Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth of calcium phosphate onto chemically-functionalized cottons

The growth of calcium phosphate onto both cotton fibers and chemically-modified cotton fibers with three different degrees of carbonyl groups contents, immersed in saturated calciumphosphate solutions at 60°C and 7.4 pH for 6, 9 and 12 days, has been investigated by scanning electron microscopy, energy dispersed spectrometry and transmission electron microscopy as a function of the type and amount of functionalized groups. The functionalized cotton fibers were obtained through the reaction of alkali cellulose with sodium chloroacetate and the immersion solution was changed every three days. Without modification no crystallization in the cotton sample was observed, whereas in the functionalized samples, with higher substitution we obtained calcium phosphate crystallization onto the fibers as long vesicles with sizes ranging from 1 μm to 24 μm wide; these vesicles are formed by long platelets with certain orientation and ordered arrangement. In the presence of the most functionalized cotton fibers we observed the highest calcium phosphate crystallization onto the fibers; the Ca/P molar ratio obtained by EDS was 1.58. One of the calcium phosphate identified by transmission microscopy was octacalcium phosphate (Ca8H2(PO4) · 6.5H2O, OCP). The chemical functionalization of the cotton via carbonyl group determined the growth and the amount of calcium phosphate onto the fiber.
Text Extraction through Video Lip Reading Using Deep Learning

Automated text extraction from video data through lip reading can overcome the language barrier and open the door of opportunities in terms of security, connectivity and physical challenges. The conversion is possible by analyzing facial expression using deep learning method. But this conversion is a challenging task due to the varieties of pronunciation and accents of the same word causing different countenance. In this research, a method of converting video data to text data through lip reading has been proposed. The proposed method includes test dataset, image frame analysis and having text output from identified words. In the proposed technique, the test dataset will be organized by combining all the possible facial expressions of different words.
Therapeutic effects of recombinant human thrombopoietin on sepsis-associated thrombocytopenia

Objective To investigate the clinical value of recombinant human thrombopoietin (rhTPO) in the treatment of sepsis-related thrombocytopenia. Methods Eighty-four patients with sepsis-related thrombocytopenia admitted to the Second Hospital of Shanxi Medical University from January 2016 to December 2017 were evenly divided into study group and control group according to random number table method, with 42 cases in each group. Patients in control group were given routine treatment, while patients in study group were given rhTPO on the basis of routine treatment. The use of blood products and the change of platelet count during treatment were recorded. Results During the treatment period, the infusion volume of blood products such as platelets, concentrated red blood cells and fresh frozen plasma in study group was less than that in control group (P 0.05); the platelet count in study group increased more rapidly than that in control group 3, 7 and 10 days after treatment (P all <0.05). Conclusions The use of rhTPO in the treatment of sepsis-related thrombocytopenia is effective, which is beneficial to improvement in platelet count and reduction in blood product consumption. Key words: Sepsis; Thrombocytopenia; Recombinant human thrombopoietin; Clinical application effect
Automated Process Control Anomaly Detection Using Machine Learning Methods

The paper discusses the features of the automated process control system, defines the algorithm for installing critical updates. The main problems in the administration of a critical system have been identified. The paper presents a model for recognizing anomalies in the network traffic of an industrial information system using machine learning methods. The article considers the network intrusion dataset (raw TCP / IP dump data was collected, where the network was subjected to multiple attacks). The main parameters that affect the recognition of abnormal behavior in the system are determined. The basic mathematical models of classification are analyzed, their basic parameters are reviewed and tuned. The mathematical model was trained on the considered (randomly mixed) sample using cross-validation and the response was predicted on the control (test) sample, where the model should determine the anomalous behavior of the system or normal as the output. The main criteria for choosing a mathematical model for the problem to be solved were the number of correctly recognized (accuracy) anomalies, precision and recall of the answers. Based on the study, the optimal algorithm for recognizing anomalies was selected, as well as signs by which this anomaly can be recognized.
Follow up of the Multiple Sclerosis Lesion's Evolution in Brain MRI by Automated Registration Approach

Magnetic Resonance Imaging is considered as a powerful tool for no-invasive diagnosis and description of brain pathologies. This is particularly the case of Multiple Sclerosis, for monitoring this disease and its treatment. Multiple sclerosis is an autoimmune inflammatory disease of the central nervous system which clinical markers are used today for diagnosis and for therapeutic evaluation. In order to automate a long and hard process for the clinician, we propose a semi-automatic segmentation approach of multi sclerosis lesions in longitudinal MRI sequences. We use firstly a robust algorithm that allows spatiotemporal extraction of these lesions by geodesic active contour model. Then, we recommend an original scheme based on an automated image registration technique for evaluating the evolution of the detected lesions. A quantitative study is presented in this paper to validate our results using the BrainWeb simulator. Very promising results are obtained in the case of clinical data.
Corporate Credit Risk Modeling and the Macroeconomy∗† Forthcoming: Journal of Banking & Finance

Despite a surge in the research efforts put into modelling credit risk during the past decade, few studies have incorporated the impact that macroeconomic conditions have on business defaults. In this paper, we estimate a duration model to explain the survival time to default for borrowers in the business loan portfolio of a major Swedish bank over the period 1994-2000. The model takes both firm-specific characteristics, such as accounting ratios and payment behaviour, loan-related information, and the prevailing macroeconomic conditions into account. The output gap, the yield curve and consumers’ expectations of future economic development have significant explanatory power for the default risk of firms. We also compare our model with a frequently used model of firm default risk that conditions only on firm-specific information. The comparison shows that while the latter model can make a reasonably accurate ranking of firms’ according to default risk, our model, by taking macro conditions into account, is also able to account for the absolute level of (default, and thus also credit) risk.
Meloidogyne incognita Penyebab Umbi Berbintil pada Kentang di Beberapa Sentra Produksi Kentang di Jawa

Root knot nematodes is an important pathogen on potatoes in tropical and sub-tropical areas. Root knot nematodes contribute a significant impact in reducing the quality and quantity of potato tuber. Meloidogyne incognita is one of the species causing the root knot. This research was conducted to identify M. incognita on potatoes in Java Island based on morphological and DNA molecular characteristic. The infected potato tubers with pimple-like knot symptom were collected from Pangalengan (West Java), Banjarnegara (Central Java), and Kota Batu (East Java). Nematode was identified based on morphological character of perineal pattern, and based on molecular DNA character by polymerase chain reaction technique using a pair of specific primer (MI-F and MI-R), followed by DNA fragment sequencing and phylogenetic analysis. Based on morphological character of perineal pattern, M. incognita was detected in all 3 locations; while based on DNA molecular character, and M. incognita was detected in Pangalengan (West Java) and Kota Batu (East Java). M. incognita from Pangalengan had high homology, i.e.99.2% to 99.8% with those isolates from China, India, and Malaysia.
Stable isotopes reveal that chironomids occupy several trophic levels within West Greenland lakes: Implications for food web studies

Stable isotope analyses of nitrogen (δ15N) and carbon (δ13C) were conducted on individual chironomid taxa from low arctic lakes to investigate the range in trophic levels covered by this diverse insect group. Five lakes were sampled, including two freshwater, two oligosaline, and one glacier influenced lake, representing the major lake types in southwest Greenland. There was a large difference in the offset of isotopic values among the different lake types. In the oligosaline lakes, we ascribe low δ13C values to the use of recycled CO2, while the high δ15N signal is possibly controlled by microbial processes. In order to compare among lakes, the δ15N signal of the chironomid taxa was normalized to common primary consumers (Psectrocladius sordidellus group and Psectrocladius limbatellus groups) that show consistently low δ15N values and belong to the subfamily Orthocladiinae. Normalized δ15N values spanned more than 5‰, indicating that the chironomid community covered at least two trophic levels in these low arctic lakes. The δ15N values of different chironomid taxa within a single habitat differed by 1‰ to 5‰. The results suggest that chironomids should not be considered as one group in food web studies. Even interpretation of trophic position on subfamily level can be problematic as a result of the large differences observed in their δ15N values. The level of detailed trophic information on individual chironomid taxa presented here has not previously been reported. Chironomids are an abundant and important group of organisms in arctic lakes, and the observed variation in their trophic level indicates a high complexity of the food web structure of arctic lakes.
Using imaging Photoplethysmography (iPPG) Signal for Blood Pressure Estimation

Blood Pressure (BP) is one of the most important vital signs of the human body, which its value provides valuable physiological information about cardiac function for physicians. In recent years, many types of research have been done in the field of BP estimation using Photoplethysmography (PPG) signal. On the other hand, the results of the studies on Heart Rate (HR) and Respiration Rate (RR) calculations have been reported using the imaging Photoplethysmography (iPPG) signal. The iPPG signal is a kind of PPG signal that is recorded in a non-contact method using a camera.This paper is among the first studies to provide a new algorithm for estimating BP values using the only iPPG signal and with a non-contact method. The validity of the proposed algorithm was evaluated in a gathered database with 40 people. The algorithm in estimation of the Diastolic Blood Pressure (DBP) was able to achieve mean error of −0.2 and standard deviation of 6.41 mmHg and in estimation of the Systolic Blood Pressure (SBP) was able to achieve mean error of 0.45 and standard deviation of 12.39 mmHg.
Self-sensing Elastomeric Membrane for Haptic Bubble Arrray.

Bubble arrays are new type of devices that can render forces to the user’s skin by pressurizing thin membranes. These devices are suitable for wearables due to being soft while having low encumbrance to the user body. In this manuscript, we describe a solution to tackle the major challenge towards controllability of this devices: state estimation of the membrane. This work describes the manufacturing of a self-sensing composite elastomeric membrane, device integration and the methods for state estimation using deep learning architectures. Additive manufacturing was used to fabricate the membrane and a pneumatic bubble actuator was operated at low pressures (10 psi). This membrane is thin $(700 \mu \mathrm{m})$, soft (3 MPa), and includes a matrix of elastomeric capacitors for distributed sensing of deformation. Our model predicted the magnitude of deformation with a mean absolute error MAE =0.321 mm corresponding to 13.1% accuracy.
An asymmetrical slot circularly polarized on-chip antenna for wireless SoC applications at 2.45 GHz

In this paper, a circularly polarized on-chip antenna has been proposed for system on chip (SoC) applications at ISM 2.45 GHz band. The low resistive and high permittivity silicon (Si) wafer ($\rho=10\ \Omega.\mathrm{cm}$ and $\varepsilon_{\mathrm{r}}=11.9$) with standard $0.18\mu\mathrm{m}$ CMOS technology has been considered for the design of on-chip antenna. The proposed slotted on-chip antenna (OCA) has the miniaturized dimension of the $30\times 25\times 0.3795\ \mathrm{mm}^{3}$. The introduction of asymmetry in slot of OCA results enhancement of the directive gain of +4.65 dB and also provide the circular polarization (CP). The 10 dB bandwidth i.e. FBW (Fractional bandwidth) of the proposed antenna is 62.8%. The proposed antenna also shows very good axial ratio (AR) bandwidth of 12.98% from 2.31-2.51 GHz range. All the simulation results have been depicted and explained in various sections of the paper.
Investigation of Supercritical Fluid Treatment for SiN/SiON/AlGaN/GaN MIS-HEMTs

Supercritical fluid treatment technology is introduced to MIS-HEMTs with Si-N related dielectric layers. The high resolution micrographs as well as energy spectrum distribution are analyzed before and after the supercritical fluid treatment. The technology effectively passivates the dielectric/III-V interfaces and improve the quality of the dielectrics. A molecular-level reaction mode was proposed to explain the positive shift of threshold voltage of ~2.5V as well as the increased maximum drain current by ~25%, increased maximum transconductance by ~34% and reduced off-state drain current by orders of magnitude. All the results reveal the potential of supercritical fluid treatment to be applied to GaN-based power switching applications as batching processing in low temperature.
Test-bench for Task Offloading Mechanisms: Modelling the Rewards of Non-stationary Nodes

In this paper, we have proposed a framework to model the server nodes' non-stationary reward distribution as an N-state Markov chain derived from the long-term probability distribution of the reward, in which the state transitions occur at an interval defined as a non-homogeneous Poisson process. The framework aims to serve as a tool for validation and as a test-bench for comparing the performance of various learning algorithms in non-stationary bandits and similar applications where the server nodes' computational reward distribution is dynamic.
Detection of small numbers of toxigenic Vibrio cholerae from environmental water samples by a novel polymerase chain reaction procedure

INTRODUCTION Cholera is a highly epidemic diarrhoeal disease which continues to devastate many developing countries where socio-economic conditions are poor, sanitary systems and public hygiene are rudimentary, and safe drinking water is not available. Cholera is usually transmitted to humans by ingestion of contaminated water and foods. It has been determined on the basis of human volunteer trials that, depending on the health of a given individual, the ingestion of approximately 10 to 10 V. cholerae O1 organisms is likely to produce clinical cholera (Cash et al. 1974). The major virulence factor produced by V. cholerae is the cholera enterotoxin (CT). The chromosomal genes encoding the A and B units of cholera toxin are designated ctxA and ctxB, respectively. Conventional microbiological methods for identifying V. cholerae involve cultural, biochemical and immunological assays which often take several days to complete. Nucleic acid-based methods, such as the polymerase chain reaction (PCR), has the potential to shorten detection times. Microbial species can be detected by amplification of gene sequences unique to that organism, and the sensitivity of PCR is such that theoretically only a single intact nucleic acid template is needed to amplify the target sequence sufficiently to visualize by electrophoresis (Saiki et al. 1988). The goal of this research was to develop a rapid and sensitive detection protocol for low levels of toxigenic V. cholerae in environmental water and drinking water sources based on PCR targeting the ctxA gene. We have subsequently developed such a protocol based on the technique of seminested PCR following cultivation of the organisms in an enrichment broth. The sensitivity of the pit-stop seminested PCR procedure was determined using seeded environmental water samples collected from natural sources. The feasibility of this method was furthermore evaluated by comparing this procedure with traditional isolation and characterization methods.
A bending sensor insensitive to pressure: soft proprioception based on abraded optical fibres

Optical sensors have recently been proposed for sensing bending, linear strains and external forces in soft actuators. However, the susceptibility of these sensors to a number of stimuli makes it difficult to comprehend the resulting data output. We address this challenge through the introduction of a low-cost flexible bending sensor based upon the abrasion of optical channels. Such sensors are responsive to bending while being insensitive to internal pressure or external forces. The developed sensor is integrated into a soft pneumatic finger in order to obtain feedback of its curvature. We further demonstrate that the sensor is insensitive to normal forces applied on a blocked finger. Through this work, we propose a simple, low-cost sensor development technique towards addressing the challenge of decoupling forces and deformations in soft robots.
A Novel PPA Method for Fluid Pipeline Leak Detection Based on OPELM and Bidirectional LSTM

Pipeline leak detection has attracted great research interests for years in the energy industry. Continuous pressure monitoring is one of the most straightforward approaches in leak detection which utilizes pressure point analysis (PPA) algorithms to exploit the transient pressure characteristics and identify leak events. However, a critical issue that jeopardizes the deployment of PPA based methods is the high false alarm rate. In this paper, a novel PPA based leak detection method is proposed which can accurately detect the leak events and dramatically decrease the number of false alarms compared to existing methods. Firstly, the proposed method takes advantage of the good approximation ability and fast learning speed of optimally-pruned extreme learning machine (OPELM) to produce a preliminary leak detection result. Then, the strong memorizing ability of bidirectional long-short term memory (BiLSTM) network is exploited to identify the true positive from the preliminary detection result, hence significantly decrease the number of false alarms. Furthermore, a feature extraction mechanism is also proposed to obtain both the dynamic and static characteristics from raw pressure wave. Experiments and verifications are performed on different real world data sets obtained from pipeline leak tests. It shows that the proposed method can achieve higher detection accuracy with significantly less false alarms. It enhances the practicality of pressure monitoring based leak detection schemes.
Phylogeny of the Wormfishes (Teleostei: Gobioidei: Microdesmidae)

Abstract Microdesmidae (sensu stricto) includes 30 species of elongate, cryptic, benthic gobioid fishes. The phylogenetic relationships among microdesmid species have been unknown, and the sister taxon to Microdesmidae has been a matter of debate. In this study, the relationships of microdesmid species are hypothesized based on morphological characters, analyzed with cladistic parsimony methods. Characters previously used as evidence for a sister-taxon relationship between Microdesmidae, s.s. , and five genera classified as a subfamily (Ptereleotrinae) in an expanded Microdesmidae are reexamined and found to be invalid. Microdesmidae s.s. is restored, including only Cerdale, Clarkichthys, Microdesmus, Gunnellichthys, and Paragunnellichthys. Monophyly of the microdesmid genera is confirmed, and characters diagnosing each genus, and other clades revealed in this analysis, are discussed. The five genera formerly placed in Ptereleotrinae are referred to Ptereleotridae (incertae sedis within Gobioidei), bringing the total number of gobioid families to nine. Microdesmidae is also placed incertae sedis in Gobioidei; some sister-taxon candidates could be excluded based on the characters surveyed in this analysis, but among other gobioids, no clear sister taxon to Microdesmidae could be identified.
Automatic Arousal Detection Using Multi-model Deep Neural Network

Arousal labeling is one of the important methods in the diagnosis and treatment of sleep-related diseases, and are usually analyzed manually by doctors based on polysomnography (PSG) signals. In order to solve the problem of time-consuming and labor-intensive manual arousal analysis in sleep physiological signals, we propose an automatic arousal detection method using multi-model deep neural networks. Combining methods such as one-to-many formulation, LSTM, and network structure improvements, the performance of deep neural network models on clinical data set has been significantly improved, and multiple indicators have been improved (precision 86.7%, recall 86.0% and F1 86.3%). At the same time, the model parameters have been greatly streamlined, making them more efficient, laying a foundation for the application of automatic arousal detection methods on wearable sleep monitoring device signal analysis.
Different coupling of dissolved amino acid, protein, and carbohydrate turnover to heterotrophic picoplankton production in the Southern Ocean in austral summer and fall

We assessed growth dynamics of heterotrophic picoplankton and concentrations and turnover of dissolved protein, amino acids, and neutral monosaccharides in the Atlantic sector of the Southern Ocean in austral summer (December‐January) and fall (March‐May). Phytoplankton biomass (chlorophyll a) and biomass production of heterotrophic picoplankton in summer was twice as high as in fall. This difference was also reflected in protein turnover rate constants and in concentrations of dissolved combined neutral polysaccharides (DCCHO). Turnover rate constants of dissolved free amino acids (DFAA) were in the same range in both seasons, but turnover rate constants of glucose were higher in fall as compared to summer. In summer, dissolved protein was the major substrate for growth of heterotrophic picoplankton, followed by DFAA and dissolved free neutral monosaccharides (DFCHO). During summer, heterotrophic picoplankton production was closely correlated to concentrations and incorporation of dissolved protein. In fall, heterotrophic picoplankton production was only significantly correlated to glucose turnover rate constants. The latter were also inversely correlated to DCCHO concentrations in fall. The reduced supply of organic substrates by phytoplankton in fall not only resulted in an equal reduction of heterotrophic picoplankton production but also in a shift of the supply in dissolved protein, DFAA, and DFCHO to heterotrophic picoplankton. Dissolved protein was the major substrate for heterotrophic picoplankton growth in summer, but in fall, when supply of dissolved protein was reduced, DFAA and DFCHO were relatively more important substrates.