bluuebunny/crossref_metadata_2025 huggingface dataset:
Gambling and Aging: An Overview of a Risky Behavior
Authors: Fontaine Maylis , Lemercier Céline , Bonnaire Céline , Giroux Isabelle , Py Jacques , Varescon Isabelle , Le Floch Valérie
Published: May 2023

Gambling is a field of study that has grown since the 2000s. Much research has focused on adolescents and youth as a vulnerable population. The rate of aging gamblers is increasing; however, evidence-based knowledge of this population is still too sparse. After introducing the issue (1), this article provides a narrative review of older adults’ gambling through three sections: (2) older adult gamblers (age, characteristics, and motivations), (3) gambling as a risky decision-making situation, and (4) gambling disorder related to older adults. By drawing on the existing literature from a problematization perspective, this type of review can highlight complex and original research topics and provoke thought and controversy to generate avenues for future research. This narrative review provides an overview of the existing literature on gambling among older adults and offers perspectives on how aging can affect decision-making and thus gambling for this population. Older adults are a specific population, not only in terms of the consequences of gambling disorders but also in terms of the motivations and cognitions underlying gambling behaviors. Studies on behavioral science focusing on decision-making in older adults could help in the development of public policy in terms of targeted prevention.
Mental Health of University Students When Returning to Face-to-Face Classes: A Cross-Sectional Study
Authors: Estrada-Araoz Edwin Gustavo , Bautista Quispe Judith Annie , Córdova-Rojas Lizbeth Maribel , Ticona Chayña Euclides , Mamani Coaquira Humberto , Huaman Tomanguilla Jhony
Published: May 2023

Depression, anxiety and stress are multifactorial affective disorders that could manifest through a set of symptoms, both physical and psychological, that affect the quality of life and performance of people who suffer from them. In this sense, the present research had the objective of evaluating depression, anxiety and stress in students of the Faculty of Engineering of a Peruvian public university when returning to face-to-face classes. The research was developed under a quantitative approach and is of a non-experimental design of the descriptive cross-sectional type. The sample consisted of 244 students who responded to the Depression, Anxiety and Stress Scale, an instrument with adequate psychometric properties. According to the results, the students presented low levels of depression and anxiety. However, they showed moderate levels of stress. On the other hand, it was found out that the three variables were directly and significantly related. In the same way, it was found that there were statistically significant differences regarding the levels of depression, anxiety and stress related to gender, age group, family responsibilities and professional career. Finally, it was concluded that there were symptoms of depression, anxiety and stress in students of the Faculty of Engineering of a Peruvian public university when returning to face-to-face classes.
Exploring the Impact of Time Spent Reading Product Information on E-Commerce Websites: A Machine Learning Approach to Analyze Consumer Behavior
Authors: Necula Sabina-Cristiana
Published: May 2023

In this study, we aim to investigate the influence of the time spent reading product information on consumer behavior in e-commerce. Given the rapid growth of e-commerce and the increasing importance of understanding online consumer behavior, our research focuses on gaining a deeper understanding of customer navigation on e-commerce websites and its effects on purchasing decisions. Recognizing the multidimensional and dynamic nature of consumer behavior, we utilize machine learning techniques, which offer the capacity to handle complex data structures and reveal hidden patterns within the data, thereby augmenting our comprehension of underlying consumer behavior mechanisms. By analyzing clickstream data using Machine Learning (ML) algorithms, we provide new insights into the internal structure of customer clusters and propose a methodology for analyzing non-linear relationships in datasets. Our results reveal that the time spent reading product-related information, combined with other factors such as bounce rates, exit rates, and customer type, significantly influences a customer’s purchasing decision. This study contributes to the existing literature on e-commerce research and offers practical implications for e-commerce website design and marketing strategies.
The Necessity for Multi-Spectral Simulations of the Indoor Non-Visual Luminous Environment: A Simplified Annual Approach
Authors: Potočnik Jaka , Košir Mitja
Published: May 2023

The difference between the functioning of the human non-visual and photopic systems has elicited the need for complex in situ measurements or time-consuming multi-spectral simulations to accurately predict the non-visual luminous content of the indoor environment. As such methodologies are time-consuming, the aim of the present study was to determine whether such complex methodologies are needed. The issue was studied through simulations of four cardinally oriented identical offices located in Ljubljana, Slovenia. Each was studied using orange, grey and blue walls. Diurnal luminous conditions were studied under clear, hazy and overcast skies on December, March and June 21st. The non-visual content was evaluated using novel metrics, the Autonomy of Circadian Potential and Circadian Autonomy, which assess temporal circadian luminous content. Diurnal results were used to construct climate-based spectral months to evaluate the monthly non-visual potential of the studied offices. Furthermore, simulations addressed the question of whether the requirements of the non-visual system might contradict the visual comfort of indoor environments. The results show that compliance with non-visual requirements for indoor spaces with spectrally neutral surfaces or those in shades of blue could be assessed using photopic methodologies. However, this is not true for spaces characterised by orange and red materials.
Experiment on the Performance of Recycled Powder of Construction Waste on Adobe Materials
Authors: Zeng Mei , Huang Huanan , Zhang Xianggang
Published: May 2023

With the widespread use of adobe materials in buildings, their durability can deteriorate under harsh weather conditions such as long-term low temperatures and rainfall, which can easily lead to safety accidents. This article takes adobe material mixed with construction waste recycled powder as the research object and adds the prepared construction waste recycled concrete powder and recycled brick powder to the adobe material in different proportions to study the mechanical and durability properties of the adobe material. The results indicate that under normal temperature curing conditions, the compressive strength of the adobe sample significantly increases with the increase in the recycled powder content, and then decreases. Under high-temperature conditions, with the increase in the recycled powder content, the compressive strength of the adobe sample first significantly increases and then decreases. When the powder content is within the range of 6% to 10%, good moisture absorption and desorption performance can be achieved. When the content of recycled powder is between 2% and 10%, the effect on the dry–wet cycling performance of the adobe is weakest. When the content of grade I/II recycled brick powder is between 2% and 6%, and the content of grade I recycled concrete powder is between 2% and 6%, the negative impact on the freeze–thaw cycle performance is relatively weak. The research results provide theoretical data support for the mixed-use of recycled powder and adobe materials.
The Current Development of Structural Health Monitoring for Bridges: A Review
Authors: Deng Zhihang , Huang Minshui , Wan Neng , Zhang Jianwei
Published: May 2023

The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.
The Comparison of Code-Based and Empirical Seismic Fragility Curves of Steel and RC Buildings
Authors: Biglari Mahnoosh , Hosseini Hashemi Behrokh , Formisano Antonio
Published: May 2023

Seismic codes were developed to reduce the structural vulnerability and risk associated with earthquakes in earthquake-prone regions of the world. The effectiveness of the code in preventing damage is dependent on the performance level defined and the construction technology employed. The seismic fragility curves for two recent versions of the seismic code of Iran are determined by using the hybrid method. The probability of damage levels is visualized by these curves. To develop these curves, only the assumptions of the code are taken into account. These curves are compared with the empirical fragility of the recent devastating earthquake in Iran. The results indicate that, despite a similar probability of damage to the different seismic-resistant systems, steel-braced frames pose a greater risk of collapse. Concerning earthquake damage, the steel and RC moment-resisting frames have shown higher damage probability than expected from the code.
A Multi-Layer Blowout Model for the Tunneling Face Stability Analysis
Authors: Vu Minh-Ngan , Vu Minh-Ngoc , Pham Duc-Tho , Nguyen-Sy Tuan , Nguyen Quoc-Bao , Dang Viet-Duc
Published: May 2023

The stability of the tunnel face during tunneling is one of the major criteria for the design and construction of the tunnel. Collapse and blowout are two modes of tunnel face failure during the excavation. The cover-to-diameter ratio is one of the main parameters controlling these failure modes. Several analytical solutions have been proposed to estimate the range of support pressure applied on the tunnel face to avoid both the collapse and the blowout. However, most of those models deal with homogeneous soils. This paper aims at proposing an analytical model to predict the blowout of the tunneling face of a tunnel in multi-layered soils. The derivation is based on a limit equilibrium analysis, which considers the water tCiable. The proposed model is validated against the real blowout data reported from the tunneling in the Second Heinenoord Tunnel project in the Netherlands. Then, the maximum support pressure exerted on the tunneling face is predicted as a function of the cover-to-diameter ratio, the tunnel diameter, and the water table level for five representative soils. Finally, the model is applied to an underground segment of the Hanoi Metro Line 3 project (in Vietnam) to show the role of the multi-layer aspect.
Exploring Sign System Design for a Medical Facility: A Virtual Environment Study on Wayfinding Behaviors
Authors: Wang Ching-Yuan , Chen Ching-I , Zheng Meng-Cong
Published: May 2023

The National Taiwan University Hospital (NTUH) is a significant institution in modern medicine in Taiwan. Its West Campus, the oldest medical center in the country, has been recognized as a municipal historic site due to its architectural and historical importance. However, visitors have faced navigation difficulties for a long time. To address this issue, a study was conducted to find navigation solutions within the constraints of architectural structure, hardware, and busy crowds. Blender and Unity were used to recreate the environment, and interactive virtual wayfinding experiments were conducted with 64 participants divided into two groups. Each group completed 12 tasks in two scenarios, and their task performance, wayfinding behavior, and questionnaire responses were collected to evaluate the overall environment. The pretest helped identify problems in the existing signage system and weak areas, leading to redesigning of a new signage system. The main objective of the posttest was to evaluate the new design’s effectiveness. Our research contributes to future signage system layout design references, enhancing readability and information coherence while recommending locations within medical facilities. We set specific design standards to facilitate wayfinding signage systems in complex environments.
A Review on the Impacts of Urban Heat Islands on Outdoor Thermal Comfort
Authors: Ren Jianlin , Shi Kaizhe , Li Zhe , Kong Xiangfei , Zhou Haizhu
Published: May 2023

The worsening urban heat island (UHI) effect poses a great challenge to the thermal comfort of people outdoors. However, there has not been a summary of the mechanisms by which UHI affects outdoor thermal comfort (OTC). This paper reviews the commonly used OTC evaluation indexes, data collection methods, and mitigation measures and discusses the relationship between UHI and OTC. The review is limited to peer-reviewed journal publications found in five databases: Science Direct, Scopus, Google Scholar, PubMed, and Web of Science. The review results indicate that physiological equivalent temperature (PET), universal thermal climate index (UTCI), and wet bulb globe temperature (WBGT) are the most widely used indexes in outdoor thermal comfort studies. The data collection methods mainly include questionnaire surveys, measurement, simulation, and formula calculation. There are four main approaches to mitigating the UHI effect in order to improve the comfort of people outdoors: vegetation strategies, water strategies, urban planning strategies, and material strategies. Future research can focus on developing OTC research methods and indexes and combine thermal comfort with visual comfort, auditory comfort, etc. to better evaluate the overall comfort.
Accurate Detection of Urothelial Bladder Cancer Using Targeted Deep Sequencing of Urine DNA
Authors: Lee Dongin , Lee Wookjae , Kim Hwang-Phill , Kim Myong , Ahn Hyun Kyu , Bang Duhee , Kim Kwang Hyun
Published: May 2023

Patients with hematuria are commonly given an invasive cystoscopy test to detect bladder cancer (BC). To avoid the risks associated with cystoscopy, several urine-based methods for BC detection have been developed, the most prominent of which is the deep sequencing of urine DNA. However, the current methods for urine-based BC detection have significant levels of false-positive signals. In this study, we report on uAL100, a method to precisely detect BC tumor DNA in the urine without tumor samples. Using urine samples from 43 patients with BC and 21 healthy donors, uAL100 detected BC with 83.7% sensitivity and 100% specificity. The mutations identified in the urine DNA by uAL100 for BC detection were highly associated with BC tumorigenesis and progression. We suggest that uAL100 has improved accuracy compared to other urine-based methods for early BC detection and can reduce unnecessary cystoscopy tests for patients with hematuria.
Cellular and Molecular Mechanisms of Liver Fibrosis in Patients with NAFLD
Authors: Sanchez Jessica I. , Parra Edwin R. , Jiao Jingjing , Solis Soto Luisa M. , Ledesma Debora A. , Saldarriaga Omar A. , Stevenson Heather L. , Beretta Laura
Published: May 2023

The expression of immune- and cancer-related genes was measured in liver biopsies from 107 NAFLD patients. The strongest difference in overall gene expression was between liver fibrosis stages F3 and F4, with 162 cirrhosis-associated genes identified. Strong correlations with fibrosis progression from F1 to F4 were observed for 91 genes, including CCL21, CCL2, CXCL6, and CCL19. In addition, the expression of 21 genes was associated with fast progression to F3/F4 in an independent group of eight NAFLD patients. These included the four chemokines, SPP1, HAMP, CXCL2, and IL-8. A six-gene signature including SOX9, THY-1, and CD3D had the highest performance detecting the progressors among F1/F2 NAFLD patients. We also characterized immune cell changes using multiplex immunofluorescence platforms. Fibrotic areas were strongly enriched in CD3+ T cells compared to CD68+ macrophages. While the number of CD68+ macrophages increased with fibrosis severity, the increase in CD3+ T-cell density was more substantial and progressive from F1 to F4. The strongest correlation with fibrosis progression was observed for CD3+CD45R0+ memory T cells, while the most significant increase in density between F1/F2 and F3/F4 was for CD3+CD45RO+FOXP3+CD8− and CD3+CD45RO−FOXP3+CD8− regulatory T cells. A specific increase in the density of CD68+CD11b+ Kupffer cells with liver fibrosis progression was also observed.
Prognostic Value of [18F]FDG PET Radiomics to Detect Peritoneal and Distant Metastases in Locally Advanced Gastric Cancer—A Side Study of the Prospective Multicentre PLASTIC Study
Authors: Pullen Lieke C. E. , Noortman Wyanne A. , Triemstra Lianne , de Jongh Cas , Rademaker Fenna J. , Spijkerman Romy , Kalisvaart Gijsbert M. , Gertsen Emma C. , de Geus-Oei Lioe-Fee , Tolboom Nelleke , de Steur Wobbe O. , Dantuma Maura , Slart Riemer H. J. A. , van Hillegersberg Richard , Siersema Peter D. , Ruurda Jelle P. , van Velden Floris H. P. , Vegt Erik ,   on behalf of the PLASTIC Study Group
Published: May 2023

Aim: To improve identification of peritoneal and distant metastases in locally advanced gastric cancer using [18F]FDG-PET radiomics. Methods: [18F]FDG-PET scans of 206 patients acquired in 16 different Dutch hospitals in the prospective multicentre PLASTIC-study were analysed. Tumours were delineated and 105 radiomic features were extracted. Three classification models were developed to identify peritoneal and distant metastases (incidence: 21%): a model with clinical variables, a model with radiomic features, and a clinicoradiomic model, combining clinical variables and radiomic features. A least absolute shrinkage and selection operator (LASSO) regression classifier was trained and evaluated in a 100-times repeated random split, stratified for the presence of peritoneal and distant metastases. To exclude features with high mutual correlations, redundancy filtering of the Pearson correlation matrix was performed (r = 0.9). Model performances were expressed by the area under the receiver operating characteristic curve (AUC). In addition, subgroup analyses based on Lauren classification were performed. Results: None of the models could identify metastases with low AUCs of 0.59, 0.51, and 0.56, for the clinical, radiomic, and clinicoradiomic model, respectively. Subgroup analysis of intestinal and mixed-type tumours resulted in low AUCs of 0.67 and 0.60 for the clinical and radiomic models, and a moderate AUC of 0.71 in the clinicoradiomic model. Subgroup analysis of diffuse-type tumours did not improve the classification performance. Conclusion: Overall, [18F]FDG-PET-based radiomics did not contribute to the preoperative identification of peritoneal and distant metastases in patients with locally advanced gastric carcinoma. In intestinal and mixed-type tumours, the classification performance of the clinical model slightly improved with the addition of radiomic features, but this slight improvement does not outweigh the laborious radiomic analysis.
New Strategies in Diagnosis and Treatments for Brain Tumors
Authors: Kantelhardt Sven
Published: May 2023

In general, cancer is one of the most frequent causes of death [...]
Efficient and Stable Ni/SBA-15 Catalyst for Dry Reforming of Methane: Effect of Citric Acid Concentration
Authors: Waris Mamoona , Ra Howon , Yoon Sungmin , Kim Min-Jae , Lee Kyubock
Published: May 2023

Citric acid, one of the representative chelate compounds, has been widely used as an additive to achieve the highly dispersed metal-supported catalysts. This study aimed to investigate the effect of citric acid concentration on the preparation of the highly dispersed Ni catalysts on mesoporous silica (SBA-15) for the dry reforming of methane. A series of Ni/SBA-15 catalysts with citric acid were prepared using the acid-assisted incipient wetness impregnation method, and the Ni/SBA-15 catalyst as a reference was synthesized via the impregnation method. First of all, the citric acid addition during the catalyst synthesis step regardless of its concentration resulted in highly dispersed Ni particles of ~4–7 nm in size in Ni/SBA-15 catalysts, which had a superior and stable catalytic performance in the dry reforming of methane (93% of CO2 conversion and 86% of CH4 conversion). In addition, the amount of coke formation was much lower in a series of Ni/SBA-15 catalysts with citric acid (~2–5 mgcoke gcat−1 h−1) compared to pristine Ni/SBA-15 catalysts (~22 mgcoke gcat−1 h−1). However, when the concentration of citric acid became higher, the more free NiO species that formed on the SBA-15 support, leading to large Ni particles after the stability test. The addition of citric acid is a very clear strategy for making highly dispersed catalysts, but its concentration needs to be carefully controlled.
Laser-Induced Nitrogen-Doped Graphene Composite Iron–Cobalt Hydroxide for Methylene Blue Degradation via Electrocatalytic Activation of Peroxymonosulfate
Authors: Chen Liqin , Liao Jianjun , Li Chen , Xu Yandong , Ge Chengjun , Xu Wen , He Xiong , Liu Wenyu
Published: May 2023

With the acceleration of industrialization, the removal of refractory organic dyes from water and how to promote its practical application remains a challenge. Herein, we synthesized an FeCo-LDH/LI-NDG composite electrode material by a simple laser-induced technique on polyimide films, which could electrocatalytically activate peroxymonosulfate (PMS) to completely degrade MB in about 6 min. The reaction rate constant (kobs) was 0.461 min−1. It was faster than most of the currently reported electrocatalysts. The reaction system demonstrated good interference resistance and catalytic effectiveness in the pH range of 3 to 9. According to the chemical quenching and electron paramagnetic resonance (EPR) experiments, the non-radical pathway of 1O2 and the radical pathways of SO4·−, ·OH and O2·− were involved in the reaction synergistically, with 1O2 playing the dominant role. 1O2 was produced through the dual pathway of PMS electron loss at the anode and O2·− intermediate transformation at the cathode. The two activation methods of electro-activation and catalytic activation of PMS had synergistic effects to achieve high efficiency in the whole process of production, reaction and recovery, providing new ideas to advance practical applications.
Transcriptome-Powered Pluripotent Stem Cell Differentiation for Regenerative Medicine
Authors: Ogi Derek A. , Jin Sha
Published: May 2023

Pluripotent stem cells are endless sources for in vitro engineering human tissues for regenerative medicine. Extensive studies have demonstrated that transcription factors are the key to stem cell lineage commitment and differentiation efficacy. As the transcription factor profile varies depending on the cell type, global transcriptome analysis through RNA sequencing (RNAseq) has been a powerful tool for measuring and characterizing the success of stem cell differentiation. RNAseq has been utilized to comprehend how gene expression changes as cells differentiate and provide a guide to inducing cellular differentiation based on promoting the expression of specific genes. It has also been utilized to determine the specific cell type. This review highlights RNAseq techniques, tools for RNAseq data interpretation, RNAseq data analytic methods and their utilities, and transcriptomics-enabled human stem cell differentiation. In addition, the review outlines the potential benefits of the transcriptomics-aided discovery of intrinsic factors influencing stem cell lineage commitment, transcriptomics applied to disease physiology studies using patients’ induced pluripotent stem cell (iPSC)-derived cells for regenerative medicine, and the future outlook on the technology and its implementation.
The Immune Response to SARS-CoV-2 Vaccine in a Cohort of Family Pediatricians from Southern Italy
Authors: Cortese Paolo , Amato Felice , Davino Antonio , De Franchis Raffaella , Esposito Speranza , Zollo Immacolata , Di Domenico Marina , Solito Egle , Zarrilli Federica , Gentile Laura , Cernera Gustavo , Castaldo Giuseppe
Published: May 2023

In Italy, from January 2021, the Ministry of Health indicated a vaccination plan against COVID for frail patients and physicians based on a three-dose scheme. However, conflicting results have been reported on which biomarkers permit immunization assessment. We used several laboratory approaches (i.e., antibodies serum levels, flow cytometry analysis, and cytokines release by stimulated cells) to investigate the immune response in a cohort of 53 family pediatricians (FPs) at different times after the vaccine. We observed that the BNT162b2-mRNA vaccine induced a significant increase of specific antibodies after the third (booster) dose; however, the antibody titer was not predictive of the risk of developing the infection in the six months following the booster dose. The antigen stimulation of PBMC cells from subjects vaccinated with the third booster jab induced the increase of the activated T cells (i.e., CD4+ CD154+); the frequency of CD4+ CD154+ TNF-α+ cells, as well as the TNF-α secretion, was not modified, while we observed a trend of increase of IFN-γ secretion. Interestingly, the level of CD8+ IFN-γ+ (independently from antibody titer) was significantly increased after the third dose and predicts the risk of developing the infection in the six months following the booster jab. Such results may impact also other virus vaccinations.
Synchrotron-Based Fourier-Transform Infrared Micro-Spectroscopy of Cerebrospinal Fluid from Amyotrophic Lateral Sclerosis Patients Reveals a Unique Biomolecular Profile
Authors: Dučić Tanja , Koch Jan Christoph
Published: May 2023

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, with the most common adult-onset neurodegenerative disorder affecting motoneurons. Although disruptions in macromolecular conformation and homeostasis have been described in association with ALS, the underlying pathological mechanisms are still not completely understood, and unambiguous biomarkers are lacking. Fourier Transform Infrared Spectroscopy (FTIR) of cerebrospinal fluid (CSF) is appealing to extensive interest due to its potential to resolve biomolecular conformation and content, as this approach offers a non-invasive, label-free identification of specific biologically relevant molecules in a few microliters of CSF sample. Here, we analyzed the CSF of 33 ALS patients compared to 32 matched controls using FTIR spectroscopy and multivariate analysis and demonstrated major differences in the molecular contents. A significant change in the conformation and concentration of RNA is demonstrated. Moreover, significantly increased glutamate and carbohydrates are found in ALS. Moreover, key markers of lipid metabolism are strongly altered; specifically, we find a decrease in unsaturated lipids and an increase in peroxidation of lipids in ALS, whereas the total amount of lipids compared to proteins is reduced. Our study demonstrates that FTIR characterization of CSF could represent a powerful tool for ALS diagnosis and reveals central features of ALS pathophysiology.
Generation of Chimeric Antigen Receptors against Tetraspanin 7
Authors: Pieper Tom , Roth Kristian Daniel Ralph , Glaser Viktor , Riet Tobias , Buitrago-Molina Laura Elisa , Hagedorn Maike , Lieber Maren , Hust Michael , Noyan Fatih , Jaeckel Elmar , Hardtke-Wolenski Matthias
Published: May 2023

Adoptive transfer of antigen-specific regulatory T cells (Tregs) has shown promising results in the treatment of autoimmune diseases; however, the use of polyspecific Tregs has limited effects. However, obtaining a sufficient number of antigen-specific Tregs from patients with autoimmune disorders remains challenging. Chimeric antigen receptors (CARs) provide an alternative source of T cells for novel immunotherapies that redirect T cells independently of the MHC. In this study, we aimed to generate antibody-like single-chain variable fragments (scFv) and subsequent CARs against tetraspanin 7 (TSPAN7), a membrane protein highly expressed on the surface of pancreatic beta cells, using phage display technology. We established two methods for generating scFvs against TSPAN7 and other target structures. Moreover, we established novel assays to analyze and quantify their binding abilities. The resulting CARs were functional and activated specifically by the target structure, but could not recognize TSPAN7 on the surface of beta cells. Despite this, this study demonstrates that CAR technology is a powerful tool for generating antigen-specific T cells and provides new approaches for generating functional CARs.
Factors That Support Technology Integration Practices in the Math Education of Children with Intellectual Disabilities
Authors: David Carmen Viorica , Costescu Cristina Anamaria , Roșan Adrian Marian
Published: May 2023

The current paper explores the relationship between technological knowledge, beliefs about technology use in education, beliefs about the limits of technology use with respect to children with disabilities, self-efficacy beliefs, and their effects on technology use or teacher practices in the special education setting regarding mathematics education. Research was conducted via an online questionnaire. A total of 83 teachers working in the field of special education answered the questionnaire. The answers were analyzed via quantitative and qualitative analyses. In addition, correlational analyses were conducted. A prediction model that included all the aforementioned variables was tested. Our regression analysis evidenced the strong predictive value of technology-related knowledge with respect to technology integration practices but not toward the other models that included beliefs. Through mediation analysis, we found that technology-related knowledge mediates the relationship between self-efficacy and technology integration practices. The qualitative findings provided information on the interplay between knowledge, practice, and beliefs that are contextualized; on the specificity of technology-related knowledge. Implications are discussed with reference to factors important for the professional development of teachers with regard to technology integration.
Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients
Authors: Alotaibi Abrar , Alnajrani Lujain , Alsheikh Nawal , Alanazy Alhatoon , Alshammasi Salam , Almusairii Meshael , Alrassan Shoog , Alansari Aisha
Published: May 2023

Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing cirrhosis. Cirrhosis typically worsens to the point of liver failure. Patients with cirrhosis may also experience brain and nerve system damage, as well as gastrointestinal hemorrhage. Treatment for cirrhosis focuses on preventing further progression of the disease. Detecting cirrhosis earlier is therefore crucial for avoiding complications. Machine learning (ML) has been shown to be effective at providing precise and accurate information for use in diagnosing several diseases. Despite this, no studies have so far used ML to detect cirrhosis in patients with hepatitis C. This study obtained a dataset consisting of 28 attributes of 2038 Egyptian patients from the ML Repository of the University of California at Irvine. Four ML algorithms were trained on the dataset to diagnose cirrhosis in hepatitis C patients: a Random Forest, a Gradient Boosting Machine, an Extreme Gradient Boosting, and an Extra Trees model. The Extra Trees model outperformed the other models achieving an accuracy of 96.92%, a recall of 94.00%, a precision of 99.81%, and an area under the receiver operating characteristic curve of 96% using only 16 of the 28 features.
A Novel Feature-Based Manufacturability Assessment System for Evaluating Selective Laser Melting and Subtractive Manufacturing Injection Moulding Tool Inserts
Authors: El Kashouty Mennatallah F. , Rennie Allan E. W. , Ghazy Mootaz
Published: May 2023

Challenges caused by design complexities during the design stages of a product must be coordinated and overcome by the selection of a suitable manufacturing approach. Additive manufacturing (AM) is capable of fabricating complex shapes, yet there are limiting aspects to surface integrity, dimensional accuracy, and, in some instances, design restrictions. Therefore, the goal is essentially to establish the complex areas of a tool during the design stage to achieve the desired quality levels for the corresponding injection moulding tool insert. When adopting a manufacturing approach, it is essential to acknowledge limitations and restrictions. This paper presents the development of a feature-based manufacturability assessment system (FBMAS) to demonstrate the feasibility of integrating selective laser melting (SLM), a metal-based AM technology, with subtractive manufacturing for any given part. The areas on the tool inserts that hold the most geometrical complexities to manufacture are focused on the FBMAS and the design features that are critical for the FBMAS are defined. Furthermore, the structural approach used for developing the FBMAS graphical user interface is defined while explaining how it can be operated effectively and in a user-friendly approach. The systematic approach established is successful in capturing the benefits of SLM and subtractive methods of manufacturing, whilst defining design limitations of each manufacturing method. Finally, the FBMAS developed was validated and verified against the criteria set by experts in the field, and the system’s logic was proven to be accurate when tested. The decision recommendations proved to correlate with the determined recommendations of the field experts in evaluating the feature manufacturability of the tool inserts.
From Meaning to Expression: A Dual Approach to Coupling
Authors: Van Campenhout Lukas , Vancoppenolle Ward , Dewit Ivo
Published: May 2023

Coupling is a key concept in the field of embodied interaction with digital products and systems, describing how digital phenomena relate to the physical world. In this paper, we present a Research through Design process in which the concept of coupling is explored and deepened. The use case that we employed to conduct our research is an industrial workplace proposed by Audi Brussels and Kuka. Our aim was to enrich this workplace with projection, or Spatial Augmented Reality, while focusing on operator interaction. We went through three successive design iterations, each of which resulted in a demonstrator. We present each of the three demonstrators, focusing on how they propelled our understanding of coupling. We establish a framework in which coupling between different events, be they physical or digital, emerges on four different aspects: time, location, direction, and expression. We bring the first three aspects together under one heading—coupling of meaning—and relate it to ease of use and pragmatic usability. We uncover the characteristics of the fourth aspect—coupling of expression—and link it to the psychological wellbeing of the operator in the workplace. We conclude this paper by highlighting its contribution to the embodied interaction research agenda.
A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches
Authors: Nilashi Mehrbakhsh , Abumalloh Rabab Ali , Alyami Sultan , Alghamdi Abdullah , Alrizq Mesfer
Published: May 2023

Diabetes in humans is a rapidly expanding chronic disease and a major crisis in modern societies. The classification of diabetics is a challenging and important procedure that allows the interpretation of diabetic data and diagnosis. Missing values in datasets can impact the prediction accuracy of the methods for the diagnosis. Due to this, a variety of machine learning techniques has been studied in the past. This research has developed a new method using machine learning techniques for diabetes risk prediction. The method was developed through the use of clustering and prediction learning techniques. The method uses Singular Value Decomposition for missing value predictions, a Self-Organizing Map for clustering the data, STEPDISC for feature selection, and an ensemble of Deep Belief Network classifiers for diabetes mellitus prediction. The performance of the proposed method is compared with the previous prediction methods developed by machine learning techniques. The results reveal that the deployed method can accurately predict diabetes mellitus for a set of real-world datasets.
Evaluation of the Utilization of Near-Infrared Fluorescent Contrast Agent ASP5354 for In Vivo Ureteral Identification in Renal Diseases Using Rat Models of Gentamicin-Induced Acute Kidney Injury
Authors: Teranishi Katsunori
Published: May 2023

ASP5354 was recently developed as a near-infrared fluorescence (NIRF) contrast agent for intraoperative ureteral identification, and its use has been evaluated in healthy animals. However, the utilization of ASP5354 for ureteral identification has not been evaluated in animals with renal injury. In this study, we assessed the application of ASP5354 for ureteral imaging using rat models of gentamicin-induced mild, moderate, and severe acute kidney injury (AKI), using a clinically available NIRF detection system. NIRF was detected in the abdominal cavity and ureters after laparotomy, and the efficiency of ASP5354 was evaluated based on the NIRF signal intensity over 60 min. After the intravenous injection of ASP5354 into rats with mild or moderate AKI, the ureters were clearly imaged at a high ratio of NIRF intensity in the ureter to that in the tissues around the ureter. Six days after intravenous injection, the use of ASP5354 in rats with moderate AKI did not affect the biochemical kidney functions or histopathological conditions of the kidney tissues, as compared to those with no injection of ASP5354. In rats with severe AKI, ureteral imaging was not effective due to the relatively strong NIRF expression in the tissues around the ureters. These data indicate that ASP5354 holds potential as a contrast agent for intraoperative ureteral identification in patients with limited renal injury.
Posterior Reversible Encephalopathy Syndrome after Pazopanib Therapy
Authors: Savaliya Madhavkumar , Surati Drishty , Surati Ramesh , Padmani Shailesh , Boussios Stergios
Published: May 2023

The term posterior reversible encephalopathy syndrome (PRES) refers to an acute syndrome characterised by a range of neurological symptoms and posterior transient changes on neuroimaging. Common clinical presentation includes headache, confusion, visual disturbances, seizures, and focal neurological deficit. With the advancement and increasing availability of neuroimaging, this syndrome is increasingly recognised. There are several underlying causes for PRES, including certain medications. Tyrosine kinase inhibitors (TKIs) such as pazopanib can increase the risk of developing PRES by markedly elevating the blood pressure due to its effect of inhibition of vascular endothelial growth factor receptors (VEGFRs). We are reporting a case of a 55-year-old male patient with the clear cell type of renal cell carcinoma (RCC) who developed PRES within a short period after starting pazopanib therapy. With the effective control of his blood pressure and discontinuation of pazopanib, his typical magnetic resonance imaging (MRI) lesion of PRES resolved in the follow-up scan after four weeks.
MicroRNAs’ Role in Diagnosis and Treatment of Subarachnoid Hemorrhage
Authors: Segherlou Zahra Hasanpour , Saldarriaga Lennon , Azizi Esaan , Vo Kim-Anh , Reddy Ramya , Siyanaki Mohammad Reza Hosseini , Lucke-Wold Brandon
Published: May 2023

Subarachnoid hemorrhage (SAH) is most commonly seen in patients over 55 years of age and often results in a loss of many productive years. SAH has a high mortality rate, and survivors often suffer from early and secondary brain injuries. Understanding the pathophysiology of the SAH is crucial in identifying potential therapeutic agents. One promising target for the diagnosis and prognosis of SAH is circulating microRNAs, which regulate gene expression and are involved in various physiological and pathological processes. In this review, we discuss the potential of microRNAs as a target for diagnosis, treatment, and prognosis in SAH.
An ETA-Based Tactical Conflict Resolution Method for Air Logistics Transportation
Authors: Li Chenglong , Gu Wenyong , Zheng Yuan , Huang Longyang , Zhang Xuejun
Published: May 2023

Air logistics transportation has become one of the most promising markets for the civil drone industry. However, the large flow, high density, and complex environmental characteristics of urban scenes make tactical conflict resolution very challenging. Existing conflict resolution methods are limited by insufficient collision avoidance success rates when considering non-cooperative targets and fail to take the temporal constraints of the pre-defined 4D trajectory into consideration. In this paper, a novel reinforcement learning-based tactical conflict resolution method for air logistics transportation is designed by reconstructing the state space following the risk sectors concept and through the use of a novel Estimated Time of Arrival (ETA)-based temporal reward setting. Our contributions allow a drone to integrate the temporal constraints of the 4D trajectory pre-defined in the strategic phase. As a consequence, the drone can successfully avoid non-cooperative targets while greatly reducing the occurrence of secondary conflicts, as demonstrated by the numerical simulation results.
Performance Analysis of Multi-Hop Flying Mesh Network Using Directional Antenna Based on β-GPP
Authors: Qin Shenghong , Peng Laixian , Xu Renhui , Wei Xianglin , Wei Xingchen , Jiang Dan
Published: May 2023

Maintaining high system performance is critical for a multi-hop flying mesh network (FlyMesh) to perform missions in different environments. Although the Poisson point process (PPP) has been widely used for the performance analysis of FlyMesh, it still has flaws in describing the spatial distribution of the UAVs since it does not restrict the minimum distance between them. The spatial deployment of FlyMesh varies depending on the environment. Considering the relevance and practicality, we modeled the multi-hop FlyMesh using the β-Ginibre point process (β-GPP) and equipped each UAV with a directional antenna. Under the condition of the decode-and-forward protocol, we derived the connection probability and ergodic capacity of a multi-hop FlyMesh utilizing the Laplace transform of interference. Then, we calculated an approximate expression for the interference Laplace transform based on the diagonal approximation and further obtained the coverage probability. Finally, the numerical simulation results verified the correctness of the theoretical derivation, indicating that it is possible to optimize the system’s performance based on the expressions derived in this paper.
A Lightweight UAV System: Utilizing IMU Data for Coarse Judgment of Loop Closure
Authors: Zhu Hongwei , Zhang Guobao , Ye Zhiqi , Zhou Hongyi
Published: May 2023

Unmanned aerial vehicles (UAVs) can experience significant performance issues during flight due to heavy CPU load, affecting their flight capabilities, communication, and endurance. To address this issue, this paper presents a lightweight stereo-inertial state estimator for addressing the heavy CPU load issue of ORB-SLAM. It utilizes nonlinear optimization and features to incorporate inertial information throughout the Simultaneous Localization and Mapping (SLAM) pipeline. The first key innovation is a coarse-to-fine optimization method that targets the enhancement of tracking speed by efficiently addressing bias and noise in the IMU parameters. A novel visual–inertial pose graph is proposed as an observer to assess error thresholds and guide the system towards visual-only or visual–inertial maximum a posteriori (MAP) estimation accordingly. Furthermore, this paper introduces the incorporation of inertial data in the loop closure thread. The IMU data provide displacement direction relative to world coordinates, which is serving as a necessary condition for loop detection. The experimental results demonstrate that our method maintains excellent localization accuracy compared to other state-of-the-art approaches on benchmark datasets, while also significantly reducing CPU load.
Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks
Authors: Dang Son Tung , Dinh Xuan Minh , Kim Thai Dinh , Xuan Hai Le , Ha Manh-Hung
Published: May 2023

This paper proposes a new adaptive controller for three-wheeled mobile robots (3WMRs) called the ABHSMC controller. This ABHSMC controller is developed through a cooperative approach, combining a backstepping controller and a Radial Basis Function (RBF) neural network-based Hierarchical Sliding Mode Controller (HSMC). Notably, the RBF neural network exhibits the remarkable capability to estimate both the uncertainty components of the model and systematically adapt its parameters, leading to enhanced output trajectory responses. A novel navigational model, constructed by the connection to the adaptive BHSMC controller, Timed Elastic Band (TEB) Local Planner, and A-star (A*) Global Planner, is called ABHSMC navigation stack, and it is applied to effectively solve the tracking issue and obstacle avoidance for the 3-Wheeled Mobile Robot (3WMR). The simulation results implemented in the Matlab/Simulink platform demonstrate that the 3WMRs can precisely follow the desired trajectory, even in the presence of disturbances and changes in model parameters. Furthermore, the controller’s reliability is endorsed on our constructed self-driving car model. The achieved experimental results indicate that the proposed navigational structure can effectively control the actual vehicle model to track the desired trajectory with a small enough error and avoid a sudden obstacle simultaneously.
Metamaterial-Based LTCC Compressed Luneburg Lens Antenna at 60 GHz for Wireless Communications
Authors: Zelenchuk Dmitry , Kirillov Vitalii , Kärnfelt Camilla , Gallée Francois , Munina Irina
Published: May 2023

In this study, a metamaterial-based LTCC compressed Luneburg lens was designed, manufactured and measured. The lens was designed at 60 GHz to utilize the unlicensed mm-wave spectrum available for short-range high-capacity wireless communication networks. The transformation optics method was applied to ensure the compression of the Luneburg lens antenna and thus maintain a low-profile structure. The two different types of unit cells for low and high permittivity regions were considered. The parametric study of the effect of compression on lens performance was presented. The antenna is implemented with a standard high-permittivity LTCC process, and details of the manufacturing process for the metamaterial lens are discussed. The low-profile lens is thinner than 2 mm and measures 19 mm in diameter. A size reduction of 63.6% in comparison with a spherical lens was achieved. The near-field to far-field mm-wave measurement technique is presented, and the measurement results show a peak antenna gain of 16 dBi at 60 GHz and a beam-scanning capacity with 1 dB scan loss within a 50° field of view.