bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of propolis supplementation on cardiovascular risk factors in women with rheumatoid arthritis: A double‐blind, placebo, controlled randomized clinical trial
Authors: Maddahi Mona , Nattagh‐Eshtivani Elyas , Jokar Mohammadhassan , Barati Mehdi , Tabesh Hamed , Safarian Mohammad , Khosravi Maryam 
Published: August 2023

AbstractPropolis has gained popularity in recent years because of its beneficial properties, which make it a possible preventative and therapeutic agent as well as a valuable food and cosmetic ingredient. The objective of this study was to evaluate the effects of propolis supplementation on cardiovascular risk factors in women with rheumatoid arthritis. This randomized, double‐blind, placebo‐controlled clinical trial was performed among 48 patients diagnosed with rheumatoid arthritis. Subjects were randomly assigned to placebo and intervention groups, supplemented with 1000 mg/day of propolis for 12 weeks. Cardiovascular risk factors including, high‐sensitivity C‐reactive protein (hs‐CRP), monocyte chemoattractant protein (MCP‐1), Nitric oxide, blood pressure, and lipid profile were assessed pre‐and post‐intervention. The atherogenic index of plasma value, as well as total cholesterol/high‐density lipoprotein cholesterol (HDL‐C), triglyceride/HDL‐C, and non‐HDL‐C/HDL‐C ratios, were significantly reduced in the intervention group, compared with the placebo group post‐intervention (p < 0.05). Moreover, there was a significant reduction in the serum level of hs‐CRP in the intervention group when compared with the placebo group (p = 0.001). Furthermore, propolis supplementation could marginally reduce MCP‐1 (p = 0.051). These data indicate that propolis supplementation may be a promising treatment strategy for cardiovascular complications among rheumatoid arthritis patients.
Observer‐based adaptive neural fault‐tolerant control for nonlinear systems with prescribed performance and input dead‐zone
Authors: Wang Haihan , Zong Guangdeng , Yang Dong , Xia Jianwei 
Published: August 2023

AbstractThis article investigates the adaptive fault‐tolerant tracking control problem for nonlinear systems with prescribed performance and input dead‐zone. First, a new composite variable is constructed by using the characteristics of actuator fault and input dead‐zone for the modeling purpose. Second, an adaptive neural network observer is designed to estimate the system states in the presence of inaccurate feedback information. Third, the proposed control strategy effectively counteracts the effects of sensor failure and unknown nonlinear functions, which makes the tracking error confined within the performance boundary and all the signals of the closed‐loop system semi‐globally uniformly ultimately bounded. Finally, an application oriented example is provided to demonstrate the effectiveness of the proposed control algorithm.
Automated visitor and wildlife monitoring with camera traps and machine learning
Authors: Mitterwallner Veronika , Peters Anne , Edelhoff Hendrik , Mathes Gregor , Nguyen Hien , Peters Wibke , Heurich Marco , Steinbauer Manuel J. 
Published: August 2023

AbstractAs human activities in natural areas increase, understanding human–wildlife interactions is crucial. Big data approaches, like large‐scale camera trap studies, are becoming more relevant for studying these interactions. In addition, open‐source object detection models are rapidly improving and have great potential to enhance the image processing of camera trap data from human and wildlife activities. In this study, we evaluate the performance of the open‐source object detection model MegaDetector in cross‐regional monitoring using camera traps. The performance at detecting and counting humans, animals and vehicles is evaluated by comparing the detection results with manual classifications of more than 300 000 camera trap images from three study regions. Moreover, we investigate structural patterns of misclassification and evaluate the results of the detection model for typical temporal analyses conducted in ecological research. Overall, the accuracy of the detection model was very high with 96.0% accuracy for animals, 93.8% for persons and 99.3% for vehicles. Results reveal systematic patterns in misclassifications that can be automatically identified and removed. In addition, we show that the detection model can be readily used to count people and animals on images with underestimating persons by −0.05, vehicles by −0.01 and animals by −0.01 counts per image. Most importantly, the temporal pattern in a long‐term time series of manually classified human and wildlife activities was highly correlated with classification results of the detection model (Pearson's r = 0.996, p < 0.001) and diurnal kernel densities of activities were almost equivalent for manual and automated classification. The results thus prove the overall applicability of the detection model in the image classification process of cross‐regional camera trap studies without further manual intervention. Besides the great acceleration in processing speed, the model is also suitable for long‐term monitoring and allows reproducibility in scientific studies while complying with privacy regulations.
14‐3: Drop Resistance Optimization Through Post‐Hoc Analysis of Chemically Strengthened Glass
Authors: Baek Myunghun , An Taeho , Kuk Seungkuk , Park Kyongtae 
Published: August 2023

In the market of the mobile cover glass, the development of chemically strengthened glass is focused on the drop resistance improvement. The cover glass which protects the display panel is a typical brittle material that the micro cracks tends to be occurred underneath a glass surface by physical impacts. The micro cracks tends to be propagated by tensile stress and it is known as a general procedures on glass breakage. In purpose of the cover glass strength improvement, compressive stress is applied to the glass surface using chemical strengthening method by ion exchange. However, since the central area of the glass is relatively subjected to tensile stress, the glass is instantly broken when the propagated cracks are reached on the tensile stress exerted area. The glass specifications have been managed for maintaining the strength using parameters of compressive stress (CS), depth of compression (DOC), and central tension (CT). However, there was no method to judge the most effective factor for impact drop resistance. In order to solve this problem, we developed a machine learning program for cover glass that can predict the breakage height based on stress and mapped evaluation data based on the measured stress profile data and the actual drop breakage simulated evaluation. Especially, eXplainable AI (XAI) method is used to identify the effective factors on drop breakage height. And the test and measurement data were applied to various chemically strengthened glass for analysis.In this study, it was possible to predict the drop breakage height only by measuring the stress profile of chemically strengthened glass. The feature value of chemically strengthened that has the most effect on the drop breakage resistance on rough surfaces was identified to be stress value at 30um in the case of alkali aluminosilicate glass. And it was proved that the drop breakage height was improved by 25% only by increasing the compressive stress at 30um depth.
20‐4: An AI‐Driven Aquarium Guide System for Intelligent Museum
Authors: Liu Te-Chih , Lin Yu-Hsin , Cheng Ting-Hsun , Chao Yu-Ju , Chen Jian-Lung 
Published: August 2023

Visitors to aquariums often struggle to access information and differentiate between similar marine species. To address this issue, ITRI has developed an AI‐driven Aquarium Guide System that uses real‐time face tracking and recognition technology, along with fish identification and gesture recognition. When visitors stand in front of a fish tank, the system detects their gaze and facial features, and identifies the species they are looking at. The backend database then displays the fish's name and provides relevant information to visitors.This system has a 98% accuracy rate in recognizing marine life and can adjust the information based on visitor gestures with an accuracy error of less than 1.0 mm. It also uses virtual and augmented reality technology, paving the way for real‐time, immersive mapping technology.The AI‐driven Aquarium Guide System improves the visitor experience at tourist attractions by offering accurate and interactive information about marine life. This increases visitor understanding and appreciation of marine creatures while providing an engaging and enjoyable experience. We are thrilled to announce that this system has won the 2023 CES Innovation Award, recognizing its excellence and innovation. It has already been installed at the National Museum of Marine Science & Technology (NMMST), transforming the way visitors experience and learn about marine life.
84‐3: A Novel 2D Diffractive Liquid Crystal Smart Window using Single‐sided Engineered Electrode Structure
Authors: Huh Jae-Won , Yoon Hong 
Published: August 2023

Smart window technology has been studied actively because it can simply control optical property of window by external power. Among various smart window technology, a phase grating liquid crystal (LC) device shows outstanding properties such as clear off state, high haze in on state, and no viewing angle dependence. Although it can have excellent optical performance, an LC phase grating device requires complicated pattern electrode structure on both top and bottom substrate. To make the device in cost effectiveness, it is required that simple and cost saving design of LC cell is essential.In this presentation, we will introduce our recent progress in LC phase grating smart window. Based on the optimal LC cell design, a trade‐off between performance and cost was minimized while maintaining the advantages of previously researched phase grating LC smart window.
P‐83: Optimizing Water‐proofing Algorithm with Machine Learning for Wearable OLED In‐Cell Touch Panel
Authors: Huang Jun-Jie , Lin Yi-Ying , Lee Chin-Lin , Lai Chih-Chang , Chou Chun-Yi , Lin Ching-Chun 
Published: August 2023

In this paper, a wearable OLED in‐cell self‐capacitive sensor using machine learning algorithm for water‐proofing is proposed.This paper also shows the experiment and the test result. The result shows that the success rate of water‐proofing on bezel can be largely improved (from 30.7% to 77.1%) by machine learning method on the wearable device with Novatek TDDI NT38350.
Generalized machine learning based on multi‐omics data to profile the effect of ferroptosis pathway on prognosis and immunotherapy response in patients with bladder cancer
Authors: Liu Xinyu , Qiu Ziran , Zhang Xiongfeng , Su Zhouhua , Yi Renzheng , Zou Debo , Xie Chaoqun , Jin Na , Long Weibing , Liu Xiaobing 
Published: August 2023

AbstractIntroductionBladder cancer (BLCA) affects millions of people worldwide, with high rates of incidence and mortality. Ferroptosis proves to be a novel form of cell death process that is triggered by oxidative stress.MethodsWe procured a total of 25 single nuclear RNA‐seq (snRNA‐seq) samples from GSE169379 in GEO database. We obtained different cohorts of BLCA patients from the TCGA and GEO databases for model training and validation. A total of 369 ferroptosis‐related genes (FRGs) were selected from the FerrDb database. AUCell analysis was performed to assign ferroptosis scores to all the cell types. Weighted Gene Co‐Expression Network Analysis (WGCNA), COX, and LASSO regression analysis were conducted to retain and finalize the genes of prognostic values. Various bioinformatic approaches were utilized to depict immune infiltration profile. We conducted a series of colony formation analysis, flow cytometry and western blot (WB) analysis to determine the role of SKAP1 in BLCA.ResultsWe divided the cells into high ferroptosis group and low ferroptosis group according to ferroptosis activity score, and then screened 2150 genes most associated with ferroptosis by differential expression analysis, which are related to UV‐induced DNA damage, male hormone response, fatty acid metabolism and hypoxia. Subsequently, WGCNA algorithm further screened 741 ferroptosis related genes from the 2150 genes for the construction of prognostic model. Lasso‐Cox regression analysis was used to construct the prognostic model, and the prognostic model consisting of 6 genes was obtained, namely JUN, SYT1, MAP3K8, GALNT14, TCIRG1, and SKAP1. Next, we constructed a nomogram model that integrated clinical factors to improving the accuracy. In addition, we performed drug sensitivity analyses in different subgroups and found that Staurosporine, Rapamycin, Gemcitabine, and BI‐2536 may be candidates for the drugs treatment in high‐risk populations. The ESTIMATE results showed higher stromal scores, immune scores, and ESTIMATE scores in the low‐risk group, indicating a higher overall immunity level and immunogenicity of tumor microenvironment (TME) in this group, and tumor immune dysfunction and exclusion (TIDE) analysis confirmed a better response to immunotherapy in the low‐risk group. Finally, we selected the oncogene SKAP1 in the prognostic gene for in vitro validation, and found that SKAP1 directly regulated BLCA cell proliferation and apoptosis.ConclusionWe identified a set of six genes, JUN, SYT1, MAP3K8, GALNT14, TCIRG1, and SKAP1, that exhibited significant potential in stratification of BLCA patients with varying prognosis. In addition, we uncovered the direct regulatory effect of SKAP1 on BLCA cell proliferation and apoptosis, shedding some light on the role of FRGs in pathogenesis of BLCA.
Holistic Analytics of Sensor Data from Renewable Energy Sources: A Vision Paper
Authors: Jensen Søren Kejser , Thomsen Christian 
Published: August 2023

AbstractModern Renewable Energy System (RES) installations, e.g., wind turbines, produce petabytes of high-frequency time series. State-of-the-art systems cannot cope with such amounts of data. Thus, practitioners generally store simple aggregates, e.g., 10-min averages. Based on discussions with practitioners, we present requirements and our vision for a next-generation time series management system that can efficiently manage vast amounts of time series across edge, cloud, and client.
A Monte Carlo permutation procedure for testing variance components in generalized linear regression models
Authors: El-Horbaty Yahia S. 
Published: August 2023

AbstractTesting zero variance components is of utmost importance in various applications empowered by the use of mixed-effects models. Focusing on generalized linear models, this article proposes a permutation test using an analogue of the ANOVA test statistic that merely requires fitting the null model with independent observations. Monte Carlo simulations reveal that the new test has correct Type-I error rate and that its power compares favorably to an existing bootstrap score test. A real data application illustrates the advantageous capability of the proposed test in detecting the need for random effects.
Performance of Lung-RADS in different target populations: a systematic review and meta-analysis
Authors: Mao Yifei , Cai Jiali , Heuvelmans Marjolein A. , Vliegenthart Rozemarijn , Groen Harry J. M. , Oudkerk Matthijs , Vonder Marleen , Dorrius Monique D. , de Bock Geertruida H. 
Published: August 2023

Abstract
Objectives
Multiple lung cancer screening studies reported the performance of Lung CT Screening Reporting and Data System (Lung-RADS), but none systematically evaluated its performance across different populations. This systematic review and meta-analysis aimed to evaluate the performance of Lung-RADS (versions 1.0 and 1.1) for detecting lung cancer in different populations.

Methods
We performed literature searches in PubMed, Web of Science, Cochrane Library, and Embase databases on October 21, 2022, for studies that evaluated the accuracy of Lung-RADS in lung cancer screening. A bivariate random-effects model was used to estimate pooled sensitivity and specificity, and heterogeneity was explored in stratified and meta-regression analyses.

Results
A total of 31 studies with 104,224 participants were included. For version 1.0 (27 studies, 95,413 individuals), pooled sensitivity was 0.96 (95% confidence interval [CI]: 0.90–0.99) and pooled specificity was 0.90 (95% CI: 0.87–0.92). Studies in high-risk populations showed higher sensitivity (0.98 [95% CI: 0.92–0.99] vs. 0.84 [95% CI: 0.50–0.96]) and lower specificity (0.87 [95% CI: 0.85–0.88] vs. 0.95 (95% CI: 0.92–0.97]) than studies in general populations. Non-Asian studies tended toward higher sensitivity (0.97 [95% CI: 0.91–0.99] vs. 0.91 [95% CI: 0.67–0.98]) and lower specificity (0.88 [95% CI: 0.85–0.90] vs. 0.93 [95% CI: 0.88–0.96]) than Asian studies. For version 1.1 (4 studies, 8811 individuals), pooled sensitivity was 0.91 (95% CI: 0.83–0.96) and specificity was 0.81 (95% CI: 0.67–0.90).

Conclusion
Among studies using Lung-RADS version 1.0, considerable heterogeneity in sensitivity and specificity was noted, explained by population type (high risk vs. general), population area (Asia vs. non-Asia), and cancer prevalence.

Clinical relevance statement
Meta-regression of lung cancer screening studies using Lung-RADS version 1.0 showed considerable heterogeneity in sensitivity and specificity, explained by the different target populations, including high-risk versus general populations, Asian versus non-Asian populations, and populations with different lung cancer prevalence.

Key Points
• High-risk population studies showed higher sensitivity and lower specificity compared with studies performed in general populations by using Lung-RADS version 1.0.
• In non-Asian studies, the diagnostic performance of Lung-RADS version 1.0 tended to be better than in Asian studies.
• There are limited studies on the performance of Lung-RADS version 1.1, and evidence is lacking for Asian populations.
An algorithm for cross-fiber separation in yarn hairiness image processing
Authors: Deng Zhongmin , Yu Ling , Wang Lijing , Ke Wei 
Published: August 2023

AbstractYarn hairiness affects yarn and fabric quality. The existing hairiness detection methods cannot discriminate crossover fibers or hairs. In order to accurately separate and detect crossover fibers, an algorithm of separating crossover hairs is proposed. By obtaining refined hair skeletons after image pretreatment, the positions of fiber intersection point were determined, and a hair information table according to the characteristics of the hair cross-point was constructed. After classifying each hair branch skeleton and screening out the hair common skeleton, the branch hair matching table by using the two end points of the true common hair skeleton adjacent to the hair branch was constructed. Through pairing the same cross-hair branch with the principle of the closest slope at the adjacent cross-end, each complete cross-hair skeleton was defined for hair count in a field of view. The detection results show that compared with the existing photoelectric hairiness detection instrument, the algorithm can realize the crossover hair separation, and calculate the length of complete crossover hairs and curved hairs with high accuracy. On average, the developed algorithm measures hair length about 11.1% longer than the manually measured results, while commercial apparatus would report hair length 62.5% shorter than the actual hair length.
Maternal diet quality and associations with plasma lipid profiles and pregnancy-related cardiometabolic health
Authors: van der Pligt Paige F. , Kuswara Konsita , McNaughton Sarah A. , Abbott Gavin , Islam Sheikh Mohammed Shariful , Huynh Kevin , Meikle Peter J. , Mousa Aya , Ellery Stacey J. 
Published: August 2023

Abstract
Purpose
To assess the relationship of early pregnancy maternal diet quality (DQ) with maternal plasma lipids and indicators of cardiometabolic health, including blood pressure (BP), gestational diabetes mellitus (GDM) and gestational weight gain (GWG).

Methods
Women (n = 215) aged 18–40 years with singleton pregnancies were recruited at 10–20 weeks gestation. Diet quality was assessed by the Dietary Guideline Index, calculated at early ([mean ± SD]) (15 ± 3 weeks) and late (35 ± 2 weeks) pregnancy. Lipidomic analysis was performed, and 698 species across 37 lipid classes were measured from plasma blood samples collected at early (15 ± 3 weeks) and mid (27 ± 3 weeks)-pregnancy. Clinical measures (BP, GDM diagnosis, weight) and blood samples were collected across pregnancy. Multiple linear and logistic regression models assessed associations of early pregnancy DQ with plasma lipids at early and mid-pregnancy, BP at three antenatal visits, GDM diagnosis and total GWG.

Results
Maternal DQ scores ([mean ± SD]) decreased significantly from early (70.7 ± 11.4) to late pregnancy (66.5 ± 12.6) (p < 0.0005). At a false discovery rate of 0.2, early pregnancy DQ was significantly associated with 13 plasma lipids at mid-pregnancy, including negative associations with six triglycerides (TGs); TG(54:0)[NL-18:0] (neutral loss), TG(50:1)[NL-14:0], TG(48:0)[NL-18:0], TG(52:1)[NL-18:0], TG(54:1)[NL-18:1], TG(50:0)[NL-18:0]. No statistically significant associations were found between early pregnancy DQ and BP, GDM or GWG.

Conclusion
Maternal diet did not adhere to Australian Dietary Guidelines. Diet quality was inversely associated with multiple plasma TGs. This study provides novel insights into the relationship between DQ, lipid biomarkers and cardiometabolic health during pregnancy.
Are there associations between hip geometry and bone quality? An analysis on 3074 adults from a general population
Authors: Fischer Cornelius Sebastian , Ittermann Till , Hannemann Anke , Schmidt Carsten Oliver , Mederake Moritz , Schüll Daniel , Histing Tina , Lange Jörn , Haralambiev Lyubomir 
Published: August 2023

Abstract
Introduction
Patients with reduced bone mineral density and altered hip geometry are susceptible for hip pathologies. Knowledge on associations between bone properties and hip geometric parameters might facilitate identification of patients at risk for hip pathologies. The aim of the present study was to identify associations of bone properties assessed by quantitative ultrasound (QUS) at the heel and hip geometric parameters like center-edge angle (CE), neck-shaft angle (NSA) and alpha angle.

Materials and methods
Hip geometric parameters (CE, NSA and alpha angle) of 3074 participants from the population-based Study of Health in Pomerania were assessed on magnetic resonance imaging. QUS was performed on both calcanei providing broadband ultrasound attenuation (BUA), speed of sound (SOS) and stiffness-index. Based on the stiffness-index the individual osteoporotic fracture risk (low, moderate or high) was determined. Associations between QUS-based and hip geometric parameters were calculated in linear regression models adjusted for age, sex, body height and weight. Interactions of QUS markers with age and sex on hip geometric parameters were tested.

Results
Significant inverse associations between BUA (β = − 0.068), SOS (β = − 0.024) as well as stiffness-index (β = − 0.056) and CE were present, while fracture risk was positively associated with CE (β for high = 1.28 and moderate = 2.54 vs. low fracture risk). Interactions between BUA and sex as well as between SOS and age were detected in the models for CE. Furthermore, there was an inverse relation between fracture risk and NSA that was restricted to the moderate risk (β for moderate vs. low fracture risk = − 0.60). There were no significant associations between QUS parameters and alpha angle.

Conclusions
In the general population, several associations between QUS-based bone properties or fracture risk and hip geometry are present. Less dysplastic hips had a lower stiffness-index and a higher fracture risk, whereas more valgus hips had a lower fracture risk.