Miracle-dz/UniFAR-FTUs huggingface dataset:
Writing Workouts to Develop Common Core Writing Skills

This book is a "best of" collection of tips and exercises to help nurture young writers and strengthen core skills. Since each activity has been tried and tested in thousands of classrooms, you'll find successful methods for turning even the most reluctant students into effective, powerful writers. Writing proficiency is more important than ever, especially with the demands of standardized testing and the rigors of new standards impacting our schools. This classroom-tested manual, created from a combination of the author's extensive in-class experience and acclaimed research in neural science and developmental biology, addresses all facets of writing competency. The content links to common core curriculum elements in state language arts standards for every state. Written by experienced educator and author Kendall Haven, the work features 13 innovative writing tips and 30 engaging activities for helping students become better writers. The first part of the book covers writing hints and techniques, while the second half contains core content activities for coaxing the best writing out of your students. A short introductory section lays out the five steps of effective student writing. Lastly, the text reveals how teachers—even those from non-literary backgrounds—can successfully teach and grade writing.
Living under more than one sun: The Nikkei Diaspora in the Americas

ABSTRACT Currently more than 2.5 million Americans living on the South and North American continents are Nikkei or descendants of Japanese migrants. The history of their forefathers’ emigration from Japan and the meaning of ethnicity and citizenship while living in the diaspora has attracted considerable scholarly attention, which was renewed by the recent wave of sojourner migration by Latin Americans of Japanese origin into Japan. Virtually nothing is known so far about the impact of “return migration” and the “returnees’ remigration” on the diaspora in Latin America. To what degree have ideas of ethnic or political loyalty, of national and cultural identity, been shifting one way or the other due to the increased proximity to their ancestors’ place of origin? And how have hostile or discriminatory treatment by homeland and hostland societies impacted on the collective image of the Nikkei in Latin America? The Nikkei experience of living abroad bears the potential for rethinking the meaning of diaspora. As the return migration to the land of their ancestors has not fulfilled the postulated ‘negation of a diaspora’ (Clifford 1994), it has squared the sensation of being diasporic in the sense of being displaced twice and having multiple relationships with distinct nations which are neither just homeland nor hostland. Based on multi-sited fieldwork in Japan, Argentina, Bolivia, and Paraguay, I analyze the shifting functions of the pillars of migrant communities, i.e. family household, school and hometown associations. I argue that the Nikkei are entangled in a “squared diaspora” in which the juxtaposition of homeland and hostland itself becomes questionable, instable and fluctuating.
Estimation of Sediment Transport Rate of Karun River (Iran)

Several types of sediment transport equations have been developed for estimation of the river sediment materials during the past decades. The estimated sediment from these equations is very different, especially when they applied for a specific river. Therefore, choice of an equation for estimation of the river sediment load is not an easy task. In this study 10 important sediment transport equations namely; Meyer-Peter and Muller (1948), Einstein (1950), Bagnold (1966), Engelund and Hansen (1972), Toffaleti (1969), Yang (1996), Van Rijn (2004), Wiuff (1985), Samaga et al. (1986) and Beg (1995) are used to estimate sediment load of the Karun River in Iran. The estimated sediment load compared with the measured field data by using statistical criteria such as root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R2). Results showed that Engelund and Hansen formula can provide reliable estimates of sediment load of the Karun River which have high suspended sediment load concentration with RMSE of 3725 ton/day, MAE of 1058.82 ton/day and R2 of 0.41. Bagnold and Wiuff formulas estimated the total sediment load 280 % and 700% more than the measured values and the Van Rijn, Tofaleti and Bagnold formulas estimated the sediment load 99 %, 71% and 93 % lower than the measured values, respectively. The comparison indicated that Samaga, Einstein, Tofaleti and Yang equations with low accuracy are not suitable for estimation of sediment load of the Karun River. The main reason for this difference is related to fact that the Karun River carries fine sediment (wash load) which these equations not considered it.
The importance of choosing precipitation datasets

Precipitation data are important for hydrometeorological analyses, yet there are many ways to measure precipitation. The impact of station density analysed by the current study by comparing measurements from the Missouri Mesonet available via the Missouri Climate Center and Community Collaborative Rain, Hail, and Snow (CoCoRaHS) measurements archived at the program website. The CoCoRaHS data utilize citizen scientists to report precipitation data providing for much denser data resolution than available through the Mesonet. Although previous research has shown the reliability of CoCoRaHS data, the results here demonstrate important differences in details of the spatial and temporal distribution of annual precipitation across the state of Missouri using the two data sets. Furthermore, differences in the warm and cold season distributions are presented, some of which may be related to interannual variability such as that associated with the El Niño and Southern Oscillation. The contradictory results from two widely‐used datasets display the importance in properly choosing precipitation data that have vastly differing temporal and spatial resolutions. With significantly different yearly aggregated precipitation values, the authors stress caution in selecting 1 particular rainfall dataset as conclusions drawn could be unrepresentative of the actual values. This issue may be remediated by increased spatiotemporal coverage of precipitation data.
Room-Temperature SO2 Gas Sensing Properties of the Ag@GO Heterogeneous Nanomaterial

The study proposes SO2 gas sensor based on a quartz crystal microbalance (QCM) coated with Ag@GO heterogeneous nanomaterial at room temperature. The heterogeneous nanomaterial was synthesized by hydrothermal method at 160 C for 90 min. The structure and morphological characteristics were analyzed by using field-emission scanning electron microscope (FE-SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Raman spectroscopy. The Ag@GO heterogeneous nanomaterial was dispersed in water and coated on an Au electrode of QCM via spray coating method. The fabricated sensor exhibits a good response to SO2 in the concentration range of 2.5-15 ppm. The highest gas sensitivity of the sensor is 8.98 Hz/ppm corresponding to the adsorbed SO2 mass density of 2.38 μg. cm-2. The gas sensing results indicated that the developed sensor can be applied for monitoring, warning, and controlling SO2 in the environment. Moreover, the gas adsorption properties of Ag@GO nanomaterial can also be considered for a filter to remove toxic gases in respirators and environmental treatment.
Accurate Capacity Prediction and Evaluation with Advanced SSA-CNN-BiLSTM Framework for Lithium-Ion Batteries

Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health indicator and proposes a data-driven method for capacity prediction. Specifically, this method mainly utilizes Convolutional Neural Network (CNN) for automatic feature extraction from raw data and combines it with the Bidirectional Long Short-Term Memory (BiLSTM) algorithm to realize the capacity prediction of LIBs. In addition, the sparrow search algorithm (SSA) is used to optimize the hyper-parameters of the neural network to further improve the prediction performance of original network structures. Ultimately, experiments with a public dataset of batteries are carried out to verify and evaluate the effectiveness of capacity prediction under two temperature conditions. The results show that the SSA-CNN-BiLSTM framework for capacity prediction of LIBs has higher accuracy compared with other original network structures during the multi-battery cycle experiments.
Unveiling macrophage diversity in myocardial ischemia-reperfusion injury: identification of a distinct lipid-associated macrophage subset

Background and objective Macrophages play a crucial and dichotomous role cardiac repair following myocardial ischemia-reperfusion, as they can both facilitate tissue healing and contribute to injury. This duality is intricately linked to environmental factors, and the identification of macrophage subtypes within the context of myocardial ischemia-reperfusion injury (MIRI) may offer insights for the development of more precise intervention strategies. Methods Specific marker genes were used to identify macrophage subtypes in GSE227088 (mouse single-cell RNA sequencing dataset). Genome Set Enrichment Analysis (GSEA) was further employed to validate the identified LAM subtypes. Trajectory analysis and single-cell regulatory network inference were executed using the R packages Monocle2 and SCENIC, respectively. The conservation of LAM was verified using human ischemic cardiomyopathy heart failure samples from the GSE145154 (human single-cell RNA sequencing dataset). Fluorescent homologous double-labeling experiments were performed to determine the spatial localization of LAM-tagged gene expression in the MIRI mouse model. Results In this study, single-cell RNA sequencing (scRNA-seq) was employed to investigate the cellular landscape in ischemia-reperfusion injury (IRI). Macrophage subtypes, including a novel Lipid-Associated Macrophage (LAM) subtype characterized by high expression of Spp1, Trem2, and other genes, were identified. Enrichment and Progeny pathway analyses highlighted the distinctive functional role of the SPP1+ LAM subtype, particularly in lipid metabolism and the regulation of the MAPK pathway. Pseudotime analysis revealed the dynamic differentiation of macrophage subtypes during IRI, with the activation of pro-inflammatory pathways in specific clusters. Transcription factor analysis using SCENIC identified key regulators associated with macrophage differentiation. Furthermore, validation in human samples confirmed the presence of SPP1+ LAM. Co-staining experiments provided definitive evidence of LAM marker expression in the infarct zone. These findings shed light on the role of LAM in IRI and its potential as a therapeutic target. Conclusion In conclusion, the study identifies SPP1+ LAM macrophages in ischemia-reperfusion injury and highlights their potential in cardiac remodeling.
Direction-Specific Changes in Trunk Muscle Synergies in Individuals With Extension-Related Low Back Pain

Background Identifying altered trunk control is critical for treating extension-related low back pain (ERLBP), a common subgroup classified by clinical manifestations. The changed coordination of trunk muscles within this group during particular trunk tasks is still not clearly understood. Objectives The objective of this study is to investigate trunk muscle coordination during 11 trunk movement and stability tasks in individuals with ERLBP compared to non-low back pain (LBP) participants. Methods Thirteen individuals with ERLBP and non-LBP performed 11 trunk movement and stability tasks. We recorded the electromyographic activities of six back and abdominal muscles bilaterally. Trunk muscle coordination was assessed using the non-negative matrix factorization (NMF) method to identify trunk muscle synergies. Results The number of synergies in the ERLBP group during the cross-extension and backward bend tasks was significantly higher than in the non-LBP group (p<0.05). The cluster analysis identified the two trunk synergies for each task with strikingly similar muscle activation patterns between groups. In contrast, the ERLBP group exhibited additional trunk muscle synergies that were not identified in the non-LBP group. The number of synergies in the other tasks did not differ between groups (p>0.05). Conclusion Individuals with ERLBP presented directionally specific alterations in trunk muscle synergies that were considered as increased coactivations of multiple trunk muscles. These altered patterns may contribute to the excessive stabilization of and the high frequency of hyperextension in the spine associated with the development and persistence of ERLBP.
Nanotechnology-based delivery systems to overcome drug resistance in cancer

Abstract Cancer nanomedicine is defined as the application of nanotechnology and nanomaterials for the formulation of cancer therapeutics that can overcome the impediments and restrictions of traditional chemotherapeutics. Multidrug resistance (MDR) in cancer cells can be defined as a decrease or abrogation in the efficacy of anticancer drugs that have different molecular structures and mechanisms of action and is one of the primary causes of therapeutic failure. There have been successes in the development of cancer nanomedicine to overcome MDR; however, relatively few of these formulations have been approved by the United States Food and Drug Administration for the treatment of cancer. This is primarily due to the paucity of knowledge about nanotechnology and the fundamental biology of cancer cells. Here, we discuss the advances, types of nanomedicines, and the challenges regarding the translation of in vitro to in vivo results and their relevance to effective therapies.
Phases or regimes? Revisiting NDVI trends as proxies for land degradation

One of the main challenges in land degradation assessment is that a rigorous and systematic approach to addressing its complex dynamics is still missing. The development and application of operative tools at regional and global scales remain a challenge. Land degradation is usually defined as a long‐term decline in ecosystem function and productivity. Due to its temporal and spatial resolution as well as data availability, the use of time series of spectral vegetation indexes obtained from satellite sensors has become frequent in recent studies in this field. Slope of linear trends of the normalized difference vegetation index is usually considered an accurate indicator and is widely used as a proxy for land degradation. Yet this method is built on a number of simplifying conceptual and methodological assumptions that prevent capturing more complex dynamics, such as cyclic or periodic behaviors. Our aim was to examine the limitations associated with using linear normalized difference vegetation index trends as proxies for land degradation by comparing outcomes with an alternative methodological procedure based on wavelet autoregressive methods. We explored these issues in 5 case studies from Africa and South America. We observed that trend explained a marginal portion of total temporal variability, whereas monotonic functions, such as linear trends, were unable to capture dynamics that were non‐unidirectional, resulting in misinterpretation of actual trends. Wavelet autoregressive method results were encouraging as a step towards the application of more accurate methods to provide sound scientific information of land degradation and restoration.
Assessing Teacher Practices Related to Precision Agriculture in Secondary Agriculture Education

Agricultural education was designed to reflect the agriculture industry, and since the recent increase in technology use in the industry, little research has been done to investigate what agricultural technologies are used in secondary agriculture classrooms. Secondary agriculture instructors in Alabama and Illinois participated in this study and provided descriptive data about their personal characteristics and their decision to incorporate precision agriculture, as well as barriers that prevent them from incorporating precision agriculture concepts. This study identifies the curriculum involving precision agriculture that is currently being taught and gains insight into teachers’ decisions to integrate precision agriculture in their classrooms. Teachers indicated the importance and relevance of precision agriculture, but only half of the participants incorporate related concepts into their curricula. A Chi Square test revealed no significant relationships between the personal characteristics of teachers and their decision to incorporate precision agriculture concepts. The most important topics in precision agriculture were identified by participants as: GPS, Soil Sampling/Land Management and Genetic Modification. Teachers indicated a need for professional development or teacher education focused on precision agriculture in multiple fashions and supports the need for similar education in the agriculture industry.
CFD Study on the Windage Power Loss of High Speed Gear

The windage power loss is main factor of the power loss in a high speed gearbox. CFD simulation method has been developed for a detailed understanding of the physical mechanism of windage power loss. Comparisons are made with experimental data from the open literature. The CFD results are in good agreement with experiment. The study focuses on the influence of the shroud on the windage power loss for rotating speed ranging from 5000rpm to 7000rpm. It is critical to capture the energy efficiency prize by shrouding the gear. Smooth shroud and grooved shroud have been selected to reduce the windage power loss. It is observed that the better performance occurs with shroud, whereas it is found that the grooved shroud provides a considerable increase in energy efficiency at about 10% over the smooth shroud.
WOMEN’S ATTACHMENT AND CHILDHOOD EXPERIENCES OF RECREATIONAL PARKS IN KLANG VALLEY, MALAYSIA

Childhood experiences influence attachment to recreational parks and green spaces in women’s adulthood. Studies have shown that the adults’ frequency of visiting recreational parks is correlated with the frequent visits during their childhood. This study examines women’s attachment and their childhood experiences. Questionnaire surveys were distributed to 510 women (as park users) involving two public parks namely Bukit Kiara and Bukit Jalil Recreational Parks. Results show majority of the respondents spent their leisure time in the parks for relaxing with family members. Most of the women were attached to the greenery of the landscape in the parks, as they associated the recreational park with relaxation and peaceful. The women’s childhood experiences with nature were identified as having a memorable family time, which was correlated with their positive attachment of the recreational park as adults. The findings can guide the park managers, planners and designers in providing a park that is responsive to the women’s needs and demands that can contribute to their emotional and social well-being.
Self-avatar: Monocular 3D human reconstruction from RGB image

3D human position and shape estimate are crucial in many computer vision applications. Despite the fact that there are numerous deep learning techniques designed to handle this problem, they frequently only use training networks with RGB images from a single point of view. In this paper, a unique approach to solve this issue is proposed by combining a regression-based multi-view picture learning loop with an optimization-based multi-view model. This is because some public datasets are collected by multi-view camera systems. A parameterized human body model's position and shape parameters are initially deduced by a convolutional neural network (CNN) from multi-view photos. This work then introduces an enhanced multi-view optimization method called MV-SMPLify, which aligns the SMPL model with multi-view images by using the regressed pose and shape as beginning values. Following that, the CNN model's training can be monitored using the optimum parameters. The Self-avatar project as a whole is a self-supervised framework that combines the advantages of both the CNN method and the optimization-based strategy. Additionally, the use of multi-view photos improves thorough supervision during training. This methodology outperforms earlier methods in a variety of ways, according to qualitative and quantitative testing using open datasets.
One-Dimensional Numerical Analysis of a 5 kN Liquid Propellant Rocket Engine Combustion Chamber Using Ethanol/LOX

Liquid bipropellant propulsion systems are the most widely used in launch rockets due to their high thrust and specific impulse, increased operational and storage safety, and better combustion control when compared to solid propellant systems. In recent years, studies on green propulsion have become increasingly common due to the advantages it offers, such as lower pollutant emissions, low toxicity, reduced environmental impact, higher operational safety, lower propellant storage and handling costs, improved safety, and energy independence. Green propellants are cleaner and more sustainable compared to traditional chemical propellants. This work presents a numerical study of a 5 kN liquid rocket engine combustion chamber, which uses Ethanol and LOX as propellants. For the simulations, a one-dimensional, reactive, multiphase, and steady-state mathematical model of combustion in a combustion chamber was used. The model considers that the chemical reaction is faster than droplet vaporization, so that the propellant vapors mix and react instantaneously. The model also accounts for combustion at subcritical pressures, non-uniform fuel and oxidizer droplet size distributions, droplet preheating, heat losses from gas to chamber walls and droplets, different droplet and gas velocities, as well as the effect of deformation on droplet drag. Discrete portions of droplets with characteristic mean diameters are considered to represent the Rosin-Rammler distribution function, thus allowing the estimation of the vaporization length of a bipropellant spray under various conditions. The combustion products include 5 main species (CO 2 , CO, H 2 O, H 2 , and O 2 ) and 3 secondary species (H, OH, and O). The obtained results allow evaluating the influence of pressure, propellant temperatures, equivalence ratio, number of propellant droplet portions, and Rosin-Rammler parameters on the complete vaporization distance. Theoretical values of vaporization distance and temperatures were determined and compared to data from the European project PERSEUS.