Miracle-dz/UniFAR-FTUs huggingface dataset:
Resolution of a Few Problems in the Application of Quasilinear Theory to Calculating Diffusion Coefficients in Heliophysics

A theory of quasilinear diffusion for obliquely propagating electromagnetic waves was developed in the 1960's and applied in the 1970's to model scattering of relativistic electrons by a prescribed distribution of waves. In the latter work, a transformation of variables, from wavevector space to the temporal frequency and tangent of the wave normal angle, was used so that simple Gaussian functions of frequency and tangent of the wave normal angle could be multiplied together to define the distribution of wave power, although arbitrary distributions of power in these two variables is also permitted. Finally, in 2005, previous work was consolidated and has been widely used in heliophysics studies that require computation of quasilinear diffusion coefficients. Here it is shown that this transformation is inppropriate when the precise wave vector distribution is known. The correct transformation is derived and used to produce diffusion coefficients that can differ by orders of magnitude from those computed using the inappropriate transformation. The differences are largest when the distribution of wave power extends to wave normal angles near the resonance cone. When the ratio of the plasma frequency to the gyrofrequency is large, only low energies (keV) are affected, but as the ratio decreases higher energies (MeV) also show differences. It is also shown that the derivation from the 1960's uses a notation that results in the diffusion coefficients depending on the distribution of wave power with respect to the wave azimuthal angle whereas there should be no such dependence.
An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification

In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset’s mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA’s performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%.
Characterization of Vadose Zone Sediment: RCRA Borehole 299-E33-338 Located Near the B-BX-BY Waste Management Area

This report was revised in September 2008 to remove acid-extractable sodium data from Table 4.8. The sodium data was removed due to potential contamination introduced during the acid extraction process. The rest of the text remains unchanged from the original report issued in June 2003. The overall goals of the of the Tank Farm Vadose Zone Project, led by CH2M HILL Hanford Group, Inc., are: 1) to define risks from past and future single-shell tank farm activities, 2) to identify and evaluate the efficacy of interim measures, and 3) to aid via collection of geotechnical information and data, future decisions that must be made by the U.S. Department of Energy (DOE) regarding the near-term operations, future waste retrieval, and final closure activities for the single-shell tank waste management areas. For a more complete discussion of the goals of the Tank Farm Vadose Zone Project, see the overall work plan, Phase 1 RCRA Facility Investigation/Corrective Measures Study Work Plan for the Single-Shell Tank Waste Management Areas (DOE 1999). Specific details on the rationale for activities performed at the B-BX-BY tank farm waste management area are found in CH2M HILL (2000).
Genome-Wide Association Studies for Body Conformation Traits in Korean Holstein Population

Simple Summary Holstein has been the most widely used dairy cattle breed in the Korean Peninsula since its introduction in 1885. Since the formal dairy herd improvement program was initiated in 1979, Holsteins have been extensively selected for Korean environments. While body conformation traits in dairy cattle are not typically considered direct economic traits for animal breeders, they are associated with various production traits, including health status, milking ability, and overall cow longevity. Consequently, these traits are recognized as significant factors in dairy cattle breeding programs worldwide. The dairy industry relies on consistent milk and calf production from cows, which presents challenges in today’s context. Conducting a genome-wide association study (GWAS) for body conformation traits in Korean Holstein cattle, using a large population, may enhance the power of detecting and mapping novel genes associated with these traits. To the best of our knowledge, this study represents the first-ever attempt to conduct a GWAS for body conformation traits in the Korean Holstein population. Abstract The objective of this study was to identify quantitative trait loci (QTL) and nearby candidate genes that influence body conformation traits. Phenotypic data for 24 body conformation traits were collected from a population of 2329 Korean Holstein cattle, and all animals were genotyped using the 50 K Illumina bovine SNP chip. A total of 24 genome-wide significant SNPs associated with 24 body conformation traits were identified by genome-wide association analysis. The selection of the most promising candidate genes was based on gene ontology (GO) terms and the previously identified functions that influence various body conformation traits as determined in our study. These genes include KCNA1, RYBP, PTH1R, TMIE, and GNAI3 for body traits; ANGPT1 for rump traits; MALRD1, INHBA, and HOXA13 for feet and leg traits; and CDK1, RHOBTB1, and SLC17A1 for udder traits, respectively. These findings contribute to our understanding of the genetic basis of body conformation traits in this population and pave the way for future breeding strategies aimed at enhancing desirable traits in dairy cattle.
Relationship Between Depression and Falls Among Nursing Home Residents: Protocol for an Integrative Review

Background Aging exposes individuals to new health disorders and debilitating chronic diseases, yet most older adults, even in functional decline, do not want to leave their homes. Nevertheless, for many, institutionalization in a nursing home (NH) may become essential to ensure their continued safety and health. Depression is one of the most common psychiatric disorders among older adults, especially among those who are institutionalized. Depressed NH residents face a high risk of future functional decline and falls, decreasing their quality of life. The relationship between depression and falls is complex and bidirectional. Previous reviews have focused on home-dwelling older adults or explored the relationship between antidepressant drugs and falls. To the best of our knowledge, no integrative literature reviews have explored the relationship between depression and falls among NH residents. Objective Analyze studies on the relationship between depression and falls among NH residents. Methods We will conduct an integrative literature review of published articles in relevant scientific journals on the relationship between depression and depressive symptomatology and falls among NH residents. As usually defined, we will consider NH residents to be people aged 65 years and older who can no longer live safely and independently in their homes. We will also consider older adults on short-term stays in an NH for rehabilitation after hospital discharge. Retrieved articles will be screened for eligibility and analyzed following previously reported steps. The most pertinent bibliographical databases will be examined for qualitative, quantitative, and mixed methods studies, from inception until August 31, 2023, thus ensuring that all relevant literature is included. We will also hand-search the bibliographies of all the relevant articles found and search for unpublished studies in any language. If appropriate, we will consider conducting a meta-analysis of the studies retrieved. Results A first round of data collection was completed in March 2023. We retrieved a total of 2276 references. A supplementary literature search to ensure the most up-to-date evidence is ongoing. We anticipate that the review will be completed in late September 2023, and we expect to publish results at the end of December 2023. Conclusions This integrative review will increase knowledge and understanding of the complex relationship between depression and falls in NH environments. Its findings will be important for developing integrated, multidisciplinary models and care recommendations, adaptable to each NH resident’s situation and health status, and for creating preventive interventions to help them maintain or recover optimal health stability. International Registered Report Identifier (IRRID) DERR1-10.2196/46995
Non-gendered pronoun processing: an investigation of the gender non-specific third person singular pronoun ‘TA’ in Chinese

ABSTRACT This study investigated the processing of the Chinese nongendered third-person singular pronoun, “TA,” in a series of self-paced reading experiments. We begin by investigating the perceived appropriateness of TA using a novel implementation of the modified maze task. We then contrasted reading latencies for TA and male- and female-gender pronouns in reference to antecedents with varying stereotypical gender (e.g., occupation terms) and definitional gender (e.g., kinship terms). In our analysis, we assessed several means of operationalizing stereotypical gender information. Optimal model performance was achieved with a continuous measure that accounted for individual differences in gender perception, suggesting the involvement of a probabilistic component. Results for reading latencies and perceived appropriateness of TA support previous findings from discourse analysis that TA is not entirely gender-neutral but rather has nuanced contexts of use in modern Chinese written discourse.
Ground Vehicle Navigation Using Magnetic Field Variation

Abstract : The Earth's magnetic field has been the bedrock of navigation for centuries. The latest research highlights the uniqueness of magnetic field measurements based on position due to large scale variations as well as localized perturbations. These observable changes in the Earth's magnetic field as a function of position provide distinct information which can be used for navigation. This dissertation describes ground vehicle navigation exploiting variation in Earth's magnetic field using a self-contained navigation system consisting of only a magnetometer and magnetic field maps. In order to achieve navigation, effective calibration enables repeatable magnetic field measurements from different vehicles and facilitates mapping of the observable magnetic field as a function of position. A new modified ellipsoid calibration technique for strapdown magnetometers in large vehicles is described, as well as analysis of position measurement generation comparing a multitude of measurement compositions using existing and newly developed likelihood techniques. Finally, navigation solutions are presented.
Digitizing weather observations from World War II US naval ship logbooks

The number and coverage of weather observations over the oceans were considerably reduced during World War II (WW2) due to disruptions to normal trade routes. The observations that do exist for this period are often unavailable to science as they are still only available as paper records or scanned images. We have rescued the detailed hourly weather observations contained in more than 28,000 logbook images of the US Navy Pacific Fleet stationed at Hawai'i during 1941–1945 to produce a dataset of more than 630,000 records. Each record contains the date and time, positional information and several meteorological measurements, totalling more than 3 million individual observations. The data rescue process consisted of a citizen science project asking volunteers to transcribe the observations from the available images, followed by additional quality control processes. This dataset not only contains hourly weather observations of air temperature, sea surface temperature, atmospheric pressure, wind speed and wind direction, mainly in the Pacific Ocean but also includes some observations from the Atlantic and Indian Oceans. The new observations are found to be of good quality by inter‐comparing independent measurements taken on ships travelling in convoy and by comparing with the 20th‐Century Reanalysis. This dataset provides invaluable instrumental weather observations at times and places during WW2, which fill gaps in existing reconstructions.
A Specific Carbohydrate Diet Virtual Teaching Kitchen Curriculum Promotes Knowledge and Confidence in Caregivers of Pediatric Patients with Inflammatory Bowel Disease

Diet-based approaches such as the Specific Carbohydrate Diet (SCD) have proposed health benefits for patients with Inflammatory Bowel Disease (IBD). Despite its potential effectiveness, patients and caregivers identified barriers towards implementing the SCD, and a majority expressed interest in formal education surrounding the SCD. This study aimed to determine the impact of a virtual teaching kitchen curriculum on caregivers’ knowledge and perspectives on implementing the SCD. Inclusion criteria included pediatric patients with IBD aged 3–21 years and their caregivers. Participants should have fewer than 12 months of experience with the SCD or have no experience with the SCD but with an interest in learning it. Twenty-three caregivers took part in a 90-min virtual teaching kitchen curriculum and completed pre- and post-session surveys. Caregivers had statistically significant increases in total curriculum scores (p < 0.0001) as well as increases in all curricular elements post-curriculum teaching. Caregivers indicated that they plan to apply the newly acquired recipes and cooking concepts and appreciated the encouragement and support they received during the course. Curricular strengths identified included the innovative multimodal curriculum structure and professional and community support. IBD centers can use this pilot study to create or expand SCD and other nutritional curricula for the IBD community.
Predicting Student Performance Using Mental Health and Linguistic Attributes with Deep Learning

Early prediction of students' academic performance is a critical research topic in educational data mining. ML models have been developed to predict academic performance, but it has become difficult to extract high-quality handcrafted attributes due to the large dataset. To solve this issue, a Deep Neural Network (DNN) was presented to automatically extract attributes from students' multi-source data. However, it didn’t deal with the student’s mental health and their mood changes (i.e., physiological attributes), which were also essential to increase the prediction performance. Thus, this article proposes a Student Accomplishment prediction using the Distinctive Deep Learning (SADDL) model. First, the student’s academic and demographic attributes are collected along with the posts shared by them about academic performance on online social networks. A Latent Dirichlet Allocation (LDA) is applied to extract the physiological attributes from the online posts data. Then, all the attributes of students’ data are given to the SADDL network, which comprises three modules: (i) a Long Short-Term Memory (LSTM) module to learn the temporal dependency; (ii) a multidimensional Deep Convolutional Neural Network (DCNN) module to learn the correlation attributes and (iii) a Multi-Layer Perceptron (MLP) module to predict the students’ academic performance. Finally, the experimental results show that the SADDL can predict students’ academic performance with an accuracy of 91.71% while using 80% training and 20% test dataset, which is 9.91% improved than the existing ML models. Similarly, the SADDL has 89.3% while using 70% training and 30% test dataset, which is 10.36% higher than the existing ML models
Nominalization in high- and low-rated L2 undergraduate writing

Nominalization in undergraduate writing Nominalizations, or nouns derived from verbs or adjectives through suffixes, are a pervasive characteristic feature of written academic discourse. To better understand the nature of nominalization in L2 student writing and its relation to assessment in first-year writing (FYW) contexts, we report findings of a comparative corpus-based analysis of nominalization use in university student papers. Data consist of high-rated (A graded) and low-rated (C graded) L2 undergraduate research papers from multiple sections of an FYW course for international and multilingual students. Nominalizations were examined in terms of frequencies, unique types, abstract/concrete and human/non-human categories, nominal stance types, and modification types. Results reveal no statistically significant differences in the examined classifications. However, the small effect sizes for certain categories point to subtle differences between the two groups, which together might have affected the instructors’ evaluations of text quality. We conclude with suggestions for incorporating nominalization instruction in English for Academic Purposes writing courses.
Experimental study on rolling segregation of cement stabilized macadam

Indoor simulated mixing building storage bin unloading, forming conical piles. The effects of falling distance, mixing time and water content on rolling segregation of cement-stabilized macadam were analyzed by three indexes, including repose Angle of pile, key sieve passing rate and cement dosage. The results show that the segregation of mixture can be increased significantly with the increase of drop distance. The separation degree of the mixture can be effectively controlled by controlling the unloading distance within 70 cm. Mixing time has little effect on the Angle of rest and gradation segregation of the mixture, but has significant effect on the distribution uniformity of cement. Indoor mixing time should reach more than 45 s. When the mixing water content is less than the optimal water content, the segregation of the mixture is serious. When the moisture content increased to 1.5 %, the mixture showed obvious bleeding phenomenon. The mixture shall not be lower than the optimal moisture content before the crushing is completed, and the moisture content increase shall not exceed 1.5 % of the optimal moisture content as far as possible.
Bioinspired Rigid-Soft Hybrid Origami Actuator With Controllable Versatile Motion and Variable Stiffness

Conventional soft pneumatic actuators (SPAs) are made of soft materials that facilitate safe interaction and adaptability. In positioning and loading tasks, however, SPAs demonstrate limited performance. In this article, we extend the current designs of SPAs upon integrating a tendon-driven parallel mechanism into a pneumatic origami chamber, inspired by the performances and structures of vertebrates. The inner rigid/outer soft actuator exploits the advantages of both, parallel mechanisms to achieve precise, versatile motion, and SPAs, to form a compliant, modular structure. With the antagonistic actuation of tendons-pulling and air-pushing, the actuator can exhibit multimode motion, tunable stiffness, and load-carrying maneuvers. Kinematic and quasi-static models are developed to predict the behavior and to control the actuator. Using readily accessible materials and fabrication methods, a prototype was built, on which validation experiments were conducted. The results prove the effectiveness of the model, and demonstrate the motion and stiffness characteristics of the actuator. The design strategy and comprehensive guidelines should expand the capabilities of soft robots for wider applications, and facilitate the development of robots with rigid-soft hybrid structures.
Beyond Money: Wealth and Wellbeing of the Business Family

This article offers a conceptual discussion about the relationship between wealth and wellbeing of the business family. It provides a psychological perspective on ownership that explains the effect of wealth on individual and collective dimensions relevant to business families, namely psychological ownership, socioemotional wealth, and ownership competences. The study provides an integrative framework and offers propositions that illustrate the theoretical and practical implications of the model as regards the relationship between wealth and wellbeing of the business family.