bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating Machine Learning Models and Their Diagnostic Value
Authors: Varoquaux Gael , Colliot Olivier
Published: July 2023

This chapter describes model validation, a crucial part of machine learning whether it is to select the best model or to assess performance of a given model. We start by detailing the main performance metrics for different tasks (classification, regression), and how they may be interpreted, including in the face of class imbalance, varying prevalence, or asymmetric cost–benefit trade-offs. We then explain how to estimate these metrics in an unbiased manner using training, validation, and test sets. We describe cross-validation procedures—to use a larger part of the data for both training and testing—and the dangers of data leakage—optimism bias due to training data contaminating the test set. Finally, we discuss how to obtain confidence intervals of performance metrics, distinguishing two situations: internal validation or evaluation of learning algorithms and external validation or evaluation of resulting prediction models.
Reproducibility in Machine Learning for Medical Imaging
Authors: Colliot Olivier , Thibeau-Sutre Elina , Burgos Ninon
Published: July 2023

Reproducibility is a cornerstone of science, as the replication of findings is the process through which they become knowledge. It is widely considered that many fields of science are undergoing a reproducibility crisis. This has led to the publications of various guidelines in order to improve research reproducibility.

This didactic chapter intends at being an introduction to reproducibility for researchers in the field of machine learning for medical imaging. We first distinguish between different types of reproducibility. For each of them, we aim at defining it, at describing the requirements to achieve it, and at discussing its utility. The chapter ends with a discussion on the benefits of reproducibility and with a plea for a nondogmatic approach to this concept and its implementation in research practice.
Machine Learning for Alzheimer’s Disease and Related Dementias
Authors: Modat Marc , Cash David M. , Dos Santos Canas Liane , Bocchetta Martina , Ourselin Sébastien
Published: July 2023

Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing.
Machine Learning for Parkinson’s Disease and Related Disorders
Authors: Faouzi Johann , Colliot Olivier , Corvol Jean-Christophe
Published: July 2023

Parkinson’s disease is a complex heterogeneous neurodegenerative disorder characterized by the loss of dopamine neurons in the basal ganglia, resulting in many motor and non-motor symptoms. Although there is no cure to date, the dopamine replacement therapy can improve motor symptoms and the quality of life of the patients. The cardinal symptoms of this disorder are tremor, bradykinesia, and rigidity, referred to as parkinsonism. Other related disorders, such as dementia with Lewy bodies, multiple system atrophy, and progressive supranuclear palsy, share similar motor symptoms although they have different pathophysiology and are less responsive to the dopamine replacement therapy. Machine learning can be of great utility to better understand Parkinson’s disease and related disorders and to improve patient care. Many challenges are still open, including early accurate diagnosis, differential diagnosis, better understanding of the pathologies, symptom detection and quantification, individual disease progression prediction, and personalized therapies. In this chapter, we review research works on Parkinson’s disease and related disorders using machine learning.
Machine Learning for Cerebrovascular Disorders
Authors: Yu Yannan , Chen David Yen-Ting
Published: July 2023

Cerebrovascular disease refers to a group of conditions that affect blood flow and the blood vessels in the brain. It is one of the leading causes of mortality and disability worldwide, imposing a significant socioeconomic burden to society. Research on cerebrovascular diseases has been rapidly progressing leading to improvement in the diagnosis and management of patients nowadays. Machine learning holds many promises for further improving clinical care of these disorders. In this chapter, we will briefly introduce general information regarding cerebrovascular disorders and summarize some of the most promising fields in which machine learning shall be valuable to improve research and patient care. More specifically, we will cover the following cerebrovascular disorders: stroke (both ischemic and hemorrhagic), cerebral microbleeds, cerebral vascular malformations, intracranial aneurysms, and cerebral small vessel disease (white matter hyperintensities, lacunes, perivascular spaces).
The Role of Artificial Intelligence in Neuro-oncology Imaging
Authors: Soun Jennifer , Masudathaya Lu-Aung Yosuke , Biswas Arabdha , Chow Daniel S.
Published: July 2023

Diagnostic imaging is widely used to assess, characterize, and monitor brain tumors. However, there remain several challenges in each of these categories due to the heterogeneous nature of these tumors. This may include variations in tumor biology that relate to variable degrees of cellular proliferation, invasion, and necrosis that in turn have different imaging manifestations. These variations have created challenges for tumor assessment, including segmentation, surveillance, and molecular characterizations. Although several rule-based approaches have been implemented that relates to tumor size and appearance, these methods inherently distill the rich amount of tumor imaging data into a limited number of variables. Approaches in artificial intelligence, machine learning, and deep learning have been increasingly leveraged to computer vision tasks, including tumor imaging, given their effectiveness for solving image-based challenges. This objective of this chapter is to summarize some of these advances in the field of tumor imaging.
Generative Adversarial Networks and Other Generative Models
Authors: Wenzel Markus
Published: July 2023

Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially been meant not to be an image analysis tool but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods and has proven to be highly successful—though by no means from the first attempt.

This chapter gives a basic introduction into the motivation for generative adversarial networks (GANs) and traces the path of their success by abstracting the basic task and working mechanism and deriving the difficulty of early practical approaches. Methods for a more stable training will be shown, as well as typical signs for poor convergence and their reasons.

Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images and also generalizes to tasks in image analysis. Examples of architectures for image semantic segmentation and abnormality detection will be acclaimed, before contrasting GANs with further generative modeling approaches lately entering the scene. This will allow a contextualized view on the limits but also benefits of GANs.
Clinical Assessment of Brain Disorders
Authors: Epelbaum Stéphane , Cacciamani Federica
Published: July 2023

The clinical evaluation of brain diseases strictly depends on patient’s complaint and observation of their behavior. The specialist, often the neurologist, chooses whether and how to assess cognition, motor system, sensory perception, and autonomic nervous system. They may also decide to request a more in-depth examination, such as neuropsychological and language assessments and imaging or laboratory tests. From the synthesis of all these results, they will be able to make a diagnosis. The neuropsychological assessment in particular is based on the collection of medical history, on the clinical observation, and on the administration of standardized cognitive tests validated in the scientific literature. It is therefore particularly useful when a neurological disease with cognitive and/or behavioral manifestation is suspected. The introduction of machine learning methods in neurology represents an important added value to the evaluation performed by the clinician to increase the diagnostic accuracy, track disease progression, and assess treatment efficacy.
Direkte Methoden der Variationsrechnung
Authors: Künnemann Andreas
Published: July 2023

In diesem Kapitel wird die Theorie der direkten Methoden der Variationsrechnung aufgegriffen. Eingangs wird deren Konzept erläutert, bevor über einen fundamentalen Satz zur schwachen Unterhalbstetigkeit von Funktionalen die Existenz eines Minimierers für eine breite Klasse von Variationsproblemen nachgewiesen wird. Dabei handelt sich um zwei grundlegende Ergebnisse der modernen Variationsrechnung, die ausführlich dargelegt werden.
Höhere Regularität von Minimierern im Inneren
Authors: Künnemann Andreas
Published: July 2023

Ziel dieses Kapitels ist der Nachweis der stetigen Differenzierbarkeit für Minimierer von Variationsproblemen. Zu Beginn erfolgt die Berechnung der ersten Variation sowie die damit verbundene Herleitung der schwachen Euler-Lagrange-Gleichung, welche eine Beziehung zur Lösungstheorie von Differentialgleichungen im Sinne des Dirichletschen Prinzips herstellt. Ausgehend von der schwachen Euler-Lagrange-Gleichung werden in einem zweiteiligen Beweis die Zugehörigkeit eines Minimierers zur Klasse $$W_{loc}^{2,2} \left( G \right)$$

W

loc

2
,
2

G

sowie die Hölder-Stetigkeit der ersten Ableitungen dieses Minimierers ausführlich dargelegt.
Minimierer vom Poissonschen Typ
Authors: Künnemann Andreas
Published: July 2023

In diesem Kapitel sollen Randwertprobleme mit partiellen Differentialgleichungen zweiter Ordnung mithilfe der Variationsrechnung gelöst werden. Dazu ist der Nachweis stetiger zweiter Ableitungen von Minimierern notwendig. Der Fokus wird hierbei auf sogenannte Minimierer vom Poissonschen Typ gelegt. Eine $$C^{2,\sigma }$$

C

2
,
σ

-Rekonstruktion, die auf der Schaudertheorie basiert, liefert in diesem Fall die gewünschte Regularität. In Anwendung dessen werden das Randwertproblem harmonischer Abbildungen in Riemannschen Räumen sowie das Dirichletproblem des H-Flächen-Systems behandelt, indem jeweils ein geeignetes Variationsproblem aufgestellt wird.

Als Erweiterung des Dirichletproblems des H-Flächen-Systems wird das allgemeine Plateausche Problem für Flächen vorgeschriebener mittlerer Krümmung mit den erarbeiteten Methoden der Variationsrechnung untersucht. Zudem wird das Plateausche Problem für Flächen vorgeschriebener mittlerer Krümmung in Kugeln, in Zylindern sowie im Einheitskegel gelöst.
Cognitive Empowerment of Students with Disabilities and Its Impact on Academic Self-Efficacy and Mental Health During the COVID-19 Pandemic—the University of Sharjah as a Model
Authors: Alomosh Ahmad Falah , Sallam Ahmed Mahmoud
Published: July 2023

Objective: This study aims to document the interventions implemented at the University of Sharjah (Disability Resource Center) to enhance learning processes to achieve cognitive empowerment for students living with disabilities during COVID-19 and its impact on academic self-efficacy, psychological well-being, and health from their point of view.Methods: The descriptive-analytical approach was used. The study tools applied include the academic self-efficacy scale, the cognitive empowerment scale, and the mental health scale: R-SCL-90. Dimensions (anxiety, depression, and phobia), (n = 120).Results: A statistically significant inverse correlation at the level (0.01) between cognitive empowerment (cognitive awareness, information availability, training and empowerment, independence, and commitment) and mental health (depression, anxiety, and phobia), where the correlation coefficients amounted to –0.522, –0.534, and –0.512 was noted. There is a positive, statistically significant correlation at the level of significance (0.01) between the dimensions of cognitive empowerment and academic self-efficacy, where the correlation coefficient reached 0.549. A statistically significant inverse correlation at 0.01 between mental health (anxiety, depression, and phobia) and academic self-efficacy, as the correlation coefficients amounted to –0.605, –0.558, and –0.557. There are no statistically significant differences between average females and males in cognitive empowerment. There is a statistically significant difference in the dimensions of cognitive and empowerment according to the disability category’s variable. The order of the categories of disability came according to the degree of response to cognitive empowerment: Physical disability, visual disability, hearing disability, and learning difficulties.
Impact of Service Quality on Student Retention in UAE Higher Education Institutions
Authors: Al-Shamsi Aisha , Al-Hawari Mohammad A. , Aderibigbe Semiyu Adejare , Omar Maher
Published: July 2023

A major goal of this study is to demonstrate how improving the quality of services can be used to enhance higher education institutions’ ability to hold on to their students and assist them in graduating on time. This study investigates the relationship between service quality and student retention in the higher education sector, as well as the influences of satisfaction, trust, and commitment on this relationship. The study discusses how Institutions can retain their students while defining service quality in the present day. Hence, the literature focused on how service quality impacts student retention today. Following the literary phase of the research, the framework specifies the study’s methodology while analyzing the importance of service quality in higher education institutions. It aims to identify the key success factor contributing to student retention and completion of graduation in higher education through a critical assessment of service quality literature. It suggests a theoretical framework using the SERVQUAL model. A qualitative method of four focus groups was conducted. The data were analyzed using coding, and the hypotheses were constructed and discussed qualitatively. The study concluded that service quality is crucial to retaining students in higher education. According to the research, service quality is important to establishing assurance with students. This study highlights the significance of operational aspects in the service sector, particularly for high-contact services. Furthermore, it demonstrates how higher education institutions can achieve long-term sustainability while providing expansion opportunities.
ESL Teachers’ Experiences on XReading as a Formative Assessment Tool in Extensive Reading: A Study of a Federal Institution in the UAE
Authors: Shehzad Naureen
Published: July 2023

Extensive reading is a widely used effective strategy for enhancing students’ reading skills. The study explores how digital libraries like XReading impact students’ extensive reading skills in the English as a Second Language program. XReading provides a Learning Management System where teachers can track and set goals to enhance students’ performance. It also has a key feature of online formative assessments that keep students motivated and accountable in their learning journey. The study using a qualitative research design was conducted at a federal college in the UAE, and it aimed to explore teachers’ perceptions of using XReading as a formative assessment tool, its benefits and challenges the students face, and how it influences students’ performance. The data were collected qualitatively using semi-structured interviews and observations to reveal the effects of XReading as a formative assessment tool in extensive reading. The results indicated that along with cooperative learning, XReading involves the students with a multitude of features it offers and fosters engagement, collaboration, and learner autonomy among students which is a twenty-first-century learning skill.
Rethinking Career Development Post COVID-19: The Career Profile of the Future Framework (CPFF), an E.I.-Based Human Skills Approach
Authors: Bouslama Faouzi , Hiasat Lana , Coombe Christine
Published: July 2023

Presently, many organizations are going through a lot of changes brought about using emerging technologies, such as Artificial Intelligence (AI), Machine Learning, and Smart Automation, to streamline their businesses and improve productivity. Moreover, environmental factors, the COVID-19 pandemic, brought attention to the importance of mental health and Emotional Intelligence (E.I.). Therefore, several existing job roles are being redefined and many young professionals in non-supervisory roles are at risk of losing their jobs. These organizations are now more than ever very keen on identifying the required knowledge, attributes, and skill set, and links between personality traits and professional success. As such, there is an urgent need to address the gap in the knowledge and skills required by this transformation, identify the jobs to be retained in the current and future workforce and create plans to close any skills gap. This paper presents an innovative Emotional Intelligence (E.I.) based human skills framework to assess and map university graduates and young professionals into typical career tracks, and hence create a sustainable professional profile, especially in post COVID-19 era. The proposed framework, entitled the “Career Profile of the Future Framework-CPFF”, has four dimensions: Knowledge, Attitude and Mindset, Skills, and Human attributes. This framework is expected to be an asset in assessing the knowledge and skills of university graduates and young professionals, and helping in finding answers to questions such as “How can I stay relevant, given that automation and AI can do the tasks related to my job?”; “What reskilling and upskilling in existing and new employment sectors do I need?”, and “How can I identify the requirements to synergize my E.I profile while catering to my mental health balance?”.
Adapting to the New Normal: Asynchronous Learning as a Promising Solution for Post-Covid-19 Challenges in Jordanian Universities - A Case Study of the American University of Madaba (AUM)
Authors: Madanat Hanan , Tarawnah Majid , Khzouz Afag
Published: July 2023

In the post Covid-19 time, educational trends have changed aiming at meeting the rising challenges which have a crucial impact on the learning environment. Asynchronous learning has become an alternative approach to traditional approaches. Investigating the challenges and opportunities has become a central topic of major concern. To this end, a two-fold approach, namely, an interview and a questionnaire, was utilized. In particular, the two surveys used included: an open-ended survey in which 30 faculty members answered the barriers and opportunities that they face. Whereas the second survey was built upon the responses of the first survey using the Likert scale. The participants were 72 from different faculties at the American University of Madaba (AUM) in Jordan. Statistical analyses were used to find answers to the questions of the study. The findings of the analysis of the three major themes reported the following results: first, asynchronous learning is flexible and allows the ability to both teachers and students to move in their teaching and learning process at their own pace. Second, asynchronous learning is an affordable and practical teaching modality. Third, the inability to adapt to asynchronous learning and the need for specialized training seem to top the challenges AUM’s instructors encounter.
Online Effectiveness of Hybrid Blended and Flipped Learning Approaches During COVID-19 Virus in Libya
Authors: Brydan Rashad , Ikraiam Fawzi
Published: July 2023

The aim of this paper is to provide an insight into hybrid online teaching and flipped learning during COVID-19 pandemic in Libya in pursuit of improving Libyan university students’ knowledge and skills as an alternative instrument to continue their studies. However, this brought about some challenges, for both lecturers and students, while teaching undergraduate courses using hybrid blended online lecturing in the context of flipped learning in different subjects and university programs. Using questionnaires and focused group data analyses helped the authors to discover the opportunities ahead to promote further development of online teaching and flipped learning in Libya. The result proved the need for information and communication technology (ICT) skills for students and staff members, considering the hybrid blended online teaching and flipped learning experience and involvement. This was achieved according to four indicators: students’ learning experience, students’ academic performance, students’ final achievements and educators’ observations. In conclusion, the study proposes a new online teaching model, called Learn, Believe and Achieve. More studies are still needed to help improving this new teaching methodology in Libya.
Proactive Targeted Academic Advising Approach for Students Under Academic Probation and At-Risk Students
Authors: Elmehdi Hussein M. , Ibrahem Almehdi M. , Zaid Zaid A.
Published: July 2023

Academic Advising in higher education has received considerable attention in recent years. This is mainly due to the increased focus on improving education quality, student retention and ranking. The current approach focuses on at-risk students, which includes students with a CGPA of less than 2.3. The Academic Success Program (ASP) is comprised of three main components, which start early in the semester where the targeted students are enrolled in zero-credit courses on the Learning Management System (LMS), i.e., Blackboard. In the first phase of the ASP, we targeted students who are currently under academic probation with the aim to expand the program to include students at risk (students with a CGPA of < 2.3). First, students are asked to answer an online questionnaire, which include carefully designed questions on student status, reasons for academic probation, study habits, social issues, challenges they are facing in their study programs and assistance or services they require to improve their academic standings. Next, students are invited to attend a 2-h workshop that focuses on explaining the various rules and regulations pertaining to academic probation, how to calculate GPA and CGPA along with study plan navigation assistance. The third part of the workshop is a motivational seminar aimed at encouraging and inspiring the students to seek help, talk about the challenges they face, get parents involved and key points on academic success. After the workshop, students are asked to answer an exit assessment that is constructed in the form of a quiz to gauge the gain or impact the program had on the students. The initial results are very encouraging, where 75% of the students who completed the program were able to overcome their academic probation status.
Lessons from Graduation Project During COVID-19 Pandemic for Future Applications, Methods, and Tools
Authors: Bande Lindita
Published: July 2023

United Arab Emirates University (UAEU), located in the city of AL Ain, is a well-established institution since 1976. The department of Architectural Engineering within the College of Engineering is ABET credited. The Graduating Projects (GP) are part of this curriculum. In pre-pandemic conditions, the students had regular weekly meetings with several faculty based on the project needs. During COVID-19 pandemic, the full program went into online mode. During this change, the students, faculty, and staff had to adapt to new teaching methods in a noticeably short time. This research aims to find and highlight the lessons learned during this change to the GP. The methodology starts with the analysis of two main case studies. Case study A started in fall of 2019 and ended in the spring of 2020 (partially developed in face-to-face mode (in person, on the campus) and partially online mode). Case Study B started in the fall of 2020 and ended in the spring of 2021 (online mode). The case study analysis describes the project’s main objectives and the steps taken by the students to achieve these objectives. The methodology follows the tool analysis. Since the tool used is innovative and complex, in addition to the program, the process of learning, application, and outcomes is crucial. The pandemic’s impact on students’ psychology and productivity is the following step that helps understand how the changes in society and the extensive use of virtual tools helped the students cooperate with the situation. The results showed an improvement in the student’s skills in using advanced tools to achieve the project goal. This research is an attempt in understanding how the pandemic impacted GP work. The outcomes of this study might improve the GP program in the AE Department by integrating a hybrid approach with face-to-face meetings and online training for students to achieve the project targets with more advanced tools.
On the Development of Fintech in Asia
Authors: Yong Liu
Published: July 2023

There are five models of fintech development in the world: the technology promotion model represented by the USA, the rule-driven model represented by the UK, the market pull model represented by China, the mixed competition model represented by Japan and Indonesia, and the model of fanning out from point to area represented by South Korea and Israel. In terms of the layout, the transformation of traditional financial hubs has been accelerated, China and the USA have outstanding advantages in fintech, and the Asia-Pacific region has great potential for fintech development. The fintech of China has been promoted to the worlds leading level; Japan boosts the rapid growth of fintech through advantages of backwardness; Singapore gathers innovative resources with a relaxed and inclusive atmosphere; South Korea promotes scale development of fintech industry by fanning out from point to area; India is gradually exerting its potential for fintech development; Israel builds the highland of fintech development through guidance plus service; Indonesia has gradually become a rising star in fintech development in Southeast Asia; Hong Kong promotes the momentum of sound fintech development with government assistance.
LLE Based K-Nearest Neighbor Smoothing for scRNA-Seq Data Imputation
Authors: Feng Yifan , Ai Yutong , Jiang Hao
Published: July 2023

The single-cell RNA sequencing (scRNA-seq) technique allows single cell level of gene expression measurements, but the scRNA-seq data often contain missing values, with a large proportion caused by technical defects failing to detect gene expressions, which is called dropout event. The dropout issue poses a great challenge for scRNA-seq data analysis. In this chapter, we introduce a method based on KNN-smoothing: LLE-KNN-smoothing to impute the dropout values in scRNA-seq data and show that the LLE-KNN-smoothing greatly improves the recovery of gene expression in cells and shows better performance than state-of-the-art imputation methods on a number of scRNA-seq data sets.
A Novel Approach for Improving Accuracy for Distributed Storage Networks
Authors: Lu Liu , Yuanyuan Ke , Yong Yuan
Published: July 2023

With the development of storage technology and Internet technology, cloud storage continues to make its impact. Scalability, reliability, and lowered costs have made cloud storage widely used with success in businesses and individuals. The advent of the blockchain has brought some changes. As the incentive layer for IPFS, Filecoin allows storage resources to become tradable, greatly extending storage capacity. However, the process of testing the integrity of data still needs constant improvement. In this chapter, we propose a new data audit proof, in which nodes continuously upload hashed data that has been added to random numbers, and the smart contract will compare the result to verify the integrity of the data. Meanwhile, data owner could calculate and then challenge to verify the data integrity. There are audit miners responsible for regulating the behavior of miners and the protection of users’ data, and audit miners in a state of semi-participation. It is demonstrated later in the chapter that this proof is accurate enough and resistant to attacks.
Optimisation of Limestone Calcined Clay Cement Based on Response Surface Method
Authors: Huang G. , Zhuge Y. , Benn T. , Liu Y.
Published: July 2023

Limestone calcined clay cement (LC3) is a new type of cement that contains Portland cement, calcined clay, and limestone. Compared with traditional cement clinker, LC3 reduces CO2 emissions by up to 40%, and is a promising technology for the cement industry to achieve its emission target. We used a numerical approach to predict the optimum composition of LC3 mortar. The experiments were performed using central composite rotational design under the response surface methodology. The method combined the design of mixtures and multi-response statistical optimization, in which the 28-day compressive strength was maximized while the CO2 emissions and materials cost were simultaneously minimized. The model with a nonsignificant lack of fit and a high coefficient of determination (R2) revealed a well fit and adequacy of the quadratic regression model to predict the performance of LC3 mixtures. An optimum LC3 mixture can be achieved with 43.4% general purpose cement, 34.16% calcined clay, 20.6% limestone and 1.94% gypsum.
Analysis of Categories That Delay Global Construction Projects
Authors: Abonassrya M. , Alam M. , Saifullah A.
Published: July 2023

Delay in construction projects is a significant issue and concern for most construction companies. Many studies have addressed this issue by identifying the top-ranked causes, which vary according to project type, location, and the research method used. The combined factors of delay/time overrun need further analysis to understand the top-ranked factors considering the project context. We identified 360 delay/time overrun factors of construction projects from articles published in the past 10 years in top-quality journals ranked as per scientific journal ranking (SJR). The factors were then coded and classified into categories based on their impact and the description in NVIVO software. Finally, the categories were analyzed and ranked by the relative importance index using SPSS software to identify the critical ones in global construction projects. In addition, the developed and developing countries affected by these delay factors were determined. The results revealed that the top five important factors are located under the following categories: orders and requirements; experience and productivity; financial problems; planning; and lastly both external and management categories. These categories were the highest ranking among the five top factors found in the reviewed studies and affect both developed and developing countries.
A Compact Review on the Waste-Based Lightweight Concrete: Advancement and Possibilities
Authors: Islam M. M. U. , Li J. , Roychand R. , Saberian M.
Published: July 2023

Lightweight concrete (LWC) has been used for more than 2000 years, and the technical development of waste-based LWC is still proceeding. Notably, the very first representative concrete mix of infrastructural LWC was introduced for building a family house in Berlin, Germany, a few decades ago. The unique and distinctive combination of waste-based LWC successfully creates an appealing alternative to traditional concrete aggregates in terms of durability, robustness, cost, energy-saving, transportation, environmental advantages, innovative architectural designs and implementations, and ease of construction. Numerous researchers have attempted to utilize waste materials to produce LWC, aiming to bring both ecological and economical solutions to the construction industry over the past few decades. Waste materials, such as crushed glass, waste tire rubber, masonry rubber, chip rubber, plastics, coconut shells, palm oil fuel ash, palm kernel shells, fly ash, and rice husks, possess lower specific gravity than traditional concrete aggregates. Thus waste-based LWC can be a significant replacement for conventional raw materials (cementitious material and aggregates) as it requires less strength than conventional concrete for both structural and non-structural applications. Although waste-based LWC is well recognized and has proven its scientific potential in a broad range of applications, there are still uncertainties and hesitations in practice. Therefore, the primary objective of this study was to demonstrate the current state-of-the-art understanding and advancement of waste-based LWC over the past decades. Furthermore, an equally critical discussion is reported to shed light on the potential benefits of LWC. We highlight how the performance of LWC has been enhanced significantly over the period, and understanding of the properties of waste-based LWC has advanced.
Creep of Slag Blended Cement Concrete with and Without Activator
Authors: Thanh H. T. , Tapas M. J. , Chandler J. , Sirivivatnanon V.
Published: July 2023

Partly replacing Portland cement (PC) with lower carbon footprint cementitious materials such as ground granulated blast furnace slag (slag) is considered as a practical method for reducing CO2 emissions in the cement concrete industry. To mitigate the slow reactivity of slag in a cementitious system and enhance early-age strength, the addition of a chemical activator is a solution. However, the effect of the activator on creep behaviour of slag-blended cement concretes remains unclear. This work presents the effect of sodium sulfate (Na2SO4) activator on the compressive creep of PC concrete blended with 50 and 70 wt% slag. Four concrete mixes (with and without 2.5% Na2SO4 activator) containing 395 kg of cementitious material were prepared. The creep strain measurements were conducted on 150 × 300 mm cylindrical specimens for 140 days under sustained compressive load. The results showed that the 70% slag concrete had lower creep strain than 50% slag-blended cement concrete. The presence of Na2SO4 helped reduce the creep strain of 50% slag concrete but slightly increased that of 70% slag-blended cement concrete. In addition, the applicability of the predictive model in AS3600:2018 for the creep behaviour of high slag content concrete was assessed.
Thermal Transfer Effects of CRTS II Slab Track Under Various Meteorological Conditions
Authors: Zhou R. , Yuan W. H. , Du Y. D. , Liu H. L. , Zhang L. H.
Published: July 2023

With the evolution of climate change, the thermal transfer effects of ballastless track in high-speed railways under complicated environmental conditions becomes increasingly important, governed by a number of meteorological factors, including solar radiation, ambient temperature, wind speed and direction, humidity, and many others (Matias SR, Ferreira PA, Constr Build Mater 322:126445, 2022).
Effect of Carbonation on the Microstructure and Phase Development of High-Slag Binders
Authors: Tapas M. J. , Yan A. , Thomas P. , Holt C. , Sirivivatnanon V.
Published: July 2023

The drive for sustainable concrete production favors the use of high replacement levels of supplementary cementitious materials (SCMs) in the concrete mix. The use of SCMs such as fly ash and slag, however, although they improve the sustainability of concrete production as well as most concrete durability properties, increases the carbonation rate. Carbonation decreases the pH of the concrete pore solution, making the steel reinforcement susceptible to corrosion. The effect of carbonation is, however, not confined to the change in pH of the pore solution. We investigated changes in the microstructure and phases of high-slag binders due to carbonation. The carbonation resistance of mortars with 50 and 70% slag replacement were investigated at exposure conditions of 2%CO2, 50%RH, 23 °C. The carbonated and non-carbonated parts of the mortars were subjected to various characterization techniques to investigate the effect of carbonation on microstructure and phase development. Results confirmed the absence of portlandite in all the carbonated regions (“colorless” by phenolphthalein test, which indicated that the change in color of the phenolphthalein solution was due to the absence of portlandite to buffer the pH). Significant reduction in the amount of C-S-H, as well as increase in the amount of calcium carbonate, were been observed in the carbonated regions. Aragonite, a polymorph of CaCO3, was very prominent in all the carbonated mortars.
Accelerated Mortar Bar Test to Assess the Effect of Alkali Concentration on the Alkali–Silica Reaction
Authors: Boyd-Weetman B. , Thomas P. , DeSilva P. , Sirivivatnanon V.
Published: July 2023

We report the outcomes of a study into the influence of alkali concentration on expansion induced by the alkali–silica reaction (ASR), a deleterious reaction that causes cracking and durability loss in concrete structures. We assessed the effect of alkali concentration on mortar bar expansion using a modified form of AS1141.60.1, the accelerated mortar bar test (AMBT). Mortar prisms were prepared with a reactive aggregate and immersed in alkali solutions of varying concentrations (from 0.4 to 1.0 M NaOH) and saturated limewater at 80 °C. Expansion was monitored for 28 days. The degree of expansion was observed to increase with increasing alkali concentration and an induction period prior to expansion was observed for the 0.4 M NaOH. No expansion was observed for mortar bars immersed in the control saturated lime water bath. Additionally, no expansion was observed for mortars using blended cements containing fly ash (FA) and ground granulated blast furnace slag, suggesting the AMBT is a viable technique for demonstrating the efficacy of mitigation strategies.
Machine Vision-Based Scanning Strategy for Defect Detection in Post-Additive Manufacturing
Authors: Zhang S. , Chen Z. , Granland K. , Tang Y. , Chen C.
Published: July 2023

The surge in 3D printer availability, and its applications over the past decade as an alternative to industry-standard subtractive manufacturing, has revealed a lack of post-manufacturing quality control. Developers have looked towards automated machine learning (ML) and machine-vision algorithms, which can be effective in developing such additive manufacturing (AM) technologies for industry-wide adoption. Currently, most research has explored in-situ monitoring methods, which aim to detect printing errors during manufacturing. A significant limitation is the single, fixed monitoring angle and low resolution, which fail to identify small or hidden defects due to part geometry. Therefore, we investigated a novel ex-situ scanning strategy that combines the advantages of robotics and machine vision to address the limitations; specifically, the viability of image-recognition algorithms in the context of post-fabrication defect detection, and how such algorithms can be integrated into current infrastructure by automatically classifying surface faults in printed parts. A state-of-the-art and widely accepted ML-based vision model, YOLO, was adapted and trained by scanning for prescribed defect categories in a sample of simple parts to identify the strengths of this method over in-situ monitoring. An automated scanning algorithm that uses a KUKA robotic arm and high-definition camera is proposed and its performance was assessed according to the percentage of accurate defect predictions, in comparison with a typical in-situ model.
Spalling Resistance of Hybrid Polyethylene and Steel Fiber-Reinforced High-Strength Engineered Cementitious Composite
Authors: Rawat S. , Zhang Y. X. , Lee C. K.
Published: July 2023

We analyzed the effect of elevated temperatures on the integrity of high-strength engineered cementitious composite (ECC) made with a hybrid combination of polyethylene (PE) and steel fibers. The 50 mm cube specimens were subjected to temperature ranging from 200 to 800 °C at three different heating rates: 1, 5, and 10 °C/min. Five different types of mixes with varying content of supplementary cementitious materials and fibers were evaluated. No spalling was observed at 1–5 °C/min heating rate and <400 °C. However, at a heating rate of 10 °C/min for temperature 600–800 °C, all ECC specimens with a PE fiber volume of 1.25 and 1% steel fiber spalled explosively. Moreover, cementitious matrix with silica fume was more prone to spalling at 800 °C and the use of slag or quaternary blend of slag and dolomite at an optimum content was effective in maintaining the integrity of the ECC specimens even at very high heating rates. Thus, the type of cementitious matrix is equally important to consider, as well as fiber type and content, while analyzing the spalling resistance of ECC.
Modeling the Alkali–Silica Reaction and Its Impact on the Load-Carrying Capacity of Reinforced Concrete Beams
Authors: Nguyen T. N. , Li J. , Sirivivatnanon V. , Sanchez L.
Published: July 2023

The alkali–silica reaction (ASR) is one of the most harmful distress mechanisms affecting concrete infrastructure worldwide. The reaction leads to cracking, loss of material integrity, and consequently compromises the serviceability and capacity of the affected structures. In this study, a modeling approach was proposed to simulate ASR-induced expansion considering three-dimensional stress/restraint conditions, and its impact on the structural capacity of reinforced concrete members. Both the losses in concrete mechanical properties and prestressing effects induced by the expansion under restraints are taken into account in the model. Validation of the developed model is conducted using reliable experimental datasets derived from different laboratory testings and field exposed sites. With the capability of modelling both ASR-induced expansion and its impact on structural capacity, the model provides valuable results to specify effective repair and/or mitigation strategies for concrete structures affected by ASR.
Guidelines for Enzymatic Soil Stabilization
Authors: O’Donnell B. , Swarup A. , Sidiq A. , Robert D. , Setunge S.
Published: July 2023

There are numerous manuals to guide practitioners in utilizing traditional additives in the construction of road, rail and dam construction but they fall short of specific guidance for non-standard additive-based ecofriendly and cost-effective soil stabilization. Increased attention has recently been on the use of non-standard additives for stabilizing weak soils due to environmental and cost concerns associated with traditional additives. We summarize the specific guidelines of using environmental-friendly enzymes to treat weak soils. We elaborate on the requirements and specifications for the Eko-Soil multi-enzyme product that is manufactured from water and proteins extracted from fermented exudes of plants. Specific tests (laboratory and field) and conditions required for soil stabilization using Eko-Soil enzyme are elaborated using the experience of past construction projects. The guide also elaborates enhancing the efficiency of enzymatic soil stabilization by correctly incorporating the required mixing proportions and pre-requisite condition tests. Professionals and practitioners will benefit from using novel eco-friendly sustainable stabilization techniques in the treatment of weak soils covering many applications including roads, foundations, water containment areas, landfills, working platforms and slope erosion control.
Transfer and Substrate Effects on 2D Materials for Their Sensing and Energy Applications in Civil Engineering
Authors: Zhang Q. , Zheng C. , Sagoe-Crentsil K. , Duan W.
Published: July 2023

The recent emergence of two-dimensional (2D) materials such as graphene and transition metal dichalcogenides (TMDs) of the family (Mo, W)(S, Se)2 has attracted interest from a broad range of engineering applications, including advanced sensing and energy harvesting and conservation, because of their distinctive properties. However, it is critical important to achieve intact delamination and transfer of these atomically thin materials, as well as to understand the effects of the target substrates on their optical and electronic properties. Therefore, we developed and compared techniques for transferring as-grown WS2 crystals to arbitrary substrates. Polystyrene-assisted wet transfer can realize improved preservation of monolayer WS2 crystals than the commonly used poly(methyl methacrylate) (PMMA)-assisted wet transfer method, due to minimal chemical etching involved in the 2D material delamination process. The intercalation of alkali ions in the PMMA-based transfer method induces chemical doping over the transferred 2D crystals, leading to the formation of trions. Moreover, the edges of the crystals on hydrophilic substrates, such as sapphire or SiO2/Si, are subject to ambient water intercalation, which locally affects the photoluminescence behavior of the monolayer WS2 by doping and changing of the dielectric environment. This non-uniform optical behavior is absent when the crystal is transferred onto a hydrophobic substrate through which ambient water cannot penetrate. These results have important implications for the choice of target substrate and transfer method adopted for 2D TMD-based applications such as next-generation strain sensing, photodetectors, gas sensing, bio sensing, solar energy harvesting and radiative cooling in which uniform behavior of the channel material is required.
Experimental and Numerical Studies on the In-Plane Shear Behavior of PVC-Encased Concrete Walls
Authors: Kildashti Kamyar , Samali Bijan
Published: July 2023

The effective application of lightweight stay-in-place concrete forms for casting shear walls subjected to wind and seismic loading is of particular concern to practitioners. Insufficient technical data available for new kinds of wall systems, such as Polyvinyl Chloride (PVC) form walls, hinder their implementation in construction practice. To that end, an effective experimental and numerical campaign was launched at Western Sydney University to investigate the structural performance of PVC form walls when subjected to in-plane shear loading. A set of push-out specimens was designated to conduct monotonic in-plane shear tests until failure. All failure phenomena, capping strengths, and ductility capacities were monitored. Test results indicated that the embedded PVC latticed webs could efficiently protect the concrete web from sudden crushing and improve ductility capacity and failure pattern of the specimens. Nonlinear finite element analysis on test specimens was also conducted and good correlation with experiment results was achieved.