bluuebunny/crossref_metadata_2025_split huggingface dataset:
Glycemic Dispersion: A New Index for Screening High Glycemic Variability
Authors: Shi R , Feng L , Tang LT , Luo BB , Cao HY , Bi QY , Liu YM 
Published: January 2023

Abstract
Objective: For patients with diabetes, high frequency and high amplitude glycemic variability may be more harmful than continuous hyperglycemia, however, there is still a lack of screening indicators that can quickly and easily assess the level of glycemic variability so far. The aim of this study was to investigate whether the glycemic dispersion index is effective for screening high glycemic variability.
Methods: A total of 170 diabetes patients hospitalized in the Sixth Affiliated Hospital of Kunming Medical University were included in this study. After admission, the fasting plasma glucose, 2-hour postprandial plasma glucose, and glycosylated hemoglobin A1c were measured. The peripheral capillary blood glucose was measured seven times in 24 h, before and after each of three meals and before bedtime. The standard deviation of the seven peripheral blood glucose values was calculated, and a standard deviation of >2.0 was used as the threshold of high glycemic variability. The glycemic dispersion index was calculated and its diagnostic efficacy for high glycemic variability was determined by the Mann–Whitney U test, receiver operating characteristic (ROC) curve and, Pearson correlation analysis.
Results: The glycemic dispersion index of patients with high glycemic variability was significantly higher than that of those with low glycemic variability (p < 0.01). The best cutoff value of the glycemic dispersion index for screening high glycemic variability was 4.21. The area under the curve (AUC) was 0.901 (95% CI: 0.856–0.945) and had a sensitivity of 0.781 and specificity of 0.905. It was correlated with the standard deviation of blood glucose values (r = 0.813, p < 0.01).
Conclusions: The glycemic dispersion index had good sensitivity and specificity for screening high glycemic variability. It was significantly associated with the standard deviation of blood glucose concentration and is simple and easy to calculate. It was an effective screening indicator of high glycemic variability.
Research on the dynamic tracking error compensation method of the linear axis of an ultra-precision lathe based on a piezo nanopositioning stage
Authors: Fu Siyuan , Yang Hong , Cui Kaihua , Sun Shouli , Duan Fang , Jiang Zhong , Zhang Yongbin 
Published: January 2023

Abstract
Single-point diamond turning technology has been widely used in processing microstructures. The accuracy of ultraprecision cutting machine tools affects the performance of the microstructure. By improving the structure of the machine tool itself, the machining accuracy of ultraprecision diamond lathes has almost become optimized. Therefore, this research identifies and analyzes the linear axis tracking error through the dynamic modeling of a macro/micro cutting system. Furthermore, the impact of the tracking error on machining accuracy is obtained. Based on the macro/micro cutting system, a servo tracking error compensation method is proposed, and the effectiveness of this error compensation strategy is verified by simulation. The proposed experimental approach includes cutting experiments of tracking error compensation for a hyperbolic sine wave surface structure, verifying the surface profile accuracy of the workpiece with and without tracking error compensation. Additionally, this study proposes a profile evaluation method for microstructure. Experimental results show that the proposed tracking error compensation strategy effectively reduces the tracking error of ultraprecision cutting machine tools. Additionally, the proposed approach significantly improves the microstructure machining profile accuracy and can be used for ultraprecision lathes with high precision.
Temperature and magnetic field effect on the thermodynamic properties of 2DEG
Authors: Bouzgarrou Sonia , Almutairi Haya Abdullah Hazza 
Published: January 2023

Abstract
A numerical calculation of the temperature and magnetic field dependence of the specific heat capacity, the magnetization, and the chemical potential is carried out. Of particular interest are the properties of the energy of a magnetic field in a two-dimensional electron gas exposed to a magnetic field. Thus, in this paper, we illustrate the effect of temperature on the oscillation dHvA of specific heat capacity and magnetization. As well a mathematical model has been developed for calculating the temperature dependence of the oscillations of the chemical potential and the density of states under the influence of a magnetic field. Using the proposed model, the results were explained at different broadening factors Γ. The calculated results show that specific heat capacity and magnetization increase as the magnetic field increases. Additionally, these increases carry out that the magnetic field is large enough to neglect the mixing of Landau levels caused by the sharp peak of Landeau levels. Moreover, the 2D dHvA effect is characterized by a sawtooth strap at a very low temperature.
Heterogeneous Graph Neural Network with Semantic-aware Differential Privacy Guarantees
Authors: Wei Yuecen , Fu Xingcheng , Yan Dongqi , Sun Qingyun , Peng Hao , Wu Jia , Wang Jinyan , Li Xianxian 
Published: January 2023

Abstract
Most social networks can be modeled as a heterogeneous graph. Recently, advanced graph learning methods exploit the rich node properties and topological relationships for downstream tasks. That means that more private information is embedded in the representation. However, the existing privacy-preserving methods only focus on protecting the single type of node attributes or relationships, which neglect the significance of high-order semantic information. To address this issue, we propose a novel Heterogeneous graph neural network with Semantic-aware Differential privacy Guarantees named HeteSDG, which provides a double privacy guarantee and performance trade-off in terms of both graph features and topology. In particular, we first reveal the privacy leakage in heterogeneous graph and define a membership inference attack with a semantic enhancement (MIS) that will improve the means of member inference attacks by obtaining side background knowledge through semantics. Then we design a two-stage mechanism, which includes the feature attention personalized mechanism and the topology gradient perturbation mechanism, where the privacy-preserving technologies are based on differential privacy. These mechanisms will defend against MIS attacks and provide stronger interpretation, but simultaneously bring in noise for representation learning. To better balance the noise perturbation and learning performance, we utilize a bi-level optimization pattern to allocate a suitable privacy budget for the above two modules. Our experiments on four public benchmarks conduct performance experiments, ablation studies, inference attack verification, etc. The results show the privacy protection capability and generalization of HeteSDG.
Prevalence of Common Mental Disorder And Associated Factors Among Pregnant Women Attending Abebebch Gobena Mother And Child Hospital Addis Ababa Ethiopia in 2022: Cross sectional study
Authors: Mohammed Kasim , Hussein Kedir , Ali Dawed , Andualem Fantahun 
Published: January 2023

Abstract

Introduction:
Common mental disorders (CMDs) are nonpsychotic mental health conditions like depression, anxiety, adjustment, and somatoform disorders that compromise daytoday functioning. Pregnancy and the transition to motherhood involve major physical and psychological changes in the expecting mothers.
Objective
To assess the prevalence of common mental disorders and associated factors among pregnant women in Abebech Gobena Hospital in 2022.
Method
Crosse sectional study was conducted from May to June 2022 at Abebech Gobena hospital. Systematic random sampling method was employed to select 421 study participants. The data were collected using structured questionnaire and face- to -face interview method. Data was entered in to Epi info 7.1 and export to (SPSS) version 20 for analysis. The association between dependent and independent variables was analyzed by using logistic regression. In bi-variable logistic regression (at p value < 0.25) was entered in to multivariable logistic regression. Adjusted odds ratio with 95% confidence interval was computed. Statistical significance was declared at p value of < 0 .05.
Results
From the total of 415 participant 85(20.5%) had common mental disorder. As of this result, participants were unplanned 2.9 times [AOR = 2.9, CI95%( 1.3, 6.7)] developed common mental disorder than planed pregnant. Pregnant women who reported history of chronic medical illness have 2.8 CMD than who didn’t (AOR = 2.8; 95%CI: 1.08—7.1). The odds of having CMD among participants who were lifetime alcohol use was 5.3 times (AOR = 5.304, 95% CI = 3.1, 7.1) than participants who did not use. Participants who had no husband support continuity of pregnant were 1.97 times more develop CMD (AOR = 1.97, 95% CI = 0.96, 4.04) as compared with who had husband support.
Conclusion and Recommendation:
In this study the prevalence of Common Mental Disorder in pregnant women was high. Chronic illness, alcohol use in lifetime, husband support and status of pregnancy were found to be significantly associated with common mental disorder. Therefore all stake holders would involve in providing special psycho-social support during pregnancy than ever at the time of ANC follow up.
Focusing a realist evaluation of peer support for paediatric mental health
Authors: Thompson Dean M. , Bernard Mark , Maxfield Bob , Halsall Tanya , Mathers Jonathan 
Published: January 2023

Abstract
Objective
Mental health problems are a leading and increasing cause of health-related burden in children across the world. Peer support interventions are increasingly used to meet this need using the lived experience of people with a history of mental health problems. However, much of the research underpinning this work has focused on adults, leaving a gap in knowledge about how these complex interventions may work for different children in different circumstances. Realist research may help us to understand how such complex interventions may trigger different mechanisms to produce different outcomes in children. This paper reports on an important first step in realist research, namely the construction of an embryonic initial programme theory to help ‘focus’ realist evaluation exploring how children’s peer support services work in different contexts to produce different outcomes in the West Midlands (UK).
Methods
A survey and preliminary semi-structured realist interviews were conducted with 10 peer support service staff. Realist analysis was carried out to produce context-mechanism-outcome configurations (CMOC).
Results
Analysis produced an initial programme theory of peer support for children’s mental health. This included 12 CMOCs. Important outcomes identified by peer support staff included hope, service engagement, wellbeing, resilience, and confidence; each generated by different mechanisms including contextualisation of psychoeducation, navigating barriers to accessing services, validation, skill development, therapeutic relationship, empowerment, and reducing stigma.
Conclusion
These data lay the groundwork for designing youth mental health realist research to evaluate with nuance the complexities of what components of peer support work for whom in varying circumstances.
Experimental verification of seafloor crustal deformation observations by UAV-based GNSS- A
Authors: Yokota Yusuke , Kaneda Masata , Hashimoto Takenori , Yamaura Shusaku , Kouno Kenji , Hirakawa Yoshiaki 
Published: January 2023

Abstract
The Global Navigation Satellite System – Acoustic ranging combination technique (GNSS-A) is the only geodetic observation method that can precisely detect absolute horizontal and vertical seafloor crustal deformations on the centimetre scale. GNSS-A has detected many geophysical phenomena and is expected to make great contributions to earthquake disaster prevention science and geodesy. However, current observations obtained using a vessel and a buoy suffer from high cost or poor real-time performance, which leads to low observation frequency and delays in obtaining and transmitting disaster prevention information. To overcome these problems, a new sea-surface platform is required. Here, we develop a sea-landing unmanned aerial vehicle (UAV) system for GNSS-A. Sub-metre-level seafloor positioning is achieved based on real-time single-frequency GNSS data for an actual site. UAV-based GNSS-A allows high-frequency, near-real-time, and low-cost seafloor geodetic observations. It could be applied for high-frequency centimetre-accuracy observations using dual-frequency GNSS.
Open Set Classification of Sound Event
Authors: You Jie , Wu Wenqin , Lee Joonwhoan 
Published: January 2023

Abstract
Sound is one of the primary forms of sensory information that we use to perceive our surroundings. Usually, a sound event is a sequence of an audio clip that is obtained from an action. The action can be rhythm patterns, music genre, people speaking for a few seconds, etc. The sound event classification address involves distinguishing what kind of audio clip it is from the given audio sequence. Nowadays, it is a common issue to solve in the following pipeline: audio pre-processing◊perceptual feature extraction◊classification algorithm. In this paper, we improve the traditional sound event classification algorithm to identify unknown sound events by using the deep learning method. The compact cluster structure in the feature space for known classes is helpful in recognizing unknown classes by allowing large room to locate unknown samples in the embedded feature space. Based on this concept: we applied center loss and supervised contrastive loss to optimize the model. The center loss tries to minimize the intra class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class features from one another. In addition, we explored the performance of self-supervised learning in detecting unknown sound events. The experimental results demonstrate that our proposed open set sound event classification algorithm and self-supervised learning approach achieve sustained performance improvements in various datasets.
Ecosystem Health in the Southwest China Hybrid Geomorphologic Region: Evolution Characteristics and Driving Factors
Authors: Chen Honglian , Li Rui , Zhang Yushan , Wu Qinglin , Yuan Jiang , Gao Jiayong 
Published: January 2023

Abstract
The ecosystem health of the Chishui River Basin (CRB)—a crucial ecological barrier in the upper reaches of the Yangtze River—is vital for the ecological security and socioeconomic sustainability of the Yangtze River Basin. However, the evolution of the ecosystem health in various CRB areas of different geomorphologic types and its driving factors remain unclear. This study combined the Revised Universal Soil Loss Equation (RUSLE) and Soil and Water Assessment Tool (SWAT) models to construct a VORES framework–based ecosystem health assessment for the CRB and evaluated the spatiotemporal evolution characteristics in the CRB in 2010–2020, and then explore the driving factors of ecosystem health based on geographical detectors.The results were as follows: (1)In 2010–2020, ecosystem service function in the CRB decreased and then increased; the overall trend was downward, and the overall ecosystem service function was higher in the Danxia area than it was in the karst area.(2)The ecosystem was generally subhealthy; the Danxia area was mostly extremely healthy, whereas the karst area was mostly subhealthy and unhealthy. (3)In the CRB, strong explanatory power for healthy spatial distribution of ecosystems was demonstrated by vegetation, precipitation, and the bedrock bareness rate in the karst area, whereas it was demonstrated by vegetation, land use, and precipitation in the Danxia area. All influencing factors demonstrated increased explanatory power after interaction, and the combinations of the dominant interaction factors of different geomorphologic types demonstrated considerable differences. These results may provide scientific support for CRB ecosystem health maintenance and conservation.
Repellent and deterrent effects of natural products against diamondback moth, Plutella xylostella (Linnaeus)
Authors: Kavita  , Sharma Sanjay Kumar , Sood A.K. 
Published: January 2023

Abstract
Four farm made natural products viz. Agniastra, Brahmastra, Darekastra, Dashparni each applied @ 10 per cent concentration and one commercial product Neem Baan containing azadirachtin @ 0.0005 per cent were evaluated for repellence and oviposition deterrence against Plutella xylostella using ‘Leaf dip’ method of bioassay under laboratory conditions. In ‘No choice’ test, repellence of natural products to adults of P. xylostella varied from 20.8 – 82.3 per cent up to 4h after adult release. However, in ‘Choice’ test, it varied from 10.4 – 70.4 per cent. Similarly, deterrence to oviposition by females of P. xylostella varied from 15.9 – 63.9 and 21.7 – 56.3 per cent up to 4h after adult release in ‘No choice’ and ‘Choice’ tests, respectively.
The potential therapeutic efficiency of pan-ERBB inhibitors for canine glioma
Authors: Noguchi Shunsuke , Yasumura Moeka 
Published: January 2023

Abstract
Canine glioma is one of the most common brain tumors and has a poor prognosis. Therefore, an effective chemotherapy is desired. A previous study has indicated that the signaling involving one of the epidermal growth factor receptor (EGFR), ERBB4 is a promising therapeutic target. In the current study, the anti-tumor effects of pan-ERBB inhibitors that can inhibit phosphorylation of ERBB4 were evaluated in a canine glioblastoma cell line in vitro and in vivo. As a result, both afatinib and dacomitinib successfully decreased the expression of phosphorylated ERBB4 and significantly decreased the number of viable cells. Furthermore, both reagents prolonged the survival time of orthotopic xenografted mice. With regard to the downstream molecules of ERBB4, afatinib suppressed the expression of phosphorylated Akt and phosphorylated Extracellular signal-related kinases1 and 2 (ERK1/2) and, in addition, induced apoptotic cell death. Thus, pan-ERBB inhibition was considered a potential promising therapeutic strategy for canine glioma.
Human Identification after Cosmetic Surgery
Authors: SABHARWAL TANUPREET 
Published: January 2023

Abstract
Many people adopt cosmetic or medical changes for aesthetic or therapeutic objectives. The paper proposes a human identification technique for differentiating between samples taken before and after surgery. The system operates in three stages. First, pre-, and post-surgery images are preprocessed (cropped/converted to grayscale), then fuzzy edge detection is performed. Next, prominent features are extracted using SURF (Speeded Up Robust Features) extractor, and finally, a KNN classifier is used to determine which pairs are genuine and which are impostor. Utilizing fuzzy edge detection as a pre-processing step for appropriate (non-redundant) feature selection is the innovation/novelty in this method (dimensionality reduction). The selected features acquired by fuzzy detection are then subjected to SURF. The purpose of SURF is to compute operators quickly by utilizing box filters, rotation invariance, and anti-blur features. Since edges are local in nature and SURF is a local extractor, we have concentrated on extracting local features since they reveal more information (high frequency components) and can represent non-linear geometrical variations brought about by medical changes. Because the image contour is not deformed and suitable edges are retained, the fuzzy detector is preferable to Sobel, Canny, Roberts, and Prewitt detectors. Surgical sample fuzzy edge detection has not yet been implemented. The anticipated scheme's evaluation measures have been documented in literature the most effectively to date.
In silico study of anti-quorum sensing activity of silver, zinc oxide, and copper oxide nanoparticles against Pseudomonas aeruginosa
Authors: Sadoq Badr-Edine , Britel Mohammed Reda , Bouajaj Adel , Touhami Ahmed , Touhami Fakhita , Maurady Amal 
Published: January 2023

Abstract
In recent years, Pseudomonas aeruginosa emerged as a significant pathogenic microorganism in the majority of hospital-acquired infections due to the elevated levels of resistance to antibacterial agents by virtue of its highly organized quorum sensing (QS). P. aeruginosa has three hierarchical major QS systems (LasI/R, RhlI/R, and PqsA/R) that are involved in the secretion of virulence factors, biofilm formation, and antimicrobial resistance. Thus, targeting the QS mechanism may be a good strategy to combat infection caused by P. aeruginosa. In the present study, in silico analysis by molecular docking simulation of anti-quorum sensing activities of silver, zinc oxide and copper oxide against P. aeruginosa strain have been evaluated. Molecular docking showed that metal/metal oxide interaction with Acyl-Homoserine-Lactone synthases (LasI, RhlI, PqsA) can prevent precursor molecule binding and therefore inhibit the synthesis of functional signaling molecules. In addition, the binding of these nanoparticles to regulatory proteins (LasR, RhlR, PqsR) decreases the expression of QS-controlled genes by competing with functional signaling molecules for interaction with regulatory proteins. Thus, our results revealed that ZnO possesses good binding properties towards the selected targets. Particularly, the PqsA-ZnO complex shows more stable active binding sites and better binding affinity (-3.83 kcal/mol) with an inhibitory constant (Ki) of 1.65 mM. The binding mode of ZnO nanoparticles (NPs) has been found to be more potent compared to Ag and CuO NPs and related to the virulence factors of Gram-negative bacterial pathogens. This suggests that ZnO NPs could potentially be developed as effective inhibitors of QS systems and biofilms.
Graph Relation Networks for Few-shot Node Classification on Attributed Networks
Authors: 里 魏丽 , 刘 刘勇 , 张 魏张 
Published: January 2023

Abstract
Few-shot learning has been solving classification problems with few labels in the image and text domains, which has achieved great success in such Euclidean domains. However, a large amount of data can be represented as attributed networks defined by non-Euclidean domains, and fewer studies have successfully applied few-shot learning to attributed networks. Firstly, they ignore the relationship between nodes, so they can not obtain an adequate and accurate representation of nodes. Secondly, the use of an artificial set distance function to measure the similarity between nodes is defective. Because some special samples can cause perturbation to the classifier. In order to solve the above problems, we propose a new framework based on relation networks - graph relation networks (GRN). Specifically, GRN first sets up an episode training mechanism to simulate a real-world few-shot scenario, then uses graph attention networks to obtain a representation of each node with importance scores, and finally learns to compare the similarity of unclassified and classified nodes through the relation network. We conduct extensive experiments on real-world datasets. The experimental results show that the method improves the performance of graph node classification.