Miracle-dz/UniFAR-FTUs huggingface dataset:
Ornithol Sci 6: 87–94 (2007)

A goal of many resource selection studies is to identify those habitats selected by a species. However, favorability of a particular habitat feature is likely contingent on such factors as landscape composition, predation risk, and an individual’s resource needs. Identifying causes of variability in habitat utilization may serve to increase our understanding of the functional aspects of a species’ habitat ecology. The Common Hill Partridge Arborophila torqueola is a species that requires broadleaf forest, but whose populations have declined as a result of fragmentation of subtropical forest. While habitat conditions during the pairing season (March–April) are thought to be important for the Common Hill Partridge’s survival and reproduction, information on habitat utilization during this period is limited. We investigated habitat utilization by Hill Partridges within Baiposhan Natural Reserve in the mountains of southwestern China. We used pointing dogs to locate Hill Partridges during the pairing seasons of 2004 and 2005, and measured habitat characteristics at 60 flush sites and 60 associated random sites (within 100 m of flush sites). We recorded information on terrain, vegetation traits and defoliation leaf layer. Hill Partridges mainly utilized sites within elevations of 2,400–2,900 m and with an east-facing slope of 28.6 2.9 degrees, close to water resources and roads. The utilized sites had greater tree cover, shrub cover and thicker defoliation layers than randomly available, whereas numbers of bamboo and bamboo cover were less at flush sites than at random sites. PCA indicated that concealment, food, terrain and water resources best explained the birds’ habitat utilization. Forest management practices that reduce poaching, livestock and logging may benefit the Common Hill Partridge through raising habitat quality and availability. Based on habitat utilization patterns of the partridges, we discussed the difference of habitat between them and Sichuan Partridge A. rufipectus, and indicated that Common Hill Partridges in the reserve should be Common Hill Partridges rather than Sichuan Partridges.
A new rain-based index for the Indian summer monsoon rainfall

Most of the studies of the observed variability of the Indian summer monsoon rainfall (ISMR), its prediction and of its impact have involved analysis of an index for ISMR derived by Parthasarathy et al. (1995) or the allIndia rainfall during the summer monsoon, available from the India Meteorological Department (IMD) website. Both these indices are based on the average rainfall over the meteorological subdivisions of India. Rajeevan et al. (2006) first derived a gridded rainfall data set for the Indian region which was at a resolution of 1° and subsequently, Pai et al. (2014) have derived a finer resolution (0.25°) rainfall data set for the same region. At present, these data sets are widely used by modelers to generate the ‘observed’ ISMR for assessment of the skill of their models. However, in different studies, different regions are used for averaging the grid data to obtain the ‘observed’ ISMR. For proper assessment and comparison of the skill of the simulations/predictions by different models/versions, it is important that the same region be used for averaging the rainfall to obtain the observed ISMR in each case. Here, we suggest what we consider as the appropriate regions for averaging the rainfall in terms of the 1° and 0.25° to derive/represent ISMR, on the basis of the present understanding of the monsoonal regions and the Indian summer monsoon. We show that the interannual variation of the ISMR thus derived (by averaging rainfall over the regions identified in this study) from gridded data sets is largely consistent with the indices derived as the area weighted sub-divisional rainfall data used in the indices used earlier.
Gross morphology and histopathology of male-killing strain larvae in the oriental tea tortrix Homona magnanima (Lepidoptera: Tortricidae)

The late male-killing phenomenon was reported in the oriental tea tortrix, Homona magnanima, but its gross pathology and histopathology have not been elucidated. The present study investigated pathological changes in larvae of a late male-killing strain of H. magnanima (LMK). Most male LMK larvae died during the last instar or pupal stage, and dead male larvae showed characteristic signs and symptoms of male killing. The body color of male LMK larvae started to change 4 d after molting into the fifth instar and became increasingly opaque white. Male LMK larvae weighed significantly less than normal-strain (NSR) males. Midgut epithelial cells of male LMK larvae developed normally and microvilli were observed on the luminal side 3 d after the final molt. Regenerative cells of male LMK larvae were observed on the basement membrane but these cells did not develop to pupal midgut cells. From 7 d after the final molt, midgut cells of male LMK larvae were discharged into the lumen as granules and regenerative cells elongated from the basement membrane. All midgut cells of male LMK larvae dropped into the lumen just before death and no cells were observed on the basement membrane. The fat body of male LMK larvae contained large fat granules in the cytoplasm but became a tumor-like cell mass that finally fused with epidermal cells. These changes were specific to LMK males; no differences in the developmental pattern or morphology were observed between female LMK and NSR larvae. From these results, it is suggested that the degradation of midgut epithelial cells may be the main reason for late male killing.
Effect of Amino Acid Type on Peptide Structure

Through intramolecular interactions, amino acids largely dictate a peptide’s structure and flexibility, both of which play significant roles in a peptide’s binding properties and function. This model explored the effects of three major residue types (uncharged hydrophilic, hydrophobic, and charged) on peptide structure in solution, by utilizing a bead-spring model and the relevant potentials, including LennardJones and screened Coulomb, to describe amino acid interactions. As indicated by contact maps and MD movies, MD simulations of fifteen residue peptides with varied amino acid configurations captured the relevant structural interactions, such as a hydrophobic core versus a flexible peptide made up solely of uncharged hydrophilic residues. Modeling these structural interactions can aid the design of peptides for a specific task, such as fitting in the catalytic region of an enzyme.
Classification Of Text Documents Using Adaptive Robust Classifier

: Classifying the documents by means of extracting the keywords has become an imperative direction of research in text mining. The important purpose of extracting the keywords is to exemplify the documents in a concise manner. The compactable exemplification of documents serves multiple applications in different ways. Classifying the documents regards to the keywords have becomes a major task. Most classifiers are suitable only for the dataset which hold the low number of documents. In this paper, adaptive robust classifier (ARC) is proposed to classify the documents in any size dataset with better accuracy. ARC is designed to segregate the documents dataset into multiple parts and perform classification in a random manner, where the existing classifiers perform classification in a sequential manner which leads to poor classification of documents. The existing classifiers were designed to fit only for a specific type of dataset either with specific size, where ARC is designed to fit for document dataset with any size. For evaluating the performance of classifiers, this research work has chosen ACM Document collection dataset, Reuters-21578, NBA Input document collection dataset of a B-School which holds 3506, 21578, and 1256 documents respectively. The results shows that ARC is having better performance in terms of Classification Accuracy and F-Measure, than baseline classifiers.
Effects of Interaction Between Dietary Zinc and Vitamin A in Broilers on Performance, Immunity, ALP and CuZn-SOD Activity and Serum Insulin Concentration

Day old Arbos Acry (AA) chickens, 480 were randomly divided into 12 treatments with 4 replicates of 10 birds per treatment, to investigate the influence of dietary zinc and vitamin A levels on production performance, immune competence, serum insulin concentration, serum ALP activity and CuZn-SOD activity in serum and liver. Supplemental levels were: 40, 80,120, 160, 200 and 320 mg of zinc/kg diet; 2,700 and 8,800 IU of vitamin A/kg diet. Results showed that zinc requirement for broilers probably differs with age, amounting 320 mg zinc/kg diet from 0 to 4 weeks, but 80-120 mg/diet from 5 to 7 weeks. Supplemental vitamin A 2,700 IU kg diet was sufficient through the whole 1 period. Dietary zinc added levels significantly (p<0.01) affected feed:gain from 1 to 28 days, feed:gain was minimum for 320 mg added zinc/kg diet. Dietary zinc significantly (p<0.05) affected HI results, thus HI values reached a peak with 80-120 mg zinc added /kg diet. Dietary zinc significantly (p<0.01) affected serum CuZn-SOD activity and insulin concentration, serum CuZn-SOD activity reached a peak with zinc added levels being 80-120 mg kg diet, serum insulin 1 concentration reached a peak with 320 mg zinc added /kg diet. 8,800 IU vitamin A/kg diet significantly (p<0.05) reduced feed: gain from 1 to 28 days and significantly (p<0.01) depressed serum insulin concentration. The interaction effect between zinc and vitamin A was significant (p<0.01) for the feed: gain from 1 to 49 days, feed:gain was lower for the diet supplied 120 mg zinc/kg diet and 2,700 IU vitamin A/kg diet from 1 to 49 days. The interaction effect significantly (p<0.01) affected T-lymphocyte transformation rate with or without PHA adding. Serum ALP activity was significantly (p<0.01) affected by the interaction effect and reached a peak with zinc added level being 80 mg kg diet and 8800 IU 1
Stochastic Classifiers for Unsupervised Domain Adaptation

A common strategy adopted by existing state-of-the-art unsupervised domain adaptation (UDA) methods is to employ two classifiers to identify the misaligned local regions between source and target domain. Following the ’wisdom of the crowd’ principle, one has to ask: why stop at two? Indeed, we find that using more classifiers leads to better performance, but also introduces more model parameters, therefore risking overfitting. In this paper, we introduce a novel method called STochastic clAssifieRs (STAR) for addressing this problem. Instead of representing one classifier as a weight vector, STAR models it as a Gaussian distribution with its variance representing the inter-classifier discrepancy. With STAR, we can now sample an arbitrary number of classifiers from the distribution, whilst keeping the model size the same as having two classifiers. Extensive experiments demonstrate that a variety of existing UDA methods can greatly benefit from STAR and achieve the state-of-the-art performance on both image classification and semantic segmentation tasks.
Molecular Screening and Characterization of dsRNA from Wild-type and Mutant Strains of Rhizoctonia solani Kuhn Isolates

Rhizoctonia solani Kuhn is a common soil-borne pathogen which infects a diversity of plant hosts. Previous studies have shown that the virulence of Rhizoctonia is affected by the presence of certain dsRNA segments which are of viral origin. This study attempted to screen dsRNA from wild type and mutant isolates of Rhizoctonia solani and characterize them as to size, concentration, and reaction to nucleases. The Rhizoctonia solani isolates were also compared as to color changes in the mycelia and in the culture media, in sclerotia formation and growth rate. There were twelve R. solani isolates utilized in the study: three wild type strains that are heterokaryons (RS29, RS 114 and TE2-4); and nine mutant strains that are homokaryons (RS29.5, RS 29.6. RS29.7, EGR4, EGR7, T2, Tom7, 123e and 115). Results of the study show that only five of the R. solani isolates had dsRNA namely: RS29, RS114, TE2-4, EGR4 and T2. The dsRNAs vary in their sizes and concentration and they showed resistance to nucleases. The R. solani isolates vary in the mycelia pigmentation: white (RS 29.6, RS 29.7, EGR4, EGR7 and 123E), light brown (RS29, RS114, RS 29.5 and 115), brown (TE2-4), and dark brown (T2 and Tom7). Color changes on the potato dextrose agar medium were light yellow (RS 29.5, RS 29.7, EGR4, EGR7), yellow brown (RS 29.6), light brown (RS 29, RS 114, TE2-4), brown (Tom 7, Strain 115) and dark brown (123E, T2).
CRF-based Initialization for Nonnegative Matrix Factorization

MF algorithms have been recently employed in several applications; however, the performance of NMF is highly dependent on three factors including: 1) choosing a suitable cost function, 2) using an effective initialization method and 3) determining the rank of factorized matrices. This paper is aimed at enhancing the NMF performance using CRF as an efficient initialization method for estimating initial components of NMF in addition to find the proper rank of decomposed matrices. The modified NMF is applied to JAFFE facial expression dataset and experimental results demonstrate the superiority of the proposed approach to NMF with different initialization schemes, in terms of relative error, robustness, sparsity and orthogonality.
Design Techniques for Elliptical Micro-Strip Patch Antenna and Their Effects on Antenna Performance

Elliptical Micro-strip Patch Antenna (EMPA) has been emerged as a peculiar and significant category among the different shaped micro-strip patch antennas because of its circular polarization and dual-resonant frequency features with a single feed. Elliptical and its derived shapes such as semielliptical, half-elliptical, slotted-elliptical and elliptical ring are found to be particularly instrumental for bandwidth enhancement and these antennas find great applications in Ultra Wide Band (UWB) and Super Wide Band (SWB) communications. Compared to antennas with circular or rectangular shapes, the design of EMPA is a research area of high potential as there is higher flexibility in its design due to more degrees of freedom. The reported literature in the field of EMPA is very less and there is ample scope for new researchers to work on. This review paper is an attempt to summarize and critically assess the-state-of-the-art design techniques as reported in literature and understand their effects on performance of elliptical patch antenna for suggesting new research fronts in the field of EMPA.
Study and Analysis of 3 -Input Logic Gates By Using Quantum Dot Cellular Automata

 Abstract : Quantum dot cellular automata (QCA) is growing technology with Nano range scale in that, QCA is extremely computational and exceptionally productive than CMOS technology. QCA the parameters like area and usage of power are very less when compared to CMOS technology. In the comparative analysis of QCA and CMOS technologies, the number of quantum cells used and the number of transistors taken place to design 3 input logic gates. In this paper, it explains about the comparison between CMOS and QCA technologies for 3 input basic logic gates like AND, OR, NOT, NAND, NOR ,XOR and XNOR by utilizing the ultra -low power and the area, time taken to outline the circuit with quantum dot cells in QCA technology. From the results, area utilized to design logic gates using by QCA technology as compared to CMOS technology is reduced up to 5%.
The mathematics resilience approach to mathematics anxiety : is this supported by self-determination theory?

One approach to the problem of mathematics anxiety, that of developing mathematical resilience (Lee & Johnston-Wilder, 2017) focuses on enabling learners to remain in the growth zone, where learners experience challenge and manage any threat. This approach, involving the use of three tools (the growth zone model, hand model of the brain and the relaxation response) has been successful in small-scale studies. We show here how the theory and practice of MR can be grounded in self-determination theory (SDT) (Deci & Ryan, 2000), with connections to SDT concepts of: autonomous motivation; the basic psychological needs of autonomy, competence and relatedness; and emotion regulation. Extensive research evidence has indicated that the satisfaction of basic psychological needs leads to well-being and that frustration of these needs leads to ill-being, indicating the potential of SDT to support research and practice in the specific area of ill-being known as mathematics anxiety.
The plasmid profile of Edwardsiellaictaluri

The plasmid content of 18 isolates of Edwardsiella ictaluri found in channel catfish was analysed by agarose gel electrophoresis. Each isolate contained an identical set of plasmids. This set consisted of two prominent plasmids of 5.2 and 4.4 kilobase pairs, and three smaller and less apparent plasmids. One plasmid (the 5.2 kilobase plasmid) was radiolabelled and used to probe a blot of DNA from all isolates. All plasmids were hybridized but no chromosomal DNA was labelled. These plasmids must share some homologous regions. DNAs from other bacteria, including the related Edwardsiella tarda, were also probed and no hybridization was seen. It is suggested that a plasmid probe may be a sensitive, specific probe for detection of E. ictaluri in channel catfish
International Journal of Innovative Technology and Exploring Engineering (IJITEE)

Images are the fastest growing content, they contribute significantly to the amount of data generated on the internet every day. Image classification is a challenging problem that social media companies work on vigorously to enhance the user’s experience with the interface. The recent advances in the field of machine learning and computer vision enables personalized suggestions and automatic tagging of images. Convolutional neural network is a hot research topic these days in the field of machine learning. With the help of immensely dense labelled data available on the internet the networks can be trained to recognize the differentiating features among images under the same label. New neural network algorithms are developed frequently that outperform the state-of-art machine learning algorithms. Recent algorithms have managed to produce error rates as low as 3.1%. In this paper the architecture of important CNN algorithms that have gained attention are discussed, analyzed and compared and the concept of transfer learning is used to classify different breeds of dogs.
Few-Shot Pill Recognition

Pill image recognition is vital for many personal/public health-care applications and should be robust to diverse unconstrained real-world conditions. Most existing pill recognition models are limited in tackling this challenging few-shot learning problem due to the insufficient instances per category. With limited training data, neural network-based models have limitations in discovering most discriminating features, or going deeper. Especially, existing models fail to handle the hard samples taken under less controlled imaging conditions. In this study, a new pill image database, namely CURE, is first developed with more varied imaging conditions and instances for each pill category. Secondly, a W2-net is proposed for better pill segmentation. Thirdly, a Multi-Stream (MS) deep network that captures task-related features along with a novel two-stage training methodology are proposed. Within the proposed framework, a Batch All strategy that considers all the samples is first employed for the sub-streams, and then a Batch Hard strategy that considers only the hard samples mined in the first stage is utilized for the fusion network. By doing so, complex samples that could not be represented by one type of feature could be focused and the model could be forced to exploit other domain-related information more effectively. Experiment results show that the proposed model outperforms state-of-the-art models on both the National Institute of Health (NIH) and our CURE database.
Laughter Repair

We investigate whether laughter can be object of clarification requests and what these clarification requests might be about. Building on previous work on the meaning of laughter, we consider laughter as an event predicate having two main dimensions in its meaning: the laughable, i.e., the argument it is predicating about, and the level of arousal. Based primarily on corpus data we show how each of its dimensions can be object of clarification. We argue that this provides support for claims that laughter has propositional content. Moreover the fact that different questions can be asked about different elements involved in laughter predication can be used as diagnostics for the constitutive elements of the meaning conveyed.
Combination of Spectral Representation and Wavelet Packets for Generating Long-Period Ground Motions

Far-field long-period ground motions (hereafter long-period ground motions) featuring low-frequency components are responsible for the resonant responses of high-rise buildings. In this context, it is beneficial to assess the dynamic performance of these buildings under long-period ground motions with the aid of time history analysis. This paper proposes a method for generating long-period motions by combining long-period components synthesized by spectral representation with high-frequency components simulated by wavelet packets. Later-arriving long-period surface waves (LALP surface waves), which are determined on the grounds of phase dispersion, represent the main long-period properties in sense of velocity spectrum at longer periods of interest. An analytical expression for power spectrum density is employed to capture the narrowband properties of LALP velocity surface waves. Meanwhile, modification of the Gaussian random process is performed in time and frequency domains to attain a modulated initial seed motion, which shows the variability of the targeted ground motion. A simulation of high-frequency components is accomplished by means of iteration, in which wavelet coefficients of the modulated seed motion are adjusted to match the targeted response spectrum and cumulative energy plot. Furthermore, comparisons between an ensemble of realizations and target motions demonstrate the feasibility of the proposed method to generate long-period simulations sharing similar properties to target motions.
The use of hollow-bearing trees by vertebrate fauna in wet and dry Eucalyptus obliqua forest, Tasmania

The loss of hollow-bearing trees in production forest areas can have large impacts on animal populations that rely on them for shelter. This study facilitates the selection of appropriate trees for retention by examining the proportion and type of trees that were used by vertebrate fauna in mature wet and dry Eucalyptus obliqua forest in Tasmania. Felled trees were searched for hollows and secondary evidence of use by fauna. Classification Trees and Bayesian logistic regression modelling were used to examine the site and tree attributes that best explained the use of a tree by fauna. We did two separate analyses: one using attributes expected to be causally related to hollow use, and a second using attributes that might be correlated with hollow use and could be easily assessed in standing trees. In all, 28% of hollow-bearing trees examined showed evidence of use, which is at the lower end of the scale found in other areas of Australia. The variables most strongly related to the use of a tree were hollow abundance, tree size and senescence. Random Forest modelling indicated that the likelihood of a hollow being used increased with hollow size, particularly hollow depth. Counting the number of hollows in standing trees was the best way to identify a tree that is likely to be used by fauna and this was particularly important for younger and healthier trees. It was recommended that trees to be retained should contain at least one large hollow. It was estimated that 8–15 trees per hectare were used by hollow-using fauna in mature wet and dry E. obliqua forest in Tasmania.