Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Treponema Pallidum Antibody among Volunteer Blood Donors in China

Background Infection with syphilis is still a major public health problem. The precise data for syphilis seroprevalence in the populations will help to develop a strategy for prevention and treatment of it. However, the data for syphilis prevalence in continuous years among volunteer blood donors in China is rare. Methods A retrospective study for Treponema pallidum (TP) antibody in blood donors was conducted from January 2010 to December 2019 at the Blood Center of Zhejiang Province, China. TP antibody was detected with two different reagents using enzyme-linked immunosorbent assay and the only sample which was reactive in the two reagents was defined as seropositive. Results A total of 992,646 volunteer blood donors were analyzed and the positive rate of TP antibody in the blood donors was 0.43%. From 2010 to 2019, the positive rates of TP antibody were 0.53%, 0.51%, 0.51%, 0.43%, 0.36%, 0.18%, 0.11%, 0.12%, 0.11%, and 0.10%, respectively. The positive rates of TP antibody were significantly different among blood donor age group (p < 0.001), with the highest positive rate in 45–54-years-old group (0.93%). The positive rates of TP antibody in male and female blood donors were 0.44% and 0.41%, respectively. The positive rate was 0.57% among the first-time blood donors, which was significantly higher than that of the repeat blood donors (0.17%). The positive rate of TP antibody in blood donors decreased gradually with the increase of educational level. Conclusion The syphilis seroprevalence is low in the blood donors of the Hangzhou area, and the positive rate of blood donors is associated with age, educational level, and times of blood donation. Increasing the number of repeat blood donations is helpful to improve blood safety.
Acute stroke management during COVID-19 pandemic

INTRODUCTION: In the coronavirus disease 2019 (COVID-19) pandemic, there may be a decrease in the number of acute stroke intervention and acute treatment, and delays in treatment periods. In this study, it was aimed to compare the clinical features of patients presenting with acute stroke during the COVID-19 pandemic and in the pre-pandemic period. METHODS: Patients hospitalized with the diagnosis of cerebrovascular disease (CVD) between January 01, 2019 and May 31, 2021 were included in the study. Demographic characteristics and stroke risk factors of the patients were recorded. Stroke type and ischemic disease subtypes were determined, and patients' admission National Institutes of Health Stroke Scale (NIHSS) score, symptom-door time, door-consultation time, door-to-door Needle time and door-groin puncture time, intravenous tissue type plasminogen activator (IV tPA) and endovascular thrombectomy (EVT) applications were recorded. Discharge modified Rankin Scale (mRS) and NIHSS scores and mortality rates were evaluated. Patients hospitalized in two separate periods of 14 months each were compared by dividing them into pre-pandemic and pandemic periods. RESULTS: Before COVID-19, 316 patients (female 45.25%, age: 66.75±13.68 years) and during the pandemic period 341 (female 41.94%, age: 68.34±13.55 years) patients were included in the study. During the pandemic period, an increase in the number of hemorrhagic CVD and transient ischemic attacks, a decrease in the number of ischemic stroke and cerebral venous thrombosis (CVT) hospitalizations, a decrease in cardioembolic strokes and an increase in lacuner ischemic CVD subtypes were observed (p<0.01). The number of large vessel atherosclerosis, IV tPA and EVT were found to be similar before and after the pandemic. In the pandemic period, although it did not reach statistical significance compared to the pre-pandemic period, prolongation was recorded in the symptom-door, door-consultation, door-needle and door-groin puncture times (p>0.05). The COVID-19 test was positive after hospitalization in 5 (1.8%) patients with ischemic stroke hospitalized during the pandemic period. During the pandemic period, admission NIHSS, discharge NIHSS and mRS scores and mortality rates were found to be significantly higher between hemorrhagic and ischemic stroke patients (p<0.01). DISCUSSION AND CONCLUSION: The COVID-19 pandemic adversely affects the management of acute stroke. The duration of acute stroke treatment is delayed due to pre-hospital and in-hospital reasons. With the heavy burden of stroke during the pandemic period, poor clinical outcome and high mortality are observed.
Star formation in M33 (HerM33es)

Within the key project "Herschel M33 extended survey" (HerM33es), we are studying the physical and chemical processes driving star formation and galactic evolution in the nearby galaxy M33, combining the study of local conditions affecting individual star formation with properties only becoming apparent on global scales. Here, we present recent results obtained by the HerM33es team. Combining Spitzer and Herschel data ranging from 3.6um to 500um, along with HI, Halpha, and GALEX UV data, we have studied the dust at high spatial resolutions of 150pc, providing estimators of the total infrared (TIR) brightness and of the star formation rate. While the temperature of the warm dust at high brightness is driven by young massive stars, evolved stellar populations appear to drive the temperature of the cold dust. Plane-parallel models of photon dominated regions (PDRs) fail to reproduce fully the [CII], [OI], and CO maps obtained in a first spectroscopic study of one 2'x2' subregion of M33, located on the inner, northern spiral arm and encompassing the HII region BCLMP302.
On the validity of drift-reduced fluid models for tokamak plasma simulation

Drift-reduced plasma fluid models are commonly used in plasma physics for analytic studies and simulations, so the validity of such models must be verified for the regions of parameter space in which tokamak plasmas exist. By deriving and comparing the linear dispersion relations for the drift-wave instability for both a drift-reduced model and a full-velocity model, the importance of the physics lost with the drift-reduction is examined. This analysis is generalised for typical tokamak parameter spaces and is then applied directly to JET data. It is found that drift-reduced models are generally more applicable to the edge plasma (<10% error), while the core plasma shows more significant disagreement (>30% error) particularly at mid-radius. The effect of drift-wave mode number and wavelength also play a key role in determining the accuracy of drift-reduced models.
Advanced security testing using a cyber‐attack forecasting model: A case study of financial institutions

As the number of cyber‐attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides them with proactive controls with which to halt an attack by predicting patterns. Advanced prediction systems also enhance the software design and security testing of new advanced cyber‐security measures by providing new testing scenarios supported by attack forecasting. This present study developed a model that forecasts future network‐based cyber‐attacks on financial institutions using a deep neural network. The dataset that was used to train and test the model consisted of some of the biggest cyber‐attacks on banking institutions over the past three years. This provided insight into new patterns that may end with a cyber‐crime. These new attacks were also evaluated to determine behavioral similarities with the nearest known attack or a combination of several existing attacks. The performance of the forecasting model was then evaluated in a real banking environment and provided a forecasting accuracy of 90.36%. As such, financial institutions can use the proposed forecasting model to improve their security testing measures.
Analysis of Students' Written Scientific Argument Generate and Evaluation Skills / Öğrencilerin Yazılı Bilimsel Argüman Oluşturma ve Değerlendirme Becerilerinin İncelenmesi

Argumanlar fen siniflarinda siklikla kullanilir. Ogrencilerin fen siniflarinda argumanla karsilasma bicimleri cogunlukla; kendilerine ogretmenleri tarafindan arguman sunulmasi ve kendilerinden arguman uretilmesinin istenmesi seklindedir. Bu calismada ogrencilerin yazili arguman yapilandirma ve sunulan argumani degerlendirme becerileri incelenmistir. Bu amacla lise 9. , 10. , 11. ve 12. sinif ogrencilerinin (n=165) kimyasal tepkimeler, erime ve cozunme, maddenin dogasi konulariyla ilgili yapilandirdiklari 495 yazili arguman, Toulmin'in Arguman Modeli'nde yer alan iddia, gerekce, delil bilesenleri ve bu bilesenlerin kalitesi acisindan incelenmis ve siniflar arasi bir karsilastirma yapilmistir. Ardindan bu yazili argumanlardan 14 tanesinin, 8 universite ogrencisi tarafindan degerlendirilmesi saglanmistir. Arastirmanin bulgulari; arguman olusturma becerisinin konu icerigine bagli oldugunu, ogrencilerin iddia olusturmada basarili iken gerekce ve delil olusturmada yetersiz olduklarini, ogrencilerin artan ogrenim ve zihinsel gelisim seviyeleri ile arguman olusturma becerileri arasinda iliski oldugu yonunde genelleme yapilamayacagini, ogrencilerin arguman degerlendirirken iddia-gerekce baglantisini test etmede ve gerekcenin iddia icin uygun destek saglayip saglamadigini belirlemede yetersiz olduklarini gostermektedir.
Student critical thinking skills and student writing ability: The role of teachers’ intellectual skills and student learning

Students’ writing ability has become an essential element for student success and may be influenced by the student and teachers skills; therefore, the subject has garnered the attention of regulators and researchers alike. Thus, the current research investigates the role of student critical thinking skills and teachers’ intellectual skills on students’ writing ability across private universities in Indonesia. The researchers also investigate the mediating impact of student learning among the nexus of student critical thinking skills, teachers’ intellectual skills and student writing ability in the context of private universities in Indonesia. The present study has used primary data collection methods and employs the survey questionnaires for data collection. The current research also use the primary data analysis tools such as smart-PLS to test the study hypotheses as well as the items’ validity and reliability. The results reveal that student critical thinking skills and teachers’ intellectual skills have a positive influence on student writing ability in private universities in Indonesia. The results also show that student learning significantly mediates between the association of student critical thinking skills, teachers’ intellectual skills and student writing ability of private universities in Indonesia. This study is meaningful for future researchers who want to investigate this area in a more in-depth manner in the future and provides the guideline to the regulating and developing authorities while formulting policies related to students’ writing ability in the universities of Indonesia. Keywords: Student critical thinking skills, teachers’ intellectual skills, student writing ability, student learnings, private universities in Indonesia
Lightweight Compound Scaling Network for Nasopharyngeal Carcinoma Segmentation from MR Images

Nasopharyngeal carcinoma (NPC) is a category of tumours with a high incidence in head-and-neck. To treat nasopharyngeal cancer, doctors invariably need to perform focal segmentation. However, manual segmentation is time consuming and laborious for doctors and the existing automatic segmentation methods require large computing resources, which makes some small and medium-sized hospitals unaffordable. To enable small and medium-sized hospitals with limited computational resources to run the model smoothly and improve the accuracy of structure, we propose a new LW-UNet network. The network utilises lightweight modules to form the Compound Scaling Encoder and combines the benefits of UNet to make the model both lightweight and accurate. Our model achieves a high accuracy with a Dice coefficient value of 0.813 with 3.55 M parameters and 7.51 G of FLOPs within 0.1 s (testing time in GPU), which is the best result compared with four other state-of-the-art models.
Evaluation of changes in image appearance with changes in displayed image size

This research focused on the quantification of changes in image appearance when images are displayed at different image sizes on LCD devices. The final results provided in calibrated Just Noticeable Differences (JNDs) on relevant perceptual scales, allowing the prediction of sharpness and contrast appearance with changes in the displayed image size. A series of psychophysical experiments were conducted to enable appearance predictions. Firstly, a rank order experiment was carried out to identify the image attributes that were most affected by changes in displayed image size. Two digital cameras, exhibiting very different reproduction qualities, were employed to capture the same scenes, for the investigation of the effect of the original image quality on image appearance changes. A wide range of scenes with different scene properties was used as a test-set for the investigation of image appearance changes with scene type. The outcomes indicated that sharpness and contrast were the most important attributes for the majority of scene types and original image qualities. Appearance matching experiments were further conducted to quantify changes in perceived sharpness and contrast with respect to changes in the displayed image size. For the creation of sharpness matching stimuli, a set of frequency domain filters were designed to provide equal intervals in image quality, by taking into account the system’s Spatial Frequency Response (SFR) and the observation distance. For the creation of contrast matching stimuli, a series of spatial domain S-shaped filters were designed to provide equal intervals in image contrast, by gamma adjustments. Five displayed image sizes were investigated. Observers were always asked to match the appearance of the smaller version of each stimulus to its larger reference. Lastly, rating experiments were conducted to validate the derived JNDs in perceptual quality for both sharpness and contrast stimuli. Data obtained by these experiments finally converted into JND scales for each individual image attribute. Linear functions were fitted to the final data, which allowed the prediction of image appearance of images viewed at larger sizes than these investigated in this research.
Impact of ANN in Revealing of Viral Peptides

All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structural components of the microbial cell membrane and have a wide range of cellular targets, bacteria are unlikely to be able to develop resistance to them in the short term. The underlying structure of AMPs is critical in determining the selectivity with which they target their respective targets. As far as we know, peptides have not been tested in a lab to see if they can fight bacteria, fungus, and viruses in real life. In this paper, we develop an artificial neural network (ANN) using a back propagation neural network (BPNN) that enables optimal classification of tendency of a peptide sequence that involves the activities of antifungal, antibacterial, or antiviral. The BPNN is trained on the datasets collected across different repositories and then the overfitting is avoided using particle swarm optimization (PSO) algorithm. Hence, at the time of testing, the BPNN clearly finds the predicted samples belonging to the same classes and this avoids the problem of finding the false positives. The simulation is conducted to test the efficacy of the model against various metrics that includes accuracy, precision, recall, and f1-measure. The effectiveness of the BPNN-PSO model in classifying instances at a faster rate than other techniques is demonstrated by its performance. The principle is straightforward, it is not difficult to programme, it converges more quickly, and it generally offers a superior solution.
Effect of herbicides on associated weeds and growth of blackgram

A field study was conducted at Research Station in Kota, Rajasthan during rainy (Kharif) season of 2020 to identify suitable herbicides including pre-emergence (PE) and post-emergence herbicides (PoE) for managing weeds and improve productivity of blackgram [ Vigna mungo (L.) Hepper]. Among weed control treatments tested, the lowest weed density, weed index and highest crop growths parameters like plant population, plant height, dry matter accumulation, nodules/ plant, dry weight of nodules was recorded with hand weeding twice at 20 and 40 days after sowing (DAS), pre-emergence application (PE) of pendimethalin 1.0 kg/ha followed by ( fb ) post-emergence application (PoE) of propaquizafop 2.5% w/w 33.3 g/ha + imazethapyr 3.75% w/w (pre-mix) ME 50 g/ha at 20 DAS and pendimethalin 1.0 kg/ha PE fb fomesafen 11.1% w/w 220 g/ha + fluazifop-p-butyl 11.1% w/w 220 g/ha (pre-mix) PoE at 20 DAS.
Optimal Planning of Microgrid-Integrated Battery Energy Storage

Battery energy storage (BES) is a core component in reliable, resilient, and costeffective operation of microgrids. When appropriately sized, BES can provide the microgrid with both economic and technical benefits. Besides the BES size, it is found that there are mainly three planning parameters that impact the BES performance, including the BES integration configuration, technology, and depth of discharge. In this dissertation, the impact of each one of these parameters on the microgridintegrated BES planning problem is investigated. Three microgrid-integrated BES planning models are developed to individually find the optimal values for the aforementioned parameters. These three microgrid-integrated BES planning models are then combined and extended, by including the impact of microgrid islanding incidents on the BES planning solution, to develop a comprehensive planning model that can be used by microgrid planners to simultaneously determine the installed BES optimal size, integration configuration, technology, and maximum depth of discharge. Besides applications in microgrids, this dissertation investigates the integration of BES to provide other types of support in distribution networks such as load management of commercial and industrial customers, distribution network expansion, and solar PV
Inferring Cognitive State of Pilot’s Brain Under Different Maneuvers During Flight

This work designs an adversarial Bayesian deep network to solve the cognitive detection of pilot fatigue. Batch normalization and data enhancement are adopted in the posterior inference of the proposed model parameters to effectively improve the generalization of neural networks. The generator is used to enhance the brain power map generated from three cognitive indicators and improve the accuracy of fatigue state recognition. This work also adds adversarial noise in the vicinity of each brain electrode to form an adversarial image, which further reveals the correlation between the cognitive state of brain and the location of brain regions. Compared with other deep models and parameter optimization methods, our model achieves better detection accuracy.
Modified Active-Power MRAS Based Adaptive Control With Reduced Sensors for PMSM Operated Solar Water Pump

In a permanent magnet synchronous motor operated solar water pump (SWP), a current sensor is used for realizing maximum power control of photovoltaic array, while a speed sensor is used for implementing vector control of motor. These sensors increase cost and reduce reliability of SWP. Besides, such sensors are temperature sensitive, which thereby limits applicability of system for applications like surface water-pumping. Thus, a modified active-power model reference adaptive system (MAP-MRAS) for elimination of these two sensors is presented in this article. The presented MRAS structure estimates speed along with permanent magnet flux linkage and armature resistance, where estimated parameters are continuously updated in adaptive model. Thus, presented MAP-MRAS is parameter independent unlike conventional MRAS. The PV power is then estimated by using estimated speed. Based on the sensed voltage and estimated power of photovoltaic (PV) array, its maximum power control is realized. Hence, there is no need for sensing PV current. Besides, developed estimated speed based maximum power control scheme does not demands extra converter. The presented MRAS structure is realized on dSPACE1202 platform, and then its effectiveness is demonstrated through test performances obtained on a laboratory prototype of system under different operating conditions.
Penerapan Algoritma Kriptografi Twofish Untuk Mengamankan Data File

The development of technology in the field of telecommunications makes people more and more often send data files in the form of doc, pdf, xls, voice, image, video via the internet. because the internet is a public medium that is vulnerable to intrusion and theft of information on data or files by unauthorized parties. The problem is that there are frequent changes or manipulation of the contents of data files from bank notes processing, cash pick-ups, document pick-ups, clearing items, atm & cdm service, atm cash replenishment, atm first line maintenance and atm second line maintenance. The purpose of this study is to prevent irresponsible people from knowing the reports that will be sent from the branch to the center by made applying the twofish cryptographic algorithm application to secure file data. The results of the tests carried out were 15 encrypted files, the average encrypted file size was 0.11%, the encryption process time was 9.5352 milliseconds and the decryption process time was 9.3294 milliseconds. In the UAT test, a questionnaire with a Likert scale scale of 5. has been used. As a result, the respondents agree (above 91.23%) that the overall application of the twofish algorithm to secure data files can be kept confidential.
An Accurate Approach of Mathematical Modeling of SRR and SR for Metamaterials

In the proposed work, we used two miniaturized resonant inclusions, spiral resonators (SRs) and split ring resonators (SRRs) used in practical implementation of metamaterials. A spiral resonator is modeled on a Rogers’s duroid substrate of permittivity 2.2. This structure is numerically analysed inside a rectangular waveguide with electromagnetic solver. A full wave simulation of a double split ring resonator with same design parameters is performed inside a waveguide. The transmission minimum frequency of SR is evaluated for varying number of turns in SR where as it is accessed for varying number of rings in SRRs. The full wave simulated transmission minimum frequency is compared with frequency derived from equivalent circuit model of each respective model. In this paper, the expressions derived for resonant frequency are simplified for evaluating effective dielectric constant of substrate after incorporation of metallic inclusion. It is found that the analytically calculated resonant frequency is in close agreement with full wave numerically analyzed frequency. It is also confirmed that SR decrease the electrical dimensions of the resonant inclusions to a great extent as compared to SRR, while synthesizing artificial metamaterials.