Miracle-dz/UniFAR-FTUs huggingface dataset:
First Indications of Omsk Haemorrhagic Fever Virus beyond Russia

Omsk haemorrhagic fever virus (OHFV) is the agent leading to Omsk haemorrhagic fever (OHF), a viral disease currently only known in Western Siberia in Russia. The symptoms include fever, headache, nausea, muscle pain, cough and haemorrhages. The transmission cycle of OHFV is complex. Tick bites or contact with infected small mammals are the main source of infection. The Republic of Kazakhstan is adjacent to the endemic areas of OHFV in Russia and febrile diseases with haemorrhages occur throughout the country—often with unclear aetiology. In this study, we examined human cerebrospinal fluid samples of patients with suspected meningitis or meningoencephalitis with unknown origins for the presence of OHFV RNA. Further, reservoir hosts such as rodents and ticks from four Kazakhstan regions were screened for OHFV RNA to clarify if this virus could be the causative agent for many undiagnosed cases of febrile diseases in humans in Kazakhstan. Out of 130 cerebrospinal fluid samples, two patients (1.53%) originating from Almaty city were positive for OHFV RNA. Screening of tick samples revealed positive pools from different areas in the Akmola region. Of the caught rodents, 1.1% out of 621 were positive for OHFV at four trapping areas from the West Kazakhstan region. In this paper, we present a broad investigation of the spread of OHFV in Kazakhstan in human cerebrospinal fluid samples, rodents and ticks. Our study shows for the first time that OHFV can not only be found in the area of Western Siberia in Russia, but can also be detected up to 1.600 km away in the Almaty region in patients and natural foci.
Exploring Nanogeochemical Environments: New Insights from Single Particle ICP-TOFMS and AF4-ICPMS

Nanogeochemistry is an emerging focus area recognizing the role of nanoparticles in Earth systems. Engineered nanotechnology has cultivated advanced analytical techniques that are also applicable to nanogeochemistry. Single particle inductively coupled plasma ICP-time-of-flight-mass spectrometry (ICP-TOF-MS) promises a significant step forward, as time-of-flight mass analyzers enable simultaneous quantification of the entire atomic mass spectrum (∼7–250 m/z+). To demonstrate the utility of this approach, samples were collected and analyzed from a large, boreal river, and its surrounding tributaries. These samples provided us with a diversity of particle compositions and morphologies, while their interconnected nature allowed for an examination of the various nanogeochemical processes present in this system. To further expand on this effort, we combined this high-throughput technique with AF4-ICPMS, focusing on major carriers of trace elements. Using spICP-TOF-MS, Al, Si, and Fe were grouped into classes having all combinations of one or more of these elements. Particle-by-particle ICP-TOF-MS analysis found chemically heterogeneous populations, indicating the predominance of diverse mineralogy or heteroaggregates. The importance of suspended Fe and Mn for the speciation of Pb was observed by single particle ICP-TOF-MS and complemented by AF4-ICPMS analysis of dissolved organic matter and nanoparticulate Fe/Mn. Our study exploits the combination of spICP-TOF-MS and AF4-ICP-MS for studying isotopic and elemental ratios (mineralogy) of individual nanoparticles, which opens the door to further explore the mechanisms of colloid facilitated transport of trace elements.
The effectiveness of narrative therapy on sexual function and couple burnout

Objective: This study aimed to evaluate the effectiveness of narrative therapy on sexual function and couple burnout in women with skin cancer. Method: The research method was quasi-experimental with a pre-test–post-test design with a control group. Among married women with skin cancer in Mashhad (Iran), 30 available individuals were selected and randomly assigned to experimental and control groups. Narrative therapy was performed in a group of 15 people in nine sessions (experimental group). The Female Sexual Function Index and Couple Burnout Measure were used to collect pre-test and post-test data. Analysis of covariance was used to analyze the data. Results: Group narrative therapy significantly improved reported sexual function (F = 40.90; p = .001) and decreased couple burnout (F = 59.03; p = .001) in women with skin cancer. Conclusions: Narrative therapy seems to effectively improve sexual function and couple burnout in women with skin cancer.
Modeling and kinematic simulation of the telescopic sleeve anti-swing device based on Adams depend on crane rotating

For offshore crane in the operation process, due to the suspension of lifting wire rope belongs to the flexible parts and wind, wave and ocean environment. These cause the load swing. A new type of off- shore crane telescopic sleeve anti-swing device was proposed. It uses the casing of the rigid constraints to re- duce the swing of the load. The telescopic sleeve device was introduced, and the mathematical model was es- tablished. The kinematic simulation was carried out by Adams. From the simulation result, we can get the conclusions that the telescopic sleeve has obvious effects of anti-swing. KEYWORD: Offshore crane; Telescopic sleeve device; Modeling; Kinematic simulation; Adams
Performance Impact of TLB on the K computer Applications

Application performance in high-performance computing depends on various factors such as algo- rithms and systems. To maximize hardware performance, it is important for collaboration among hardware, system software, and application developers. We report the performance impact of TLB on the application by simultaneously running several applications on the K computer. TLB is a mechanism that translates virtual and physical addresses in any architecture. Performance impact of TLB on the application is predicted by the data access pattern, which is similar to that of the cache. It is difficult to analyze when and where a TLB miss may occur, because it is often measured not only as performance degradation but also as performance fluctuation. In this paper, we discuss not only calculation results based on some applications that measure the performance impact by TLB misses but also their causes and characteristics, as well as some ways to resolve them.
Comparative Evaluation of Corneal Storage Medias Used as Tooth Avulsion Medias in Maintaining the Viability of Periodontal Ligament Cells Using the Cell Counting Kit-8 Assay

Purpose The prime factor in determining the success of reimplantation of an avulsed tooth is the maintenance of the viability of periodontal ligament fibroblast cells (PDFC). This study aims to evaluate and compare Mc Carey Kaufman media (MK), Cornisol, Dulbecco’s Modified Eagles Medium (DMEM), Hanks Balanced Salt Solution (HBSS) and distilled water in preserving the viability of the PDFC using the Cell Counting Kit-8 assay (CCK-8). Methods Cryopreserved PDFC were suspended in DMEM and incubated in CO2 incubator at 370C with 95% humidity and 5% CO2 for attachment. Once cells attained 80% confluence, they were trypsinised and passed into T-25 culture flasks to expand the culture population. Cells from passage 5 were pooled for experimentation. Trypan blue exclusion test was performed before each experiment to measure cell viability and batches showing more than 95% viability were used in the experiment. The viable PDFC with 1×105 were seeded in 96 well plates and incubated in CO2 incubator at 370C, 95% humidity and 5% CO2 for 24 hours to allow cell attachment. A 100µL of the experimental media were added in the wells and the cells were exposed for 1, 24 and 48 hours respectively. The viability was determined using the CCK-8. Experiment was performed in triplicates and data was subjected to statistical analysis. Results Statistical analysis was performed using repeated measure ANOVA, ANOVA, and post-hoc Bonferroni test with the significance level p<0.05. The values are as follows: MK (1.3146 ±0.0588, 1.9012±0.0511, 2.0723±0.1211) > Cornisol (1.2399±0.0548, 1.9596±0.0652, 1.9592±0.1361) >DMEM (1.1914±0.0691, 1.8479±0.0116, 2.0718±0.0795) > HBSS (0.3665±0.0814, 0.0184±0.0010, 0.0248±0.0042) >distilled water (0.0122±0.0033, 0.0225±0.0085, 0.0104±0.0008) at 1 hour, 24 hours and 48 hours respectively. MK >Cornisol>DMEM>HBSS>distilled water. Conclusion It can be concluded that the corneal preservation solutions showed promising results in preserving periodontal ligament cell viability for extended time periods.
Characterization and Antibacterial Activity of Green Copper Nanoparticles Synthesized by Saponaria officinalis L., a plant with high saponin content

Characterization and Antibacterial Activity of Green Copper Nanoparticles Synthesized by Saponaria officinalis L. , a Plant with High Saponin Content. European Journal of Science and Technology , (35), 341-348. Abstract Evaluation of antimicrobial metal ions and nanoparticles generally concludes that metallic nanoparticles are more stable and active than their metal ion counterparts from which they were synthesized. The antimicrobial effect of these metal ions and nanoparticles typically covers gram-positive, gram-negative bacteria along with fungi. And unlike antibiotics that target specific essential synthetic cellular pathways, antimicrobial actions of metallic nanoparticles are nonspecific. Due to this fact, NPs with antibacterial properties are foreseen as potential agents against stubborn bacterial infections that show multidrug resistance. In this work, we report copper nanoparticle (CuNP) synthesis through the green route, its’ structural characterization, and evaluation of its’ antibacterial properties. The source of a reducing agent selected was soapwort ( Saponaria officinalis L.) root extracts known to be high in saponin content. The main aim of generating green nanoparticles from copper with S. officinalis root extracts was to combine the antibacterial effects of both CuNPs and bioactive phytochemicals from soapwort. Structural characterizations of the Green-CuNPs were performed through scanning electron microscopy (SEM), UV-Visible spectroscopy (UV-Vis), Fourier transform infrared spectrophotometer (FTIR), and X-ray diffraction (XRD) analyses. Green-CuNPs, probably coated with saponins and other active phytochemicals, were tested for their antimicrobial activities against two common bacteria ( Escherichia coli and Staphylococcus aureus ). The bulk of the generated NPs were pure nanocrystalline structures (XRD analysis) with an average diameter of 17 nm. FTIR analysis data has confirmed both CuNP formation and functionalization with aromatic hydrocarbon structures (possibly saponins). In the evaluation of antimicrobial properties, it was observed that NPs had weak to moderate but statistically significant antimicrobial effects against E. coli and S. aureus when used at high doses. Further diagnostics and experimentations may reveal the actual mechanisms of antimicrobial activity of Green-CuNPs synthesized with S. officinalis .
Automatic detection of coronary artery stenosis in X-ray angiograms using Gaussian filters and genetic algorithms

This paper presents a new automatic coronary artery segmentation method based on Gaussian matched filters (GMF) and genetic algorithms in X-ray angiograms. This method is used for detecting coronary stenosis based on the second-order derivatives of the vessel size, and it consists of the steps of vessel enhancement and vessel segmentation. In the enhancement step, the proposed method uses genetic algorithms to obtain the best performance of the GMF by tuning the optimal parameters. The results of the detection method are compared with those obtained by different state-of-the-art methods based on GMF using the area (Az) under the receiver operating characteristic curve. Moreover, in the segmentation step, the inter-class variance thresholding method has been applied for classifying vessel and non-vessel pixels. The segmentation results obtained by the proposed method demonstrated high performance achieving an accuracy of 0.962 with a test set of 20 angiograms of a database of 40 angiograms outlined by a sp.
A New Text Summarization Approach based on Relative Entropy and Document Decomposition

—In the era of the fourth industrial revolution, the rapid relay on using the Internet made online resources explosively grow. This revolution emphasized the demand for new approaches to utilize the use of online resources such as texts. Thus, the difﬁculty to compare unstructured resources (text) is urging the demand of proposing a new approach, which is the core of this paper. In fact, text summarization technology is a vital part of text processing, therefore. The focus is on the semantic information not just on the basic information. It requires mining topic features in order to obtain topic-words and topic-sentences relationships. This automatic text summarization is document decomposition according to relative entropy analysis; which means measuring the difference of the probability distribution to measure the correlation between sentences. This paper introduced a new method for document decomposition, which categorizes the sentences into three types of content. The performance demonstrated the efﬁciency of using the relative entropy of the topic probability distribution over sentences, which enriched the horizon of text processing and summarization research ﬁeld.
Spectral Methods for Response Enhancement of Microwave Resonant Sensors in Continuous Non-Invasive Blood Glucose Monitoring

In this paper, the performance of three recent algorithms for the frequency-response enhancement of microwave resonant sensors are compared. The first one, a single-step algorithm, is based on a couple of direct-inverse Fourier transforms, giving a densely sampled response as a result. The second algorithm exploits an iterative procedure to progressively restricts the frequency response. The final one is based on the super-resolution MUSIC algorithm. The comparison is carried out through a Monte Carlo analysis. In particular, synthetic signals are firstly exploited to mimic the frequency response of a resonant microwave sensor. Then, experimental data collected from water-glucose solutions are adopted as validation test for potential applications in noninvasive blood-glucose monitoring.
Identification of a mutation in a Vietnamese family with Emery-Dreifuss Muscular Dystrophy using whole exome sequencing

Emery-Dreifuss muscular dystrophy (EDMD) is a degenerative neuromuscular disease associated with at least nine genes, including EMD, LMNA, FHL1, TMEM43, SUN1, SUN2, TTN, SYNE1, and SYNE2. Herein, we identified a heterozygous missense LMNA mutation (NM_170707.4: c.1357C>T,p.R453W) in three members of a Vietnamese family using whole-exome sequencing (WES), in which the proband was an 11-year-old girl presenting humeroperoneal muscle weaknesses and generalized contracture. Her father and one other relative also exhibited multiple signs of muscular atrophy and contracture. Sanger sequencing in the extended family verified the causative nature of this mutation, establishing a confirmed diagnosis of autosomal dominant Emery-Dreifuss muscular dystrophy (EDMD2). The clinical presentations of each patient in this study are different from each other, demonstrating the intrafamilial phenotypic variability of this mutation. Early identification of the underlying genetic course of the disease by sequencing, combined with clinical findings provides solid evidence to diagnosis process, genetic counseling and management strategy.
Metodologi Paradigma Nusantara

Hal yang paling dikhawatirkan Bangsa Indonesia ialah tercerabutnya kebangsaan dan kebudayaan dari akarnya. Sehingga, akar harus diperkuat melalui penelitian-penelitian terapan membumikan nilai Pancasila sehingga menghasilkan kebijakan-kebijakan yang berpihak. Metode terapan diharapkan menghasilkan produk yang mampu secara langsung mengatasi permasalahan. Paradigma kenusantaraan adalah cara pandang terhadap segala sesuatu dari pemikiran kenusantaraan. Kenusantaraan adalah kesatuan yang integral antara materi dan spiritualitas. Penelitian ini menghasilkan metode terapan dalam ranah paradigma kenusantaraan untuk membumikan nilai Pancasila. Nilai Pancasila ialah spirit (spiritualitas) dari Bangsa Indonesia yang harus menyatu dengan raganya (materi). Penelitian ini memberikan tiga tahapan mulai dari Paradigma Kenusantaraan (historisitas), selanjutnya menerapkannya (beyond rasionalitas), dan tahapan akhir menghasilkan metode terapan (beyond aktualitas).
Self-Efficacy Beliefs Instrument: Use of History of Mathematics in Mathematics Teaching

This study is related to the use of the tool of self-efficacy beliefs in teaching mathematics through using history of mathematics, based on Self-efficacy Theory of Bandura (1977). The instrument is adapted from Enoch, Smith and Huinker’s (2000) instrument which contains 21 statements grouped into two dimensions of self-efficacy beliefs: personal mathematics teaching efficacy (PMTE) and mathematics teaching outcome expectancy (MTOE). Exploratory factor analysis was employed to maintain construct validity; and reliability was maintained by Cronbach alpha (α = .829, N = 305) to assess internal consistency of the items. Pre-service mathematics teachers’ self-efficacy beliefs can be increased through the use of this tool.
Supervised Learning Techniques for Intrusion Detection System based on Multi-layer Classification Approach

—The goal of this study is to discover a solution to two problems: first, the signature-based intrusion detection system SNORT can identify a new attack signature without human intervention; and second, signature-based IDS cannot detect multi-stage attacks. The interesting aspect of this study is the growing ways to address the aforementioned issues. We introduced a multi-layer classification strategy in this study, in which we employ two layers, the first of which is based on a decision tree, and the second of which includes machine learning technique fuzzy logic and neural networks. If the first layer fails to identify fresh attacks, the second layer takes over and detects new signature assaults, updating the SNORT signature automatically.
Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems

Due to the growing number of users, power, and spectral effectiveness, most communication systems are complex and difficult to implement on a large scale. Artificial Intelligence (AI) has played an outstanding role in the implementation of theoretical systems in the real world, with less complexity achieving better results. In this direction, we compare the Non-Orthogonal Multiple Access (NOMA) technique for a multiuser Visible Light Communication (VLC) system with Successive Interference Cancellation (SIC) for two types of detectors: (1) the deep learning-based system and (2) the traditional maximum likelihood (ML) decoder-based system. For multiplexing, we compare the variations of novel Orbital Angular Momentum (OAM) multiplexing and Orthogonal Frequency Division Multiplexing (OFDM) with Index Modulation (IM). In this article, we implement OFDM-IM and OAM-IM for four users for the Gaussian fading MIMO Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) VLC channels. The suggested systems’ bit error rate (BER) performances are compared in simulations for a wide range of Signal-to-Noise Ratios (SNRs), which shows that deep learning-based systems outperform the ML-based system for both users to ensure better decoding at the receiver end, especially at higher SNR values. The detection error is lower in a deep learning-based system at around 20% and around 30% for low SNR and high SNR values, respectively.
Computational and Mathematical Methods in Medicine Prediction of COVID-19 in BRICS Countries: An Integrated Deep Learning Model of CEEMDAN-R-ILSTM-Elman

Since the outbreak of COVID-19, BRICS countries have experienced different epidemic spread due to different health conditions, social isolation measures, vaccination rates, and other factors. A descriptive analysis is conducted for the spread of the epidemic in the BRICS countries. Considering the nonlinear and nonstationary characteristics of COVID-19 data, a principle of decomposition-reconstruction(R)-prediction-integration is proposed. Correspondingly, this paper constructs an integrated deep learning prediction model of CEEMDAN-R-ILSTM-Elman. Specifically, the prediction model is integrated by complete ensemble empirical mode decomposition (CEEMDAN), improved long-term and short-term memory network (ILSTM), and Elman neural network. First, the data is decomposed by adopting CEEMDAN. Then, by calculating the permutation entropy and average period, the decomposed eigenmode component IMFs are reconstructed into four sequences of high, medium, low level, and trend term. Thus, ILSTM and Elman algorithms are used for component sequence prediction, whose results are integrated as the final results. The ILSTM is established based on the LSTM model and the improved beetle antennae search algorithm (IBAS). The ILSTM mainly considers that the prediction accuracy of LSTM model is vulnerable to the influence of parameter selection. The IBAS with adaptive step size is used to automatically optimize the super parameters of LSTM model and to improve the modeling efficiency and prediction accuracy. Experimental results indicate that compared with other benchmark models, CEEMDAN-R-ILSTM-Elman integrated model predicts the number of newly confirmed cases of COVID-19 in BRICS countries with higher accuracy and lower error. Strict social policies have a greater impact on the infection rate and mortality rate of the epidemic. During July-August 2021, epidemic spread in BRICS countries will slow down, and the overall situation is still quite severe.
CODED OFDM FOR AERONAUTICAL TELEMETRY

Three Quadrature Phase Shift Keying (QPSK) mapped COFDM systems demonstrating a continuum of complexity levels are simulated over an evolving three ray model of the multipath fading channel with parameters interpolated from actual channel sounding experiments. The first COFDM system uses coherent QPSK and convolutional coding with interleaving in frequency, channel equalization and soft decision decoding; the second uses convolutional coding with interleaving in frequency, Differential Phase Shift Keying (DPSK) and soft decision decoding; the third system uses a quaternary BCH code with DPSK mapping and Error and Erasure Decoding (EED). All three systems are shown to be able to provide reliable data communication during frequency selective fade events. Simulations demonstrate QPSK mapped COFDM with reasonable complexity performs well in a multipath frequency selective fading environment under parameters typically encountered in aeronautical telemetry.
IMD-Net: a Deep Learning-based Icosahedral Mesh Denoising Network

In this work, we propose a novel denoising technique, the icosahedral mesh denoising network (IMD-Net) for closed genus-0 meshes. IMD-Net is a deep neural network that produces a denoised mesh in a single end-to-end pass, preserving and emphasizing natural object features in the process. A preprocessing step, exploiting the homeomorphism between genus-0 mesh and sphere, remeshes an irregular mesh using the regular mesh structure of a frequency subdivided icosahedron. Enabled by gauge equivariant convolutional layers arranged in a residual U-net, IMD-Net denoises the remeshing invariant to global mesh transformations as well as local feature constellations and orientations, doing so with a computational complexity of traditional conv2D kernel. The network is equipped with carefully crafted loss function that leverages differences between positional, normal and curvature fields of target and noisy mesh in a numerically stable fashion. In a first, two large shape datasets commonly used in related fields, ABC and ShapeNetCore, are introduced to evaluate mesh denoising. IMD-Net’s competitiveness with existing state-of-the-art techniques is established using both metric evaluations and visual inspection of denoised models.