Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Validation of e-Books during the Post-Pandemic to Improve Attitude towards Environmental Care in Case of Indonesia

— Indonesia has natural diversity in the form of tropical rain forests, the sea, flora, and fauna. Information technology adaptation is needed considering its development and use is getting higher, especially during the pandemic and post-pandemic periods. Through information technology in the form of E-books based on local wisdom, an attitude of caring for the environment grows. This research aims to combine information technology, environmental-based science learning, and local wisdom in growing students' environmental care character. Through the use of e-books based on Google's website as information technology in implementing a green science learning model that is oriented to local wisdom. This study develops and validates an e-book based on local wisdom through the ADDIE research design. The results of the analysis and discussion are that the development of an e-book based on Google's website with a green science model oriented to local wisdom is declared valid and feasible to use with an average percentage of 90.74% from the three experts. Then the results of student responses also showed a positive response from students with a percentage of 90.29%. The application of google site-based e-books in the green science model oriented to local wisdom in learning can increase the growth of environmental care characters for junior high school students as evidenced by the results of multiple regression analysis of significance 0.00 below 0.05 .
Culturally Responsive Computing for American Indian Youth: Making Activities With Electronic Textiles in the Native Studies Classroom

By providing access to hands-on activities and the physical and digital tools necessary to complete them, maker activities encourage cross-disciplinary, interest-driven learning and problem solving in schools. However, maker movement efforts to broaden participation into computer science have largely ignored Indigenous populations. In this dissertation, I examine how electronic textiles (e-textiles) materials connects to the heritage craft practices found in many Indigenous communities. By design, e-textiles materials combine low-tech craft practices like sewing with high-tech engineering and programming. Framing learning computing within these two distinct but overlapping cultural contexts provides youth will a familiar context in which to learn something new (programming), promotes positive identity development, and fosters connections across multiple dimensions of youth’s lives. At the core of this work is design-based research into the development and implementation of a three-week electronic textiles unit in gender-segregated Native Studies class with American Indian youth (12-14 years old) at a charter school located on tribal lands in the Southwest. This unit was implemented four times over the course of the school year. Findings highlight how different groups of students (American Indian girls and American Indian boys) engaged with e-textiles activities and how their perspectives on computing developed through participation in the unit. In addition, the teacher’s perspective on integrating digital technologies in the Native Studies classroom is explored within the context of contemporary Federal Indian educational policy and practice. This work makes three significant contributions to ethnography, computing education, and American Indian education. First, it proposes a new methodology through the integration of ethnography with design-based research and critical Indigenous research approaches. Second, it contributes to the emerging field of culturally responsive computing by exploring what happens when computing moves beyond the screen and into the tangible realm. Third, it furthers our understandings of the role of digital technologies in American Indian education, with a particular focus on how making activities might contribute to increased educational sovereignty for Indigenous peoples throughout the United States. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Education First Advisor Yasmin B. Kafai Second Advisor John L. Jackson, Jr. This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/1995
Assessment of Probability Defaults Using K-Means Based Multinomial Logistic Regression

—Classification analysis is a key and easy tool in machine learning and prediction. Because of the large amount of data and the need to convert this data into useful information and knowledge, machine learning has gotten a lot of attention in the information industry and also in society because of the large amount of data and the issues that come with it. In this paper, a K-Means based Multinomial Logistic Regression (MLR) prediction algorithm is used for evaluating the performance of Probability Defaults (PD), and suggestions are made to improve financial status. The necessary information about the members of PD has been collected from the UCI machine learning repository. The parameters are chosen for the study using the feature selection method. The research goal is to find default risk probabilities and they are assessed by accuracy, RMSE (Root Mean Squared Error), error rate, and time. K-means based Multinomial Logistic Regression (MLR) significantly outperforms other classifier models. Assessment of PD will have an impact on the financial industry.
Influence of climate dynamics and liming on physicochemical soil properties and crop- rotation productivity of North-Western Polissya in Ukraine

© 2022 by the authors. Licensee Soil Science Society of Poland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY NC ND 4.0) license (https://creativecommons.org/licenses/by-nc-nd/4.0/). * Infl uence of climate dynamics and liming on physicochemical soil properties and croprotation productivity of North-Western Polissya in Ukraine
Anti-Inflammatory Effects of (3S)-Vestitol on Peritoneal Macrophages

The isoflavone (3S)-vestitol, obtained from red propolis, has exhibited anti-inflammatory, antimicrobial, and anti-caries activity; however, few manuscripts deal with its anti-inflammatory mechanisms in macrophages. The objective is to elucidate the anti-inflammatory mechanisms of (3S)-vestitol on those cells. Peritoneal macrophages of C57BL6 mice, stimulated with lipopolysaccharide, were treated with 0.37 to 0.59 µM of (3S)-vestitol for 48 h. Then, nitric oxide (NO) quantities, macrophages viability, the release of 20 cytokines and the transcription of several genes related to cytokine production and inflammatory response were evaluated. The Tukey–Kramer variance analysis test statistically analyzed the data. (3S)-vestitol 0.55 µM (V55) lowered NO release by 60% without altering cell viability and diminished IL-1β, IL-1α, G-CSF, IL-10 and GM-CSF levels. V55 reduced expression of Icam-1, Wnt5a and Mmp7 (associated to inflammation and tissue destruction in periodontitis) and Scd1, Scd2, Egf1 (correlated to atherosclerosis). V55 increased expression of Socs3 and Dab2 genes (inhibitors of cytokine signaling and NF-κB pathway), Apoe (associated to atherosclerosis control), Igf1 (encoder a protein with analogous effects to insulin) and Fgf10 (fibroblasts growth factor). (3S)-vestitol anti-inflammatory mechanisms involve cytokines and NF-κB pathway inhibition. Moreover, (3S)-vestitol may be a candidate for future in vivo investigations about the treatment/prevention of persistent inflammatory diseases such as atherosclerosis and periodontitis.
Dual rotor flux switching permanent magnet machines with phase-group concentrated-coil windings for high performance

This paper proposes a novel dual rotor radial field flux switching permanent magnet machine (FSPMM) with phase- group concentrated-coil (PGCC) windings and two misaligned rotors to obtain high performance, including high torque density and low cogging torque, as well as low torque ripple. The proposed FSPMM features a new combination of stator slots and rotor poles, which is determined by the winding configurations. The PGCC windings are adopted to obtain a unity displacement winding factor, and enhance the flux focusing effects together with the use of spoke-type PM constructions. The unaligned arrangement of two rotors will help to not only achieve further flux magnification by an alternate operating principle for PM flux concentration, but also suppress the cogging torque. To highlight the advantages of the proposed FSPMM, two conventional FSPMMs with typical machine configurations having concentrated windings are adopted for performance comparison based on a finite element method (FEM) using JMAG-Designer.
Towards Quantification and Visualization of the Effects of Concretization During Concolic Testing

Concolic testing is a software testing technique, which improves the scalability of symbolic execution by allowing efficient concretization of symbolic expressions. Concretization converts a symbolic expression to a concrete value, e.g., when the constraints of a symbolic expression become too complex for the utilized solver to handle. Unfortunately, concretization negatively impacts completeness of the performed analysis. For example, if a branch in the tested program depends on a previously symbolic value, which is now concrete, this branch will not be tracked by the symbolic execution engine. As such, the tested code is not explored in its entirety and errors may remain undetected. In order to allow a verification engineer to identify code parts which have not been tested sufficiently, due to concretization, we propose a novel metric, which quantifies performed concretizations. Furthermore, we contribute a visualization of this metric, which eases identifying code parts, which depend, directly or indirectly, on symbolic values affected by concretization. To the best of our knowledge, this is the first work proposing a concretization metric for concolic testing, to stimulate further research on this topic we have released our implementation as open-source software.
Multimodal Emotion Recognition Algorithm for Artificial Intelligence Information System

Emotional expression is an essential and important link in human social activities and daily communication activities. Emotion recognition is a technology that uses computer technology to recognize the emotion of recognized objects. When emotion recognition technology was born, the research on human emotion recognition mainly focused on single-modal emotion recognition. Single-modal emotion recognition has been the most commonly used emotion recognition method. However, in recent years, with the continuous development of science and technology and the continuous improvement of people’s awareness of the defects of single-modal emotion recognition, the research on multimodal emotion recognition is increasingly emerging. This paper is aimed at analyzing and studying the multimodal emotion recognition algorithm in artificial intelligence information system. Based on this research topic, this paper designs a single-modal and multimodal emotion recognition comparison simulation experiment for the four emotional characteristics of happiness, sadness, shock, and anger extracted from artificial intelligence information systems. The experiment concluded that the comprehensive emotion recognition accuracy rate of the multimodal emotion recognition algorithm in the artificial intelligence information system reached 93%. Compared with single-mode emotion recognition, the comprehensive recognition accuracy is improved by 7%.
Updating Indoor Air Quality (IAQ) Assessment Screening Levels with Machine Learning Models

Indoor air quality (IAQ) standards have been evolving to improve the overall IAQ situation. To enhance the performances of IAQ screening models using surrogate parameters in identifying unsatisfactory IAQ, and to update the screening models such that they can apply to a new standard, a novel framework for the updating of screening levels, using machine learning methods, is proposed in this study. The classification models employed are Support Vector Machine (SVM) algorithm with different kernel functions (linear, polynomial, radial basis function (RBF) and sigmoid), k-Nearest Neighbors (kNN), Logistic Regression, Decision Tree (DT), Random Forest (RF) and Multilayer Perceptron Artificial Neural Network (MLP-ANN). With carefully selected model hyperparameters, the IAQ assessment made by the models achieved a mean test accuracy of 0.536–0.805 and a maximum test accuracy of 0.807–0.820, indicating that machine learning models are suitable for screening the unsatisfactory IAQ. Further to that, using the updated IAQ standard in Hong Kong as an example, the update of an IAQ screening model against a new IAQ standard was conducted by determining the relative impact ratio of the updated standard to the old standard. Relative impact ratios of 1.1–1.5 were estimated and the corresponding likelihood ratios in the updated scheme were found to be higher than expected due to the tightening of exposure levels in the updated scheme. The presented framework shows the feasibility of updating a machine learning IAQ model when a new standard is being adopted, which shall provide an ultimate method for IAQ assessment prediction that is compatible with all IAQ standards and exposure criteria.
Geomorphological slope units of the Himalayas

ABSTRACT Slope units represent surface slopes by means of polygons delimited by drainage and divide lines obtained on a digital topography. Objective slope unit delineation for a given digital elevation model is still an open issue and, often, a limitation that may dictate the use of a more traditional pixel-based approach for spatial analysis. Availability of slope unit maps facilitates many kinds of studies and allows scholars to focus on specific scientific issues rather than on preparing sound mapping units from scratch for their research. Here, we present a slope unit map of a large portion of the Himalayas. The map is prepared following a widely tested, parameter-free optimization algorithm. The area encompassed by the map is relevant to studies of the well-known 2015 Gorkha earthquake and monsoons, which makes it relevant to a vast portion of the scientific community working in natural hazards including, but not limited to, landslide scientists and practitioners. The map contains 112,674 polygons with average area of 0.38 km and is published in vector form. The map is accompanied by a selection of data including morphometric and thematic quantities. In addition to describing the rationale behind the delineation of the polygonal map and selected data, we describe an application devoted to unsupervised terrain classification. We applied a k-means clustering procedure with two strategies: one at (coarser) basin scale and one at (finer) slope unit scale. We show similarities and differences between the two classification strategies, highlighting the role of the slope unit subdivision in the two cases.
A Quantitative Study of Mathematics Identity and Achievement among LatinX Secondary School Students

ABSTRACT Using data from the National Center of Educational Statistics’ (NCES) 2009 High School Longitudinal Study, this article studies the relationship between mathematics identity and mathematics success for LatinX students by relying on critical race theory and intersectionality as theoretical frameworks. The quantitative analysis relies on hierarchical regression modeling to examine the relative impact of demographic variables, school characteristics, parents social capital, and parental involvement on the mathematics grade point averages at the conclusion of 11th grade of a national sample of LatinX students. The article concludes with a discussion of the ways in which we as educators, policy makers, and researchers can work toward supporting positive mathematics identity development and, by extension, mathematical attainment and success for LatinX students. Specifically, the article discusses ways in which the experiences, cultures and abilities of these students can be acknowledged, celebrated, and built upon.
Quality Concept in Persian Precedent Architecture: A Lesson in Eco-Building Design

Architecture as a process of change, converts a set of in-puts into out-puts. This process creates building through dealing with four aspects of social, economical, environmental and design related issues. In this process technology and conceptual framework are two pivotal factors balancing the aspects needed for the design context equilibrium. Application of technology and specific strategies in design concept as well as prudent selection of materials and energy resources can decrease the rate of environmental impacts. Hence, the process management in general and the out-put management in particular are addressed in design stage. There are many lessons, thoughts and philosophies in precedent works in architecture long term education, which could be engaged in current projects experience. Here, Persian precedents architecture, principles and characteristics are investigated in order to extract some lost values, found in this heritage. Then the paper points out how these values might be used in establishing a scope in sustainable architecture through eco-concepts in eco-building design approach. Quality concept originated in Iranian past architecture codes indicates that their architecture not only includes eco-concepts but also has a great respect for the environment. This paper is addressing some of the lost values in order to be revitalised.
Distinct AMPK-Mediated FAS/HSL Pathway Is Implicated in the Alleviating Effect of Nuciferine on Obesity and Hepatic Steatosis in HFD-Fed Mice

Nuciferine (Nuci), the main aporphine alkaloid component in lotus leaf, was reported to reduce lipid accumulation in vitro. Herein we investigated whether Nuci prevents obesity in high fat diet (HFD)-fed mice and the underlying mechanism in liver/HepG2 hepatocytes and epididymal white adipose tissue (eWAT) /adipocytes. Male C57BL/6J mice were fed with HFD supplemented with Nuci (0.10%) for 12 weeks. We found that Nuci significantly reduced body weight and fat mass, improved glycolipid profiles, and enhanced energy expenditure in HFD-fed mice. Nuci also ameliorated hepatic steatosis and decreased the size of adipocytes. Furthermore, Nuci remarkably promoted the phosphorylation of AMPK, suppressed lipogenesis (SREBP1, FAS, ACC), promoted lipolysis (HSL, ATGL), and increased the expressions of adipokines (FGF21, ZAG) in liver and eWAT. Besides, fatty acid oxidation in liver and thermogenesis in eWAT were also activated by Nuci. Similar results were further observed at cellular level, and these beneficial effects of Nuci in cells were abolished by an effective AMPK inhibitor compound C. In conclusion, Nuci supplementation prevented HFD-induced obesity, attenuated hepatic steatosis, and reduced lipid accumulation in liver/hepatocytes and eWAT/adipocytes through regulating AMPK-mediated FAS/HSL pathway. Our findings provide novel insight into the clinical application of Nuci in treating obesity and related complications.
Wear Resistance and Biocompatibility of Co-Cr Dental Alloys Fabricated with CAST and SLM Techniques

Cobalt–chromium (Co-Cr) alloys have been widely used as dental-restoration materials for many years. This study sought to investigate whether selective laser melting (SLM) is a more appropriate process than traditional casting (CAST) for fabricating dental Co-Cr alloys. Metallurgical microscopy, X-ray photoelectron spectroscopy (XPS), Vickers hardness and nanoindentation tests, and friction and wear tests were used to evaluate the microstructure, surface compositions, mechanical properties, and wear resistance, respectively. Additionally, the biocompatibilities and cell adhesion of the alloys were evaluated with L-929 fibroblasts via CCK-8 assay, Live/Dead staining, flow cytometric analysis, scanning electron microscopy (SEM) observation and real-time PCR (RT-PCR) assay. The XPS results showed that the two alloys were all mainly comprised of Co, Cr, and O. The hardness in the CAST group equaled 7.15 ± 0.48 GPa, while in the SLM group, it equaled 9.06 ± 0.49 GPa. The friction coefficient of SLM alloys remained at approximately 0.46, but the CAST specimens fluctuated significantly. SLM alloys exhibited shallower wear scars and less wear debris compared with CAST alloys, simultaneously. Additionally, there were higher survival and expression of cell-adhesion-related genes on SLM alloys of L-929 cells, which meant that the deleterious effect on L-929 cells was significantly reduced compared with that for the CAST alloys. Overall, the wear resistances and biocompatibilities of the Co-Cr dental alloys were dramatically affected by the fabrication technique. The SLM technique is advantageous over the CAST technique for fabricating Co-Cr dental alloys.
Probiotic-Mediated Biosynthesis of Silver Nanoparticles and Their Antibacterial Applications against Pathogenic Strains of Escherichia coli O157:H7

The present study aimed to suggest a simple and environmentally friendly biosynthesis method of silver nanoparticles (AgNPs) using the strain Bacillus sonorensis MAHUQ-74 isolated from kimchi. Antibacterial activity and mechanisms of AgNPs against antibiotic-resistant pathogenic strains of Escherichia coli O157:H7 were investigated. The strain MAHUQ-74 had 99.93% relatedness to the B. sonorensis NBRC 101234T strain. The biosynthesized AgNPs had a strong surface plasmon resonance (SPR) peak at 430 nm. The transmission electron microscope (TEM) image shows the spherical shape and size of the synthesized AgNPs is 13 to 50 nm. XRD analysis and SAED pattern revealed the crystal structure of biosynthesized AgNPs. Fourier transform infrared spectroscopy (FTIR) data showed various functional groups associated with the reduction of silver ions to AgNPs. The resultant AgNPs showed strong antibacterial activity against nine E. coli O157:H7 pathogens. Minimum inhibitory concentration (MIC) values of the AgNPs synthesized by strain MAHUQ-74 were 3.12 μg/mL for eight E. coli O157:H7 strains and 12.5 μg/mL for strain E. coli ATCC 25922. Minimum bactericidal concentrations (MBCs) were 25 μg/mL for E. coli O157:H7 ATCC 35150, E. coli O157:H7 ATCC 43895, E. coli O157:H7 ATCC 43890, E. coli O157:H7 ATCC 43889, and E. coli ATCC 25922; and 50 μg/mL for E. coli O157:H7 2257, E. coli O157: NM 3204-92, E. coli O157:H7 8624 and E. coli O157:H7 ATCC 43894. FE-SEM analysis demonstrated that the probiotic-mediated synthesized AgNPs produced structural and morphological changes and destroyed the membrane integrity of pathogenic E. coli O157:H7. Therefore, AgNPs synthesized by strain MAHUQ-74 may be potential antibacterial agents for the control of antibiotic-resistant pathogenic strains of E. coli O157:H7.
Back in My Day: Generational Beliefs About School Shootings

Following a school shooting, the public and media search to understand what factors led to such tragedy. Faced with grief, fear, and confusion, people often seek to make sense of traumatic events. As such, this study uses a 2020 Amazon Mechanical Turk survey (N = 739) to examine the impact of generational cohort on the blameworthiness of various perceived causes of school shootings. Findings support some generational differences. Baby Boomers were more likely to believe in societal-related causes of school shootings compared to Millennials and Generation Z. Conversely, Millennials and Generation Z were more likely than Baby Boomers to attribute the cause of school shootings to bullying, mental health, and school security. These findings suggest that future school shooting policies will seek to address bullying, mental health, and school security, while policies surrounding societal factors may be phased out.