Reviewerly/exHarmony huggingface dataset:
Geochemistry and Structural Inputs of the Mineralized Quartz Veins from Imonga Mining Area, Maniema, DR Congo: General Overview

The quartz veins in the Imonga region are confined to the primary ENE-WSW trend, which is quite consistent with the Kibara metasediments.The objective of this work was to study the topographic, structural and geochemical features of the mineralized quartz veins in the Imonga area.Field investigations have shown that the area is composed of seristoschists and quartzites, with seristoschists more abundant than quartzite.Mineralized quartz veins found with sericite schist have different compositions between quartz, iron oxide ± sericite and mafic minerals.Mineralized quartz veins contain 0.50 to 3.44 ppm Au, while quartz veins that are considered sterile contain less than 0.50 ppm Au.Since the concentration correlates with Au in samples with moderate Au content, Au exhibits a positive correlation with only Mo while it is negatively correlated with other metals such as Cu, Pb, Zn and Co.The studied ridges show signs of multiphase strain and it is associated with D2 strain in the region.The presence of quartz veins in the shear zone suggests that the site of gold mineralization is epigenetic.The main type of weathering includes sericitization.The general directional trend of Imonga quartz veins can be exploited for future geological exploration.
Internet Penetration’s Impact on Gross Domestic Product per Capita of African Countries

The internet has become an essential part of everyday life and is impacting various aspects of society.One field of interest is how internet usage affects economic growth, particularly in terms of GDP per person.This article aims at highlighting the effect of internet penetration on Africa countries gross domestic product per capita (GDP per capita).To achieve this, we used panel data for African countries over the period 1996 to 2019.The study conducted various statistical tests, such as causal effect, co-integration, and mediation tests, to identify which variables are useful in predicting GDP per capita.The findings show that the internet has a significant impact on the economy of African countries.Findings suggest that internet significantly impacts GDP per capita in African countries, whether evaluating within-effect (over time) or between-effect (across countries at a given time).In addition, the result reveals that Secure internet servers are fundamental if a country is to rely on the internet to boost its economy.The study highlights the need to invest in internet infrastructure and increase internet penetration to promote economic growth in Africa, due to the importance of the internet in today's world.
Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure

Risk stratification of patients with congestive heart failure (HF) is vital in clinical practice. The aim of this study was to construct a machine learning model to predict the in-hospital all-cause mortality for intensive care unit (ICU) patients with HF.eXtreme Gradient Boosting algorithm (XGBoost) was used to construct a new prediction model (XGBoost model) from the Medical Information Mart for Intensive Care IV database (MIMIC-IV) (training set). The eICU Collaborative Research Database dataset (eICU-CRD) was used for the external validation (test set). The XGBoost model performance was compared with a logistic regression model and an existing model (Get with the guideline-Heart Failure model) for mortality in the test set. Area under the receiver operating characteristic cure and Brier score were employed to evaluate the discrimination and the calibration of the three models. The SHapley Additive exPlanations (SHAP) value was applied to explain XGBoost model and calculate the importance of its features.The total of 11,156 and 9,837 patients with congestive HF from the training set and test set, respectively, were included in the study. In-hospital all-cause mortality occurred in 13.3% (1,484/11,156) and 13.4% (1,319/9,837) of patients, respectively. In the training set, of 17 features with the highest predictive value were selected into the models with LASSO regression. Acute Physiology Score III (APS III), age and Sequential Organ Failure Assessment (SOFA) were strongest predictors in SHAP. In the external validation, the XGBoost model performance was superior to that of conventional risk predictive methods, with an area under the curve of 0.771 (95% confidence interval, 0.757-0.784) and a Brier score of 0.100. In the evaluation of clinical effectiveness, the machine learning model brought a positive net benefit in the threshold probability of 0%-90%, prompting evident competitiveness compare to the other two models. This model has been translated into an online calculator which is accessible freely to the public (https://nkuwangkai-app-for-mortality-prediction-app-a8mhkf.streamlit.app).This study developed a valuable machine learning risk stratification tool to accurately assess and stratify the risk of in-hospital all-cause mortality in ICU patients with congestive HF. This model was translated into a web-based calculator which access freely.
Machine Learning and Bibliographic Data Universe: Assessing Efficacy of Backend Algorithms in Annif through Retrieval Metrics

This research study utilizes an open source AI/ML framework named Annif, developed by the National Library of Finland, to explore the feasibility of automated subject indexing. The framework loads the linked open data format of LCSH and trains the model with a comprehensive training dataset comprising MARC records downloaded from different libraries all over the world. It then compares a set of selected machine learning backends of Annif, namely TF-IDF, Omikuji, and Neural Network, against a set of retrieval metrics to measure the suitability of these backends for the bibliographic data universe. The study concludes that the fusion backend in Annif named Neural Network has the potential to provide support for an automated subject indexing system.
Geochemistry and Petrology of Metasediments Associated with Gold Mineralization in Imonga Area, Eastern DR Congo

The Imonga sector is located in the Eastern of DR Congo, precisely within the Maniema province located in the Central African Karagwe Ankole belt, which forms together with the Kibara belt a Mesoproterozoic geological structure.This study gives fresh information on the origin of the metasediments in this zone, their geochemical and petrographic characteristics, and the genetic model of the gold mineralization that they contain.Field observations, as well as petrographic and geochemical studies reveal that the metasediments consist of sericitoschists and quartzites.Petrographic work revealed varying proportions of sericite and quartz with subordinate iron-oxide minerals.Geochemically, most of the analyzed metasediments displayed higher SiO2 contents and enriched in Fe2O3.They belong to the groups of shales and sand (sometimes rich in iron), litharenite and grauwakes derived from quartzose sedimentary and mafic igneous provenance.Their protholites are intermediate altered (70 < CIA < 90) andesitic, granite and granodioritic rocks emplaced in a geotectonic active continental margin.Gold mineralization in the Imonga sector occur in veins.Gold mineralization is controlled by veins which occur as micro veins with small thickness.The mineralization consists of specific grains and nanoparticles pyrite associated with gold, hematite and goethite.The hydrothermal alteration accompanying this mineralization consists of silica, goethite and sericite-carbonate.
Development of manufacturable, testable, and reliable transmit-receive modules for satellite radar imaging payload

Most modern computer applications use reversible logic gates to solve power dissipation issues. This manuscript uses an efficient multi-functional reversible arithmetic and logical unit (MF-RALU) to perform 30 operations. The 32-bit MF-RALU includes arithmetic, logical, complement, shifters, multiplexers, different adders, and multipliers. The multi-bit reversible multiplexers are used to construct the MF-RALU structure. The Reduced instruction set computer (RISC) processor is designed to realize the functionality of the MF-RALU. The MF-RALU can perform its operation in a single clock cycle. The 1-bit RALU is developed and compared with existing approaches with improvements in performance metrics. The 32-bit reversible arithmetic units (RAUs) and reversible logical units (RLUs) are constructed using 1-bit RALU. The MF-RALU and RISC processor are synthesized individually in the Vivado environment using Verilog-HDL and implemented on Artix-7 field programmable gate array (FPGA). The MF-RALU utilizes a &amp;lt;11% chip area and consumes 332 mW total power. The RISC processor utilizes a &amp;lt;3% chip area and works at 483 MHZ frequency by consuming 159 mW of total power on Artix-7 FPGA.
Detection and Classification of Cyberbullying Using CR*

Abstract: Cyberbullying is one of the latest threats in the online world, affecting millions of people worldwide. The detection of cyberbullying became a challenging task due to the complexity involved. In this paper, we propose a novel approach for cyberbullying detection using CR* which concatenates the deep learning model’s features such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) which helps in detecting text, audio, and emoji. Initially, we collect a dataset of cyberbullying messages from social media platforms. Then we integrate convolutional neural networks and recurrent neural networks to build our deep learning model entitled Convolutional Recurrent (CR*) to extract features from a combination of the text data, emoji, and audio. The multimodal features were then concatenated and passed through fully connected layers for classification. The proposed approach can be useful for detecting cyberbullying on various online platforms and can help prevent the spread of cyberbullying.
Implementation of FAST in Two Digital Repositories: Breaking Silos, Unifying Subject Practices

This study traces evolving approaches to the use of the FAST (Faceted Application of Subject Terminology) in digital repositories at Atkins Library at the University of North Carolina at Charlotte, where changes in staffing, the launch of an institutional repository, and efforts to address problematic language in metadata have necessitated a shift from an in-depth indexing approach to FAST to a lightweight “tagging” model more suited to present-day metadata needs. Despite the subject schema’s apparent simplicity, Atkins’ experience with FAST has shown that it still requires significant time, effort, and experimentation in order to deploy it to best effect.
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Information Technology Organisations

The study's main objective is to analyse the level of cloud computing adoption and usage during COVID-19 in Sri Lanka, especially in Information Technology (IT) organisations.Using senior IT employees, this study investigates what extent their organisation adopts with cloud computing, the level of cloud computing usage, current use of cloud service model, usage of cloud deployment model, preferred cloud service providers and reasons for adopting and not adopting cloud computing.The study also describes why cloud computing is a solution for new normal situations and the cloud-enabled services used during and after the COVID-19 pandemic.The finding suggests that 87.7% of the organisations currently use cloud-enabled services, whereas 12.3% do not and intend to adopt.Considering the benefits, cloud computing is the solution post COVID-19 pandemic to run the business way forward.
Understanding the Importance and Challenges of Animatronic Humanoid Prototypes Production in the Robotic Field in the United of States of America: Policy Implications

This study analyzes the importance and challenges of animatronic humanoid prototypes production within the robotic field in the United States of America to influence Artificial Intelligence (AI) policy.In fact, animatronic humanoid prototype has greatly inspired more designers and developers' interest in the study of human and robot interaction in both scientists and enthusiast alike to aid in robotic production.The study adopted a narrative literature review and Boolean search technique to identify 22 researches and review articles that are related to applications, challenges and importance of animatronic humanoid robotics production and applications.As part of the findings for the study utilized for the article, many scholars made specific inferences to the robotic applications in firms, businesses, and nations.Out of the twenty-two articles, five of the researchers, thus 22%, underscored and also perceived that robots and machines with biped locomotion is one of the achievements of humanoid robotic production.Researchers of 4 articles-thus 18% --explicitly stated in their research that one of the achievements of humanoid robotic production is "learning capabilities in robots".The study further revealed that some of the evidenced-based research applications for humanoid robotic products include the following: Mitsuo Kawato of ATR Japan proposed using humanoid robots to study human behavior; In Europe-EU-funded projects, which include the large-scale NEUROBOTICS project; RobotCub project; Human Brain Project; Atlas humanoid robot by Boston Dynamics; and Wisdom of the robot Sophia in engaging in conversations, etc. Above all, this study revealed that complex environment, perception, human robot interaction, and collaboration in real life are some of the challenges identified in the literature.Therefore, in order to overcome such challenges-for the body of a robot, designers need to rethink the materials that robots are made of and leverage morphological computation to intrinsically balance and compensate for motion and dynamic behavior.Also, investors, policymakers, and public officials should invest more in innovative robotic production in order to promote businesses, bring about efficiency in operations, and to increase productivity.
The Kyoto School and Sound Art

This paper explores the thought of key Kyoto School philosophers Nishida Kitar_ō, Tanabe Hajime, and Nishitani Keiji as a novel framework through which to consider the historical development and philosophical underpinnings of sound art. Connections and divergences between these thinkers and key developments in the history of sound art are analyzed and explored. After an introduction to Kyoto School thought, the intersections between the Kyoto School and relevant 20th-century art movements are examined. The paper then spotlights four important themes developed in Kyoto School thought and analyzes key sound art pieces in relation to these themes. The article closes with a brief concluding reflection.
Formation of the communicative competence of Russian and foreign electrical students during the socio-cultural project

Introduction. The problem of the formation of communicative competence among Russian and foreign students in the classroom in a foreign language requires the introduction of an integrated approach to learning. Such an innovative form of education as a project, namely, a socio-cultural project that includes professional tests, provides an opportunity for intercultural mutual enrichment and the use of language knowledge in everyday and professional communication. Theoretical analysis of the material on this issue led to the conclusion that it is necessary to introduce professional tests in Russian and foreign languages into the practice of training future engineers. Empirical analysis. The author of the article offers an experimental experience – intercultural interaction in the system of professional trials of Russian and foreign students. Foreign students get acquainted with the basics of electrical engineering already at the stage of pre-university training, which contributes to the development of interest in their future specialty. Russian students gain experience of communication in a foreign language as part of their professional activities. Conclusion. The novelty of this form of education lies in the fact that the cooperation of Russian and foreign students is not limited to one-time joint professional practices, but is combined into a sociocultural professionally oriented project.
Discussion competence in the structure of modern engineer training

Introduction. In the course of the study we have identified a contradiction consisting, on the one hand, in the increased requirements to the level of communicative training of technical specialists in post-industrial society. On the other hand, there is an almost complete lack of research on the problem of forming a discussion competence of future engineers. Based on the abovementioned, the author has formulated the goal of this paper. It consists in defining the place of the discussion competence in the structure of modern engineer training, caused by the networking and socio-technical transformation of technical activity. Theoretical analysis. The generalization of philosophical and pedagogical approaches to the study of engineering activity (EA) allowed us to classify the functions of a modern engineer according to the nature of the tasks to be solved. The classification revealed that under the influence of networking and socio-technical transformation of engineering activity there is a growing demand for engineers who are ready to implement professional tasks based on socio-technical functions. The correlation between the engineers’ mastery of the discussion competence and the effectiveness of their implementation of sociotechnical functions: design, research, organizational and managerial, was revealed. Conclusion. The study made it possible to draw a general conclusion about the need to distinguish the discussion competence as an independent element in the structure of universal competences and to include discussion training in the curriculum of technical universities in two directions: traditional (offline discussions) and network (online discussions).
Errors in Author Names and Affiliation

Errors in Tables 1 and2 and in eTables 1 and 3 in the Supplement: In the Original Investigation titled "Association of Whole Blood With Survival Among Patients Presenting With Severe Hemorrhage in US and Canadian Adult Civilian Trauma Centers," 1,2 published online January 18, 2023, data errors appeared in the "time to first blood product transfusion" row in Table 1 and eTable 3 in the Supplement and in multiple cells in Table 2 and eTable 1 in the Supplement.This article has been corrected online.1.
Object Detection Optimization by Comparing SSD and SSDLite

Convolutional Neural Networks are being widely used in computer vision tasks including object detection. For resource-limited environments, the combination of MobileNetV2 and SSDLite is a feasible solution, but the structure of SSDLite has not been well analyzed. This paper compares the structure of SSDLite with that of the previous version, SSD (Single-Shot multibox Detector), finding out possible variations. With the application of the variations, the experimental results shows that SSDLite can be improved by enhancing the capability of detecting small objects. The MS COCO AP can be raised by 0.6% point with complexity increased a little.
Design-based research for the development of augmented reality applications: the case of virtual playground

Augmented Reality applications for education change the classroom experience for both teachers and students. However, the use of these tools is not widespread yet. Current applications still have some limitations, such as: hard to use interfaces, lack of freedom to create content, lack of support for a shared experience among others. This work presents the update of Virtual Playground, an application collaboratively created with language teachers, designers and engineers; and Virtual Playground Creator, an authorship tool for this application. This process was developed following Design-based research methodology in two cycles of ideation, implementation and testing. We present the updates and results regarding the second round of six tests of Virtual Playground with teachers. The main goals were to evaluate if Virtual Playground Creator allows teachers to create AR experiences to use in classrooms; and (2) investigate if there were missing or unnecessary features in Virtual Playground Creator. Results have shown that the tool allowed teachers to work with diverse skills and competences. The Virtual Playground Creator prototype allowed the teacher to create their AR experiences proposed, although, in some cases modifications were needed. The main issues were related to the limited 3D library in the prototype evaluated and lack of creation possibilities.
A multiscale algorithm for computing realistic image transformation: application to the modelling of fetal brain growth

We propose to establish a continuous trajectory of brain growth across pregnancy using geodesic regression in the Large Deformation Diffeomorphic Metric Mapping framework. One is usually faced with two issues when estimating high dimensional transformations: the elevated risk of trapping the optimization in an unrealistic local minimum and the fact that deformations are constrained to a single scale. To tackle these issues, we introduce a coarse-to-fine optimization strategy based on multiscale parametrizations of objects and deformations. Experiments on fetal brain Magnetic Resonance Images show that the multiscale strategy can generate more natural images of the fetal brain across pregnancy, which offers an interesting perspective for the quantitative analysis of normal and abnormal brain growth.
Implementing arbitrary quantum operations via quantum walks on a cycle graph

The quantum circuit model is the most commonly used model for implementing quantum computers and quantum neural networks whose essential tasks are to realize certain unitary operations. The circuit model usually implements a desired unitary operation $U(N)$ by a sequence of single-qubit and two-qubit unitary gates from a universal set. Although this certainly facilitates the experimentalists as they only need to prepare several different kinds of universal gates, the number of gates required to implement an arbitrary desired unitary operation is usually large. Hence, the efficiency in terms of the circuit depth or running time is not guaranteed. Here we propose an alternative approach; we use a simple discrete-time quantum walk (DTQW) on a cycle graph to model an arbitrary unitary operation $U(N)$ without the need to decompose it into a sequence of gates of smaller sizes. Our model is essentially a quantum neural network based on DTQW. Firstly, it is universal as we show that any unitary operation $U(N)$ can be realized via an appropriate choice of coin operators. Secondly, our DTQW-based neural network can be updated efficiently via a learning algorithm, i.e., a modified stochastic gradient descent algorithm adapted to our network. By training this network, one can promisingly find approximations to arbitrary desired unitary operations. With an additional measurement on the output, the DTQW-based neural network can also implement general positive-operator-valued measure (POVM) measurements. We show its capacity in implementing arbitrary two-outcome POVMs via numeric simulation. We further demonstrate that the network can be simplified and can overcome device noises during the training so that it becomes more friendly for laboratory implementations. Our work shows the capability of the DTQW-based neural network in quantum computation and its potential in laboratory implementations.