Reviewerly/exHarmony huggingface dataset:
SIL Simulation Test Platform Based on Prescan and ROS

The existing control execution algorithms are mainly developed through the Matlab/Simulink, while the environmental perception and planning decision algorithms mostly developed by ROS. Most of the algorithm development stages are performed separately, which is not convenient for SIL tests of unmanned algorithms. In order to break the barriers between various software and improve the developing efficiency, in this paper, a SIL simulation platform based on Prescan and ROS was proposed, the obstacle avoidance function was simulated and tested through model establishment, message compilation, topic definition and communication connection in turn. The results suggested that the simulation platform in this article has a full stack SIL simulation capabilities for environmental perception, path planning and control, which can quickly perform functional verification in the early stage of algorithm development while improving the developing efficiency.
Evaluation of Simulated Petroleum Hydrocarbon on the Physicochemical Properties of Soil

Petroleum hydrocarbon contamination of soils has become a global concern, which is often caused by anthropogenic activities, posing serious threat to all living beings. The study for evaluation of the variability of crude oil on the physicochemical characteristics of sandy loam soil was conducted at demonstration farm, Rivers state university, Port-Harcourt, Nigeria. The Bonny light crude oil was obtained from an oil and gas production company. Uncontaminated soil was mixed with crude oil. Standard procedures were adopted for the laboratory analysis, the parameters analyzed include bulk density, total porosity, pH, available phosphorus (P), Total hydrocarbon content (THC), organic carbon, organic matter, exchangeable cation (Mg, K, Na, Ca), effective cation exchange capacity (ECEC), total exchangeable acidity (TEA), and base saturation were monitored for a period of 21 days. 10 kg of sandy loam soils were mixed with 100, 300, 500, and 700ml of crude oil while no crude oil serves as the control using plastic reactors. The reactor with 700ml of crude oil recorded the highest THC of 1734.33 mg/kg, followed by 500ml with a THC of 1601mg/kg while the control with no contamination recorded the least THC of 534.33mg/kg. However, the values of all concentrations did not meet 5000 mg/kg of Department of Petroleum Resources (2018) intervention value but exceeded the target value of 50 mg/kg. Other parameters followed same trend except porosity which decreased with increase in crude oil volume. There were significant differences at P&lt; 0.05 except for pH, available P, and base saturation. Therefore, this study calls for the utilization of its findings for adoption of remediation on crude oil contaminated soils.
Reflections on the Construction of the Key Elements of Vocational Education and Training System

Campus education and social training are the two most important functions of vocational colleges. Attaching importance to campus education while neglecting social training is a long-standing problem in vocational colleges. Vigorously developing higher vocational education is an inevitable requirement for the high-quality development of education in China and the ardent expectation of the public. Only through the construction a scientific and efficient education and training system and the deep integration of campus education with social training can the design of complementary resources and the improvement of talent training quality take place. The vocational education and training system contains many elements. This study takes teaching resources, teaching staff, and management mechanism as the main research subject and carries out related research work. Firstly, this paper expounds the policy documents of the Chinese government and emphasizes the importance of vocational education. Secondly, the relevant research results of scholars are reviewed. Thirdly, this paper proposes several specific suggestions for the construction of key elements of the vocational education and training system. Then, the main conclusions and prospects are summarized.
Institutional Repositories as a Global Dissemination Tool of Educational And Scientific Information

The authors present an overview of publications on the introduction and use of digital institutional repositories (DIR) in different countries and in Ukraine. A variety of reference and scientific literature, and publications in international scientometric databases have been analyzed. The role of digital institutional repositories in providing access to full-text electronic achievements, disseminating research results, and maximizing their accessibility and scientific influence is highlighted. The problem of creating and using digital institutional repositories in the training of specialists in higher education, as well as the formation of their information and research competence, is considered. It is substantiated that institutional repositories are a powerful tool in pedagogical universities to conduct and disseminate scientific research results in education, pedagogy, and psychology. Particular attention is paid to the role of library materials in the formation and operation of digital institutional repositories. The experience of Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University Library (Ukraine) is reflected separately. Keywords: digital institutional repository, information and research competence, open electronic scientific and educational systems, open science; pedagogical university library
Performance analysis of frequent pattern mining algorithm on different real-life dataset

&lt;span lang="EN-US"&gt;The efficient finding of common patterns: a group of items that appear frequently in a dataset is a critical task in data mining, especially in transaction datasets. The goal of this paper is to look into the efficiency of various algorithms for frequent pattern mining in terms of computing time and memory consumption, as well as the problem of how to apply the algorithms to different datasets. In this paper, the algorithms investigated for mining the frequent patterns are; Pre-post, Pre-post+, FIN, H-mine, R-Elim, and estDec+ algorithms. These algorithms have been implemented and tested on four real-life datasets that are: The retail dataset, the Accidents dataset, the Chess dataset, and the Mushrooms dataset. From the results, it has been observed that, for the Retail dataset, estDec+ algorithm is the fastest among all algorithms in terms of run time as well as consumes less memory for its execution. Pre-post+ algorithm performs better than all other algorithms in terms of run time and maximum memory for the Mushrooms dataset. Pre-Post outperforms other algorithms in terms of performance. And for Accident datasets, in terms of execution time and memory consumption, the FIN method outperforms other algorithms.&lt;/span&gt;
Empirical Research on Intention to Rebuy Cryptocurrencies such as Bitcoin

Cryptocurrencies, such as Bitcoin, allow creative and quick transactions without tangible assets and online payments and, thus, they can be used in potential economic system revolutions. In Pakistan, a lesser number of people are using Bitcoin. It will be crucial to look at the Pakistani people's intentions to rebuy Bitcoin. Smart PLS 3 was used to evaluate the empirical data. It is found that expectations positively affect perceived enjoyment and ease of use, and there is a relationship between perceived enjoyment and perceived ease of use, whereas perceived enjoyment, expectations, and perceived ease of use influence satisfaction. Furthermore, satisfaction, perceived ease of use, perceived enjoyment, and trust substantially impact Bitcoin rebuy intentions. Social influence and experience are found to have an impact on trust. This is the first project to look at factors affecting intentions toward the rebuy of Bitcoin in Pakistan; thus, we proposed a unique model.
FEATURES OF TEACHING ARCHITECTURE IN CONDITIONS MANDATORY REMOTE PROCESS

In the article, the author present a brief overview of the successful experience of implementation an educational process based on the newly introduced modules of the Platonus system and the Teams platform in higher education, which expanded the possibilities of remote interaction. The article analyzes the technical capabilities and main advantages of Platonus, Teams, which were chosen as an electronic information and educational environment by the Eurasian National University named after L.N. Gumilyov (Nur-Sultan, Kazakhstan).
Graph Learning for Attributed Graph Clustering

Due to the explosive growth of graph data, attributed graph clustering has received increasing attention recently. Although deep neural networks based graph clustering methods have achieved impressive performance, the huge amount of training parameters make them time-consuming and memory- intensive. Moreover, real-world graphs are often noisy or incomplete and are not optimal for the clustering task. To solve these problems, we design a graph learning framework for the attributed graph clustering task in this study. We firstly develop a shallow model for learning a fine-grained graph from smoothed data, which sufficiently exploits both node attributes and topology information. A regularizer is also designed to flexibly explore the high-order information hidden in the data. To further reduce the computation complexity, we then propose a linear method with respect to node number n, where a smaller graph is learned based on importance sampling strategy to select m(m≪n) anchors. Extensive experiments on six benchmark datasets demonstrate that our proposed methods are not only effective but also more efficient than state-of-the-art techniques. In particular, our method surpasses many recent deep learning approaches.
Detection of Leukemia Using Deep learning Approach

The project's goal is to create leukemia visuals using smart phone images of small blood smears.The convolution neural network model Res Net was used to identify leukemia imaging.In order to deliver a trustworthy diagnosis, particularly in places with fewer resources, such rural areas, the suggested project uses an in-depth study technique.This method also helps to lower the cost of diagnosis.They of far efficiency and cost of gathering photo datasets in a brief period of time, similar to microscopic blood smear images caught by the camera.Additionally, it can instantly transfer pictures of blood smears for early detection.An internet service compilesrealtimeimagesfromthehospitalaswellasmicroscopicbloodsmearimages.Intheproposedwork, the images are transferred to a convolution layer containing residual units defined by Re Lu and Batch normalization.Finally, a fully integrated layer is developed to give the predicted result of infected leukemia or virus-free images.Training to ensure accuracy and loss graphs are planned and performance metrics of the model are tested.
Peran Perempuan dalam Video Musik Bertaut Karya Nadin Amizah dan Implementasinya dalam Pendidikan Karakter

The music video with title Bertaut by Nadin Amizah depicts the life of a family consisting of only women. The absence of men in a family shows a new order that is separated from the habits, structures, and social systems of society that regard men as the control gender in the family. Women are able to act as single parents, act as mothers and fathers because they have strong, independent, and responsible characters. The results of this study indicate the role of women at home and outside the home as breadwinners. Women who have loyalty always accompanies family members in happy and sad times. Women are strong figures and able to strengthen other family members who are in trouble. The results of the study can be used as guidelines for teachers in shaping the character of students who are strong, independent, responsible, and have empathy for others. Through literary learning, students can experience catharsis, reflect on the figure that is well described in the lyrics of the song so that they can adapt the good character to be themselves.
Autosuggestion of Relevant Cases and Statutes

In this paper, we describe a method to help legal practitioners in citing the relevant case laws and statute laws for the specified legal issue. In this method, we consider the cited case and statute law as single tokens where we try to find the relevant tokens based on the words around these tokens. We observed that context-based representations outperformed lexical-based representations and distributional representations. Also, we observed that the method works better for statute law retrieval compared to case law retrieval.
OS AGRAVOS DA COVID-19 EM PACIENTES COM SÍNDROME DE DOWN

The objective of this study was to verify the social representations of cardiovascular diseases and cancer, in the conception of family members of hospitalized users.Methods: This qualitative research is part of a Residency Completion Work.Eighteen interviews were carried out with family members and the data collected were submitted for content analysis, showing the social representation of cardiovascular diseases and cancer for family members.Results: it was noticed how much cancer requires greater dedication from family members who assume the role of
Certifying emergent genuine multipartite entanglement with a partially blind witness

Genuine multipartite entanglement underlies correlation experiments corroborating quantum mechanics and it is an expedient empowering many quantum technologies. One of many counterintuitive facets of genuine multipartite entanglement is its ability to exhibit an emergent character, that is, one can infer its presence in some multipartite states merely from a set of its separable marginals. Here, we show that the effect can be found also in the context of Gaussian states of bosonic systems. Specifically, we construct examples of multimode Gaussian states carrying genuine multipartite entanglement which can be verified solely from separable nearest-neighbour two-mode marginals. The key tool of our construction is a genuine multipartite entanglement witness acting only on some two-mode reductions of the global covariance matrix, which we find by a numerical solution of a semi-definite programme. We also propose an experimental scheme for preparation of the simplest three-mode state, which requires interference of three correlatively displaced squeezed beams on two beam splitters. Besides revealing the concept of emergent genuine multipartite entanglement in the Gaussian scenario and bringing it closer to experimentally testable form, our results pave the way to effective diagnostics methods of global properties of multipartite states without complete tomography.
EFEKTIVITAS PEMBELAJARAN PAK DENGAN METODE STAD BERBANTUAN YOUTUBE TERHADAP HASIL BELAJAR SISWA KELAS IV SD ALOYSIUS SEMARANG

There is a problem that in grade IV SD Aloysius Semarang found students who feel bored and bored because of monotonous methods and unattractive media so that students are not responsible for student learning so that the impact on student learning outcomes is low, so many students have not reached the KKM which is determined by the school is 80. So this study aims to:&#x0D; 1) To determine the extent to which students' sense of responsibility has a positive effect on learning outcomes in fourth grade students of SD Aloysius Semarang; 2) To find out how far the learning of the Student Teams Achievement Division (STAD) method assisted by YouTube is more effective in improving student learning outcomes than the conventional method for fourth grade students of SD Aloysius Semarang; 3) to find out to what extent learning using the Student Teams Achievement Division (STAD) method can increase the number of students who reach the KKM in fourth grade students of SD Aloysius Semarang. This research applies an experimental quantitative approach. This type of experimental research is referred to as true experiments. The research design used was Prettest-posttest Only Control Group. with research subjects amounted to 57 fourth grade students of SD Aloysius Semarang. This study consisted of two classes: 1) the experimental class applied the STAD learning method; 2) the control class implements the Conventional method. The instrument used was a multiple choice test with 20 questions and a student responsibility observation sheet. The data analysis of this study used the influence test, homogeneity test and comparative test, N-Gain test and descriptive statistics assisted by SPSS version 21. The results showed that there was a positive influence of students' sense of responsibility on learning outcomes, both groups had a significant value &gt;5% then is homogeneous. Because the data is homogeneous, the hypothesis H0 is accepted and H1 is rejected. Then the Independent t test is carried out at t value sig = 0.000 = 0.0% &lt;5%, then H0 is rejected or H1 is accepted. The results of the N-gain test analysis showed a significant difference, namely the N-Gain value of the experimental class was 59.8052 or 59.8% and was included in the Quite Effective category, while the N-Gain value of the control class was 43.1052% or 43.5% included in the less category. effective. After treating the Student Team Achievement Divisions (STAD) method assisted by YouTube in the experimental class, there were 68.9% or 20 of the students who reached the KKM. Thus, it can be concluded that the use of the YouTube-assisted Student Team Achievement Divisions (STAD) method is more effective on learning outcomes.
The binocular structured light three-dimensional imaging method for measurement of high-reflective objects

Structured light three-dimensional (3D) imaging has been widely used for 3D surface measurement applications. However, it is limited for objects with high reflectivity in complex scenes. There are flying points or missing point clouds for the 3D imaging due to the overexposure by strong reflection or multiple reflections. In this work, we propose a method using two cameras to share the same projector. Gray codes with multi-step phase shift images compose the projected patterns. We adopt the partition fusion method for the captur images from different perspectives to reduce the influence of strong reflection and multiple reflections. The 3D measurement with the fusional captured images is realized.
Puterea modelelor de interval în problema calculării indicilor de centralitate

Food webs, some scheduling problems and DNA molecules all have in common a "linear structure" which can be captured through the idealized model of interval graphs (intersection graphs of intervals on a line).However, real data is prone to errors and noise, thus raising the question of whether the algorithmic results obtained for the interval graphs could be extended to more realistic models of "almost interval" graphs.We address this question in the context of computing the vertex eccentricities, one of the most studied centrality indices in order to determine the relative importance of nodes in a network.We give a positive answer for the interval+kv graphs and a negative one (assuming plausible complexity hypotheses) for the graphs of bounded interval number.In particular, we present an almost linear-time algorithm for computing all vertex eccentricities in an interval+kv graph, for any fixed k, thus improving on the recent quadratic-time algorithm of (Bentert & Nichterlein, 2022) for this problem.
Problems using structural MRIs from the oldest‐old, and some solutions: Lessons learned from The 90+ Study and ADNI

Abstract Background The oldest‐old (≥85 years) have the highest rates of dementia. However, because of pronounced brain atrophy, standard neuroimaging programs face unique challenges in this cohort. Here, using data from both The 90+ Study and Alzheimer’s Disease Neuroimaging Initiative (ADNI), we report the prevalence of errors when processing brain MRIs from oldest‐old participants using standard approaches and present some strategies for ameliorating these issues. Method First, we analyzed image quality issues and segmentation errors (FreeSurfer) for the last available scans by age‐groups (oldest‐old, ≥85 years, vs younger‐old, &lt;85 years) in ADNI. Second, we evaluated the utility of an age‐specific template (the 90+ T1w Template) for realigning MRIs from the oldest‐old compared to a standard template (Montreal Neurological Institute T1w template, MNI) using linear and nonlinear registration (FSL) and evaluating alignment quality using visual and deformation (Jacobian) measures for oldest‐old participants in both The 90+ Study and ADNI. Lastly, we evaluated whether stringent preprocessing (bias field removal, denoising, brain extraction, intensity normalization using Computational Anatomy Toolbox (CAT12)) helped reduce FreeSurfer segmentation errors in frontal, parietal, and temporal regions in MRIs from the oldest‐old. Comparisons were performed using Chi‐square tests. Result In ADNI data ( Table‐1 ), MRIs from oldest‐old participants (∼10% of cohort) were around twice as likely to have image quality issues (20% vs 13%, P&lt;0.001) and brain segmentation errors (45% vs 29%, P&lt;0.001) compared to younger‐old participants. Figure‐1 shows a histogram of image quality issues and segmentation errors, which become more common with greater age. Compared to the MNI template, MRIs from participants aged 90 and older from both The 90+ Study (N=163, Table‐2 ) and ADNI (N=75) had fewer registration errors (∼5% vs ∼23%, P&lt;0.001 for all) and less severe deformation when using the 90+ T1w Template ( Figure‐2 ). Our proposed preprocessing pipeline (see Figure‐3 for example) roughly halved segmentation errors in the oldest‐old (P&lt;0.001 for all regions, Figure‐4 ). Conclusion Our results show that adapting pipelines specifically for processing brain MRIs of oldest‐old participants minimizes errors, enabling more effective use of neuroimaging to study neural correlates of neurodegeneration and dementia in this important portion of our aging population.
Information Technology at the Service of the Transformation of Medical Education, Medical Science and Telemedicine Application. Medical Project Lab Business Accelerator Project

Digital transformation also plays a special role in the strategic renewal of a modern medical university. We are talking not only about re-equipping IT resources in medical education, but also about a kind of resetting the potential of a medical worker, upgrading his professional capital, modernizing the corporate culture, socialization, communication, and optimizing all his internal processes. The purpose of the study is to summarize the experience of organizing and creating a set of interrelated events designed to involve and train students in the skills of using effective information technologies in order to successfully master the curriculum in the context of the digital transformation of medical education. The methodological basis of this study was system-activity, competency-based, informational, individually differentiated approaches. The basis of the research materials was the results of the activities of students and teachers of the Ural State Medical University (USMU). Data on the dynamics of the number of students' involvement, their reports on the topic of the direction, the expansion of the range of research topics, the dynamics of the number of student publications, lectures and master classes by members of the expert council and involved medical IT specialists were used. In general, for four years at the USMU a team of expert teachers interested in the active development of this area at the university has been created. It was possible to attract to work a large number of students from USMU, other universities in Russia and neighboring countries. The study revealed the effectiveness of the work carried out at the USMU in the direction of "Information technologies for the digital transformation of medical education, medical science and telemedicine". The main problems and contradictions in the development of digital literacy and the interest of students and teachers of a medical university are identified, and directions for its improvement are identified.