bluuebunny/crossref_metadata_2025 huggingface dataset:
Direct and Indirect Contributions of Three Aspects of Morphological Knowledge to Second Language Reading Comprehension
Authors: Yamashita Junko , Kusanagi Kunihiro
Published: March 2024

Growing attention has been devoted to the contribution of morphological knowledge to reading comprehension. Because of the complex nature of morphological knowledge, more fine-grained approaches are sought on this topic by exploring multiple aspects of morphological knowledge and multiple pathways through which each aspect contributes to reading comprehension. This study measured three aspects of affix knowledge (form, meaning, and use) and vocabulary breadth and examined how each aspect contributes to EFL (English as a foreign language) reading comprehension by modeling direct and indirect effects with vocabulary as a mediator. The participants were 211 Japanese university students. All variables were measured using standardized tests. Direct effects of meaning, use, and vocabulary and indirect effects of meaning and use via vocabulary were significant. However, form displayed no significant effect. The lack of significant effects for form may be due to the design of this study, which did not include word reading (a variable that may mediate form’s effect). In sum, although the form aspect did not show any effect, semantic and syntactic aspects demonstrated direct and indirect contributions. Overall, this study endorsed the criticality of a more fine-grained approach, shedding light on what and how morphological knowledge supports L2 reading comprehension.
Domain Adaptive Subterranean 3D Pedestrian Detection via Instance Transfer and Confidence Guidance
Authors: Liu Zengyun , Zheng Zexun , Qin Tianyi , Xu Liying , Zhang Xu
Published: March 2024

With the exploration of subterranean scenes, determining how to ensure the safety of subterranean pedestrians has gradually become a hot research topic. Considering the poor illumination and lack of annotated data in subterranean scenes, it is essential to explore the LiDAR-based domain adaptive detectors for localizing the spatial location of pedestrians, thus providing instruction for evacuation and rescue. In this paper, a novel domain adaptive subterranean 3D pedestrian detection method is proposed to adapt pre-trained detectors from the annotated road scenes to the unannotated subterranean scenes. Specifically, an instance transfer-based scene updating strategy is designed to update the subterranean scenes by transferring instances from the road scenes to the subterranean scenes, aiming to create sufficient high-quality pseudo labels for fine-tuning the pre-trained detector. In addition, a pseudo label confidence-guided learning mechanism is constructed to fully utilize pseudo labels of different qualities under the guidance of confidence scores. Extensive experiments validate the superiority of our proposed domain adaptive subterranean 3D pedestrian detection method.
Secure Multiparty Computation Using Secure Virtual Machines
Authors: Miladinović Danko , Milaković Adrian , Vukasović Maja , Stanisavljević Žarko , Vuletić Pavle
Published: March 2024

The development of new processor capabilities which enable hardware-based memory encryption, capable of isolating and encrypting application code and data in memory, have led to the rise of confidential computing techniques that protect data when processed on untrusted computing resources (e.g., cloud). Before confidential computing technologies, applications that needed data-in-use protection, like outsourced or secure multiparty computation, used purely cryptographic techniques, which had a large negative impact on the processing performance. Processing data in trusted enclaves protected by confidential computing technologies promises to protect data-in-use while possessing a negligible performance penalty. In this paper, we have analyzed the state-of-the-art in the field of confidential computing and present a Confidential Computing System for Artificial Intelligence (CoCoS.ai), a system for secure multiparty computation, which uses virtual machine-based trusted execution environments (in this case, AMD Secure Encrypted Virtualization (SEV)). The security of the proposed solution, as well as its performance, have been formally analyzed and measured. The paper reveals many gaps not reported previously that still exist in the current confidential computing solutions for the secure multiparty computation use case, especially in the processes of creating new secure virtual machines and their attestation, which are tailored for single-user use cases.
Synergistic Hybrid Marine Renewable Energy Harvest System
Authors: Cui Liang , Amani Sadra , Gabr Mohammed , Kumari Wanniarachchige Gnamani Pabasara , Ahmed Aziz , Ozcan Hasan , Horri Bahman Amini , Bhattacharya Subhamoy
Published: March 2024

This paper proposes a novel hybrid marine renewable energy-harvesting system to increase energy production, reduce levelized costs of energy and promote renewable marine energy. Firstly, various marine renewable energy resources and state-of-art technologies for energy exploitation and storage were reviewed. The site selection criteria for each energy-harvesting approach were identified, and a scoring matrix for site selection was proposed to screen suitable locations for the hybrid system. The Triton Knoll wind farm was used to demonstrate the effectiveness of the scoring matrix. An integrated energy system was designed, and FE modeling was performed to assess the effects of additional energy devices on the structural stability of the main wind turbine structure. It has been proven that the additional energy structures have a negligible influence on foundation/structure deflection (<1%) and increased system natural frequency by 6%; thus, they have a minimum influence on the original wind system but increased energy yield.
Research on a Real-Time Monitoring System for Campus Woodland Fires via Deep Learning
Authors: Xu Dengwei , Chen Jie , Wu Qi , Wang Zheng
Published: March 2024

To solve the problems of low recognition accuracy and large amounts of computation required in forest fire detection algorithms, this paper, aiming to make improvements in these two aspects, proposes a G-YOLOv5n-CB forest fire detection algorithm based on the YOLOv5 algorithm and develops a set of real-time fire monitoring systems applicable to campus forest land with the aid of deep learning technology. The system employs an unmanned vehicle to navigate automatically and collect image information through a camera and deploys its algorithm on the unmanned vehicle’s Jetson Nano hardware platform. The results demonstrate that the proposed YOLOv5n-CB algorithm increased the mAP value index by 1.4% compared with the original algorithm on the self-made forest fire dataset. The improved G-YOLOv5n-CB model was deployed on the Jetson Nano platform for testing, and its detection speed reached 15 FPS. It can accurately detect and display real-time forest fires on campus and has, thus, a high application value.
Reimagining Peer-to-Peer Lending Sustainability: Unveiling Predictive Insights with Innovative Machine Learning Approaches for Loan Default Anticipation
Authors: Nguyen Ly , Ahsan Mominul , Haider Julfikar
Published: March 2024

Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustainable growth. It is imperative to create a system that can correctly predict loan defaults to lessen the damage brought on by defaulters. The goal of this study is to fill the gap in the literature by exploring the feasibility of developing prediction models for P2P loan defaults without relying heavily on personal data while also focusing on identifying key variables influencing borrowers’ repayment capacity through systematic feature selection and exploratory data analysis. Given this, this study aims to create a computational model that aids lenders in determining the approval or rejection of a loan application, relying on the financial data provided by applicants. The selected dataset, sourced from an open database, contains 8578 transaction records and includes 14 attributes related to financial information, with no personal data included. A loan dataset is first subjected to an in-depth exploratory data analysis to find behaviors connected to loan defaults. Subsequently, diverse and noteworthy machine learning classification algorithms, including Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naïve Bayes, and XGBoost, were employed to build models capable of discerning borrowers who repay their loans from those who do not. Our findings indicate that borrowers who fail to comply with their lenders’ credit policies, pay elevated interest rates, and possess low FICO ratings are at a higher likelihood of defaulting. Furthermore, elevated risk is observed among clients who obtain loans for small businesses. All classification models, including XGBoost and Random Forest, successfully developed and performed satisfactorily and achieved an accuracy of over 80%. When the decision threshold is set to 0.4, the best performance for predicting loan defaulters is achieved using logistic regression, which accurately identifies 83% of the defaulted loans, with a recall of 83%, precision of 21% and f1 score of 33%.
Characterization, Stability and Antioxidant Activity of Vanilla Nano-Emulsion and Its Complex Essential Oil
Authors: Xu Fei , Shi Yucong , Li Bin , Liu Chengmei , Zhang Yanjun , Zhong Junzhen
Published: March 2024

As a natural flavoring agent, vanilla essential oil has a special aroma and flavor, but its volatility and instability limit its value. Therefore, in this study, vanilla essential oil was compounded with cinnamon essential oil to prepare nano-emulsions (composite nano-emulsions called C/VT and C/VM), and the stability of the composite essential oil emulsions was investigated. Transmission electron microscopy (TEM) images showed that the nano-emulsions were spherical in shape and some flocs were observed in C/VM and C/VT. The results showed that the average droplet sizes of C/VM and C/VT increased only by 14.99% and 15.01% after heating at 100 °C for 20 min, and the average droplet sizes were less than 120 nm after 24 days of storage at 25 °C. Possibly due to the presence of reticulated flocs, which have a hindering effect on the movement of individual droplets, the instability indices of C/VM and C/VT were reduced by 34.9% and 39.08%, respectively, in comparison to the instability indices of C/VM and C/VT. In addition, the results of antioxidant experimental studies showed that the presence of composite essential oil flocs had no significant effect on the antioxidant capacity. These results indicate that the improved stability of the composite essential oil nano-emulsions is conducive to broadening the application of vanilla essential oil emulsions.
For a Psychoanalysis of the Flesh
Authors: Torlasco Domietta
Published: March 2024

This essay takes the notion of “flesh” as the point of departure for exploring the viability and contemporary relevance of what Maurice Merleau-Ponty has called an “ontological psychoanalysis”. Primary interlocutors will be Octavia Butler’s novel Kindred and Hortense Spillers’s essay, “Mama’s Baby, Papa’s Maybe: An American Grammar Book”.
Can LED Lighting Be a Sustainable Solution for Producing Nutritionally Valuable Microgreens?
Authors: Vrkić Roberta , Šic Žlabur Jana , Dujmović Mia , Benko Božidar
Published: March 2024

With its quality, intensity, and photoperiod, light is a decisive abiotic factor that directly influences plant biomass and the accumulation of specialized metabolites (SMs). Photosynthetically active radiation (PAR) has significant effects on primary and secondary plant metabolism and thus influences the morphological characteristics of plants and their antioxidant systems. The aim of this study was to investigate the effects of blue, red, and a 50:50 combination of blue and red LED lighting on the SM content in broccoli, mustard, and garden cress microgreens grown in an indoor farm using the zero-acreage farming technique (ZFarming). This research aims to provide valuable insights into the optimization of light spectra to improve the nutritional quality of microgreens, with a focus on sustainable and space-saving cultivation methods. After eight days, the samples were cut in the cotyledon phenophase and analyzed in a fresh state. The microgreens grown under the blue spectrum LED lighting had the highest content of ascorbic acid (112.70 mg·100 g fw−1), total phenolics (412.39 mg GAE·100 g fw−1), and the highest antioxidant capacity (2443.62 µmol TE·L−1). The results show that the highest content of SMs in all the studied microgreens species was accumulated under the blue spectrum LED lighting. This study underlines the favorable influence of the blue spectrum (400–500 nm) on the nutrient content, especially the enhancement of SMs, in the microgreens investigated. Furthermore, the use of supplemental LED lighting proves to be a sustainable and effective means of producing microgreens with superior nutritional properties through the innovative practice of the zero-acreage farming technique.
What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA
Authors: Yang Yalin , Wu Yanan , Yuan May
Published: March 2024

In-person social events bring people to places, while people and places influence where and what social events occur. Knowing what people do and where they build social relationships gives insights into the distribution and availability of places for social functions. We developed a Bayesian Network model, integrating points of interest (POIs) and sociodemographic characteristics, to estimate the probabilistic effects of places and people on the presence of social events. A case study in Dallas demonstrated the utility and performance of the model. The Bayesian Network model predicted the presence likelihoods for seven types of social events with an R2 value around 0.83 (95% confidence interval). For both the presence and absence of social events at locations, the model predictions were within a 20% error for most event types. Furthermore, the model suggested POI, age, education, and population density configurations as important contextual variables for place–event associations across locations. A spatial cluster analysis identified likely multifunctional hotspots for social events (i.e., socially vibrant places). While psychological and cultural factors likely contribute further to local likelihoods of social event occurrences, the proposed conceptually informed geospatial data-science approach elucidated intricate place–people–event relationships and implicates inclusive, participatory places for urban development.
Effects of Hemorrhage on Hematopoietic Cell Depletion after a Combined Injury with Radiation: Role of White Blood Cells and Red Blood Cells as Biomarkers
Authors: Kiang Juliann G. , Woods Akeylah K. , Cannon Georgetta
Published: March 2024

Combined radiation with hemorrhage (combined injury, CI) exacerbates hematopoietic acute radiation syndrome and mortality compared to radiation alone (RI). We evaluated the effects of RI or CI on blood cell depletion as a biomarker to differentiate the two. Male CD2F1 mice were exposed to 8.75 Gy γ-radiation (60Co). Within 2 h of RI, animals were bled under anesthesia 0% (RI) or 20% (CI) of total blood volume. Blood samples were collected at 4–5 h and days 1, 2, 3, 7, and 15 after RI. CI decreased WBC at 4–5 h and continued to decrease it until day 3; counts then stayed at the nadir up to day 15. CI decreased neutrophils, lymphocytes, monocytes, eosinophils, and basophils more than RI on day 1 or day 2. CI decreased RBCs, hemoglobin, and hematocrit on days 7 and 15 more than RI, whereas hemorrhage alone returned to the baseline on days 7 and 15. RBCs depleted after CI faster than post-RI. Hemorrhage alone increased platelet counts on days 2, 3, and 7, which returned to the baseline on day 15. Our data suggest that WBC depletion may be a potential biomarker within 2 days post-RI and post-CI and RBC depletion after 3 days post-RI and post-CI. For hemorrhage alone, neutrophil counts at 4–5 h and platelets for day 2 through day 7 can be used as a tool for confirmation.
Correction: Noureddine et al. Impact of the Renin–Angiotensin System on the Endothelium in Vascular Dementia: Unresolved Issues and Future Perspectives. Int. J. Mol. Sci. 2020, 21, 4268
Authors: Noureddine Fatima Y. , Altara Raffaele , Fan Fan , Yabluchanskiy Andriy , Booz George W. , Zouein Fouad A.
Published: March 2024

The authors would like to make the following correction to the publication dealing with Reference #3 [...]
Nitrite Attenuates the In Vitro Inflammatory Response of Immune Cells to the SARS-CoV-2 S Protein without Interfering in the Antioxidant Enzyme Activation
Authors: Ferrer Miguel D. , Reynés Clara , Jiménez Laura , Malagraba Gianluca , Monserrat-Mesquida Margalida , Bouzas Cristina , Sureda Antoni , Tur Josep A. , Pons Antoni
Published: March 2024

SARS-CoV-2 induces a hyperinflammatory reaction due to the excessive release of cytokines during the immune response. The bacterial endotoxin lipopolysaccharide (LPS) contributes to the low-grade inflammation associated with the metabolic syndrome, enhancing the hyperinflammatory reaction induced by the SARS-CoV-2 infection. The intake of sodium nitrate, a precursor of nitrite and nitric oxide, influences the antioxidant and pro-inflammatory gene expression profile after immune stimulation with LPS in peripheral blood mononuclear cells from metabolic syndrome patients. We aimed to assess the inflammatory and antioxidant responses of immune cells from metabolic syndrome patients to exposure to the SARS-CoV-2 spike protein (S protein) together with LPS and the effect of nitrite in these responses. Whole blood samples obtained from six metabolic syndrome patients were cultured for 16 h at 37 °C with four different media: control medium, control medium plus LPS (100 ng/mL), control medium plus LPS (100 ng/mL) plus S protein (10 ng/mL), and control medium plus LPS (100 ng/mL) plus S protein (10 ng/mL) plus nitrite (5 µM). Immune stimulation with the LPS/S protein enhanced nitrate biosynthesis from nitrite oxidation and probably from additional organic precursors. In vitro incubations with the LPS/S protein enhanced the expression and/or release of pro-inflammatory TNFα, IL-6, IL-1β, and TLR4, as well as the expression of the anti-inflammatory IL-1ra and IL-10 and antioxidant enzymes. Nitrite attenuated the pro- and anti-inflammatory response induced by the S protein without interfering with the activation of TLR4 and antioxidant enzyme expression, raising the possibility that nitrite could have potential as a coadjutant in the treatment of COVID-19.
Changes in Inflammatory Cytokines in Responders and Non-Responders to TNFα Inhibitor and IL-17A Inhibitor: A Study Examining Psoriatic Arthritis Patients
Authors: Skougaard Marie , Søndergaard Magnus Friis , Ditlev Sisse Bolm , Kristensen Lars Erik
Published: March 2024

This study aimed to examine the changes in biomarker levels in responders and non-responders to tumor necrosis factor alpha inhibitor (TNFi) and interleukin-17A inhibitor (IL-17Ai) in psoriatic arthritis (PsA) patients over a 4-month period after treatment initiation. A total of 68 PsA patients initiating either TNFi, IL-17Ai, or methotrexate treatment were included. Blood plasma and clinical outcome measures were collected adjacent to treatment initiation and after four months. A commercially available multiplex immunoassay was included to evaluate 54 biomarkers. Mean changes were used to evaluate change over time. A statistically significant decrease in pro-inflammatory cytokines IL-6 (log-transformed mean change −0.97, 95%CI −4.30; 2.37, [p = 0.032]) and an increase in anti-inflammatory IL-10 (0.38, 95%CI 1.74; 2.50 [p = 0.010]) were seen in TNFi responders. Meanwhile, a statistically significant increase in the target cytokine IL-17A was seen in both IL-17Ai responders (2.49, 95%CI −1.84; 6.85 [p = 0.031]) and non-responders (2.48, 95%CI −1.46; 6.41 [p = 0.001]). This study demonstrated differing changes in cytokine levels when comparing treatment responders and non-responders, highlighting the need to improve the understanding of the different immune response mechanisms explaining different responses to medical treatment in PsA patients.
Acute and Chronic Exposure to Linagliptin, a Selective Inhibitor of Dipeptidyl Peptidase-4 (DPP-4), Has an Effect on Dopamine, Serotonin and Noradrenaline Level in the Striatum and Hippocampus of Rats
Authors: Łupina Małgorzata , Wąsik Agnieszka , Baranowska-Bosiacka Irena , Tarnowski Maciej , Słowik Tymoteusz , Listos Piotr , Kotlińska Jolanta , Kosik-Bogacka Danuta , Gutowska Izabela , Listos Joanna
Published: March 2024

Linagliptin is a selective dipeptidyl peptidase-4 (DPP-4) inhibitor that indirectly elevates the glucagon-like peptide-1 (GLP-1) level. The aim of the present study was to check whether linagliptin has an influence on neurotransmission in rat brain. Rats were acutely and chronically exposed to linagliptin (10 and 20 mg/kg, intraperitoneally (i.p.)). Twenty-four hours later, the striatum and hippocampus were selected for further studies. In neurochemical experiments, using high-performance liquid chromatography with electrochemical detection (HPLC-ED), the concentrations of three major neurotransmitters—dopamine, serotonin and noradrenaline—and their metabolites were measured. The analysis of mRNA expression of dopamine (D1 and D2), serotonin (5-HT-1 and 5-HT-2) and noradrenaline (α1 and α2a) receptors was also investigated using real-time quantitative reverse transcription polymerase chain reaction (RQ-PCR) in the same brain areas. Linagliptin has the ability to influence the dopaminergic system. In the striatum, the elevation of dopamine and its metabolites was observed after repeated administration of that linagliptin, and in the hippocampus, a reduction in dopamine metabolism was demonstrated. Acute linagliptin exposure increases the serotonin level in both areas, while after chronic linagliptin administration a tendency for the mRNA expression of serotoninergic receptors (5-HT1A and 5-HT2A) to increase was observed. A single instance of exposure to linagliptin significantly modified the noradrenaline level in the striatum and intensified noradrenaline turnover in the hippocampus. The recognition of the interactions in the brain between DPP-4 inhibitors and neurotransmitters and/or receptors is a crucial step for finding novel discoveries in the pharmacology of DPP-4 inhibitors and raises hope for further applications of DPP-4 inhibitors in clinical practices.
Plasma Circulating mRNA Profile for the Non-Invasive Diagnosis of Colorectal Cancer Using NanoString Technologies
Authors: Tsang Hin Fung , Pei Xiao Meng , Wong Yin Kwan Evelyn , Wong Sze Chuen Cesar
Published: March 2024

Colorectal cancer (CRC) is one of the most prevalent cancers and the second leading cause of cancer deaths in developed countries. Early CRC may have no symptoms and symptoms usually appear with more advanced diseases. Regular screening can identify people who are at increased risk of CRC in order to offer earlier treatment. A cost-effective non-invasive platform for the screening and monitoring of CRC patients allows early detection and appropriate treatment of the disease, and the timely application of adjuvant therapy after surgical operation is needed. In this study, a cohort of 71 plasma samples that include 48 colonoscopy- and histopathology-confirmed CRC patients with TNM stages I to IV were recruited between 2017 and 2019. Plasma mRNA profiling was performed in CRC patients using NanoString nCounter. Normalized data were analyzed using a Mann–Whitney U test to determine statistically significant differences between samples from CRC patients and healthy subjects. A multiple-group comparison of clinical phenotypes was performed using the Kruskal–Wallis H test for statistically significant differences between multiple groups. Among the 27 selected circulating mRNA markers, all of them were found to be overexpressed (gene expression fold change > 2) in the plasma of patients from two or more CRC stages. In conclusion, NanoString-based targeted plasma CRC-associated mRNAs circulating the marker panel that can significantly distinguish CRC patients from a healthy population were developed for the non-invasive diagnosis of CRC using peripheral blood samples.
Involvement of Ferroptosis Induction and Oxidative Phosphorylation Inhibition in the Anticancer-Drug-Induced Myocardial Injury: Ameliorative Role of Pterostilbene
Authors: Fujii Kiyomu , Fujiwara-Tani Rina , Nukaga Shota , Ohmori Hitoshi , Luo Yi , Nishida Ryoichi , Sasaki Takamitsu , Miyagawa Yoshihiro , Nakashima Chie , Kawahara Isao , Ogata Ruiko , Ikemoto Ayaka , Sasaki Rika , Kuniyasu Hiroki
Published: March 2024

Patients with cancer die from cardiac dysfunction second only to the disease itself. Cardiotoxicity caused by anticancer drugs has been emphasized as a possible cause; however, the details remain unclear. To investigate this mechanism, we treated rat cardiomyoblast H9c2 cells with sunitinib, lapatinib, 5-fluorouracil, and cisplatin to examine their effects. All anticancer drugs increased ROS, lipid peroxide, and iron (II) levels in the mitochondria and decreased glutathione peroxidase-4 levels and the GSH/GSSG ratio. Against this background, mitochondrial iron (II) accumulates through the unregulated expression of haem oxygenase-1 and ferrochelatase. Anticancer-drug-induced cell death was suppressed by N-acetylcysteine, deferoxamine, and ferrostatin, indicating ferroptosis. Anticancer drug treatment impairs mitochondrial DNA and inhibits oxidative phosphorylation in H9c2 cells. Similar results were observed in the hearts of cancer-free rats treated with anticancer drugs in vitro. In contrast, treatment with pterostilbene inhibited the induction of ferroptosis and rescued the energy restriction induced by anticancer drugs both in vitro and in vivo. These findings suggest that induction of ferroptosis and inhibition of oxidative phosphorylation are mechanisms by which anticancer drugs cause myocardial damage. As pterostilbene ameliorates these mechanisms, it is expected to have significant clinical applications.
Predicting Spin-Dependent Phonon Band Structures of HKUST-1 Using Density Functional Theory and Machine-Learned Interatomic Potentials
Authors: Strasser Nina , Wieser Sandro , Zojer Egbert
Published: March 2024

The present study focuses on the spin-dependent vibrational properties of HKUST-1, a metal–organic framework with potential applications in gas storage and separation. Employing density functional theory (DFT), we explore the consequences of spin couplings in the copper paddle wheels (as the secondary building units of HKUST-1) on the material’s vibrational properties. By systematically screening the impact of the spin state on the phonon bands and densities of states in the various frequency regions, we identify asymmetric -COO- stretching vibrations as being most affected by different types of magnetic couplings. Notably, we also show that the DFT-derived insights can be quantitatively reproduced employing suitably parametrized, state-of-the-art machine-learned classical potentials with root-mean-square deviations from the DFT results between 3 cm−1 and 7 cm−1. This demonstrates the potential of machine-learned classical force fields for predicting the spin-dependent properties of complex materials, even when explicitly considering spins only for the generation of the reference data used in the force-field parametrization process.
Quantification of Blade Vibration Amplitude in Turbomachinery
Authors: Schneider Alexandra P. , Paoletti Benoit , Ottavy Xavier , Brandstetter Christoph
Published: March 2024

Experimental monitoring of blade vibration in turbomachinery is typically based on blade-mounted strain gauges. Their signals are used to derive vibration amplitudes which are compared to modal scope limits, including a safety factor. According to industrial guidelines, this factor is chosen conservatively to ensure safe operation of the machine. Within the experimental campaign with the open-test-case composite fan ECL5/CATANA, which is representative for modern lightweight Ultra High Bypass Ratio (UHBR) architectures, measurements close to the stability limit have been conducted. Investigation of phenomena like non-synchronous vibrations (NSV) and rotating stall require a close approach to the stability limit and hence demand for accurate (real-time) quantification of vibration amplitudes to ensure secure operation without exhaustive safety margins. Historically, short-time Fourier transforms of vibration sensors are used, but the complex nature of the mentioned coupled phenomena has an influence on amplitude accuracy, depending on evaluation parameters, as presented in a previous study using fast-response wall-pressure transducers. The present study investigates the sensitivity of blade vibration data to evaluation parameters for different spectral analysis methods and provides guidelines for fast and robust surveillance of critical vibration modes.
Comparison of Cluster-Based Sampling Approaches for Imbalanced Data of Crashes Involving Large Trucks
Authors: Tahfim Syed As-Sadeq , Chen Yan
Published: March 2024

Severe and fatal crashes involving large trucks result in significant social and economic losses for human society. Unfortunately, the notably low proportion of severe and fatal injury crashes involving large trucks creates an imbalance in crash data. Models trained on imbalanced crash data are likely to produce erroneous results. Therefore, there is a need to explore novel sampling approaches for imbalanced crash data, and it is crucial to determine the appropriate combination of a machine learning model, sampling approach, and ratio. This study introduces a novel cluster-based under-sampling technique, utilizing the k-prototypes clustering algorithm. After initial cluster-based under-sampling, the consolidated cluster-based under-sampled data set was further resampled using three different sampling approaches (i.e., adaptive synthetic sampling (ADASYN), NearMiss-2, and the synthetic minority oversampling technique + Tomek links (SMOTETomek)). Later, four machine learning models (logistic regression (LR), random forest (RF), gradient-boosted decision trees (GBDT), and the multi-layer perceptron (MLP) neural network) were trained and evaluated using the geometric mean (G-Mean) and area under the receiver operating characteristic curve (AUC) scores. The findings suggest that cluster-based under-sampling coupled with the investigated sampling approaches improve the performance of the machine learning models developed on crash data significantly. In addition, the GBDT model combined with ADASYN or SMOTETomek is likely to yield better predictions than any model combined with NearMiss-2. Regarding changes in sampling ratios, increasing the sampling ratio with ADASYN and SMOTETomek is likely to improve the performance of models up to a certain level, whereas with NearMiss-2, performance is likely to drop significantly beyond a specific point. These findings provide valuable insights for selecting optimal strategies for treating the class imbalance issue in crash data.
Analysis of Near-Field Magnetic Responses on ZrTe5 through Cryogenic Magneto-THz Nano-Imaging
Authors: Haeuser Samuel , Kim Richard H. J. , Park Joong-Mok , Chan Randall K. , Imran Muhammad , Koschny Thomas , Wang Jigang
Published: March 2024

One manifestation of light-Weyl fermion interaction is the emergence of chiral magnetic effects under magnetic fields. Probing real space magnetic responses at terahertz (THz) scales is challenging but highly desired, as the local responses are less affected by the topologically trivial inhomogeneity that is ubiquitous in spatially averaged measurements. Here, we implement a cryogenic THz microscopy instrument under a magnetic field environment—a task only recently achieved. We explore the technical approach of this system and characterize the magnetic field’s influence on our AFM operation by statistical noise analysis. We find evidence for local near-field spatial variations in the topological semimetal ZrTe5 up to a 5-Tesla magnetic field and obtain near-field THz spectra to discuss their implications for future studies on the chiral magnetic effect.
Effects of Perforated Plates on Shock Structure Alteration for NACA0012 Airfoils
Authors: Gall Mihnea , Dumitrescu Oana , Drăgan Valeriu , Crunțeanu Daniel Eugeniu
Published: March 2024

This research investigated a passive flow control technique to mitigate the adverse effects of shock wave–boundary layer interaction on a NACA 0012 airfoil. A perforated plate with a strategically positioned cavity beneath the shock wave anchoring spot was employed. Airfoils with perforated plates of varying orifice sizes (ranging from 0.5 to 1.2 mm) were constructed using various manufacturing techniques. Experimental analysis utilized an “Eiffel”-type open wind tunnel and a Z-type Schlieren system for flow visualization, along with static pressure measurements obtained from the bottom wall. Empirical observations were compared with steady 3D density-based numerical simulations conducted in Ansys FLUENT for comprehensive analysis and validation. The implementation of the perforated plate induced a significant alteration in shock structure, transforming it from a strong normal shock wave into a large lambda-type shock. The passive control case exhibited a 0.2% improvement in total pressure loss and attributed to the perforated plate’s capability to diminish the intensity of the shock wave anchored above. Significant fluctuations in shear stress were introduced by the perforated plate, with lower stress observed in the plate area due to flow detachment from cavity blowing. Balancing shock and viscous losses proved crucial for achieving a favorable outcome with this passive flow control method.
Left Ventricular Diastolic Dysfunction Is Associated with Poor Functional Outcomes after Endovascular Thrombectomy
Authors: Li Tony Y. W. , Toh Emma M. S. , Koh Ying Ying , Leow Aloysius S. T. , Chan Bernard P. L. , Teoh Hock-Luen , Seet Raymond C. S. , Gopinathan Anil , Yang Cunli , Sharma Vijay K. , Yeo Leonard L. L. , Chan Mark Y. , Kong William K. F. , Poh Kian-Keong , Tan Benjamin Y. Q. , Sia Ching-Hui
Published: March 2024

Introduction: With the advent of endovascular thrombectomy (ET), patients with acute ischaemic strokes (AIS) with large vessel occlusion (LVO) have seen vast improvements in treatment outcomes. Left ventricular diastolic dysfunction (LVDD) has been shown to herald poorer prognosis in conditions such as myocardial infarction. However, whether LVDD is related to functional recovery and outcomes in ischaemic stroke remains unclear. We studied LVDD for possible relation with clinical outcomes in patients with LVO AIS who underwent ET. Methods: We studied a retrospective cohort of 261 LVO AIS patients who had undergone ET at a single comprehensive stroke centre and correlated LVDD to short-term mortality (in-hospital death) as well as good functional recovery defined as modified Rankin Scale of 0–2 at 3 months. Results: The study population had a mean age of 65-years-old and were predominantly male (54.8%). All of the patients underwent ET with 206 (78.9%) achieving successful reperfusion. Despite this, 25 (9.6%) patients demised during the hospital admission and 149 (57.1%) did not have good function recovery at 3 months. LVDD was present in 82 (31.4%) patients and this finding indicated poorer outcomes in terms of functional recovery at 3 months (OR 2.18, 95% CI 1.04–4.54, p = 0.038) but was not associated with increased in-hospital mortality (OR 2.18, 95% CI 0.60–7.99, p = 0.240) after adjusting for various confounders. Conclusion: In addition to conventional echocardiographic indices such as left ventricular ejection fraction, LVDD may portend poorer outcomes after ET, and this relationship should be investigated further.
Acute Kidney Injury and Chronic Kidney Disease and Their Impacts on Prognosis among Patients with Severe COVID-19 Pneumonia: An Expert Center Case–Cohort Study
Authors: Klimkiewicz Jakub , Grzywacz Anna , Michałowski Andrzej , Gutowski Mateusz , Paryż Kamil , Jędrych Ewelina , Lubas Arkadiusz
Published: March 2024

Background: Acute kidney injury (AKI) is associated with substantial mortality. In this case–control study, we analyzed the impacts of AKI and chronic kidney disease (CKD) on outcomes in a group of 323 patients with severe COVID-19. The correlation of clinical and laboratory data with AKI and CKD was also analyzed. Methods: A retrospective case–control study was conducted among AKI, CKD, and normal kidney function (NKF) groups hospitalized in a COVID-19 center in 2021. Results: AKI patients had higher in-hospital mortality (55.2 vs. 18.8%, p < 0.001), more frequent transfers from the HDU to ICU (57.5 vs. 12.9%, p < 0.001), and prolonged hospital stays (15.4 ± 10.7 vs. 10.7 ± 6.7 days, p < 0.001) compared to the NKF group. AKI was a predictor of death (OR 4.794, 95%CI: 2.906–7.906, p < 0.001). AKI patients also had broader lung parenchymal involvement and higher inflammatory markers compared to the NKF group. Patients with prior CKD had higher in-hospital mortality compared to the NKF group (64.0 vs. 18.8%, p < 0.001, OR 4.044, 95%CI: 1.723–9.490, p = 0.013); however, transfers from the HDU to ICU were not more frequent (16.0 vs. 12.9%, p = 0.753). Conclusions: AKI among COVID-19 patients was correlated with more ICU transfers, higher morbidity, and greater markers of severe disease. Patients with CKD had a higher mortality; however, the rate of ICU transfer was not substantially higher due to their poor prognosis.
Outcomes of “Over the Top” Anterior Cruciate Ligament Reconstruction Associated with a Lateral Extra-Articular Tenodesis in Children
Authors: Gomez-Caceres Abel , Tamimi-Mariño Iskandar , Martinez-Malo Francisco Javier , Idiart-Charrier Raphael Pierre , Vieitez-Riestra Ignacio , Medina-Porqueres Ivan
Published: March 2024

(1) Purpose: The incidence of anterior cruciate ligament (ACL) ruptures in children and adolescents has considerably increased during the last decades due to higher levels of competitive athletic activity, and early sport specialization and professionalization. Contemporary ACL reconstruction techniques have recently been subject to renewed interest in this population. The objective of this study is to report the short- and mid-term results of our physis-sparing ACL reconstruction technique using an “over the top” technique associated with a modified Lemaire procedure. (2) Methods: A retrospective series of 12 junior soccer players who presented to our clinic with a torn ACL between January 2019 and September 2021 was reviewed. The inclusion criteria were patients under 15 years with open tibial and femoral physes, with a stable contralateral knee, a minimum follow-up of 6 months, and a time frame from injury to surgery of <3 months. Patients with previous knee surgery, structural concomitant injuries, muscular, neurological, or vascular abnormalities, or hypersensitivity to metal alloys were excluded. The functional evaluation was performed using the International Knee Documentation Committee (IKDC) rating, Lysholm score, and Tegner activity level. Moreover, clinical and radiological assessments were also performed, including KT-1000 and knee X-rays. (3) Results: We identified 1 female and 11 male patients with ACL tears, with a mean age of 13.17 ± 0.9 months. Concomitant injuries include isolated vertical and bucket-handle tears of the medial meniscus, lateral meniscus tears, bilateral tear of both menisci. The mean follow-up time was 26 ± 12.6 months. The average IKDC, Lysholm and Tegner scores were 93.29 ± 11.04, 95.08 ± 13.2 and 9 ± 0.0 points, respectively. The average KT-1000 score of the participants was 0.96 ± 1.6 points. None of the included patients reported post-surgical complications or required additional surgeries. (4) Conclusions: Our novel ACL reconstruction with LET technique is a safe procedure that resulted in good clinical outcomes, lower failure rate and return to sports in skeletally immature patients.
Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites
Authors: Deb Jayanta Bhusan , Gou Jihua , Song Haonan , Maiti Chiranjit
Published: March 2024

Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high melting temperatures, strong resistance to oxidation, corrosion, and ablation, minimal creep, and advantageous thermal cycling behavior, ceramic matrix composites (CMCs) show great promise as a material to meet the strict requirements in these kinds of environments. Furthermore, the addition of boron nitride nanoparticles with continuous fibers to the CMCs can offer thermal resistivity in harsh conditions, which will improve the composites’ strength and fracture toughness. Therefore, in extreme situations, it is crucial to understand the thermal resistivity period of composite materials. To forecast the ablation performance of composites, we developed six machine learning regression methods in this study: decision tree, random forest, support vector machine, gradient boosting, extreme gradient boosting, and adaptive boosting. When evaluating model performance using metrics including R2 score, root mean square error, mean absolute error, and mean absolute percentage error, the gradient boosting and extreme gradient boosting machine learning regression models performed better than the others. The effectiveness of machine learning models as a useful tool for forecasting the ablation behavior of ceramic matrix composites was effectively explained by this study.
In-Season Test–Retest Reliability of Visual Smooth-Pursuit (EyeGuide Focus) Baseline Assessment in Female and Male Field-Sport Athletes
Authors: Walshe Ayrton , Daly Ed , Pearce Alan J. , Ryan Lisa
Published: March 2024

Sport-related concussions (SRCs) are a mild traumatic brain injury (mTBI) that induces transient symptoms in athletes. These symptoms provide avenues for developing emerging technologies to diagnose SRCs, in particular ocular and vestibular dysfunction. The following study aims to assess the reliability of visual smooth-pursuit technology (EyeGuide Focus) in amateur field-sport athletes. A convenience sample of 30 mixed-gender athletes (mean age = 24.89 ± 6.81 years) completed two testing sessions separated by 2–7 days. Participants were tested at rest, free from distraction, and completed a 10 s smooth pursuit while seated. Participants completed 2–4 practice trials before completing three tests at each session. Appropriate difference, reliability, and repeatability tests were performed in Statistical Packages for the Social Sciences. No significant difference existed between the time points (p > 0.05). The reliability between sessions was poor (ICC = 0.24; 95% CI = 0.03–0.42), and the mean coefficients of variation were 20% and 21% for each session, indicating poor repeatability. However, the implementation of practice trials did prevent the familiarization effects that are evident in the previous literature (p > 0.05). The within-session reliability of EyeGuide Focus has varied from poor (ICC ≤ 0.50) to good (ICC = 0.75–0.90) in the previous literature, indicating the fact that greater research is required before this tool can be implemented in applied settings.
Accurate Identification for CW Direct Signal in Underwater Acoustic Ranging
Authors: Li Jing , Fu Jin , Zou Nan
Published: March 2024

The underwater channel is bilateral, heterogeneous, uncertain, and exhibits multipath transmission, sound line curvature, etc. These properties complicate the structure of the received pulse, causing great challenges in direct signal identification for ranging purposes and impacts on back-end data processing, even accurate acoustic positioning. Machine learning (ML) combined with underwater acoustics has emerged as a prominent area of research in recent years. From a statistical perspective, ML can be viewed as an optimization strategy. Nevertheless, the existing ML-based direct-signal discrimination approaches rely on independent assessment, utilizing a single sensor (beacon or buoy), which is still insufficient for adapting to the complex underwater environment. Thus, discrimination accuracy decreases. To address the above issues, an accurate CW direct signal detection approach is performed using the decision tree algorithm, which belongs to ML. Initially, the pulse parameter characteristics in the underwater multipath channel are investigated and the parameter models are built. Then, based on multi-sensor localization performance feedback, fusion characteristics for diverse pulse are created. Next, the pulse parameter characteristics are preprocessed to mitigate the impact of varying magnitudes and units of magnitude on data processing. Then, the decision tree is built to obtain the desired output results and realize accurate recognition of the ranging direct signals. Finally, the feasibility and reliability of this paper’s method are verified by computer simulation and field testing.
Special Issue “Personalized Medicine in Blood Disease of Children”
Authors: Ceci Adriana , Kountouris Petros , Didio Antonella , Bonifazi Fedele
Published: March 2024

Personalized medicine is defined as a medical model using the characterization of individuals’ phenotypes and genotypes (e [...]
The Impact of a Computing Curriculum Accessible to Students with ASD on the Development of Computing Artifacts
Authors: Arslanyilmaz Abdu , Briley Margaret L. , Boerio Gregory V. , Petridis Katie , Ilyas Ramlah , Yu Feng
Published: March 2024

There has been no study examining the effectiveness of an accessible computing curriculum for students with autism spectrum disorder (ASD) on their learning of computational thinking concepts (CTCs), flow control, data representation, abstraction, user interactivity, synchronization, parallelism, and logic. This study aims to investigate the effects of an accessible computing curriculum for students with ASD on their learning of CTCs as measured by the scores of 312 computing artifacts developed by two groups of students with ASD. Conducted among 21 seventh-grade students with ASD (10 in the experimental group and 11 in the control), this study involved collecting data on the computing projects of these students over 24 instructional sessions. Group classification was considered the independent variable, and computing project scores were set as the dependent variables. The results showed that the original curriculum was statistically significantly more effective for students in learning logic than the accessible one when all seven CTCs were examined as a single construct. Both curriculums were statistically significantly effective in progressively improving students’ learning of data representation, abstraction, synchronization, parallelism, and all CTCs as a single construct when examining the gradual increase in their computing artifact scores over the 24 sessions. Both curriculums were statistically significantly effective in increasing the scores of synchronization and all CTCs as a single construct when the correlations between CTCs and sessions for individual groups were analyzed. The findings underscore that students with ASD can effectively learn computing skills through accessible or standard curriculums, provided that adjustments are made during delivery.