Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and Associated Factors of Chronic Obstructive Pulmonary Disease Among Adults in Neno District, Malawi: A Cross-Sectional Analytical Study

Introduction Chronic obstructive pulmonary disease (COPD) continues to pose a global public health challenge. However, literature is scarce on the burden of COPD in Malawi. We assessed the prevalence and risk factors for COPD among adults in Neno, Malawi. Methodology We conducted a population-based analytical cross-sectional study in Neno District between December 2021 and November 2022. Using a multi-stage sampling technique, we included 525 adults aged≥40 years. All participants underwent spirometry according to the American Thoracic Society (ATS) guidelines and were interviewed using the IMPALA questionnaire. For this study, we utilized the definition of COPD as a post-bronchodilator FEV1/FVC <0.70. We collected data using Kobo collect, exported to Microsoft Excel, and analysed using R software. We used descriptive statistics and logistic regression analysis; a p-value of <0.05 was considered statistically significant. Results Out of 525 participants, 510 participants were included in the final analysis. Fifty-eight percent of the participants were females (n=296), and 62.2% (n=317) were between 40 and 49 years with a median (IQR) age of 46 (40–86). For patient characteristics, 15.1% (n=77) were current smokers, and 4.1% (n=21) had a history of pulmonary tuberculosis (PTB). Cough was the most commonly reported respiratory symptom (n=249, 48.8%). The prevalence of COPD was 10.0% (n=51) and higher (15.0%) among males compared to females (6.4%). Factors significantly associated with COPD were age 60 years and above (adjusted odds ratio [aOR] = 3.27, 95% CI: 1.48–7.34, p<0.004), ever smoked (aOR = 6.17, 95% CI:1.89–18.7, p<0.002), current smoker (aOR = 17.6, 95% CI: 8.47–38.4, p<0.001), and previous PTB (aOR = 4.42, 95% CI: 1.16–15.5, p<0.023). Conclusion The cross-sectional prevalence of COPD in rural Malawi is high, especially among males. Factors significantly associated were older age (60 years and above), cigarette smoking, and previous PTB. Longitudinal studies are needed to better understand disease etiology and progression in this setting.
Utilization of Online and Offline Mixed Education Model in ESP Teaching—Taking Financial English Teaching as an Example

: With the deepening of educational informatization, especially the emergence of Massive Open Online Course (MOOC), the blended online and offline teaching mode has brought profound impact on classroom teaching and greatly improved student learning efficiency. This article attempted to apply this new teaching model to financial English classrooms and organically combined the ESP (English for Specific Purposes) teaching method with online and offline blended teaching methods to improve students' English learning abilities. In order to obtain objective and accurate teaching experiment results, based on the principle of comparison, students from undergraduate English classes majoring in economics were selected as the statistical samples for the experiment, and they were compared and analyzed to minimize differences between samples. Before the experiment, a score test was used to determine the experimental subjects. In the comparison of grades using different teaching methods, online and offline teaching scored 75.3 points; ESP teaching scored 84.2 points; this article's teaching scored 93.7 points. This article helps to enhance students' interest in financial English classes.
A Deep Neural Network Translator for Edge Site Implementation

Implementing deep learning models (DNN) on edge sites remains challenging. Besides the high complexity of DNN models in their architecture, programming directly in hardware languages requires high-level verification to render these models run on edge devices. By utilizing High-Level Synthesis or HLS technology to generate HDL (Hardware Description Language) containing the DNN model, in this study, we built a DNN translator. DNN translator translates the model and its weights from the Pytorch-based DNN model to C++. The generated C++ files can be synthesized using HLS tools such as Vitis HLS or Vivado HLS. HDLs resulting from the synthesis of generated C++ are provided with HLS Pragmas to optimize the design. This DNN translator provides a solution to expand the application of the DNN model to edge devices, especially FPGA, as one of the potential edge devices. In the implementation, our DNN translator successfully generated C++ files for synthesizing the Multi-layer Perceptron (MLP) and LeNet5 and implemented them in an FPGA, Arty A7-100.
Emoji use in social media posts: relationships with personality traits and word usage

Prior research has demonstrated relationships between personality traits of social media users and the language used in their posts. Few studies have examined whether there are relationships between personality traits of users and how they use emojis in their social media posts. Emojis are digital pictographs used to express ideas and emotions. There are thousands of emojis, which depict faces with expressions, objects, animals, and activities. We conducted a study with two samples (n = 76 and n = 245) in which we examined how emoji use on X (formerly Twitter) related to users’ personality traits and language use in posts. Personality traits were assessed from participants in an online survey. With participants’ consent, we analyzed word usage in posts. Word frequencies were calculated using the Linguistic Inquiry Word Count (LIWC). In both samples, the results showed that those who used the most emojis had the lowest levels of openness to experience. Emoji use was unrelated to the other personality traits. In sample 1, emoji use was also related to use of words related to family, positive emotion, and sadness and less frequent use of articles and words related to insight. In sample 2, more frequent use of emojis in posts was related to more frequent use of you pronouns, I pronouns, and more frequent use of negative function words and words related to time. The results support the view that social media users’ characteristics may be gleaned from the content of their social media posts.
Application and Development of Intelligent Teaching Based on ChatGPT

Based on applying ChatGPT in smart classroom, explore the development and reform of ChatGPT-driven classroom teaching. Firstly, the related concepts and applications of smart classroom and ChatGPT are introduced. Then discuss the application of ChatGPT combined with constructivism theory in teaching and its impact on teaching methods, teaching methods, teaching content, etc., and how to promote the development and reform of teaching through ChatGPT. In terms of methods and technologies, use ChatGPT to design, complete and evaluate teaching content, and use ChatGPT to improve teaching efficiency and student learning effects. The application of ChatGPT in smart classroom teaching can provide new ideas and methods for the development of education and teaching. Help teachers better understand the needs of students, design more personalized and diversified teaching content and methods. In terms of teaching evaluation, ChatGPT can help teachers scientifically evaluate teaching effects, and accurately evaluate the teaching results. This study provides some practical ideas and methods for the application of ChatGPT technology in smart classroom teaching, which will help promote the development and reform of education and teaching.
Concept Designs for Vibration Energy Harvesting Employing Nonlinear Dynamics

The era of Industrial Internet of Things requires the development of broadband energy harvesters with the capability to energize low-power electronics for self-powered sensing applications. Although miniaturized vibration energy harvesters can provide an energy source for wireless sensing, major challenges are the energy harvesting capability, bandwidth, device size and reliability in harnessing broadband energy sources. The use of nonlinear dynamics features can play a key role in the design of the mechanical part of the harvester in order to increase its performance. Some fundamentals and methods adopted for this purpose are discussed in this seminar.
Purification Method of Extracellular Vesicles Derived from Human T-Cell Leukemia Virus Type 1-Infected Cells without Virions

To mediate intercellular communication, cells produce extracellular vesicles (EVs). These EVs transport many biomolecules such as proteins, nucleic acids, and lipids between cells and regulate pathophysiological actions in the recipient cell. However, EVs and virus particles produced from virus-infected cells are of similar size and specific gravity; therefore, the separation and purification of these two particles is often controversial. When analyzing the physiological functions of EVs from virus-infected cells, the presence or absence of virus particle contamination must always be verified. The human T-cell leukemia virus type 1 (HTLV-1)-infected cell line, MT-2, produces EVs and virus particles. Here, we validated a method for purifying EVs from MT-2 cell culture supernatants while avoiding HTLV-1 viral particle contamination. EV fractions were collected using a combination of immunoprecipitation with Tim-4, which binds to phosphatidylserine, and polymer precipitation. The HTLV-1 viral envelope protein, gp46, was not detected in the EV fraction. Proteomic analysis revealed that EV-constituted proteins were predominant in this EV fraction. Furthermore, the EVs were found to contain the HTLV-1 viral genome. The proposed method can purify EVs while avoiding virus particle contamination and is expected to contribute to future research on EVs derived from HTLV-1-infected cells.
Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network

With the rapid development of artificial intelligence and Unmanned Aerial Vehicle (UAV) technology, AI-based UAVs are increasingly utilized in various industrial and civilian applications. This paper presents a distributed Edge-Cloud collaborative framework for UAV object detection, aiming to achieve real-time and accurate detection of ground moving targets. The framework incorporates an Edge-Embedded Lightweight (<inline-formula><tex-math notation="LaTeX">${{\text{E}}^{2}}\text{L}$</tex-math><alternatives><mml:math><mml:mrow><mml:msup><mml:mrow><mml:mtext>E</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mtext>L</mml:mtext></mml:mrow></mml:math><inline-graphic xlink:href="yuan-ieq1-3361858.gif"/></alternatives></inline-formula>) object algorithm with an attention mechanism, enabling real-time object detection on edge-side embedded devices while maintaining high accuracy. Additionally, a decision-making mechanism based on fuzzy neural network facilitates adaptive task allocation between the edge-side and cloud-side. Experimental results demonstrate the improved running rate of the proposed algorithm compared to YOLOv4 on the edge-side NVIDIA Jetson Xavier NX, and the superior performance of the distributed Edge-Cloud collaborative framework over traditional edge computing or cloud computing algorithms in terms of speed and accuracy
Zinc Finger and BTB Domain-Containing 20: A Newly Emerging Player in Pathogenesis and Development of Human Cancers

Zinc finger and BTB domain-containing 20 (ZBTB20), which was initially identified in human dendritic cells, belongs to a family of transcription factors (TFs) with an N-terminal BTB domain and one or more C-terminal DNA-binding zinc finger domains. Under physiological conditions, ZBTB20 acts as a transcriptional repressor in cellular development and differentiation, metabolism, and innate immunity. Interestingly, multiple lines of evidence from mice and human systems have revealed the importance of ZBTB20 in the pathogenesis and development of cancers. ZBTB20 is not only a hotspot of genetic variation or fusion in many types of human cancers, but also a key TF or intermediator involving in the dysregulation of cancer cells. Given the diverse functions of ZBTB20 in both health and disease, we herein summarize the structure and physiological roles of ZBTB20, with an emphasis on the latest findings on tumorigenesis and cancer progression.
Omega-3-Enriched and Oxidative Stable Mayonnaise Formulated with Spray-Dried Microcapsules of Chia and Fish Oil Blends

There is a growing demand for nutritious food products that contain specific ingredients, such as long-chain polyunsaturated fatty acids (LCPUFAs). In the case of LCPUFAs, protection against lipid peroxidation is difficult, and microencapsulation emerges as an alternative. The aim of this research work is to develop mayonnaise containing spray-dried microcapsules (SDM). Fortified mayonnaise was developed using various treatments such as (T1) incorporating chia seed oil (CSO), (T2) incorporating fish oil (FO), (T3) incorporating blend of chia and fish oil, (T4) incorporating the SDM of CSO, (T5) incorporating the SDM of FO, and (T6) incorporating the SDM of chia and fish oil blend as well as controls. Thereafter, during the 15-day storage period, the fatty acids (FAs) composition, free fatty acids (FFAs), peroxide value (PV), and sensory properties of fortified mayonnaise were examined every 5 days. The overall results showed that the oxidative stability of mayonnaise formulated with SDM has been improved, and it can be used as a fortifying agent in the processing of many food products. Treatments containing SDM of up to 4% did not differ from the control in sensory analysis. Sensory scores of SDM samples showed a slight decrease in off-flavor scores and were in an acceptable range. Therefore, SDM developed from CSO and FO blends can be recommended for supplementation in different food products for long-time storage.
Psychiatric Disorders Associated with Internet Addiction in A Sample of Preteens Attending Damietta Specialized Hospital

Background : High prevalence rates of internet addiction (IA) have become a global phenomenon; yet, these percentages differ from region to country. The use of different disease classi ﬁ cations or diagnostic criteria may explain these differences. IA is frequently dif ﬁ cult to diagnose. In contrast to drug dependency and substance misuse, the internet offers some distinct bene ﬁ ts. We aimed to review IA, assess psychiatric disorders, and detect the relationship between IA and psychiatric disorders in preteens. Patients and methods : This observational cross-sectional study was performed on 317 preteens aged from 9 to 12 years old and using internet who were not yet diagnosed as IA or psychiatric disorders. Those preteens were subjected to complete medical and neurological history and examination, complete psychiatric interview based on The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria, IA test, and mini-international neuropsychiatric interview for children and adolescents. Results : Mood disorders were signi ﬁ cantly higher in preteens with IA than those with no IA ( P ¼ 0.049). Anxiety disorders were signi ﬁ cantly increased in preteens with IA compared with those with no IA ( P < 0.001). Obsessive compulsive disorder was signi ﬁ cantly higher in preteens with IA than those with no IA ( P ¼ 0.044). TIC disorders were found to be signi ﬁ cantly more frequent in preteens with IA compared with those with no IA ( P ¼ 0.045). Behavioral disorders were signi ﬁ cantly increased in preteens with IA than those with no IA ( P < 0.001). Conclusion : Preteens with IA suffered from psychiatric disorders in more frequencies than those with no IA and these differences were signi ﬁ cant except for substance use disorder and suicide.
Dimensionality Reduction: A Comparative Review using RBM, KPCA, and t-SNE for Micro-Expressions Recognition

—Facial expressions are the main ways how humans display emotions. Under certain circumstances, humans can do facial expression, but emotions can also appear in the special form of micro-expressions. A micro-expression is a very brief facial expression faced on people’s faces under some circumstances. Micro-expressions are shown in the situations when a person tries to lie or hide something. Studying micro-expressions sounds very attractive but considering the number of pixels that an image contains becomes difficult. Feature extraction techniques are the most popular ones for reducing data dimensionality. Those techniques create a new low-dimensional dataset, which tries to represent as much information as original dataset. Many and many methods are used for dimensionality reduction. Restricted Boltzmann Machine (RBM), Kernel Principal Component Analyses (KPCA) and t-distributed stochastic neighbor embedding (t-SNE) are currently widely used by researchers. Choosing the right dimensionality reduction technique is time consuming. This study proposes one framework for micro-expression recognition. The two key processes of this framework are the facial feature extraction (Dlib) and dimensionality reduction using RBM, KPCA and t-SNE. We will select the technique that generates new dataset which represents as much the original dataset as possible. The framework will be trained with images from the CASMEII database, which is a database built specially for research purposes. The framework will be tested with new images unseen before. Software used for conducting the experiments is Python.
Deep Crustal Structure of the Northeastern Japan Forearc by a Seismic Exploration Using an Airgun-array

Aki ITO, Ryota HINO, Minoru NISHINO and Hiromi FUJIMOTO Research Center for Prediction of Earthquakes and Volcanic Eruptions, Graduate School of Science, Tohoku University, Aoba-ku, Sendai 980-8578, Japan Seiichi MIURA and Shuichi KODAIRA Institute for Frontier Research on Earth Evolution (IFREE) Japan Marine Science and Technology Center, 2-15 Natsushima-cho, Yokosuka 237-0061, Japan Akiko HASEMI Faculty of Science, Yamagata University, Koshirakawa-cho 1-4-12, Yamagata 990-8560, Japan (Received July 26, 2001; Accepted January 28, 2002)
Learning Colored Petri Nets Using Object-Centric Event Data (OCED2CPN)

Traditional process mining assumes that each event is related to precisely one case. This can be compared to mainstream modeling notations using Petri nets (in particular WF-nets), BPMN diagrams, directly-follows graphs, flowcharts, or UML activity diagrams. These notations describe the life-cycle of a process instance (often called case in process mining). Currently, we see an uptake of Object-Centric Process Mining (OCPM), where events can refer to any number of objects and where objects can be related. OCPM includes process discovery starting from Object-Centric Event Data (OCED) to produce process models that describe different object types in a single diagram. Since Colored Petri Nets (CPNs) have been around for decades and have been widely used for modeling, verification, and simulation, CPNs are an obvious target model for OCPM. The transition from traditional process mining to OCPM can be compared with the transition from classical Petri nets to CPNs. However, it turns out to be very difficult to discover arbitrary CPNs. This keynote paper summarizes what has been done before and why it is challenging to discover CPNs. However, starting from OCED, subclasses of CPNs can be discovered (which we refer to as OCED2CPN). These insights are also relevant for conformance checking and forward-looking forms of process mining starting from OCED.
Post-Transplant Bone Disease in Kidney Transplant Recipients: Diagnosis and Management

Kidney transplantation is the preferred gold standard modality of treatment for kidney failure. Bone disease after kidney transplantation is highly prevalent in patients living with a kidney transplant and is associated with high rates of hip fractures. Fractures are associated with increased healthcare costs, morbidity and mortality. Post-transplant bone disease (PTBD) includes renal osteodystrophy, osteoporosis, osteonecrosis and bone fractures. PTBD is complex as it encompasses pre-existing chronic kidney disease–mineral bone disease and compounding factors after transplantation, including the use of immunosuppression and the development of de novo bone disease. After transplantation, the persistence of secondary and tertiary hyperparathyroidism, renal osteodystrophy, relative vitamin D deficiency and high levels of fibroblast growth factor-23 contribute to post-transplant bone disease. Risk assessment includes identifying both general risk factors and kidney-specific risk factors. Diagnosis is complex as the gold standard bone biopsy with double-tetracycline labelling to diagnose the PTBD subtype is not always readily available. Therefore, alternative diagnostic tools may be used to aid its diagnosis. Both non-pharmacological and pharmacological therapy can be employed to treat PTBD. In this review, we will discuss pathophysiology, risk assessment, diagnosis and management strategies to manage PTBD after kidney transplantation.
Evolution of Directional Wave Spectra in the Marginal Ice Zone: A New Model Tested with Legacy Data

AbstractField experimental data from a 1980s program in the Greenland Sea investigating the evolution of directional wave spectra in the marginal ice zone are reanalyzed and compared with the predictions of a new, phase-resolving, three-dimensional model describing the two-dimensional scattering of the waves by the vast number of ice floes that are normally present. The model is augmented with a dissipative term to account for the nonconservative processes affecting wave propagation. Observations reported in the experimental study are used to reproduce the ice conditions and wave forcing during the experiments. It is found that scattering alone underestimates the attenuation experienced by the waves during their passage through the ice field. With dissipation, however, the model can replicate the observed attenuation for most frequencies in the swell regime. Model predictions and observations of directional spreading are in agreement for short to midrange wave periods, where the wave field quickly becomes.
Efficient Video Summarization with Hydra Attentive Vision Transformer

A video summary’s objective is to produce succinct and condensed synopses that accurately depict the content of the original video while sacrificing none of its vital features. Effective deep summarization models have arisen in the field of video summarizing, made feasible by the advancement of gated recursive unit (GRU) and long and short-term memory (LSTM) technologies. However, if the video is very long, GRU and LSTM models are unlikely to capture long-term dependencies as well as they could otherwise. In recent years, significant progress in the field of supervised video summarization has been accomplished through the use of techniques involving sequence-to-sequence learning. However, it is important to remember that traditional recurrent neural networks (RNNs) have some limitations when it comes to modeling long sequences and that using transformers to represent sequences necessitates a large number of input parameters, resulting in increased computational complexity. We present a new video summarizing methodology that addresses the aforementioned issues by utilizing a Hydra Attention-based Vision Transformer framework. The suggested method captures long-term dependencies and extracts significant characteristics from video sequences well. The proposed architecture improves the accuracy and reduces the processing time of video summaries by using the capabilities of Hydra Attention and transformer-based sequence modeling. Our solution outperforms state-of-the-art approaches in terms of performance and computing economy in empirical evaluations of the SumMe and TVSum datasets.