bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing THP-1 Macrophage Culture for an Immune-Responsive Human Intestinal Model
Authors: Phuangbubpha Pornwipa , Thara Sanya , Sriboonaied Patsawee , Saetan Puretat , Tumnoi Wanwiwa , Charoenpanich Adisri
Published: May 2023

Previously established immune-responsive co-culture models with macrophages have limitations due to the dedifferentiation of macrophages in long-term cultures. This study is the first report of a long-term (21-day) triple co-culture of THP-1 macrophages (THP-1m) with Caco-2 intestinal epithelial cells and HT-29-methotrexate (MTX) goblet cells. We demonstrated that high-density seeded THP-1 cells treated with 100 ng/mL phorbol 12-myristate 13-acetate for 48 h differentiated stably and could be cultured for up to 21 days. THP-1m were identified by their adherent morphology and lysosome expansion. In the triple co-culture immune-responsive model, cytokine secretions during lipopolysaccharide-induced inflammation were confirmed. Tumor necrosis factor-alpha and interleukin 6 levels were elevated in the inflamed state, reaching 824.7 ± 130.0 pg/mL and 609.7 ± 139.5 pg/mL, respectively. Intestinal membrane integrity was maintained with a transepithelial electrical resistance value of 336.4 ± 18.0 Ω·cm2. Overall, our findings suggest that THP-1m can be effectively employed in models of long-term immune responses in both normal and chronic inflammatory states of the intestinal epithelium, making them a valuable tool for future research on the association between the immune system and gut health.
Uranium Retention in Silica-Rich Natural Glasses: Nuclear Waste Disposal Aspect
Authors: Poluektov Valeri V. , Petrov Vladislav A. , Ojovan Michael I. , Yudintsev Sergey V.
Published: May 2023

Uranium-containing glass samples with an age of 140–145 million years were collected within the volcanic rocks of the largest volcanic-related uranium ore deposit in the world. Main features of their composition are high concentrations of silica and uranium, the largest for the rocks of this type. In contrast to this, the ages of fresh (unaltered) low-silica natural glasses of a basic composition (basalts) usually do not exceed a few million years. The volcanic low-silica glass is unstable at longer times and in older ancient rocks is transformed into a crystalline mass. The geochemistry of uranium including the behavior in solids and solutions is similar to that of long-lived transuranic actinides such as radioactive Np and Pu from high-level radioactive waste. This allows uranium to be used as a simulant of these long-lived hazardous radionuclides both at the synthesis and for the study of various nuclear wasteforms: glasses, glass crystalline materials and crystalline ceramics. The data obtained on long-term behavior of natural glasses are of importance for prognosis and validation of stability of nuclear wasteforms disposed of in geological disposal facilities (GDF).
LIBS-MLIF Method: Stromatolite Phosphorite Determination
Authors: Wang Hongpeng , Xin Yingjian , Fang Peipei , Jia Jianjun , Zhang Liang , Liu Sicong , Wan Xiong
Published: May 2023

The search for biominerals is one of the core targets in the deep space exploration mission. Stromatolite phosphorite is a typical biomineral that preserves early life on Earth. The enrichment of phosphate is closely related to microorganisms and their secretions. Laser-induced breakdown spectroscopy (LIBS) has become an essential payload in deep space exploration with the ability to analyze chemical elements remotely, rapidly, and in situ. This paper aims to evaluate the rapid identification of biological and non-biological minerals through a remote LIBS payload. LIBS is used for element analysis and mineral classification determination, and molecular laser-induced fluorescence (MLIF) is used to detect halogenated element F to support the existence of fluorapatite. This paper analyzes the LIBS-MLIF spectral characteristics of stromatolites and preliminarily evaluates the feasibility of P element quantification. The results show that LIBS technology can recognize biological and non-biological signals. This discovery is significant because it is not limited to detecting and analyzing element composition. It can also realize the detection of molecular spectrum based on selective extraction of CaF molecule. Therefore, the LIBS payload still has the potential to search for biomineral under the condition of adjusting the detection strategy.
Pediatric Pancreatic Endocrine Tumor Presenting as Acute Pancreatitis: A Case Report
Authors: Fukuda Shigetaka , Suzuki Mitsuyoshi , Minowa Kei , Koga Hiroyuki , Yamataka Atsuyuki , Shimizu Toshiaki
Published: May 2023

Pancreatic neuroendocrine tumors (PNETs) are relatively rare, especially in the pediatric age group. This report describes a pediatric case of acute pancreatitis secondary to stenosis of the main pancreatic duct due to a PNET. The patient was a boy, thirteen and a half years old, who presented with persistent low-grade fever, nausea, and abdominal pain. He was diagnosed with acute pancreatitis based on the elevation of serum pancreatic enzyme levels and abdominal ultrasonography findings of enlargement of the pancreas and dilatation of the main pancreatic duct. Abdominal contrast-enhanced computed tomography (CT) showed a 5.5 mm, contrast-enhanced mass in the head of the pancreas. His symptoms resolved with conservative treatment, although the pancreatic tumor grew slowly. At fifteen years and four months, since the tumor had enlarged to 8.0 mm, the patient underwent pancreaticoduodenectomy for therapeutic and diagnostic purposes. Based on the pathological evaluation, he was diagnosed with PNET (grade: G1). The patient has been free of tumor recurrence for 10 years and requires no additional therapy. In this report, the clinical characteristics of PNETs are also discussed, comparing the clinical features of adult-onset and pediatric-onset cases that initially present as acute pancreatitis.
Evaluating Contemporary and Future-Scenario Substantial-Precipitation Events in the Missouri River Basin Using Object-Oriented Analysis
Authors: Fisel Brandon J. , Erickson Nathan E. , Young Colin R. , Ellingworth Ada L. , Gutowski William J.
Published: May 2023

The Missouri River Basin is the largest single river basin in the United States, and, as such, it plays an important role in natural ecosystems as well as the country’s economy, through agriculture, hydroelectric power generation, and transportation. Episodes of heavy precipitation can have a substantial negative impact on all these aspects of the basin, so understanding how well these episodes are simulated and projected to change in the future climate is important. We analyzed contemporary and projected mid-century behavior of heavy-precipitation episodes using an object-oriented analysis to diagnose short-term (≥5-day) and extended-period (≥30-day) events with substantial precipitation, using PRISM gridded, observed precipitation and RegCM4 regional-climate simulations that used outputs from two different GCMs for boundary conditions. The simulations were produced for the North American portion of the CORDEX program. A 25 km grid was used for the simulations and for aggregated PRISM precipitation. Overall, the simulated contemporary-climate events compared favorably with the PRISM events’ frequency and duration. The simulated event areas tended to be larger than the areas in the PRISM events, suggesting that the effective resolution of the simulations is greater than 25 km. Event areas and durations change little going from contemporary to scenario climate. The short-term events increase in frequency by an amount commensurate with the increase in mean precipitation simulated for the basis. However, the extended-term events showed little change in frequency, despite the average precipitation increase. Roughly half the extended-period events overlapped with at least one short-term event in both the observations and the simulations. Extended-period events that overlap a short-term event generally have larger areas and longer durations compared to their counterparts with no overlapping short-term events. Understanding the climate dynamics yielding the two types of extended-period events could be useful for assessing future changes in the Missouri River Basin’s heavy precipitation events and their impact.
Febuxostat–p-Toluenesulfonic Acid Multi-Component Crystal: Characterization, Crystal Growth and Elucidation of the Salt/Co-Crystal Nature
Authors: Ungur Doriana T. , Santiso-Quinones Gustavo , Pop Mihaela M. , Tamas Tudor L. , Guguta Carmen , Stam Danny , Mija Alice , Iordache Coca A.
Published: May 2023

The multi-component solid form of febuxostat (FEB) with p-toluenesulfonic acid was synthesized by solvent-drop grinding and cooling-evaporative crystallization and characterized by powder X-ray diffraction (XRPD), thermogravimetry (TGA), differential scanning calorimetry (DSC), and infrared spectroscopy (FT-IR). The multi-component form was stable after exposure at elevated temperature and relative humidity and powder dissolution measurements revealed five-fold aqueous solubility improvement relative to FEB. Additionally, the decrease in pH after dissolution suggests a potential for enhancing the drug absorption in the lower stomach. In the context of the regulatory requirements, the salt/co-crystal nature of the form was elucidated by a combination of crystallization process development and crystal growth, followed by SC-XRD and FT-IR. Despite the very weak basicity of the drug, crystal structure determination combined with spectroscopy analysis revealed salt formation by the transfer of the acidic proton from p-toluenesulfonic acid to FEB. Our study emphasizes the importance of the crystal structure knowledge in understanding the type of interactions present in multi-component crystals as well as complying with the specific regulatory requirements.
Laser Textured Superhydrophobic SiC Ceramic Surface and the Performance of Condensation Heat Transfer
Authors: Lou Deyuan , Lu Gengxin , Li Heng , Chen Pengjian , Tao Qing , Yang Qibiao , Liu Dun
Published: May 2023

Chemical modification is usually utilized for preparing superhydrophobic SiC surfaces, which has the problems of long processing time, high environmental contamination risk, and high cost. To enhance the condensation heat transfer efficiency of SiC, the superhydrophobic SiC surface was fabricated through laser texturing and heat treatment. In this study, the SiC surface was processed by laser texturing with a nanosecond laser, followed by heat treatment. Surface microstructures and compositions were investigated with SEM and XPS, and the heat transfer coefficient of the superhydrophobic SiC surface was tested. The results indicated that the laser-textured SiC surface had a super hydrophilic contact angle of 0°; after heat treatment, SiC ceramic became superhydrophobic (surface contact angle reaches 164°) because organic contamination on the original SiC surface could be cleaned by using laser texturing, which caused a chemical reaction and the formation of SiO2 on the surface. Moreover, the distribution of relatively low-energy SiOX was formed after heat treatment; then, SiC ceramic became superhydrophobic. Due to the formation of nanoscale sheet-like protrusion structures by heat treatment, the SiC superhydrophobic surface exhibited typical dropwise condensation, and the condensation heat transfer coefficient reached 331.8 W/(m2·K), which was 2.3 times higher than that of the original surface.
The Coming of Age of Breast Radiotherapy
Authors: Corn Benjamin W. , Galper Shira , Ben-David Merav
Published: May 2023

Exactly 50 years ago, the investigators of the National Surgical Adjuvant Breast and Bowel Project began to design the B-06 trial [...]
Formica gagatoides Ruzsky, 1904, and Siberian F. kozlovi Dlussky, 1965 (Hymenoptera: Formicidae); Two or One Species?
Authors: Chesnokova Svetlana V. , Vaulin Oleg V. , Zhigulskaya Zoya A. , Novgorodova Tatiana A.
Published: May 2023

Ants of the genus Formica play an important role in biogenesis by participating in various processes, including the formation of complex trophic networks. The role of ants in an ecosystem depends on their species and geographic population, which can be difficult to identify. Formica gagatoides with a wide range and F. kozlovi are among some examples. The question is whether the Siberian populations of F. kozlovi really belong to this species or are local populations of F. gagatoides. Based on the materials collected in Russia (Murmansk Region, the north of the Krasnoyarsk Territory, Altai, Far East), a morphological analysis (key diagnostic features) and molecular genetic analysis (COI, ITS1, D2 28S) were carried out. In all localities, there were individuals with pure (gagatoides, kozlovi) and mixed (gagatoides/kozlovi) morphotypes, with the exception of the Magadan Region, where the kozlovi morphotype was absent. According to the phylogenetic trees, F. gagatoides formed separate geographical branches, with the Siberian F. kozlovi being close and clearly conspecific to the Asian branch of F. gagatoides. A relatively high COI divergence, along with some differences in the ITS1 sequences, between the Asian and European F. gagatoides raises the question about the conspecificity of the Asian and European branches of this species.
Virtual Monoenergetic Imaging of Lower Extremities Using Dual-Energy CT Angiography in Patients with Diabetes Mellitus
Authors: Bucolo Giuseppe Mauro , D’Angelo Tommaso , Yel Ibrahim , Koch Vitali , Gruenewald Leon D. , Othman Ahmed E. , Alizadeh Leona Soraja , Overhoff Daniel P. , Waldeck Stephan , Martin Simon S. , Mazziotti Silvio , Ascenti Giorgio , Blandino Alfredo , Vogl Thomas J. , Booz Christian
Published: May 2023

Background: Type 2 diabetes mellitus (DM) is the most common metabolic disorder in the world and an important risk factor for peripheral arterial disease (PAD). CT angiography represents the method of choice for the diagnosis, pre-operative planning, and follow-up of vascular disease. Low-energy dual-energy CT (DECT) virtual mono-energetic imaging (VMI) has been shown to improve image contrast, iodine signal, and may also lead to a reduction in contrast medium dose. In recent years, VMI has been improved with the use of a new algorithm called VMI+, able to obtain the best image contrast with the least possible image noise in low-keV reconstructions. Purpose: To evaluate the impact of VMI+ DECT reconstructions on quantitative and qualitative image quality in the evaluation of the lower extremity runoff. Materials and Methods: We evaluated DECT angiography of lower extremities in patients suffering from diabetes who had undergone clinically indicated DECT examinations between January 2018 and January 2023. Images were reconstructed with standard linear blending (F_0.5) and low VMI+ series were generated from 40 to 100 keV, in an interval of 15 keV. Vascular attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for objective analysis. Subjective analysis was performed using five-point scales to evaluate image quality, image noise, and diagnostic assessability of vessel contrast. Results: Our final study cohort consisted of 77 patients (41 males). Attenuation values, CNR, and SNR were higher in 40-keV VMI+ reconstructions compared to the remaining VMI+ and standard F_0.5 series (HU: 1180.41 ± 45.09; SNR: 29.91 ± 0.99; CNR: 28.60 ± 1.03 vs. HU 251.32 ± 7.13; SNR: 13.22 ± 0.44; CNR: 10.57 ± 0.39 in standard F_0.5 series) (p < 0.0001). Subjective image rating was significantly higher in 55-keV VMI+ images compared to the other VMI+ and standard F_0.5 series in terms of image quality (mean score: 4.77), image noise (mean score: 4.39), and assessability of vessel contrast (mean value: 4.57) (p < 0.001). Conclusions: DECT 40-keV and 55-keV VMI+ showed the highest objective and subjective parameters of image quality, respectively. These specific energy levels for VMI+ reconstructions could be recommended in clinical practice, providing high-quality images with greater diagnostic suitability for the evaluation of lower extremity runoff, and potentially needing a lower amount of contrast medium, which is particularly advantageous for diabetic patients.
Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning
Authors: Yong Ming Ping , Hum Yan Chai , Lai Khin Wee , Lee Ying Loong , Goh Choon-Hian , Yap Wun-She , Tee Yee Kai
Published: May 2023

Gastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the need for early detection to improve patient survival rates. The current clinical gold standard for detection is histopathological image analysis, but this process is manual, laborious, and time-consuming. As a result, there has been growing interest in developing computer-aided diagnosis to assist pathologists. Deep learning has shown promise in this regard, but each model can only extract a limited number of image features for classification. To overcome this limitation and improve classification performance, this study proposes ensemble models that combine the decisions of several deep learning models. To evaluate the effectiveness of the proposed models, we tested their performance on the publicly available gastric cancer dataset, Gastric Histopathology Sub-size Image Database. Our experimental results showed that the top 5 ensemble model achieved state-of-the-art detection accuracy in all sub-databases, with the highest detection accuracy of 99.20% in the 160 × 160 pixels sub-database. These results demonstrated that ensemble models could extract important features from smaller patch sizes and achieve promising performance. Overall, our proposed work could assist pathologists in detecting gastric cancer through histopathological image analysis and contribute to early gastric cancer detection to improve patient survival rates.
Deep Infiltrating Endometriosis: Diagnostic Accuracy of Preoperative Magnetic Resonance Imaging with Respect to Morphological Criteria
Authors: Harth Sebastian , Roller Fritz C. , Zeppernick Felix , Meinhold-Heerlein Ivo , Krombach Gabriele A.
Published: May 2023

Several current guidelines recommend imaging in the diagnostic work-up of deep infiltrating endometriosis (DIE). The purpose of this retrospective diagnostic test study was to evaluate the diagnostic accuracy of MRI compared to laparoscopy for the identification of pelvic DIE, considering lesion morphology using MRI. In all, 160 consecutive patients were included who received pelvic MRI for evaluation of endometriosis between October 2018 and December 2020 and underwent subsequent laparoscopy within 12 months of the MRI examination. MRI findings were categorized for suspected DIE using the Enzian classification and were additionally graded using a newly suggested deep infiltrating endometriosis morphology score (DEMS). Endometriosis was diagnosed in 108 patients (all types, i.e., purely superficial and DIE), of which 88 cases were diagnosed with DIE and 20 with solely superficial peritoneal endometriosis (i.e., not deep infiltrating endometriosis/DIE). The overall positive and negative predictive values of MRI for the diagnosis of DIE, including lesions with assumed low and medium certainty of DIE on MRI (DEMS 1–3), were 84.3% (95% CI: 75.3–90.4) and 67.8% (95% CI: 60.6–74.2), respectively, and 100.0% and 59.0% (95% CI: 54.6–63.3) when strict MRI diagnostic criteria were applied (DEMS 3). Overall sensitivity of MRI was 67.0% (95% CI: 56.2–76.7), specificity was 84.7% (95% CI: 74.3–92.1), accuracy was 75.0% (95% CI: 67.6–81.5), positive likelihood ratio (LR+) was 4.39 (95% CI: 2.50–7.71), negative likelihood ratio (LR-) was 0.39 (95% CI: 0.28–0.53), and Cohen’s kappa was 0.51 (95% CI: 0.38–0.64). When strict reporting criteria are applied, MRI can serve as a method to confirm clinically suspected DIE.
FIP1L1–PDGFRα-Positive Loeffler Endocarditis—A Distinct Cause of Heart Failure in a Young Male: The Role of Multimodal Diagnostic Tools
Authors: Varga Andreea , Moldovan Diana Andreea , Pop Marian , Benedek Istvan , Kövecsi Attila , Dumbrava Robert Adrian , Iancu Dragos Gabriel , Cristescu Liviu , Huma Laurentiu , Tilea Ioan
Published: May 2023

The presence of the Fip1-Like1-platelet-derived growth factor receptor alpha (FIP1L1–PDGFRα) fusion gene represents a rare cause of hypereosinophilic syndrome (HES), which is associated with organ damage. The aim of this paper is to emphasize the pivotal role of multimodal diagnostic tools in the accurate diagnosis and management of heart failure (HF) associated with HES. We present the case of a young male patient who was admitted with clinical features of congestive HF and laboratory findings of hypereosinophilia (HE). After hematological evaluation, genetic tests, and ruling out reactive causes of HE, a diagnosis of positive FIP1L1–PDGFRα myeloid leukemia was established. Multimodal cardiac imaging identified biventricular thrombi and cardiac impairment, thereby raising suspicion of Loeffler endocarditis (LE) as the cause of HF; this was later confirmed by a pathological examination. Despite hematological improvement under corticosteroid and imatinib therapy, anticoagulant, and patient-oriented HF treatment, there was further clinical progression and subsequent multiple complications (including embolization), which led to patient death. HF is a severe complication that diminishes the demonstrated effectiveness of imatinib in the advanced phases of Loeffler endocarditis. Therefore, the need for an accurate identification of heart failure etiology in the absence of endomyocardial biopsy is particularly important for ensuring effective treatment.
Strain Rate Changes during Stress Echocardiography Are the Most Accurate Predictors of Significant Coronary Artery Disease in Patients with Previously Treated Acute Coronary Syndrome
Authors: Shenouda Rafik , Bytyçi Ibadete , El Sharkawy Eman , Hisham Noha , Sobhy Mohamed , Henein Michael Y.
Published: May 2023

Background and Aims. Dobutamine stress echocardiography (DSE) is a well-established non-invasive investigation for the detection of ischemic myocardial dysfunction. The aim of this study was to evaluate the accuracy of myocardial deformation parameters measured by speckle tracking echocardiography (STE) in predicting culprit coronary artery lesions in patients with prior revascularization and acute coronary syndrome (ACS). Methods. We prospectively studied 33 patients with ischemic heart disease, a history of at least one episode of ACS and prior revascularization. All patients underwent a complete stress Doppler echocardiographic examination, including the myocardial deformation parameters of peak systolic strain (PSS), peak systolic strain rate (SR) and wall motion score index (WMSI). The regional PSS and SR were analyzed for different culprit lesions. Results. The mean age of patients was 59 ± 11 years and 72.7% were males. At peak dobutamine stress, the change in regional PSS and SR in territories supplied by the LAD showed smaller increases compared to those in patients without culprit LAD lesions (p < 0.05 for all). Likewise, the regional parameters of myocardial deformation were reduced in patients with culprit LCx lesions compared to those with non-culprit LCx lesions and in patients with culprit RCA legions compared to those with non-culprit RCA lesions (p < 0.05 for all). In the multivariate analysis, the △ regional PSS (1.134 (CI = 1.059–3.315, p = 0.02)) and the △ regional SR (1.566 (CI = 1.191–9.013, p = 0.001)) for LAD territories predicted the presence of LAD lesions. Similarly, in a multivariable analysis, the △ regional PSS and the △SR predicted LCx culprit lesions and RCA culprit lesions (p < 0.05 for all). In an ROC analysis, the PSS and SR had higher accuracies compared to the regional WMSI in predicting culprit lesions. A △ regional SR of −0.24 for the LAD territories was 88% sensitive and 76% specific (AUC = 0.75; p < 0.001), a △ regional PSS of −1.20 was 78% sensitive and 71% specific (AUC = 0.76, p < 0.001) and a △ WMSI of −0.35 was 67% sensitive and 68% specific (AUC = 0.68, p = 0.02) in predicting LAD culprit lesions. Similarly, the △ SR for LCx and RCA territories had higher accuracies in predicting LCx and RCA culprit lesions. Conclusions. The myocardial deformation parameters, particularly the change in regional strain rate, are the most powerful predictors of culprit lesions. These findings strengthen the role of myocardial deformation in increasing the accuracy of DSE analyses in patients with prior cardiac events and revascularization.
Comparing CT and MR Properties of Artificial Thrombi According to Their Composition
Authors: Viltužnik Rebeka , Kaučič Aleš , Blinc Aleš , Vidmar Jernej , Serša Igor
Published: May 2023

This study aims to determine whether and to what extent the structure and composition of thrombi can be assessed using NMR and CT measurements. Seven different thrombus models, namely, six RBC thrombi with hematocrit levels (HTs) of 0%, 20%, 40%, 60%, 80% and 100% and one platelet thrombus model, were analyzed using proton NMR at 100 MHz and 400 MHz, with measurements of T1 and T2 NMR relaxation times and measurements of the apparent diffusion coefficient (ADC). In addition, the thrombus models were CT-scanned in a dual-energy mode (80 kV and 140 kV) and in a single-energy mode (80 kV) to measure their CT numbers. The results confirmed that RBC thrombi can be distinguished from platelet thrombi by using ADC and CT number measurements in all three settings, while they cannot be distinguished by using T1 and T2 measurements. All measured parameters allowed for the differentiation of RBC thrombi according to their HT values, but the best sensitivity to HT was obtained with ADC and single-energy CT measurements. The importance of this study also lies in the potential application of its results for the characterization of actual thrombi in vivo.
A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling
Authors: Chakraborty Gouri Shankar , Batra Salil , Singh Aman , Muhammad Ghulam , Torres Vanessa Yelamos , Mahajan Makul
Published: May 2023

COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.
Yield Prediction of Four Bean (Phaseolus vulgaris) Cultivars Using Vegetation Indices Based on Multispectral Images from UAV in an Arid Zone of Peru
Authors: Saravia David , Valqui-Valqui Lamberto , Salazar Wilian , Quille-Mamani Javier , Barboza Elgar , Porras-Jorge Rossana , Injante Pedro , Arbizu Carlos I.
Published: May 2023

In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.
Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism
Authors: Wang Guoying , Ai Jiafeng , Mo Lufeng , Yi Xiaomei , Wu Peng , Wu Xiaoping , Kong Linjun
Published: May 2023

Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sensing data, which often performs poorly in multidimensional data scenarios due to weak computational scalability and the problem of dimensional catastrophe, ignoring potential correlations between sensing data and some important information of certain characteristics. In order to capture the correlation of multidimensional sensing data and improve the accuracy of anomaly detection effectively, GTAF, an anomaly detection model for multivariate sequences based on an improved graph neural network with a transformer, a graph attention mechanism and a multi-channel fusion mechanism, is proposed in this paper. First, we added a multi-channel transformer structure for intrinsic pattern extraction of different data. Then, we combined the multi-channel transformer structure with GDN’s original graph attention network (GAT) to attain better capture of features of time series, better learning of dependencies between time series and hence prediction of future values of adjacent time series. Finally, we added a multi-channel data fusion module, which utilizes channel attention to integrate global information and upgrade anomaly detection accuracy. The results of experiments show that the average accuracies of GTAF, the anomaly detection model proposed in this paper, are 92.83% and 96.59% on two datasets from unmanned systems, respectively, which has higher accuracy and computational efficiency compared with other methods.
A Causal Analysis of Young Adults’ Binge Drinking Reduction and Cessation
Authors: Cheng Tyrone C. , Lo Celia C.
Published: May 2023

Background: This study, using the multiple disadvantage model (MDM), sought to identify factors (disadvantaging social disorganization, social structural, social integration, health/mental health, co-occurring substance use, and substance treatment access factors) in young adults’ binge drinking reduction and cessation in the United States. Methods: We extracted data on 942 young adult binge drinkers (25–34 years, 47.8% female) from the National Longitudinal Study of Adolescent to Adult Health (Add Health), carrying out a temporal-ordered causal analysis, meaning the evaluation of select variables’ impacts on an outcome at a subsequent time. Results: MDM found a relatively high reduction likelihood for non-Hispanic African Americans and respondents with relatively more education. MDM found a relatively low reduction likelihood accompanying an alcohol-related arrest, higher income, and greater number of close friends. Change to nondrinking was found more likely for non-Hispanic African Americans, other non-Hispanic participants having minority ethnicity, older respondents, those with more occupational skills, and healthier respondents. Such change became less likely with an alcohol-related arrest, higher income, relatively more education, greater number of close friends, close friends’ disapproval of drinking, and co-occurring drug use. Conclusions: Interventions incorporating a motivational-interviewing style can effectively promote health awareness, assessment of co-occurring disorders, friendships with nondrinkers, and attainment of occupational skills.
An Infrared and Visible Image Fusion Algorithm Method Based on a Dual Bilateral Least Squares Hybrid Filter
Authors: Lu Quan , Han Zhuangding , Hu Likun , Tian Feiyu
Published: May 2023

Infrared and visible images of the same scene are fused to produce a fused image with richer information. However, most current image-fusion algorithms suffer from insufficient edge information retention, weak feature representation, and poor contrast, halos, and artifacts, and can only be applied to a single scene. To address these issues, we propose a novel infrared and visual image fusion algorithm based on a bilateral–least-squares hybrid filter (DBLSF) with the least-squares and bilateral filter hybrid model (BLF-LS). The proposed algorithm utilizes the residual network ResNet50 and the adaptive fusion strategy of the structure tensor to fuse the base and detail layers of the filter decomposition, respectively. Experiments on 32 sets of images from the TNO image-fusion dataset show that, although our fusion algorithm sacrifices overall time efficiency, the Combination 1 approach can better preserve image edge information and image integrity; reduce the loss of source image features; suppress artifacts and halos; and compare favorably with other algorithms in terms of structural similarity, feature similarity, multiscale structural similarity, root mean square error, peak signal-to-noise ratio, and correlation coefficient by at least 2.71%, 1.86%, 0.09%, 0.46%, 0.24%, and 0.07%; and the proposed Combination 2 can effectively improve the contrast and edge features of the fused image and enrich the image detail information, with an average improvement of 37.42%, 26.40%, and 26.60% in the three metrics of average gradient, edge intensity, and spatial frequency compared with other algorithms.
Mechanism Analysis of Multiple Disturbance Factors and Study of Suppression Strategies of DFIG Grid-Side Converters Caused by Sub-Synchronous Oscillation
Authors: Sun Dong-Yang , Qian Zi-Jie , Shen Wen-Qiang , Zhou Kai , Jin Ning-Zhi , Chen Qing-Guo
Published: May 2023

With the increasing utilization of electronic equipment in the power system, sub-synchronous oscillation (SSO) has occurred many times and caused off-grid accidents because of power oscillation. SSO has become one of the main problems that restrict the development of new energy. In this paper, power oscillation in grid-side converters (GSCs) in doubly-fed induction generators (DFIGs) under SSO is studied. Firstly, the influence mechanism of SSO on GSC multipath disturbance is studied. Secondly, the problem of coupling oscillation caused by PLL output errors after coordinate transformation is studied, and the mathematical model of GSC output power considering SSO multipath disturbance is established. By analyzing the oscillation suppression ability of the quasi-resonant controller under variable SSO states, the key influencing factors of SSO for GSC power oscillation suppression strategies are determined. Furthermore, based on the above theoretical analysis and research, an improved PLL is designed to eliminate the influence of its output errors on the disturbance of GSC. At the same time, a DFIG-GSC power oscillation suppression strategy using an adaptive quasi-resonant controller is designed to eliminate the influence of SSO on the multi-path disturbance of GSC. Finally, the effectiveness of the proposed suppression strategy is verified using simulation and experimental results.
An Evaluation of Link Prediction Approaches in Few-Shot Scenarios
Authors: Braken Rebecca , Paulus Alexander , Pomp André , Meisen Tobias
Published: May 2023

Semantic models are utilized to add context information to datasets and make data accessible and understandable in applications such as dataspaces. Since the creation of such models is a time-consuming task that has to be performed by a human expert, different approaches to automate or support this process exist. A recurring problem is the task of link prediction, i.e., the automatic prediction of links between nodes in a graph, in this case semantic models, usually based on machine learning techniques. While, in general, semantic models are trained and evaluated on large reference datasets, these conditions often do not match the domain-specific real-world applications wherein only a small amount of existing data is available (the cold-start problem). In this study, we evaluated the performance of link prediction algorithms when datasets of a smaller size were used for training (few-shot scenarios). Based on the reported performance evaluation, we first selected algorithms for link prediction and then evaluated the performance of the selected subset using multiple reduced datasets. The results showed that two of the three selected algorithms were suitable for the task of link prediction in few-shot scenarios.
MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection
Authors: Zeng Fanrui , Sun Yingjie , Li Yizhou
Published: May 2023

Social media bots pose potential threats to the online environment, and the continuously evolving anti-detection technologies require bot detection methods to be more reliable and general. Current detection methods encounter challenges, including limited generalization ability, susceptibility to evasion in traditional feature engineering, and insufficient exploration of user relationships. To tackle these challenges, this paper proposes MRLBot, a social media bot detection framework based on unsupervised representation learning. We design a behavior representation learning model that utilizes Transformer and a CNN encoder–decoder to simultaneously extract global and local features from behavioral information. Furthermore, a network representation learning model is proposed that introduces intra- and outer-community-oriented random walks to learn structural features and community connections from the relationship graph. Finally, the behavioral representation and relationship representation learning models are combined to generate fused representations for bot detection. The experimental results of four publicly available social network datasets demonstrate that the proposed method has certain advantages over state-of-the-art detection methods in this field.
Memory-Efficient Fixed-Length Representation of Synchronous Event Frames for Very-Low-Power Chip Integration
Authors: Schiopu Ionut , Bilcu Radu Ciprian
Published: May 2023

The new event cameras are now widely used in many computer vision applications. Their high raw data bitrate levels require a more efficient fixed-length representation for low-bandwidth transmission from the event sensor to the processing chip. A novel low-complexity lossless compression framework is proposed for encoding the synchronous event frames (EFs) by introducing a novel memory-efficient fixed-length representation suitable for hardware implementation in the very-low-power (VLP) event-processing chip. A first contribution proposes an improved representation of the ternary frames using pixel-group frame partitioning and symbol remapping. Another contribution proposes a novel low-complexity memory-efficient fixed-length representation using multi-level lookup tables (LUTs). Complex experimental analysis is performed using a set of group-size configurations. For very-large group-size configurations, an improved representation is proposed using a mask-LUT structure. The experimental evaluation on a public dataset demonstrates that the proposed fixed-length coding framework provides at least two times the compression ratio relative to the raw EF representation and a close performance compared with variable-length video coding standards and variable-length state-of-the-art image codecs for lossless compression of ternary EFs generated at frequencies bellow one KHz. To our knowledge, the paper is the first to introduce a low-complexity memory-efficient fixed-length representation for lossless compression of synchronous EFs, suitable for integration into a VLP event-processing chip.
Can Deep Generative Models Explain Brain Function in People with Developmental Dyslexia?
Authors: Ogawa Hiroto , Ogoshi Sakiko , Ogoshi Yasuhiro , Nakai Akio
Published: May 2023

Many developmental disorders are diagnosed based on symptoms, which may result in lumping together multiple causes. This is thought to be a factor that complicates the research and treatment of developmental disorders. The purpose of this study is to provide hypotheses on the causes of brain functions in developmental dyslexia (DD) by constructing and analyzing a simple computational model of visual information processing using a deep generative model. We then analyze three symptoms observed in DD and investigate their functions and causes.
Modeling of Electrical Systems for Uninterrupted Operation of Drives in Case of Short-Term Distortions in the Supply Networks
Authors: Pupin Valery , Orlov Victor
Published: May 2023

The article considers the problem of increasing the stability of an electrical system with running synchronous and asynchronous electric motors, which are electrically connected through an intra-factory network and for which the operating modes affect the stability of the entire load. A method is proposed for studying electrical systems with an electric motor load to assess the effect of short-term power supply interruptions (voltage dips) in supply networks on the dynamic stability of synchronous and asynchronous drives. The influence of considering operating synchronous and asynchronous electric motors on transient processes and levels of residual voltages of sections was studied. It was found that to ensure the stability of the drives, to avoid shutdowns in case of various types of short circuits, it is desirable to use an automatic reserve input with a response time of less than 12 ms. A device for the instantaneous activation of a reserve is proposed, providing a response time of 9 ms and a total switching time to a reserve input within 17 ÷ 65 ms. The results of experiments and calculations according to the author’s developed programs are presented, confirming the possibility of the uninterrupted operation of technical electric systems consumers. The requirements for such automation devices are determined.
An Analysis of the Effects of Renewable Energy Intermittency on the 2030 Korean Electricity Market
Authors: Do Insu , Lee Siyoung , Seo Gab-Su , Kim Sungsoo
Published: May 2023

Republic of Korea has unique geographical characteristics similar to those of an island, resulting in an isolated power system. For this reason, securing sufficient operating reserves for the system’s stability and reliability in the face of the intermittency of increasing variable renewable energy (VRE) is paramount, and this will pave the way to achieving the nation’s decarbonization target and carbon neutrality. However, the current reserve-operation method in Republic of Korea does not take into account energy-system conditions, such as the intermittency of the VRE. Therefore, this paper presents an analysis of the impact of changes in reserve-operation methods on the electricity market in the future Republic of Korean power system, with the increased levels of VRE that are currently envisioned. Specifically, three reserve-operation methods, including Korea’s current reserve-power-operation standards, were applied to the two power-system plans announced by the Korean government to analyze the annual generator operation and costs. The analysis results show that securing reserves proportional to the VRE would exert negative effects, such as increased power-generation costs and the curtailment of nuclear and VRE generation. These results can contribute to the estimation of operational reserves needed for high levels of VRE and to the design of new the Korean reserve market, to be introduced in 2025.
Conversion of a Small-Size Passenger Car to Hydrogen Fueling: Evaluating the Risk of Backfire and the Correlation to Fuel System Requirements through 0D/1D Simulation
Authors: Irimescu Adrian , Vaglieco Bianca Maria , Merola Simona Silvia , Zollo Vasco , De Marinis Raffaele
Published: May 2023

Hydrogen is an effective route for achieving zero carbon dioxide emissions, with a contained cost compared to electric powertrains. When considering the conversion of spark ignition (SI) engines to H2 fueling, relatively minor changes are required in terms of added components. This study looks at the possibility of converting a small-size passenger car powered by a turbocharged SI unit. The initial evaluation of range and peak power showed that overall, the concept is feasible and directly comparable to the electric version of the vehicle in terms of powertrain performance. Injection phasing effects and cylinder imbalance were found to be potential issues. Therefore, the present work applied an 0D/1D simulation for investigating the effects of hydrogen fueling with respect to the likelihood of backfire. A range of engine speeds and load settings were scrutinized for evaluating the possibility of achieving the minimal risk of abnormal combustion due to pre-ignition. Ensuring the correct flow was predicted to be essential, especially at high loads and engine speeds. Fuel delivery phasing with respect to valve intake and closing events was also found to be a major factor that influenced not only backfire occurrence but conversion efficiency as well. Interactions with the electronic control unit were also evaluated, and additional requirements compared to standard conversion kits for LPG or CNG fueling were identified.
Renewable Energies and Blue Economy: New Trends in Global Research
Authors: Pires Manso José Ramos , Martínez Vázquez Rosa M. , Milán García Juan , de Pablo Valenciano Jaime
Published: May 2023

In recent years, increasing attention has been paid to the blue economy and its economic and environmental benefits. Renewable energies are an alternative to fossil fuels and can contribute to reducing the carbon footprint of maritime sectors and of the general economy. This article aims to analyze the scientific production in the Web of Science (WoS) database and to propose some measures for future actions. The results indicate new trends in scientific articles related to climate change, aquaculture, blue growth and marine spatial planning, and private administration offering proposals that can be useful for economic progress based on the oceans of the nations that have bet on this model of sustainable development and the need for promoting the green and digital conversion of firms, namely those acting in sea-related activities.