Miracle-dz/UniFAR-FTUs huggingface dataset:
SIMULATION ANALYSES ON PHASE TRANSITION IN COUNTER FLOW SITUATIONS BY USING A PEDESTRIAN AGENT MODEL WITH SIMPLIFIED BEHAVIOR RULES

In this study, by using a simplified pedestrian agent model with eight behavior rules, we analyzed the phase transition of the counter flows and clarified the relationships between the probability of the diagonal stepping with the evasion rules and the density level. As a result, we classified six phases of the pedestrian flows: (1)Static Layered Flow; (2)Stable Layered Flow; (3)Emergence and Collapse of Layered Flow; (4)Chaotic Flow; (5)Incomplete Clogging; and (6)Clogging. We also confirmed the phase transitions and showed that the small deflection of the diagonal stepping directions affects these phases.
Bayesian Multiple Comparison of Bivariate Exponential Populations based on Fractional Bayes Factor

In this paper, we consider the Bayesian multiple comparisons problem for K bivariate exponential populations to make inferences on the relationships among the parameters based on observations. And we suggest the Bayesian procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. Also, we give a numerical examples to illustrate our procedure.
The Synergistic Effect of Cyclic Tensile Force and Periodontal Ligament Cell-Laden Calcium Silicate/Gelatin Methacrylate Auxetic Hydrogel Scaffolds for Bone Regeneration

The development of 3D printing technologies has allowed us to fabricate complex novel scaffolds for bone regeneration. In this study, we reported the incorporation of different concentrations of calcium silicate (CS) powder into fish gelatin methacrylate (FGelMa) for the fabrication of CS/FGelMa auxetic bio-scaffolds using 3D printing technology. Our results showed that CS could be successfully incorporated into FGelMa without influencing the original structural components of FGelMa. Furthermore, it conveyed that CS modifications both the mechanical properties and degradation rates of the scaffolds were improved in accordance with the concentrations of CS upon modifications of CS. In addition, the presence of CS enhanced the adhesion and proliferation of human periodontal ligament cells (hPDLs) cultured in the scaffold. Further osteogenic evaluation also confirmed that CS was able to enhance the osteogenic capabilities via activation of downstream intracellular factors such as pFAK/FAK and pERK/ERK. More interestingly, it was noted that the application of extrinsic biomechanical stimulation to the auxetic scaffolds further enhanced the proliferation and differentiation of hPDLs cells and secretion of osteogenic-related markers when compared to CS/FGelMa hydrogels without tensile stimulation. This prompted us to explore the related mechanism behind this interesting phenomenon. Subsequent studies showed that biomechanical stimulation works via YAP, which is a biomechanical cue. Taken together, our results showed that novel auxetic scaffolds could be fabricated by combining different aspects of science and technology, in order to improve the future chances of clinical applications for bone regeneration.
Rational Number Interventions for Students With Mathematics Difficulties: A Meta-Analysis

Understanding rational numbers is critical for secondary mathematics achievement. However, students with mathematics difficulties (MD) struggle with rational number topics, including fractions, decimals, and percentages. The purpose of this systematic review was to describe the instructional foci of rational number interventions, determine the overall effect size, and explore potential moderators. Forty-three studies were included and 150 effect sizes were meta-analyzed using robust variance estimation. The majority of studies focused on teaching fraction magnitude and arithmetic. An overall effect size of g = 1.02 [0.80, 1.25] was found for rational number interventions favoring treatment conditions over business as usual control. Proximal measures contributed to higher effect sizes than distal measures. Limitations included a high number of fraction interventions contributing to the overall effect size and a large amount of heterogeneity among study effect sizes.
Utility of anthropometric measures to identify small for gestational age newborns: A study from Eastern India

Introduction: Small-for-gestational-age (SGA) is one of the important factors for neonatal mortality. Early identification and necessary intervention of these newborns is crucial to increase their chances of survival and reduce long-term disabilities. However, in low- and middle-income countries a large portion of pregnant women are unaware of their accurate gestational age (GA) due to the limited availability of ultrasonography. The purpose of our study was to build an alternative tool to identify SGA. Methods: A institutional-based, prospective observational study was conducted from August-2018 to February-2020, with 1451 live singleton-newborns of 30-40 weeks of gestation. Ultrasonography was used to evaluate accurate GA in early pregnancy and a reference chart for the Asian population, constructed by the National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies was used to classify newborns as SGA. Neonatal anthropometry was measured within 48 hours of birth. Receiver operating characteristic curves were developed to identify the best cut-off point for each anthropometric parameter and the area under the curve (AUC) was estimated to assess the overall precision. Results: Prevalence of SGA was 34.3%. The AUC was 0.888 for head circumference (HC), 0.890 for chest circumference (CC), and 0.865 for mid-upper arm circumference (MUAC). The optimal cut-offs to classify SGA were ≤32.45 cm for HC, ≤29.75 cm for CC and ≤8.55 cm for MUAC with sensitivities of 85.9%, 86.9% and 85.4%, specificities of 75.5%, 85.1% and 72.1%, positive predictive values of 0.64, 0.75 and 0.61 and negative predictive values of 0.91, 0.93 and 0.90 respectively. Conclusion: All three anthropometric measurements could be used to identify SGA but, overall CC is the best.
Sources of Energy Poverty: A Factor Analysis Approach for Spain

This study estimates housing quality and features explaining energy poverty in Spain and its regions. By using the EU-SILC dataset for 2008–2019, it calculates the hidden links between energy poverty indicators and housing features, controlled by other variables such as type of household, poverty, and housing tenancy. Confirmatory factor analysis is used to identify the role of different dimensions in explaining energy poverty at the household level. The empirical evidence finds three hidden factors associating energy poverty with poverty, poor housing quality, and housing size and outskirts location. These three factors enable classifying households accordingly, revealing their distribution across Spain and three of its 17 Spanish regions: Madrid, Cataluña, and Valencian Community. Findings indicate how the impact of energy poverty differs by region, rejecting the general hypothesis that all households in poverty live in poor housing because they cannot afford the maintenance costs, thus causing energy poverty. Results suggest that energy poverty due to poor housing quality and location affects many households that are not necessarily poor, with different impacts depending on location. The association between energy poverty and larger houses located on the outskirts represents new evidence in the literature and is one of the contributions of this study, together with the methodology for classification. Results suggest that retrofitting investment would be crucial in reducing energy poverty problems in Spain.
An Exploration of Wikipedia Contributions in the Era of COVID-19

With the declaration of COVID-19 as a pandemic by the World Health Organization and associated public health concerns and emergency measures, the lives of many came to a grinding halt. Whether due to stay-at-home orders and lockdowns, or quarantines, many were required to change their day-to-day way of life overnight. This not only changed the mechanical activities performed daily, such as driving to the office, going out to a restaurant, or attending a party, but this also changed the resultant implications of the utility we received from those activities. Utility theory posits that individuals choose what activities to perform based on some utility function, where the utility satisfies some need. As these activities to meet existing needs were disrupted, we seek to use this as an opportunity to explore how contribution in open-source communities changed, specifically the English Wikipedia. We utilize a natural experiment methodology, centered around the COVID-19 pandemic, and explore contribution patterns before and after the pandemic. Our project will contribute to the understanding of IS during unusual times, specifically in the context of open-source communities that rely on volunteer time and effort.
Defining the Realized Niche of the Two Major Clades of Trichodesmium: A Study on the West Florida Shelf

The cyanobacterium Trichodesmium plays an essential role supporting ocean productivity by relieving nitrogen limitation via dinitrogen (N2) fixation. The two common Trichodesmium clades, T. erythraeum and T. thiebautii, are both observed in waters along the West Florida Shelf (WFS). We hypothesized that these taxa occupy distinct realized niches, where T. thiebautii is the more oceanic clade. Samples for DNA and water chemistry analyses were collected on three separate WFS expeditions (2015, 2018, and 2019) spanning multiple seasons; abundances of the single copy housekeeping gene rnpB from both clades were enumerated via quantitative PCR. We conducted a suite of statistical analyses to assess Trichodesmium clade abundances in the context of the physicochemical data. We observed a consistent coastal vs. open ocean separation of the two clades: T. erythraeum was found in shallow waters where the concentrations of dissolved iron (dFe) and the groundwater tracer Ba were significantly higher, while T. thiebautii abundance was positively correlated with water column depth. The Loop Current intrusion in 2015 with entrained Missisippi River water brought higher dFe and elevated abundance of both clades offshore of the 50 m isobath, suggesting that both clades are subject to Fe limitation on the outer shelf. Whereas, previous work has observed that T. thiebautii is more abundant than T. erythraeum in open ocean surface waters, this is the first study to examine Trichodesmium niche differentiation in a coastal environment. Understanding the environmental niches of these two key taxa bears important implications for their contributions to global nitrogen and carbon cycling and their response to global climate change.
Dose reduction potential in dual-energy subtraction chest radiography based on the relationship between spatial-resolution property and segmentation accuracy of the tumor area

We investigated the relationship between the spatial-resolution property of soft tissue images and the lesion detection ability using U-net. We aimed to explore the possibility of dose reduction during energy subtraction chest radiography. The correlation between the spatial-resolution property of each dose image and the segmentation accuracy of the tumor area in the four regions where the tumor was placed was evaluated using linear regression analysis. The spatial-resolution property was determined by task-based evaluation, and the task-based modulation transfer function (TTF) was computed as its index. TTFs of the reference dose image and the 75 % dose image showed almost the same frequency characteristics regardless of the location of the tumor, and the Dice coefficient also high. When the tumor was located in the right supraclavicular region and under 50 % dose, the frequency characteristics were significantly reduced, and the Dice coefficient was also low. Our results showed a close relationship between the spatial-resolution property and the segmentation accuracy of tumor area using deep learning in dual-energy subtraction chest radiography. In conclusion, a dose reduction of approximately 25 % compared to the conventional method can be achieved. The limitations are the shape of the simulated mass and the use of chest phantom.
Video-based emotion sensing and recognition using convolutional neural network based kinetic gas molecule optimization

Human facial expressions are thought to be important in interpreting one's emotions. Emotional recognition plays a very important part in the more exact inspection of human feelings and interior thoughts. Over the last several years, emotion identification utilizing pictures, videos, or voice as input has been a popular issue in the field of study. Recently, most emotional recognition research focuses on the extraction of representative modality characteristics and the definition of dynamic interactions between multiple modalities. Deep learning methods have opened the way for the development of artificial intelligence products, and the suggested system employs a convolutional neural network (CNN) for identifying real-time human feelings. The aim of the research study is to create a real-time emotion detection application by utilizing improved CNN. This research offers information on identifying emotions in films using deep learning techniques. Kinetic gas molecule optimization is used to optimize the fine-tuning and weights of CNN. This article describes the technique of the recognition process as well as its experimental validation. Two datasets such as video-based and image-based datasets, which are employed in many scholarly publications, are also investigated. The results of several emotion recognition simulations are provided, along with their performance factors.
Fortification of bread with bee pollen, and its effects on quality attributes and antioxidant activity

Bee pollen (BP) is a good source of nourishing substances with preventative effects against metabolic syndrome and subsequent type II diabetes mellitus and cardiovascular diseases. In the present work, the addition of BP changed the characteristics and nutritional values of bread. It was found that the addition of BP significantly increased dietary fibre, thiamine, and riboflavin levels, as well as phenolic contents and antioxidant activity. When blending the wheat flour with 20 - 25% BP, the antioxidant activity of bread increased effectively, while at 20% BP addition level, the specific volume, springiness, hardness, and chewiness were significantly detrimentally affected. Addition of 10% BP was beneficial to water retention, and delayed bread staling. These results suggest that the addition of 10 - 15% BP could effectively improve the nutritional and antioxidant properties of wheat bread with textural and sensory quality being closer to the white bread.
Genome-Wide Identification of MAPKK and MAPKKK Gene Family Members and Transcriptional Profiling Analysis during Bud Dormancy in Pear (Pyrus x bretschneideri)

The mitogen-activated protein kinase (MAPK) cascade consisting of three types of reversibly major signal transduction module (MAPKKK, MAPKK, and MAPK) is distributed in eukaryotes. MAPK cascades participate in various aspects of plant development, including hormone responses, cell division and plant dormancy. Pear is one of the most economically important species worldwide, and its yield is directly affected by dormancy. In this study, genome-wide identification of MAPKK and MAPKKK gene family members in Pyrus x bretschneideri and transcriptional expression analysis of MAPK cascades during pear dormancy were performed. We identified 8 MAPKKs (PbrMKKs) and 100 MAPKKKs (PbrMAPKKKs) in Pyrus using recent genomic information. PbrMAPKKs were classified into four subgroups based on phylogenetic analysis, whereas PbrMAPKKKs were grouped into 3 subfamilies (MEKK, Raf, and ZIK). Most PbrMAPKKKs and PbrMAPKKs in the same subfamily had similar gene structures and conserved motifs. The genes were found on all 17 chromosomes. The comprehensive transcriptome analysis and quantitative real-time polymerase chain reaction (qRT–PCR) results showed that numerous MAPK cascade genes participated in pear bud dormancy. The interaction network and co-expression analyses indicated the crucial roles of the MAPK member-mediated network in pear bud dormancy. Overall, this study advances our understanding of the intricate transcriptional control of MAPKKK-MAPKK-MAPK genes and provides useful information on the functions of dormancy in perennial fruit trees.
Prescribed-Time Input-to-State Stabilization of Normal Nonlinear Systems by Bounded Time-Varying Feedback

This paper studies the prescribed-time input-to-state stabilization problem of normal nonlinear systems. With the help of some key properties of a class of parametric Lyapunov equations, the prescribed-time input-to-state stabilization problem of normal nonlinear systems under matched uncertainties is studied. Some bounded time-varying controllers are proposed. It is shown that the closed-loop systems are prescribed-time input-to-state stable. Finally, the effectiveness of the established methods is illustrated by two physical systems.
Pergerakan Robot Lengan Pengambil Objek Dengan Sistem Perekam Gerak Berbasis Komputer

Teknologi robot telah digunakan dalam bidang otomasi industri, medis, pertanian, dan lain-lain. Umumnya robot terdiri dari dua tipe yaitu mobile dan non-mobile. Robot tipe mobile dapat berpindah dari satu posisi menuju ke posisi lain, sedangkan robot tipe non-mobile bersifat statis pada basis. Salah satu contoh dari robot tipe non-mobileialah berbentuk manipulator atau lengan. Pada penelitian ini dirancang lengan robot dengan sistem perekem gerak dalam mengambil objek. Lengan robot ini memiliki empat derajat kebebasan (4 Degree of Freedom). Prototype robot ini telah dilengkapi dengan gripper untuk mengambil objek. Adapun sistem kendali pada robot menggunakan sistem perekam koordinat yang ter-program di dalam Komputer menggunakan Cubic Trajectory. Percobaan robot lengan dilakukan di Laboratorium, dan objek menyesuaikan keadaan robot. Robot tersebut dikendalikan oleh program komputer berdasarkan pergerakan yang diinginkan, setelah itu komputer memberikan perintah kepada motor servo pada setiap joint. Adapun hasil percobaan menunjukkan bahwa robot lengan dapat dikendalikan secara manual oleh operator menggunakan komputer,Robot juga mampu ber-navigasi (pergerakan) otomatis berdasarkan rekam gerak yang ditentukan oleh operator.
A Non-Intrusive Residential Electric Bicycle Load Monitoring Method Based on Hybrid Feature Bitmap and DeiT

Taking into account energy management and fire safety, electric bicycles are one of the most significant household loads that require real-time sensing for nonintrusive load monitoring. V–I trajectories, power quantities, and harmonic characteristics are the basic selection in feature space for appliance identification. Based on the study of the charging mode of electric bicycles, this study expands the V–I trajectory into V-△I trajectory for gaining the load signature with multi appliances working simultaneously. We perform linear interpolation and pixelation to obtain a bitmap of the V-△I trajectory. Meanwhile, active and harmonic features are encoded and combined to form a hybrid feature bitmap, which is unique to compensate for the high harmonic feature loss caused by the pixelation of the V-△I trajectory. Furthermore, we trained the DeiT model on the self-built dataset and UK-DALE dataset and performed two experiments under single and superposition working conditions for electric bicycles. Our case results indicate that the DeiT model using hybrid feature bitmap offers better overall precision in the prediction of electric bicycles, against other deep convolutional neural networks.
Numerical Analyses on Seismic Behaviour of Concrete - filled Steel Tube Composite Columns Based on OpenSEES Program

In order to study seismic behaviour of concrete-filled steel tube composite columns under low-cyclic load, using finite element software OpenSEES, numerical simulation is carried out in this paper. The results indicate that OpenSEES can simulate well hysteretic curves and skeleton curves of concrete-filled steel tube composite columns under low-cyclic load, and the simulated peak load showed good agreement with the test results, with errors no more than 10%. The simulative hysteretic curve shapes are nearly similar to that of test results, and can describe degradation of their stiffness and strength in the cyclic loading process.
Label-Free Optical Detection of Pathogenic Bacteria and Fungi at Extremely Low Cell Densities for Rapid Antibiotic Susceptibility Testing

Antibiotic resistance is a rapidly expanding public health problem across the globe leading to prolonged hospital admissions, increased morbidity and mortality, and associated high healthcare costs. Effective treatment of bacterial infections requires timely and correct antibiotic administration to the patients which relies on rapid phenotyping of disease-causing bacteria. Currently, antibiotic susceptibility tests can take several days and as a result, indiscriminate antibiotic use has exacerbated the evolution and spread of antibiotic resistance in clinical and community settings. In order to address this problem, we have developed a novel optical apparatus that we called RUSD (Rapid Ultra-Sensitive Detection). RUSD is built around a hollow silica fiber and utilizes bacterial cells as spatial light modulators. This generates a highly sensitive modulation transfer function due to the narrow reflectivity angle in the fiber-media interface. We leveraged the RUSD technology to allow for robust bacterial and fungal detection. RUSD can now detect pathogenic cell densities in a large dynamic window (OD600 from ∼10−7 to 10−1). Finally, we can generate dose response curves for various pathogens and antimicrobial compounds within one to three hours by using RUSD. Our antibiotic- susceptibility testing (AST) assay that we call iFAST (in-Fiber-Antibiotic-Susceptibility-Testing) is fast, highly sensitive, and does not change the existing workflow in clinical settings as it is compatible with FDA-approved AST. Thus, RUSD platform is a viable tool that will expedite decision-making process in the treatment of infectious diseases and positively impact the antibiotic resistance problem in the long term by minimizing the use of ineffective antibiotics.
Engaging Teams: Missions, Management and Everyday Life

Drawing upon the extended symbolic interactionist tradition, this paper examines management activities in the context of office holders and teams. As researchers, if we wish to fully appreciate management in everyday life, then we must pay particular attention to teams, team creation and teamwork, for it is through people doing things together that organizational life is realized. Specifically, I examine the relevance of performance teams, legacy teams, legislated teams, mission-based teams, and the relevance of secrecy for enacting teams. Encouraging an attentiveness to perspectives and activities of office holders, this paper resists more structural renderings of organizational life and encourages researcher to attend to management in the making.