Miracle-dz/UniFAR-FTUs huggingface dataset:
PERENCANAAN POLA PENDISTRIBUSIAN PROPYLENE UNIT RCC OFF GAS TO PROPYLENE PROJECT (ROPP) MENGGUNAKAN SIMULASI PENDISTRIBUSIAN SEDERHANA

Dengan bertambahnya Propylene dari ROPP (RCC Offgas to Propylene Project)sebesar 309 Ton per hari, produksi propylene RU VI Balongan juga menjadi semakin meningkat, yaitu menjadi 719 Ton per hari. Dampak dari semakin meningkatnya produksi propylene ini adalah meningkatnya jumlah produk propylene pada tangki.PadaRU VI Balongan terdapat 8 tangki propylene dengan kapasitas maksimal 10008 Ton.Yang menjadi Permasalahanadalah belum adanya rencana penjadwalan pengkapalan propylene yang optimal.Untuk mengatasi permasalahan tersebut dibuat Simulasi Pendistribusian Sederhana menggunakan Ms.Excell. Dengan simulasi inidiharapkandapat mengontrol stock tangki, mencegah kekurangan atau kelebihan stock, dan untuk tercapainya pemenuhan permintaan konsumen tanpa terjadinya kekurangan stock atau kelebihan stock. Dari hasilsimulasidiperolehusulanpenambahankapaldanusulaneksportuntukmengatasimasalahkeleb ihanstok. Kata Kunci :Penjadwalan, Simulasi Pendistribusian Sederhana, Stok Tangki
Experimental Comparison of Direct and Active Throttle Control of a 7 kW Turboelectric Power System for Unmanned Aircraft

This article compares direct turbine throttle control and active turbine throttle control for a turboelectric system; the featured turboprop is rated for 7 kW of shaft output power. The powerplant is intended for applications in unmanned aerial systems and requires a control system to produce different amounts of power for varying mission legs. The most straightforward control scheme explored is direct turbine control, which is characterized by the pilot controlling the throttle of the turbine engine. In contrast, active control is characterized by the turbine reacting to the power demanded by the electric motors or battery recharge cycle. The transient response to electric loads of a small-scale turboelectric system is essential in identifying and characterizing such a system’s safe operational parameters. This paper directly compares the turbogenerator’s transient behavior to varying electric loads and categorizes its dynamic response. A proportional, integral, and derivative (PID) control algorithm was utilized as an active throttle controller through a microcontroller with battery power augmentation for the turboelectric system. This controller manages the turbine’s throttle reactions in response to any electric load when applied or altered. By comparing the system’s response with and without the controller, the authors provide a method to safely minimize the response time of the active throttle controller for use in the real-world environment of unmanned aircraft.
Method for biological purification of wastewater.

PROCESS FOR THE PURIFICATION BIOLOGICAL WASTEWATER psicrofila medium-low concentrations of polluting substances, which involves a first treatment ANAEROBIC SLUDGE on a bed VASB TYPE WITH EXPANDED low flow rate; SECOND, A TREATMENT IN FLUID BED ON SUPPORT FINO HIGH FLOW RATE, AND FINALLY, TREATMENT ANAEROBIC for nitrification and Oxydizing TERMINAL various substances reduced by previous treatments, which are then RECYCLED TO THE PRIMEWROS STAGES TO EXTRACT ASI POSSIBLE nutrients like nitrogen and phosphorus.
Análise de técnicas de pré-processamento de imagem para reconhecimento facial baseada em VGG Faces e Ball tree

Nos últimos anos o reconhecimento facial se tornou amplamente presente em diversos dispositivos e sistemas. No entanto, apesar de sua praticidade, o desempenho do reconhecimento facial é afetado por fatores como variação de iluminação, pose, expressão facial e qualidade da câmera que realiza a captura. Sendo assim, é necessário o uso de técnicas de pré-processamento de imagens para o tratamento destes problemas. Com isso, neste artigo é realizado um estudo comparativo de 6 métodos de pré-processamento (escala de cinza, filtro gaussiano, filtro da mediana, filtro linear, equalização de histograma e transformação logarítmica). Os testes foram feitos utilizando o descritor VGG Faces, e Ball tree como método de reconhecimento. Além das bases em sua forma original, cada base foi modificada adicionando ruídos artificiais, o que proporcionou novos testes que permitiram investigar a inﬂuência de cada técnica de pré-processamento em relação ao ruído presente nas imagens. Os experimentos sugerem que o uso do filtro da mediana produz melhores resultados em imagens com ruídos tipo Sal e Pimenta.
Comparison of experimental car-pedestrian collisions performed with various modified side-impact dummies and cadavers

Experimental car-pedestrian collisions were performed with a modified PART 572 dummy and cadavers; they involved some reconstructions of real accidents. These collisions brought to light the differences between the kinematics and the impact responses when dummy and human subject are compared under identical and realistic test conditions to simulate a pedestrian struck sideways. These differences are mainly due to the overall relative stiffness of the PART 572 dummy when compared to cadavers. Same-type collisions were therefore carried out again with other dummies which were designed so as to simulate human response in lateral impact better; thus they were also assumed to display better kinematics as pedestrians. APROD and ONSER dummies were used; when compared to PART 572, their flexibility and deformation capabilities are greater, in particular as regards their thoraxes and shoulders.
Evaluation of the classification method using ancestry SNP markers for ethnic group

Various probabilistic methods have been proposed for using interpopulation allele frequency differences to infer the ethnic group of a DNA specimen. The selection of the statistical method is critical because the accuracy of the statistical classification results vary. For the ancestry classification, we proposed a new ancestry evaluation method that estimate the combined ethnicity index as well as compared its performance with various classical classification methods using two real data sets. We selected 13 SNPs that are useful for the inference of ethnic origin. These single nucleotide polymorphisms (SNPs) were analyzed by restriction fragment mass polymorphism assay and followed by classification among ethnic groups. We genotyped 400 individuals from four ethnic groups (100 African-American, 100 Caucasian, 100 Korean, and 100 Mexican-American) for 13 SNPs and allele frequencies that differed among the four ethnic groups. Additionally, we applied our new method to HapMap SNP genotypes for 1,011 samples from 4 populations (African, European, East Asian, and CentralSouth Asian). Our proposed method yielded the highest accuracy among statistical classification methods. Our ethnic group classification system based on the analysis of ancestry informative SNP markers can provide a useful statistical tool to identify ethnic groups.
A Decade of Pollen Phosphoproteomics

Angiosperm mature pollen represents a quiescent stage with a desiccated cytoplasm surrounded by a tough cell wall, which is resistant to the suboptimal environmental conditions and carries the genetic information in an intact stage to the female gametophyte. Post pollination, pollen grains are rehydrated, activated, and a rapid pollen tube growth starts, which is accompanied by a notable metabolic activity, synthesis of novel proteins, and a mutual communication with female reproductive tissues. Several angiosperm species (Arabidopsis thaliana, tobacco, maize, and kiwifruit) were subjected to phosphoproteomic studies of their male gametophyte developmental stages, mostly mature pollen grains. The aim of this review is to compare the available phosphoproteomic studies and to highlight the common phosphoproteins and regulatory trends in the studied species. Moreover, the pollen phosphoproteome was compared with root hair phosphoproteome to pinpoint the common proteins taking part in their tip growth, which share the same cellular mechanisms.
Avaliação de Redes de Segmentação de Deep Learning para Segmentar Melanoma

Dentre os cânceres de pele, o melanoma é o mais grave e o motivo da maioria das mortes dentre os tipos de câncer de pele. Além disso, sua incidência está aumentando cada vez mais pelo mundo, assim mostrando a importância dos sistemas de detecção do câncer através de imagens médicas para a obtenção de um diagnóstico mais rápido. Uma das etapas é a segmentação, que trata do isolamento da região lesionada. Nesta pesquisa realizou-se a comparação de resultados utilizando diferentes backbones com as redes neural U-Net e FPN. Com a utilização das bases PH2 e DermIS, foram obtidos 0,66 e 0,56 de valores de Dice, respectivamente. Assim acredita-se que esse conjunto pode favorecer a obtenção de resultados mais próximos ao estado da arte.
New record of the encrusting alga Ramicrusta textilis overgrowing corals in the lagoon of Dongsha Atoll, South China Sea

The crustose calcareous alga, Ramicrusta textilis Pueschel and G.W. Saunders (Peyssonneliaceae, Rhodophyta), was recently observed to overgrow dead and living corals in shallow lagoon patch reefs of Dongsha Atoll, South China Sea. DNA barcoding revealed that R. textilis frequently co-occurred in a complex with other crustose algae of the family Peyssonneliaceae and the genus Lobophora, forming patchy crusts that cover 3%–29% of the benthic substrate. This is the first record R. textilis overgrowing corals in the Pacific Ocean.
Improving Face Alignment Accuracy on Clinical Populations and its effect on the Video-based Detection of Neurological Diseases

Background: Automatic facial landmark localization is an essential component in many computer vision applications, including video-based detection of neurological diseases. Machine learning models for facial landmarks localization are typically trained on faces of healthy individuals, and we found that model performance is inferior when applied to faces of people with neurological diseases. Fine-tuning pre-trained models with representative images improves performance on clinical populations significantly. However, questions related to the characteristics of the database used to fine-tune the model and the clinical impact of the improved model remain. Methods: We employed the Toronto NeuroFace dataset – a dataset consisting videos of Healthy Controls (HC), individuals Post-Stroke, and individuals with Amyotrophic Lateral Sclerosis performing speech and non-speech tasks with thousands of manually annotated frames - to fine-tune a well-known deep learning-based facial landmark localization model. The pre-trained and fine-tuned models were used to extract landmark-based facial features from videos, and the facial features were used to discriminate clinical groups from HC. Results: Fine-tuning a facial landmark localization model with a diverse database that includes HC and individuals with neurological disorders resulted in significantly improved performance for all groups. Our results also showed that fine-tuning the model with representative data greatly improved the ability of the subsequent classifier to classify clinical groups vs. HC from videos. Conclusions: Using a diverse database for model fine-tuning might result in better model performance for HC and clinical groups. We demonstrated that fine-tuning a model for landmark localization with representative data results in improved detection of neurological diseases.
Machine Learning in Apache Spark Environment for Diagnosis of Diabetes

Disease-related data and information collected by physicians, patients, and researchers seem insignificant at first glance. Still, the same unorganized data contain valuable information that is often hidden. The task of data mining techniques is to extract patterns to classify the data accurately. One of the various Data mining and its methods have been used often to diagnose various diseases. In this study, a machine learning (ML) technique based on distributed computing in the Apache Spark computing space is used to diagnose diabetics or hidden pattern of the illness to detect the disease using a large dataset in real-time. Implementation results of three ML techniques of Decision Tree (DT) technique or Random Forest (RF) or Support Vector Machine (SVM) in the Apache Spark computing environment using the Scala programming language and WEKA show that RF is more efficient and faster to diagnose diabetes in big data.
Complementary Feeding Practices and Influencing Factors Among Children Under 2 Years of Age: A Cross-Sectional Study in Indonesia

Purpose This study aimed to analyze the practice of complementary feeding and its influencing factors in children under 2 years of age in Indonesia. Methods This cross-sectional study used data obtained from the 2017 National Socioeconomic Survey. The sample totaled 1,751 households with children under 2 years of age, who received complementary feeding in urban and rural communities. Furthermore, the practice of complementary feeding was evaluated on the basis of the variations in provided food grouped into two categories: complete and incomplete. This study applied bivariate and multivariate analytical methods. Multivariate analysis was performed using binary logistic regression. Results The proportion of children under 2 years of age who received complementary feeding with complete variant food was 15.9%, while that with incomplete variant food was 84.1%. Furthermore, the factor influencing the practice of complementary feeding among the children was the mother's educational level (odds ratio: 1.481, 95% confidence interval: 0.245-0.943). Conclusion Complete complementary feeding, which involves a variety of food sources, is the best approach to improve the nutritional status of infants. Therefore, the source of food for complementary feeding must be accessible to all communities.
Role of Peripheral Vision in Brake Reaction Time During Safety Critical Events

The criticality of a rear end event depends on the brake reaction time (BRT) of the driver. Therefore, distracted driving poses greater threat in such events. Evidence accumulation model (EAM) that uses looming of the lead vehicle as main stimuli has shown significant success in estimating drivers’ BR Ts. It is often argued that drivers collect evidence for braking through peripheral vision, especially during off-road glances, and transition to forward. In this work, we have modeled evidence accumulation as a function of gaze eccentricity for off-road glances while approaching safety critical events. The model is tested with real world crash and near crash event data from SHRP2 naturalistic study. Our model shows that linear relation between gaze eccentricity and evidence accumulation rate during off road glances helps to improve EAM estimation in predicting BRT. We have also shown that brake-light onset does not influence EAM in presence of active looming.
Remotely Sensed Fine-Fuel Changes from Wildfire and Prescribed Fire in a Semi-Arid Grassland

The spread of flammable invasive grasses, woody plant encroachment, and enhanced aridity have interacted in many grasslands globally to increase wildfire activity and risk to valued assets. Annual variation in the abundance and distribution of fine-fuel present challenges to land managers implementing prescribed burns and mitigating wildfire, although methods to produce high-resolution fuel estimates are still under development. To further understand how prescribed fire and wildfire influence fine-fuels in a semi-arid grassland invaded by non-native perennial grasses, we combined high-resolution Sentinel-2A imagery with in situ vegetation data and machine learning to estimate yearly fine-fuel loads from 2015 to 2020. The resulting model of fine-fuel corresponded to field-based validation measurements taken in the first (R2 = 0.52, RMSE = 218 kg/ha) and last year (R2 = 0.63, RMSE = 196 kg/ha) of this 6-year study. Serial prediction of the fine-fuel model allowed for an assessment of the effect of prescribed fire (average reduction of −80 kg/ha 1-year post fire) and wildfire (−260 kg/ha 1-year post fire) on fuel conditions. Post-fire fine-fuel loads were significantly lower than in unburned control areas sampled just outside fire perimeters from 2015 to 2020 across all fires (t = 1.67, p < 0.0001); however, fine-fuel recovery occurred within 3–5 years, depending upon burn and climate conditions. When coupled with detailed fuels data from field measurements, Sentinel-2A imagery provided a means for evaluating grassland fine-fuels at yearly time steps and shows high potential for extended monitoring of dryland fuels. Our approach provides land managers with a systematic analysis of the effects of fire management treatments on fine-fuel conditions and provides an accurate, updateable, and expandable solution for mapping fine-fuels over yearly time steps across drylands throughout the world.
Elastic modulus measurement of ultrathin layer using gas cluster ion beam

Nanoindentation is generally used to measure the hardness or Young’s modulus of thin films. However, when the hardness or Young’s modulus is measured on the ultrathin layer, it cannot sometimes obtain exact results due to some influential factors such as wear of indentation tip, surface roughness, or substrate effect. We focused on the elastic modulus measurement method of ultrathin layers using the time-of-flight secondary ion mass spectroscopy (TOF-SIMS) with Ar gas cluster ion beam (GCIB). The dissociated ion yield defined as the intensity ratio (Ar 2+ /(Ar 2+ +Ar 3+ ) on the positive ion spectrum and impulsive stress were compared and correlated to be proportional to both on the reference materials. The Young’s moduli of the silicon oxide layer of 1.7 nm thickness on monocrystal silicon wafer and ultrathin diamond-like carbon (DLC) layer with approximately 3 nm thickness on magnetic disks were estimated using the dissociated ion yield measured using Ar-GCIB and correlation result. The Young’s modulus was measured at various depths using Ar-GCIB etching with a very slow etching speed. We confirmed a difference between the Young’s moduli of these ultrathin layers and those of under layers. From these results, it was seen that the elastic modulus measurement method using TOF-SIMS with Ar-GCIB is a powerful tool to compare the elastic modulus of ultra-thin layers and improve the accuracy of structural analysis simulations of ultra-thin film structures with nanometer order thickness, which was impossible with conventional methods.
Negative Regulation of Semaphorin-3A Expression in Peripheral Blood Mononuclear Cells Using MicroRNA-497-5p

Background: Semaphorin-3A (Sema3A), as a secreted semaphorin, is an immune modulator molecule participating in the pathogenesis of autoimmune diseases. MicroRNAs (miRNAs) modulate the target-gene expression at the post-transcriptional level. It has been proposed that miRNAs may be crucial to the modulation of the function of semaphorins. Previous findings have proven that miR-497-5p is upregulated and Sema3A is downregulated in some autoimmune disorders. Thus, we aimed to examine the presence of any correlation between Sema3A and miR-497-5p in peripheral blood mononuclear cells (PBMCs). Methods: PBMCs were cultured and transfected with miR-497-5p mimic using the X-tremeGENE™ reagent. The expression level of Sema3A was assessed after 48 hours in supernatants and cells via the enzyme-linked immunosorbent assay and quantitative real-time polymerase chain reaction, respectively. Cell viability was evaluated using the methylthiazol tetrazolium assay. All the experiments were done in triplicate, and the statistical analyses were performed with SPSS, version 20. P values equal to or less than 0.05 were considered significant. Results: We observed downregulation of the Sema3A gene (P=0.0001) and its secretion (P=0.032) in the PBMCs through miR-497-5p transfection. Moreover, transfection with miR-497-5p mimic and downregulation of Sema3A did not affect the viability of the PBMCs (P=0.061). Conclusion: Based on the obtained results, we suggest that miR-497-5p has a high suppressive effect on Sema3A expression and both Sema3A and miR-497-5p can be considered critical targets for further studies on future therapeutic attempts for the treatment of autoimmune diseases such as multiple sclerosis and rheumatoid arthritis.
Developing a new human-autonomy team cohesion Scale

Cohesion is an important construct in understanding human-autonomy team dynamics and effectiveness, yet methods to adequately measure this construct are still being developed. Here we describe the initial steps of the development of a new human-autonomy team cohesion scale: item development, content validation, and preliminary scale item evaluation. The initial item pool was developed resulting in 134 items and underwent content validation with subject matter experts to reduce the item pool to 82. For the content validation, participants watched a set of video clips displaying examples of high and low human-autonomy team cohesion and rated the clips using the new human-autonomy team cohesion scale. Preliminary results revealed that our new human-autonomy team cohesion scale is a reliable and valid measure of cohesion in these contexts. Additionally, ratings for the high cohesion clips were somewhat higher than the low cohesion clips. Further analyses will determine if each of the items are contributing to the overall validity of the scale and will be further evaluated for possible removal.
CRISPR Screening: Molecular Tools for Studying Virus–Host Interactions

CRISPR/Cas is a powerful tool for studying the role of genes in viral infections. The invention of CRISPR screening technologies has made it possible to untangle complex interactions between the host and viral agents. Moreover, whole-genome and pathway-specific CRISPR screens have facilitated identification of novel drug candidates for treating viral infections. In this review, we highlight recent developments in the fields of CRISPR/Cas with a focus on the use of CRISPR screens for studying viral infections and identifying new candidate genes to aid development of antivirals.
Interchannel Interference and Mitigation in Distributed MIMO RF Sensing

In this paper, we analyze and mitigate the cross-channel interference, which is found in multiple-input multiple-output (MIMO) radio frequency (RF) sensing systems. For a millimeter wave (mm-Wave) MIMO system, we present a geometrical three-dimensional (3D) channel model to simulate the time-variant (TV) trajectories of a moving scatterer. We collected RF data using a state-of-the-art radar known as Ancortek SDR-KIT 2400T2R4, which is a frequency-modulated continuous wave (FMCW) MIMO radar system operating in the K-band. The Ancortek radar is currently the only K-band MIMO commercial radar system that offers customized antenna configurations. It is shown that this radar system encounters the problem of interference between the various subchannels. We propose an optimal approach to mitigate the problem of cross-channel interference by inducing a propagation delay in one of the channels and apply range gating. The measurement results prove the effectiveness of the proposed approach by demonstrating a complete elimination of the interference problem. The application of the proposed solution on Ancortek’s SDR-KIT 2400T2R4 allows resolving all subchannel links in a distributed MIMO configuration. This allows using MIMO RF sensing techniques to track a moving scatterer (target) regardless of its direction of motion.
Testing, monitoring, and dating structural changes in maximum likelihood models

A unified toolbox for testing, monitoring, and dating structural changes is provided for likelihood-based regression models. In particular, least-squares methods for dating breakpoints are extended to maximum likelihood estimation. The usefulness of all techniques is illustrated by assessing the stability of de facto exchange rate regimes. The toolbox is used for investigating the Chinese exchange rate regime after China gave up on a fixed exchange rate to the US dollar in 2005 and tracking the evolution of the Indian exchange rate regime since 1993.