Miracle-dz/UniFAR-FTUs huggingface dataset:
Syngas Production from Rubberwood Biomass in Downdraft Gasifier Combined with Wet Scrubbing: Investigation of Tar and Solid Residue

Production of synthesis gas by gasification is still a challenge due to the tar in the synthesis gas (syngas). This tar needs to be eliminated by appropriate methods before using the syngas as a fuel. Moreover, the solid residue after gasification also needs to be properly managed or destroyed. Therefore, the aim of this study was to investigate tar and solid residue generated by gasification of rubberwood biomass, including rubberwood chips (RWC), rubberwood pellets (RWP), rubberwood unburned char (UBC), and their blends, in a downdraft gasifier. Waste vegetable oil (WVO) and water were used as scrubbing media. Properties of the biomass samples were characterized by proximate and ultimate analysis, as well as for the higher heating value. The downdraft gasifier was operated at 850 °C and equivalence ratio (ER) of 0.25. The concentrations of tar in syngas both before and after passing through the wet scrubber were determined. Chemical compounds in the tar were analysed by GC-MS. The solid residue remaining after gasification was separated into biochar and ash. The biochar was characterized by CHNS/O analyser, FTIR, SEM, and for the iodine number. The compounds in ash were determined by XRF. The results show that biomass type and scrubbing media affected the tar removal efficiency. Scrubbing syngas with WVO had better tar removal efficiency than scrubbing with water. The highest tar removal efficiency with WVO was 82.16%. The tar sample consisted of complex compounds as indicated by GC-MS, and these compounds depended on type of biomass feedstock. The solid residue obtained after gasification process contained biochar (unburned carbon) and ash. Some biochars can be used as solid fuels, depending on carbon content and energy content. The biochar also had a highly porous structure based on SEM imaging, and a high iodine number (930-1134 mg/g). The biochar contained the functional groups OH, C-O, and C-H, as indicated by FTIR. CaO, K2 O, SiO2 , and MgO were the major components in ash. The spent WVO, biochar, and ash need to be properly managed or utilized for sustainable gasification operations, and these results support that pursuit.
Temporal Models for Mobile Robot Visual Navigation

This thesis focuses on modelling environmental changes depending on the time for long-term mobile robot visual navigation and integrating these models into an existing functional navigation system. The goal of this thesis is to create a temporal environment model, which would allow to capture and predict operational environment changes providing long-term autonomous operating of a mobile robot in a changing environment. This thesis divides the problem of a temporal model creation into two sub problems: "How recorded changes should be interpreted? " and "How to predict current environment model usable for navigation? ". This thesis extends a system that uses image features for visual navigation, but the abstraction of the solution allows using different methods instead. The system is implemented in the Robotic Operating System in C++ programming language.
Students ’ Reading Material Choices and Their L 1 and FL Comprehension Levels

Reading has changed its shape throughout history and turned into a new shape named as screen reading. By considering the value of reading and its new shape, this survey design study which was supported with the correlational design was used to find students’ general, L1 and FL reading choices and L1 and FL comprehension levels of the students who read printed or electronic sources. A questionnaire and a comprehension test were used to collect data. Results showed that students choose to read electronic materials more than printed ones. Especially, social sites and internet blogs have a high reading amount in both languages and there are no comprehension differences among students who read different kinds of materials. Considering that students read electronic materials more than printed ones and there is not a comprehension deficiency among the students who read those kinds of materials, it is thought that using these kinds of sources in the class-room as course materials will support the success by increasing the motivation and, so, it will be beneficial especially for foreign language teachers to use authentic electronic materials in their courses. Keywords
Statistical Analysis of Sacral Bone Density using a CNN-based Atlas Creation on a Large-scale CT database

Fragility fracture of the sacrum has been an issue for elderly people. Research has been limited for the sacrum partly because its shape is complex with large inter-subject variation. Also, large-scale statistical analysis of its shape and density distribution has been limited mainly due to the computational load in establishment of the voxel correspondences, i.e., deformable registration. In this study, we employed a convolutional neural network (CNN)-based deformable registration algorithm in the analysis of the sacral bone. The algorithm we employed, VoxelMorph (Dalca et al. Med Image Anal 2019), is characterized as an unsupervised algorithm where no ground truth deformation field is required. The algorithm also allows to create a conditional deformable template, which is a volume image exhibiting smallest deformation field from all samples with specific attributes (e.g., age, sex, e tc.), which in short represents the “average” (or “centroid”) image among the specific age and sex group. We applied it on a database consisting of 837 CTs (149 males, 688 females, 58.14 ± 14.73 y.o.) of the pelvis region, where the sacral bone was segmented and masked. We computed the templates corresponding to ages of 20 to 80 for male and female. The templates visually illustrated reduction of the bone density with aging in both male and female. The quantitative analysis showed that the average CT value over the sacrum region was reduced from 135.73 HU to
Multiple-Wavelength Source based on SOA and Sagnac Loop Mirror

A simple multi-wavelength fiber laser based on a semiconductor optical amplifier (SOA) and Sagnac loop is proposed. At a 150 mA bias current, 6 lines are obtained with at least -40 dBm output power and 25 dB signal to noise ratio (SNR). The 1.49 nm channel spacing and number of lines is determined by the length of the PMF used in the loop mirror. The multi-wavelength comb output can be tuned by adjusting the operating temperature of the SOA. The source has the advantage of a simple configuration, stability at room temperature and a broad wavelength band.
EFFECT OF DIFFERENT ABUTMENT MATERIALS ON STRESS DISTRIBUTION IN PERIPHERAL BONE AND DENTAL IMPLANT SYSTEM

In this study, the effect of different abutment materials on the stress distribution in bone tissue around the dental implant was investigated using ANSYS packet program based on finite element method. The study aims to examine the stresses, strains and deformations that will occur in the bone, dental implants and abutments with the replacement of abutment materials on the titanium dental implant. Modeling has been prepared in Solidworks program. Titanium was chosen as the dental implant material. Abutment materials were selected as titanium, zirconium, chrome-cobalt and polyetheretherketone. After modeling, necessary load was applied to the abutments and analyzed. This process was repeated for each abutment material. As a result of the analyzes, the stresses, strains and deformations that occurred in the bone, dental implants and abutments were combined into tables. In the conclusion, the differences between the abutment materials were evaluated. Generally, it was found that the lowest values were obtained in the group that chrome-cobalt was used as an abutment material. Therefore, chrome-cobalt can be preferred as an abutment material among all tested materials.
Application of heavy-tailed distribution using PROC IML, NLMIXED, and SEVERITY

Probabilistic heavy-tailed distribution (Pareto, Weibull, Burr etc.) theory has vast applications involving in many real-life situations and natural phenomena. This area of research attracts not only for theoretical probabilistic nature but also for various branches of statistics. Heavy-tailed distribution are also used for modeling various biological, actuarial, financial, economic, hydrological, and engineering data. In this paper, we are modeling the cancer patient dataset and electrical blackouts dataset in the USA with generalized Pareto distribution. We also simulate the data arising from generalized Pareto distribution and estimate the parameters of generalized Pareto distribution (GPD) using maximum likelihood estimation. A suite of SAS procedure is used for all computation, specifically Procedure IML, SEVERITY, NLMIXED.
Specimen Copy: Not for sale

Twenty selected genetically diverse okra strains were evaluated using Principal Component and cluster analysis for the extent of variability and relationship between various economically important traits for the purpose of genetic improvement. The trial was laid out in a randomized block design (RBD). Positive signifi cant correlation for days to 50% fl owering (DF) with days to fi rst harvest (DFH), number of pod per plant (NP) with pod yield per plant (PY) and pod yield per plot (PYP) (P≤0.001) and PY with PYP (P≤0.001) and negative correlation was observed for pod weight (PW) with NP (P≤0.01). The analysis of extracted components, component pattern and Eigen values revealed that the fi rst two principal components alone accounted for 53.25% of variance. First component was found heavily loaded with days to 50% fl owering (DF), days to fi rst harvest (DFH), pod length (PL), pod diameter (PD) and pod weight (PW), which comprised of fourteen genotypes in three clusters. The matrix obtained from principal component analysis revealed that the genotype Pb57 and HRB-9-2 found their positions in same cluster in principal space. Dominating similar prominent phenotypic characters formed separate place in principal space as coherent cluster. Cluster based inter breeding of genotypes would exhibit high hetrosis and is also likely to produce new recombinants with desired characters in okra.
The Complementary Nature of Perceived and Actual Time Spent Online in Measuring Digital Well-being

As online platforms become ubiquitous, there is growing concern that their use can potentially lead to negative outcomes in users' personal lives, such as disrupted sleep and impacted social relationships. A central question in the literature studying these problematic effects is whether they are associated with the amount of time users spend on online platforms. This is often addressed by either analyzing self-reported measures of time spent online, which are generally inaccurate, or using objective metrics derived from server logs or tracking software. Nonetheless, how the two types of time measures comparatively relate to problematic effects -- whether they complement or are redundant with each other in predicting problematicity -- remains unknown. Additionally, transparent research into this question is hindered by the literature's focus on closed platforms with inaccessible data, as well as selective analytical decisions that may lead to reproducibility issues. In this work, we investigate how both self-reported and data-derived metrics of time spent relate to potentially problematic effects arising from the use of an open, non-profit online chess platform. These effects include disruptions to sleep, relationships, school and work performance, and self-control. To this end, we distributed a gamified survey to players and linked their responses with publicly-available game logs. We find problematic effects to be associated with both self-reported and data-derived usage measures to similar degrees. However, analytical models incorporating both self-reported and actual time explain problematic effects significantly more effectively than models with either type of measure alone. Furthermore, these results persist across thousands of possible analytical decisions when using a robust and transparent statistical framework. This suggests that the two methods of measuring time spent measure contain distinct, complementary information about problematic usage outcomes and should be used in conjunction with each other.
A prototype stochastic parameterisation of regime behaviour in the stably stratified atmospheric boundary layer

Recent research has demonstrated that hidden Markov model (HMM) analysis is an effective tool to classify atmospheric observations of the stably stratified nocturnal boundary layer (SBL) into weakly stable (wSBL) and very stable (vSBL) regimes. Here we consider the development of explicitly stochastic representations of SBL regime dynamics. First, we analyse if HMM-based SBL regime statistics (the occurrence of regime transitions, subsequent transitions after the first, and very persistent nights) can be accurately represented by ‘freely-running’ stationary Markov chains (FSMC). Our results show that despite 5 the HMM-estimated regime statistics being relatively insensitive to the HMM transition probabilities, these statistics cannot all simultaneously be captured by a FSMC. Furthermore, by construction a FSMC cannot capture the observed non-Markov regime duration distributions. Using the HMM classification of data into wSBL and vSBL regimes, state-dependent transition probabilities conditioned on the bulk Richardson number (RiB) or the stratification are investigated. We find that conditioning on stratification produces more robust results than conditioning on RiB. A prototype explicitly stochastic parameterisation is 10 developed based on stratification-dependent transition probabilities, in which turbulence pulses (representing intermittent turbulent events) are added during vSBL conditions. Experiments using an idealised single column model demonstrate that such an approach can simulate realistic-looking SBL regime dynamics.
Identifying Swing Dynamic Coherency by Koopman Mode Analysis

This paper provides a new technique to identify coherent machines groups of electrical power systems swinging together in frequency and phase. Modal analysis of swing dynamics under large disturbances in multi-machine networks is performed based on a mathematical technique named Nonlinear Koopman Mode Analysis. The study is conducted on two tested networks: the Kundur 2-area 4-machines power system and the IEEE 9-bus 4-machines power system. A comparison of the Discrete Fourier Transform analysis is carried out with the nonlinear Koopman mode analysis in terms of frequencies and coherency identification. This comparison reveals that the Koopman modes extract spatial single-frequency oscillations and may identify coherent swings dynamics. Keywords— Electrical Power Systems, Nonlinear Oscillations, Koopman Mode Analysis, Transient stability, Coherency Identification.
DISEASE PREDICTION USING MACHINE LEARNING OVER BIGDATA

: The Advanced Healthcare System applies Machine Learning Algorithms for the implementation and development of medical facilities, enabling precise decisions about patient treatment and diagnosis. This System helps people to process and analyze, generate deep insights of large datasets of medical procedures. Physicians could use these advanced application, further in the provision of medical care. Therefore the significant contribution towards Machine Learning applications in healthcare could improve patient satisfaction. In this paper, we are attempting to incorporate Healthcare-Machine learning functionalities inside a single program. This sophisticated technology of machine leaning has made the healthcare system intelligent such that instead of diagnosis, the disease could be predicted using predictive algorithms of machine learning. Some cases can occur when diagnosing a disease early is not within reach. It is also possible to successfully apply the prediction of disease. As widely stated "Prevention is better than cure," disease prediction and epidemic outbreak would lead to an early prevention of a disease occurrence. This paper focuses mainly on the implementation of a program or we might claim an immediate medical service that would integrate the symptoms gathered from multisensory tools and other medical data and store them in a healthcare dataset. This dataset would then be analyzed using Decision tree, Random Forest, NaïveBayes machine learning algorithms to ensure maximum accuracy of results.
Human, cattle and goat blood as substitutes for sheep blood in blood-supplemented culture media

Introduction and Objective: Sheep blood is the recommended type of blood for supplementation of agar media. In Sri Lanka, due to lack of availability of sheep blood, expired citrated human blood is used which gives poor haemolysis and causes difficulties in identification of some organisms. In addition, human blood contains antibodies and other antibacterial factors including antibiotics which may inhibit bacterial growth. Human blood may also contain blood borne pathogens which could be a risk for laboratory staff. The objective of this study was to explore available alternatives in the Sri Lankan setting. Methods: Clinical isolates and standard strains of Streptococcus pyogenes, Streptococcus agalactiae, Streptococcus pneumoniae and Haemophilus influenzae were tested for growth, identification characteristics and antibiotic susceptibility on human, cattle, goat and sheep blood agars. The performances were compared. Identification of Listeria monocytogenes as well as Streptococcus agalactiae was carried out using the CAMP test. Results: All tested organisms gave similar isolation rates at the tested dilutions in the four tested agar plates. Human blood gave noticeably smaller colonies. S. pyogenes and S. agalactiae gave equally large zones of beta-haemolysis and S. pneumoniae gave obvious alpha-haemolysis on all animal blood agar plates. Both types of haemolysis were faint on human blood. Typical arrow head shape haemolysis for S. agalactiae and match-head shape haemolysis for Listeria were seen in the CAMP test on the three animal blood agar plates whereas human blood gave negative results. All blood agar plates gave comparable positive results in the satellitism test for H. influenzae. There was no difference in bacitracin and optochin sensitivity tests for identification of S. pyogenes and S. pneumoniae respectively. Inhibitory zones were unreadable when antibiotic susceptibility was done for H. influenzae on goat and cattle chocolate agar. Sheep and human chocolate agar were inferior to Haemophilus test medium. ABST results were equivalent but goat blood gave hazy, irregular margins for other organisms. Conclusions: Cattle and goat blood show similar performance to sheep blood in growth and identification tests for common fastidious pathogens. Cattle blood is equivalent to sheep blood for ABST, but goat blood is inferior.
Abundance and seasonal population dynamics of the potentially ciguatera-causing dinoflagellate Gambierdiscus in Japanese coastal areas between 2007 and 2013

Ciguatera fish poisoning (CFP) is caused by toxins originating from an epiphytic/benthic dinoflagellate of the genus Gambierdiscus. In Japan, CFP cases have been increasingly reported not only in subtropical areas but also in temperate areas. It is therefore important to study Gambierdiscus cell occurrences, cell densities, and population dynamics to address CFP outbreaks in Japan. This study assessed the densities in Japanese shallow waters (0.1–3 m depths) and revealed that the densities were lower than those in tropical and subtropical areas worldwide. In the shallow waters of Tosa Bay, a Japanese temperate area, population dynamics of Gambierdiscus cells were assessed monthly between 2007 and 2013. Gambierdiscus did not show substrate preferences for macroalgal species. The cell densities in the area ranged from 0 to 232.2 cells g−1 wet weight algae. The average cell densities in spring, summer, autumn, and winter were 0.1±0.4, 0.9±2.6, 4.0±20.6, and 0.4±1.4 cells g−1 wet weight algae, respectively. The cell densities in summer and autumn were not significantly different (p>0.05), whereas those in summer and autumn were significantly higher than those in spring and winter (p<0.01). A significant positive correlation between cell densities and sea surface temperatures (SSTs) was observed (rs=0.21, p<0.001), while a significant negative correlation between cell densities and salinity was recognized (rs=−0.18, p<0.001). These results suggest that cell densities of Gambierdiscus in Japanese temperate shallow waters increase in summer and autumn when the SST is high and salinity is moderately low.
A prototype stochastic parameterisation :::::::::::::::::::::::::: parameterization of regime behaviour in the stably stratified atmospheric boundary layer

Recent research has demonstrated that hidden Markov model (HMM) analysis is an effective tool to classify atmospheric observations of the stably stratified nocturnal boundary layer (SBL) into weakly stable (wSBL) and very stable (vSBL) regimes. Here we consider the development of explicitly stochastic representations of SBL regime dynamics. First, we analyse :::::: analyze if HMM-based SBL regime statistics (the occurrence of regime transitions, subsequent transitions after the first, and very persistent nights) can be accurately represented by ‘freely-running’ stationary Markov chains (FSMC). Our 5 results show that despite the HMM-estimated regime statistics being relatively insensitive to the HMM transition probabilities, these statistics cannot all simultaneously be captured by a FSMC. Furthermore, by construction a FSMC cannot capture the observed non-Markov regime duration distributions. Using the HMM classification of data into wSBL and vSBL regimes, state-dependent transition probabilities conditioned on the bulk Richardson number (RiB) or the stratification are investigated. We find that conditioning on stratification produces more robust results than conditioning on RiB. A prototype explicitly 10 stochastic parameterisation :::::::::::::: parameterization is developed based on stratification-dependent transition probabilities, in which turbulence pulses (representing intermittent turbulent ::::::::: turbulence events) are added during vSBL conditions. Experiments using an idealised ::::::: idealized : single column model demonstrate that such an approach can simulate realistic-looking SBL regime dynamics.
Complete mitochondrial DNA genome of grey pomfret Pampus cinereus (Bloch, 1795) (Perciformes: Stromateidae)

Abstract In this study, we sequenced the complete mitochondrial genome of Pampus cinereus (Bloch, 1795). This mitochondrial genome, consisting of 16,540 base pairs (bp), contains 13 protein-coding genes, two ribosomal RNAs, 22 transfer RNAs, and two mainly noncoding regions (control region and origin of light-strand replication) as those found in other vertebrates. Control region with 846 bp in length, is located between tRNAPro and tRNAPhe. The overall base composition of the heavy strand shows 27.4% of T, 27.5% of C, 30.0% of A, and 15.2% of G, with a slight A + T rich bias (57.4%). The complete mitochondrial genome data will provide useful genetic markers for the studies on the molecular identification, population genetics, phylogenetic analysis and conservation genetics.