Miracle-dz/UniFAR-FTUs huggingface dataset:
Chronic Kidney Disease in a Large National Human Immunodeficiency Virus Treatment Program

Tenofovir disoproxil fumarate (TDF) is associated with a risk of chronic kidney disease (CKD), especially in Asian populations. Data from the Thai national health insurance system was used to assess CKD incidence in patients receiving antiretroviral therapy in real-world practice. We analyzed data from patients who initiated one of the following first-line regimens: zidovudine + lamivudine + nevirapine (AZT + 3TC + NVP); zidovudine + lamivudine + efavirenz (AZT + 3TC + EFV); tenofovir + lamivudine + nevirapine (TDF + 3TC + NVP); tenofovir + lamivudine/emtricitabine + efavirenz (TDF + 3TC/FTC + EFV); and tenofovir +lamivudine +lopinavir/ritonavir (TDF + 3TC + LPV/r). CKD was defined as glomerular filtration rate <60 mL/min/1.73 m2 for >3 months, or a confirmed 2010 WHO diagnosis (ICD-10 code N183, N184, or N185). Death competing risk survival regression models were used. Among 27,313 participants, with a median age of 36.8 years and median follow-up of 2.3 years, 245 patients (0.9%) were diagnosed with CKD (incidence 3.2 per 1000 patient-years; 95% CI 2.8–3.6). Compared with patients receiving AZT + 3TC + NVP, the risk of CKD measured by adjusted sub-distribution hazard ratio (aSHR) was 6.5 (95% CI 3.9–11.1) in patients on TDF + 3TC + LPV/r, 3.8 (95% CI 2.3–6.0) in TDF + 3TC + NVP, and 1.6 (95% CI 1.2–2.3) in TDF + 3TC/FTC + EFV. Among patients receiving TDF, compared with those receiving TDF + 3TC/FTC + EFV, the aSHR was 4.0 (95% CI 2.3–6.8) in TDF + 3TC + LPV/r and 2.3 (95% CI 1.4–3.6) in TDF + 3TC + NVP. TDF was associated with an increased risk of CKD, especially when combined with LPV/r or NVP.
Students Engagement Detection in Online Learning During Covid-19 Pandemic Using R Programming Language

Nowadays, Covid-19 is a serious issue, which is outspread all over the world. As, this is a contagious illness, so people maintaining social distance to prevent it. Government of every country announced lockdown to the respective countries to stop its rapid spread. For this reason, most of the sectors especially the education sector is going through a crisis. Students cannot go to their institution because of this pandemic. Therefore, Government of every country decided to start online class in this pandemic situation. It is very much tough to continue study through online rather than intuitional class. Not only students but also the teachers also faced many problems to do the online class properly because this is a new process for both of them. In online class, teachers have to identify that the students are present or not. If the students turn on their webcam, then the teachers can take their attendance easily. In this research, researchers tried to develop a prototype using R programming language and machine learning tools that can detect and recognize students’ face easily that might help teachers to take attendance without any hassle. Researchers took help of Artificial Intelligence as well as used Machine Learning tools to complete this research. People using artificial intelligence because people do mistake but machine cannot do mistake so the in here the error rate is low. Machine learning is also important because it is time consuming, this machine have to trained up so that it is act as human and solve all the problems easily. That is why various types of programming language are needed to train up the machine. In here, Researchers mainly used OpenCV that is a built-in package of R programming language, which is used for real time face detection and so on. Keyword: Face Detection, Face Recognition, R Programming Language, Artificial Intelligence (AI), Machine Learning, OpenCV DOI: 10.24818/issn14531305/25.3. 2021.03
Application of Deep Learning in Lung Cancer Imaging Diagnosis

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.
Evaluation of the Radial Procurvatum Using the Center of Rotation of Angulation Methodology in Chondrodystrophic Dogs

The radial joint orientation angles were calculated using the center of rotation of angulation (CORA) methodology within the frontal and sagittal planes in chondrodystrophic dog breeds, including Welsh Corgi, Dachshund, Pekinese, Poodle, Beagle and Maltese, and it was compared whether there is a statistically significant difference between the breeds. Radial joint orientation angles were obtained in eighty-eight dogs, including 23 Welsh Corgis, 16 Dachshunds, 14 Pekinese, 13 Maltese, 12 Poodles and 10 Beagles. Using the CORA methodology, the cranial proximal radial angle (CrPRA) and caudal distal radial angle (CdDRA) in the sagittal plane and medial proximal radial angle (MPRA) and lateral distal radial angle (LDRA) in the frontal plane were measured for the six breeds studied. The mean values of joint angles for each breed were compared statistically were observed. The CrPRA, CdDRA, and LDRA mean values of Dachshund and Welsh Corgi breeds were significantly smaller than other breeds, and in MPRA, Pekingese showed significantly smaller values than other breeds. This study confirms that the mean values of radial joint orientation angles can be significantly different among chondrodystrophic breeds. To accurately evaluate the degree of angular deformity of the radius, it may be helpful to refer to the average value for each breed with chondrodystrophy.
Behavioral problems and its associated factors among siblings of children with developmental disabilities: analytical cross-sectional study

ABSTRACT The typically developing siblings of children with disabilities are vulnerable to behavioral problems as they grow up in an environment of considerable stress. Yet, the needs and behavior problems of these siblings are often neglected especially in Pakistan. In this cross-sectional study, we assessed the behavior problems among siblings of children with developmental disabilities. The strength and difficulties questionnaire (SDQ) was administered on 250 siblings (aged 6–17 years) of children with a developmental disability (Autism, Intellectual disability (ID), Down Syndrome (DS), and Attention Deficit Hyperactive Disorder (ADHD)) attending special education institutions in Karachi, Pakistan. Multiple linear regression was applied to assess the relationship between SDQ scores and other demographic, parental, and disability-related factors. Adjusted beta coefficients were reported along with a 95% confidence interval. The average SDQ score was found to be 14 ± 6 among the siblings of children with developmental disabilities. Multivariable analysis showed that the estimated mean SDQ score was almost twice among siblings of children with ADHD (95% CI: 0.73–3.48) and ID (95% CI: 0.64–3.16) than siblings of children with Down syndrome. Also, the mean SDQ score was 8 units higher among siblings of children with a severe developmental disability compared to mild disability (95% CI: 6.79–9.47). The findings of the current study emphasize the need for appropriate mental health services, using a family-focused approach from therapeutic and counseling platforms to the siblings, parents, and disabled brother/sister for early identification and management of behavior issues. Abbreviations: ADHD: Attention Deficit Hyperactive Disorder; AKU: Aga Khan University; β: Beta Coefficient (Slope); βo: Intercept; CHS: Community Health Sciences; DS: Down syndrom; ERC: Ethical Review Committee; ID: Intellectual Disability; MLR: Multiple Linear Regression; SDQ: Strengths and Difficulties Questionnaire; SES: Socioeconomic Status; TD: Typically Developing
Two-stage model of prepreg bonding interface formation in automated fiber placement process

While Automated Fiber Placement (AFP) of thermoset matrix composites are widely used in the aviation industry, there is little conclusive research on the relationship between the physical model of bonding interface formation process and the actual bonding strength between prepreg layers formed in AFP process. Although massive amounts of experimental data on prepreg tack have been achieved from existing research, engineers are unable to use these data as a decisive criterion in choosing process parameters. In this research, a prepreg layup physical model based on reptation model and viscoelastic mechanical model is built, in which the bonding interface formation process is divided into two stages, namely, diffusion and viscous stage. Layup-peeling experiments are conducted via a special designed high-speed layup experimental platform so that practical AFP process parameters can be imitated, and a logarithmic curve of layup velocity-peeling energy under different layup pressure is achieved. The slope of the logarithmic curve and the surface morphology of the sample after peeling prove the correctness of the established model. Simultaneously, the experimental data proves that when prepreg is peeled off, the transition from the cohesive failure mode to the interface failure mode occurs at the laying speed between 100 mm/s and 200 mm/s. These results can be used as a reference for choosing AFP process parameters to realize the balance between good bonding quality and harmless separation of adjacent prepreg layers.
Effect of Spraying in Complex Salt Fog Tests Studied by Grey Relational Analysis

Grey relational analysis was adopted to investigate the effect of spraying in complex salt fog tests simulating corrosion in splash zone.The corrosion weight loss and corrosion potential of flame sprayed zinc coatings under four different accelerated testing conditions,including fog plus preserving,fog plus spraying,fog plus drying,and fog plus drying and spraying,were calculated.The grey relational degrees between the results in laboratory and those in actual splash zone were analyzed by using grey relational analysis method.Results show that fog plus spraying test and fog plus drying and spraying test,both involving spraying process,have better simulation performance,which indicates that introducing spraying procedure into traditional fog test and fog plus drying test could better simulate the coupling effects among fog,alternating dry and humid atmosphere,and splashing in the splash zone.
Sequential Cesium Incorporation for Highly Efficient Formamidinium-Cesium Perovskite Solar Cells

Although pure formamidinium iodide perovskite (FAPbI3) possesses an optimal gap for photovoltaics, their poor phase stability limits the long-term operational stability of the devices. A promising approach to enhance their phase stability is to incorporate cesium into FAPbI3. However, state-of-the-art formamidinium-cesium (FA-Cs) iodide perovskites demonstrate much worse efficiency compared with FAPbI3, limited by different crystallization dynamics of formamidinium and cesium, which result in poor composition homogeneity and high trap densities. We develop a novel strategy of crystallization decoupling processes of formamidinium and cesium via a sequential cesium incorporation approach. As such, we obtain highly reproducible and highly efficient solar cells based on FA1-xCsxPbI3 films, with uniform composition distribution and low defect densities. In addition, our cesium-incorporated perovskites demonstrate much enhanced stability compared with FAPbI3, as a result of suppressed ionic migration due to reduced electron-phonon coupling.
Multi-Encoder Sequential Attention Network for Context-Aware Speech Recognition in Japanese Dialog Conversation

Japanese automatic speech recognition (ASR) in conversation is considered challenging and highly ambiguous because the subjects are usually omitted and the number of homonyms is quite large. Since the speaker's utterance alone may not provide enough information, the context of the previous utterance from the dialog partner might help. A limited number of studies have addressed the idea of incorporating dialog-context information in end-to-end ASR tasks. However, these have mainly focused on the English language. Furthermore, although such models exploit dialog context history, the decoder handles the current input and the context simultaneously, ignoring the sequence of a conversation in the process. In this study, we propose a Japanese ASR that considers the dialog partner's conversation context in a sequential manner. Specifically, we use a multi-encoder sequential attention network and investigate several possible architectures. The experimental results reveal that context information helps to improve recognition performance over standard ASR and systems that process the context at the same time.
Research on DTC Strategy of Doubly-fed Induction Generator Wind Power Systems Base on PSCAD

In order to solve the vector control dependence on the precise parameters of doubly-fed induction generator,direct torque control strategy was applied to the doubly-fed induction generator system.According to the number of sectors and logic output of the rotor flux and torque hysteresis comparator,the optimal switching vector which is got in pre-designed forms acts on the rotor windings to achieve rapid control of electromagnetic torque and rotor flux amplitude.And on the basis of direct torque control,doubly-fed induction generator wind power system is set up in the power system simulation software PSCAD / EMTDC,which achieves maximum wind energy capture and meets the demand of reactive power grid.
Highway Network Structure Characteristics Based on Complex Network Theory

In order to further analyze route importance of complex highway network,using dual topology method,highway section was abstracted into node according to its name and road intersection was abstracted into edge which were used to convert real highway network to topology structure. Based on this transportation network structure topology method,authors constructed network reliability index and applied evaluation indexes of complex network,such as degree,closeness,betweenness,and connectivity reliability to analysis of regional highway network structure characteristics. Then,grade structure classification method of highway network was put forward based on fuzzy clustering. Analysis result shows that the complex network theory based on dual topology can reflect route importance from the point of view of connectivity,centrality,intermediate and reliability.
Computational Geometry Computation and KNN Segmentation in ITK

This work describes the implementation of computational geometry algorithms developed within the Insight Toolkit (ITK): Distance Transform (DT), Voronoi diagrams, k Nearest Neighbor (kNN) transform, and finally a K Nearest Neighbor classifier for multichannel data, that is used for supervised segmentation. We have tested this algorithm for 2D and 3D medical datasets, and the results are excellent in terms of accuracy and performance. One of the strongest points of the algorithms described here is that they can be used for many other applications, because they are based on the ordered propagation paradigm. This idea consists in actually not raster scan the image but rather in start from the image objects and propagate them until the image is totally filled. This has been demonstrated to be a good approach in many algorithms as for example, computation of Distance Transforms, Voronoi Diagrams, Fast Marching, skeletons computation, etc. We show here that these algorithms have low computational complexity and it provides excellent results for clinical applications as the segmentation of brain MRI.
A Framework for Fast Automatic Robot Ultrasound Calibration

Ultrasound (US) has been increasingly used as medical imaging technology across various clinical diagnostic and therapeutic scenarios thanks to its availability and non-radiative nature. While 3D US probes are becoming available, most systems are still using 2D probes. For 3D US reconstruction based on 2D probes, US image calibration forms an essential step. Through calibration, one can find the transformation matrix between a coordinate frame attached to an optical marker or the robot’s end effector towards the coordinate frame of the US probe. Current US calibration methods usually require hereto lengthy free hand gestures as well as some manual interventions, which hampers the use and integration with advanced robotic systems. This paper introduces a reliable automatic calibration framework that is also fast. Demonstrated on a KUKA lightweight robot and 2D US probe, the full calibration procedure was completed in 224.8 seconds with a 1.29 mm mean 3D localization error. Within this procedure, camera-to-robot calibration was accomplished within only 47 seconds and reached a 0.17 mm mean error. Validation of the US image calibration was done through 3D printed model, leading to a mean deviation of 1.05 mm from the respective CAD models.
Bandwidth Boost Method of Current Control for Servo Motor Drives with Current Observer

The purpose of this paper is to propose a bandwidth boost method of current control loop for servo motor drives. The proposed method is to improve the time delay of feedback loop using current observer and redesigning the current controller when time delay improvement is considered. Comparing that of current observer method, the proposed method can significantly boost the bandwidth of current control. The Sigma-Delta ADC by Sinc3 filter is used for current feedback to intensify the precision and noise immunity. A dual-core controller, consisting of DSP and FPGA, is used for the implementation of the proposed bandwidth boost method. Experimental and simulation results will be presented to fully support the claims.
O2 Physisorption in Coal Pore: Effects of Pore Size, Pressure, Temperature and Function Group

In this paper, the physical adsorption characteristics of oxygen in coal pores were systematically investigated by the Grand Canonical Monte Carlo and the COMPASS force field. Firstly, coal pore structures of different sizes were constructed by graphite slit models and different groups. Secondly, the physisorption behavior of oxygen in graphite slit models of different sizes was simulated. Finally, the physisorption behavior of oxygen in graphite slit models at different pressures and temperatures was analyzed. The results showed that the physisorption density and excess physical adsorption of oxygen were divided into the rapidly decreasing stage (0.4-0.7 nm), the slowly decreasing stage (0.7-1.4 nm), and the stable stage (1.4 nm-5 nm) with the increase of coal pores, and the excess oxygen physisorption amount was more sensitive to the change of pressure. The O2 isosteric heat of physisorption decreased with increasing pore size of coal. Oxygen is more strongly adsorbed by hydroxyl and ether bonds than by methyl, carboxyl and carbonyl groups. Through this study, the mechanism of oxygen physical adsorption in coal pores and the characteristics influenced by temperature and pressure can be better understood.
Critical appraisal of minimally invasive keyhole surgery for intracranial meningioma in a large case series

Background Meningioma surgery has evolved over the last 20 years with increased use of minimally invasive approaches including the endoscopic endonasal route and endoscope-assisted and gravity-assisted transcranial approaches. As the “keyhole” concept remains controversial, we present detailed outcomes in a cohort series. Methods Retrospective analysis was done for all patients undergoing meningioma removal at a tertiary brain tumor referral center from 2008–2021. Keyhole approaches were defined as: use of a minimally invasive “retractorless” approach for a given meningioma in which a traditional larger approach is often used instead. The surgical goal was maximal safe removal including conservative (subtotal) removal for some invasive locations. Primary outcomes were resection rates, complications, length of stay and Karnofsky Performance Scale (KPS). Secondary outcomes were endoscopy use, perioperative treatments, tumor control and acute MRI FLAIR/T2 changes to assess for brain manipulation and retraction injury. Results Of 329 patients, keyhole approaches were utilized in 193(59%) patients (mean age 59±13; 30 (15.5%) had prior surgery) who underwent 213 operations; 205(96%) were skull base location. Approaches included: endoscopic endonasal (n = 74,35%), supraorbital (n = 73,34%), retromastoid (n = 38,18%), mini-pterional (n = 20,9%), suboccipital (n = 4,2%), and contralateral transfalcine (n = 4,2%). Primary outcomes: Gross total/near total (>90%) resection was achieved in 125(59%) (5% for petroclival, cavernous sinus/Meckel’s cave, spheno-cavernous locations vs 77% for all other locations). Major complications included: permanent neurological worsening 12(6%), CSF leak 2(1%) meningitis 2(1%). There were no DVTs, PEs, MIs or 30-day mortality. Median LOS decreased from 3 to 2 days in the last 2 years; 94% were discharged to home with favorable 90-day KPS in 176(96%) patients. Secondary outcomes: Increased FLAIR/T2 changes were noted on POD#1/2 MRI in 36/213(17%) cases, resolving in all but 11 (5.2%). Endoscopy was used in 87/139(63%) craniotomies, facilitating additional tumor removal in 55%. Tumor progression occurred in 26(13%) patients, mean follow-up 42±36 months. Conclusions & relevance Our experience suggests minimally invasive keyhole transcranial and endoscopic endonasal meningioma removal is associated with comparable resection rates and low complication rates, short hospitalizations and high 90-day performance scores in comparison to prior reports using traditional skull base approaches. Subtotal removal may be appropriate for invasive/adherent meningiomas to avoid neurological deficits and other post-operative complications, although longer follow-up is needed. With careful patient selection and requisite experience, these approaches may be considered reasonable alternatives to traditional transcranial approaches.