Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing sense of place through a place-based Indigenous education for sustainable development curriculum

Abstract The life experiences of Indigenous students are often overlooked in school learning, thereby causing dissonance in learning. To address this issue, this study incorporated local and traditional ecological knowledges of community in this year-long course of ‘place-based Indigenous education for sustainable development’, using place-based design principles, which provided many experiential learning opportunities for the students to develop cognitive, socio-emotional, and behavioral aspects of sense of place. To ensure self-determination of Indigenous peoples in educational research, this study engaged and empowered knowledgeable Elders, community members, and Indigenous teachers in this community-based participatory design research. From student worksheets, student interviews, classroom observations, and teacher feedback, we found that students began to think about hometown and its sustainability issues in a broader perspective as they enhanced their local knowledge and sense of place. Follow-up research can be conducted to determine the generalizability of this model in cross-cultural contexts or for Indigenous youth, as the notion of sense of place is rather holistic and contextualized.
Bounds on the Error Variance when Estimating the Position of an Image

We illustrate Cramer–Rao lower bounds (CRLBs) on the root-mean-square (RMS) error in estimating the position of an image on a detector array. Several models of the image intensity distribution are treated. The case of Poisson arrivals and a Gaussian image profile, which was treated in [1], is extended to treat an unknown image mean and a skew-normal profile with unknown skew. We show the bounds are insensitive to knowledge of the mean while unknown skew can result in a loss of ≈1.5 dB. We show that a time-varying image profile, with pixel variance quadratic in the image mean, leads to a floor in the RMS error as a function of the mean.
Chloroplast‐localized PITP7 is essential for plant growth and photosynthetic function in Arabidopsis

Abstract Recent studies of chloroplast‐localized Sec14‐like protein (CPSFL1, also known as phosphatidylinositol transfer protein 7, PITP7) showed that CPSFL1 is necessary for photoautotropic growth and chloroplast vesicle formation in Arabidopsis (Arabidopsis thaliana). Here, we investigated the functional roles of CPSFL1/PITP7 using two A. thaliana mutants carrying a putative null allele (pitp7‐1) and a weak allele (pitp7‐2), respectively. PITP7 transcripts were undetectable in pitp7‐1 and less abundant in pitp7‐2 than in the wild‐type (WT). The severity of mutant phenotypes, such as plant developmental abnormalities, levels of plastoquinone‐9 (PQ‐9) and chlorophylls, photosynthetic protein complexes, and photosynthetic performance, were well related to PITP7 transcript levels. The pitp7‐1 mutation was seedling lethal and was associated with significantly lower levels of PQ‐9 and major photosynthetic proteins. pitp7‐2 plants showed greater susceptibility to high‐intensity light stress than the WT, attributable to defects in nonphotochemical quenching and photosynthetic electron transport. PITP7 is specifically bound to phosphatidylinositol phosphates (PIPs) in lipid‐binding assays in vitro, and the point mutations R82, H125, E162, or K233 reduced the binding affinity of PITP7 to PIPs. Further, constitutive expression of PITP7H125Q or PITP7E162K in pitp7‐1 homozygous plants restored autotrophic growth in soil but without fully complementing the mutant phenotypes. Consistent with a previous study, our results demonstrate that PITP7 is essential for plant development, particularly the accumulation of PQ‐9 and photosynthetic complexes. We propose a possible role for PITP7 in membrane trafficking of hydrophobic ligands such as PQ‐9 and carotenoids through chloroplast vesicle formation or direct binding involving PIPs.
The gut microbiome and cognition in Parkinson’s disease: a systematic review

ABSTRACT Background The pathology underlying cognitive changes in people with Parkinson’s disease (PD) is not well understood. In healthy older adults, gut microbiome composition has been associated with cognitive function. In people with PD, preliminary evidence suggests that cortical spreading of abnormal alpha-synuclein aggregates may be associated with cognitive impairment. As changes in the gut have been linked to PD onset and associated Lewy body pathology, an investigation of the gut microbiome and cognition in PD is warranted. Objective To synthesise existing evidence on the relationship between the gut microbiome and cognitive function in PD. Methods A systematic review was conducted to search for peer-reviewed articles and grey literature published to July 2021 across seven electronic databases (MEDLINE, EMBASE, PsycINFO, Scopus, Cochrane Library, ProQuest, and ProQuest Dissertations and Theses). English language articles reporting the relationship between cognition and the gut microbiome in human participants with PD were considered for inclusion. Results were qualitatively synthesised and evidence quality was assessed using the QualSyst tool for quantitative studies. Results Five cross-sectional studies reporting the association between the gut microbiome and cognition in 395 participants with PD were included. Studies provided preliminary evidence of a relationship between cognition and gut microbiota within the Bacteroidetes and Firmicutes phyla, however, associations with specific genera were inconsistent across studies. Conclusions Some species of short-chain fatty acid-producing bacteria (e.g. acetate, butyrate, and propionate producers) appear to be reduced in participants with PD with cognitive impairment. More research with larger samples and more consistent methodology is needed to substantiate these findings.
Production and quality assessment of biodiesel obtained by thermal process from wet microalgae biomass of Choricystis minor var. minor

Abstract The microalgae Choricystis minor var. minor stand out as a potential source of lipids. In this study, it was evaluated the cultivate, on a pilot scale, the microalgae freshwater Choricistis minor var. minor, and the evaluation of biodiesel production from wet biomass using the pressure heating route of wet biomass followed by esterification and subsequent transesterification, as well as how the quality of the produced biodiesel was evaluated. The process of centrifugation was adequate since it allows obtaining a concentrated biomass with a water content of approximately 76%. The biomass showed high total lipid content (22%) and adequate conversion of these lipids into biodiesel. The pilot scale cultivation in open tanks showed a volumetric productivity with a production potential of six tons of biodiesel per hectare per year. Biodiesel production via the biomass pressure heating route followed by subsequent transesterification produced a biodiesel that meets most quality parameters. Some parameters still need to be adjusted, such as sulfur and ester contents. The processes of cultivation, isolation and conversion to biodiesel proposed in this work are an adequate alternative to the production of biodiesel using microalgae, with some improvements in the biodiesel purification process.
Infinitesimal Time Scale Calculus

Calculus has historically been fragmented into multiple distinct theories such as differential calculus, difference calculus, quantum calculus, and many others. These theories are all about the concept of what it means to “change”, but in various contexts. Time scales calculus (introduced by Stefan Hilger in 1988) is a synthesis and extension of all the various calculi into a single theory. Calculus was originally approached with “infinitely small numbers” which fell out of use because the use of these numbers could not be justified. In 1960, Abraham Robinson introduced hyperreal numbers, a justification for their use, and therefore the original approach to calculus was indeed logically valid. In this thesis, we combine Abraham Robinson’s hyperreal numbers with Stefan Hilger’s time scale calculus to create infinitesimal time scale calculus.
Synthesis and Characterization of Mango Wood Nanocellulose Fibers

Agricultural biomasses are the prominent natural sources of cellulose currently available on the planet. When this was treated chemically, this acquire the properties such as toughness, good biocompatibility, and higher thermal stability. In this study, Nanocellulose was extracted from Mango wood (Mangifera indica) a largely available agricultural biomass. The cellulose was alkali-treated followed by bleaching to remove lignin and hemicellulose, pectins, and waxes. The green solvent i.e Ionic liquid (1-butyl-3-methylimidazolium chloride ([Bmim] Cl) was used to dissolve cellulose to yield Nanocellulose through sonication and centrifugation. The functional groups and significant conversion of cellulose to nanocellulose are confirmed by FT-IR spectra. The crystallinity of synthesized nanocellulose is illustrated by XRD. The surface architecture and size obtained are represented by SEM and TEM monographs. The TEM images show the synthesized nanocellulose has a dimension between 27.33 to 34.85 nm. The thermal stability of the obtained nanocellulose was evidenced using TGA/DTA. The thermal studies records that synthesized nanocellulose shows superior thermal stability up to 473.8°C. The efficient conversion of agricultural waste into value added material is achieved through a greener pathway.
Predictive analysis of injury severity of person across angle crashes using machine learning models

Abstract Human injury in a vehicle crash is a critical subject of analysis. The injury severity of a person in crashes helps the transportation agency to determine crash conditions. This will in turn helps road safety manager or engineer to implement the counter measures and to improve and enhance the level of safety at the roadside. In this research study, the injury severity of a person in crash analysis across angles is analyzed using various machine learning models. For the injury severity prediction 2018 Fatality Analysis Reporting System (FARS) NHTSA (National Highway Traffic Safety Administration) dataset of United States is used. The person injury severity is predicted with the help of machine learning models like Multinomial logistic regression, Naive Bayes Classifier, Random Forest, Extra Trees, XGB Classifier and optimized XGBoost model. It is observed that optimized XGBoost method performs better than other machine learning models in terms of performance metrics like Accuracy, Error rate, Cohen-kappa-score, Loss and Misclassified samples.
Implementation of the Winnowing Algorithm in Detecting Plagiarism in Title and Abstract of Student's Final Project

In this modern era, technology has become more sophisticated. All information can be accessed quickly and efficiently. Nevertheless, when surfing the internet to search for information, people frequently find it almost similar and even have no different. Moreover, to news information content on the internet, similarities are found in final semester student research titles and abstracts as well. In preventing this, the researchers explored how to establish a website design by checking the similarity of titles and abstracts with winnowing method, which is exacted to complete examining the similarity of titles and abstracts on students' final assignments. The researchers applied dummy data obtained from Google Scholar for this study. The results revealed that the application of the winnowing method can aid in achieving similar results from the student's final assignments. It is deliberated as a benchmark for accepting a title and abstract at a university and reducing plagiarism.
Characterizing (un)binding mechanism of USP7 inhibitors to unravel the cause of enhanced binding potencies at allosteric checkpoint

The ability to predict the intricate mechanistic behavior of ligands and associated structural determinants during protein–ligand (un)binding is of great practical importance in drug discovery. Ubiquitin specific protease‐7 (USP7) is a newly emerging attractive cancer therapeutic target with bound allosteric inhibitors. However, none of the inhibitors have reached clinical trials, allowing opportunities to examine every aspect of allosteric modulation. The crystallographic insights reveal that these inhibitors have common properties such as chemical scaffolds, binding site and interaction fingerprinting. However, they still possess a broader range of binding potencies, ranging from 22 nM to 1,300 nM. Hence, it becomes more critical to decipher the structural determinants guiding the enhanced binding potency of the inhibitors. In this regard, we elucidated the atomic‐level insights from both interacting partners, that is, protein–ligand perspective, and established the structure–activity link between USP7 inhibitors by using classical and advanced molecular dynamics simulations combined with linear interaction energy and molecular mechanics‐Poisson Boltzmann surface area. We revealed the inhibitor potency differences by examining the contributions of chemical moieties and USP7 residues, the involvement of water‐mediated interactions, and the thermodynamic landscape alterations. Additionally, the dissociation profiles aided in the establishment of a correlation between experimental potencies and structural determinants. Our study demonstrates the critical role of blocking loop 1 in allosteric inhibition and enhanced binding affinity. Comprehensively, our findings provide a constructive expansion of experimental outcomes and show the basis for varying binding potency using in‐silico approaches. We expect this atomistic approach to be useful for effective drug design.
Genetic Profile and Clinical Characteristics of Brugada Syndrome in the Chinese Population

Background: Brugada syndrome (BrS) is an inheritable arrhythmia syndrome that can lead to sudden cardiac death in patients while the heart structure is normal. However, the genetic background of more than 65% of BrS probands remains unclear. Objectives: The purpose of this study is to report the variant spectrum in a Chinese cohort with suspected BrS and to analyze their distinct clinical and electrocardiographic features. Methods: Patients with suspected BrS from Tongji Hospital between 2008 and 2021 were analyzed retrospectively. Results: A total of 79 probands were included in this study. Patients with type 1 BrS electrocardiogram (ECG) had a prolonged QRS duration compared to patients with type 2/3 BrS ECG. Of them, 59 probands underwent genetic testing. Twenty-five patients (42.37%) showed abnormal genetic testing results, and eight of them (13.56%) carried pathogenic/likely pathogenic (P/LP) mutations. Mutation carriers presented much more prominent depolarization and repolarization abnormalities than non-carriers, including a prolonged P-wave duration, QRS duration, QTc interval, decreased QRS amplitude, and deviation of the electrocardiographic axes (T-wave axis and R-wave axis). Furthermore, our study identified four novel P/LP mutations: Q3508X in TTN, A990G in KCNH2, G1220E, and D372H (in a representative pedigree) in SCN5A. Conclusions: Our study showed the variant spectrum of a suspected Chinese BrS cohort, and we identified four novel P/LP mutations in TTN, KCNH2, and SCN5A.
Resolving infrared singularities of QCD through the vertex paradigm

We furnish details and extensions for the vertex paradigm and related ideas. The vertex paradigm is a method for dealing non-perturbatively with the Schwinger-Dyson equations (SDE) of asymptotically-free (AF) gauge theories such as QCD, even in the face of necessary approximations. It provides a useful truncation for the infinitely-many SDE of the gauge- and renormalization-group invariant Pinch Technique (PT-RGI). We implement the vertex paradigm by successive approximations at the one-dressed-loop level, postulating input tree-level gluon and ghost propagators and a 3-gluon vertex that are well-behaved in the infrared and also satisfy several crucial PT-RGI Ward identities that are QED-like and ghost-free. Good IR behavior is assured by including a (non-running) gauge-invariant dynamical gluon and ghost mass as part of the input. The non-trivial part of the vertex paradigm is that, with our inputs, the one-loop output vertex then satisfies the correct Ward identity from which we can construct the output gluon propagator, by taking account of Nambu-Goldstone-like massless scalars and related technical problems that arise whenever there is dynamical gluon mass generation. The one-loop outputs show a number of desirable features: They are PT-RGI; free of any reference to a coupling (dimensional transmutation); satisfy the ghost-free Ward identities connecting them; give exactly the known one-loop UV behavior; and are free of IR singularities. We give a much simpler illustration of the main principles of the vertex paradigm in a modified $\phi^3_6$ model that is AF. Our successive-approximation scheme is not designed to estimate the gluon dynamical mass, but it shows that there is a lower limit to the mass below which the AF theory breaks down.
Comparison of a Nintendo Wii balance board with a laboratory-grade force plate on measurement of transitional movements

The aim of this study was to verify whether the Nintendo Wii-Balance-Board was valid and reliable for assessing sit-to-stand and return-to-sit tasks by comparing it with a gold-standard force plate. Ten elderly (age = 78.21±14.82 years; males=4; females=6) and eleven young (age =24.25±12.43 years; males=6; females=5) participants of both genders performed five sit-to-stand and return-to-sit tasks consecutively by placing their feet on the Wii-Balance-Board; after two days of rest, they repeated the same assessment. The Wii-Balance- Board was positioned over the force plate to concurrently acquire the vertical component of ground reaction forces. Relevant kinetic and temporal parameters were estimated from these signals. Both the Wii-Balance-Board and force plate measurements resulted in a high level of correlation for almost all the parameters (Pearson’s product-moment r ranged from 0.91 to 0.99, p<.001) and, for the same parameters, intra-class correlation coefficients revealed a high level of agreement between the devices (ranged from 0.93 to 0.99). Bland-Altman plots and regression analysis detected systematic and fixed biases for two parameters (i.e., the inclination of force in standing and rising), while other parameters resulted with none systematic biases; the absolute magnitude of those differences was trivial or small (standardized biases ranged from 0.01 to 0.4). A high level of intra-device reliability was measured for all the parameters (intra-class correlation coefficients ranged from 0.85 to 0.99). The Wii-Balance-Board proved valid and reliable in comparison with a force plate for assessing transition movements so it can be considered a valuable solution for supporting the assessment procedures of average practitioners.
Using bilateral filtering and autoencoder to defend against adversarial attacks for object detection

Abstract. With the development of adversarial attacks, the performance of object detection based on deep learning is threatened. When adversarial examples are introduced into the detection task, the detector will suffer from poor detection performance, causing a large number of false detections. To handle this problem, we propose a defense method by combing bilateral filtering and the denoising autoencoder. Taking the you only look once (YOLO) v4 detection model as the research target, the proposed method proceeds as follows. First, it performs weighted average in the spatial domain and the pixel-range domain. The method retains important edge texture information when it reduces the perturbations in the image. Then, a three-layer denoising reduction autoencoder is designed, and a new optimization algorithm is proposed to minimize the distance between the input and output. Finally, experiments show that the method proposed has a better defense effect than the existing defense methods. When facing the projected gradient descent-based object detection bounding box disappearance adversarial attack, our defense method can improve the detection true-box rate indicator to 83.04% on the visual object classes challenge (VOC) dataset and 72.20% on the common objects in context (COCO) data. The number of bounding boxes correctly detected is 88.09% and 86.09% of the original one on the PASCAL VOC dataset and the Microsoft COCO dataset, respectively.
Mobile wallets in cash-based economies during COVID-19

PurposeThe coronavirus disease 2019 (COVID-19) pandemic altered business and personal activities globally especially stimulating contactless financial transactions. However, despite the similar national lockdowns in cash-based economies, the adoption of contactless transactions through the widely available mechanism, mobile wallets, remained low. This research aimed to identify the factors surrounding this peculiarity.Design/methodology/approachThe study was investigated using a composite model based on the diffusion of innovation theory (DIT), technology acceptance model (TAM) and information systems success model (ISSM). Data were collected from 621 Cameroonian mobile wallet users and analyzed using partial least squares structural equation (PLS-SEM) modeling.FindingsThe key findings revealed that the usage of mobile wallets, in the current form, were not affected by the perceived ease of use and did not match the existing lifestyle of users in Cameroon (no compatibility). The branding of mobile wallets (image) which was based on global messaging did not appeal to Cameroonians; in fact, the branding gave mobile wallets a negative image.Originality/valueThese key findings reveal the dangers of assuming that global strategies which have been effective in dealing with the pandemic will be effective in low-income or cash-based economies. The findings suggest that considering essential contextual dispositions is critical.
Ecological and contamination assessment of soil in the region of coal-fired thermal power plant

ABSTRACT This study was carried out to determine the heavy metal pollution and possible sources of agricultural soils in Tavşanlı district, Which energy power plant is located. Total 83 soil samples were taken and 8 (Cu, Cr, Pb, Co, Fe, Mn, Ni, and Zn) heavy metals were analyzed in soil samples The mean concentration of heavy metals were determined as Cu (32.89 mg kg−1), Cr (285.69 mg kg−1), Co (36.37 mg kg−1), Mn (860.20 mg kg−1), Ni (457.59 mg kg−1), Pb (22.14 mg kg−1), Fe (30,250 mg kg−1) and Zn (65.05 mg kg−1), were determined. The mean concentrations of Cu, Cr Co, Mn and Ni found to be higher than both the upper continental crust values and the European soil mean values. Contamination factor Co (2.1), Cr (3.10) and Ni (9.73), enrichment factor Co (2.73), Cr (3.75) and Ni (11.42) and geoaccumulation index Co (0.18), Cr (0.50) and Ni (1.98) values showed that the soils were polluted by Co, Cr, and Ni. In addition, it was determined that Ni (48.65) poses a “moderate ecological risk” in the study area. Pearson correlation anaysis and principal component analysis determined that Cr, Co and Ni have both lithogenic and anthropogenic origin.