Miracle-dz/UniFAR-FTUs huggingface dataset:
WhatsApp as a tool for Building a Learning Community

WhatsApp groups are considered useful for creating and supporting virtual communities. This mixed method case study explores the patterns that emerged when we used WhatsApp to create a community of learning during the multimodal presentation of a postgraduate course in research proposal writing. Three questions drive the study: (1) What kinds of messages were sent? (2) Who participated and how? (3) How did the learners experience the use of WhatsApp in support of the community of learning? Data were collected during the presentation of a five-evening proposal writing workshop that was conducted using Zoom, WhatsApp, and YouTube as communication platforms. To answer the first question, all WhatsApp messages were analysed through both manual (individual) and automated (meta) content analysis. The third question was answered by analysing students’ responses to the end-of session online questionnaire that was administered via Google Forms. Results indicate that many messages were administrative in nature, while the most notable academic messages concerned a discussion of empirical research, research paradigms, and research design. The presenter and co-presenter together accounted for almost half of the messages sent, and the other messages were evenly distributed between members of the class, with five notably vocal students. Student responses indicated that they liked the high level of interactivity and the content, but that they disliked the time it took to form groups. The paper contributes to the literature by showing how this use of multiple WhatsApp groups was effective in creating a sense of community by facilitating access, relationships, vision, and function (West and Williams, 2017). More research is needed in determining the extent to which students might support one another with conversations outside of the main WhatsApp group.
RAT-RS: a reporting standard for improving the documentation of data use in agent-based modelling

ABSTRACT This article describes and justifies a reporting standard to improve data use documentation in Agent-Based Modelling. Following the development of reporting standards for models themselves, empirical modelling has now developed to the point where these standards need to take equally effective account of data use (which previously has tended to be an afterthought to model description). It is particularly important that a standard should allow the reporting of the different uses to which data may be put (specification, calibration and validation), but also that it should be compatible with the integration of different kinds of data (for example, survey, ethnographic and experimental) sometimes known as mixed methods research. The article motivates the need for standards generally, and positions the distinctive contribution of the RAT-RS reporting standard. It describes how the standard was developed to ensure its usability, presents and explains it, and describes possibilities for future development.
Development and Functions of Mitochondria in Early Life

Mitochondria are highly dynamic organelles of bacterial origin in eukaryotic cells. These play a central role in metabolism and adenosine triphosphate (ATP) synthesis and in the production and regulation of reactive oxygen species (ROS). In addition to the generation of energy, mitochondria perform numerous other functions to support key developmental events such as fertilization during reproduction, oocyte maturation, and the development of the embryo. During embryonic and neonatal development, mitochondria may have important effects on metabolic, energetic, and epigenetic regulation, which may have significant short- and long-term effects on embryonic and offspring health. Hence, the environment, epigenome, and early-life regulation are all linked by mitochondrial integrity, communication, and metabolism.
A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management

Nowadays, Artificial Intelligence (AI) and its subfields, Machine Learning (ML) and Deep Learning (DL), are used for a variety of medical applications. It can help clinicians track the patient’s illness cycle, assist with diagnosis, and offer appropriate therapy alternatives. Each approach employed may address one or more AI problems, such as segmentation, prediction, recognition, classification, and regression. However, the amount of AI-featured research on Inherited Retinal Diseases (IRDs) is currently limited. Thus, this study aims to examine artificial intelligence approaches used in managing Inherited Retinal Disorders, from diagnosis to treatment. A total of 20,906 articles were identified using the Natural Language Processing (NLP) method from the IEEE Xplore, Springer, Elsevier, MDPI, and PubMed databases, and papers submitted from 2010 to 30 October 2021 are included in this systematic review. The resultant study demonstrates the AI approaches utilized on images from different IRD patient categories and the most utilized AI architectures and models with their imaging modalities, identifying the main benefits and challenges of using such methods.
Automatic Traffic Sign Recognition System Using CNN

In recent times, self-driving vehicles have been widely adopted across different countries as they are equipped to drastically reduce the number of road accidents and congestion on the road thereby improving the traffic efficiency. To detect, identify and label the traffic signs on the road in order to help the “Advanced Driver Assistance Systems (ADAS)” in these autonomous vehicles with navigation details, a “Traffic Sign Recognition (TSR) System” using a deep convolutional neural network model, Mask RCNN (Mask Regional Convolutional Neural Network) is proposed in this paper that aims to help the autonomous vehicles comprehend the road ahead and safely navigate to the desired destination. This paper presents the detection and labelling of Indian and European Signs and also the results of the system working efficiently under various challenging visibility conditions. The results obtained show that the Mask RCNN model has recorded higher performance compared to all the other CNN models that have been previously used for traffic sign recognition.
Stent Ureteral Olvidado, Nuestra experiencia en Instituto Salvadoreño del Seguro Social (ISSS).

Objetivo: comparar el manejo dado al stent ureteral calcificado en nuestra institución con el propuesto por protocolo FECAL, identificar la causa principal de stent ureteral olvidado. Material y método: Se realizó un estudio descriptivo, retrospectivo, observacional, transversal, de los pacientes manejados con catéter olvidado en ISSS periodo de enero 2018 a marzo 2019. Resultados: se comparó el manejo dado al catéter calcificado olvidado con base al protocolo FECAL del centro médico de Loyola, Maywood, Ilinois. Se describe el manejo de 15 pacientes con estén retenido según el protocolo FECAL con la diferencia en la utilización de litotricia neumática intracorporea en lugar de utilizar energía laser, se encontró que la falta de información es el factor principal para el desarrollo de esta complicación. Conclusiones: Al no contar con un protocolo adecuado para el manejo de pacientes con catéter doble j y dificultades propias del sistema asistencial que puedan evitar que la falta de información sea un factor crítico para el desarrollo de la complicación estudiada.
Medical Education Research Priorities for Master’s Students in Sudan: A Qualitative Study

Background: Believing research prioritization can increase the research value and augment advances in the scientific base of any profession, this study aimed to identify research priorities relevant for medical education in Sudan. Methods: The study was designed to capture a qualitative exploration of multiple stakeholders’ opinions. Data were collected from two stakeholder groups: experts and students of medical education in Sudan. Semi-structured individual interviews and focus groups from 10 experts and 41 learners were incorporated. The categories and subcategories, derived from experts’ data inductively through constant comparison, enhanced the development of a coding framework. This framework was used deductively to analyze the beliefs and opinions of the learners leading to a list that exemplifies priorities for medical education research. Results: A set of seven principal and three minor themes were identified, the principal themes were: Curriculum Content, Design, and Delivery; Faculty Development; Assessment Methods; Research; Accreditation, Evaluation, and Quality; Professionalism; and Student Selection and Support. Four themes were identified to justify participants’ selections: Quality education and patient care; Accreditation of schools; Curricula contextualization; and Documentation of success stories. Conclusion: This instrumental research fulfilled its aim to mount a set of medical education research priorities grounded in collected perceptions with optimal stakeholder engagement. Importantly, there were many more similarities than differences between these findings and those from other countries which suggests that some topics are relevant across the international arena and one may propose the commencements of an international medical education agenda.
WGANVO: odometría visual monocular basada en redes adversarias generativas

Los sistemas tradicionales de odometría visual (VO), directos o basados en características visuales, son susceptibles de cometer errores de correspondencia entre imágenes. Además, las configuraciones monoculares sólo son capaces de estimar la localización sujeto a un factor de escala, lo que hace imposible su uso inmediato en aplicaciones de robótica o realidad virtual. Recientemente, varios problemas de Visión por Computadora han sido abordados con éxito por algoritmos de Aprendizaje Profundo. En este trabajo presentamos un sistema de odometría visual monocular basado en Aprendizaje Profundo llamado WGANVO. Específicamente, entrenamos una red neuronal basada en GAN para regresionar una estimación de movimiento. El modelo resultante recibe un par de imágenes y estima el movimiento relativo entre ellas. Entrenamos la red neuronal utilizando un enfoque semi-supervisado. A diferencia de los sistemas monoculares tradicionales basados en geometría, nuestro método basado en Deep Learning es capaz de estimar la escala absoluta de la escena sin información extra ni conocimiento previo. Evaluamos WGANVO en el conocido conjunto de datos KITTI. Demostramos que nuestro sistema funciona en tiempo real y la precisión obtenida alienta a seguir desarrollando sistemas de localización basados en Aprendizaje Profundo.
Learning Virtual Reality: Developing Immersive Experiences and Applications for Desktop, Web, and Mobile

As virtual reality approaches mainstream consumer use, a vibrant development ecosystem has emerged in the past few years. This hands-on guide takes you through VR development essentials for desktop, mobile, and browser-based applications. You'll explore the three go-to platforms-OculusVR, Gear VR, and Cardboard VR-as well as several VR development environments, programming tools, and techniques. If you're an experienced programmer familiar with mobile development, this book will help you gain a working knowledge of VR development through clear and simple examples. Once you create a complete application in the final chapter, you'll have a jumpstart on the next major entertainment medium. Learn VR basics for UI design, 3D graphics, and stereo rendering Explore Unity3D, the current development choice among game engines Create native applications for desktop computers with the Oculus Rift Develop mobile applications for Samsung's Gear VR with the Android and Oculus Mobile SDKs Build browser-based applications with the WebVR Javascript API and WebGL Create simple and affordable mobile apps for any smartphone with Google's Cardboard VR Bring everything together to build a 360-degree panoramic photo viewer
Vortex structure on twist-morphing micro air vehicle wings

Vortex formation has numerous influences on the aerodynamic characteristics of fixed-wing micro air vehicle wings. Despite the mature understanding of vortices on fixed-wing and flapping micro air vehicle wings, the behavior of vortices over the morphing micro air vehicle wing has not been fully explored. Thus, the current work is conducted to investigate the influence of vortex structure over a series of twist-morphing micro air vehicle wings. Twist morphing micro air vehicle and baseline wings are simulated through fluid–structure interaction analysis. The validation results for each wing exhibited good correlation in the overall lift coefficient distribution trend. The vortex formation results show that vortex formations are significantly altered throughout angle of attack changes. For a given angle of attack cases below the stall angle, each morphing wing exhibited higher intensities of tip vortex structure formations and leading edge vortex–tip vortex interactions compared to the baseline wings. Stronger leading edge vortex–tip vortex interactions improved the low-pressure region over the morphing wing surface and further induce better lift performance. In fact, the morphing wing with higher morphing force induces better lift performance.
FTIR and GCMS analysis of Euphorbia hirtaL. and its In-vitroAntibacterial and Antioxidant Activities

Introduction: Euphorbia hirtaL. is a common weed plant belongs to family Euphorbiaceae. Thepresent study brings out the importance of weed plants like E. hirtain medicine and health thecare system. Material and Methods:Plant E. hirtahas been collected from thegarden of Department of Botany, University of Delhi and plant leaves were extractedin ethanol by thesoxhlet method. Antibacterial screening has been done by disc-diffusion method. Theantioxidant potential has been determined by TPC, TFC, FRAP, Total antioxidants through phosphomolybdate assay and by DPPH radical scavenging activity. FTIR and GCMS analysis havebeen done for characterization.Result and Discussions:Plant E. hirtashowed significant antibacterial activity against Escherichia coli,Staphylococcus aureus, Bacilus subtilisand Pseudomonas aeruginosawith maximum ZOI diameter 19±0.5mmagainst S.aureusandP. aeruginosaboth. Plant E. hirtaL. showedsignificant antioxidant potential with 196.32 mg/g, 39.7133 mg/g, 595.99 mg/g, 525.84 mg/g values for TPC, TFC, FRAP activity and total antioxidants through phosphomolybdate assay respectively. Plant E. hirtaalso showedsignificant radical scavenging activity against DPPH free radical with amaximum % inhibition 68.505% at 1000μg/ml concentration. FTIR and GCMS of plant E. hirtahave confirmed the presence of various active functional groups and compounds.Conclusion:The current study justifies the importance of E. hirta L. a common weed asasource of natural antioxidants having antibacterial potential and its role in herbal medicines. ©2022iGlobal Research and PublishingFoundation. All rights reserved.
Sustainable Agroforestry for Soil Chemical Properties Improvement and Nutrients Availability in Agriculture Landscape Around Cyamudongo Isolated Forest, Rwanda

The protected areas of Rwanda are facing various challenges resulting from the anthropogenic activities of the surrounding communities, especially in the adjacent area to Cyamudongo isolated rain forest, which results in soil degradation. Therefore, this study aims to broaden current knowledge on the impact of sustainable Agroforestry (AF) on soil-selected chemical and physical properties. To understand this, the permanent sample plots (PSPs) were established mainly in the designed four transects of four km long originating on the boundary of the Cyamudongo isolated rain forest following the slope gradient ranging from 1286 to 2015 m asl. A total number of 73 PSPs were established in the Cyamudongo study area. The Arc Map GIS 10.4 was used to design and map the sampling areas while GPS was used for localization of plots centers. Statistical significance was analyzed through R-software. The recorded soil pH means value across in Cyamudongo study area is 4.2, which is strongly acidic. The tests revealed that the soil pH, C, N, C: N ratio, OM, NH4+, NO3-+NO2-, PO43-, and CEC were significantly different in various soil depths. The pH, N, C: N ratio, CEC, NH4+, PO43-, and Al3+ showed a significant difference across land uses whereas the C and NO3-+NO2- did not show any statistical difference. All tested chemical elements showed a statistical difference as far as altitude ranges are concerned. The only NH4+, PO43-, and CEC showed significant differences with time whereas all other remaining chemical elements did not show any statistical significance. The soil pH was very strongly correlated with CEC, Mg, and Ca in cropland (CL) whereas it was strongly correlated in both AF and natural forest (NF) except for Mg, which was moderately correlated in AF. Furthermore, its correlation with K was strong in CL, and moderate in AF while it was weak in NF. Finally, the pH correlation with Na was weak in both AF and CL whereas it was negligible in NF.
Materials And Design The Art And Science Of Material Selection In Product Design

Thank you for reading materials and design the art and science of material selection in product design. Maybe you have knowledge that, people have search hundreds times for their favorite books like this materials and design the art and science of material selection in product design, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some infectious virus inside their computer.
Intelligence-Led Policing and the New Technologies Adopted by the Hellenic Police

In the never-ending search by Law Enforcement Agencies (LEAs) for ways to reduce crime more effectively, the prevention of criminal activity is always considered the ideal solution. Since the 1990s, Intelligence-led Policing (ILP) was implemented in some forms by many LEAs around the world for crime prevention. Along with ILP, LEAs nowadays more and more turn to various new surveillance technologies. As a result, there are numerous studies and reports introducing some compelling results from LEAs that have implemented ILP, offering robust data around how the future of policing could be. In this context, this paper explores the most recent literature, identifying where ILP stands today in Greece and to what extent it could be a viable, practical approach to crime prevention. In addition, it is researched to what degree new technologies have been adopted by the European Union and the Hellenic Police in their “battle” against crime. It is concluded that most technologies are at the research stage, and studies are underway in many areas.
Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.
Shining Damaged Hearts: Immunotherapy-Related Cardiotoxicity in the Spotlight of Nuclear Cardiology

The emerging use of immunotherapies in cancer treatment increases the risk of immunotherapy-related cardiotoxicity. In contrast to conventional chemotherapy, these novel therapies have expanded the forms and presentations of cardiovascular damage to a broad spectrum from asymptomatic changes to fulminant short- and long-term complications in terms of cardiomyopathy, arrythmia, and vascular disease. In cancer patients and, particularly, cancer patients undergoing (immune-)therapy, cardio-oncological monitoring is a complex interplay between pretherapeutic risk assessment, identification of impending cardiotoxicity, and post-therapeutic surveillance. For these purposes, the cardio-oncologist can revert to a broad spectrum of nuclear cardiological diagnostic workup. The most promising commonly used nuclear medicine imaging techniques in relation to immunotherapy will be discussed in this review article with a special focus on the continuous development of highly specific molecular markers and steadily improving methods of image generation. The review closes with an outlook on possible new developments of molecular imaging and advanced image evaluation techniques in this exciting and increasingly growing field of immunotherapy-related cardiotoxicity.
Influence of substrate biasing on structural, chemical and electrical properties of Al2O3 thin films deposited by PEALD

High-k materials are needed to minimise the gate leakage current in high-speed and high-power switching applications. In this regard, aluminium oxide (Al2O3) deposited by plasma enhanced atomic layer deposition (PEALD) is gaining extensive attention to be used as high-k material in microelectronics. In this work, we studied the effect of substrate biasing during the oxidizing plasma step on physical, chemical and electrical properties of Al2O3 thin films grown by PEALD on silicon substrate. We show that the structural and electrical properties such as the flat band voltage, and chemical composition can be tuned with the applied substrate bias. Indeed, we highlight that the dielectric constant of the MIS capacitor decreases from 8.5 to 6.5 and the charge polarity of the film is modulated from negative to positive when the applied substrate bias is increased. Using morphological and structural characterisations, we show that the substrate bias significantly affects the chemical composition of Al2O3 thin film layer. Moreover, we highlight by cross-sectional transmission electron the presence of an interfacial layer between Si and Al2O3 which could significantly influence the electrical properties of the deposited thin film. The chemical composition of this interfacial layer can be controlled by the applied substrate bias. Using a series of energy dispersive x-ray experiments, we further confirm the formation of aluminosilicate under low substrate bias condition while silicon oxide is formed under high bias. These findings show that the substrate biasing plays a critical role in defining physical, chemical as well as electrical properties of the PEALD Al2O3 thin films.