bluuebunny/crossref_metadata_2025_split huggingface dataset:
Image Registration: Fundamentals and Recent Advances Based on Deep Learning
Authors: Chen Min , Tustison Nicholas J. , Jena Rohit , Gee James C. 
Published: July 2023

AbstractRegistration is the process of establishing spatial correspondences between images. It allows for the alignment and transfer of key information across subjects and atlases. Registration is thus a central technique in many medical imaging applications. This chapter first introduces the fundamental concepts underlying image registration. It then presents recent developments based on machine learning, specifically deep learning, which have advanced the three core components of traditional image registration methods—the similarity functions, transformation models, and cost optimization. Finally, it describes the key application of these techniques to brain disorders.
Classic Machine Learning Methods
Authors: Faouzi Johann , Colliot Olivier 
Published: July 2023

AbstractIn this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest neighbor methods, linear and logistic regressions, support vector machines, and tree-based algorithms. We also describe the problem of overfitting as well as strategies to overcome it. We finally provide a brief overview of unsupervised learning methods, namely, for clustering and dimensionality reduction. The chapter does not cover neural networks and deep learning as these will be presented in Chaps.3,4,5, and6.
Main Existing Datasets for Open Brain Research on Humans
Authors: Couvy-Duchesne Baptiste , Bottani Simona , Camenen Etienne , Fang Fang , Fikere Mulusew , Gonzalez-Astudillo Juliana , Harvey Joshua , Hassanaly Ravi , Kassam Irfahan , Lind Penelope A. , Liu Qianwei , Lu Yi , Nabais Marta , Rolland Thibault , Sidorenko Julia , Strike Lachlan , Wright Margie 
Published: July 2023

AbstractRecent advances in technology have made possible to quantify fine-grained individual differences at many levels, such as genetic, genomics, organ level, behavior, and clinical. The wealth of data becoming available raises great promises for research on brain disorders as well as normal brain function, to name a few, systematic and agnostic study of disease risk factors (e.g., genetic variants, brain regions), the use of natural experiments (e.g., evaluate the effect of a genetic variant in a human population), and unveiling disease mechanisms across several biological levels (e.g., genetics, cellular gene expression, organ structure and function). However, this data revolution raises many challenges such as data sharing and management, the need for novel analysis methods and software, storage, and computing.Here, we sought to provide an overview of some of the main existing human datasets, all accessible to researchers. Our list is far from being exhaustive, and our objective is to publicize data sharing initiatives and help researchers find new data sources.
Machine Learning in Neuroimaging of Epilepsy
Authors: Lee Hyo Min , Gill Ravnoor Singh , Bernasconi Neda , Bernasconi Andrea 
Published: July 2023

AbstractEpilepsy is a prevalent chronic condition affecting about 50 million people worldwide. A third of patients suffer from seizures unresponsive to medication. Uncontrolled seizures damage the brain, are associated with cognitive decline, and have negative impact on well-being. For these patients, the surgical resection of the brain region that gives rise to seizures is the most effective treatment. In this context, due to its unmatched spatial resolution and whole-brain coverage, magnetic resonance imaging (MRI) plays a central role in detecting lesions. The last decade has witnessed an increasing use of machine learning applied to multimodal MRI, which has allowed the design of tools for computer-aided diagnosis and prognosis. In this chapter, we focus on automated algorithms for the detection of epileptogenic lesions and imaging-derived prognostic markers, including response to anti-seizure medication, postsurgical seizure outcome, and cognitive reserves. We also highlight advantages and limitations of these approaches and discuss future directions toward person-centered care.
Transformers and Visual Transformers
Authors: Courant Robin , Edberg Maika , Dufour Nicolas , Kalogeiton Vicky 
Published: July 2023

AbstractTransformers were initially introduced for natural language processing (NLP) tasks, but fast they were adopted by most deep learning fields, including computer vision. They measure the relationships between pairs of input tokens (words in the case of text strings, parts of images for visual transformers), termed attention. The cost is exponential with the number of tokens. For image classification, the most common transformer architecture uses only the transformer encoder in order to transform the various input tokens. However, there are also numerous other applications in which the decoder part of the traditional transformer architecture is also used. Here, we first introduce the attention mechanism (Subheading 1) and then the basic transformer block including the vision transformer (Subheading 2). Next, we discuss some improvements of visual transformers to account for small datasets or less computation (Subheading 3). Finally, we introduce visual transformers applied to tasks other than image classification, such as detection, segmentation, generation, and training without labels (Subheading 4) and other domains, such as video or multimodality using text or audio data (Subheading 5).
Neuroimaging in Machine Learning for Brain Disorders
Authors: Burgos Ninon 
Published: July 2023

AbstractMedical imaging plays an important role in the detection, diagnosis, and treatment monitoring of brain disorders. Neuroimaging includes different modalities such as magnetic resonance imaging (MRI), X-ray computed tomography (CT), positron emission tomography (PET), or single-photon emission computed tomography (SPECT).For each of these modalities, we will explain the basic principles of the technology, describe the type of information the images can provide, list the key processing steps necessary to extract features, and provide examples of their use in machine learning studies for brain disorders.
Electroencephalography and Magnetoencephalography
Authors: Corsi Marie-Constance 
Published: July 2023

AbstractIn this chapter, we present the main characteristics of electroencephalography (EEG) and magnetoencephalography (MEG). More specifically, this chapter is dedicated to the presentation of the data, the way they can be acquired and analyzed. Then, we present the main features that can be extracted and their applications for brain disorders with concrete examples to illustrate them. Additional materials associated with this chapter are available in the dedicated Github repository.
Einleitung
Authors: Künnemann Andreas 
Published: July 2023

ZusammenfassungIn diesem Kapitel wird zunächst eine Einführung in die Thematik vorgenommen. Dabei erfolgt ein historischer Abriss zur Entwicklung der Variationsrechnung zu einem eigenständigen Teilgebiet der Mathematik, welches in vielen Anwendungsbereichen von zentraler Bedeutung geworden ist. Des Weiteren werden die Aufgaben und Ziele der Arbeit formuliert, bevor abschließend der Aufbau dieses Buches ausführlich dargelegt wird.
Objectives of Using Massive Open Online Courses (MOOCs) by Omani Teachers During the Covid-19
Authors: Al-Badi Aaisha , Al Harassi Nabahan , Alkharusi Hussain 
Published: July 2023

AbstractThis paper aimed to explore the objectives of the Omani teachers' use of MOOCs during the pandemic of Covid-19. The quantitative approach was applied to collect the data through questionnaires to reveal the teachers' objectives and reasons for using MOOCs during Covid-19. Therefore, the population of this study was the Omani teachers who teach in the Muscat Governorate, in public schools, and from different subject specializations, with various years of teaching experience. The findings of this study showed that the majority (97%) of the Omani teachers, in Muscat specifically, use MOOCs during the Covid-19 pandemic as MOOCs give a meaningful learning through providing access to good quality education with no fees given, following with (94.7%) use MOOCs to get new insights and experiences in their teaching subject. The same percentage (#%) use MOOCs to update their knowledge and skills related to innovative technologies and teaching strategies. However, 31.5% of the participants disagree with using MOOCs for enhancing peer support. In addition, 26% of them disagree with using MOOCs because of encouraging peer support. Also, the study revealed no significant gender differences in the objectives of the Omani teachers’ use of MOOCs during the pandemic of Covid-19. This study recommended having a database of the MOOCs learners’ profiles to make it easier to reach them, analyze their needs, and develop MOOCs for them to make their learning more effective and meaningful.
How to Make Learning Process Smoother for Students Through Teaching Practices: Action Research
Authors: Alreshidi Nawaf Awadh 
Published: July 2023

AbstractThis action research seeks to make students’ learning of mathematics smoother through teaching. This kind of research aims to improve students’ learning by improving the teacher’s performance. The study comprises two stages. In the first stage, the sample consisted of four students (age M = 12 years; SD = 1), who were taught about ratios, proportions, and percentages over a total of five lessons. Data were collected through field observations. The data were then thematically analyzed to develop an improvement plan for the next teaching session. The findings revealed that the students suffered from a lack of understanding, a lack of big ideas, a lack of necessary prior knowledge, and misconceptions. Planned improvements were implemented for four different students in the second stage, followed by reflection (age M = 12 years; SD = 1). In the second teaching session, students were taught the same topics over five lessons. The plan worked well, as no surface-level learning or learning difficulties emerged. The overall results suggested that posing questions in different learning situations, developing big ideas, and assessing prior knowledge are helpful in making students’ learning deeper and easier.
Evaluating the Effectiveness of E-Learning in Private Schools of Amman, Jordan During and After the Corona Pandemic
Authors: Hasan Nivin , Murshid Emran Abu 
Published: July 2023

AbstractThis paper aimed to measure the impact of COVID-19 on education in private schools of Amman, Jordan during and after corona pandemic. A questionnaire consisting of (10) paragraphs aimed to answer the study's questions and hypotheses. The study population consisted of (20) private schools in Jordan distributed in the capital city of Amman, where they were selected using the simple random method. Results show that the effectiveness of e-learning during corona pandemic was ranging from medium to low and there is no noticeable difference of teacher’s gender on the effectiveness of e-learning, teacher’s years of experiences has a positive relationship with the effectiveness of e-learning. As for the success of e-learning during corona pandemic, results show that it was low due to the presence of several challenges, the most important of which are technical, social, economic, and infrastructural challenges. The study recommended that e-learning could act as a support education system to the traditional (face-to-face) education, and it could be more effective when applied to literary courses, in addition to promoting the efficiency and readiness of teaching staff, students and parents to implement e-learning through holding workshops in this regard.
The Reality of E-learning Assessment Practices in Higher Education Institutions
Authors: Attiat Madher Mohammad 
Published: July 2023

AbstractThe aim of this study was to investigate the reality of e-learning assessment practices in Jordanian universities during the Corona pandemic, as well as the challenges they faced, by investigating Jordanian university students’ attitudes using an electronic questionnaire whose psychometric properties were validated. The measure included four areas: The conduct of controlling online exams, the diversity of its forms and practices, its quality, and the challenges of the online assessment. A total of 312 randomly selected male and female students participated in the study. The results revealed two types of procedures for controlling online exams, namely, controlling the exam characteristics and conditions of its applications. In addition, controlling online exams using the available technical means. It also demonstrated a great diversity in the practices of formative assessment compared to a small diversity in the practices of the final assessment. A prominent level of quality in the characteristics of the questions, their structure, the gradation of their difficulty, and their levels of knowledge was evident. As for the challenges facing online assessment only, they were related to technical, financial technical aspects. The study suggested identifying the available sources for use during the exam, diversifying the type of questions in the final exams, and non-exam assessment in classwork, and focusing on the quality characteristics of the exam to prevent cheating, more than focusing on technical tuning procedures for the virtual environment of the exams.
Post-COVID Education: Virtualization as the Way Forward
Authors: Ogadimma Emenyeonu C. , Raza Syed Hassan , Gorpe T. Serra , Irshad Farah 
Published: July 2023

AbstractOnline education has been in practice before COVID-19. However, COVID-19 pandemic has popularized online education. This study aims to explore the application of Process Virtualization Theory (PVT) in an e-learning process to gain new insights into the education context. Data was collected through an online survey from 362 university students in Pakistan. Results of the structural equational modeling indicate that eradication of digital divide can improve students’ engagement in virtual learning environments and thus entrench e-learning as a viable mode of education. Thus, this study has unfolded the understudied factor of digital divide in a developing country setting. To this end, the study underscored the threatening potential of digital divide to e-learning. Furthermore, the study has validated the notion of PVT theory that when a virtual process or a system reduces the need for sensory requirements, there are more chances of adoption. For educational institutions, the findings recommend incorporating offline learner-material and interaction capability (e.g., feedback) in their e-learning management system to promote students’ adoption of e-learning.
The Practices of Digital Citizenship Among Undergraduates at Sultan Qaboos University in Oman During COVID-19
Authors: Alsaadi Mouza , Alharassi Nabhan , Alsalmi Jamal , Alkindi Salim 
Published: July 2023

AbstractDigital citizenship (DC) has been an essential subject in educational research for the last few years. This study aimed at discovering the degree to which Omani undergraduate students practiced digital citizenship during the COVID-19 pandemic. The participants were 262 undergraduate students (116 males and 164 females) who took an online university elective course at Sultan Qaboos University (SQU) in Spring of 2021. The study adopted a quantitative approach to data collection to investigate students’ digital citizenship practices. An online situational judgment test was developed and conducted to address six dimensions of digital citizenship: digital identity, digital civic engagement, digital ethics, digital literacy, digital safety, and global digital communication. The findings show that the level of practicing digital citizenship dimensions ranged from intermediate to low. Additionally, the results show that the least practiced dimensions are digital civic engagement and digital identity. Further, the results indicated some gender differences in students’ practices of digital citizenship. Considering the results, more efforts are required from stakeholders to enhance the level of DC among undergraduates. The findings of the study are considered good indicators to be taken when designing and evaluating curricula at higher education institutions.
Cybersecurity Awareness Among Students During the COVID-19 Digital Transformation of Education: A Case Study at the Muscat (Oman) Schools
Authors: Al Shabibi Ayida Mohammed , Al-Suqri Mohammed Nasser 
Published: July 2023

AbstractThe COVID-19 pandemic has developed numerous complicated challenges, and it has equally opened the door to another deadly force, known as cybercrime; many reports have indicated that cyberattacks have increased by 500% since the pandemic. While many awareness campaigns are conducted, these campaigns go through many challenges to develop effectiveness, cost coverage, and sustainability. Increasing public awareness of cybersecurity can be a critical strategy in preventing cybercrime or mitigating its impact at some level. Unfortunately, young people are an essential part of the community, and they are immature to recognize the dangers of these kinds of crimes to avoid being a target by cybercriminals. Therefore, the paper's viewpoint is to reform and update the education curricula through the following two objectives: discovering the consequences of COVID-19 on children's digital lives and generating information about current cybersecurity awareness levels among post-Basic education students in Muscat. The survey was conducted in early 2021 on 265 participants from six private schools. The results show that 83% of the students were exposed to cybersecurity incidents during the pandemic, and two-thirds think cybersecurity should be integrated into school curricula. However, awareness campaigns seem limited since 77% of students did not attend or hear about them.
Job Shadowing: An Evaluation of a Training Program for Enhancing the Communication Skills of Students During Covid-19 in Sharjah, UAE
Authors: Radwan Ahmed Farouk , Mousa Sheren Ali 
Published: July 2023

AbstractThis study aims to explore how the job shadowing program offered by the Sharjah government media bureau was experienced by communication and media students and professionals, and how it develops students’ skills and experience. The study also explores the impacts of the program during the Covid-19 period and the expectations from the program after the pandemic. The study works to help in creating effective training programs for media students with collaboration between universities and media organizations. A group discussion was conducted with 12 trainers and an online questionnaire was distributed to a sample of program attendees to monitor their viewpoints. 100 students responded to the survey. Results revealed that the job shadowing program has an effective role in developing students’ skills by enabling them to interact with professionals and benefiting from their experience. Students considered the program as an integrated way with the academic courses to support their practical skills. Results assured the need to design programs concerned with new topics, skills, and perspectives in media production to meet the needs of the media profession and academic requirements. Results revealed that coexistence and integration between academic programs and media institutions will be effective methods to develop the program.
Nonlinear Wind-Induced Vibration Behaviors of Multi-tower Suspension Bridges Under Strong Wind Conditions
Authors: Zhou R. , Ge Y. J. , Yang Y. , Du Y. D. , Zhang L. H. 
Published: July 2023

AbstractThe aerodynamic characteristics of a multispan suspension bridge differ from those of a two-span suspension bridge. In this study we investigated the nonlinear aerodynamic characteristics of the Maanshan Bridge under nonstationary flow using combination quasi-3D finite element (FE) bridge models of 2D nonlinear aerodynamic force models and 3D nonlinear FE bridge models. The developed model predictions were validated by wind tunnel tests involving a 2D sectional stiffness model and 3D full-bridge aeroelastic model. Results showed that the developed model could potentially describe the nonlinear and unsteady aerodynamic effects on the bridge. Furthermore, the flutter behavior of the Maanshan Bridge under uniform flow changed from the stable limit cycle of soft flutter to unstable limit cycle with the disconnection of two hangers at the 1/2L of the right main span, while the flutter behavior of the bridge under turbulence flow could be defined as the fracture failure of the hangers from the 1/2L of the left main span.
Role of Aggregate Reactivity, Binder Composition, and Curing Temperature on the Delayed Ettringite Formation and Associated Durability Loss in Concrete
Authors: Martin L. , Thomas P. , De Silva P. , Sirivivatnanon V. 
Published: July 2023

AbstractThe durability of concrete is critical to its worldwide use as a structural material for buildings and infrastructure, with the lifetime service of concrete greatly affecting its economic, environmental, and social costs. Causes of durability loss in some concrete structures can be attributed to the alkali–silica reaction (ASR) and delayed ettringite formation (DEF). Both are chemical reactions that have the potential to cause expansion and strength loss in affected elements. Significant overlap exists in the factors contributing to ASR and DEF in concrete structures, with widely reported evidence of deleterious DEF frequently occurring in conjunction with mild or moderate ASR. For precast concrete, experiments in mortars have provided limits in the alkali and sulfate content of the binder and maximum curing temperatures used to minimize DEF risk. The role of other constituents in concrete specimens, notably the aggregate, has been overlooked. We investigated the role of reactive aggregates and ASR in the susceptibility of concrete to deleterious DEF.
Partially-Unzipped Carbon Nanotubes as Low-Concentration Amendment for Cement Paste
Authors: Iffat S. , Matta F. , Gaillard J. , Elvington M. , Sikder M. , Baalousha M. , Tinkey S. , Meany J. 
Published: July 2023

AbstractPartially-unzipped multiwalled carbon nanotubes (PUCNTs) combine the chemical structure and basic mechanical properties of multiwalled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) with exceptionally high graphene-edge content. As a result, PUCNTs can be effectively oxidized for dispersion in aqueous solutions and have a specific surface area that is larger than that of MWCNTs with a comparable aspect ratio. Thus, the incorporation of relatively small concentrations of PUCNTs in cement composites may result in significant physicomechanical enhancements. In the proof-of-concept study presented here, cement paste specimens were manufactured with oxidized PUCNT concentrations of 0, 0.001, and 0.005% in weight of cement (wt%), that is, one order of magnitude smaller than lower-bound concentrations for MWCNTs reported in the literature. Stable dispersion in water was verified through dynamic light scattering analysis. Physicomechanical changes and PUCNT dispersion in the cement matrix were investigated through uniaxial compression tests on 25 × 25 × 76 mm prism specimens, and visual inspection of scanning electron microscopy (SEM) micrographs, respectively. The incorporation of 0.001 wt% and 0.005 wt% of PUCNTs resulted in an average increase in compressive strength of 10% and 29%, respectively, compared with plain cement paste. In both instances, representative SEM micrographs show the preferential formation of cement hydrates in cement paste that accorded with well-dispersed PUCNTs.