bluuebunny/crossref_metadata_2025_split huggingface dataset:
VR Scene Detail Enhancement Method Based on Depth Reinforcement Learning Algorithm
Authors: Feng Changbao , Tong Xin , Zhu Meili , Qu Feng 
Published: June 2024

AbstractIn virtual reality, due to factors such as light sources and surface materials of objects, the details of the scene exhibit extremely complex changes, making it difficult to capture environmental modeling relationships and reducing the quality of scene details. Therefore, a VR scene detail enhancement method based on deep reinforcement learning algorithm is proposed. Using the Total Variation (TV) denoising algorithm to decompose the VR scene, the VR scene is divided into two parts: noisy and non-noisy, to complete the noise removal. Based on the denoised VR scene, a Hessian matrix is constructed to extract VR scene features using the SURF algorithm. Using deep reinforcement learning algorithms to train and process the extracted VR visual features, introducing meta-learning within the gradient descent method, updating the parameters of the deep reinforcement learning Expose framework, and accelerating the training speed of the deep reinforcement learning Expose framework. By designing L1 loss, structural similarity loss, content perception loss, and sharpness loss functions, the enhancement effect of VR visual details can be improved. The experimental results show that the proposed method enhances the gray-scale values and distribution of VR scene detail images to be higher and more uniform. When the magnification is different, the studied method can effectively enhance the signal-to-noise ratio of VR scenes. Therefore, it indicates that the VR scene detail enhancement effect of the method proposed in this article is good.
Analysis of the ion collection model in the ExB probe
Authors: Yamauchi Toyofumi , Rovey Joshua 
Published: June 2024

AbstractAn ExB probe is a pass-band velocity filter for an ion beam. The ExB probe measurement is related to the ion velocity distribution function (IVDF); however, the finite pass-band filter window size leads to differences in the true IVDF and measured ExB probe spectrum. We derive for the first time an analytical ExB probe transmittancy matrix and use it to examine differences in the IVDF and the probe spectrum. The probe spectra are compared to a synthetically defined test IVDF to study how probe geometry and ion species affect the differences. It is found that the difference in the probe spectrum and the true IVDF is dependent on the ion species, and the deviation is larger for heavier ions. The peak velocity of the spectrum was shifted by up to 13%, and the velocity spread was broadened by up to 276%. The relative ion species fraction is calculated from the probe spectra by two different methods and compared to the true fraction. Using the approach of this work, direct integration of the spectrum resulted in a 2% difference, while a more common approach from literature overestimated one ion species by 184%.
Analyzing the urban–rural divide: the role of location, time, and breach characteristics in U.S. hospital security incidents, 2012–2021
Authors: Munoz-Cornejo Gilbert , Sakowski Julie , Lee Joonghee , Parks Ashley 
Published: June 2024

AbstractThis analysis describes how cybersecurity breaches vary between urban and rural hospitals, how they differ over time, and how they differ across different types and locations of breaches within hospitals. Utilizing data from the U.S. Department of Health and Human Services Office for Civil Rights on incidents from 2012 to 2021 that affected 500 or more individuals, we studied breaches in 237 community hospitals, distinguishing between 185 urban and 52 rural facilities. Using Poisson and Quasi-Poisson regression models, we found a significant yearly increase in data breaches, especially in urban hospitals. Hacking/IT incidents were the most common breaches, particularly prevalent in urban settings, while unauthorized access/disclosure occurred frequently. Email and network servers were the primary locations of breaches in urban and rural hospitals. These findings indicate differences between urban and rural hospitals in data security incidents, suggesting areas for further research.
Machine learning optimization for hybrid electric vehicle charging in renewable microgrids
Authors: Hassan Marwa 
Published: June 2024

AbstractRenewable microgrids enhance security, reliability, and power quality in power systems by integrating solar and wind sources, reducing greenhouse gas emissions. This paper proposes a machine learning approach, leveraging Gaussian Process (GP) and Krill Herd Algorithm (KHA), for energy management in renewable microgrids with a reconfigurable structure based on remote switching of tie and sectionalizing. The method utilizes Gaussian Process (GP) for modeling hybrid electric vehicle (HEV) charging demand. To counteract HEV charging effects, two scenarios are explored: coordinated and intelligent charging. A novel optimization method inspired by the Krill Herd Algorithm (KHA) is introduced for the complex problem, along with a self-adaptive modification to tailor solutions to specific situations. Simulation on an IEEE microgrid demonstrates efficiency in both scenarios. The predictive model yields a remarkably low Mean Absolute Percentage Error (MAPE) of 1.02381 for total HEV charging demand. Results also reveal a reduction in microgrid operation cost in the intelligent charging scenario compared to coordinated charging.
Immunohistochemical-properties of the dermal embryonic telocytes
Authors: Soliman Soha A. 
Published: June 2024

AbstractThe current investigation aims to study the embryonic dermis formed in the early stages of development and identify the initial interstitial components of the dermis that serve as biological and structural scaffolds for the development of the dermal tissue. To investigate the dermal structure, the current study used morphological and immunological techniques. TCs identified by TEM. They had a cell body and unique podomeres and podoms. They formed a 3D network spread throughout the dermis. Homocellular contact established between them, as well as heterocellular contacts with other cells. Immunohistochemical techniques using specific markers for TCss CD34, CD117, and VEGF confirmed TC identification. TCs represent the major interstitial component in the dermal tissue. They established a 3D network, enclosing other cells and structures. Expression of VEGF by TC promotes angiogenesis. TCs establish cellular contact with sprouting endothelial cells. At the site of cell junction with TCs, cytoskeletal filaments identified and observed to form the pseudopodium core that projects from endothelial cells. TCs had proteolytic properties that expressed MMP-9, CD68, and CD21. Proteolytic activity aids in the removal of components of the extracellular matrix and the phagocytosis of degraded remnants to create spaces to facilitate the development of new dermal structures. In conclusion, TCs organized the scaffold for the development of future dermal structures, including fibrous components and skin appendages. Studying dermal TCs would be interested in the possibility of developing therapeutic strategies for treating different skin disorders and diseases.
Effective recognition design in 8-ball billiards vision systems for training purposes based on Xception network modified by improved Chaos African Vulture Optimizer
Authors: Pan WenKai , Zhu Dong , Wang Jutao , Zhu Haiyan 
Published: June 2024

AbstractThis research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries.
The influence of intraoral cryotherapy on postoperative pain and substance P in symptomatic apical periodontitis: randomized clinical study
Authors: Hamza Esraa Mohammed , Abd El Aziz Tarek Mustafa , Obeid Maram Farouk 
Published: June 2024

AbstractCryotherapy is widely utilized in medicine, particularly for pain management. This randomized clinical trial aimed to assess the effect of intraoral cold pack application (cryotherapy) on postoperative pain (POP) and the level of Substance P (SP) in patients with symptomatic apical periodontitis (SAP). Enrolled patients were randomly assigned to either cryotherapy or control group. After adequate anesthesia, access cavity, and biomechanical preparation of the root canal system were completed, the first apical fluid (AF) sample (S1) was obtained. A custom-made intraoral ice-gel pack was applied for 30 min in the cryotherapy group, while no intervention was performed in the control group. The second AF sample (S2) was collected 30 min later in both groups. Patients were asked to complete the Visual Analogue Scale (VAS) questionnaire to assess their POP. Quantification of SP in AF samples was performed using the enzyme-linked immunosorbent assay (ELISA) test. Data were analyzed statistically, revealing a significant reduction in POP and SP levels in the cryotherapy group compared to the control group (P ≤ 0.05). Furthermore, a moderate positive correlation was observed between SP levels and POP (P ≤ 0.05). In conclusion, intraoral cryotherapy represents a simple and cost-effective option for controlling POP and reducing inflammation levels in patients with SAP.
Prediction of post-delivery hemoglobin levels with machine learning algorithms
Authors: Aghajanian Sepehr , Jafarabady Kyana , Abbasi Mohammad , Mohammadifard Fateme , Bakhshali Bakhtiari Mina , Shokouhi Nasim , Saleh Gargari Soraya , Bakhtiyari Mahmood 
Published: June 2024

AbstractPredicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care centers on patient delivery were incorporated into the current study. Patients with preexisting coagulopathy, traumatic cases, and allogenic blood transfusion were excluded from all analyses. The association of pre-delivery variables with 24-h post-delivery hemoglobin level was evaluated using feature selection with Elastic Net regression and Random Forest algorithms. A suite of ML algorithms was employed to predict post-delivery Hb levels. Out of 2051 pregnant women, 1974 were included in the final analysis. After data pre-processing and redundant variable removal, the top predictors selected via feature selection for predicting post-delivery Hb were parity (B: 0.09 [0.05–0.12]), gestational age, pre-delivery hemoglobin (B:0.83 [0.80–0.85]) and fibrinogen levels (B:0.01 [0.01–0.01]), and pre-labor platelet count (B*1000: 0.77 [0.30–1.23]). Among the trained algorithms, artificial neural network provided the most accurate model (Root mean squared error: 0.62), which was subsequently deployed as a web-based calculator: https://predictivecalculators.shinyapps.io/ANN-HB. The current study shows that ML models could be utilized as accurate predictors of indirect measures of PPH and can be readily incorporated into healthcare systems. Further studies with heterogenous population-based samples may further improve the generalizability of these models.
Comparison of the diagnostic accuracy of the Pluslife Mini Dock RHAM technology with Abbott ID Now and Cepheid GenXpert: A retrospective evaluation study
Authors: Herrmann Laura , Breuer Juliana , Duc Tuan Ngo , Thomé Nicole , Ghazaani Fatemeh , Kamhieh-Milz Sundrela , Kamhieh-Milz Julian , Pfützner Andreas 
Published: June 2024

AbstractRapid and sensitive detection of pathogens is critical in interrupting the transmission chain of infectious diseases. Currently, real-time (RT-)PCR represents the gold standard for the detection of SARS-CoV-2. RNase HII-assisted amplification (RHAM) is a promising technology, enabling reliable point-of-care (PoC) testing; however, its diagnostic accuracy has not yet been investigated. The present study compared the Pluslife Mini Dock (RHAM technology), with Abbott ID Now and Cepheid GeneXpert IV. The positive percent agreement (PPA) and negative percent agreement (NPA) were determined in 100 SARS-CoV-2 positive and 210 SARS-CoV-2 negative samples. Further, the reliability of the Pluslife Mini Dock was investigated in different SARS-CoV-2 variants (Delta and Omicron subvariants). The PPA was 99.00% for Pluslife, 100.00% for Abbott ID Now, and 99.00% for Cepheid GeneXpert, with an NPA of 100.00%, 98.90%, and 93.72%, respectively. Abbott ID Now demonstrated the highest rate of invalid results. All SARS-CoV-2 analysed variants were detected by the Pluslife device. Altogether, the Pluslife Mini Dock demonstrated a PPA of 99.16% (235/237) for CT < 36 and an NPA of 100.00% (313/313), respectively. In conclusion, the Pluslife Mini Dock demonstrated better analytical performance than Abbott ID Now and Cepheid GeneXpert IV, representing a highly accurate and rapid PoC alternative to RT-PCR.
Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy
Authors: Yao Rucheng , Zheng Bowen , Hu Xueying , Ma Baohua , Zheng Jun , Yao Kecheng 
Published: June 2024

AbstractPatients diagnosed with hepatocellular carcinoma (HCC) often present with multimorbidity, significantly contributing to adverse outcomes, particularly in-hospital mortality. This study aimed to develop a predictive nomogram to assess the impact of comorbidities on in-hospital mortality risk in HCC patients undergoing palliative locoregional therapy. We retrospectively analyzed data from 345 hospitalized HCC patients who underwent palliative locoregional therapy between January 2015 and December 2022. The nomogram was constructed using independent risk factors such as length of stay (LOS), hepatitis B virus (HBV) infection, hypertension, chronic obstructive pulmonary disease (COPD), anemia, thrombocytopenia, liver cirrhosis, hepatic encephalopathy (HE), N stage, and microvascular invasion. The model demonstrated high predictive accuracy with an AUC of 0.908 (95% CI: 0.859–0.956) for the overall dataset, 0.926 (95% CI: 0.883–0.968) for the training set, and 0.862 (95% CI: 0.728–0.994) for the validation set. Calibration curves indicated a strong correlation between predicted and observed outcomes, validated by statistical tests. Decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility in predicting in-hospital mortality. This nomogram offers a practical tool for personalized risk assessment in HCC patients undergoing palliative locoregional therapy, facilitating informed clinical decision-making and improving patient management.
Endothelial dysfunction in retinal vessels of hemodialysis patients compared to healthy controls
Authors: Günthner Roman , Lorenz Georg , Braunisch Matthias Christoph , Angermann Susanne , Matschkal Julia , Hausinger Renate , Kuchler Timon , Glaser Patrizia , Schicktanz Felix , Haller Bernhard , Heemann Uwe , Streese Lukas , Hanssen Henner , Kotliar Konstantin , Schmaderer Christoph 
Published: June 2024

AbstractEndothelial dysfunction is a key factor promoting atherosclerosis and cardiovascular complications. Hemodialysis patients typically show various cardiovascular complications and impaired retinal venular dilation has been described as a risk factor for mortality. Non-invasive retinal vessel analysis provides insight into the microvasculature and endothelial function. Static retinal vessel analysis determines arteriolar and venular vessel diameters and dynamic retinal vessel analysis measures microvascular function by flicker-light induced stimulation, which results in physiological dilation of retinal vessels. We measured 220 healthy individuals and compared them to our preexisting cohort of hemodialysis patients (275 for static and 214 for dynamic analysis). Regarding static vessel diameters, hemodialysis patients and healthy individuals did not significantly differ between vessel diameters. Dynamic retinal vessel analysis showed attenuated dilation of the arteriole of hemodialysis patients with 1.6% vs 2.3% in healthy individuals (p = 0.009). Case–control matching for age (mean 65.4 years) did not relevantly diminish the difference. Hemodialysis patients also exhibited reduced venular dilation after matching for age (3.2% vs 3.8%, p = 0.019). Hemodialysis patients showed microvascular dysfunction compared to healthy individuals when using dynamic retinal vessel analysis. Further studies should focus on dynamic retinal vessel analysis which can add insights into the microvascular function and risk factors in multimorbid patients.
Europe as a secondary distribution hub in the worldwide invasion of the potato cyst nematode Globodera rostochiensis
Authors: Esquibet Magali , Mwangi James M. , Kiewnick Sebastian , Wang Xiaohong , Mimee Benjamin , Handayani Nurul Dwi , Bert Wim , Helder Johannes , Wainer John , Sakata Itaru , Garcia Nathan , Grenier Eric , Montarry Josselin 
Published: June 2024

AbstractThe potato cyst nematode Globodera rostochiensis originates from the Andean Mountain region in South America and has unintentionally been introduced to all inhabited continents. Several studies have examined the population genetic structure of this pest in various countries by using microsatellite markers. However, merging microsatellite data produced from different laboratories is challenging and can introduce uncertainty when interpreting the results. To overcome this challenge and to explore invasion routes of this pest, we have genotyped 22 G. rostochiensis populations from all continents. Within populations, the highest genetic diversity was observed in the South American populations, the European populations showed an intermediate level of genetic diversity and the remaining populations were the less diverse. This confirmed pre-existing knowledge such as a first introduction event from South America to Europe, but the less diverse populations could originate either from South America or from Europe. At the continental scale, STRUCTURE genetic clustering output indicated that North America and Asia have experienced at least two introduction events. Comparing different evolutionary scenarios, the Approximate Bayesian Computation analysis showed that Europe served as a secondary distribution centre for the invasion of G. rostochiensis into all other continents (North America, Africa, Asia and Oceania).
Study of the impact of structural factors and cleaning efficiency in reducing hazardous litter density and its related pollution in urban environment
Authors: Morovati Maryam , Parsa Sheida , Panahande Maryam , Hossaini Motlagh Amin , Parseh Iman 
Published: June 2024

AbstractCigarette filter is the most common hazardous litter that contains many pollutants including PAHs. The durability of this litter in the urban environment has an important effect on the rate of pollutant leakage. In this study, the leakage rate of PAHs from the littered cigarette filters was estimated by considering the affecting parameters on their durability in the urban environment. The results showed that the density of littered cigarette filters in the studied locations was 0.00048–0.13563 g/m2. The maximum spatial variation of the littered cigarette filter was 225 times. The average leakage of the total studied PAHs was estimated to be 2.048 µg/10 m2. The impact of structural factors and efficiency of urban cleaning in the estimated leakage was at most 2.4 times. It is necessary to change the behavior of citizens in littering the cigarette filter, considering its durability in the urban environment, to reduce the environmental and health consequences caused by the leakage of PAHs.
Integrated analysis of single-cell RNA sequencing and bulk RNA data reveals gene regulatory networks and targets in dilated cardiomyopathy
Authors: Zhang Min , Zhang Xin , Niu Jiayin , Hua Cuncun , Liu Pengfei , Zhong Guangzhen 
Published: June 2024

AbstractDilated cardiomyopathy (DCM) is a common cause of heart failure, thromboembolism, arrhythmias, and sudden cardiac death. The quality of life and long-term survival rates of patients with dilated DCM have greatly improved in recent decades. Nevertheless, the clinical prognosis for DCM patients remains unfavorable. The primary driving factors underlying the pathogenesis of DCM remain incompletely understood. The present study aimed to identify driving factors underlying the pathogenesis of DCM from the perspective of gene regulatory networks. Single-cell RNA sequencing data and bulk RNA data were obtained from the Gene Expression Omnibus (GEO) database. Differential gene analysis, single-cell genomics analysis, and functional enrichment analysis were conducted using R software. The construction of Gene Regulatory Networks was performed using Python. We used the pySCENIC method to analyze the single-cell data and identified 401 regulons. Through variance decomposition, we selected 19 regulons that showed significant responsiveness to DCM. Next, we employed the ssGSEA method to assess regulons in two bulk RNA datasets. Significant statistical differences were observed in 9 and 13 regulons in each dataset. By intersecting these differentiated regulons and identifying shared targets that appeared at least twice, we successfully pinpointed three differentially expressed targets across both datasets. In this study, we assessed and identified 19 gene regulatory networks that were responsive to the disease. Furthermore, we validated these networks using two bulk RNA datasets of DCM. The elucidation of dysregulated regulons and targets (CDKN1A, SAT1, ZFP36) enhances the molecular understanding of DCM, aiding in the development of tailored therapies for patients.