Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of climate factors on Hand-Foot-Mouth Disease: A generalized additive model approach

Hand, foot, and mouth disease (HFMD) has become an endemic childhood disease in Asia, including Malaysia, over the last few decades. This infectious disease caused by the Entero and Coxsackie viruses has been a major public health threat in Malaysia since 1997. Climate change has been considered an influential factor in HFMD cases and has been explored in other countries using various statistical analyses. The most popular is the Generalized Linear Model (GLM). However, GLM often fails to capture the non-linearity effect of the variables. The study, therefore, proposes to use the Generalized Additive Model (GAM) to analyse the non-linear effects of temperature, humidity, rainfall, and wind speed at varying time lags of HFMD in Selangor. In summary, the result indicates that the weekly temperature, humidity, and rainfall were significantly associated with HFMD cases in Selangor and clarified with two weeks of lag time. This disease’s risk increased in the subsequent two weeks with a temperature range of 27°C to 30°C, 70% to 85% of humidity, and 5mm to 20mm of rainfall. Besides, this study also found that the seasonal distribution of HFMD in Selangor has a large peak during the Southwest monsoon. A small peak was observed at the end of the year during the Northeast monsoon. The findings of this study could be a practical guide for HFMD intervention strategies, especially in Malaysia.
An Investigation of Factors Affecting Measurement Accuracy of Nanoparticles Real-time Sensor using Corona Discharge integrated with Quartz Crystal Microbalance

A nanoparticles real-time sensor in the present study was designed using corona discharge integrated with quartz crystal microbalance (QCM) or Nano-QCM detector. We investigated the following factors affecting the measurement accuracy of the detector; needle to plate distance, flow rate, and number of particles on QCM. DC high voltage power supply of 7 kV was used to maintain the positive corona voltages difference in the charger. The mass sensitivity of 10 MHz AT-cut QCMs coated by plate rigid gold film were investigated. The monodisperse aerosol particles smaller than 300 nm were initially used for the testing of the system. They were charged with negative corona voltages and then deposited on the surface of QCM electrode. Sauerbrey equation was used to describe the frequency-mass relationship at the QCM surface. Results show that the above factors significantly affect the accuracy of the detector measurement. The particles can be collected on a QCM electrode using the partial breakdown high voltage (reach to breakdown points) in terms of needle-to-plate corona discharge. The optimal needle-to-plate distance is 6 mm with maximum mass change (frequency shift) on QCM electrode of 0. 607μg/min. The QCM collection efficiency of nanoparticles on the QCM electrode surface using corona discharge technique can be as high as 95% of total particles. Collection efficiency of PM0.1 is under investigation.
Implementation of Compressed Point Cloud Streams in ROS

—In this paper, we write and test a program that compresses point cloud data generated in real-time through integration with Robot Operating System (ROS). By splitting the data into its composite depth map and RGB image streams, we are able to separately compress these streams to massively reduce the amount of space required for storing the original point cloud data. We then provide a way of recreating the original point cloud from the depth map and RGB image. This allows for essentially lossless storage of point cloud data making transport more efﬁcient and effective. We found that we could reduce the original ﬁle by over 3300%, in some cases, through our approach. To accomplish this, we encoded the RGB image stream into a video ﬁle using H.264 and then utilized LZ4 frame compression for the depth map images. We then uncompressed each RGB and depth image frame by frame based on timing data and then generated a point cloud in real-time.
Four years of global carbon cycle observed from OCO-2 version 9 and in situ data, and comparison to OCO-2 v7

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) satellite has been provided information to estimate carbon dioxide (CO2) fluxes at global and regional scales since 2014 through the combination of CO2 retrievals with top-down atmospheric inversion methods. Column average CO2 dry air mole fraction retrievals has been constantly improved. A bias correction has been applied in the OCO-2 version 9 retrievals compared to the previous OCO-2 version 7r improving data accuracy and coverage. We study an ensemble of ten atmospheric inversions all characterized by different transport models, data assimilation algorithm and prior fluxes using first OCO-2 v7 in 2015-2016 and then OCO-2 version 9 land observations for the longer period 2015- 2018. Inversions assimilating in situ (IS) measurements have been also used to provide a baseline against which to compare the satellite-driven results. The times series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiments using either OCO-2 or IS data. We then evaluate the inversion ensemble based on dataset from TCCON, aircraft, and in-situ observations, all independent from assimilated data. While we find a similar constraint of global total carbon emissions between the ensemble spread using IS and both OCO-2 retrievals, differences between the two retrieval versions appear over regional scales and particularly in tropical Africa. A difference in the carbon budget between v7 and v9 is found over this region which seems to show the impact of corrections applied in retrievals. However, the lack of data in the tropics limits our conclusions and the estimation of carbon emissions over tropical Africa require further analysis.
Toward artificial cells/living cells communication in hybrid ensembles

Recent progress in “artificial” (or “synthetic”) cell research has allowed the construction of sophisticated systems capable of sending/receiving chemical signals to/from biological cells. This new scenario paves the way to conceptually new technologies and scientific investigations based on interfacing organisms (natural cells) and robots (artificial cells) and exploiting their built-in molecular communication capacity. The state-of-the-art is presented and commented from the perspective of Artificial Life and synthetic biology.
Extraction of Retinal Blood Vessels from Fundus Images using Laplacian Pyramids and Anisotropic Diffusion

Retinal Fundus images are commonly studied by ophthalmologists and bio-medical researchers to diagnose or predict the onset of various systemic diseases. These Fundus images are examined for anomalies to make accurate diagnoses. Since the process of manually inspecting and annotating retinal blood vessels from Fundus images is time consuming and relies entirely on the skill and experience of the human experts, computer-aided approaches are progressively becoming better alternatives, thus creating a growing need for more efficient retinal vessel segmentation algorithms. These computer-aided approaches could be used for clinical studies, pre-screening the patient for signs of systemic diseases and for developing effective treatment plans. In this thesis, we propose a framework for extracting retinal blood vessels from Fundus images based on their multi-layer, multi-scale Laplacian Pyramid representation. The algorithm is implemented using two publicly available datasets, the High-Resolution Fundus dataset and the DRIVE dataset. We provide a robust pre-processing pipeline for enhancing retinal vessel features in Fundus images. We evaluate the performance of the technique by comparing the segmentation results with a manually annotated ground-truth image, based on performance metrics like specificity, sensitivity, accuracy, precision and recall. The framework is capable of detecting and extracting both, thin and coarse retinal vessels and is competitive with other state-of-the-art techniques in terms of computation time, quality of segmentation, accuracy, sensitivity and specificity. It is robust to parameter changes and is capable of handling noise in the image without blurring the edges of retinal vessels. Our proposed technique shows an average accuracy of 95.85%, sensitivity of 79.33%, and specificity of 97.54% on High-Resolution Fundus images of healthy patients. School of Computer Science & Statistics Trinity College Dublin, Ireland ©Abishek Vaithylingam September 7, 2020 Page 4/59
Data Mining to Determine Behavioral Patterns in Respiratory Disease in Pediatric Patients

There are several varieties of respiratory diseases which mainly affect children between 0 and 5 years of age, not having a complete report of the behavior of each of these. This research seeks to conduct a study of the behavior of patterns in respiratory diseases of children in Peru through data mining, using data generated by the health sector, organizations and research between the years 2015 to 2019. This process was given by means of the K-Means clustering algorithm which allowed performing an analysis of this data identifying the patterns in a total of 10,000 Peruvian clinical records between the years mentioned, generating different behaviors. Through the grouping obtained in the clusters, it was obtained as a result that most of the cases in all the ages studied, they presented diseases with codes between the range of 000 and 060 approximately. This research was carried out in order to help health centers in Peru for further study, documentation and due decision-making, waiting for optimal prevention strategies regarding respiratory diseases. Keywords—Respiratory diseases; data mining; cluster algorithms; K-Means algorithm
The Healing Eff ects of The Topical Mesenchymal Stem Cells Application on Colonic Anastomosis Subjected to Ischemia Reperfusion Injury

Intestinal ischemia reperfusion injury (IRI) is a challenging problem and it adversely affects the healing of colonic anastomosis. Our experimental study aimed to investigate the role of mesenchymal stem cells (MSC) administration in the healing of colonic anastomosis. A total of 33 rats were grouped as Control, IRI and MSC treatment groups. Three rats were reserved for obtaining MSCs. Colonic resection and anastomosis procedure was performed in all groups. Anastomotic line was wrapped with MSCs impregnated spongostan after colonic anastomosis in the rats of the MSC treatment group. All rats were sacrificed and anastomotic line were sampled for examination on the post operative seventh day. Tissue hydroxyproline (HP) levels and anastomotic bursting pressures were statistically compared. Anastomotic bursting pressures were found to be significantly high in MSC treatment group rats. The lowest anastomotic bursting pressure was detected in IRI group rats. Hydroxyproline content of the anastomotic sites were also found to be significantly higher in the rats of the MSC treatment group when compared with the IRI group rats. Our study showed that the detrimental effects of IRI on the healing process of colonic anastomosis in an experimental model may be alleviated with the treatment of MSCs.
Evaluating and developing a model of specific degradation using geospatial analysis for sediment erosion management in South Korea

Abstract. The South Korean Peninsula is subject to hydrological extremes, and 70 % of its terrain is mountainous, with sharp ridges and steep valley flanks. Recently, rapid urbanization has created an emerging demand for large-scale water resources, such as dams and reservoirs. Accordingly, complicated sediment-related problems have become an issue, with abundant soil loss during typhoons transported to the reservoirs, and downstream, riverbed degradation is caused by intercepting sediment. Thus, a reliable approach is required for predicting sediment yields of soil erosion and sedimentation. In this study, the specific degradation (SD) of 62 stream-river watersheds and 14 reservoir watersheds were calculated from field measurements of sediment concentration and deposition. Estimated SD ranged between 10 and 1,500 tons·km−2·yr−1. Furthermore, existing empirical models of sediment yield are insufficient for predicting specific degradation upstream of the reservoirs; therefore, a new model was developed based on multiple regression analysis and model tree data mining of 47 watersheds (~75 % national land cover). Accuracy of the developed model was enhanced with the following significant parameters: (1) drainage area, (2) mean annual precipitation, (3) percent urbanized area, (4) percent water, (5) percent wetland and water, (6) percent sand at effective soil depths of 0–10 cm, (7) slope of the hypsometric curve, and (8) watershed minimum elevation. Additionally, erosion maps from the revised universal soil loss equation (RUSLE) were generated to validate model variables and further understand the sediment regime in South Korea. The gross erosion results for 16 ungauged watersheds were used to validate the empirical model by comparing sediment delivery ratios of other references. The modeled meaningful parameters were examined via remote sensing analyses of satellite and aerial imagery and revealed the features affecting erosion and sedimentation with an erosion loss map at 5-m resolution. Vulnerable areas of soil loss, including construction sites, and croplands, as well as sedimentation features, such as wetlands and agricultural reservoirs, were highlighted.
Development of Learning Analytics Dashboard based on Moodle Learning Management System

Digitalization catalyzes drastic changes to a particular subject or area. Digitalization is an operational structure transformation process, such as in the educational domain. Digitalization in the academic field has brought the classroom to the users' fingertips with the prevalence of elearning applications, learning management systems, etc. However, with the increasing number of digital learning platform users, educators find it hard to monitor their students' progress. Analytics that analyze data generated from the usage pattern of the users contribute to giving the educators an insight regarding the performance of their students. With that, they can apply early intervention and modification of their delivery method to suit the students' needs and, at the same time, increase the quality of the content. This study illustrates the development of a learning analytics dashboard that can improve learning outcomes for educators and students. Keywords—Learning analytics; learning management system; moodle
NetworkPharmacologyApproach forPredictingTargets ofZishen Yutai Pills on Premature Ovarian Insufficiency

Guangzhou University of Chinese Medicine, Guangzhou 510405, China Department of Gynecology, First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510405, China Lingnan Medical Research Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China Singapore (ong Chai Medical Institution, 169874, Singapore Shenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China
ADUVC-an EPICS Areadetector Driver for USB Video Class Devices

Most devices supported by the Experimental Physics and Industrial Control System [1] (EPICS) areaDetector [2] project fall under one of two categories: detectors and cameras. Many of the cameras in this group can be classified as industrial cameras, and allow for fine control of exposure time, gain, framerate, and many other image acquisition parameters. This flexibility can come at a cost; however, with most such industrial cameras’ prices starting near one thousand dollars, with the price rising for cameras with more features and better hardware. While these prices are justified for situations that require a large amount of control over the camera, for monitoring tasks, and some basic data acquisition, the use of consumer devices may be sufficient while being less cost-prohibitive. The solution we developed was to write an areaDetector driver for USB Video Class (UVC) [3] devices. This driver allows for a variety of cameras and webcams to be used through EPICS and areaDetector, with most costing under $100.
Quasi Maximum Likelihood for MESS Varying Coefficient Panel Data Models with Fixed Effects

Article history Received: 4 June 2021 Accepted: 23 June 2021 Published Online: 20 July 2021 The study of spatial econometrics has developed rapidly and has found wide applications in many different scientific fields, such as demography, epidemiology, regional economics, and psychology. With the deepening of research, some scholars find that there are some model specifications in spatial econometrics, such as spatial autoregressive (SAR) model and matrix exponential spatial specification (MESS), which cannot be nested within each other. Compared with the common SAR models, the MESS models have computational advantages because it eliminates the need for logarithmic determinant calculation in maximum likelihood estimation and Bayesian estimation. Meanwhile, MESS models have theoretical advantages. However, the theoretical research and application of MESS models have not been promoted vigorously. Therefore, the study of MESS model theory has practical significance. This paper studies the quasi maximum likelihood estimation for matrix exponential spatial specification (MESS) varying coefficient panel data models with fixed effects. It is shown that the estimators of model parameters and function coefficients satisfy the consistency and asymptotic normality to make a further supplement for the theoretical study of MESS model.
Bandgap prediction for molecular crystals using Geometric Deep learning

Deep Learning (DL) has achieved the state of the art results on many machine learning tasks starting from image classification, video processing to natural language understanding and speech recognition. The input data to the model in all of these tasks lie in the Euclidean space. However, there are many applications where the input data is not present in Euclidean space and are represented using graphs. For example, in chemistry, molecules are represented as graphs. A citation network can also be modelled as a graph where the nodes represent papers and an edge between the nodes indicate citation relationship. Graph Neural Networks (GNNs), i.e., Geometric Deep Learning concerns generalized convolution methods that can work on a non-linear structure like graphs. GNNs can also be applied on images where the pixels (nodes) are connected by neighbouring pixels. GNNs are classified into spectral and spatial approaches depending on how the convolutions are defined. In this thesis, we have examined the performance of GNNs and SchNet (a continuous filter convolution) model on OMDB dataset. The dataset contains the band gap values of 12,500 3D organic molecular crystals calculated using Density Functional Theory (DFT) and the task is to predict the bandgap value given the structure of the molecule. Among all of the models tested, we found that our slightly modified version of SchNet achieved the Mean Absolute Error (MAE) of 0.28eV which is better than the state of the art. In this SchNet model, we also identified the atoms that are significant in contributing to the bandgap value of the molecule. Finally, we built an ensemble of SchNet models which attained a slightly lower MAE of 0.268eV. Although the GNN approaches that were tried did not improve the estimation accuracy, they still hold a promise in terms of improved explainability of results due to the graph-based nature of molecules.
Natural Language to Long-Range Robot Navigation in Outdoor Environments

Tremendous progress has been made in the field of natural language processing for robots. Advanced language models have been integrated with multiple types of robots, paving a path towards seamless human-robot interaction. Still, there remains significant work to accomplish. Consider a pilot instructing an autonomous drone to “travel from Boston to Providence and go through towns with charging stops. ” To fulfill the pilot’s request, the robot must resolve “Boston,” “Providence,” and “towns with charging stops” to real-world coordinates and identify a sequence of locations that satisfies the pilot’s non-sequential instruction. These tasks are computationally expensive in small domains, let alone a miles-wide geographic region. Further, the robot should not assume advance training on the Boston and Providence regions. Such training is expensive and difficult to scale. There is need for a computationally modest, domain-generalized system that enables the robot to fulfill natural language instructions in large outdoor environments.
Revisiting Recommended BGP Route Flap Damping Configurations

—BGP Route Flap Damping (RFD) is recommended to suppress BGP churn. Current conﬁguration recommendations for RFD, however, are based on a study from 2010. Since then, BGP churn increased by one order of magnitude, which may lead to outdated RFD parameters and introduce more loss of reachability of stable networks. In this paper, we revisit current recommendations to conﬁgure RFD. First, we develop an accurate and scalable emulation of Cisco and Juniper RFD implementations and make it publicly available. Second, we successfully reproduce the 2010 measurement study that justiﬁed the current RFD recommendations using current data. Third, we consider the RFD implementation of an additional major router vendor (Juniper), which penalizes BGP churn differently compared to the previously studied Cisco implementation. Fourth, we include IPv6 data from 2020. Our results show that the recommended RFD conﬁguration parameters from 2010, though seemingly rarely used, still hold today in IPv4 and IPv6 and across vendors, even though BGP churn increased signiﬁcantly. Our study revises metrics to assess the impact of incorrectly conﬁgured RFD, discusses collateral damage, and gives insights into sweet spots when damping routes.