Miracle-dz/UniFAR-FTUs huggingface dataset:
Remarkable confusion in some Western Palearctic Clepsis leads to a revised taxonomic concept (Lepidoptera, Tortricidae)

Abstract The taxonomy of some Palearctic species of the genus Clepsis Guenée, 1845 (Lepidoptera, Tortricidae), in particular C. neglectana sensu auctt. and C. consimilana sensu auctt. , is revised based on combined characters of external and internal adult morphology, including everted vesicae in male genitalia, and DNA barcodes. Clepsis striolana (Ragonot, 1879), stat. rev., C. acclivana (Zerny, 1933), C. trivia (Meyrick, 1913), stat rev., and C. xylotoma (Meyrick, 1891), stat. rev. are resurrected from synonymy with C. neglectana (Herrich-Schäffer, 1851), C. semiana (Chrétien, 1915), stat. nov. is considered as a valid species; C. eatoniana (Ragonot, 1881), stat. rev., is resurrected from synonymy with C. consimilana (Hübner, 1817), and C. razowskii Gastón, Vives & Revilla, 2017, syn. nov. is synonymised with C. eatoniana.
Synthesis and Characterization of Zero-Valent Iron Nanoparticles, and the Study of Their Effect against the Degradation of DDT in Soil and Assessment of Their Toxicity against Collembola and Ostracods

Of late, novel magnetic nanomaterials have drawn worldwide attention because of the uniqueness in their properties and uses. In our studies, we have prepared nearly monodisperse zero-valent iron nanoparticles (nZVIs) of diameter of less than 60 nm in aqueous medium by a reductive precipitation process and pectin as stabilizing agent. The characterization of these nanoparticles was done by dynamic light scattering and transmission electron microscopy (TEM) techniques. The TEM images confirmed that the average size of the nZVIs was about 25 nm. The resultant nZVIs were then employed to degrade DDT (dichlorodiphenyltrichloroethane) in spiked soil, and their toxicity toward Collembola (Folsomia candida) and Ostracods (Heterocypris incongruens) was measured. The fabricated nZVIs degraded DDT in soil quite effectively. Further, the effects of nZVIs on Collembola and Ostracods were found to be negative. This was due to the oxidation of nZVIs and creation of anoxic conditions thereupon, and the generation of excess Fe(II) in soil. In addition, the negative effects of DDT on ostracod development and Collembola reproduction were found to be quite weak.
Genome-wide identification and characterization of heat shock protein family 70 provides insight into its divergent functions on immune response and development of Paralichthys olivaceus

Flatfish undergo extreme morphological development and settle to a benthic in the adult stage, and are likely to be more susceptible to environmental stress. Heat shock proteins 70 (hsp70) are involved in embryonic development and stress response in metazoan animals. However, the evolutionary history and functions of hsp70 in flatfish are poorly understood. Here, we identified 15 hsp70 genes in the genome of Japanese flounder (Paralichthys olivaceus), a flatfish endemic to northwestern Pacific Ocean. Gene structure and motifs of the Japanese flounder hsp70 were conserved, and there were few structure variants compared to other fish species. We constructed a maximum likelihood tree to understand the evolutionary relationship of the hsp70 genes among surveyed fish. Selection pressure analysis suggested that four genes, hspa4l, hspa9, hspa13, and hyou1, showed signs of positive selection. We then extracted transcriptome data on the Japanese flounder with Edwardsiella tarda to induce stress, and found that hspa9, hspa12b, hspa4l, hspa13, and hyou1 were highly expressed, likely to protect cells from stress. Interestingly, expression patterns of hsp70 genes were divergent in different developmental stages of the Japanese flounder. We found that at least one hsp70 gene was always highly expressed at various stages of embryonic development of the Japanese flounder, thereby indicating that hsp70 genes were constitutively expressed in the Japanese flounder. Our findings provide basic and useful resources to better understand hsp70 genes in flatfish.
The effect of progressive muscle relaxation on anxiety and stress in nursing students at the beginning of the internship program

Background and aims: Anxiety is a complicated process appearing from low to high which may reduce the learning ability. It is common among nurses as they struggle to cope with increasingly complicated stressors such as examinations, article preparation and practical experiences. The aim of this study was to evaluate the effect of muscle relaxation on reduction of anxiety in nursing students before starting the internship program. Methods: In this study, 40 nursing students who were going to start their internship program were randomly divided into two equal groups. Before starting the internship program, all students were asked to fill a questionnaire (Spielberger Test Anxiety) to measure their anxiety. In addition, physiological parameters (blood pressure, pulse, respiration, temperature) were collected before and after the study in both groups. During a 45-min session, muscle relaxation training was given to the test group. Muscle relaxation practice was continued three times per day for two weeks. Two weeks after starting the internship, Spiel Berger Test Anxiety and physiological parameters were collected again in both groups. Data were analyzed using Chi-square and t tests. Results: The average age of the students in both groups was 22.7±2 (range=21 to 25 years) and the majority of the students were female (75%). No significant difference was observed between two groups for willingness to the nursing profession, birth rank and the average scores of the students from the previous years. The average anxiety score in control group before and after the study was 27.5±10.2 and 28.3±8.8 respectively (P>0.05). Average anxiety score in test group before and after the study was 25.4±19.5 and 29.2±14.1 respectively (P<0.05). Conclusion: To alleviate stressors associated with practical practice and create a healthy work environment for practice, it is recommended that nursing students have some progressive muscle relaxation before entering practical environments.
Current/Voltage Dual-Mode Single-Wire Simultaneous Bidirectional Interface Architecture for Sensor System

This paper presents a simultaneous bidirectional asymmetrical serial interface architecture for sensor systems. The proposed current/voltage dual-mode signaling scheme provides system synchronous clock and continuous data transmission between sensor integrated circuit (IC) and system-on-a-chip (SoC) using a single wire, which minimizes pin requirements on packages. Two types of transceiver circuits were implemented in a 65 nm CMOS technology for the sensor IC and the SoC, and they were designed for transmission rates of 1 Mb/s and 250 Kb/s, core areas of 0.008 mm2 and 0.142 mm2, and power consumptions of 7.1 μW and 145.8 μW, respectively. The transceiver circuit for the sensor IC was also applied to a monolithic PPG sensor implemented in 180 nm CMOS, and the acquisition and transmission of PPG sensor data with the transceiver for the SoC, implemented in 65 nm, was successfully achieved.
Monitoring the Stability of Tide Gauges Using Time-Series INSAR Analysis: A Case Study in Pohang, South Korea

Coastal Subsidence is a global phenomenon that caused by both natural and anthropogenic activities. The impact of coastal subsidence can affect the monitoring of sea level rise by in-situ tide gauges. The Korean peninsula is one of such areas that reported considerably rapid sea level rise than the global average in the recent years. The objective of this study is to estimate the earth’s surface movements along the coastal areas of Pohang, South Korea. To achieve that, we acquired C-band Sentinel-1 data during 2016 – 2017. Time-series InSAR technique is applied to measure displacements at high-resolution with reduced decorrelation noise from interferograms. The results showed about 1.83 cm/year subsidence in the Pohang coastal area, where significant sea level rise is observed in this region. The results will provide further insight into the localized subsidence rates in the study sites and its impact on the sea level monitoring.
Alzheimer's Disease Diagnosis and Severity Level Detection Based on Electroencephalography Modulation Spectral “Patch” Features

Over the last two decades, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). Typically, resting-state EEG (rsEEG) signals have been used, and traditional frequency bands (delta, theta, alpha, beta and gamma) have been explored. Recent studies, however, have suggested that non-conventional bands may lead to improved diagnostic performance. In this work, we propose a new type of features derived from the 2-dimensional modulation spectral domain representation of the rsEEG signal in order to characterize the neuromodulatory deficit emergent with AD. The proposed features are computed as the power in specific “patches” or regions of interest in the power modulation spectrogram, which are shown to be highly discriminant of AD severity levels. The proposed features were compared with traditional features used in the rsEEG AD monitoring literature. Results showed the proposed features not only achieving improved performance at discriminating between healthy normal elderly controls (Nold) and AD patients with varying severity levels, but also at monitoring severity levels (i.e., mild AD versus moderate AD). Moreover, the proposed features were shown to outperform traditional rsEEG features. Finally, we validated the biological origin of the proposed features by using source localization and comparing the obtained results with ones reported in the AD literature.
An Optimal Sampling Design for Land Surface Temperature Validation with Spatial and Diurnal Variations

The development of ground-based sampling strategies is vital to the validation of medium- or coarse-resolution satellite-derived land surface temperature (LST) products, extremely over heterogeneous ground with dramatic diurnal LST change. An optimal sampling strategy in support of LST validation across both spatial and diurnal scales (SDS) was proposed in this study. The SDS integrated prior knowledge of land-cover, multi-temporal feature information and spatial distribution of samples to improve the representativeness of the samples. The SDS were also compared with three sampling strategies including random, systematic, and land-cover base sampling. The results obtained by the remote sensing simulation data indicated that the SDS performed best with stable root mean square errors (RMSE) less than 0.1k when sample ratio was more than 2%, and the representativeness of samples selected by the SDS in both diurnal space and spatial space was superior to the current sampling strategies.
Using the Two-Level Model with Tandem-X for Large-Scale Forest Mapping

This study applies the two-level model to predict stem volume (VOL), presented as wall-to-wall rasters. The SAR data were acquired with the TanDEM-X system and 518 scenes covered the entire Sweden. For comparison, a multiple linear regression model is also evaluated. Compared to earlier studies, the model parameters are fitted separately for each satellite scene. The prediction accuracy at the stand-level is evaluated using field inventoried reference stands within one scene, located in Northern Sweden and provided by a Swedish forest company. The results from the two models were similar, with an RMSE of 34.8 m3/ha and 32.9 m3/ha at the stand-level, respectively, and the corresponding biases were 14.3 m3/ha and 12.1 m3/ha. The error is significantly lower, compared to a previous study (52-65 m3/ha) where a universal multiple linear regression model was used for all scenes. It can be concluded, that using model parameters fitted at the local scene appears to improve the prediction performance in terms of RMSE, but no significant difference could be determined between predictions based on the two- level model or multiple linear regression, evaluated in this study.
Polyp Location in Colonoscopy Based on Deep Learning

Colorectal cancer is one of the most common cancers in China. The occurrence of most colorectal cancer is closely related to colorectal polyps. Colonoscopy is the gold standard for the diagnosis of intestinal lesions. Usually, existing colonoscopy is performed by physicians to determine the location of polyps by observing the results of detection with the naked eye. The detection rate of polyps is also affected by the doctor’s experience, fatigue, detection rate, and other factors, so there is a certain degree of polyp missed detection. Therefore, to improve diagnostic accuracy and reduce the rate of missed diagnosis, the paper proposes an improved_ssd model based on deep learning. The model is extended from the ssd_inception_v2 model, and the inception_v2 basic framework is used to extract features from multiple dimensions and fuse them, which improve the accuracy of polyp location. The test results show that the AP of this method is 94.92%, the accuracy is 96.04%, the sensitivity is 93.67%, and the specificity is 98.36%. This method realizes the accurate localization of polyps in colonoscopy and provides a reference for doctors' diagnosis.
Revisiting an Iterative Speckle Filtering Technique

Because of speckle noise, estimating extended target scattering properties from Single Look Complex PolSAR data is a difficult problem. Speckle filtering is aiming at reducing noise within extended targets while preserving point target, polarimetric signatures and meaningful details. However, optimal speckle filtering requires some knowledge about the underlying target properties. Also, using all the terms of the quadpol polarimetric matrix in order to drive speckle filtering is still an open problem. Deep Learning techniques have been successful in tackling difficult computer vision tasks and show promising results when applied to SAR and PolSAR data. The goal of this paper is to investigate the use of Convolutional Neural Networks (CNN) in order to extract information from the full quadpol covariance matrix and help improve speckle filtering. Experiments are conducted using simulated PolSAR data and results show that we can potentially recover information from the off-diagonal terms.
High-Speed Aircraft Single Channel SAR-GMTI Based on Neural Network

For traditional ground moving target indication, multiple channels are necessary to cancel ground clutter. For high-speed aircraft, slow moving target detection is a difficult problem because ground clutter cannot be eliminated completely by channel cancellation. In this paper, we propose a method, which is implemented by single-channel SAR images and improved Faster R-CNN, to detect the moving target and the stationary target. Synthetic aperture radar image which contains amplitude and phase information is put into the neural network to detect the moving and stationary target. We make a dataset to verify the availability of the proposed method. In order to increase the credibility of the dataset, we use FEKO to calculate the target electromagnetic scattering characteristics and use measured data scattering characteristics to generate the ground echo. The simulation proves that the proposed method has good performance in moving target detection and the performance of the proposed method is better than Faster R-CNN.
LED visible light communication system using frequency division multiplexing (FDM) technique

A LED visible light communication system is proposed based on frequency division multiplexing (FDM) technique. Which can transmit analog and digital signals by different channels with a single LED. The main idea of this system is to modulate above two signals with different frequency carriers, and then add them together through an adder to finally drive a LED. The receiving modulated light signals is amplified, filtered, reshaped, and finally demodulated to restore the original input signal.
Missing data and sensitivity analysis for binary data with implications for sample size and power of randomized clinical trials

Despite our best efforts, missing outcomes are common in randomized controlled clinical trials. The National Research Council's Committee on National Statistics panel report titled The Prevention and Treatment of Missing Data in Clinical Trials noted that further research is required to assess the impact of missing data on the power of clinical trials and how to set useful target rates and acceptable rates of missing data in clinical trials. In this article, using binary responses for illustration, we establish that conclusions based on statistical analyses that include only complete cases can be seriously misleading, and that the adverse impact of missing data grows not only with increasing rates of missingness but also with increasing sample size. We illustrate how principled sensitivity analysis can be used to assess the robustness of the conclusions. Finally, we illustrate how sample sizes can be adjusted to account for expected rates of missingness. We find that when sensitivity analyses are considered as part of the primary analysis, the required adjustments to the sample size are dramatically larger than those that are traditionally used. Furthermore, in some cases, especially in large trials with small target effect sizes, it is impossible to achieve the desired power.
Object Grasping using Convolutional Neural Networks

We have described learning based approach for a robotic arm to grasp an object or clear the clutter kept in front of it. We have used AlexNet pre-trained ImageNet for our CNN (Convolutional neural network). We continuously feed the image to our Deep neural network and this deep neural network identifies the objects and gives out an output in the form of grasp angle and the coordinates of the object that is to be picked. These coordinates are fed to the robotic arm to do the action. Thus, implementing hand eye coordination. Our experimental results demonstrate that the robotic arm is able to grasp novel objects successfully. We got an accuracy of 70% for previously known objects and 64% accuracy for novel objects.
Mammary microbiome of lactating organic dairy cows varies by time, tissue site, and infection status

Infections of the cow udder leading to mastitis and reducing milk quality are a critical challenge facing all dairy farmers. Mastitis may be linked to the ecological disruption of an endogenous mammary microbial community, suggesting an ecosystems approach to management and prevention of this disease. The teat end skin represents a first point of host contact with mastitis pathogens and may offer an opportunity for microbially mediated resistance to infection, yet we know little about the microbial community of teat end skin or its potential interaction with the microbial community of intramammary milk of organic dairy cattle. High-throughput sequencing of marker genes for bacterial and fungal communities was used to characterize the skin and milk microbiome of cows with both a healthy and infected gland (i.e., udder quarter) and to assess the sharing of microbial DNA between these tissue habitat sites. The mammary microbiome varied among cows, through time, and between skin and milk. Microbiomes of milk from healthy and infected quarters reflected a diverse group of microbial DNA sequences, though milk had far fewer operational taxonomic units (OTUs) than skin. Milk microbiomes of infected quarters were generally more variable than healthy quarters and were frequently dominated by a single OTU; teat end skin microbiomes were relatively similar between healthy and infected quarters. Commonly occurring genera that were shared between skin and milk of infected glands included Staphylococcus spp. bacteria and Debaryomyces spp. fungi. Commonly occurring genera that were shared between skin and milk of healthy glands included bacteria SMB53 (Clostridiaceae) and Penicillium spp. fungi. Results support an ecological interpretation of the mammary gland and the notion that mastitis can be described as a dysbiosis, an imbalance of the healthy mammary gland microbiome.
Artificial Intelligence Managed Network Defense System against Port Scanning Outbreaks

Cybercriminals predominantly uses port scanner to discover potential weak points or vulnerabilities they could exploit. Port scanning is one of the penetration testing technique used by attackers. It is the first step of reconnaissance in cyber kill chain used by most of the scammers when trying to penetrate a network or steal or damage sensitive data. Conventional network intrusion detection systems and firewalls are usually preconfigured to detect this kind of malicious network scans. Now-a-days attackers have become smarter and can try avoiding common detection rules. There are a few targeted areas where Artificial Intelligence will deliver the greatest evolution for Cybersecurity. To build proactive defenses, the system has to understand the intelligence of threats that are currently targeting the organization. The implementation of machine learning and Threat intelligent-based solutions into the mix can revolutionize the landscape in cybersecurity industry against those kinds of network attacks. This paper proposed an Artificial Intelligence managed network defense system to protect the computer network from recon of known and future threats. Thus, the proposed system will not only detect anomalous network traffic as in classical IDSs, but also it learns itself and update the rules in IDS which offer the deadlock towards malicious network traffic.
Optical Remote Sensing Water-Land Segmentation Representation Based on Proposed SNS-CNN Network

For water resource analysis applications, due to very high resolution and large observation scope, optical remote sensing images can present more visible object characters. Water-land segmentation from optical remote sensing images is wildly used and becomes a hot research topic. However, since large scale complex background scenes include many interferences, the water-land segmentation from optical remote sensing images becomes a challenge task. Aim to achieve better water area feature description from complex land cover background, we apply a sub-neighbor system convolutional neural network (SNS-CNN) to the water-land segmentation in harbor scene areas. First, on the basis of the U-net structure, an optimized up-sampling process is proposed to enhance water area feature expression. Second, a novel sub-neighbor system constraint of each predicted pixel point is leaded into the loss function to make the model producing water mask more coherent. Furthermore, experiments on our collected variety of optical remote sensing images demonstrate that this paper proposed water-land segmentation method can produce better performance than state of the art methods.
Large Scale Digital Surface Model Production on Cloud Using Big Data Technologies for Future EO Mission

Earth Observation (EO) remote sensing missions are producing an increasing volume of data due to higher spatial and spectral resolutions, and higher frequency of acquisitions. To prepare new EO missions, CNES, the French space agency, develop in partnership with CS a demonstrator that aims at implementing and validating image processing chains using Big Data and Cloud technologies in an environment similar to an operational image processing center. The image processing pipeline chosen for the study is a 3D image processing chain that will be used for a future earth observation mission. The demonstrator is currently being tested at a large scale on various Cloud infrastructures with numerous stereo and tri-stereo images.