bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large eddy simulation on wind-induced interference effects of staggered chamfered square cylinders
Authors: Cao Wenlong , Wang Xueyan , Liu Yubo , Yin Yujie , Yang Jian , An Jianhui 
Published: September 2023

Abstract
Chamfered corners in buildings are the main means to reduce the control effect of wind load on the structure, and the interference effect of chamfered buildings cannot be ignored. At present, only the mutual interference coefficients of square and rectangular section buildings are given in the Chinese code, without the interference effect of chamfered buildings being specified. Therefore, in this paper, aerodynamic force and wind pressure coefficients of chamfered square cylinders of different spacing are obtained by the large eddy simulation method. Wind load characteristics, non-Gaussian characteristics and interference effects of chamfered square cylinders with different arrangements are studied based on aerodynamic coefficients, wind pressure coefficients and interference coefficients. The results show that when the wall y plus value is 1, the large eddy simulation is the most accurate to simulate the wind load and wind field parameters. Besides, the aerodynamic effects, non-Gaussian characteristics and interference effects between the chamfered square cylinders are mainly controlled by the cross-wind interval and the spacing (4.0, 4.0) is the characteristic coordinate. That means, when the spacing is smaller than this co-ordinate, the interference effect of the square cylinder is more obvious. When the spacing coordinate is greater than (4.0, 4.0), the aerodynamic coefficients and non-Gaussian regional distributions of the principal square cylinder and the isolated cylinder are the same, and the interference factor approaches 1.
Resting-state networks and their relationship with MoCA performance in PD patients
Authors: Galvez Victor , Romero-Rebollar César , Estudillo-Guerra María A. , Fernandez-Ruiz Juan 
Published: September 2023

Abstract

Although mild cognitive impairment is a common non-motor symptom experienced by individuals with Parkinson’s Disease, the changes in intrinsic resting-state networks associated with its onset in Parkinson’s remain underexamined. To address the issue, our study sought to examine resting-state network alterations and their association with total performance in the Montreal Cognitive Assessment and its cognitive subdomains in Parkinson’s by means of functional magnetic resonance imaging of 29 Parkinson’s patients with normal cognition, 25 Parkinson’s patients with mild cognitive impairment, and 13 healthy controls. To contrast the Parkinson’s groups with each other and the controls, the images were used to estimate the Z-score coefficient between the regions of interest from the default mode network, the salience network and the central executive network. Our first finding was that default mode and salience network connectivity decreased significantly in Parkinson’s patients regardless of their cognitive status. Additionally, default mode network nodes had a negative and salience network nodes a positive correlation with the global assessment in Parkinson’s with normal cognition; this inverse relationship of both networks to total score was not found in the group with cognitive impairment. Finally, a positive correlation was found between executive scores and anterior and posterior cortical network connectivity and, in the group with cognitive impairment, between language scores and salience network connectivity. Our results suggest that specific resting-state networks of Parkinson’s patients with cognitive impairment differ from those of Parkinson’s patients with normal cognition, supporting the evidence that cognitive impairment in Parkinson’s Disease displays a differentiated neurodegenerative pattern.
An automated fluorescent defect detection system in precision castings by convolutional neural network
Authors: Yu Huipeng , KANG Maodong , Ding Chengyang , Liu Yahui , Wang Jun 
Published: September 2023

Abstract
Fluorescent Penetrant Inspection (FPI) is a widely used Non-Destructive Testing (NDT) method in the aerospace industry to inspect precision casting components. Currently, FPI inspection relies on visual examination, which can lead to challenges in distinguishing between defects and false indications. Moreover, human factors introduce variability in the results, impacting the consistency and reliability of the inspection process. This highlights the desirability of the automation of FPI to increase consistency, reliability and productivity. The deep learning method is gradually replacing the traditional approaches that involve image processing and machine learning classifiers in automated defect detection system. Deep learning method offers automatic feature extraction and high robustness, which contribute to more accurate and efficient defect detection. The use of various convolutional neural networks (CNN) in defect detection for flat superalloy plates processed with FPI and photographed to create digital images was investigated. Among the CNN models, MobileNetV2 exhibited outstanding performance, with a remarkable recall rate of 99.2% and an accuracy of 99.2%. Additionally, the effect of dataset imbalance on model performance was carefully examined. Moreover, the features extracted by the model are visualized using Guided Grad-CAM to reveal the attention of the CNN model to the fluorescent display features. These results underscore the strong capability of deep learning architectures in detect defects in aerospace precision casting components, paving the way for the automation of the entire FPI process.
A Decentralized Smart Healthcare Monitoring System using Deep Federated Learning Technique for IoMT
Authors: V Anbazhagu U. , Tamilarasi K. , Maheswari K. , Ramesh S. , Isaac Samson , Rajaram A. 
Published: September 2023

Abstract
The likelihood of privacy and security problems rises as a result. Additionally, it performs poorly due to a lack of datasets. By utilising a new Federated Learning (FL) model, this research work enhances privacy and decentralises the analysis of medical data. We created a three-tier architecture for our IoMT: In first tier, the medical data is generated by medical sensors. In first, tier we introduced data normalisation technique in order to eliminate the redundant data and uncertain data. This process reduces the complexity of the system. In the second tier, the data is submitted to the decentralised edge servers where the Deep Learning (DL) algorithm is employed. By FL model, each DL is trained with the limited data samples. The DL approach used here is Recurrent Neural Network (RNN) model. The RNN is trained to detect abnormalities in the data received from the first tier. In the final tier, the data is further stored in repositories and enabled to end users. The overall system is analysed with COVID-19 data and proved efficacy in accuracy, precision, recall and f-score.
Nomogram for Predicting Severe Abdominal Pain After Initial Conventional Transarterial Chemoembolisation for Hepatocellular Carcinoma: A Retrospective Study
Authors: Qin Huixia , Xiao Xuhua , Li Jinhai , Ya Houxiang , Liang Fugang , Jin Haiqing , Liao Lianghuan , Li Yaohua , Qin Jiahun , Jue Yu , Jing Gu , Zhou Chunmei , Jin Ming , Miao Ying , Li Shuqun 
Published: September 2023

Abstract
Transarterial chemoembolisation (TACE) is a standard therapy for hepatocellular carcinoma (HCC). However, adverse events, including abdominal pain, are common. This study aimed to investigate and verify the feasibility of a nomogram model to predict severe abdominal pain after first conventional TACE (cTACE) among patients with HCC. Patients with HCC treated with cTACE between October 28, 2019, and August 5, 2022, at a single centre were enrolled (n=216). Patients were divided into training and validation cohorts (ratio, 7:3). A visual analogue scale score between 7 and 10 was considered severe abdominal pain. A total of 127 (58.8%) patients complained of severe abdominal pain after first cTACE treatment. The nomogram considered age and tumour number and size. The nomogram demonstrated good discrimination, with a C-index of 0.749 (95% confidence interval [CI], 0.617, 0.881). Further, the C-index in the validation cohort reached 0.728 (95% CI: 0.592, 0.864). The calibration curves showed ideal agreement between the prediction and real observations, and the nomogram decision curve analysis performed well. The nomogram model can provide an accurate prediction of severe abdominal pain in patients with HCC after first cTACE, aiding in the personalization of pain management and providing novel insights into hospital nursing.
Advancing Rheumatology Practice with AI Assistance: Evaluating ChatGPT's Performance in Real-world Cases
Authors: Parsaei Amirhossein , Torabi Pouya , Naviafar Anahita , Kooshki Ashkan Mohammadi , Masoumi Maryam , Amini Behnam , Azadnajafabad Sina 
Published: September 2023

Abstract

Background
The launch of ChatGPT, an advanced language model created by OpenAI, has sparked attention throughout the world. ChatGPT offers human-like responses and the potential for a wide range of applications, including medical decision-making. However, questions remain regarding its validity, the spread of false information, and its ethical implications for healthcare. While other studies have investigated ChatGPT's diagnostic capabilities, this study fills a research gap by assessing how well it performs in real-world rheumatology case scenarios, offering light on its possible use in managing rheumatologic patients.
Methods
The study encompassed 32 challenging rheumatology cases. Data for each case was divided into four categories: 1) initial presentation, history, and review of systems; 2) physical examinations; 3) workup results; and 4) final diagnosis. Data was transformed into prompts for ChatGPT, simulating real-time interaction. Four stages of questioning were used to progressively build the case. Recommendations were evaluated based on correctness, completeness, and potential harm or cost implications.
Results
The percentage of comprehensive answers (totally correct, totally complete, no extra-costs, no harm) for physical examinations, workups, differential diagnosis, and treatment were 65.6%, 50%, 40,6% and 40,6% respectively. ChatGPT was successful in 65.6% of the cases to suggest the correct diagnosis first in the list of differential diagnoses. None of ChatGPT responses included suggestions that would result in unnecessary costs or harm to the patient. ChatGPT recommendations for physical examinations, workups, differential diagnosis and treatment were totally correct in 75%, 65.63%, 53.13% and 50% of instances; and they were totally complete in 81.25%, 62.50%, 62.50%, 59.38% of instances respectively.
Conclusions
Our study highlights the effectiveness of ChatGPT in supporting rheumatology practice by offering precise and comprehensive suggestions across various stages of patient cases. While the AI model displays potential, its performance is inconsistent when faced with higher levels of scenario complexity.
Evaluation of Serum Zonulin Level and Intestinal Permeability in Patients with Chronic Idiopathic Urticaria and the Relationship between Serum Zonulin Level and Disease Severity
Authors: Ünal Simge , Kartal Selda Pelin , ÖZSOY Ahmet , AYDIN Fevzi Nuri 
Published: September 2023

Abstract

Background
The development of Chronic Spontaneous Urticaria (CSU) and increased intestinal permeability may be related.
Aim
It was aimed to evaluate the role of intestinal permeability in the etiopathogenesis of CSU and the relationship between disease severity and zonulin levels by measuring the zonulin level, which is an indicator of the increase in intestinal permeability, in the serum of CSU and control groups.
Methods
61 CSU patients and 59 healthy controls were included. Demographic characteristics, personal and family history, urticaria activity score, age of onset, duration of last attack, antihistamine dose used, and concomitant diseases of CSU patients were recorded.
Result
Between the patient and control groups, there was a statistically significant difference in zonulin levels (p=0.000). Patients with angioedema had considerably greater zonulin levels than patients without angioedema, and there was a statistically significant difference in zonulin levels depending on whether angioedema was present (p=0.023).
Conslusion
These findings suggest that intestinal permeability may play an important role in the pathogenesis of CSU and angioedema.
Delineation of Intersegmental Plane: Application of Blood Flow Blocking Method in Pulmonary Segmentectomy
Authors: Cai Lei , Wang Changchun , Luo Taobo , Fu Shuang , Liang Jingxiao , Huang Xiancong , Pan Chao , Feng Yiding , Mo Wenkui , Xu Haoting , Chen Qixun , Zeng Jian 
Published: September 2023

Abstract

Background: The Modified Inflation-Deflation Method (MIDM) is widely used in China in pulmonary segmentectomies. We optimized the procedure, which was named as Blood Flow Blocking Method (BFBM), also known as "No-Waiting Segmentectomy”. This method has produced commendable clinical outcomes in segmentectomies. The aim of this research is to confirm whether the intersegmental planes formed by MIDM and BFBM techniques during segmentectomies have high degree of concordance.
Methods: We utilized the Open Sequential Test design in our study. Using both MIDM and BFBM techniques, intersegmental planes were created in the same patient, one after the other. The degree of alignment between the planes formed by the two techniques was assessed by two experienced chief surgeons. Based on the results obtained in each case, a test line was plotted until it intersected the effective or ineffective line.
Results: In every case studied, the intersegmental planes created by the MIDM and BFBM displayed high congruity. The test line crossed the effective line during the 12th case. When comparing the time taken to form the intersegmental plane using either MIDM or BFBM technique, no significant difference was observed. However, the application of the BFBM technique resulted in an average time savings of 13.8 minutes.
Conclusions: In segmentectomies, the intersegmental planes formed by MIDM and BFBM techniques exhibit high concordance. However, given that BFBM affords a time-saving advantage, we propose that BFBM could potentially replace MIDM in performing lung segmentectomies.
Effects of the Mn/Ni Ratio on the Battery Performance of Layered Na-Ni-Mn Oxide Cathode Materials in Sodium-ion Batteries
Authors: Xiao qingmei , Guo Ziting , Huang Jinchao , Zhong Shengwen 
Published: September 2023

Abstract
The development of efficient sodium-ion batteries is essential to overcome the issue of limited lithium sources for preparing lithium-ion batteries. Layered Mn-based cathode materials have significant application potential because of their simple structure and high specific capacities. However, sodium-ion batteries with these cathode materials demonstrate considerable voltage attenuation and phase transition during battery operation. To eliminate these issues, in this study, we investigated the effects of different Mn/Ni ratios in Na-Ni-Mn cathode materials on their structural stability and electrochemical performances. Na0.8MnO2 (NNM-8010), Na0.8Ni0.1Mn0.9O2 (NNM-819), Na0.8Ni0.2Mn0.8O2 (NNM-828), and Na0.8Ni0.3Mn0.7O2 (NNM-837) were synthesized and characterized using X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy, and electrochemical analyses. The addition of Ni+ increased the Mn oxidation state from + 3 to + 4, thus reducing the Jahn–Teller effect of Mn3+ and stabilizing the material structure. NNM-819 exhibited the best electrochemical performance. Its initial discharge specific capacity was 198.5mAh g− 1 at a current density of 0.2C, and the capacity retention rate after 100 cycles was 86.9% at 0.5C. Moreover, its capacity retention rate at 1.0C high-rate cycling after 100 cycles remained high 81.9%.
Machine learning-based approach for sentiment analysis from recorded social media movie reviews
Authors: Jassim Mustafa Abdalrassual , Abd Dhafar Hamed , Omri Mohamed Nazih 
Published: September 2023

Abstract
The goal of Sentiment Analysis (SA), especially in social media, is to identify useful information in large amounts of unstructured data from many different sources. Text mining involves extracting revision texts and categorizing them by positive and negative attitudes. However, the value of the extracted features also lies in those that contribute most to the classification process. That's where dimensional reduction comes into play to remove noise and reduce the high area of the feature while maintaining the required accuracy. As part of this work, we propose a new approach for extracting words from a specific text and then classifying them. Thanks to a phase of pre-processing and extraction of words depending on the frequency. For this purpose, we considered a test group database of 50,000 film reviews, of which 25,000 were rated positive and 25,000 negatives. We have selected 4,000 words that have an impact on the feelings of the documents, To predict the rating which is based on a textual customer review. We evaluated the algorithms we proposed, as part of this approach, and compared the effectiveness of the following machine learning techniques as classifiers for SA: extreme gradient boosting (XGBoost), artificial neurons (ANN), and stochastic gradient descent (SGD). with a vectorial construction by applying the techniques of ”Term Frequency” (TF) and that of the ”Term frequency-Inverse Document Frequency” (TF-IDF). We noticed that our approach based on the SGD algorithm recorded the best performance. Indeed, we recorded an accuracy of 88.093\%, a precision of 89\%, a recall of 86\%, and an F1-score of 88\%.
\textbf{Keywords :} Sentiment analysis; Opinion mining; movie review; classification; Machine learning; XGBoost; Artificial neural network; stochastic gradient descent.
Fuzzy Clustering of CT scans to assess image noise
Authors: Ray Monika , Romano Patrick S. 
Published: September 2023

Abstract
Computed Tomography (CT) scans are the largest source of radiation exposure in diagnostic imaging. Studies show that CT scan radiation doses are relatively high in the United States compared to Japan, Israel, and Switzerland even af- ter adjusting for population effects. The increased use of CT scans in the United States has caused great concern in the public health community. Numerous stud- ies have proposed methods for noise estimation in CT scans to help reduce dose. However, there are few efforts that have leveraged fuzzy logic, which is mod- elled after human reasoning and perception to assess the relationship between CT image quality and dose. We show a new application of fuzzy c-means (FCM) clustering to group images. Image characteristics such as noise, contrast, reso- lution, etc. are identified by the algorithm and used to cluster the images. Our dataset included CT scans from the abdominal and pelvic region of adults, re- sulting in a total of 4,951 images, which vary by radiation dose level, image slice thickness, reconstruction filter and machine specifications. One of the disadvan- tages of FCM is its sensitivity to image noise and artifacts. However, we exploit this disadvantage for our purpose to obtain clusters that would correlate well with image noise as calculated by noise algorithms. Our results showed that FCM was successful at clustering images based on image noise if the difference in radiation dose levels was significant. This implies that intelligent image classi- fication algorithms can detect differences in image noise. Results also indicated that the noise from high dose scans were similar to that from low dose scans, thereby indicating that there is potential to lower doses. It was also evident that the choice of the convolution kernel and slice thickness had a greater effect on image noise than radiation dose. We would like to draw attention of the fuzzy logic community to this area of work that will particularly benefit from percep- tion rules in fuzzy logic. While fuzzy logic has been used in image analyses and medical decision-making, it has not been applied to this objective of lowering radiation dose. We aim to show some of the work performed to achieve this goal, aligning with national initiatives to reduce radiation dose that Americans receive, and identified areas that need further exploration.
COVID-19 Under-reporting: Spillovers and Stringent Containment Strategies of Global Cases*
Authors: Wang Yulu , Kumbhakar Subal 
Published: September 2023

Abstract

Owing to the rapid spread of COVID-19 pandemic, accurately determining the true global infection count has become an exceedingly challenging endeavor. In this context, our study delves into the spatial spillover analysis of COVID-19 cases and assesses the impact of containment policy stringency on these spillovers. Furthermore, we explore the extent of under-reporting of COVID-19 cases at a country-specific level.To account for the diverse spatial dependencies, we employ a semiparametric spatial autoregressive model in which the coefficients are smooth unknown functions of countries' stringency indices. Country-specific under-reporting, represented as a one-sided deterministic function of exogenous variables, is estimated using the sieves method.Our analysis relies on COVID-19 infection data spanning from 2020 to 2021 for 57 countries. We have discovered that spillovers exhibit significant variations at different levels of containment stringency. Moreover, the true number of infections is estimated to be 1.38 to 17.12 times higher than the reported cases. These results align with prior research and bear significant policy implications for improving the precision of COVID-19 reporting and managing spillover effects effectively.
JEL Classification No.: C23, C14, I18.
How to Communicate Robot Motion Intent: A Scoping Review
Authors: Pascher Max , Gruenefeld Uwe , Schneegass Stefan , Gerken Jens 
Published: September 2023

Abstract

Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot's motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.