Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometric Method based Covariance Matrix Estimator with Application to Radar Target Detection

Accurate covariance matrix estimation is important to radar target constant false alarm rate (CFAR) detection. In this paper, a 2-step method derived from geometric framework independent of clutter statistical distribution is employed to estimate the clutter covariance matrix. Firstly, the covariance matrix with Toeplitz Hermitian positive definite (THPD) structure of each reference cell is calculated by the correlation coefficient of the received data. Then the geometric barycentre of THPD matrix is estimated using different geometric measures on the matrix manifold. Furthermore, we obtain several modified adaptive normalized matched filter (ANMF) methods, which combine the proposed matrix estimation with the classical ANMF method. Finally, the effectiveness of the improved ANMF methods is verified through simulation experiments, which also indicate the reliability of the proposed clutter covariance matrix estimation.
Wideband Jamming Cancellation for Wideband Phased Array Radar: Digital Beamforming Architecture and Preliminary Results

The increasing desire for high resolution measurements in modern radar has motivated the development of wideband phased array radar system. However, the wideband jamming is introduced, a threat to detection and recognition. In order to suppress the wideband jamming, the highly efficient subbanding technique is exploited by decomposing the entire wide band into multiple subbands in which narrowband adaptive processing can still be practiced. Due to low-rate hardware, the conventional architecture intended to do all the other processing over narrowband so that subband decomposition is required right after IF digitalization. Utilizing the state-of-the-art technology of high ADC and digital processor, this paper contributes to proposing an enhanced wideband beamforming architecture. By converting the wideband signals from IF to baseband, true wideband processing is enabled at baseband and only jamming cancellation is remained within subband. Advantages include improved beam steering accuracy, less subbanding distortion, lower computation cost and so on. Cancellation performance is evaluated both by simulation and with an experimental wideband digital phased array system. Fruitful results are presented using data collected from the array in presence of wideband jamming.
New band-pass and band-stop filters with three memory devices

This paper presents a new kind of electrical circuit which replace traditional resistor, capacitor and inductor with memristor, memcapacitor and meminductor. These 3 memory devices constitute a new kind of RLC circuit. The paper will show and prove the characteristics of band-pass and band-stop filter with the circuit including memristor, memcapasitor and meminductor. Unlike the traditional electrical components, the value of mem-devices is time-varying, which has higher controlability and usage. Band-pass filter and band-stop filter with mem-devices has further application such as signal processing
Financial Network Approach for Modeling about Company Bankruptcy

We construct a financial network that models the complex system representing the bankruptcy effect of a company. In particular, we study the influence of PT Sariwangi AEA and Indorub bankruptcy, which affects at least three banks and four other companies directly. We present the interconnectivity of the banks and other corporations regarding the ownerships and liabilities using graphs. From these resulting graphs, we use techniques in network science to provide several important measurements and statistics, such as the centrality of each network. We successfully constructed a financial network surrounding PT Sariwangi AEA and Indorub. Centrality measures obtained from the graph shows that Rabobank, BCA, Rabobank UA, ICBC, and Interbank are the most important and influential entities in the financial network.
Integrated Environmental Assessment and Management

The calculation of a species sensitivity distribution (SSD) is a commonly accepted approach to derive the predicted no‐ effect concentration (PNEC) of a substance in the context of environmental risk assessment. The SSD approach usually is data demanding and incorporates a large number of ecotoxicological values from different experimental studies. The probabilistic SSD (PSSD) approach is able to fully consider the variability between different exposure conditions and material types, which is of great importance when constructing an SSD for any chemical, especially for nanomaterials. The aim of our work was to further develop the PSSD approach by implementing methods to better consider the uncertainty and variability of the input data. We incorporated probabilistic elements to consider the uncertainty associated with uncertainty factors by using probability distributions instead of single values. The new PSSD method (named “PSSD+”) computes 10 000 PSSDs based on a Monte Carlo routine. For each PSSD calculated, the hazardous concentration for 5% of species (HC5) was extracted to provide a PNEC distribution based on all data available and their associated uncertainty. The PSSD+ approach also includes the option to consider a species weighting according to a typically constituted biome. We applied this PSSD+ approach to a previously published data set on carbon nanotubes and silver nanoparticles. The evaluation of the uncertainty factor distributions and species weighting have shown that the proposed PSSD method is robust with respect to the calculation of the PNEC value. Furthermore, we demonstrated that the PSSD+ can handle both small and more comprehensive data sets because the PNEC distributions are a close representation of the data available. Finally, the sensitivity testing toward data set variations showed that the maximum variation of the mean PNEC was of a factor of about 2, so that the method is relatively insensitive to missing data points as long as the most sensitive species is included. Integr Environ Assess Manag 2020;16:211–222. © 2019 SETAC
DRPAN: A novel Adversarial Network Approach for Retinal Vessel Segmentation

Retinal Vessel segmentation is the key to automatic detection of retinal diseases in ophthalmoscope images. This paper proposes a method for generating accurate probability maps of retinal blood vessels using generative adversarial network. Unlike the traditional generative adversarial network (GAN) structure, the segmentation model of the GAN in this paper is represented by the encoder-decoder network. The encoder network consists of an dilated residual layer and a decoder network is composed of the pyramid pooling network and the three-layer convolution layer. Due to traditional GAN suffer from training instability, we use Wasserstein GAN-gp (WGAN-gp) in this paper to improve training. Experimental results show that the method proposed in this paper has a satisfactory effect on the segmentation of retinal vessels on the datasets of DRIVE and STARE.
Energy Conservation and Hydrogenase Function in Methanogenic Archaea, in Particular the Genus Methanosarcina

The biological production of methane is vital to the global carbon cycle and accounts for ca. 74% of total methane emissions. The organisms that facilitate this process, methanogenic archaea, belong to a large and phylogenetically diverse group that thrives in a wide range of anaerobic environments. Two main subgroups exist within methanogenic archaea: those with and those without cytochromes. SUMMARY The biological production of methane is vital to the global carbon cycle and accounts for ca. 74% of total methane emissions. The organisms that facilitate this process, methanogenic archaea, belong to a large and phylogenetically diverse group that thrives in a wide range of anaerobic environments. Two main subgroups exist within methanogenic archaea: those with and those without cytochromes. Although a variety of metabolisms exist within this group, the reduction of growth substrates to methane using electrons from molecular hydrogen is, in a phylogenetic sense, the most widespread methanogenic pathway. Methanogens without cytochromes typically generate methane by the reduction of CO2 with electrons derived from H2, formate, or secondary alcohols, generating a transmembrane ion gradient for ATP production via an Na+-translocating methyltransferase (Mtr). These organisms also conserve energy with a novel flavin-based electron bifurcation mechanism, wherein the endergonic reduction of ferredoxin is facilitated by the exergonic reduction of a disulfide terminal electron acceptor coupled to either H2 or formate oxidation. Methanogens that utilize cytochromes have a broader substrate range, and can convert acetate and methylated compounds to methane, in addition to the ability to reduce CO2. Cytochrome-containing methanogens are able to supplement the ion motive force generated by Mtr with an H+-translocating electron transport system. In both groups, enzymes known as hydrogenases, which reversibly interconvert protons and electrons to molecular hydrogen, play a central role in the methanogenic process. This review discusses recent insight into methanogen metabolism and energy conservation mechanisms with a particular focus on the genus Methanosarcina.
Detection of Differentially Methylated Regions Using Bayes Factor for Ordinal Group Responses

Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation, because they play an important role in regulating gene expression without changes in the DNA sequence. There have been significant advances in developing statistical methods to detect differentially methylated regions (DMRs) associated with binary disease status. Most of these methods are being developed for detecting differential methylation rates between cases and controls. We consider multiple severity levels of disease, and develop a Bayesian statistical method to detect the region with increasing (or decreasing) methylation rates as the disease severity increases. Patients are classified into more than two groups, based on the disease severity (e.g., stages of cancer), and DMRs are detected by using moving windows along the genome. Within each window, the Bayes factor is calculated to test the hypothesis of monotonic increase in methylation rates corresponding to severity of the disease versus no difference. A mixed-effect model is used to incorporate the correlation of methylation rates of nearby CpG sites in the region. Results from extensive simulation indicate that our proposed method is statistically valid and reasonably powerful. We demonstrate our approach on a bisulfite sequencing dataset from a chronic lymphocytic leukemia (CLL) study.
Scalable Deep Hashing for Large-Scale Social Image Retrieval

Recent years have witnessed the wide application of hashing for large-scale image retrieval, because of its high computation efficiency and low storage cost. Particularly, benefiting from current advances in deep learning, supervised deep hashing methods have greatly boosted the retrieval performance, under the strong supervision of large amounts of manually annotated semantic labels. However, their performance is highly dependent upon the supervised labels, which significantly limits the scalability. In contrast, unsupervised deep hashing without label dependence enjoys the advantages of well scalability. Nevertheless, due to the relaxed hash optimization, and more importantly, the lack of semantic guidance, existing methods suffer from limited retrieval performance. In this paper, we propose a SCAlable Deep Hashing (SCADH) to learn enhanced hash codes for social image retrieval. We formulate a unified scalable deep hash learning framework which explores the weak but free supervision of discriminative user tags that are commonly accompanied with social images. It jointly learns image representations and hash functions with deep neural networks, and simultaneously enhances the discriminative capability of image hash codes with the refined semantics from the accompanied social tags. Further, instead of simple relaxed hash optimization, we propose a discrete hash optimization method based on Augmented Lagrangian Multiplier to directly solve the hash codes and avoid the binary quantization information loss. Experiments on two standard social image datasets demonstrate the superiority of the proposed approach compared with state-of-the-art shallow and deep hashing techniques.
Performability Model for Assessing NoSQL DBMS Consistency

Over the last decades, NoSQL database management systems (DBMS) have been adopted as a prominent technology for big data applications. NoSQL provides high performance and scalability, but there are trade-offs for meeting these non-functional requirements. A major issue is related to eventual consistency between database replicas to achieve improved performance and availability. Usually, NoSQL DBMSs allow the adoption of distinct consistency levels to mitigate problems related to outdated information, though raising the consistency level implies in loss of performance. Such a trade-off may not be trivial to be assessed, since some consistency levels may waste or overuse system resources without obtaining better performance or availability. This paper presents a technique based on generalized stochastic Petri nets to assess NoSQL DBMS consistency regarding latency and availability. A case study is presented to demonstrate the influence of consistency and replication factors on system performance
Rhinovirus C15 Induces Airway Hyperresponsiveness Via Calcium Mobilization in Airway Smooth Muscle.

Rhinovirus (RV) exposure evokes exacerbations of asthma that markedly impact morbidity and mortality worldwide. The mechanisms by which RV induces airway hyperresponsiveness (AHR) or by which specific RV serotypes differentially evoke AHR remain unknown. We posit that RV infection evokes AHR and inflammatory mediator release, which correlate with levels of RV infection. Further, we posit that RVC-induced airway hyperresponsiveness requires paracrine or autocrine mediator release from epithelium that modulates agonist-induced calcium mobilization in human airway smooth muscle (HASM). In these studies we utilized an ex vivo model to measure bronchoconstriction and mediator release from infected airways in human precision cut lung slices (hPCLS) to understand how RV exposure alters airway constriction. We found that RV-C15 infection augmented carbachol-induced airway narrowing, and significantly increased release of IP-10, MIP1β, but not IL-6. RV-C15 infection of human airway epithelial cells (HAEC) augmented agonist-induced intracellular calcium flux and phosphorylation of myosin light chain in co-cultured HASM to carbachol, but not following histamine stimulation. Our data suggest that RV-C15-induced structural cell inflammatory responses are be associated with viral load, but that inflammatory responses and alterations in agonist-mediated constriction of human small airways are uncoupled from viral load of the tissue.
A Prospective Study of Intraprostatic Inflammation, Focal Atrophy, and Progression to Lethal Prostate Cancer

Background: Inflammation and focal atrophy are common features adjacent to prostate tumors. Limited evidence exists on whether these features have prognostic significance. Methods: In the Health Professionals Follow-Up Study and Physicians' Health Study, we studied 1,035 men diagnosed with prostate cancer. A genitourinary pathologist centrally reviewed tumor and normal areas of hematoxylin and eosin slides from prostate cancer specimens for the presence of acute and chronic inflammation, and four subtypes of focal atrophy. Cox proportional hazards models adjusted for potential confounders were used to estimate HRs and 95% confidence intervals (CI) for the association of these features with lethal prostate cancer, defined as development of metastatic disease or death during follow-up. Results: During a median of 12 years of follow-up, 153 men developed lethal prostate cancer. A total of 84% of men had histologic evidence of chronic inflammation and 30% had acute inflammation. Both chronic and acute inflammation were inversely associated with lethal prostate cancer in age- and lifestyle-adjusted models. Chronic inflammation remained inversely associated with lethal prostate cancer after additionally adjusting for prognostic clinical features (HR = 0.45; 95% CI, 0.30–0.69 for mild and HR = 0.51; 95% CI, 0.33–0.80 for moderate to severe). None of the atrophic lesions were associated with lethal prostate cancer. Conclusions: Our data suggest that the presence of inflammation, particularly chronic inflammation, in prostate cancer tissue is associated with better prognosis among patients with prostate cancer. Impact: This is the largest prospective cohort study to examine the association between inflammation, focal atrophy, and lethal prostate cancer.
Prediction of Agriculture and Mining Stock Value Listed in Kompas100 Index Using Artificial Neural Network Backpropagation

Company's stock prices in the market are very fluctuating, where its value experiences up and down according to market condition. Based on that fact, stock price become very difficult to be predicted or even estimated. Therefore, analysis of stock price movement predictions is heavily required as investment decision reference for investor. This paper uses technical analysis with Artificial Neural Network Backpropagation method as one of machine learning methodology which has commonly used along with rapid development of data mining and big data analytic that produces a prediction with high accuracy and the least error. The objects in this paper were four companies in the agricultural and mining sector, which was among Kompas100 index with the largest market capitalization in the period of 2013 to 2018. Based on the results of research with several variations of the experiment using varying hidden layer values 10, 20, 30 with variations in SGD values: 0.01, 0.001 and 0.0001 as well as variations in epoch 100, 200 and 300 values, resulting in network predictive models that provide training with error rates the minimum error is in the 5-20-1 model with a value of SGD 0.01 and epoch 300. Accuracy performance prediction of using MAE, MSE, RMSE and MAPE shows that the deviation of the predicted values of Artificial Neural Network Backpropagation from the actual values at agricultural and mining stock prices is low enough to conclude that stock predictions are accurate
Isolation and Identification of Lactic Acid Bacteria Probiotic Culture Candidates for the Treatment of Salmonella enterica Serovar Enteritidis in Neonatal Turkey Poults

Simple Summary Salmonella spp. continues to be one of the most important foodborne bacterial pathogens. S. enterica serotype Enteritidis (SE) that emerged as an important human illness during the 1980s is currently one of the most common non-typhoidal Salmonella serotypes worldwide. Poultry and their products (eggs and meat) are considered as one of the most important source of SE infection in humans. Due to restrictions in the addition of antibiotics in the feed of animals intended for human consumption, alternatives to these antibiotics have been sought. Probiotics have shown to reduce infection in turkey poults. However, studies are lacking to show how these probiotics influence the intestinal microbiome as well as how this microbiome is related to a lower infection by Salmonella. In the present study the effect of a Lactobacillus spp. -based probiotic on SE colonization was evaluated in two separate experiments. In both trials, a significant reduction in the incidence and log10 cfu/g of SE were observed in poults treated with the probiotic when compared with control poults (p ≤ 0.05). Results showed that the application of this probiotic culture could reduce SE cecal colonization in day-of-hatch turkey poults, although further research is needed to elucidate the mechanism of this response. Abstract The effect of Lactobacillus spp. -based probiotic candidates on Salmonella enterica serovar Enteritidis (SE) colonization was evaluated in two separate experiments. In each experiment, sixty-one day-of-hatch female turkey poults were obtained from a local hatchery. In both experiments, poults were challenged via oral gavage with 104 cfu/poult of SE and randomly allocated to one of two groups (n = 30 poults): (1) the positive control group and (2) the probiotic treated group. Heated brooder batteries were used for housing each group separately and poults were allowed ad libitum access to water and unmedicated turkey starter feed. 1 h following the SE challenge, poults were treated with 106 cfu/poult of probiotic culture via oral gavage or phosphate-buffered saline (PBS) to control groups. A total of 24 h post-treatment, poults were euthanized and the ceca and cecal tonsils from twenty poults were collected aseptically for SE recovery. In both trials, a significant reduction in the incidence and log10 cfu/g of SE were observed in poults treated with the probiotic when compared with control poults (p ≤ 0.05). The results of the present study suggest that the administration of this lactic acid-producing bacteria (LAB)-based probiotic 1 h after an SE challenge can be useful in reducing the cecal colonization of this pathogen in neonatal poults.
Disseminated Well-Differentiated Gastro-Entero-Pancreatic Tumors Are Associated with Metabolic Syndrome

The association of well-differentiated gastro-entero-pancreatic neuroendocrine tumors (WD GEP-NETs) with metabolic syndrome (MetS), abdominal obesity, and fasting glucose abnormalities was recently described. The aim of this study was to evaluate whether the presence of MetS or any MetS individual component was also influenced by GEP-NET characteristics at diagnosis. A cohort of patients with WD GEP-NETs (n = 134), classified according to primary tumor location (gastrointestinal or pancreatic), pathological grading (G1 (Ki67 ≤ 2%) and G2 (>3 ≤ 20%) (WHO 2010), disease extension (localized, loco-regional, and metastatic), and presence of hormonal secretion syndrome (functioning/non-functioning), was evaluated for the presence of MetS criteria. After adjustment for age and gender, the odds of having MetS was significantly higher for patients with WD GEP-NET grade G1 (OR 4.35 95%CI 1.30–14.53) and disseminated disease (OR 4.52 95%CI 1.44–14.15). GEP-NET primary tumor location or secretory syndrome did not influence the risk for MetS. None of the tumor characteristics evaluated were associated with body mass index, fasting plasma glucose category, or any of the individual MetS components. Patients with GEP-NET and MetS depicted a higher risk of presenting a lower tumor grade and disseminated disease. The positive association between MetS and GEP-NET characteristics further highlights the potential link between the two conditions.
Research on surface defect detection and grinding path planning of steel plate based on machine vision

In the process of surface defect detection and grinding of steel plate, it needs a lot of manpower, not only low efficiency, but also not high detection accuracy. The surface defect detection of steel plate based on machine vision has been applied and paid more and more attention. In this paper, the image processing algorithm is studied to identify the surface defects of steel plate, and to obtain the location information of the defect on the steel. A method integrating with defect classification and merging and polishing path planning is proposed, and the coordinate information of each point on the path is obtained. Under the Windows system, a surface defect detection software is designed to detect the surface of the steel plate, and the detected defect location information is transmitted to the robot, and then the robot grinds the steel plate. Experimental results show that the algorithm is effective.