Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Cardiovascular Arrhythmia Classification Based on Through Nonlinear Features and Tunable-Q Wavelet Transform (TQWT) Based Decomposition

Today, cardiovascular disease has become an epidemic. Statistics show that one person dies every 33 seconds due to cardiovascular disease. It is estimated that 33% of men and 10% of women have a heart attack before the age of 60. Arrhythmias are abnormal beats that cause the heart to beat too fast or too slow to pump. Automatic electrocardiogram analysis is critical to the diagnosis and treatment of heart patients. There are several learning methods for analyzing ECG signals to diagnose arrhythmias. In the proposed method, the heart rate signals are decomposed into different sub bands using the Tunable Q-Factor Wavelet Transform (TQWT) method, then the features are extracted and modified using classification with the aim of better classifying and separating data in the process of identifying the clinical features of the class. They are classified so that normal people and people with cardiac arrhythmias can be distinguished from their ECG signals. The results showed that the proposed method classifies the ECG signal with 99.25% accuracy. Since accuracy in diagnosing cardiac arrhythmias in medicine is a vital and important factor, so the proposed method can be very effective for the decision of cardiologists.
Communicative Competence of College English Language Teachers: Using Results as Basis For An Action Plan

This study determined the communicative competence of the college English language teachers of Northern Iloilo Polytechnic State College (NIPSC) in the 5th District of Iloilo through a researcher-developed instrument which had undergone validity and reliability testing. Utilizing mixed method approach and with forty-five (45) English language teachers who were chosen through complete enumeration, result showed that the respondents’ level of communicative competence in the areas of grammatical, sociolinguistic, strategic and discourse was “very good”, while the extent of their English language exposure was “sometimes” for the 95.56% and “always” for only 4. 44%. There was no significant relationship between the respondents’ number of years in teaching, written language proficiency, relevant seminars and trainings attended and all areas of communicative competence. Among the four areas of communicative competence, grammatical and sociolinguistic competence had significant relationship with their oral language proficiency and discourse competence had significant relationship with highest educational attainment. There was no significant relationship between their communicative competence and extent of English language exposure. There was no significant difference between the level of communicative competence of the respondents when they were grouped according to campus. The following factors were perceived to contribute to their communicative competence: the practice of the English language inside and outside the classroom, exposure to mainstream media, experience as English teachers, inherent intelligence, seminars or trainings attended, while the extent of their language exposure was perceived to be influenced by time, attitude or preference of the teacher, environment, teaching load, co-workers and students. Based on the result of the study an action plan was proposed to improve the efficiency, competence, and performance of the English language teachers at the College.
The Role of PDGF Gene in Tissue Regeneration of Digit Tip Mice (Mus musculus)

Some genes play a role in the tissue regeneration process and they have specific functions. One of them is the platelet-derived growth factors (PDGF) gene that have a role in the wound healing process because it induces cell mitosis and thereby causes cell proliferation. PDGF protein expression is thought to chemotactically recruit many cell types including smooth muscle, fibroblasts, and monocytes to migrate to the wound site. These research aims are to analyze the role of PDGF gene expression in the process of digit tip mice tissue regeneration. This study used 24 mice (Mus musculus) were amputated at the digit tip and the growth was observed on days 0, 1, 3, 5, 10, 15, and 25. We isolated RNA from each tissue after amputation on days 0, 1, 3, 5, 10, 15, and 25 and analyzed the RNA expression of the PDGF gene using the qPCR method. PDGF gene expression reached its peak on day 10 on the granule phase and decreased until day 25. The peak expression of the PDGF gene indicates the role of the gene in the tissue regeneration of digit tip mice. The normality test for PDGF gene expression was normally distributed (p> 0, 05) and the variation test between each treatment was different significantly (p <0.05).
A Deep Learning Approach for Generating Pleasant Music

Advancement in deep neural networks have made it possible to compose music that mimics music composition by humans. The capacity of deep learning architectures in learning musical style from arbitrary musical corpora have been explored in this paper. The paper proposes a method for generated from the estimated distribution. Musical chords have been extracted for various instruments to train a sequential model to generate the polyphonic music on some selected instruments. We demonstrate a simple method comprising a sequential LSTM models to generate polyphonic music. The results of the model evaluation show that generated music is pleasant to hear and is similar to music played by humans. This has great application in entertainment industry which enables music composers to generate variety of creative music.
Efficient conversion of glucosamine to levulinic acid in a sulfamic acid-catalyzed hydrothermal reaction

Glucosamine is a monomer of chitosan, which is a biopolymer derived from the exoskeletons of crustaceans. This work investigated the conversion of glucosamine into the platform chemical levulinic acid (LA), in a sulfamic acid-catalyzed hydrothermal reaction. The optimized results of LA production showed that the conditions of 200 °C, 125 g L−1 glucosamine, 0.3 M sulfamic acid, and 15 min produced a 33.76 ± 0.19 mol% LA yield. The same conditions produced only 0.14 mol% 5-HMF yield. These results show that glucosamine is a potential bioresource to produce platform chemicals. Also, in the field of biofuels and chemical synthesis processes, the catalytic system using sulfamic acid is significant.
Big accents in Stockholm Swedish: Nuclear accents, prenuclear accents, and initiality accents

Stockholm Swedish has a distinction between so-called big accents and small accents (in addition to a lexical contrast between tone accent 1 and tone accent 2). The function and distribution of the big versus small accent has never been fully understood. West Germanic languages lack a corresponding distinction. While it is known that big accents appear on information-structural focus, this fact cannot account for all big accents, nor can it explain the existence of the big accent in relation to the domains of the prosodic hierarchy. In this article, the distribution of big accents in preverbal subjects is studied. A production study is presented where native speakers read sentences with preverbal subjects of four different lengths, with or without information-structural focus. Based on the results, it is argued that heads of prosodic phrases (φ) serve as hosts of big accents (one and only one big accent per φ). The rightmost big accent in the intonation phrase (ι) is referred to as the nuclear accent, and all other (non-rightmost) big accents are referred to as prenuclear accents. Heads of φ are aligned with the right edge of φ by default. However, the leftmost φ inside an ι may (non-obligatorily) be left-headed, causing a big accent to appear at the leftmost prosodic word (ω) of ι. Such left-aligned prenuclear accents are referred to as initiality accents. These assumptions regarding the big accent in Stockholm Swedish account for the large observed variation in terms of big accent distribution in the preverbal subjects.
Oxygen and Al‐Mg isotopic constraints on cooling rate and age of partial melting of an Allende Type B CAI, Golfball

Coarse‐grained, igneous Ca‐Al‐rich inclusions (CAIs) in CV chondrites formed through multiple melting events. We conducted in situ O‐isotope analysis and Al‐Mg systematics by secondary ion mass spectrometry of relict and overgrown minerals from a partial melting event in an Allende Type B CAI, Golfball. Golfball has a Type B CAI bulk composition and a unique structure: a fassaite‐rich mantle enclosing a melilite‐rich core. Many of the blocky melilite crystals in the core have irregularly shaped, Al‐rich (Åk5–15) cores enclosed in strongly zoned (Åk30–70) overgrowths. Since the Al‐rich melilite grains could not have formed from a melt of Golfball, they are interpreted as relict grains that survived later melting events. The O‐isotopic compositions of the blocky melilite crystals plot along the carbonaceous chondrite anhydrous mineral line, ranging between Δ17O ~ −14‰ and −5‰. The Al‐rich relict melilite grains and their overgrowths exhibit the same O‐isotopic compositions, while the O‐isotopic compositions are varied spatially among melilites. We found that the O‐isotopic compositions steeply change across several melilite crystals within few tens of micrometers, indicating the O‐isotopic compositions of the melt could not have been homogenized during the partial melting in that scale. According to the time scale of O self‐diffusivity in the melt, the cooling rate of the partial melting event is calculated to be >6 × 104 K h−1. Al‐Mg isotope data for core minerals plot on a straight line on an Al‐Mg evolution diagram. A mineral isochron for Golfball gives initial 26Al/27Al of (4.42 ± 0.20) × 10–5 and initial δ26Mg* of −0.035 ± 0.050‰. The chemical and O‐isotopic compositions of melilite and those initial values imply that its precursor consisted of fluffy Type A and/or fine‐grained CAIs. The partial melting event for Golfball may have occurred in very short order after the precursor formation.
Citrus species: Modern functional food and nutraceutical-based product ingredient

Citrus is the most cultivated fruit crop in the world and occupies a place of considerable importance in the country’s economy. Almost 33% of the citrus fruits are processed for juice production; however, a great amount of wastes, including peels, segment membranes, and seeds are also produced. Indeed, citrus fruits consist of 45% juice, 26% pulp, 27% peels, and 2% seeds. Pruning, a cultural practice involving the removal of tree branches and limbs, was applied to improve fruit’s quality. A large amount of leaves are produced through pruning. These agri-food matrices contain a wide range of bioactive phytochemicals compared to fruits. The present review covers the past 5 years of research carried out in chemistry, health properties, and applications in food and nutraceutical industries of all portions of citrus fruit and its major bioactive compounds. Additionally, patents are also included.
Green synthesis of silver nanoparticles using seed extract of Alpinia katsumadai, and their antioxidant, cytotoxicity, and antibacterial activities

The green synthesis of nanoparticles using plants and their extracts is considered as a representative approach in material syntheses for environmental benignity. Numerous metal nanoparticles with potential bioactivities have been prepared based on plant extracts. In this paper, we report an eco-friendly technique for the preparation of silver nanoparticles (AgNPs) using seed extract of Alpinia katsumadai, and the evaluation of their antioxidant, antibacterial, and cytotoxicity activities. UV-visible spectroscopy, FETEM, EDX, SAED, XRD, DLS and FT-IR analysis were used to characterize the morphology and crystalline phase of AgNPs. The FETEM and DLS analysis showed AgNPs were quasi-spherical in shape, with an average diameter of 12.6 nm for the size distribution. The SAED spectrum of the AgNPs indicated their crystalline nature, which was further confirmed by the XRD studies. The FT-IR spectra revealed that phytochemicals from Alpinia katsumadai seed extract act as a capping and reducing agent for AgNP formation. The AgNPs showed distinctive free radical scavenging, potent antibacterial activity against different types of bacteria, and cytotoxicity activities. The results confirmed that Alpinia katsumadai is a potential bio-resource/biomaterial for synthesizing AgNPs with applications in antibacterial and antioxidant agents.
Concomitant Use of CD138- and CD19-directed Chimeric Antigen Receptor-modified T Cells Enhances Cytotoxicity Towards Multiple Myeloma

Multiple myeloma (MM) is a malignancy characterized by abnormal proliferation of clonal plasma cells, and it is the second most common hematologic malignancy in the world after non-Hodgkin lymphoma. In recent years, significant progress has been made in the clinical treatment of MM. In particular, certain novel drugs, such as bortezomib, lenalidomide, and carfilzomib, have greatly improved the survival rate of patients with MM. However, because of drug resistance, most MM patients eventually suffer a relapse and die of the disease. In this study, the chimeric antigen receptor-modified T cell (CAR-T cell) technology, which has achieved success in recent clinical trials for B-cell acute lymphoblastic leukemia (B-ALL), was used. In view of the high CD138 expression in MM cells and the presence of the CD138–/CD19+ phenotype in a small subset of MM cells, and based on preliminary findings of effective killing of MM cells by CD19-CAR-T cells in clinical studies, CD138- and CD19-directed CAR-T cells were constructed. Through in vitro experiments and the use of a mouse model, we proved that these two types of CAR-T cells possess strong biological activity in the specific killing of target cells, and that the concomitant use of these cells significantly enhances the killing effect in an MM mouse model.
Semi-Supervised Training of Transformer and Causal Dilated Convolution Network with Applications to Speech Topic Classification

Aiming at the audio event recognition problem of speech recognition, a decision fusion method based on the Transformer and Causal Dilated Convolutional Network (TCDCN) framework is proposed. This method can adjust the model sound events for a long time and capture the time correlation, and can effectively deal with the sparsity of audio data. At the same time, our dataset comes from audio clips cropped by YouTube. In order to reliably and stably identify audio topics, we extract different features and different loss function calculation methods to find the best model solution. The experimental results from different test models show that the TCDCN model proposed in this paper achieves better recognition results than the classification using neural networks and other fusion methods.
Regulatory sanction risk and going-concern reporting practices: evidence for privately held firms

We study the temporal evolution of going-concern reporting from 2004 to 2013 and test whether sanction risk is related to the likelihood of a going-concern opinion using samples of privately held firms. In 2009, the Supervisory Board of Public Accountants (SBPA) in Sweden started to issue significantly more going-concern-related disciplinary sanctions, and we test whether and how auditors at different audit firms adjust their reporting practices (Type I and Type II errors) in response to the increased sanction risk. Our findings reveal that auditors are more likely to issue going-concern opinions to bankrupt and non-bankrupt firms when the sanction risk is higher, suggesting that sanction risk is positively associated with conservatism in auditors’ reporting. Furthermore, we find that auditors at Big 4 firms alter their reporting to conservative more than non-Top 7 firms when sanction risk increases. Finally, results on the informativeness of going-concern opinions indicate that a going-concern opinion increases the bankruptcy probability during both the lower and higher sanction risk periods, but the impact is higher under the higher sanction risk period.
Understanding the climate impacts on decadal vegetation change in northern Alaska

The Arctic is experiencing rapid climate change. This research documents tundra vegetation changes near Atqasuk and Utqiaġvik, Alaska. At each location, 30 plots were sampled annually from 2010 to 2019 using a point frame. For every encounter, we recorded the height and classified it into eight groupings (deciduous shrubs, evergreen shrubs, forbs, graminoids, bryophytes, lichens, litter and standing dead vegetation); for vascular plants we also identified the species. We found an increase in plant stature and cover over time consistent with regional warming. Graminoid cover and height increased at both sites, with a fivefold increase in cover in Atqasuk. At Atqasuk shrub and forb cover and height increased. Species diversity decreased at both sites. Year was generally the strongest predictor of vegetation change suggesting a cumulative change over time; however, soil moisture and soil temperature were also predictors of vegetation change. We anticipate plants in the region will continue to grow taller as the region warms, resulting in greater plant cover, especially graminoids and shrubs. The increase of plant cover and accumulation of litter may impact nonvascular plants negatively. Continued changes in community structure will impact energy balance and carbon cycling and may result in regional and global consequences.
Feasibility of a deep learning-based diagnostic platform to evaluate lower urinary tract disorders in men using simple uroflowmetry

Purpose To diagnose lower urinary tract symptoms (LUTS) in a noninvasive manner, we created a prediction model for bladder outlet obstruction (BOO) and detrusor underactivity (DUA) using simple uroflowmetry. In this study, we used deep learning to analyze simple uroflowmetry. Materials and Methods We performed a retrospective review of 4,835 male patients aged ≥40 years who underwent a urodynamic study at a single center. We excluded patients with a disease or a history of surgery that could affect LUTS. A total of 1,792 patients were included in the study. We extracted a simple uroflowmetry graph automatically using the ABBYY Flexicapture® image capture program (ABBYY, Moscow, Russia). We applied a convolutional neural network (CNN), a deep learning method to predict DUA and BOO. A 5-fold cross-validation average value of the area under the receiver operating characteristic (AUROC) curve was chosen as an evaluation metric. When it comes to binary classification, this metric provides a richer measure of classification performance. Additionally, we provided the corresponding average precision-recall (PR) curves. Results Among the 1,792 patients, 482 (26.90%) had BOO, and 893 (49.83%) had DUA. The average AUROC scores of DUA and BOO, which were measured using 5-fold cross-validation, were 73.30% (mean average precision [mAP]=0.70) and 72.23% (mAP=0.45), respectively. Conclusions Our study suggests that it is possible to differentiate DUA from non-DUA and BOO from non-BOO using a simple uroflowmetry graph with a fine-tuned VGG16, which is a well-known CNN model.
Interval Type-2 Fuzzy Logic-Second Order Sliding Mode Based Fault Detection and Active Fault-Tolerant Control of Brushless DC Motor

This paper addresses the detection of the short-circuit faults and the active fault tolerant control (AFTC) of the brushless direct current motor (BLDCM) based on the interval type-2 fuzzy-second order sliding mode. In this article, the main idea consists of using an algorithm to detect the fault in an electric current. This algorithm corrects the detected fault. In this study, a hybrid technique of fault tolerant control is proposed. This technique based on interval type 2 fuzzy logic and second order sliding mode. Also, it facilitates the procedures for setting and controlling the velocity of BLDCM. For that, a dynamic model for direct current has been established. Furthermore, short circuit faults have also been introduced between turns to test the robustness of the control laws. Finally, a theoretical analysis is presented and the simulations are presented in order to validate the proposed control strategy. The proposed AFTC can then achieve favorable tracking performance.
Optimizing the neural network hyperparameters utilizing genetic algorithm

Neural networks (NNs), as one of the most robust and efficient machine learning methods, have been commonly used in solving several problems. However, choosing proper hyperparameters (e.g. the numbers of layers and neurons in each layer) has a significant influence on the accuracy of these methods. Therefore, a considerable number of studies have been carried out to optimize the NN hyperparameters. In this study, the genetic algorithm is applied to NN to find the optimal hyperparameters. Thus, the deep energy method, which contains a deep neural network, is applied first on a Timoshenko beam and a plate with a hole. Subsequently, the numbers of hidden layers, integration points, and neurons in each layer are optimized to reach the highest accuracy to predict the stress distribution through these structures. Thus, applying the proper optimization method on NN leads to significant increase in the NN prediction accuracy after conducting the optimization in various examples.
Updating MoonProt From Home: An Online Student Research Project During the COVID-19 Pandemic

During the spring of 2020, labs around the world suddenly closed to help slow the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the deadly COVID-19 pandemic. Among the many effects on science and education, the lab closures resulted in undergraduates losing the opportunity to work on research projects during that spring and summer and throughout the 2020–2021 academic year. Participating directly in a research project is important for undergraduate students to gain research experience and with it the mentoring and training needed to prepare them for graduate school or professional school and a future career in science. To address this need during the pandemic, I organized an online, remote, collaborative project for a team of undergraduates at the University of Illinois at Chicago (UIC) that grew to include additional undergraduates from other universities as well as several high school students and their teachers. My experience in organizing this project could serve as a model for organizing online student research projects in the future.