Miracle-dz/UniFAR-FTUs huggingface dataset:
DNA barcodes in identification of some species of hawkmoths (Lepidoptera: Sphingidae)

The use of DNA barcodes is a molecular method commonly used for the identification of specimens that are difficult to pinpoint accurately by traditional morphological methods. DNA barcoding uses standard short genomic regions that are universally present in target lineages and has sufficient sequence variation to identify species in the genus. The hawkmoth genus Cechetra (Zolotuhin & Ryabov, 2012) was proposed for a group of morphologically similar, stripe-patterned species. Based on the seven specimens of the three species of Cechetra genus collected from the Central of Vietnam, we used morphological analysis to initially identified their scientific name of Cechetra lineosa (Walker, 1856) for 04 specimens (LB_F01, LB_G01, LB_H01 and LK_G05); 02 specimens (MK_H04 and MK_B07) were identified with the scientific name of Cechetra minor (Butler, 1875) and 01 specimen (SG_D7) with scientific name Cechetra subangustata (Rothschild, 1920). Then, the COI (Cytochrome C oxidase I) sequence was used for molecular analysis. Total DNA was extracted from the legs of specimens, COI with over 600 bp in length from each specimen was amplified by PCR reactions. The PCR products are hence purified and sequenced. COI gene cloning results showed a very high level of genetic similarity (over 99%) and 7 specimens are of the genus Cechetra, Sphingidae family. For three homomorphic species belonging to the genus Cechetra Zolotuhin & Ryabov, 2012 including Cechetra lineosa (Walker, 1856); Cechetra minor (Butler, 1875) and Cechetra subangustata (Rothschild, 1920). The studied sequences have been added to the current database at GenBank (NCBI) with accession numbers MT994230, MT994231, MT994232, MT994233, MT994234, MT994235, and MT994236.
Preliminary Phytochemical Screening and Antimicrobial Studies on the Aerial Parts Extracts of Crinum latifolium L. (Amaryllidaceae)

The present study was undertaken to find the antimicrobial activities, phytochemical presence in various aerial parts extract of Crinum latifolium (C. latifolium). Qualitative analysis of various phytochemical constituents and quantitative analysis of total phenolics, flavonoids and alkaloids was determined by the well-known test protocol available in the literature. Quantitative analysis of phenolic, flavonoids and alkaloids was carried out by Folins Ciocalteau reagent, aluminium chloride and bromocresol green method respectively. The in vitro antimicrobial activity was done by well diffusion assay method against S. mutans and S. bongori using standard ofloxacin, ciprofloxacin (10-30μg/ml). The antimicrobial activity was determined by measuring the diameter of the zone of inhibition in term of millimeter (mm). The phytochemical analysis showed the presence of tannins, glycosides, flavonoids and alkaloids ect. The antimicrobial activity of hydroalcoholic extract of aerial parts against all microorganisms was concentration dependent manner but less than standard drug. It is concluded that the antimicrobial activity showed by the plant is due to the presence of these phytochemicals. For future studies, phytochemcials responsible for these activities can be isolated and modified for pharmacological purpose.
Shaping the Topology of a Single Phase BLDC Motor for Reduced Cogging Torque and Improved Performance

In the paper is presented a step-by-step development of the topology for a novel single phase permanent magnet (PM) brushless DC (BLDC) motor, aiming to better its operation with more efficient utilisation of the active materials. The studied motor is based on a generic design, where asymmetrical stator poles and respective shaping of the permanent magnets is applied. Accurate 2D finite element simulations were employed for numerical prototyping of the motor design, until the expected results were obtained. Based on FEA, cogging and static torque waveforms, along with other relevant performance characteristics were determined and examined. The results show that the motor topology, and in particular, the detailed shape of the stator poles has the most important impact on the smooth operation of the single phase BLDC permanent magnet motor.
Hierarchical Structure-Feature Aware Graph Neural Network for Node Classification

In recent years, graph neural network is used to process graph data and has been successfully applied to graph node classification task. Due to the complexity of graph structure and the difficulty of obtaining node labels, node classification in datasets with fewer labels becomes a challenge. Existing node classification problems with few-label nodes datasets usually use neighbor aggregation schemes. However, these methods lack the “graph pooling mechanism,” which makes these models impossible to make full use of the global graph information. To solve the problem, we propose a Hierarchical Structure-Feature aware Graph Neural Network (HSFGNN) model. The model learns node features by a hierarchical mechanism that repeatedly utilizes coarsening and refining methods at different levels. Firstly, we use topological information, node characteristics and connectivity of the graph to construct an intuitive and effective coarsening part based on multivariable analysis technology. Secondly, we aggregate the unselected nodes’ features into the top-k nodes, so that the coarser nodes contain more abundant valid graph information. Finally, we propose an additional attention mechanism to obtain more accurate final representations of nodes. Experimental results show that the HSFGNN model achieves outstanding classification accuracy on the dataset with less labeled data.
Systemic seismic vulnerability and loss assessment: Validation studies. SYNER-G Reference Report 6

The SYNER-G project aims at developing a methodology to evaluate the seismic vulnerability to earthquakes of a complex system of interconnected infrastructural systems of regional/urban extension. This report summarizes the application of the SYNER-G methodology and tools to selected case studies of regional and urban extension: the city of Thessaloniki; the city of Vienna; the gas system of L’Aquila in Italy; the road network of Calabria region in Southern Italy; the electric power network of Sicily; a hospital facility in Italy; the harbor of Thessaloniki. For each case study the following items are given: general description of the test site, seismic hazard issues, systemic vulnerability methodology, software developments and implementation, system topology and characteristics, description of the input, results of the application. Through these studies the different steps of the SYNER-G methodology are validated and demonstrated.
Association between Ambient Air Pollutants and Pneumonia in Wuhan, China, 2014–2017

Objectives: To assess associations between short-time air pollution exposure and outpatient visits for pneumonia by the distributed lag nonlinear model (DLNM). Methods: Daily outpatient visits for pneumonia and air pollutant data were collected from Wuhan Basic Medical Insurance Database in China and 10 national air quality monitoring stations in Wuhan from 2014 to 2017, respectively. Taking the first percentile of the concentration as the reference, DLNM was used to estimate the impact of moderate (50th) and high levels (99th) of pollutants on pneumonia. Results: A total of 133,882 outpatient visits were identified during the period of the study. Moderate-level (P50) fine particulate matter (PM2.5) or sulfur dioxide (SO2) and high-level nitrogen dioxide (NO2) (P99) can increase the risk of pneumonia. The maximum RR was 1.198 (95% CI: 1.094–1.311) at lag0-11, 1.304 (95% CI: 1.166–1.458) at lag0-13, and 1.286 (95% CI: 1.060–1.561) at lag0-14, respectively. Females and children had greater risks. Conclusions: Short-time PM2.5, SO2, and NO2 exposure were associated with outpatient visits for pneumonia in Wuhan, China.
Single-Cell RNA-Seq Analysis of Cells from Degenerating and Non-Degenerating Intervertebral Discs from the Same Individual Reveals New Biomarkers for Intervertebral Disc Degeneration

In this study, we used single-cell transcriptomic analysis to identify new specific biomarkers for nucleus pulposus (NP) and inner annulus fibrosis (iAF) cells, and to define cell populations within non-degenerating (nD) and degenerating (D) human intervertebral discs (IVD) of the same individual. Cluster analysis based on differential gene expression delineated 14 cell clusters. Gene expression profiles at single-cell resolution revealed the potential functional differences linked to degeneration, and among NP and iAF subpopulations. GO and KEGG analyses discovered molecular functions, biological processes, and transcription factors linked to cell type and degeneration state. We propose two lists of biomarkers, one as specific cell type, including C2orf40, MGP, MSMP, CD44, EIF1, LGALS1, RGCC, EPYC, HILPDA, ACAN, MT1F, CHI3L1, ID1, ID3 and TMED2. The second list proposes predictive IVD degeneration genes, including MT1G, SPP1, HMGA1, FN1, FBXO2, SPARC, VIM, CTGF, MGST1, TAF1D, CAPS, SPTSSB, S100A1, CHI3L2, PLA2G2A, TNRSF11B, FGFBP2, MGP, SLPI, DCN, MT-ND2, MTCYB, ADIRF, FRZB, CLEC3A, UPP1, S100A2, PRG4, COL2A1, SOD2 and MT2A. Protein and mRNA expression of MGST1, vimentin, SOD2 and SYF2 (p29) genes validated our scRNA-seq findings. Our data provide new insights into disc cells phenotypes and biomarkers of IVD degeneration that could improve diagnostic and therapeutic options.
Performance Evaluation of Temporal and Frequential Analysis Approaches of Electromyographic Signals for Gestures Recognition using Neural Networks

—Now-a-days, human-machine interfaces are increasingly intuitive and straightforward to design, but there is difficulty capturing electromyographic signal data using the least amount of hardware. This work takes the signals of a human forearm as input parameters describing a series of five gestures, using a dataset of 8 channels of electromyographic signals, using as a capture device a Thalmic Labs Inc. handle called Myo armband. The aim is to compare the performance of the artificial neural network using data in the time domain as input to the learning system. The same data are pre-processed to the frequency domain, looking for an improvement in the neural network's performance since transforming the input signals of the system to the frequency domain minimizes the problems inherent to this type of signal. This transformation is achieved using the fast Fourier transform. Consequently, it seeks to reach a neural network architecture that recognizes the gestures captured with the Myo armband in a high percentage of performance to be used in stand-alone applications, using the TensorFlow libraries of Python for its design. As a result, a comparison of the neural network trained with data in time versus the same data expressed in the frequency domain is obtained, seen from the increase in performance and the percentage of gesture detection.
From offline learning to online learning: Moderating effect of relationship inertia and satisfaction

The progress of educational technology and the demand of home learning under COVID‐19 have promoted the rapid development of online learning all over the world. The existing research focuses on offline learning or online learning but seldom discusses their switching process. This paper uses the questionnaire method and structural equation model to analyze the influencing factors of switching intention and behavior from offline learning to online learning. The results show that the switching intention is significantly affected by perceived usefulness, perceived ease of use, and computer self‐efficacy, and negatively affected by the perceived risk. The switching intention makes a significant positive effect on switching behavior. When the learner's offline learning relationship inertia and satisfaction are higher, the relationship between switching intention and switching behavior will be weakened. At the end of the paper, some suggestions are put forward for reference for the related parties of online learning.
The use of support vector machines in semi-supervised classification

Semi-supervised learning has gained signiﬁcant attention in recent applications. In this article, we provide a selective overview of popular semi-supervised methods and then propose a simple but e ﬀ ective algorithm for semi-supervised classiﬁcation using support vector machines (SVM), one of the most popular binary classiﬁers in a machine learning community. The idea is simple as follows. First, we apply the dimension reduction to the unlabeled observations and cluster them to assign labels on the reduced space. SVM is then employed to the combined set of labeled and unlabeled observations to construct a classiﬁcation rule. The use of SVM enables us to extend it to the nonlinear counterpart via kernel trick. Our numerical experiments under various scenarios demonstrate that the proposed method is promising in semi-supervised classiﬁcation.
Soil organic carbon fractions comparison after 40-year long-term fertilisation in a wheat-corn rotation field

Several experimental methods have been developed to fractionate soil organic carbon (SOC) into functional sub-pools. However, which fractions had the potential to better reflect the SOC dynamics responding to fertilisation are still under discussion. Thus, we compared different SOC fractions (microbial biomass carbon, MBC; dissolved organic carbon, DOC; permanganate-oxidisable carbon, POXC; particle organic carbon, POC, and aggregation organic carbon fractions) and the soil respiration rate in a wheat-corn rotation field after 40 years of manure and N fertilisation in North China to search for the most sensitive SOC fractions to fertilisation. Manure increased the organic carbon (OC) contents of all the soil fractions (26.5 to 362.8%) and the POC (18.0 to 43.7%) and macro-aggregation percentages (3.0 to 4.4%), which indicated an increasing physical-protected aggregated OC fraction. N fertilisation alone slightly increased the OC contents of all the soil fractions and DOC percentage, but decreased the macro-aggregation OC percentage, which suggests the increasing possibility that the SOC is exposed to microbial communities causing a decreasing aggregation formation. However, when a high level of both the manure and N fertiliser were applied, the excessive N in the soil stimulates the soil microbial activity and decreases the SOC content comparing it to the same level of the manure fertiliser addition.
Brassica oleracea Extracts Prevent Hyperglycemia in Type 2 Diabetes Mellitus

This study investigated the protective effect of extracts from flowers of Brassica oleracea L. var. italica Plenck on type 2 diabetes mellitus and its associated disorders. Three different doses of each extract (petroleum ether, ethanol, and aqueous) were administered orally for 42 days. Biochemical parameters, behavioral studies, and histological studies were measured at different periods. Mortality was found to be nil up to 2,000 mg/kg. Statistically significant (P<0.001) improvement in serum glucose level was observed in the groups receiving 400 mg/kg of petroleum ether, aqueous, or ethanol extracts compared with the negative control group. Insulin level was decreased by aqueous extracts, whereas lipid profiles were improved by aqueous and ethanol extracts. A reduction in transfer latency was observed in treatments of all three extract types. Ethanol extract treatment (400 mg/kg) showed maximum percentage inhibition in a lipid peroxidation assay. Additionally, the aqueous and ethanol extract treatments markedly reduced tumor necrosis factor-α, interleukin-6, and glycosylated hemoglobin levels. Histological results showed that high doses of extracts alleviated the damages induced by type 2 diabetes mellitus in various organs and bones. Based on the results of this study, it can be concluded that B. oleracea has the potential to alleviate type 2 diabetes mellitus.
Exploring herding behavior in an innovative-oriented stock market: evidence from ChiNext

ABSTRACT We adopt the cross-sectional absolute deviation model (CSAD) to test the herding behavior of ChiNext, a decade-old NASDAQ-style stock market in China, based on its stocks from 2015-2019. Our findings show that the herding behavior is prevalent, implying that such behavior is widespread in a relatively new stock market themed with growth-oriented innovative enterprises and dominated by individual investors instead of institutional investors. Moreover, we find that herding tends to be more severe during the periods of falling market than rising market. We explain that several distinct attributes of the individual investors cause them to sell during the falling market, an act contrary to the standard account of the “disposition effect of holding the losers” in behavioral finance. We contribute to the herding behavior literature for a relatively new innovative-oriented stock market as well as our understanding of the investors’ circumstances, which may disprove the often-quoted disposition effect.
Dislocation Arthropathy of the Shoulder

Glenohumeral osteoarthrosis (OA) may develop after primary, recurrent shoulder dislocation or instability surgery. The incidence is reported from 12 to 62%, depending on different risk factors. The risk of severe OA of the shoulder following dislocation is 10 to 20 times greater than the average population. Risk factors include the patient’s age at the first episode of instability or instability surgery, bony lesions, and rotator cuff tears. For mild stages of OA, arthroscopic removal of intraarticular material, arthroscopic debridement, or arthroscopic arthrolysis of an internal rotation contracture might be sufficient. For severe stages, mobilization of the internal rotation contracture and arthroplasty is indicated. With an intact rotator cuff and without a bone graft, results for anatomical shoulder arthroplasty are comparable to those following primary OA. With a bone graft at the glenoidal side, the risk for implant loosening is ten times greater. For the functional outcome, the quality of the rotator cuff is more predictive than the type of the previous surgery or the preoperative external rotation contracture. Reverse shoulder arthroplasty could be justified due to the higher rate of complications and revisions of non-constrained anatomic shoulder arthroplasties reported. Satisfactory clinical and radiological results have been published with mid to long term data now available.
The Modeling of Semiconductor Circuit Elements by Using Artificial Neural Networks

Bu calismada, elektronik devrelerde cok sik kullanilan yariiletken devre elemanlarinin Yapay Sinir Aglari (YSA) tarafindan modellenmesi gerceklestirilmistir. Elektronik devre elemanlarinin modellenmesi hem muhendislik hem de pratik matematik acisindan cok onemlidir. YSA modelleme yontemleri, ozellikle fiziksel gerceklestirmenin zor oldugu hassas uygulamalar icin uygundur. Bu modelleme sistemlerinin temel amaci simulasyon zamanini kisaltmak ve gercek uygulamalarda kullanilan elemanlarin yerine modelini kullanarak bilgisayar ortaminda gercek fiziksel sistem uygulamalarini incelemektir. Bunun icin, bir PNP bipolar transistor, ornek olarak kullanilmaktadir. PNP bipolar transistorun modellemesi icin kullanilacak YSA yapisi gelistirilmis ve MATLAB arac kutusunda egitilmistir. YSA aginin egitimi icin bir hibrit ogrenme algoritmasi olan geriye yayilim algoritmasi ve en kucuk kareler yontemi kullanilmaktadir. Elde edilen YSA modeli ile PNP bipolar transistor elemani basarili bir sekilde modellenmistir. Elde edilen sonuclar gostermistir ki YSA modelleme teknigi hem PNP bipolar transistor elemani hem de diger elektronik devre elemanlarinin modellenmesi icin basit bir yazilim araci olarak kullanilabilir.
A software platform to administer the Canadian Digit Triplet Test

Originally designed in Dutch as an automatic self-screening test (Smits et al., IJA 43(1):15–28, 2004), the Digit Triplet Test has been developed in about 15 different languages. Owing to its wide success, and to facilitate the comparison between languages, a working group on multilingual speech testing of the International Collegium of Rehabilitative Audiology (ICRA) has provided recommendations for constructing such tests (Akeroyd et al., IJA 54 Suppl 2:17-22, 2015). The development of the Canadian-English and Canadian-French versions of the Digit Triplet Test includes preparing speech and noise materials and implementing a testing platform. The digits were recorded in both languages by two fluently bilingual adult talkers (1 male, 1 female). The recordings were processed and optimized and a speech-shaped noise signal was developed for each language-talker combination according to the ICRA recommendations and ISO standard on speech audiometry (ISO 8253-3:2012). The present work describes the software interface of the testing platform, the supported features as well as the required hardware components. The test will be deployed in large-scale multi-site longitudinal studies across Canada on aging and neurodegeneration in aging. In addition, the test may be a useful tool for lab-based research, the audiologic assessment of francophone speakers in minority settings in Canada, and for studies of English or French as a second language.
Characterization of genotype and beer fermentation properties of Norwegian Farmhouse Ale Yeasts

Eight different Norwegian Farmhouse Ale Yeasts from Voss, Stranda, Lærdal, Hornindal and Olden were donated to IBT for analysis. The yeast were phenotypically characterized and two beer styles were produced under laboratory conditions, a pale ale and a traditional Honndalsøl. The sensory properties were assessed on an untrained panel as a Duo-trio test and the composition of volatile compounds produced during fermentation and after bottling at different fermentation temperatures was determined using dynamic Head space GC-MS. A genetic analysis of the yeasts internally transcribed spacer 1 and 2 region was performed to assess their species and genetic relationship between the strains. The Kveik strains were compared to reference yeast. All Kveik except one was classified as the genus Saccharomyces, two were classified as S. boulardii, two were classified as S. cerevisiae, one was classified as S. bayanus / S. pastorianus, two were unclassified specie of the Saccharomyces genus. Phenotypical characterization showed a variation of maximum growth rate with over 20 % of selected Kveik strains. Flocculation varies between fully flocculating and non-flocculating. As fermentation temperature increase, the concentration of esters and alcohols increase except longer chained esters. During the fermentation, the concentration of alcohols decrease, esters decrease over time as well except ethyl acetate, ethyl octanoate and ethyl decanoate. A sensory panel could distinguish between 3 of the 5 tested yeast strains.
The Study of Recycled Poly(Ethylene Terephthalate) Nanofibres from Pet Bottle

Bottles made from poly (ethylene terephthalate) (PET) can be recycled and reused the material to reduce the amount of waste going into landfills. In this study, electrospun fibres from recycled PET were produced by a melt-electrospinning method. The effect of the melting temperature, applied voltage and distance between die and collector on the morphology of the electrospun fibres was investigated. Thermal properties of recycled PET were characterized by a differential scanning calorimetry (DSC) and a thermo gravimetric analysis (TGA). It was observed that recycled PET granules were melted at a temperature of 260, 290 and 310 °C, the melt polymer was electrospun at a high voltage of 38 kV and electrospinning was carried out at a distance of 12 cm. The Recycled PET electrospun had diameters ranging from 45 to 65 µm.