Miracle-dz/UniFAR-FTUs huggingface dataset:
Coal Dust Exposure Characteristic and Impact on Respiratory Impairment from Coal Unloading Station in Palembang, South Sumatra, Indonesia

Coal hauling, loading, and transportation activities impacted the emergence of coal dust which is harmful to health. The coal dust exposed from coal unloading stations and coal waterway transportation has escaped attention. This study aimed to determine the characteristics of coal dust, the influence of climate parameters on the spread of coal dust, and its impact on the health of children under five in the exposed area. The coal dust characteristics and concentrations of PM PM 2.5 and PM 10 were analyzed from ten points spread across three mining companies (A, B, and C). The effect of climate parameters on PM 2.5 and PM 10 was tested statistically. The results of the chemical analysis revealed that coal dust was dominated by the high content of Si, Al, S, and Fe. The concentration of PM 2.5 and PM 10 is affected by wind speed. PM 2.5 and PM 10 can exceed the annual threshold value, which has caused a high incidence of respiratory problems in two sub-districts, namely Makrayu and Gandus.
Performance Assessment of Solar based Air Conditioning System

The conventional air-conditioning system suffers from (i) harmonic distortion at the input ac side due to extensive use of nonlinear loads and (ii) not being friendly with the environment and climate. In this paper, a conventional air conditioning system (ACS), which uses a single-phase ac supply, is replaced by a solar-powered ACS to mitigate mentioned shortcomings. Due to the non-availability of solar energy during night/low-intensity levels, a battery and a dc-dc converter are used to maintain constant voltage, a prime requirement for ACS. Further, if an ac supply is used during the daytime, the battery is not required. Thus, a self-sustained solar-powered air conditioner of a capacity of 1.5 tons is designed here. The solar system is designed using Simulink, and various parameters such as torque and speed are being measured to study & analyze the solar-based ACS. The designed air conditioner gives an acceptable performance despite the variation of solar irradiations.
Bioactivity evaluation of commercial root samples of Hemidesmus indicus (L.) R.Br.

Four commercial root samples (CS1, CS2, CS3 and CS4) of Hemidesmus indicus alongside an authentic sample (HI) were evaluated following antibacterial potential, cytototoxicity and DPPH free radical scavenging activity assay. The inhibition zones produced by commercial samples ranged between 9.5 to 13.5 mm against the tested bacteria in antibacterial sensitivity test. HI produced highest inhibition zone recorded as 20.63 mm against Staphylococcus aureus followed by 17.4 mm against Bacillus subtilis at 150 μg/ml concentration. HI also showed highest cytotoxicity with LC50 value of 1.726 mg/ml, 4.754 mg/ml and 13.247 mg/ml after 6 hours, 12 hours and 24 hours respectively in brine shrimp lethality assay. Sample CS1 and CS2 showed less cytotoxicity compared to CS3 and CS4. Depending on the concentration of extracts, the DPPH free radical scavenging activities of commercial samples were in close proximity with HI. The total phenolic content in HI was 54.52 mg/ml while in four commercial sample, it ranged between 52.28 to 75.37 mg/ml gallic acid equivalent (GAE) per 100 mg extract. Jahangirnagar University J. Biol. Sci. 8(2): 61-69, 2019 (December)
Study on magnetohydrodynamics angular rate sensor under non-uniform magnetic field

Purpose The evaluations of the magnetohydrodynamics angular rate sensor (MHD ARS) in its applications necessitate further improvements in the sensor’s dynamic measurement ability. The magnetic field of the MHD ARS is a key factor in the sensor’s modeling and error analysis. The aim of this study is to illustrate the influence of a non-uniform magnetic field on the sensor. Design/methodology/approach Numerical simulation is made using ANSYS FLUNET with the magnetic field calculated by 3D-Magnetostatic. The comparison of the simulation results between uniform and non-uniform magnetic fields is made to reveal and explain the effects of magnetic field inhomogeneity (MFI) on the flow and electric field in detail. Two different structures with different MFIs are designed to confirm the MFI effect on the sensor’s output in simulation and experiment. A cross-correlation experiment and an adaptive filter are carried out to extract the signal to identify the error of the sensor output caused by MFI. Findings The MFI effect on the flow field in MHD ARS is found to be insignificant, while its effect on the electric potential is considerable. The comparisons between two kinds of MHD ARS in numerical simulation and experiment show that the MFI effect on the sensor error can be identified by fitting the sensor output. The deviation is mainly generated at the peaks and valleys of an angular vibration. Originality/value The study of the MHD ARS under the influence of a non-uniform magnetic field can offer an understanding of the MFI effect on the sensor and an evaluation method of the sensor error caused by the MFI effect.
Monitoring the well-being of vulnerable transit riders using machine learning based sentiment analysis and social media: Lessons from COVID-19

Using open-source data, we show that despite significant reductions in global public transit during the COVID-19 pandemic, ∼20% of ridership continues during social distancing measures. Current urban transport data collection methods do not account for the distinct behavioural and psychological experiences of the population. Therefore, little is known about the travel experience of vulnerable citizens that continue to rely on public transit and their concerns over risk, safety and other stressors that could negatively affect their health and well-being. We develop a machine learning approach to augment conventional transport data collection methods by curating a population segmented Twitter dataset representing the travel experiences of ∼120,000 transit riders before and during the pandemic in Metro Vancouver, Canada. Results show a heightened increase in negative sentiments, differentiated by age, gender and ethnicity associated with public transit indicating signs of psychological stress among travellers during the first and second waves of COVID-19. Our results provide empirical evidence of existing inequalities and additional risks faced by citizens using public transit during the pandemic, and can help raise awareness of the differential risks faced by travellers. Our data collection methods can help inform more targeted social-distancing measures, public health announcements, and transit monitoring services during times of transport disruptions and closures.
Regional power system transitions towards carbon neutrality: The case of North China

Abstract China has announced its goal of reaching carbon neutrality by 2060, which will have a profound impact on its energy and economic systems. During this process, the power sector will play a key role in helping the country on its road towards carbon neutrality. This study develops a multi-regional power dispatch and capacity expansion model to combine long-term capacity expansion with short-term power dispatch. The model is built to optimise the carbon-neutral transition pathway from the perspective of economics, focussing on power system stability and reliability under high penetrations of renewables. Using the case study of North China, the cost-effective methods for power system transitions under the targets of reaching peak emissions by 2030 and achieving carbon neutrality by 2060 are discussed under different scenarios. We find that the future power supply system will rely heavily on renewable energy which will account for more than 89% of power generation. Inner Mongolia will produce more than 84% of power in the North China region. The inter-regional power transmission capacity will be five times higher than its current level. Policy choices will lead to different transition pathways as the early-stage installation decisions will have a lock-in effect on the following carbon emission reduction strategies and determine the importance of technologies adopted in achieving carbon neutrality. Although the transition to a low-carbon economy will result in an increase in cost by 17–19%, it will bring huge climate benefits to the world.
Need Analysis of Augmented Reality Based Virtual Laboratory for Chemistry Practicum Purposes

To keep following the Covid-19 protocol, students are not recommended to take courses on campus. As a result, they are not able to do essential lab work as usual. In this paper, we address the early stage of the developing process of an Augmented Reality run on Android devices to help students understand the theoretical framework of atoms. The need analysis was conducted to acquire the teachers‘ and students‘ thoughts toward the development of the AR. The questionnaire was developed based on the Likert scale. 113 respondents including students and teachers volunteered and filled the online questionnaire. The results show that most respondents agree that the AR application is needed for online practicum and they are willing to support and use the application. Android platform is prefered over other platforms, and the concept of atom and molecule was chosen by the vast majority of respondents. Therefore, the AR application about atom will be developed on the Android platform.
Implicit Learning in Preschool Children with and without Developmental Language Disorder

The study below was designed to compare implicit learning in fifty-two preschool-aged children with and without developmental language disorder (DLD) to test the Procedural Deficit Hypothesis (PDH). The PDH claims that procedural memory, the basis of implicit learning, is the main impairment in DLD. Using a computer-based program to conduct sound-meaning-mapping tasks, we asked children to select one of two images according to the corresponding sound they heard. After all the participants were tested, an additional cue was found in the experiment that impacted the data. Instead of the children attuning to the sounds to depict the correct answer, the children picked up on an unintended visual cue: the target image tended to alternate from one side to another. Both children with DLD and TLD showed implicit learning of the visual alternation, in contradiction to the PDH. We relate these findings to the debate over the PDH and alternative hypotheses regarding the etiology of DLD. CHAPTER 1: Introduction 1.1 Developmental Language Disorder The purpose of this study was to test implicit learning in preschool-age children with and without developmental language disorder (DLD). The experiment included fifty-two age-matched participants (4-5 years), half with typical language development (TLD) and half with DLD. The children were monolingual English speakers. The experimental design intended to test implicit learning through sound-meaning-mapping activities delivered through a computer program. However, after the experiment was completed, an additional visual cue was discovered
Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning

Compressed hyperspectral imaging is a powerful technique for satellite-borne and airborne sensors that can effectively shift the complex computational burden from the resource-constrained encoding side to a presumably more capable base-station decoder. Reconstruction algorithms play a pivotal role in compressive imaging systems. Traditional model-based reconstruction approaches are computationally burdensome and achieve limited success. Deep learning-based approaches, while improving in reconstruction accuracy and speed, depend heavily on data, which is a major challenge for satellite-borne hyperspectral compressed imaging. In this article, we combine the respective advantages of model-based and learning-based approaches in a distributed compressed hyperspectral sensing framework, employing linear mixed model assumptions and spectral library learning to simultaneously improve the reconstruction speed and accuracy without using a large amount of additional hyperspectral data. First, the relationship between the CS band and the key band is learned from the spectral library to ensure that the key band endmembers can be accurately predicted. Then, the joint horizontal and vertical difference operators are proposed to enhance the estimation of the initial values of abundance. Finally, the CS band endmembers and residuals are updated in the reconstruction module to deal with the endmember and abundance mismatch. Extensive experimental results on five real hyperspectral datasets demonstrate that the proposed spectral library learning, abundance initialization, and reconstruction strategy can effectively improve the compressed sensing reconstruction accuracy, outperforming the existing state-of-the-art methods.
Dual-fast Greedy Heuristic Algorithm for Green ICT

— The Internet power consumption represents 3.6% to 6.2% of the annual worldwide power consumption and is continually expanding. The awareness of this problem has increased, hence, a few strategies are being put into place to decrease the power consumption of the Information Communication Technology (ICT) sectors, in general. Backbone networks are the main part of the Internet power consumption because their line cards expend a lot of energy, also their links are commonly bundled and provide larger capacity than needed. Therefore, bundled links are partially shut down during times of low demand to reduce power consumption. Literature introduces a few heuristic algorithms that are run periodically to shut down bundled links partially. This paper proposes a Dual-Fast Greedy Heuristic algorithm (DGH), which significantly speeds up the power savings. DGH is examined on the topology and traffic of the Abilene backbone. The experimental results show that DGH provides competitive power savings with minimum execution time.
Evaluation of the Dynamics of Large Scale Covid-19 Related Literature through Bibliometric Analysis from a Mathematical Standpoint

This study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms. The PubMed, ScienceDirect, and ResearchGate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed "all articles" and ScienceDirect published articles. Analyzed databases were (1A) PubMed (01/12/2019-12/06/2020) "all_articles" (16) PubMed Review articles) and (1C) PubMed Clinical Trials (2) ScienceDirect all publications (01/12/2019- 25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020 - 30/4/2020). Total publications in the observation period for PubMed, ScienceDirect, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, ScienceDirect and RG were 70.0 +/- 128.6, 77.6 +/- 125.3 and 255.6 +/- 205.8 respectively. PubMed shows an avalanche in the number of publications around May 10, the number of publications jumped from 6.0 +/- 8.4/day to 282.5 +/- 110.3/ day. The average doubling time for PubMed, ScienceDirect, and RG was 10.3 +/- 4 days, 20.6 days, and 2.3 +/- 2.0 days respectively. The average number of publications per author for PubMed, ScienceDirect, and RG was 1.2 +/- 1.4, 1.3 +/- 0.9, and 1.1 +/- 0.4 respectively. Subgroup analysis, PubMed review articles mean review <0 vertical bar 17 +/- 17 vertical bar 77> days: and reducing at a rate of -0.21 days (count)/day. The number of publications related to the COVID-19 until now is huge and growing very fast with time. It is essential to rationalize and limit the publications.
Effective intrusion detection system using concept drifting data stream and support vector machine

Due to the widespread distribution of network connectivity, the demand for network security and protection against cyber‐attacks is increasing rapidly. Intrusion detection system (IDS) plays a vital role in the network security. Previously, the concept‐drift based IDS is performed, in which the suspicious activity at specific IP address is detected on the real time network. However, in that research the intrusion detection is performed on single attribute. In this work, an efficient adaptive window with support vector machine (ADWIN‐SVM) is developed to detect the intrusions on large datasets with multiple attributes. The initial process in the proposed system is data cleaning. To clean the input data, the data normalization technique is utilized. Then, the data are streamed as online data by including the timestamp attribute to the dataset. This process assists to extract the patterns and detect the changes in the data. ADWIN algorithm identifies the sudden generation of drift and separates the window based on the split point. The data can be classified as normal and attack by the SVM classifier. For the performance evaluation of the proposed IDS, two datasets such as KDD Cup 1999 and CICIDS 2017 are utilized. Python is the implementation tool and the performance metrics considered for the proposed approach are precision, recall, F1 score, accuracy, and false positive rate. The proposed ADWIN‐SVM performance is compared with various existing techniques such as random forest, decision tree, naive Bayes, logistic regression, AdaBoost, K‐nearest neighbor, and SVM‐rbf. From the comparative analysis, the proposed ADWIN‐SVM is outperformed in intrusion detection.
Conforming an Extracorporeal Lithotripter System for Video Urodynamic Studies

Objectives: This study aimed to evaluate the efficiency using existing fluoroscopic unit and lithotripter table of an extracorporeal lithotripter system for video urodynamic studies (VUDS) to determine anatomical abnormalities in patients with neurogenic lower urinary tract dysfunction (NLUTD). Methods: The extracorporeal lithotripsy system was adapted to obtain optimum fluoroscopic view according to body shape and observed organs of patients. We reviewed the VUDS data of 25 patients with NLUTD. Results: “Christmas tree bladder” (CTB) was found in 5 (20%) patients. Vesicoureteral reflux (VUR) and external detrusor sphincter dyssynergia (DESD) were detected in 3 (12%) and 4 (16%) patients, respectively. Four (16%) patients with normal coordination between detrusor contraction and external sphincter relaxation were proven by VUDS. CTB, VUR, or DESD was not observed in 10 (40%) patients with flaccid bladder. Hematuria, urinary tract infection, or autonomic dysreflexia did not occur in any of the patients. Conclusions: VUDS can discern anatomical abnormalities of the urinary tract, and patients in undeveloped areas of the world who have NLUTD can have easier access to VUDS because of the decreasing capital cost of VUDS.
Oxidant system and ABA drive germination in seeds of palm species with differences in desiccation tolerance

Abstract We investigated the thermal thresholds to seed germination and the variations in abscisic acid (ABA) levels and oxidative metabolism during seed dormancy-breaking and germination in two palm species with differences in desiccation tolerance. We used Mauritia flexuosa (buriti palm, desiccation-sensitive seeds) from swampy habitats (Veredas) and Attalea speciosa (babassu, desiccation-tolerant seeds) from the transition zone between the forest and semi-arid region (drained soils). Germination was evaluated at 15–40°C after dormancy-breaking (operculum removal). At optimal temperature for both species (30°C), embryos were sampled in distinct germination phases – dry, imbibed, after operculum removal and at early germination – and used for quantifying ABA and hydrogen peroxide (H2O2) content, antioxidant enzyme activities and for histolocalization of superoxide anion (O2−). Seeds of M. flexuosa germinated only in a narrow temperature range (25–35°C), while A. speciosa seeds germinated between 15 and 40°C. After operculum removal, reduced ABA levels in embryos of M. flexuosa were accompanied by constant H2O2 levels, while in A. speciosa, similar levels of ABA and H2O2 were maintained throughout all germination phases. The presence of O2− was restricted to the haustorium, and an increase in O2− accumulation was observed in both species after operculum removal. Similarities were noted between both species regarding enzyme activities; however, the activities were higher in embryos from M. flexuosa. The presence of O2− only in the haustorium indicates that this region of the embryo is an active structure following imbibition and is involved in germination itself, not just functioning in reserve mobilization.
A multi-agent framework for capability-based reconfiguration of industrial assembly systems

Rapidly changing market requirements and shorter product lifecycles demand assembly systems that are able to cope with frequently changing resources, resource capabilities and product specifications. This paper presents a multi-agent framework that can adapt an assembly system in order to cope with such changes. The focus of this work is on the ability to plug resources (such as PLCs) into and out of the system, and dynamically aggregate resource capabilities to form more complex ones as resources are plugged in. In addition, an implementation of the framework on an industrial assembly system is discussed, and some insights are provided into some of the key features that product specification languages ought to have to be useful in real world assembly systems, and into the added value of using the proposed framework.
Deep Learning-based 3D Magnetic Microrobot Tracking using 2D MR Images

Magnetic resonance imaging (MRI)-guided robots emerged as a promising tool for minimally invasive medical operations. Recently, MRI scanners have been proposed for actuating and localizing magnetic microrobots in the patient’s body using two-dimensional (2D) MR images. However, three-dimensional (3D) magnetic microrobots tracking during motion is still an untackled issue in MRI-powered microrobotics. Here, we present a deep learning-based 3D magnetic microrobot tracking method using 2D MR images during microrobot motion. The proposed method comprises a convolutional neural network (CNN) and complementary particle filter for 3D microrobot tracking. The CNN localizes the microrobot position relative to the 2D MRI slice and classifies the microrobot visibility in the MR images. First, we create an ultrasound (US) imaging-mentored MRI-based microrobot imaging and actuation system to train the CNN. Then, we trained the CNN using the MRI data generated by automated experiments using US image-based visual servoing of a microrobot with a <inline-formula><tex-math notation="LaTeX">$500\,{\mu }$</tex-math></inline-formula>m-diameter magnetic core. We showed that the proposed CNN can localize the microrobot and classified its visibility in an <italic>in vitro</italic> environment with <inline-formula><tex-math notation="LaTeX">$\pm \mathbf {0.56}$</tex-math></inline-formula> mm and <inline-formula><tex-math notation="LaTeX">$\mathbf {87.5}$</tex-math></inline-formula>% accuracy in 2D MR images, respectively. Furthermore, we demonstrated <italic>ex-vivo</italic> 3D microrobot tracking with <inline-formula><tex-math notation="LaTeX">$\pm \mathbf {1.43}$</tex-math></inline-formula> mm accuracy, improving tracking accuracy by 60% compared to the previous studies. The presented tracking strategy will enable MRI-powered microrobots to be used in high-precision targeted medical applications in the future.