Miracle-dz/UniFAR-FTUs huggingface dataset:
Antibiogram and genetic relatedness of clinical isolates of Enterococcus spp. in Mangalore, India.

INTRODUCTION Drug-resistant Enterococcus species is a persisting clinical problem and may serve as a reservoir of resistant genes. The present study was undertaken in Mangalore, India to know the antibiogram and genetic relatedness of Enterococcus spp. isolated from clinical samples. METHODOLOGY A total of 150 non-repetitive Enterococcus spp. isolated from clinical samples were subjected to antimicrobial susceptibility testing by Kirby Bauer disk diffusion method. Molecular typing of the isolates was done by Random amplification of polymorphic DNA (RAPD). RESULTS Among the 150 isolates, 79 were from urine, 68 from pus and three from blood samples. Of this 58.7 % were E. faecalis and the remaining were E. faecium. Urinary isolates of E. faecium showed a higher percentage of antibiotic resistance when compared to E. faecium isolates from pus (p < 0.001). E. faecium from blood samples were resistant to ampicillin, penicillin, ciprofloxacin and were sensitive to vancomycin and teicoplanin. E. faecalis blood isolates were resistant to ciprofloxacin, penicillin, and erythromycin. 73% of Enterococcus isolates from pus were resistant to erythromycin. All the Enterococcus spp. were sensitive to vancomycin. Among the total Enterococcus isolates 44 were high-level aminoglycoside resistant (HLAR) by disc diffusion method which corresponded to MIC of > 500 µg/mL for gentamicin and > 1000 µg/mL for streptomycin. These isolates were subjected to RAPD, which showed similarity and differences in the banding patterns. CONCLUSIONS Our study showed a baseline resistance among Enterococcus spp. in our area, which poses a challenge to the treating physicians and a reservoir for transmission of antibiotic resistant genes.
Influence of Stimuli Color Combination on Online SSVEP-based BCI Performance

The stimuli color greatly affects the performance of a steady-state visual evoked potential (SSVEP)-based brain computer interface (BCI). However, few studies have evaluated the effect of stimuli color combination on the multiple-target BCI when the stimuli adopt different colors. In this study, 16 sets of colors were tested by using an online dual-task BCI, and the detection accuracy, time and information transmission rate (ITR) were measured. The results showed that the yellow-red combination achieved the highest accuracy, shortest time and highest ITR, which is the optimal choice for improving the performance of BCI.
Imaging Magnetization Switching Induced by Spin-Orbit Torque in Perpendicularly Magnetized Ta/CoFeB Structure

We report the study of current-induced magnetization switching in the Ta/CoFeB heterostructures by simultaneous magneto-electrical transport and magneto-optical Kerr effect (MOKE) microscopy measurements. Although the anomalous Hall hysteresis loop summarizes the general switching behaviors, the supplementary MOKE data can provide spatial and temporal information to visualize the magnetic domain evolution as functions of the applied current and the in-plane magnetic field. More importantly, when the initial magnetic field is not sufficient enough, the interfacial Dzyaloshinskii–Moriya interaction (DMI) would modulate the spin-orbit torque (SOT) current switching dynamics and lead to the formation of the intermediate Hall resistance plateau with meta-stable tilted domain texture. Our work elucidates the importance of the applied in-plane field and unveils an effective approach to investigate the SOT/DMI-related phenomena in symmetry-breaking magnetic heterostructures.
The Frequencies of Oppositely Charged, Uncharged Polar, and β-Branched Amino Acids Determine Proteins’ Thermostability

Enhancing proteins’ thermostability is an important aspect of enzyme engineering. Many studies have investigated the properties that determine the proteins’ thermostability. However, no consensus has emerged. To understand the mechanisms underlying the high thermostability of thermophilic proteins, we evaluated the relative importance of the amino acid frequencies in protein sequences for discriminating thermophilic and non-thermophilic proteins based on machine learning algorithms together with a three-step feature selection procedure and a principal component (PC) analysis to remove noisy and redundant information. Our results showed that the frequencies of oppositely charged amino acids, i.e., Lys and Glu, were higher in thermophilic proteins, suggesting that electrostatic interactions are fundamentally important for protein stabilization at high temperatures. Further, we found that the frequencies of uncharged polar amino acids, which are thermolabile or actively interact with water molecules, were lower in thermophilic proteins. Moreover, the frequencies of $\beta $ -branched aliphatic amino acids tended to increase with increasing thermostability. Overall, these results suggest that proteins’ thermostability is determined by a few protein features, which were well captured by the first two PCs. A classifier based on only the first two PCs achieved a high accuracy of 90%, suggesting that our classifier could be an effective and efficient tool for engineering stable proteins.
Data-Enhanced Stacked Autoencoders for Insufficient Fault Classification of Machinery and its Understanding via Visualization

As a practical tool for big data processing, deep learning not only has drawn extensive attentions in the inherent law and representation level of sample data, but also has been widely concerned in the field of mechanical intelligent fault diagnosis. In deep learning models, autoencoder (AE) and its derivative models can automatically extract useful features from big data, and many researchers have successfully applied them to the field of intelligent fault diagnosis. However, these studies always neglect two important points as follows: (1) the model training process will not be ideal when the original training dataset is insufficient; (2) the learning content of the network model is not clear. In order to surmount the above deficiencies, this paper proposes a novel framework named Data-enhanced Stacked Autoencoders (DESAE), which consists of a data enhancement module and a fault classification module. In the data enhancement module, SAE is adopted to generate simulated signals to strengthen the insufficient training data. In the fault classification module, the enhanced dataset is used to train another SAE model for fault type recognition. Meanwhile, two bearing datasets are employed to validate the efficiency of the proposed method. The experimental results show that the proposed method is superior to the method without enhanced data. In addition, the visual analysis of the learning characteristics in each layer of DESAE is presented, which is helpful to understand the working process of DESAE.
Open-World Person Re-Identification With RGBD Camera in Top-View Configuration for Retail Applications

Person re-identification (re-ID) is currently a notably topic in the computer vision and pattern recognition communities. However, most of the existing works on re-ID have been designed for closed world scenarios, rather than more realistic open world scenarios, limiting the practical application of these re-ID techniques. In a common real-world application, a watch-list of known people is given as the gallery/target set for searching through a large volume of videos where the people on the watch-list are likely to return. This aspect is fundamental in retail for understanding how customers schedule their shopping. The identification of regular and occasional customers allows to define temporal purchasing profiles, which can put in correlation the customers’ temporal habits with other information such as the amount of expenditure and number of purchased items. This paper presents the first attempt to solve a more realistic re-ID setting, designed to face these important issues called Top-View Open-World (TVOW) person re-id. The approach is based on a pretrained Deep Convolutional neural Network (DCNN), finetuned on a dataset acquired by using a top-view configuration. A special loss function called triplet loss was used to train the network. The triplet loss optimizes the embedding space such that data points with the same identity are closer to each other than those with different identities. The TVOW is evaluated on the TVPR2 dataset for people re-ID that is publicly available. The experimental results show that the proposed methods significantly outperform all competitive state-of-the-art methods, bringing to different and significative insights for implicit and extensive shopper behaviour analysis for marketing applications.
ZnO Schottky Thin Film Transistors: Single Gate and Dual Gate Frameworks and Electrical Characterizations: A Comparative Analysis

Over the years, the implementation of oxide-contained thin-film transistors (TFTs) have been magnified deliberately. Aside from empowering faster TFT response, semiconducting oxides guarantee to boost up TFT disciplines to power amplifiers or accomplishment of flat panel display in pliable electronics for authentic operations and for making noticeably economical production as well. ZnO is the most enormously investigated semiconducting oxide material in pliable electronics as with it noticeably economical fabrication at minimum processing temperature is possible. Receiving cognizance of the ZnO based TFTs, the differences in output characteristics of single and dual gate TFT structures have been investigated by varying the gate voltages using 2D numerical device simulator ATLASTM for two different device structures. Based on the transfer characteristics, a comparative study on the performance parameters of both the single and dual gate TFTs have been figured out in relation to threshold voltage, drain current, mobility, on-off ratio, subthreshold swing and transconductance with respect to the reported literatures.
Diagnosis of Cardiac Disorders in ECG Signal using Artificial Neural Network Algorithm

Heart disease was and still one of the critical disease that may affect our normal heart activity. Electrocardiograph (ECG) which represent heart cycle contain many components in form of waves, intervals, segments etc. In years, many techniques were developed to detect heart disorder and in this paper a method of cardiac arrhythmia detection and classification was developed to differentiate between normal an abnormal ECG signals, this process had included three main steps, First as known all signals even it was biological or other need to remove all the noises that may contained in this case filters were designed to remove these noises, and each kind need specific filter to be remove for example band-reject filter with cutoff frequencies (57Hz-43Hz) was designed to remove power line interferences and band-pass filter with cutoff frequencies (0.05Hz-30Hz) was designed to remove patient motion and patient chest movement. In the next step variables were calculated and features were extracted using a technique called wavelet transform algorithm which decompose the signals using many functions called wavelets, the one had been used was daubechies, It used because it has similar formulation. The extracted features and parameters were used as an input to the final stage, which is the artificial neural network (ANN) algorithm which is an algorithm works like the human brain when dealing with specific tasks, also it gains knowledge by training and evolving. The used signals were downloaded from an online website Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database which used to train and test the classifier. Classifier performance was calculated using four statistical values accuracy (98.7), sensitivity (98.7), positive predicatively (98.6), specify (90.91). The results showed new illations and support implemented ideas which will help the researcher in the future to compare between the found results and the current one, so noticing the progress, all this was implemented using Mat Lab (R2014b) program.
Adaptive Emotion Detection Using the Valence-Arousal-Dominance Model and EEG Brain Rhythmic Activity Changes in Relevant Brain Lobes

Contrary to popular belief that only the frontal lobe is concerned with emotions, recent neuroscience experiments show that in each emotional state, some of the brain lobes fired clearly whereas some did not. Unlike most of the previous works, which focused on choosing a fixed set of electrodes to detect emotions, this work presents a novel approach that consists of investigating the cerebral activity while experiencing emotions to identify the brain lobes that are showing significant and relevant changes. The identification process of relevant brain lobes is adaptive as the brain activity varies from one person to another and differs from one lobe to another during the same emotional state. The electrodes of the selected brain lobes will be tuned before being used as resources to extract the features that are required for the proposed three phases classification process. Using a clustering technique, the tuning process excludes every electrode that hardly separates between relevant and irrelevant rhythmic brain changes. The results show that the proposed method outperforms all previous approaches. Compared to the best performance obtained by previous studies, the proposed method enhanced the accuracy of both valence and arousal dimensions by 5%. Besides, the accuracy of the dominance dimension was improved by 2%.
Design and Analysis of Pattern Reconfigurable Microstrip Patch Antenna for X Band Applications

In this paper, pattern reconfigurable microstrip patch antenna has been designed and analysed which uses PIN diodes for switching between different radiating modes. The proposed antenna is able to function in three states and two operating frequencies depending on the biasing of PIN diodes used. The antenna has a compact size of 40 mm × 40 mm and a thickness of 1.6 mm. It operates in the X band at a frequency of 9.7 GHz. The proposed design can shift the radiation beam to 60 degrees in state 1 and 2 in opposite directions. The structure implements 8 PIN diodes. The ON and OFF state of PIN diodes have been analysed using copper strips for simulation purpose. The proposed antenna can be implemented to function in modern wireless communication and cellular communication networks.
STEER AND TALL FESCUE PASTURE RESPONSES TO GRAZING INTENSITY AND CHEMICAL SEEDHEAD SUPPRESSION

OF DISSERTATION STEER AND TALL FESCUE PASTURE RESPONSES TO GRAZING INTENSITY AND CHEMICAL SEEDHEAD SUPPRESSION Tall fescue (Lolium arundinaceum) is the principal cool-season species within pastures of the southeastern USA and is known to have a mutualistic relationship with a fungal endophyte (Neotyphodium coenophialum) that produces the ergot alkaloids responsible for tall fescue toxicosis. Management of the reproductive growth of tall fescue is necessary, as the seedheads contain the highest concentrations of ergot alkaloids, and livestock have been documented to selectively graze these tissues. Recently, the herbicide Chaparral TM has been shown to be an effective method to prevent seedhead production in tall fescue pastures while also increasing steer gains at a low stocking rate. The objective of this study was to compare the impact of Chaparral on steer and pasture production under multiple grazing intensities (GI). Chaparral (0 and 140 g ha -1 ) and two levels of GI (low: 3300±250 kg ha -1 & moderate: 2500±250 kg ha -1 ) treatments were arranged in a factorial combination as RCBD with three replications. Tall fescue seedhead densities were decreased (P 0.15) between the Chaparral-moderate GI and control-low GI treatments. The higher ADG compensated for the lower CC of the Chaparral and low GI treatments and resulted in no difference (P > 0.60) in total gain per hectare (GPH) between grazing intensities and herbicide treatments in 2011. In 2012, the GPH were higher within the control and moderate GI treatments due to a lessening in the magnitude of difference between the herbicide and GI treatments. The effects of these treatments for alleviating symptoms of tall fescue toxicosis were inconclusive due to the low levels of ergot alkaloids production.
Extending the Measurement Error Model of a Direct Visual Odometry Algorithm to Improve its Accuracy for Planetary Rover Navigation

In this paper, the accuracy of a direct monocular visual odometry algorithm is improved. The algorithm is able to determine the position and orientation of a robot directly from intensity differences measured at observation points between consecutive images, captured by a monocular camera, rigidly attached to one side of its structure, tilted downwards. The improvement was achieved by extending the stochastic model of the intensity-difference measurement error, from considering only the camera noise, to one that also considers the intensity-difference measurement error due to the 3D shape error between the assumed and the true planetary surface shape. The corresponding covariance matrix was incorporated into a Maximum Likelihood estimator. According to the experimental results on irregular surfaces, where the 3D shape error is usually large, the accuracy of the visual odometry algorithm improved by a factor of 2 but with the cost of increasing the processing time also by the same factor.
Diet composition and social environment determine food consumption, phenotype and fecundity in an omnivorous insect

Nutrition is the single most important factor for individual's growth and reproduction. Consequently, the inability to reach the nutritional optimum imposes severe consequences for animal fitness. Yet, under natural conditions, organisms may face a mixture of stressors that can modulate the effects of nutritional asymmetry. For instance, stressful environments caused by intense interaction with conspecifics. Here, we subjected the house cricket Acheta domesticus to (i) either of two types of diet that have proved to affect cricket performance and (ii) simultaneously manipulated their social environment throughout their complete life cycle. We aimed to track sex-specific consequences for multiple traits during insect development throughout all life stages. Both factors affected critical life-history traits with potential population-level consequences: diet composition induced strong effects on insect development time, lifespan and fitness, while the social environment affected the number of nymphs that completed development, food consumption and whole-body lipid content. Additionally, both factors interactively determined female body mass. Our results highlight that insects may acquire and invest resources in a different manner when subjected to an intense interaction with conspecifics or when isolated. Furthermore, while only diet composition affected individual reproductive output, the social environment would determine the number of reproductive females, thus indirectly influencing population performance.
Wild dogs at stake: deforestation threatens the only Amazon endemic canid, the short-eared dog (Atelocynus microtis)

The persistent high deforestation rate and fragmentation of the Amazon forests are the main threats to their biodiversity. To anticipate and mitigate these threats, it is important to understand and predict how species respond to the rapidly changing landscape. The short-eared dog Atelocynus microtis is the only Amazon-endemic canid and one of the most understudied wild dogs worldwide. We investigated short-eared dog habitat associations on two spatial scales. First, we used the largest record database ever compiled for short-eared dogs in combination with species distribution models to map species habitat suitability, estimate its distribution range and predict shifts in species distribution in response to predicted deforestation across the entire Amazon (regional scale). Second, we used systematic camera trap surveys and occupancy models to investigate how forest cover and forest fragmentation affect the space use of this species in the Southern Brazilian Amazon (local scale). Species distribution models suggested that the short-eared dog potentially occurs over an extensive and continuous area, through most of the Amazon region south of the Amazon River. However, approximately 30% of the short-eared dog's current distribution is expected to be lost or suffer sharp declines in habitat suitability by 2027 (within three generations) due to forest loss. This proportion might reach 40% of the species distribution in unprotected areas and exceed 60% in some interfluves (i.e. portions of land separated by large rivers) of the Amazon basin. Our local-scale analysis indicated that the presence of forest positively affected short-eared dog space use, while the density of forest edges had a negative effect. Beyond shedding light on the ecology of the short-eared dog and refining its distribution range, our results stress that forest loss poses a serious threat to the conservation of the species in a short time frame. Hence, we propose a re-assessment of the short-eared dog's current IUCN Red List status (Near Threatened) based on findings presented here. Our study exemplifies how data can be integrated across sources and modelling procedures to improve our knowledge of relatively understudied species.
Experimental and numerical investigation of the fatigue behaviour and crack evolution mechanism of granite under ultra-high-frequency loading

Assisted ultrasonic vibration technology has received great interest in the past few years for petroleum and mining engineering related to hard rock breaking. Understanding the fatigue behaviour and damage characteristics of rock subject to ultra-high-frequency loading is vital for its application. In this research, we conducted ultrasonic vibration breaking rock experiments combined with an ultra-dynamic data receiver and strain gauges to monitor the development of strain in real time. The experimental results show that the strain curve is U-shaped, and it can be divided into three stages: the strain first decreases, then remains steady (with light fluctuations) and finally increases. The sample first underwent compressive deformation, and no rupture occurred. As the vibration continued, the compressive deformation decreased with the initiation and propagation of cracks, and fragmentation occurred. To elucidate the crack evolution mechanism of the granite specimens, numerical simulations were performed using particle flow code in two dimensions (PFC2D), and an improved fatigue damage model based on the flat-joint contact model was proposed. The numerical results indicate that this model can effectively reproduce the fatigue characteristics of hard brittle rocks under ultrasonic vibration. By analysing the stress and strain fields and cracking process, the crack evolution mechanism in the brittle hard rock under ultra-high-frequency loading is revealed. These experimental and numerical results are expected to improve the understanding of the fragmentation mechanism of rock under assisted ultrasonic vibration.