Reviewerly/exHarmony huggingface dataset:
Cadomian Iron ore bodies in the Bafq-Saghand metallogenic province, Iran: genetic constraints from field observations and laser ablation ICP-MS mineral studies

ABSTRACTThe volcano-sedimentary unit in the Bafq-Saghand metallogenic province (BSMP) of Central Iran hosts various types of iron ore bodies including Fe-oxides with jaspilite beds, Mn-dominated iron lenses, magnetite-apatite lenses, and massive magnetite-apatite deposits. The Fe-oxides with jaspilite beds and Mn-dominated iron lenses have limited outcrops and show volcano-sedimentary features in the field. The main features are the presence of syngenetic submarine rhyolite-hosted magnetite, lamination and bedding of iron ore body, jaspilite and dropstone. Volcano-sedimentary origin is testified by magnetite composition showing affinity to BIF and positive Eu and negative Ce anomaly. Magnetite-apatite lenses and massive magnetite-apatite deposits that having spatio-temporal correlation form the main economically important deposits. They have magmatic to high-temperature hydrothermal field characteristics (lava flow-like ore bodies, the presence of degassing tubes in ore bodies, occurrence of brecciated texture and developed Na-Ca metasomatism). Mineral composition (enrichment of LREE, magnetite and apatite negative Eu anomaly and Co/Ni < 1 in pyrites) is in agreement with magmatic and high temperature hydrothermal origin. We propose a hydrous and oxidizing mantle derived iron-rich basic magma that formed in an extensional post-collisional setting for origin of Kiruna-type IOA ore bodies of the BSMP. Fe-oxides with jaspilite beds and Mn-dominated iron lenses resulted from degassing or eruption of iron-rich magma during Ediacaran in a submarine environment. However, magnetite-apatite lenses and massive magnetite-apatite deposits formed from an Early Cambrian magmatic-hydrothermal transition system from a magnetite-crystal mush zone.KEYWORDS: IranBafq-Saghand metallogenic provinceIron mineralizationvolcano-sedimentary iron bodymagmatic-hydrothermal transition iron mineralization AcknowledgementsWe thank Prof. Stern for his help and efficient editorial handling of this paper. We are grateful to Dr. F. Sepidbar and two anonymous reviewers for their constructive comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/00206814.2023.2238210Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.
Correlation between sentence topic and discourse topic in French and Chinese: annotation issues and contrastive analysis

Abstract This paper aims to investigate the correlation between “sentence topic (ST)” and “discourse topic (DT)”, both based on an “aboutness” relation. It also compares the degrees of correlation between French and Chinese, considered respectively as a subject‐prominent and a topic‐prominent language. Kendall's correlation tests, Poisson models and z ‐tests were conducted using manually annotated textual data. The results confirm a positive correlation between STs and DTs in both languages. Although the correlation coefficients are higher in Chinese, z ‐tests' results do not show a significant difference between the two languages. We explain that the two languages differ not only in the more or less frequent topic‐comment constructions, but also in the choice of more or less accessible referential markers which represent the STs, or in the more or less tacit way that STs of different sentences are related to each other.
Reading Literacy Model Based Tri Hita Karana for Student's Thinking Skills

Critical thinking skills are fundamental skills in 21st-century learning. One of these skills is influenced by students' reading literacy. However, reading literacy activities in elementary schools have yet to be maximized. This study aimed to analyze teachers' needs in elementary school learning and produce a prototype of the Tri Hita Karana Reading Literacy model. This type of research is research and development (RnD). The research and development model design used was adapted from Borg and Gall's theory. The subjects involved were 55 teachers in the preliminary study, 6 people, and 2 practitioners for the product feasibility test. Methods of data collection for preliminary study are using a questionnaire. Data were analyzed using quantitative descriptive analysis techniques. The research results show that teachers must develop a model to maximize students' literacy activities. Second, the average calculation of the validator's assessment results is 4.3, which means it is in the very good category, with an average number of content aspects of 4.3, layout and design aspects of 4.2, and grammar of 4,4. These results indicate that the THK Literacy Model is considered to have met the validity criteria. The conclusion of the experts regarding the development of this model is that the model can be used with several revisions.
PENINGKATAN PENGETAHUAN SISWA SEKOLAH MENENGAH PERTAMA TENTANG PEMASANGAN DAN EFEK SAMPING ALAT ORTODONTI CEKAT

Introduction: Malocclusion, or dental occlusion disorder, is one of the main factors disrupting the function of the oral cavity, especially the function of mastication. Malocclusion can be treated using orthodontic appliances. Orthodontic appliances are sometimes used not only for the benefit of dental treatment but as mere fashion due to a lack of knowledge, causing negative effects. Incompetent operators and poor maintenance procedures in orthodontic treatment will have a detrimental impact. Aims: The objective was to determine the knowledge level of junior high school students about the side effects of using fixed orthodontic appliances and the treatment of fixed orthodontic appliances by general dentists, orthodontists, and dental specialists. Methods: The type of research used is quasi-experimental, with one group and two measurements (pretest and post-test). Participants in this study were junior high school students in Central Jakarta. The sampling technique used total sampling. Data collection techniques using a questionnaire and analysis of data using the Wilcoxon Test P&lt;0.05 are considered significant. The total number of students included in the study was 344. Result: There was a significant difference in student knowledge between the pretest and post-test after receiving an explanation among junior high school students (p&lt;0.05). Conclusion: A fixed orthodontic treatment case was determined to be treated by an orthodontic specialist, and the general dentist has a limited range of cases treated with removable orthodontic appliances. Dental artisans can legally only treat removable dental prostheses. This information was conveyed to junior high school students; hence, they have been cautioned about choosing the right operator.
Online motion planning of mobile cable-driven parallel robots for autonomous navigation in uncertain environments

Abstract Mobile cable-driven parallel robots (MCDPRs) offer expanded motion capabilities and workspace compared to traditional cable-driven parallel robots (CDPRs) by incorporating mobile bases. However, additional mobile bases introduce more degree-of-freedom (DoF) and various constraints to make their motion planning a challenging problem. Despite several motion planning methods for MCDPRs being developed in the literature, they are only applicable to known environments, and autonomous navigation in unknown environments with obstacles remains a challenging issue. The ability to navigate autonomously is essential for MCDPRs, as it opens up possibilities for the robot to perform a broad range of tasks in real-world scenarios. To address this limitation, this study proposes an online motion planning method for MCDPRs based on the pipeline of rapidly exploring random tree (RRT). The presented approach explores unknown environments efficiently to produce high-quality collision-free trajectories for MCDPRs. To ensure the optimal execution of the planned trajectories, the study introduces two indicators specifically designed for the mobile bases and the end-effector. These indicators take into account various performance metrics, including trajectory quality and kinematic performance, enabling the determination of the final following trajectory that best aligns with the desired objectives of the robot. Moreover, to effectively handle unknown environments, a vision-based system utilizing an RGB-D camera is developed, allowing for precise MCDPR localization and obstacle detection, ultimately enhancing the autonomy and adaptability of the MCDPR. Finally, the extensive simulations conducted using dynamic simulation software (CoppeliaSim) and the on-board real-world experiments with a self-built MCDPR prototype demonstrate the practical applicability and effectiveness of the proposed method.
Cost‐ and performance‐aware resource selection for parallel software on heterogeneous cloud

Summary Cloud providers offer flexible infrastructures and on‐demand services, including the capability to deploy low cost virtual resources of many different types. However, the diversity of cloud resources followed by the important trade‐off between cost and performance makes the resource selection a challenging task for users in the case of parallel communication‐intensive software. The paper presents cost‐ and performance‐aware resource selection for parallel discrete element method (DEM) software as a service (SaaS) on heterogeneous OpenStack cloud. The developed resource selection uses preliminary application‐specific benchmarks of size smaller than targeted problems and the performance prediction based on speedup of parallel computations to obtain Pareto optimal solutions and to select the best configuration of containers from user's perspective. Hybrid parallelization of DEM software is developed by using OpenCL for shared‐memory multi‐core architectures and MPI for internode communications on distributed‐memory computer clusters. Round up and proportional pricing schemes are examined and compared from a user's perspective. Lower cost of computations obtained by using the proportional pricing scheme is always preferable for users. However, the difference approaches 1.0% of the cost calculated by using proportional pricing scheme, when long lasting computations are performed. The prediction tends to underestimate the execution time of DEM SaaS, but its accuracy is sufficient to obtain the same Pareto optimal solutions by using measured and predicted execution times. Pareto front and linear scalarization propose to select configurations of containers capable of exploiting higher memory bandwidth, which is specific to memory bandwidth bound DEM computations.
Distributed Secure Storage System Using TPA and The Blockchain

With the fast growth and development of cloud computing technology, grabs the more users nad requires big storage of data in the available storage. This integrity meeting the performance requirements has become a important research area among various academic and research community. Provable Data Ownership (PDP) based on Trusted Party Auditor (TPA) is an important method to solve the big data and storage issues. The recent emergence of decentralized storage based on block-chain technology become the most easy way to prove the ownership of given data. However, it does not seamlessly connect to powerful cloud storage. In this paper, we propose a PDP model based on blockchain and TPA (BTPDP).Based on this, a distributed secure file system (FS) is implemented to achieve his BTPDP authentication of large amounts of electronic data.
ECHO: A hierarchical combination of classical and multi-agent epistemic planning problems

Abstract The continuous interest in Artificial Intelligence (AI) has brought, among other things, the development of several scenarios where multiple artificial entities interact with each other. As for all the other autonomous settings, these multi-agent systems require orchestration. This is, generally, achieved through techniques derived from the vast field of Automated Planning. Notably, arbitration in multi-agent domains is not only tasked with regulating how the agents act, but must also consider the interactions between the agents’ information flows and must, therefore, reason on an epistemic level. This brings a substantial overhead that often diminishes the reasoning process’s usability in real-world situations. To address this problem, we present ECHO, a hierarchical framework that embeds classical and multi-agent epistemic (epistemic, for brevity) planners in a single architecture. The idea is to combine (i) classical; and(ii) epistemic solvers to model efficiently the agents’ interactions with the (i) ‘physical world’; and(ii) information flows, respectively. In particular, the presented architecture starts by planning on the ‘epistemic level’, with a high level of abstraction, focusing only on the information flows. Then it refines the planning process, due to the classical planner, to fully characterize the interactions with the ‘physical’ world. To further optimize the solving process, we introduced the concept of macros in epistemic planning and enriched the ‘classical’ part of the domain with goal-networks. Finally, we evaluated our approach in an actual robotic environment showing that our architecture indeed reduces the overall computational time.
Application of The Think Pair and Share on Writing Skills of Story Text in Class X SMA Students

This study was conducted to determine the application of the think, pair, and share models to the skills of writing saga texts. The method used when researching experimental methods is to determine the application of the think pair and share learning model in the experimental class with the ordinary learning model in the control class. Collecting data using pre-test and post-test in the control and experimental classes. The data were analyzed using the SPSS 24 application by looking at the scores obtained by the students, the normality test to determine whether the class was usually distributed, and finally, the t-test to determine the application of the think, pair, and share model to determine the students' scores. The mean score of the control class in the initial test was 77.53 and the final test was 79.97. The result of the average value of the experimental class in the initial test was 79.94 and in the final test. get 82.28. Based on the results of the normality test obtained 0.200 data indicates that the distribution is normal. The t-test obtained data 0.000 &lt;0.05, meaning that H0 was rejected and Haa accepted, so implementing the think, pair, and share model on the skills of writing saga texts was affected.
Word Sense Disambiguation: Adaptive Word Embedding with Adaptive-Lexical Resource

Word sense disambiguation (WSD) is a subdomain of natural language processing (NLP). WSD mainly deals with identifying the correct sense of ambiguous words. The discussed disambiguation model is tested for homonymous and polysemous words, and the same is tested with SemCor, OMSTI, and the newly generated Adaptive-Lex dataset. The Adaptive-Lex dataset is generated by considering the scope of SemCor and OMSTI. The discussed dataset is constructed by considering the dynamic features of ambiguous words. The main objective of this dataset is to represent ambiguous words with dynamic features. While dealing with ambiguous words, disambiguation is expected by considering available context information. The word vectors are constructed dynamically, and the dataset is also created by considering the countable features of ambiguous words. If the word vector is prepared concerning context information, it generates more fine results for WSD.
On the validity of crowdsourced data

This chapter demonstrates the validity of crowdsourced data by comparing the crowdsourced data from the VinKo project with traditionally collected data from the AThEME project. Both datasets target non-standard language varieties of the South Tyrol, Trentino, and Veneto regions in north-eastern Italy. Three different morphosyntactic phenomena are discussed, each relating to a particular language variety, providing evidence that the crowdsourced data is of comparable quality to the traditionally gathered data and has the added advantage of yielding a larger overall dataset covering a denser location network.
PC Voice Navigation using Python

With the use of a software programmer called PC Voice Navigation System, users may navigate and operate their laptops and desktops by speaking instructions. The development of a PC voice navigation system utilizing the Python programming language is covered in this research report. An intuitive and hands-free user experience is offered by the system thanks to the utilization of text-to-voice synthesis, natural language [1]. processing, and speech recognition. A personal assistant for Linux-based systems is what it seeks to create. Cortana for Windows and Siri for iOS are two examples of virtual assistants that inspire Jarvis. It's been built to offer a User-Friendly Interface (UFI) for carrying out a range of operations by using certain Well-Defined Commands. Either voice commands or
A cross‐sectional study of discipline‐based self‐perceived digital literacy competencies of nursing students

This study offers an empirical exploration of self-assessed digital competencies of students, most of whom studied in nursing courses, using a discipline-based self-assessment survey tool. A range of digital competencies were explored: information and communication technology proficiency and productivity, information literacy, digital creation, digital research, digital communication, digital learning and development, digital innovation, digital identity management and digital well-being.A cross-sectional empirical study.Quantitative data were collected from November to December 2021 via a questionnaire survey administered to students. Quantitative results were reported through descriptive statistical analysis. Mann-Whitney (U-test) and Kruskal-Wallis non-parametric statistical tests were used to identify statistically significant differences based on age demographics and pre- or post-registration course. Thematic analysis was utilized for survey open-ended questions data.Students reported low competencies in the following digital literacy dimensions, all of which were imperative for their studies and for their future professional careers: information literacy, digital research, digital innovation. Significant statistical subgroup differences were found between age demographics and pre/post-registration within most of the digital competence dimensions. The survey open-ended comments revealed that students encountered challenges around digital skills they had mostly developed via everyday life experiences and trial-and-error approaches.Increasing awareness of existing digital gaps and offering tailored digital skills enhancement can empower students as future-proof evidence-based practitioners in an evolving digital healthcare landscape.Highlights the importance of embedding digital literacy within nursing study programmes, as preparation for comprehensive patient healthcare.Offers insights into digital competencies gaps of nursing students. Proposes targeted educational digital skills training interventions. Stresses the value of academic staff supporting nursing students to develop digital skills in important areas of professional practice.JBI critical appraisal checklist. No patient or public contribution.
A New Approach based on Fuzzy Clustering and Enhancement Operator for Medical Image Contrast Enhancement

Background: Image enhancement is a very significant topic in image processing that improves the quality of the images. The methods of image enhancement are classified into 3 categories. They include histogram method, fuzzy logic method and optimal method. Studies on image enhancement are often based on rules: if it's bright then it's brighter, if it's dark then it's darker and using the global approach. So, it's hard to enhance objects in all dark and light areas, as in the medical images. Objective: Input data is downloaded from the link: http://www.med.harvard.edu/AANLIB. Method: This paper introduces a new algorithm for enhancing medical images that is called the medical image enhancement based on cluster enhancement (MIECE). Firstly, the input image is clustered by the algorithm of fuzzy clustering. Then, the upper bound, and lower bound are calculated according to cluster. Next, the sub-algorithm is implemented for clustering enhancement using an enhancement operator. For each pixel, the gray levels for each channel (R, G, B) are transformed with this sub-algorithm to generate new corresponding gray levels. Because after clustering, each pixel belongs to each cluster with the corresponding membership value. Therefore, the output gray level value will be aggregated from the enhanced gray levels by the sub-algorithm with the weight of the corresponding cluster membership value. Results: This paper experiences the method MIECE with input data downloaded from the link: http://www.med.harvard.edu/AANLIB. The experimental results are compared with some recent methods that include: SGHIE (2017), Ying (2017) and KinD++ (2021). Conclusion: This paper introduces the new algorithm which is based on cluster enhancement (MIECE) to enhance the medical image contrast. The experimental results show that the output images of the proposed algorithm are better than some other recent methods for enhancing dark objects.
What can monetary policy tell us about Bitcoin?

Abstract Bitcoin enthusiasts argue that it is free from central banks decisions and it is a hedge against inflation. Using high-frequency monetary surprises associated with decisions made by the Fed and the ECB, I show that these claims are not supported by the data. Bitcoin systemically reacts to monetary and central bank information shocks. I find that these reactions vary over time: not only by changing the magnitude but sometimes sign of reaction. Fed’s disinflationary shocks increase Bitcoin price, while the ECB’s decrease, hence providing little support for it as an inflation hedge.
Analysis on the Optimum Tilt and Azimuth Angles for Fixed-Tilt PV Systems

The integration of photovoltaics (PV) will impact the future prices of electricity, and with their increased integration expected to lead to a reduction in the demand for traditional power sources, resulting in a decrease in electricity prices. As more PV is used, it is expected that the shape of the daily price of electricity will become much more predictable, with lower prices during the middle of the day and higher prices during the morning and evening. This paper analyses the tilt and azimuth angles that will yield maximum revenue for fixed-tilt PV systems taking into account the electricity prices on the day ahead markets.
A Comparative Evaluation of Augmented Reality Frameworks: A Plane Mapping and Resource Utilisation Perspective

Augmented reality (AR) plane detection is an important application in several areas such as advertising, training, and supporting visually impaired people. This work compared the capabilities of ARCore and ARKit, which are two major AR frameworks, in plane detection using several Android and Apple mobile devices. The test examined plane detection and mapping under different lighting conditions as well as runtime, overall system efficiency and optimization across the different frameworks. Findings within the context of the study show that ARKit was on average more reliable and accurate at plane detection and mapping in comparison to ARCore. ARKit had a 14.84% computational optimization lead over ARCore, while ARCore was more efficient in RAM utilisation than ARKit within the parameters that were tested in this study. The result of this study could guide future prototyping and mobile AR plane detection application development.