demands for the sale or purchase of data allowances. Together with the analytical results presented, our simulation experiments also indicate that knowledge of user behavior can significantly improve the performance of data allowance transactions, leading to highly efficient allocations among users.
ARID1A loss in neuroblastoma promotes the adrenergic-to-mesenchymal transition by regulating enhancer-mediated gene expression

ARID1A is a tumor suppressor in neuroblastoma, whose loss promotes the adrenergic-to-mesenchymal transition. Mutations in genes encoding SWI/SNF chromatin remodeling complexes are found in approximately 20% of all human cancers, with ARID1A being the most frequently mutated subunit. Here, we show that disruption of ARID1A homologs in a zebrafish model accelerates the onset and increases the penetrance of MYCN-driven neuroblastoma by increasing cell proliferation in the sympathoadrenal lineage. Depletion of ARID1A in human NGP neuroblastoma cells promoted the adrenergic-to-mesenchymal transition with changes in enhancer-mediated gene expression due to alterations in the genomic occupancies of distinct SWI/SNF assemblies, BAF and PBAF. Our findings indicate that ARID1A is a haploinsufficient tumor suppressor in MYCN-driven neuroblastoma, whose depletion enhances tumor development and promotes the emergence of the more drug-resistant mesenchymal cell state.
Cyclic Tensile Strain Acts as an Antagonist of IL-1β Actions in Chondrocytes1

Inflammatory cytokines play a major role in cartilage destruction in diseases such as osteoarthritis and rheumatoid arthritis. Because physical therapies such as continuous passive motion yield beneficial effects on inflamed joints, we examined the intracellular mechanisms of mechanical strain-mediated actions in chondrocytes. By simulating the effects of continuous passive motion with cyclic tensile strain (CTS) on chondrocytes in vitro, we show that CTS is a potent antagonist of IL-1β actions and acts as both an anti-inflammatory and a reparative signal. Low magnitude CTS suppresses IL-1β-induced mRNA expression of multiple proteins involved in catabolic responses, such as inducible NO synthase, cyclo-oxygenase II, and collagenase. CTS also counteracts cartilage degradation by augmenting mRNA expression for tissue inhibitor of metalloproteases and collagen type II that are inhibited by IL-1β. Additionally, CTS augments the reparative process via hyperinduction of aggrecan mRNA expression and abrogation of IL-1β-induced suppression of proteoglycan synthesis. Nonetheless, the presence of an inflammatory signal is a prerequisite for the observed CTS actions, as exposure of chondrocytes to CTS alone has little effect on these parameters. Functional analysis suggests that CTS-mediated anti-inflammatory actions are not mediated by IL-1R down-regulation. Moreover, as an effective antagonist of IL-1β, the actions of CTS may involve disruption/regulation of signal transduction cascade of IL-1β upstream of mRNA transcription. These observations are the first to show that CTS directly acts as an anti-inflammatory signal on chondrocytes and provide a molecular basis for its actions.