bluuebunny/crossref_metadata_2025 huggingface dataset:
Unveiling Glucosinolate Diversity in Brassica Germplasm and In Silico Analysis for Determining Optimal Antioxidant Potential
Authors: Iwar Kanivalan , Desta Kebede Taye , Ochar Kingsley , Kim Seong-Hoon
Published: March 2024

This study explored the glucosinolate (GSL) content in Brassica plants and utilized in silico analysis approach to assess their antioxidant capabilities. GSLs, present abundantly in Brassica vegetables, offer potential health advantages, including antioxidant effects. Employing Ultra-Performance Liquid Chromatography (UPLC) coupled with tandem mass spectrometry (MS/MS), major GSLs were identified in 89 accessions from diverse species and subspecies. Statistical analysis and principal component analysis unveiled significant GSL variation and potential correlations among the Brassica germplasms. This study unveils the dominance of aliphatic GSLs over aromatic and indolyl compounds in all the accessions. Notably, Gluconapin (GNA) (33,049.23 µmol·kg−1 DW), Glucobrassicanapin (GBN) (9803.82 µmol·kg−1 DW), Progoitrin (PRO) (12,780.48 µmol·kg−1 DW) and Sinigrin (SIN) (14,872.93 µmol·kg−1 DW) were the most abundant compounds across the analyzed accessions. Moreover, in silico docking studies predicted promising antioxidant activity by evaluating the interactions of each GSL with antioxidant enzymes. Specifically, Sinigrin and Gluconapin exhibited a notably weaker influence on antioxidant enzymes. This provides key insights into the antioxidant potential of Brassica germplasm and highlights the importance of in silico analysis for evaluating bioactive properties. In general, the results of this study could be utilized in breeding programs to maximize GSL levels and antioxidant properties in Brassica crops and for developing functional foods with enhanced health benefits.
Designing an Educational Metaverse: A Case Study of NTUniverse
Authors: Sim Jing Kai , Xu Kaichao William , Jin Yuyang , Lee Zhi Yu , Teo Yi Jie , Mohan Pallavi , Huang Lihui , Xie Yuan , Li Siyi , Liang Nanying , Cao Qi , See Simon , Winkler Ingrid , Cai Yiyu
Published: March 2024

An up-and-coming concept that seeks to transform how students learn about and study complex systems, as well as how industrial workers are trained, metaverse technology is characterized in this context by its use in virtual simulation and analysis. In this work, a virtual environment is created that duplicates real-world situations and enables immersive and interactive learning in the educational metaverse. For this purpose, we built a digital twin of the Nanyang Technological University (NTU) campus as a foundation, called NTUniverse. It is designed as an educational metaverse in which various academic and analytical applications are digitized as 3D content embedded within this virtual campus. The approach to digitally twinning educational systems and embedding them within virtual campuses enables remote and collaborative learning as well as professional technical skills training. It also makes feasible the analysis of abstract concepts, complicated structures, dynamic processes, and sensitive industrial procedures virtually, which is otherwise challenging if not impossible to perform in the real world. The work offers important insights into the behaviors and interactions of systems in the metaverse by evaluating design choices and user interests. NTUniverse is an attempt to explore a novel approach that addresses remote education and training challenges. Three efforts with NTUniverse will be discussed in this work, including (1) digitalization of the NTU campus; (2) campus train modelling and simulation; and (3) science, technology, engineering and mathematics education.
Enhanced Tracking in Legged Robots through Model Reduction and Hybrid Control Techniques: Addressing Disturbances, Delays, and Saturation
Authors: Zhao Yongyong , Wang Jinghua , Cao Guohua , Yao Xu
Published: March 2024

This study introduces a reduced-order leg dynamic model to simplify the controller design and enhance robustness. The proposed multi-loop control scheme tackles tracking control issues in legged robots, including joint angle and contact-force regulation, disturbance suppression, measurement delay, and motor saturation avoidance. Firstly, model predictive control (MPC) and sliding mode control (SMC) schemes are developed using a simplified model, and their stability is analyzed using the Lyapunov method. Numerical simulations under two disturbances validate the superior tracking performance of the SMC scheme. Secondly, an Nth-order linear active disturbance rejection control (LADRC) is designed based on a simplified model and optimization problems. The second-order LADRC-SMC scheme reduces the contact-force control error in the SMC scheme by ten times. Finally, a fourth-order LADRC-SMC with a Smith Predictor (LADRC-SMC-SP) scheme is formulated, employing each loop controller independently. This scheme simplifies the design and enhances performance. Compared to numerical simulations of the above and existing schemes, the LADRC-SMC-SP scheme eliminates delay oscillations, shortens convergence time, and demonstrates fast force-position tracking responses, minimal overshoot, and strong disturbance rejection. The peak contact-force error in the LADRC-SMC-SP scheme was ten times smaller than that in the LADRC-SMC scheme. The integral square error (ISE) values for the tracking errors of joint angles θ1 and θ2, and contact force f, are 1.6636×10−28 rad2⋅s, 1.7983×10−28 rad2⋅s, and 1.8062×10−30 N2⋅s, respectively. These significant improvements in control performance address the challenges in single-leg dynamic systems, effectively handling disturbances, delays, and motor saturation.
Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
Authors: Gao Hongfeng , Xu Tiexin , Li Renlong , Cai Chaozhi
Published: March 2024

Because the gearbox in transmission systems is prone to failure and the fault signal is not obvious, the fault end cannot be located. In this paper, a gearbox fault diagnosis method grounded on improved complete ensemble empirical mode decomposition with adaptive noise, a multiscale permutation entropy and adaptive wavelet thresholding (ICEEMDAN-MPE-AWT) denoising method and an SE-ResNeXt50 transfer learning model are proposed. Initially, the vibration signal is denoised by ICEEMDAN-MPE-AWT, the denoised vibration signal is then converted into a Gram angle field (GAF) diagram, and then the parameters are transferred by the fine-tuning transfer learning strategy. Finally, a GAF diagram is input into the model for training to achieve fault extraction and classification. In this paper, the open gear dataset of Southeast University is used for experimental research. The experimental results show that when using the ICEEMDAN-MPE-AWT and when the signal-to-noise ratio (SNR) of the experimental data is −4 dB, the average accuracy of the GASF+TSE-ResNeXt50 and the GASF+TSE-ResNeXt18 can reach 98.8% and 97.5%, respectively. When the SNR is 6 dB, the accuracy of the above two models reaches 100% and 99.3%, respectively. Moreover, when compared to alternative approaches, the noise reduction method in this paper can better remove noise interference so that the model can better extract fault features. Therefore, the method proposed in this article shows significant improvement in noise reduction and fault classification accuracy compared to other methods.
DentalArch: AI-Based Arch Shape Detection in Orthodontics
Authors: Tamayo-Quintero J. D. , Gómez-Mendoza J. B. , Guevara-Pérez S. V.
Published: March 2024

Objective: This study aims to introduce and assess a novel AI-driven tool developed for the classification of orthodontic arch shapes into square, ovoid, and tapered categories. Methods: Between 2016 and 2019, we collected 450 digital dental models. Applying our inclusion and exclusion criteria, we refined our dataset to 50 models, ensuring a focused and detailed analysis. Plaster casts were digitized into 3D models with AutoScan-DS-EX. Three trained evaluators then measured mesiodistal and arch widths using MeshLab. The development of DentalArch was undertaken in two versions: the first version incorporates 18 input parameters, including mesiodistal widths (from the first molar to the first molar, totaling 14) and arch widths (1 intercanine, 2 interpremolar, and 1 intermolar, totaling 4); the second version uses only 4 parameters related to arch widths. Both versions aim to predict the arch shape. An evaluation of 28 machine learning methods through a k = 5-fold cross-validation was conducted to determine the most effective techniques. Results: In the tests, the performance evaluation of the DentalArch software in detecting arch shapes revealed that version 1, which analyzes 18 parameters, achieved an accuracy of 94.7% for the lower arch and 93% for the upper arch. The more streamlined version 2, which assesses only four parameters, also showed high precision with an accuracy of 93.0% for the lower arch and 92.7% for the upper arch. Conclusions: DentalArch provides a tool with potential use in orthodontic diagnostics, particularly in the task of arch shape classification. The software offers a less subjective and data-driven approach to arch shape determination. Moreover, the open-source nature of DentalArch ensures its global availability and encourages contributions from the orthodontic community.
Special Issue on eHealth Innovative Approaches and Applications
Authors: Silvestri Stefano , Gargiulo Francesco
Published: March 2024

Innovative ICT technologies, approaches and applications are becoming increasingly pervasive in several domains, including in medicine and healthcare [...]
A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan
Authors: Lin Shiu-Shin , Zhu Kai-Yang , Wang Chen-Yu
Published: March 2024

This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season.
Cancer Detection Using a New Hybrid Method Based on Pattern Recognition in MicroRNAs Combining Particle Swarm Optimization Algorithm and Artificial Neural Network
Authors: Molaei Sepideh , Cirillo Stefano , Solimando Giandomenico
Published: March 2024

MicroRNAs (miRNAs) play a crucial role in cancer development, but not all miRNAs are equally significant in cancer detection. Traditional methods face challenges in effectively identifying cancer-associated miRNAs due to data complexity and volume. This study introduces a novel, feature-based technique for detecting attributes related to cancer-affecting microRNAs. It aims to enhance cancer diagnosis accuracy by identifying the most relevant miRNAs for various cancer types using a hybrid approach. In particular, we used a combination of particle swarm optimization (PSO) and artificial neural networks (ANNs) for this purpose. PSO was employed for feature selection, focusing on identifying the most informative miRNAs, while ANNs were used for recognizing patterns within the miRNA data. This hybrid method aims to overcome limitations in traditional miRNA analysis by reducing data redundancy and focusing on key genetic markers. The application of this method showed a significant improvement in the detection accuracy for various cancers, including breast and lung cancer and melanoma. Our approach demonstrated a higher precision in identifying relevant miRNAs compared to existing methods, as evidenced by the analysis of different datasets. The study concludes that the integration of PSO and ANNs provides a more efficient, cost-effective, and accurate method for cancer detection via miRNA analysis. This method can serve as a supplementary tool for cancer diagnosis and potentially aid in developing personalized cancer treatments.
A Quest for Survival: A Review of the Early Biomarkers of Pancreatic Cancer and the Most Effective Approaches at Present
Authors: Bestari Muhammad Begawan , Joewono Ignatius Ronaldi , Syam Ari Fahrial
Published: March 2024

Pancreatic cancer (PC) is the most lethal type of cancer; it has the lowest 5-year survival rate among all other types of cancers. More than half of PC cases are diagnosed at an advanced stage due to PC’s insidious and non-specific symptoms. Surgery remains the most efficacious treatment option currently available, but only 10–20% of PC cases are resectable upon diagnosis. As of now, the sole biomarker approved by the United States Food and Drug Administration (US-FDA) for PC is carbohydrate antigen 19-9 (CA19-9); however, its use is limited for early diagnosis. An increasing number of studies have investigated a combination of biomarkers. Lately, there has been considerable interest in the application of a liquid biopsy, including the utilization of microRNAs (miRNAs), circulating tumor DNA (ctDNA), and circulating tumor cells (CTCs). Screening for PC is indicated for high-risk patients; studies on new diagnostic models combined with biomarkers for early detection have also shown promising results in terms of the ability of these models and biomarkers to aid clinicians in deciding on whether to start screening. This review seeks to provide a concise overview of the advancements in relation to existing biomarkers and explore novel strategies for the early detection of PC.
TSG-6 Inhibits the NF-κB Signaling Pathway and Promotes the Odontogenic Differentiation of Dental Pulp Stem Cells via CD44 in an Inflammatory Environment
Authors: Wang Ying , Xie Yulang , Xue Ningning , Xu Hao , Zhang Dunfang , Ji Ning , Chen Qianming
Published: March 2024

In pulpitis, dentinal restorative processes are considerably associated with undifferentiated mesenchymal cells in the pulp. This study aimed to investigate strategies to improve the odonto/osteogenic differentiation of dental pulp stem cells (DPSCs) in an inflammatory environment. After pretreatment of DPSCs with 20 ng/mL tumor necrosis factor-induced protein-6 (TSG-6), DPSCs were cultured in an inflammation-inducing solution. Real-time polymerase chain reaction and Western blotting were performed to measure the expression levels of nuclear factor kappa B (NF-κB) and odonto/osteogenic differentiation markers, respectively. Cell Counting Kit-8 and 5-ethynyl-2′-deoxyuridine assays were used to assess cell proliferation and activity. Subcutaneous ectopic osteogenesis and mandibular bone cultures were performed to assess the effects of TSG-6 in vivo. The expression levels of odonto/osteogenic markers were higher in TSG-6-pre-treated DPSCs than nontreated DPSCs, whereas NF-κB-related proteins were lower after the induction of inflammation. An anti-CD44 antibody counteracted the rescue effect of TSG-6 on DPSC activity and mineralization in an inflammatory environment. Exogenous administration of TSG-6 enhanced the anti-inflammatory properties of DPSCs and partially restored their mineralization function by inhibiting NF-κB signaling. The mechanism of action of TSG-6 was attributed to its interaction with CD44. These findings reveal novel mechanisms by which DPSCs counter inflammation and provide a basis for the treatment of pulpitis.
Role of Genetics in Diagnosis and Management of Hypertrophic Cardiomyopathy: A Glimpse into the Future
Authors: Abbas Mohammed Tiseer , Baba Ali Nima , Farina Juan M. , Mahmoud Ahmed K. , Pereyra Milagros , Scalia Isabel G. , Kamel Moaz A. , Barry Timothy , Lester Steven J. , Cannan Charles R. , Mital Rohit , Wilansky Susan , Freeman William K. , Chao Chieh-Ju , Alsidawi Said , Ayoub Chadi , Arsanjani Reza
Published: March 2024

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy. It follows an autosomal dominant inheritance pattern in most cases, with incomplete penetrance and heterogeneity. It is familial in 60% of cases and most of these are caused by pathogenic variants in the core sarcomeric genes (MYH7, MYBPC3, TNNT2, TNNI3, MYL2, MYL3, TPM1, ACTC1). Genetic testing using targeted disease-specific panels that utilize next-generation sequencing (NGS) and include sarcomeric genes with the strongest evidence of association and syndrome-associated genes is highly recommended for every HCM patient to confirm the diagnosis, identify the molecular etiology, and guide screening and management. The yield of genetic testing for a disease-causing variant is 30% in sporadic cases and up to 60% in familial cases and in younger patients with typical asymmetrical septal hypertrophy. Genetic testing remains challenging in the interpretation of results and classification of variants. Therefore, in 2015 the American College of Medical Genetics and Genomics (ACMG) established guidelines to classify and interpret the variants with an emphasis on the necessity of periodic reassessment of variant classification as genetic knowledge rapidly expands. The current guidelines recommend focused cascade genetic testing regardless of age in phenotype-negative first-degree relatives if a variant with decisive evidence of pathogenicity has been identified in the proband. Genetic test results in family members guide longitudinal clinical surveillance. At present, there is emerging evidence for genetic test application in risk stratification and management but its implementation into clinical practice needs further study. Promising fields such as gene therapy and implementation of artificial intelligence in the diagnosis of HCM are emerging and paving the way for more effective screening and management, but many challenges and obstacles need to be overcome before establishing the practical implications of these new methods.
A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems
Authors: Hussien Abdelazim G. , Pop Adrian , Kumar Sumit , Hashim Fatma A. , Hu Gang
Published: March 2024

The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb’s law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome these challenges, this paper introduces a modified version of AEFA, named mAEFA, which leverages the capabilities of Lévy flights, simulated annealing, and the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms. While Lévy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity. The integration of these mechanisms in AEFA aims to expand its search space, enhance exploration potential, avoid local optima, and achieve improved performance, robustness, and a more equitable equilibrium between local intensification and global diversification. In this study, a comprehensive assessment of mAEFA is carried out, employing a combination of quantitative and qualitative measures, on a diverse range of 29 intricate CEC’17 constraint benchmarks that exhibit different characteristics. The practical compatibility of the proposed mAEFA is evaluated on five engineering benchmark problems derived from the civil, mechanical, and industrial engineering domains. Results from the mAEFA algorithm are compared with those from seven recently introduced metaheuristic algorithms using widely adopted statistical metrics. The mAEFA algorithm outperforms the LCA algorithm in all 29 CEC’17 test functions with 100% superiority and shows better results than SAO, GOA, CHIO, PSO, GSA, and AEFA in 96.6%, 96.6%, 93.1%, 86.2%, 82.8%, and 58.6% of test cases, respectively. In three out of five engineering design problems, mAEFA outperforms all the compared algorithms, securing second place in the remaining two problems. Results across all optimization problems highlight the effectiveness and robustness of mAEFA compared to baseline metaheuristics. The suggested enhancements in AEFA have proven effective, establishing competitiveness in diverse optimization problems.
Microwave Imaging System Based on Signal Analysis in a Planar Environment for Detection of Abdominal Aortic Aneurysms
Authors: Martínez-Lozano Andrea , Gutierrez Roberto , Juan Carlos G. , Blanco-Angulo Carolina , García-Martínez Héctor , Torregrosa Germán , Sabater-Navarro José María , Ávila-Navarro Ernesto
Published: March 2024

A proof-of-concept of a microwave imaging system for the fast detection of abdominal aortic aneurysms is shown. This experimental technology seeks to overcome the factors hampering the fast screening for these aneurysms with the usual equipment, such as high cost, long-time operation or hazardous exposure to chemical substances. The hardware system is composed of 16 twin antennas mastered by a microcontroller through a switching network, which connects the antennas to the measurement instrument for sequential measurement. The software system is run by a computer, mastering the whole system, automatizing the measurement process and running the signal processing and medical image generation algorithms. Two image generation algorithms are tested: Delay-and-Sum (DAS) and Improved Delay-and-Sum (IDAS). Own-modified versions of these algorithms adapted to the requirements of our system are proposed. The system is carefully calibrated and fine-tuned with known objects placed at known distances. An experimental proof-of-concept is shown with a human torso phantom, including an aorta phantom and an aneurysm phantom placed in different positions. The results show good imaging capabilities with the potential for detecting and locating possible abdominal aortic aneurysms and reporting acceptable errors.
Adventitia Layer-Focused Microsurgical Flow Reconstruction for Long-Segment Tubular Stenosis of the Cervical Segment (C1) Internal Carotid Artery: Clinical Valuable Experience in 20 Cases
Authors: Cekic Efecan , Ustun Mehmet Erkan
Published: March 2024

To evaluate the efficacy of perivascular sympathectomy in managing adventitia layer-related long-segment tubular stenosis of cervical segment (C1) internal carotid arteries (ICAs) in a cohort where conventional medical and endovascular interventions were not viable options, we retrospectively analyzed 20 patients (8 males, 12 females, aged 41–63 years) who underwent perivascular sympathectomy for long-segment (>5 cm) tubular cervical ICA stenosis (non-atherosclerotic, non-intima related, and nondolichoarteriopathic) between 2017 and 2023. The procedure aimed to alleviate symptoms such as hemiparesis, pulsatile tinnitus, and migraines associated with transient ischemic attacks (TIAs). Preoperative and postoperative symptoms were assessed, and patient follow-up was conducted by MR angiography and perfusion studies. Postoperatively, 10 out of 11 migraine sufferers (90.9%) reported complete cessation of symptoms, while one patient (9.09%) experienced reduction in frequency and intensity. In cases of tinnitus, six out of nine patients (66.6%) reported complete resolution, two (22.2%) had reduced symptoms, and one (11.1%) saw no change. Regarding motor function, all 12 patients (100%) with initial hemiparesis (30–40% loss of motor function) showed complete recovery postoperatively. There was no TIA attack among the patients after the procedure in the mean two-year follow-up. Perivascular sympathectomy has shown promising results in alleviating symptoms and preventing recurrent cerebrovascular events in long-segment tubular stenosis of cervical ICAs.
An Investigation of Employment Hope as a Key Factor Influencing Perceptions of Subjective Recovery among Adults with Serious Mental Illness Seeking Community Work
Authors: Kukla Marina , McGuire Alan B. , Weber Kenneth C. , Hatfield Jessi , Henry Nancy , Kulesza Eric , Rollins Angela L.
Published: March 2024

Introduction: Employment is an important contributor to recovery in people with serious mental illness (SMI), yet studies have not explored how subjective elements of employment hope contribute to perceptions of global recovery in this population. Methods: The current study examined the relationship between employment hope and subjective recovery in 276 unemployed adults with SMI participating in a multi-site clinical trial of a cognitive behavioral group intervention tailored toward work and combined with vocational rehabilitation. Participants had diagnoses of schizophrenia spectrum, bipolar, depressive, and posttraumatic stress disorders, and were receiving services at three Veterans Affairs healthcare facilities in the United States. Data were collected at study baseline. Linear regression analysis examined the relationship between employment hope (Short Employment Hope Scale; EHS-14) and subjective recovery (Recovery Assessment Scale; RAS) after controlling for psychiatric symptom severity and mental-health-related burden on daily life. Results: After accounting for covariates, employment hope significantly contributed to the regression model explaining subjective recovery. The overall model of predictor variables explained 52.5% of the variance in recovery. The results further explore the relationships between EHS-14 and RAS subscales. Conclusions: The findings suggest that employment hope is a key intervention target to bolster subjective recovery in this vulnerable population.
Towards the Uptake of Digital Technologies for Construction Information Management: A Partial Least Squares Structural Equation Modelling Approach
Authors: Adekunle Peter , Aigbavboa Clinton , Akinradewo Opeoluwa , Ikuabe Matthew , Otasowie Kenneth
Published: March 2024

The primary objective of this study survey is to close knowledge gaps by measuring the responses from construction experts and investigating the significant effects of using digital technologies in construction information management (CIM). This is attributed to the lack of thorough knowledge among construction professionals on the implications and efficacy of incorporating digital tools in construction information management. A thorough analysis of the literature on the use of digital technologies revealed outcomes related to digitized ways of managing construction information, which were then contextually tailored through a pilot study and presented in the form of a postulated model. A total of 257 stakeholders in the building industry were given questionnaire surveys to complete in order to gather primary data. The final model of the result of adopting digital technology was statistically validated using partial least squares structural equation modelling (PLS-SEM). By concentrating on the quantitative contribution of the most important result to the adoption of digital technologies throughout the process of CIM, this study closes this knowledge gap. The three primary benefits that digital technologies have the most influence on are communication, operational efficiency, and market intelligence, according to this paper’s conclusions. The research showed that encouraging relationships that enable the use of digital technologies should be promoted between technology providers and construction companies. In order to adopt and improve digital solutions, construction firms and technology providers will be able to collaborate in an ecosystem. By shedding light on the implementation and impact of digital technologies in the construction sector, the study helps to close this knowledge gap. The study offers valuable information for upcoming initiatives that support digital transformation through construction methods. The results serve as instructions for the government authorities to help them focus their efforts and distribute their resources more effectively.
Study on the Ultimate Load-Bearing Capacity of Disc Buckle Tall Formwork Support Considering Uncertain Factors
Authors: Huang Hua , Peng Zhenfeng , Hou Jinkun , Zheng Xudong , Ding Yuxi , Wu Han
Published: March 2024

Disc buckle steel pipe brackets are widely used in building construction due to the advantages of its simple structure, large-bearing capacity, rapid assembling and disassembling, and strong versatility. In complex construction projects, the uncertainties affecting the stability of disc buckle steel pipe support need to be considered to ensure the safety of disc buckle steel pipe supports. A surrogate model based on a deep neural network is built and trained to predict the ultimate load-carrying capacity of a stent. The results of the finite element model calculations are used to form the sample set of the surrogate model. Then, we combined the computationally efficient DNN surrogate model with the Monte Carlo method to consider the distribution of the ultimate load capacity of the disc buckle bracket under the uncertainties of the bracket node pin wedge tightness, the wall thickness of the steel pipe, and the connection of the connecting wall member. At the same time, based on the DNN model, the SHapley Additive exPlanations (SHAP) interpretability analysis method was used to study the degree of influence of various uncertainty factors on the ultimate bearing capacity of the stent. In practical engineering, the stability analysis of a disc buckle tall formwork support has shown that a surrogate model based on a deep neural network is efficient in predicting the buckling characteristic value of the support. The error rate of the prediction is less than 2%. The buckling characteristic values of the bracket vary in the range of 17–25. Among the various factors that influence the buckling characteristic value of the bracket, the joint wedge tightness has the greatest impact, followed by the bottom and top wall-connecting parts.
Conversion of Biomass-Derived Tars in a Fluidized Catalytic Post-Gasification Process
Authors: Rojas Chaves Floria , Torres Brauer Nicolas , Torres Cindy , de Lasa Hugo
Published: March 2024

The present study deals with the development, characterization, and performance of a Ni-based catalyst over a ceria-doped alumina support as a post-gasification step, in the conversion of biomass-derived tars. The catalysts were prepared using the incipient wetness technique and characterized chemically and physically using NH3-TPD, CO2-TPD, H2-TPR, XRD, Pyridine-FTIR, N2 physisorption, and H2-Pulse Chemisorption. It was observed that the 5 wt% CeO2 reduced the strong and very strong acid sites of the alumina support and helped with the dispersion of nickel. It was noticed that the nickel crystallite sizes and metal dispersion remained unchanged as the nickel loading increased. The performance of the catalysts was studied in a mini-fluidized CREC Riser Simulator at different temperatures and reaction times. The selected tar surrogate was 2-methoxy-4-methylphenol, given its functional group similarities with lignin-derived tars. A H2/CO2 gas blend was used to emulate the syngas at post-gasification conditions. The obtained tar surrogate conversion was higher than 75%, regardless of the reaction conditions. Furthermore, the catalysts used in this research provided an enhancement in the syngas product composition when compared to that observed in the thermal experiments. The presence of hydrocarbons greater than CH4 (C1+) was reduced at 525 °C, from 96 ± 3% with no catalyst, to 85 ± 2% with catalyst and steam, to 68 ± 4% with catalyst and steam-H2/CO2. Thus, the catalyst that we developed promoted tar cracking, tar reforming, and water-gas shift reactions, with a H2/CO ratio higher than 3.8, providing a syngas suitable for alcohol synthesis.
Foot-and-Mouth Disease Virus Capsid Protein VP1 Antagonizes Type I Interferon Signaling via Degradation of Histone Deacetylase 5
Authors: Gong Qing , Ren Shanhui , Dou Yongxi , Tadele Berihun , Hu Tao , Zhou Luoyi , Wang Tao , Yao Kaishen , Xu Jian , Yin Xiangping , Sun Yuefeng
Published: March 2024

Foot-and-mouth disease (FMD) is a highly contagious and economically important disease of cloven-hoofed animals that hampers trade and production. To ensure effective infection, the foot-and-mouth disease virus (FMDV) evades host antiviral pathways in different ways. Although the effect of histone deacetylase 5 (HDAC5) on the innate immune response has previously been documented, the precise molecular mechanism underlying HDAC5-mediated FMDV infection is not yet clearly understood. In this study, we found that silencing or knockout of HDAC5 promoted FMDV replication, whereas HDAC5 overexpression significantly inhibited FMDV propagation. IFN-β and IFN-stimulated response element (ISRE) activity was strongly activated through the overexpression of HDAC5. The silencing and knockout of HDAC5 led to an increase in viral replication, which was evident by decreased IFN-β, ISG15, and ISG56 production, as well as a noticeable reduction in IRF3 phosphorylation. Moreover, the results showed that the FMDV capsid protein VP1 targets HDAC5 and facilitates its degradation via the proteasomal pathway. In conclusion, this study highlights that HDAC5 acts as a positive modulator of IFN-β production during viral infection, while FMDV capsid protein VP1 antagonizes the HDAC5-mediated antiviral immune response by degrading HDAC5 to facilitate viral replication.
Primary and Secondary Variants of Callous-Unemotional Traits in Community Youths: Differences in Anticipatory Fear
Authors: Gao Yu , Raine Adrian
Published: March 2024

Callous-unemotional (CU) traits refer to a cluster of characteristics such as low empathy, lack of remorse, and insensitivity to the emotions of others, delineating a group of youth at high risk for severe antisocial behavior. Two variants—primary and secondary CU—have been theorized to have different underlying mechanisms, although mixed findings have been reported. The current study examined if the variants differ in their level of anticipatory fear in 92 youths from the community (mean age = 14.2 years, range = 12.3–16.4 years; 43.5% female). Participants completed a countdown task while their heart rate and skin conductance responses were recorded. Parents and youths completed the inventory of callous-unemotional traits and the child behavior checklist. Compared to the control group (low CU/low anxiety), the primary CU group (high CU/low anxiety) showed prolonged heart rate deceleration in anticipation of the impending aversive stimulus. The secondary CU group (high CU/high anxiety) did not differ from the other two groups on heart rate or skin conductance responses. This prolonged heart rate deceleration in the primary CU group is interpreted in the context of the passive vagal coping theory of antisocial behavior which hypothesizes that an over-engagement of the parasympathetic nervous system reduces the impact of a socializing punishment, which in turn predisposes individuals to antisocial behavior. Findings provide further support for the distinct etiology of two variants of CU traits.
The Relationship between Physical Activity, Physical Fitness, Cognition, and Academic Outcomes in School-Aged Latino Children: A Scoping Review
Authors: Marrero-Rivera J. P. , Sobkowiak Olivia , Jenkins Aimee Sgourakis , Bagnato Stefano J. , Kline Christopher E. , Gordon Benjamin DH , Taverno Ross Sharon E.
Published: March 2024

This scoping review provides an overview of the relationship between physical activity, physical fitness, cognition, and academic outcomes in Latino school-aged children and identifies areas for future research. A primary search was conducted in PubMed, PsycINFO, Web of Science, and ERIC for original-research articles meeting the inclusion criteria; the search results were uploaded into PICO Portal and assessed by two independent reviewers. Of the 488 initial search results, 50 articles were eligible for full-text review, and 38 were included in this review. Most studies were cross-sectional, conducted in the United States or Chile, and included children 5–18 years old. Overall, the majority of articles reported positive associations between physical activity or physical fitness and cognitive outcomes (n = 11/12; 91.7%), and physical activity or physical fitness and academic outcomes (n = 22/28; 78.6%). In sum, this review provided consistent evidence for higher amounts of physical activity and greater physical fitness to be associated with various positive cognitive and academic outcomes in a school-aged Latino population. This scoping review also elucidated a substantial gap in the research regarding study design, with a discernible lack of interventional efforts. Future studies should test physical activity interventional strategies to optimize cognitive and academic outcomes in school-aged Latino populations.
Anti-Aging Properties of Cannabis sativa Leaf Extract against UVA Irradiation
Authors: Luangpraditkun Kunlathida , Pimjuk Preeyanuch , Phimnuan Preeyawass , Wisanwattana Wisanee , Wisespongpand Chothip , Waranuch Neti , Viyoch Jarupa
Published: March 2024

Hemp extract has garnered interest as a potential cosmeceutical agent with multifunctional activities, particularly in protecting against UV-induced skin cell aberrations and restoring aged skin cells. The ethanolic extract of Cannabis sativa leaves was prepared into an aqueous solution (CLES) to investigate its anti-photoaging ability. HPLC analysis revealed that the CLES contained 1.64 ± 0.01% w/w of cannabidiol and 0.11% w/w of ∆9-tetrahydrocannabinol. Additionally, the total phenolic content was found to be 4.08 ± 0.30 mg gallic acid equivalent per g of solution using the Folin–Ciocalteu method. The CLES exhibited potent scavenging activity using a DPPH assay, with an EC50 value of 277.9 ± 2.41 μg/mL, comparable to L-ascorbic acid, with 2.19 ± 0.28 μg/mL. The anti-photoaging potential of the CLES was evaluated using UVA-irradiated and in vitro-aged fibroblasts as a model. Pre-treatment with 20 μg/mL CLES for 24 h significantly alleviated the reduction in type I procollagen and suppressed the overproduction of MMP-1 and IL-6 induced by UVA. Moreover, the percentage of senescence-associated β-galactosidase-expressing cells decreased significantly to 11.9 ± 0.5% in the aged cells treated with CLES compared with untreated cells (18.8 ± 3.8%). These results strongly indicate the cosmeceutical potential of the CLES as an effective active agent for the anti-photoaging prevention and/or treatment.
Local Diagnostic Reference Levels for Adult Computed Tomography Urography Exams
Authors: Husremović Faruk , Muharemović Orhan , Đedović Edis , Efendić Alma , Mušanović Jasmin , Omerović Rifat , Osmanović Hedim , Busuladžić Mustafa
Published: March 2024

A Computed Tomography Urography (CTU) scan is a medical imaging test that examines the urinary tract, including the bladder, kidneys, and ureters. It helps diagnose various urinary tract diseases with precision. However, patients undergoing CTU imaging receive a relatively high dose of radiation, which can be a concern. In our research paper, we analyzed the Computed Tomography Dose Index (CTDIvol) and Dose-Length Product (DLP) for 203 adult patients who underwent CTU at one of the most important regional centers in Bosnia and Herzegovina that sees a large number of patients. Our study included the distribution of age and sex, the number of phases within one examination, and different clinical indications. We compared our findings with the results available in the scientific literature, particularly the recently published results from 20 European countries. Furthermore, we established the local diagnostic reference levels (LDRLs) that can help set the national diagnostic reference levels (NDRLs). We believe our research is a significant step towards optimizing the protocols used in different hospitals in our country.
The Role of Cardiovascular Magnetic Resonance Imaging in the Assessment of Mitral Regurgitation
Authors: Botis Ioannis , Bazmpani Maria-Anna , Daios Stylianos , Ziakas Antonios , Kamperidis Vasileios , Karamitsos Theodoros D.
Published: March 2024

Mitral regurgitation (MR), a primary cause of valvular disease in adults, affects millions and is growing due to an ageing population. Cardiovascular magnetic resonance (CMR) has emerged as an essential tool, offering insights into valvular and myocardial pathology when compared to the primary imaging modality, echocardiography. This review highlights CMR’s superiority in high-resolution volumetric assessment and tissue characterization, including also advanced techniques like late gadolinium enhancement imaging, parametric mapping, feature tracking and 4D flow analysis. These techniques provide a deeper understanding of MR’s pathophysiology and its effect on cardiac chambers, enabling CMR to surpass echocardiography in predicting hard clinical outcomes and left ventricular (LV) remodelling post mitral valve surgery. Despite its advantages, CMR’s application faces limitations like cost, lack of standardization, and susceptibility to arrhythmia artifacts. Nonetheless, as technological advancements continue and new evidence emerges, CMR’s role in MR assessment is set to expand, offering a more nuanced and personalized approach to cardiac care. This review emphasizes the need for further research and standardized protocols to maximize CMR’s potential in MR management.
Comparison of Disease Severity Classifications of Chronic Obstructive Pulmonary Disease: GOLD vs. STAR in Clinical Practice
Authors: Nishimura Koichi , Kusunose Masaaki , Shibayama Ayumi , Nakayasu Kazuhito
Published: March 2024

Background: In chronic obstructive pulmonary disease (COPD), there are two known classifications for assessing what is called disease severity. One is the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, which is based on the post-bronchodilator value of FEV1 (% reference). The other is the STaging of Airflow obstruction by Ratio (STAR), with four grades of severity in subjects with an FEV1/FVC ratio <0.70: STAR 1 ≥0.60 to <0.70, STAR 2 ≥0.50 to <0.60, STAR 3 ≥0.40 to <0.50, and STAR 4 <0.40. Purpose: The aim of this study was to compare the staging of COPD using the GOLD and STAR classifications in clinical practice. Methods: We reanalyzed data from our outpatient cohort study, which included 141 participants with COPD from 2015 to 2023. We compared mortality and COPD-specific health status between the GOLD 1 to 4 groups and the STAR 1 to 4 groups. Results: By simple calculation, GOLD and STAR severity classes coincided in 75 participants (53.2%). The weighted Bangdiwala B value with linear weights was 0.775. The participants were observed for up to 95 months, with a median of 54 months. Death was confirmed in 29 participants (20.5%). In univariate Cox proportional hazards analyses, there was a significant difference in mortality between the GOLD 1 and GOLD 3 + 4 groups, with the GOLD 1 group used as the reference [hazard ratio 4.222 (95% CI 1.298–13.733), p = 0.017]. However, there was no statistically significant predictive relationship between STAR 1 and STAR 2, or between STAR 1 and STAR 3 + 4. St. George’s Respiratory Questionnaire (SGRQ) Total and COPD Assessment Test (CAT) scores were significantly different between all GOLD groups, except for the CAT score between GOLD 1 and GOLD 2. The SGRQ Total and CAT scores were significantly different between STAR 1 and STAR 3 + 4, but not between STAR 1 and STAR 2. Conclusion: From the perspective of all-cause mortality and COPD-specific health status, the GOLD classification is more discriminative than STAR.
Relationship of Maxillary Sinus Volume and Nasal Septum Deviation: A Cone Beam Computed Tomography Study
Authors: Rodriguez Betancourt Amanda B. , Martinez Somoza Leidy J. , Romero Mesa Carlos , Tozum Tolga Fikret , Mourão Carlos Fernando , Shibli Jamil Awad , Suárez Lina J.
Published: March 2024

The present study was designed to test the hypothesis that there would be a correlation between nasal septum deviation (NSD) and a decreased maxillary sinus volume (MSV) in a Colombian population, using Cone Beam Computed Tomography (CBCT); other sinusal anatomical structures found during the reading were described and analyzed. A retrospective analysis of 537 CBCT scans of adult patients taken between January 2014 and January 2017 included measuring the maxillary sinus diameter in the vertical, horizontal, and sagittal planes. NSD was quantified and related to MSV using the same field of view (FOV). The volume of the right and left maxillary sinuses showed a median and interquartile range (IQR) of 8.18 mm3 (IQR: 6.2–10.33) and 8.3 mm3 (IQR: 6.4–10.36). Statistically significant differences were observed between sex and right and left MSV (p = 0.000), with higher MSV in men. The presence of NSD was observed in 96.81% of the sample and was evaluated in degrees, observing a median of 11° (IQR: 7–16) where 40% of the sample had moderate angles (9–15°). There was no correlation between NSD and a decreased MSV in the population studied. Detailed CBCT analysis with a large FOV is crucial for the analysis of anatomical structures before performing surgical procedures that involve the MS as a preventive diagnostic and therapeutic step for appropriate treatment.
The Sound of Surveillance: Enhancing Machine Learning-Driven Drone Detection with Advanced Acoustic Augmentation
Authors: Kümmritz Sebastian
Published: March 2024

In response to the growing challenges in drone security and airspace management, this study introduces an advanced drone classifier, capable of detecting and categorizing Unmanned Aerial Vehicles (UAVs) based on acoustic signatures. Utilizing a comprehensive database of drone sounds across EU-defined classes (C0 to C3), this research leverages machine learning (ML) techniques for effective UAV identification. The study primarily focuses on the impact of data augmentation methods—pitch shifting, time delays, harmonic distortion, and ambient noise integration—on classifier performance. These techniques aim to mimic real-world acoustic variations, thus enhancing the classifier’s robustness and practical applicability. Results indicate that moderate levels of augmentation significantly improve classification accuracy. However, excessive application of these methods can negatively affect performance. The study concludes that sophisticated acoustic data augmentation can substantially enhance ML-driven drone detection, providing a versatile and efficient tool for managing drone-related security risks. This research contributes to UAV detection technology, presenting a model that not only identifies but also categorizes drones, underscoring its potential for diverse operational environments.