bluuebunny/crossref_metadata_2025 huggingface dataset:
Characteristics and Outcome of FLT3-ITD-Positive Pediatric Acute Myeloid Leukemia—Experience of Polish Pediatric Leukemia and Lymphoma Study Group from 2005 to 2022
Authors: Czogała Małgorzata , Czogała Wojciech , Pawińska-Wąsikowska Katarzyna , Książek Teofila , Bukowska-Strakova Karolina , Sikorska-Fic Barbara , Łaguna Paweł , Fałkowska Anna , Drabko Katarzyna , Muszyńska-Rosłan Katarzyna , Krawczuk-Rybak Maryna , Kozłowska Marta , Irga-Jaworska Ninela , Zielezińska Karolina , Urasiński Tomasz , Bartoszewicz Natalia , Styczyński Jan , Skalska-Sadowska Jolanta , Wachowiak Jacek , Rodziewicz-Konarska Anna , Kałwak Krzysztof , Ciebiera Małgorzata , Chaber Radosław , Mizia-Malarz Agnieszka , Chodała-Grzywacz Agnieszka , Karolczyk Grażyna , Bobeff Katarzyna , Młynarski Wojciech , Mycko Katarzyna , Badowska Wanda , Tomaszewska Renata , Szczepański Tomasz , Machnik Katarzyna , Zamorska Natalia , Balwierz Walentyna , Skoczeń Szymon
Published: September 2023

Background: The FMS-like tyrosine kinase 3 (FLT3) gene mutated in 10–15% of pediatric acute myeloid leukemia (AML) is associated with an inferior outcome. The aim of the study was to analyze the outcome and characteristics of FLT3-ITD-positive pediatric AML. Methods: We retrospectively analyzed the nationwide pediatric AML database from between 2005 and 2022. FLT3-ITD was found in 54/497 (10.7%) patients with available analysis. Three consecutive treatment protocols were used (AML-BFM 2004 Interim, AML-BFM 2012 Registry, AML-BFM 2019 recommendations). Results: Probabilities of 5-year overall (OS), event-free (EFS) and relapse-free survival were significantly lower in the FLT3-ITD-positive patients compared to FLT3-ITD-negative (0.54 vs. 0.71, p = 0.041; 0.36 vs. 0.59, p = 0.0004; 0.47 vs. 0.70, p = 0.0029, accordingly). An improvement in the outcome was found in the analyzed period of time, with a trend of better survival in patients treated under the AML-BFM 2012 and AML-BFM 2019 protocols compared to the AML-BFM 2004 protocol (5-year EFS 0.52 vs. 0.27, p = 0.069). There was a trend of improved outcomes in patients treated with FLT3 inhibitors (n = 9, 2-year EFS 0.67 vs. 0.33, p = 0.053) and those who received stem cell transplantation (SCT) (n = 26; 5-year EFS 0.70 vs. 0.27, p = 0.059). The co-occurrence of the WT1 mutation had a dismal impact on the prognosis (5-year EFS 0.23 vs. 0.69, p = 0.002), while the NPM1 mutation improved survival (5-year OS 1.0 vs. 0.44, p = 0.036). Conclusions: It seems that SCT and FLT3 inhibitors have a beneficial impact on the prognosis. Additional genetic alterations, like the WT1 and NPM1 mutations, significantly influence the outcome.
Mesenchymal Stem Cell-Derived Long Noncoding RNAs in Cardiac Injury and Repair
Authors: Tran Talan , Cruz Claudia , Chan Anthony , Awad Salma , Rajasingh Johnson , Deth Richard , Gurusamy Narasimman
Published: September 2023

Cardiac injury, such as myocardial infarction and heart failure, remains a significant global health burden. The limited regenerative capacity of the adult heart poses a challenge for restoring its function after injury. Mesenchymal stem cells (MSCs) have emerged as promising candidates for cardiac regeneration due to their ability to differentiate into various cell types and secrete bioactive molecules. In recent years, attention has been given to noncoding RNAs derived from MSCs, particularly long noncoding RNAs (lncRNAs), and their potential role in cardiac injury and repair. LncRNAs are RNA molecules that do not encode proteins but play critical roles in gene regulation and cellular responses including cardiac repair and regeneration. This review focused on MSC-derived lncRNAs and their implications in cardiac regeneration, including their effects on cardiac function, myocardial remodeling, cardiomyocyte injury, and angiogenesis. Understanding the molecular mechanisms of MSC-derived lncRNAs in cardiac injury and repair may contribute to the development of novel therapeutic strategies for treating cardiovascular diseases. However, further research is needed to fully elucidate the potential of MSC-derived lncRNAs and address the challenges in this field.
Calcineurin Is a Universal Regulator of Vessel Function—Focus on Vascular Smooth Muscle Cells
Authors: Nolze Alexander , Matern Sebastian , Grossmann Claudia
Published: September 2023

Calcineurin, a serine/threonine phosphatase regulating transcription factors like NFaT and CREB, is well known for its immune modulatory effects and role in cardiac hypertrophy. Results from experiments with calcineurin knockout animals and calcineurin inhibitors indicate that calcineurin also plays a crucial role in vascular function, especially in vascular smooth muscle cells (VSMCs). In the aorta, calcineurin stimulates the proliferation and migration of VSMCs in response to vascular injury or angiotensin II administration, leading to pathological vessel wall thickening. In the heart, calcineurin mediates coronary artery formation and VSMC differentiation, which are crucial for proper heart development. In pulmonary VSMCs, calcineurin/NFaT signaling regulates the release of Ca2+, resulting in increased vascular tone followed by pulmonary arterial hypertension. In renal VSMCs, calcineurin regulates extracellular matrix secretion promoting fibrosis development. In the mesenteric and cerebral arteries, calcineurin mediates a phenotypic switch of VSMCs leading to altered cell function. Gaining deeper insights into the underlying mechanisms of calcineurin signaling will help researchers to understand developmental and pathogenetical aspects of the vasculature. In this review, we provide an overview of the physiological function and pathophysiology of calcineurin in the vascular system with a focus on vascular smooth muscle cells in different organs. Overall, there are indications that under certain pathological settings reduced calcineurin activity seems to be beneficial for cardiovascular health.
The Effect of Electroplating Nickel on the Mechanical Properties of Brittle Mg-Based Bulk Metallic Glasses
Authors: Zhang Jingyao , Li Jing , Jing Mei , Zhao Lichen , Qi Yumin , Yang Wei , Wang Xin
Published: September 2023

Magnesium-based bulk metallic glasses (BMGs) are typical intrinsic brittle lightweight BMG alloys, and their improvement in plasticity has attracted widespread attention in the field of BMGs. We used the electroplating method to modify the surface of Mg59.5Cu22.9Ag11Gd6.6 BMGs and investigated the geometric confinement effect of the Ni coating on the mechanical properties of the BMG. The results show that under the plating conditions of adding 1 g/L nano Al2O3 to the plating solution, adjusting the plating temperature to 50 °C, and plating time to 3 h, a smooth and dense nickel coating with a thickness of about 150 μm can be formed on the surface of the Mg-based BMG. The uniaxial compression tests showed that the average fracture strength of the BMG was increased from 565 MPa to 598 MPa by a 50 μm Ni coating, and the fluctuation range of strength was decreased from 429 MPa to 265 MPa, a reduction of 36%. The Weibull analysis showed that the Weibull modulus m was increased from 4.3 to 4.8 by the coating, and the safety stress was increased from 54 MPa to 235 MPa, indicating that electroplating nickel could improve the reliability of the Mg-based BMG alloy. However, no significant improvement of the compression plasticity was found, which indicated that improving the room temperature plasticity of brittle Mg-based BMG alloys by the geometric confinement of electroplating Ni was limited. The influence of the thickness of the Ni coating on the maximum stress level and stress distribution in the BMG samples was analyzed by ANSYS finite element simulation. It was found that when the thickness of the coating was 30% of the radius of the cylindrical compressed sample, the stress distribution caused by the Ni coating was the most uniform, and the maximum stress level was relatively reduced, which is beneficial for improving the geometric confinement effect. As a result, the Mg-based BMG sample coated with a Ni coating of 150 μm thickness exhibited ~0.3% macroscopic compressive plasticity. This is of great significance for understanding the plastic deformation mechanism of brittle BMGs improved by geometric confinement.
Retrieving the Intrinsic Microwave Permittivity and Permeability of Ni-Zn Ferrites
Authors: Shiryaev Artem , Rozanov Konstantin , Kostishin Vladimir , Petrov Dmitry , Maklakov Sergey , Dolmatov Arthur , Isaev Igor
Published: September 2023

Mixing rules may be extremely useful for predicting the properties of composite materials and coatings. The paper is devoted to the study of the applicability of the mixing rules to permittivity and permeability and the possibility of retrieving the intrinsic properties of inclusions. Magnetically soft Ni-Zn ferrites are chosen as the object of the study due to their low permittivity and the negligible influence of the skin effect. Due to this, the microwave properties of bulk ferrites may be measured by standard techniques. It is suggested to perform the analysis of the microwave properties of composites filled with Ni-Zn ferrite powder in terms of the normalized inverse susceptibility defined as the volume fraction of inclusions divided by the effective dielectric or magnetic susceptibility of the composite. The measured properties of the bulk ferrite are compared with those obtained by mixing rules from composite materials. The experimental evidence for difference between the mixing rules for permittivity and permeability of a composite, which was previously predicted only theoretically, is obtained. The reason for the difference is considered to be the effect of non-ideal electrical contacts between neighboring inclusions. It is also experimentally shown that the measured permeability of the bulk material may differ from the retrieved one. The measured static permeability is 1400 and the retrieved one is 12. The reason for the discrepancy is the difference between the domain structures and demagnetizing fields of particles and bulk ferrite.
Ytterbium Disilicate/Monosilicate Multilayer Environmental Barrier Coatings: Influence of Atmospheric Plasma Spray Parameters on Composition and Microstructure
Authors: Di Iorio Giulia , Paglia Laura , Pedrizzetti Giulia , Genova Virgilio , Marra Francesco , Bartuli Cecilia , Pulci Giovanni
Published: September 2023

SiC/SiC ceramic matrix composites (SiCf/SiC CMCs) are regarded as the new materials for the hot-section components of aircraft gas turbine engines, since they have one-third of the density of metallic superalloys, a higher temperature capability, good mechanical strength, and excellent thermal shock resistance. However, high-temperature water-vapor-rich combustion gases can induce severe surface recession phenomena in SiC/SiC leading to component failure. For this reason, it is necessary to design protective coatings, i.e., environmental barrier coatings (EBCs), able to protect the SiC/SiC surface in combustion environments. In the present work, ytterbium monosilicate (Yb2SiO5), stable when exposed to water vapor at high temperatures, and ytterbium disilicate (Yb2Si2O7), characterized by a thermal expansion coefficient closer to that of the substrate, were selected for a multilayer EBC system. EBCs were processed using the atmospheric plasma spray (APS) technique. A set of deposition parameters were tested, varying the power of the torch, and the composition and microstructure of the deposited coatings were studied in terms of porosity, crack density, and post-deposition phase retention by performing SEM, EDS, and XRD analysis. The results allow for the definition of the influence of deposition parameters on the final properties of multilayer EBC coatings.
The Behavior of TiAlN and TiAlCrSiN Films in Abrasive and Adhesive Tribological Contacts
Authors: Schulz Wadim , Joukov Vitalij , Köhn Florian , Engelhart Wolfgang , Schier Veit , Schubert Tim , Albrecht Joachim
Published: September 2023

Chromium and silicon are often introduced to increase the performance of TiAlN hard coatings in dry tribological contacts. The addition of Cr and Si during a high-power impulse magnetron sputtering (HiPIMS) deposition process leads to high-quality TiAlCrSiN films. In this paper, the analysis of friction and wear of these films is conducted by oscillation tribometry under dry conditions with a subsequent mapping of the surface topography. Both abrasion- and adhesion-dominated conditions are realized using different steel counter bodies. Oscillation-frequency-dependent experiments show a significant impact of the compositional variation on friction and wear. It is shown that the TiAlCrSiN coating investigated has a higher coefficient of friction and a lower wear resistance compared to counterparts made of 100Cr6. The friction coefficient could be reduced by using a V2A counterpart. The results can be understood in terms of a reduced adhesion of both oxidic and metallic wear debris at the TiAlCrSiN surface. The study provides valuable progress towards the development of advanced cutting tools, e.g., for stainless steel.
Effect of Argon Glow Plasma Pretreatment of Pure Ta on Hf Coating Preparation
Authors: Yang Kai , Dang Bo , Jia Xingqi , Lu Fenghua , Ding Feng , Li Fengkun , Wei Dongbo , Zhang Pingze
Published: September 2023

In order to enhance the bonding strength between coatings and substrates, argon glow plasma pretreatment with various times was conducted on tantalum substrates, followed by hafnium coating deposition. The coating, consisting of deposition and a diffusion layer with nanocrystalline grains of dimensions ranging from 10 to 20 nm, obtained on the substrate pretreated for 1 h, manifested the optimal structure, with a maximum thickness of approximately 14 μm and the best adhesion performance of approximately 9.5 N. The study found that the pretreatment led to grain refinement at a depth of approximately 150 nm and an increase in the crystal face spacing of substrate and high-energy defects. In addition, the crystal defects and lightly increased surface roughness enhanced the surface energy, while the Ta (200) and Ta (211) crystal faces, which had a lower combination energy and a more stable state with Hf atoms than the Ta (110) crystal face, were considerably increased on pretreated substrates with a decrease in the Ta (110) crystal face. Consequently, coating elements exhibited enhanced diffusion within the substrate, leading to the better formation of a gradient structure, which effectively improved the adhesion of coatings. Further, this study offers an efficacious approach to enhance coating adhesion and provides a deeper understanding of plasma pretreatment.
Artificial Neural Network-Based Prediction and Morphological Evolution of Cu2O Crystal Surface Energy
Authors: Shi Yongguo , Wang Man , Zhou Zhiling , Zhao Min , Hu Yanqiang , Yang Jian , Tong Shengfu , Lai Fuming
Published: September 2023

In this study, we investigate the crystal structure, surface energy, and atomic arrangement of Cu2O. Understanding these properties is crucial for exploring the potential applications and understanding the behavior of this material. We employ the Wulff construction method and an artificial neural network (ANN) model to analyze the relative surface energies of different crystal facets and predict the surface energy of Cu2O. The ANN model exhibits excellent performance, demonstrating its effectiveness in predicting material properties and providing automated feature-learning and nonlinear-modeling capabilities. Moreover, we analyze the atomic arrangements on various crystal facets and observe the presence of oxygen atoms on the {100} facet, as well as exposed under-coordinated copper atoms on the {111} and {110} facets. High-index facets such as {211} exhibit a higher atomic step density and screw dislocation density. By precisely controlling the synthesis process, it is possible to manipulate the proportion of high-index facets. These findings highlight the significance of understanding the surface energy and atomic arrangement of Cu2O crystals for comprehending their properties and surface reactions. In summary, this study provides valuable insights into the crystal structure, surface energy, and atomic arrangement of Cu2O, offering inspiration for its properties and potential applications. The combination of the Wulff construction method and ANN modeling provides a comprehensive understanding of Cu2O crystals and their surface behavior, contributing to the field of materials science and laying the foundation for various future applications utilizing the unique properties of Cu2O.
Process-Oriented Requirements Definition and Analysis of Software Components in Critical Systems
Authors: Intrigila Benedetto , Della Penna Giuseppe , D’Ambrogio Andrea , Campagna Dario , Grigore Malina
Published: September 2023

Requirements management is a key aspect in the development of software components, since complex systems are often subject to frequent updates due to continuously changing requirements. This is especially true in critical systems, i.e., systems whose failure or malfunctioning may lead to severe consequences. This paper proposes a three-step approach that incrementally refines a critical system specification, from a lightweight high-level model targeted to stakeholders, down to a formal standard model that links requirements, processes and data. The resulting model provides the requirements specification used to feed the subsequent development, verification and maintenance activities, and can also be seen as a first step towards the development of a digital twin of the physical system.
The Crystal Structure of Pb10(PO4)6O Revisited: The Evidence of Superstructure
Authors: Krivovichev Sergey V. , Engel Günther
Published: September 2023

The crystal structure of Pb10(PO4)6O, the proposed matrix for the potential room-temperature superconductor LK-99, Pb10−xCux(PO4)6O (x = 0.9–1.0), has been reinvestigated via single-crystal X-ray diffraction using crystals prepared by Merker and Wondratschek (Z. Anorg. Allg. Chem. 1960, 306, 25–29). The crystal structure is trigonal, P3¯, a = 9.8109(6), c = 14.8403(12) Å, V = 1237.06(15), R1 = 0.0413 using 3456 unique observed reflections. The crystal structure of Pb10(PO4)6O is a superstructure with regard to the ‘standard’ P63/m apatite structure type. The doubling of the c parameter is induced through the ordering of the split sites of ‘additional’ O’ atoms within the structure channels running parallel to the c axis and centered at (00z). The O’ atoms form short bonds to the Pb1 atoms, resulting in splitting the Pb1 site into two, Pb1A and Pb1B. The structural distortions are further transmitted to the Pb phosphate framework formed by four Pb2 sites and PO4 groups. The structure data previously reported by Krivovichev and Burns (Z. Kristallogr. 2003, 218, 357–365) may either correspond to the Pb10(PO4)6Ox(OH)2−2x (x ~ 0.4) member of the Pb10(PO4)6O—Pb10(PO4)6(OH)2 solid solution series, or to the high-temperature polymorph of Pb10(PO4)6O (with the phase with doubled c parameter being the low-temperature polymorph).
Numerical Simulation of Temperature Field during Electron Beam Cladding for NiCrBSi on the Surface of Inconel 718
Authors: Zhao Guanghui , Zhang Yu , Li Juan , Li Huaying , Ma Lifeng , Li Yugui
Published: September 2023

This study investigates the Inconel 718 alloy coated with NiCrBSi powder using the ABAQUS software. An accurate conical heat source model is constructed based on the three-dimensional Fourier heat conduction law. The heat source subroutine Dflux.for is successfully integrated to achieve a highly realistic simulation of the welding heat source. Using this model, the analysis focuses on the temperature distribution in electron beam melting. Furthermore, the accuracy and reliability of the simulation are validated through actual coating experiments. By examining the impact of various procedural factors on the temperature distribution, it is found that optimal coating results and a tightly formed elliptical molten zone are attained at an electron beam current of 18 mA, and the scanning speed is 300 mm/min. The peak temperature in the melt pool in the coating area is 5087 K, while the lowest temperature on the isothermal in the heat-affected zone is 1409 K. Over time, there is a swift rise in temperature for the data points taken along both the X and Z trajectories, followed by rapid cooling after rapid heating. Coating experiments conducted under the optimal parameters demonstrate a dense coating layer and good bonding with the substrate, thereby validating the accuracy of the simulation.
Amantadine and Rimantadine Analogues—Single-Crystal Analysis and Anti-Coronaviral Activity
Authors: Shishkova Kalina , Stoymirska Antoniya , Chayrov Radoslav , Shishkov Stoyan , Sbirkova-Dimitrova Hristina , Rusew Rusi , Shivachev Boris , Stankova Ivanka
Published: September 2023

In this study, we utilized the human coronavirus 229E (HCoV-229E) for evaluating the in vitro efficacy of some analogues of the ion-channel inhibitors and Amantadine and Rimantadine. The application of these investigated compounds did not result in any detectable cytotoxic effects. Furthermore, we observed that the derivatives of both analogues did not affect the viability of MDBK cells. Amantadine and Rimantadine applied at a concentration of 50 μg/mL inhibited the replication by 47% and 36%, respectively. The derivatives of Amantadine displayed a slightly weaker inhibitory effect in comparison with its own inhibitory effect. The derivative 4R of Rimantadine exhibited the same antiviral effect as the Rimantadine control, inhibiting viral replication by 37%. However, the other two derivatives of Rimantadine demonstrated lower activities. The molecular structures of the newly synthesized compounds were investigated thoroughly using single-crystal X-ray analysis. Molecular docking studies were performed using Autodock Vina. Two of the studied compounds 2A and 4A showed a promising binding affinity (−8.3 and −8.0 kcal/mol) towards SARS-CoV-2 RNA-dependent polymerase RNA site and SARS-CoV-2 Nsp3 (207-379, MES site) respectively.
New Trends and Advances in MRI and PET Hybrid Imaging in Diagnostics
Authors: Crimì Filippo , Zanon Chiara , Crimì Alberto , Cabrelle Giulio , Quaia Emilio
Published: September 2023

Imaging holds an irreplaceable role in routine clinical practice [...]
Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence
Authors: Gabralla Lubna Abdelkareim , Hussien Ali Mohamed , AlMohimeed Abdulaziz , Saleh Hager , Alsekait Deema Mohammed , El-Sappagh Shaker , Ali Abdelmgeid A. , Refaat Hassan Moatamad
Published: September 2023

Colon cancer is the third most common cancer type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. This paper aims to propose a heterogenic stacking deep learning model to predict colon cancer. Stacking deep learning is integrated with pretrained convolutional neural network (CNN) models with a metalearner to enhance colon cancer prediction performance. The proposed model is compared with VGG16, InceptionV3, Resnet50, and DenseNet121 using different evaluation metrics. Furthermore, the proposed models are evaluated using the LC25000 and WCE binary and muticlassified colon cancer image datasets. The results show that the stacking models recorded the highest performance for the two datasets. For the LC25000 dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (100). For the WCE colon image dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (98). Stacking-SVM achieved the highest performed compared to existing models (VGG16, InceptionV3, Resnet50, and DenseNet121) because it combines the output of multiple single models and trains and evaluates a metalearner using the output to produce better predictive results than any single model. Black-box deep learning models are represented using explainable AI (XAI).
An Anaplastic Thyroid Carcinoma of the Giant-Cell Type from a Mediastinal Ectopic Thyroid Gland
Authors: Nguyen Daniel , Htun Nyein Nyein , Wang Beverly , Lee Bonnie , Johnson Cary
Published: September 2023

Anaplastic thyroid carcinoma is a rare, aggressive form of thyroid carcinoma with a mean survival of less than 6 months. Ectopic thyroid tissue can be present in the mediastinum due to faulty embryogenesis with improper descent. Primary thyroid malignancies may arise from this ectopic tissue. A 90-year-old male with a history of prostatic adenocarcinoma, hypothyroidism, and occupational and therapeutic exposure to radiation presented with a rash on his chest. A review of the dermatopathology and excised mediastinal specimen revealed rare papillary foci that tested positive for thyroid markers from a background of poorly differentiated components. Molecular analysis confirmed a BRAF V600E mutation in the specimen. The final diagnosis was anaplastic thyroid carcinoma of the giant-cell type. Given the atrophic cervical thyroid tissue in the patient’s neck with no evidence of previous surgery, this carcinoma was believed to arise from ectopic mediastinal tissue associated with cutaneous and bony metastasis. In conclusion, anaplastic thyroid carcinoma is an aggressive and rare thyroid malignancy that can arise from ectopic thyroid tissue in the mediastinum and should be considered in the differential diagnosis of primary undifferentiated mediastinal malignancies with bony involvement.
Myocardial Tissue Characterization in Patients with Hypertensive Crisis, Positive Troponin, and Unobstructed Coronary Arteries: A Cardiovascular Magnetic Resonance-Based Study
Authors: Talle Mohammed A. , Doubell Anton F. , Robbertse Pieter-Paul S. , Lahri Sa’ad , Herbst Philip G.
Published: September 2023

Hypertensive crisis can present with cardiac troponin elevation and unobstructed coronary arteries. We used cardiac magnetic resonance (CMR) imaging to characterize the myocardial tissue in patients with hypertensive crisis, elevated cardiac troponin, and unobstructed coronary arteries. Patients with hypertensive crisis and elevated cardiac troponin with coronary artery stenosis <50% were enrolled. Patients with troponin-negative hypertensive crisis served as controls. All participants underwent CMR imaging at 1.5 Tesla. Imaging biomarkers and tissue characteristics were compared between the groups. There were 19 patients (63% male) with elevated troponin and 24 (33% male) troponin-negative controls. The troponin-positive group was older (57 ± 11 years vs. 47 ± 14 years, p = 0.015). The groups had similar T2-weighted signal intensity ratios and native T1 times. T2 relaxation times were longer in the troponin-positive group, and the difference remained significant after excluding infarct-pattern late gadolinium enhancement (LGE) from the analysis. Extracellular volume (ECV) was higher in the troponin-positive group (25 ± 4 ms vs. 22 ± 3 ms, p = 0.008) and correlated strongly with T2 relaxation time (rs = 0.701, p = 0.022). Late gadolinium enhancement was 32% more prevalent in the troponin-positive group (82% vs. 50%, p = 0.050), with 29% having infarct-pattern LGE. T2 relaxation time was independently associated with troponin positivity (OR 2.1, p = 0.043), and both T2 relaxation time and ECV predicted troponin positivity (C-statistics: 0.71, p = 0.009; and 0.77, p = 0.006). Left ventricular end-diastolic and left atrial volumes were the strongest predictors of troponin positivity (C-statistics: 0.80, p = 0.001; and 0.82, p < 0.001). The increased T2 relaxation time and ECV and their significant correlation in the troponin-positive group suggest myocardial injury with oedema, while the non-ischaemic LGE could be due to myocardial fibrosis or acute necrosis. These CMR imaging biomarkers provide important clinical indices for risk stratification and prognostication in patients with hypertensive crisis.
Hyperoxic BOLD-MRI-Based Characterization of Breast Cancer Molecular Subtypes Is Independent of the Supplied Amount of Oxygen: A Preclinical Study
Authors: Bartsch Silvester J. , Ehret Viktoria , Friske Joachim , Fröhlich Vanessa , Laimer-Gruber Daniela , Helbich Thomas H. , Pinker Katja
Published: September 2023

Hyperoxic BOLD-MRI targeting tumor hypoxia may provide imaging biomarkers that represent breast cancer molecular subtypes without the use of injected contrast agents. However, the diagnostic performance of hyperoxic BOLD-MRI using different levels of oxygen remains unclear. We hypothesized that molecular subtype characterization with hyperoxic BOLD-MRI is feasible independently of the amount of oxygen. Twenty-three nude mice that were inoculated into the flank with luminal A (n = 9), Her2+ (n = 5), and triple-negative (n = 9) human breast cancer cells were imaged using a 9.4 T Bruker BioSpin system. During BOLD-MRI, anesthesia was supplemented with four different levels of oxygen (normoxic: 21%; hyperoxic: 41%, 71%, 100%). The change in the spin–spin relaxation rate in relation to the normoxic state, ΔR2*, dependent on the amount of erythrocyte-bound oxygen, was calculated using in-house MATLAB code. ΔR2* was significantly different between luminal A and Her2+ as well as between luminal A and triple-negative breast cancer, reflective of the less aggressive luminal A breast cancer’s ability to better deliver oxygen-rich hemoglobin to its tissue. Differences in ΔR2* between subtypes were independent of the amount of oxygen, with robust distinction already achieved with 41% oxygen. In conclusion, hyperoxic BOLD-MRI may be used as a biomarker for luminal A breast cancer identification without the use of exogenous contrast agents.
Age of Synchronization Minimization Algorithms in Wireless Networks with Random Updates under Throughput Constraints
Authors: He Yuqiao , Chen Guozhi , Chen Yuchao , Wang Jintao , Song Jian
Published: September 2023

This study considers a wireless network where multiple nodes transmit status updates to a base station (BS) through a shared bandwidth-limited channel. Considering the random arrival of status updates, we measure the data freshness with the age of synchronization (AoS) metric; specifically, we use the time elapsed since the latest synchronization as a metric. The objective of this study is to minimize the weighted sum of the average AoS of the entire network while meeting the minimum throughput requirement of each node. We consider both the central scheduling scenario and the distributed scheduling scenario. In the central scheduling scenario, we propose the optimal stationary randomized policy when the transmission feedback is unavailable and the max-weight policy when it is available. In the distributed scenario, we propose a distributed policy. The complexity of the three scheduling policies is significantly low. Numerical simulations show that the policies can satisfy the throughput constraint in the central controlling scenario and the AoS performance of the max-weight policy is close to the lower bound.
Detection of Subsurface Damage Morphology of Lapped Optical Components by Analysis of the Fluorescence Lifetimes of Quantum Dots
Authors: Cui Yana , Wang Chunyang , Liu Xuelian , Wu Yajie , Li Zhengze , Li Rong , He Wen
Published: September 2023

Optical components inevitably suffer subsurface damage (SSD) during grinding and lapping, and this SSD substantially degrades the performance of optical systems. Moreover, given the surface roughness of optical components after grinding or lapping, it is impossible to non-destructively and accurately detect SSD generated by these processes, especially regarding the morphological details of such SSD. Accordingly, a novel method for detecting the morphological details of SSD in lapped optical components via analysis of the fluorescence lifetimes of quantum dots (QDs) is developed. This paper shows that, (1) compared with other methods, this novel method detects more morphological details of SSD in lapped optical components and that this detection is unaffected by the elemental composition/size/fluorescence lifetime of the QDs; (2) SSD detection achieved by analyzing the QD fluorescence lifetime can detect more SSD details on the premise of achieving the detection of SSD distribution and depth; and (3) the SSD in lapped optical components exhibits textural features, and “hole”-type SSD is detected in addition to “solid”-point and strip SSD. These findings will facilitate research on the formation mechanism of SSD, thereby enabling improvements in optical manufacturing techniques.
Novel Blockchain and Zero-Knowledge Proof Technology-Driven Car Insurance
Authors: Qiu Zhuoliang , Xie Zhijun , Jiang Xianliang , Ran Chuan , Chen Kewei
Published: September 2023

It is crucial to ensure the privacy and authenticity of the owner’s information in car insurance claims. However, the current traditional car insurance claims scenario suffers from inefficiency, complex service, unreliable data, and data leakage. Therefore, considering the privacy and sensitivity of insurance information and car owner data, we can use blockchain, smart contracts, and zero-knowledge proof technology to improve the current problems. This paper proposes a novel car insurance claim scheme based on smart contracts, blockchain, and zero-knowledge proof. Our scheme focuses on preserving privacy in the car insurance authorization and claim process. We design a private smart contract for the creation and revocation of car insurance and public smart contract for the authorization and validation of car insurance. By using ZoKrates, generating zero-knowledge proofs off chain and verifying the proofs on chain reduces the amount of data storage and computation on chain and provides privacy protection for sensitive information. Experimental results confirm the efficacy of our scheme in terms of security and performance.
Enhanced Real-Time Maintenance Management Model—A Step toward Industry 4.0 through Lean: Conveyor Belt Operation Case Study
Authors: Mendes David , Gaspar Pedro D. , Charrua-Santos Fernando , Navas Helena
Published: September 2023

Conveyor belts (CBs) are widely used for the continuous transport of bulk materials. CBs must be extremely reliable due to the cost associated with their failure in continuous production systems. Thus, it is highly relevant in terms of maintenance and planning to find solutions to reduce the existing stoppages from these assets. In this sense, it is essential to monitor and collect real-time data from this piece of equipment. This work presents a case study, where a model that combines the Lean Philosophy, Total Productive Maintenance (TPM), and the enabling technologies of Industry 4.0 is applied to a CB. The proposed model monitors the CB and provides data on its operation, which, using the calculation of indicators, allows a more accurate and thorough view and evaluation, contributing to improving and supporting decision making by those responsible for maintenance. The data collected by the sensor help those responsible for maintenance and production, in the readjustment of more accurate and optimized planning, programming, and execution, supporting decision making in these areas. During the field test of a two-hour monitoring period (10 a.m. to 12 p.m.), the model identified six stoppages, resulting in approximately 88.6% of operational time for the conveyor. The field test showed that this model can result in more accurate maintenance decision making than conventional approaches. This research also contributes to the advancement of electronics and industrial automation sectors by empowering companies to transform maintenance methodologies. The potential of this approach and its implications for enhanced productivity and overall performance are therefore highlighted.
Research on Efficient Multiagent Reinforcement Learning for Multiple UAVs’ Distributed Jamming Strategy
Authors: Ran Weizhi , Luo Rong , Zhang Funing , Luo Renwei , Xu Yang
Published: September 2023

To support Unmanned Aerial Vehicle (UAV) joint electromagnetic countermeasure decisions in real time, coordinating multiple UAVs for efficiently jamming distributed hostile radar stations requires complex and highly flexible strategies. However, with the nature of the high complexity dimension and partial observation of the electromagnetic battleground, no such strategy can be generated by pre-coded software or decided by a human commander. In this paper, an initial effort is made to integrate multiagent reinforcement learning, which has been proven to be effective in game strategy generation, into the distributed airborne electromagnetic countermeasures domain. The key idea is to design a training simulator which close to a real electromagnetic countermeasure strategy game, so that we can easily collect huge valuable training data other than in the real battle ground which is sparse and far less than sufficient. In addition, this simulator is able to simulate all the necessary decision factors for multiple UAV coordination, so that multiagents can freely search for their optimal joint strategies with our improved Independent Proximal Policy Optimization (IPPO) learning algorithm which suits the game well. In the last part, a typical domain scenario is built to test, and the use case and experiment results manifest that the design is efficient in coordinating a group of UAVs equipped with lightweight jamming devices. Their coordination strategies are not only capable of handling given jamming tasks for the dynamic jamming of hostile radar stations but also beat expectations. The reinforcement learning algorithm can do some heuristic searches to help the group find the tactical vulnerabilities of the enemies and improve the multiple UAVs’ jamming performance.
Auto-Inspection System Using Optimized Fuzzy Sliding Mode Control Strategy for Tunnel Inspection
Authors: Gao Xinbiao , Ding Zhong , Shi Shaojie , Zhou Jun , Huang Panling , Zheng Haibin , Wang Chuang
Published: September 2023

Cities composed of many mountainous areas necessitate the use of many tunnels for roads and highways with a potential safety hazard. To determine the safety of tunnels, periodic tunnel inspections mainly rely on manual work, which is dangerous and slow. Therefore, this paper proposes an auto-inspection system for tunnel inspection consisting of a robotic arm, laser sensors, and an inspection radar to free inspectors from hazardous environments and high-intensity work. Based on the mathematical model in the inspection process, a sliding mode controller is designed and optimized with fuzzy control and hyperbolic tangent functions, and is used in a tunnel inspection robot system for the first time. The simulation results show that optimized fuzzy sliding mode control can improve the tracking accuracy and stability during the tracking process compared with the traditional algorithm. Curved line inspection and arch line inspection experiment tests demonstrate that our system can automatically inspect the tunnel, and that the optimized fuzzy sliding mode control provides a superior performance in terms of the tracking process, with the average error decreasing by 37.8% when compared to traditional algorithms. Therefore, the proposed system is of great significance for high-precision and high-stability unmanned automated tunnel inspection.
DFFA-Net: A Differential Convolutional Neural Network for Underwater Optical Image Dehazing
Authors: Hou Xujia , Zhang Feihu , Wang Zewen , Song Guanglei , Huang Zijun , Wang Jinpeng
Published: September 2023

This paper proposes DFFA-Net, a novel differential convolutional neural network designed for underwater optical image dehazing. DFFA-Net is obtained by deeply analyzing the factors that affect the quality of underwater images and combining the underwater light propagation characteristics. DFFA-Net introduces a channel differential module that captures the mutual information between the green and blue channels with respect to the red channel. Additionally, a loss function sensitive to RGB color channels is introduced. Experimental results demonstrate that DFFA-Net achieves state-of-the-art performance in terms of quantitative metrics for single-image dehazing within convolutional neural network-based dehazing models. On the widely-used underwater Underwater Image Enhancement Benchmark (UIEB) image dehazing dataset, DFFA-Net achieves a peak signal-to-noise ratio (PSNR) of 24.2631 and a structural similarity index (SSIM) score of 0.9153. Further, we have deployed DFFA-Net on a self-developed Remotely Operated Vehicle (ROV). In a swimming pool environment, DFFA-Net can process hazy images in real time, providing better visual feedback to the operator. The source code has been open sourced.
A Distributed Conflict-Free Task Allocation Method for Multi-AGV Systems
Authors: Guo Qiang , Yao Haiyan , Liu Yi , Tang Zhipeng , Zhang Xufeng , Li Ning
Published: September 2023

In the era of Industry 4.0, as the main force of intelligent logistics systems, multi-Automated Guided Vehicle (AGV) systems have developed rapidly. At present, multi-AGV systems are a research hotspot, where task allocation as a key technology is being paid much attention. In this study, a new task allocation scheme for multi-AGV systems is proposed based on a distributed framework. The AGVs can autonomously select tasks, plan paths, and communicate with its neighbors to ensure that all tasks are completed at a low cost and conflicts are avoided. While ensuring total connectivity, the proposed method can avoid the calculation pressure of task center surges when the number of AGVs increases sharply, and has the advantages of good flexibility and good real-time performance. In addition, some examples are provided to demonstrate the effectiveness of the connectivity maintainer and task allocation method.
Machine and Deep Learning Algorithms for COVID-19 Mortality Prediction Using Clinical and Radiomic Features
Authors: Verzellesi Laura , Botti Andrea , Bertolini Marco , Trojani Valeria , Carlini Gianluca , Nitrosi Andrea , Monelli Filippo , Besutti Giulia , Castellani Gastone , Remondini Daniel , Milanese Gianluca , Croci Stefania , Sverzellati Nicola , Salvarani Carlo , Iori Mauro
Published: September 2023

Aim: Machine learning (ML) and deep learning (DL) predictive models have been employed widely in clinical settings. Their potential support and aid to the clinician of providing an objective measure that can be shared among different centers enables the possibility of building more robust multicentric studies. This study aimed to propose a user-friendly and low-cost tool for COVID-19 mortality prediction using both an ML and a DL approach. Method: We enrolled 2348 patients from several hospitals in the Province of Reggio Emilia. Overall, 19 clinical features were provided by the Radiology Units of Azienda USL-IRCCS of Reggio Emilia, and 5892 radiomic features were extracted from each COVID-19 patient’s high-resolution computed tomography. We built and trained two classifiers to predict COVID-19 mortality: a machine learning algorithm, or support vector machine (SVM), and a deep learning model, or feedforward neural network (FNN). In order to evaluate the impact of the different feature sets on the final performance of the classifiers, we repeated the training session three times, first using only clinical features, then employing only radiomic features, and finally combining both information. Results: We obtained similar performances for both the machine learning and deep learning algorithms, with the best area under the receiver operating characteristic (ROC) curve, or AUC, obtained exploiting both clinical and radiomic information: 0.803 for the machine learning model and 0.864 for the deep learning model. Conclusions: Our work, performed on large and heterogeneous datasets (i.e., data from different CT scanners), confirms the results obtained in the recent literature. Such algorithms have the potential to be included in a clinical practice framework since they can not only be applied to COVID-19 mortality prediction but also to other classification problems such as diabetic prediction, asthma prediction, and cancer metastases prediction. Our study proves that the lesion’s inhomogeneity depicted by radiomic features combined with clinical information is relevant for COVID-19 mortality prediction.
A Multi-Criteria Approach to Evaluate Sustainability: A Case Study of the Navarrese Wine Sector
Authors: Mainar-Toledo María Dolores , Gómez Palmero Maider , Díaz-Ramírez Maryori , Mendioroz Iñaki , Zambrana-Vasquez David
Published: September 2023

The present work introduces a multi-criteria approach focused on the evaluation of wine production, considering the three dimensions of sustainability: environmental, economic and social. In this sense, the most relevant key performance indicators were selected within each dimension and disaggregated into sub-indicators to address the different sustainability aspects within the wine value chain. Furthermore, the analytic hierarchy process was applied as the method to weight the relevance of the three dimensions and corresponding key performance indicators, in order to allow the producers to understand which aspects need to be covered to improve their production sustainability. Results demonstrate how the approach proposed is able to identify, for both the vineyard and winery, the strengths and weaknesses regarding the sustainability performance of their production. Additionally, this study also contributes by shedding light on the most suitable and recommended actions to increase the company’s sustainability from a sustainable perspective. Additionally, it is important to highlight that, although the developed approach is specifically designed for the wine sector in the Navarrese region, it can be replicated by adjusting the key performance indicator selection to apply this methodology to the reality in other wine production regions.
Numerical Assessment of a Two-Phase Model for Propulsive Pump Performance Prediction
Authors: Avanzi Filippo , Baù Alberto , De Vanna Francesco , Benini Ernesto
Published: September 2023

The present work provides a detailed numerical investigation of a turbopump for waterjet applications in cavitating conditions. In particular, the study focuses on the complexities of cavitation modelling, serving as a pivotal reference for future computational research, especially in off-design hydro-jet scenarios, and it aims to extend current model assessments of the existing methods, by disputing their standard formulations. Thus, a computational domain of a single rotor-stator blade passage is solved using steady-state Reynolds-Averaged Navier–Stokes equations, coupled with one-, two-, and four-equation turbulence models, and compared with available measurements, encompassing both nominal and thrust breakdown conditions. Through grid dependency analysis, a medium refinement with the Shear Stress Transport turbulence model is chosen as the optimal configuration, reducing either computational time and relative error in breakdown efficiency to 1%. This arrangement is coupled with a systematic study of the Zwart cavitation model parameters through multipliers ranging from 10−2 to 102. Results reveal that properly tuning these values allows for a more accurate reconstruction of the initial phases of cavitation up to breakdown. Notably, increasing the nucleation radius reduces the difference between the estimated head rise and experimental values near breakdown, reducing the maximum error by 4%. This variation constrains vapour concentration, promoting cavitation volume extension in the passage. A similar observation occurs when modifying the condensation coefficient, whereas altering the vaporization coefficient yields opposite effects.