bluuebunny/crossref_metadata_2025_split huggingface dataset:
Shear damage mechanisms of jointed rock mass: a macroscopic and mesoscopic study
Authors: Wang Gang , Liu Wenhao , Jiang Feng , He Peng , Huang Na , Xiao Zhiyong , Zheng Chengcheng 
Published: April 2024

AbstractThe joints are existing throughout the underground rock mass. It is of great significance to investigate the shear performance of the rock mass to maintain the stability of the underground structure. In this study, we conducted orthogonal tests to determine the proportion of rock-like materials, and used JRC curves to make specimen molds and then prepare the specimens. We conducted straight shear tests and uniaxial compression tests to determine the various mechanical parameters of the rock-like materials. Next, we carried out the compression and shear tests to investigate the shear characteristics of the specimens, and study the damage pattern and shear strength of the jointed rock mass under different confining pressures and roughness levels. The mesoscopic displacements in the shear process of joints were analyzed by using ABAQUS. The test results show that the effect of the confining pressure on the shear strength of the joint plane is relatively obvious, and a larger confining pressure indicates a larger shear strength. The effects of different joint plane roughness and shear rated on the shear characteristics of the joint plane are also significant. The mesoscopic displacement difference inside the joint plane with higher roughness is relatively large, and the stress concentration phenomenon is obvious and lasts longer, which leads to the faster destruction of the specimen with higher roughness and the higher destruction degree. Therefore, we suggest that the priority should be given to the reinforcement of jointed rock mass with high roughness during the construction to prevent sudden destabilization and failure.
The relationship between cardiac oxidative stress, inflammatory cytokine response, cardiac pump function, and prognosis post-myocardial infarction
Authors: Duan Dongpeng , Li Hongjun , Chai Shiyun , Zhang Linlin , Fan Tianfeng , Hu Zhenfeng , Feng Yan 
Published: April 2024

AbstractThis study delves into the potential connections between cardiac oxidative stress, inflammatory cytokine response, cardiac pump function, and prognosis in individuals following myocardial infarction. A total of 276 patients were categorized into two groups: the control group (n = 130) and the observation group (n = 146), based on the drug intervention strategies. The control group received standard drug treatment, while the observation group received early drug intervention targeting antioxidant and anti-inflammatory treatment in addition to standard treatment. Serum levels of inflammatory factors, including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-9 (IL-6), were assessed using enzyme-linked immuno sorbent assay (ELISA) kits. The Forkhead Box Protein A2 (FOX2) reagent was used to determine the overall oxidation level. Left Ventricular End-Diastolic Diameter (LVEDD), Left Ventricular Ejection Fraction (LVEF), and End-Systolic Diameter (ESD) were measured using Doppler ultrasound. The observation group exhibited significantly reduced serum levels of TNF-α, IL-1β, and IL-6 compared to the control group (P < 0.05). Moreover, the observation group exerted lower total oxidation levels, OSI, EDD, and ESD compared to the control group (P < 0.05), while the LVEF and TAS levels in the observation group were higher than those in the control group (P < 0.05). Remarkably, the observation group experienced a significant reduction in the incidences of reinfarction, heart failure, arrhythmia, and abnormal valve function compared to the control group (P < 0.05). Decreased cardiac pump function and a more unfavorable prognosis were associated with elevated levels of cardiac oxidative stress and inflammatory factors (P < 0.05). Timely intervention with appropriate medications have a crucial effect in decreasing inflammatory marker levels, mitigating oxidative pressure, and enhancing cardiac pumping capacity and overall prognosis.
A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network
Authors: Lei Dongyang , Dong Chaoyi , Guo Hongfei , Ma Pengfei , Liu Huanzi , Bao Naqin , Kang Hongzhuo , Chen Xiaoyan , Wu Yi 
Published: April 2024

AbstractFor the brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is difficult to obtain satisfactory classification performance for short-time window SSVEP signals by traditional methods. In this paper, a fused multi-subfrequency bands and convolutional block attention module (CBAM) classification method based on convolutional neural network (CBAM-CNN) is proposed for discerning SSVEP-BCI tasks. This method extracts multi-subfrequency bands SSVEP signals as the initial input of the network model, and then carries out feature fusion on all feature inputs. In addition, CBAM is embedded in both parts of the initial input and feature fusion for adaptive feature refinement. To verify the effectiveness of the proposed method, this study uses the datasets of Inner Mongolia University of Technology (IMUT) and Tsinghua University (THU) to evaluate the performance of the proposed method. The experimental results show that the highest accuracy of CBAM-CNN reaches 0.9813 percentage point (pp). Within 0.1–2 s time window, the accuracy of CBAM-CNN is 0.0201–0.5388 (pp) higher than that of CNN, CCA-CWT-SVM, CCA-SVM, CCA-GNB, FBCCA, and CCA. Especially in the short-time window range of 0.1–1 s, the performance advantage of CBAM-CNN is more significant. The maximum information transmission rate (ITR) of CBAM-CNN is 503.87 bit/min, which is 227.53 bit/min-503.41 bit/min higher than the above six EEG decoding methods. The study further results show that CBAM-CNN has potential application value in SSVEP decoding.
Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN) classifier
Authors: Prasad Kerehalli Vinayaka , Vaidya Hanumesh , Rajashekhar Choudhari , Karekal Kumar Swamy , Sali Renuka , Nisar Kottakkaran Sooppy 
Published: April 2024

AbstractThe cultivation of grapes encounters various challenges, such as the presence of pests and diseases, which have the potential to considerably diminish agricultural productivity. Plant diseases pose a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. This study employs a Convolutional neural network (CNN) with and without data augmentation, in addition to a DCNN Classifier model based on VGG16, to classify grape leaf diseases. A publicly available dataset is utilized for the purpose of investigating diseases affecting grape leaves. The DCNN Classifier Model successfully utilizes the strengths of the VGG16 model and modifies it by incorporating supplementary layers to enhance its performance and ability to generalize. Systematic evaluation of metrics, such as accuracy and F1-score, is performed. With training and test accuracy rates of 99.18 and 99.06%, respectively, the DCNN Classifier model does a better job than the CNN models used in this investigation. The findings demonstrate that the DCNN Classifier model, utilizing the VGG16 architecture and incorporating three supplementary CNN layers, exhibits superior performance. Also, the fact that the DCNN Classifier model works well as a decision support system for farmers is shown by the fact that it can quickly and accurately identify grape diseases, making it easier to take steps to stop them. The results of this study provide support for the reliability of the DCNN classifier model and its potential utility in the field of agriculture.
Prediction of jumbo drill penetration rate in underground mines using various machine learning approaches and traditional models
Authors: Heydari Sasan , Hoseinie Seyed Hadi , Bagherpour Raheb 
Published: April 2024

AbstractEstimating penetration rates of Jumbo drills is crucial for optimizing underground mining drilling processes, aiming to reduce costs and time. This study investigates various regression and machine learning methods, including Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forests (RF), to predict the penetration rates (ROP) using multivariate inputs such as operation parameters and rock mass characteristics. The Rock Mass Drillability Index (RDi), incorporating both intact rock properties and structural parameters, was utilized to characterize the rock mass. The dataset was split into 80% for training and 20% for testing. Performance metrics including correlation coefficient (R2), variance accounted for (VAF), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were calculated for each method to evaluate the accuracy of the predictions. SVR exhibited the best prediction performance for ROP, achieving the highest R2, lowest RMSE, MAE, and MAPE, as well as the largest VAF values of 0.94, 0.15, 0.11, 4.84, and 94.13 during training, and 0.91, 0.19, 0.13, 6.02, and 91.11 during testing, respectively. With this high accuracy, we conclude that the proposed machine learning algorithms are valuable and efficient predictors for estimating jumbo drill penetration rates in underground mining operations.
Omics-based approaches for discovering active ingredients and regulating gut microbiota of Actinidia arguta exosome-like nanoparticles
Authors: Chen Chunping , Xia Guangjun , Zhang Song , Tian Yuxin , Wang Yuchen , Zhao Duanduan , Xu Hongyan 
Published: April 2024

This study highlights that active ingredients and regulating gut microbiota of Actinidia arguta exosome-like nanoparticles.
Dielectric and thermal properties characterisation and evaluation of novel epoxy materials for high‐voltage power module packaging
Authors: Wang Zhengdong , Yang Ganqiu , Cao Xiaolong , Li Mengli , Zhang Tong , Liu Chenxin , Zhou Yuanhang , Cheng Yonghong 
Published: April 2024

AbstractInternal insulation of high‐voltage power modules is facing interesting failure risks, including high temperature overheating, breakdown fault, material cracking etc., so it is imperative to urgently develop new dielectric materials with high thermal conductivity (λ), outstanding electrical insulation, and thermal stability properties. A method to construct controllable liquid crystalline cross‐linking networks based on the synthesis of biphenyl epoxy monomer and the change of curing agent structures and curing temperature is proposed. The uniform nematic rod‐like liquid crystalline domains were obtained by using 4,4‐diaminodiphenylmethane as a curing agent under a pre‐curing temperature of 105°C. The resulting film (abbreviated as TD‐105) exhibited λ up to 0.53 W m−1 K−1 and a dielectric breakdown strength of 57.69 kV mm−1, which showed a simultaneous enhancement of 178% and 16%, respectively, compared to traditional bisphenol A epoxy resin. Moreover, it also exhibited lower dielectric loss and magnitude of partial discharge while having higher glass‐transition temperature (190°C). A novel idea for the development of high‐performance epoxy insulating materials for the application of high‐voltage and large‐power electrical equipment is provided.
Mapping civil society elites: multi-dimensional measure of resource stratification in civil society (MMRSC)
Authors: Scaramuzzino Roberto , Lee Jayeon 
Published: April 2024

AbstractThis paper presents a novel methodological approach, multi-dimensional measure of resource stratification in civil society (MMRSC). The method aims at mapping civil society organisations based on different types of resources for studying resource stratification in civil society. The approach is based on six indicators consisting of economic, political, and media resources, internal and external to civil society. The MMRSC is a development and adaptation of the positional method within the elite research tradition aiming at identifying individuals holding central positions in powerful organisations. To be able to apply the positional method to civil society studies, the paper argues for a multi-dimensional understanding of resources taking into account diverse organisational forms and goals within civil society. The method is presented in a systematic step-by-step structure with exemplifications based on how it has been used in a study of civil society elites in four European countries and at the EU level.
Properties and ceramic transformation of Si–Zr–O–C precursor ceramics with porous structure
Authors: Lin Jia , Wang Shuai , Jin Hua , Cui Shuyuan , Wang Rongwen , Yang Yihang , Huang Guimei , Wang Jinhuo 
Published: April 2024

The preparation of ceramic materials with complex porous structures through photopolymerization-based 3D printing requires the development of stable and printable slurries. In this study, zirconium acetylacetonate was incorporated into the thiol vinyl organosilicon prepolymer to create a photosensitive Si–Zr–O–C slurry. Regarding the natural bone structures and the Tyson polygon principle, a gradient pore structure was designed and then printed using a digital light processing 3D printer. After printing, the effects of sintering temperatures on the phase composition and structure of Si–Zr–O–C ceramics were systematically investigated. Subsequently, a comparative analysis of structure and properties was performed on sintered samples with different zirconium acetylacetonate contents. The results revealed that the sample containing 30 wt. % zirconium acetylacetonate exhibited a higher compressive strength of 9.70 ± 0.28 MPa and a lower room temperature thermal conductivity of 0.528 W m−1 K−1. This study confirmed the significant potential of using 3D printing technology to prepare Si–Zr–O–C precursor ceramics with a porous structure.
Non-destructive three-dimensional imaging of artificially degraded CdS paints by pump-probe microscopy
Authors: Zhou Yue , Grass David , Warren Warren S , Fischer Martin C 
Published: April 2024

Abstract
Cadmium sulfide (CdS) pigments have degraded in several well-known artworks, but the influence of pigment properties and environmental conditions on the degradation process have yet to be fully understood. Traditional non-destructive analysis techniques primarily focus on macroscopic degradation, whereas microscopic information is typically obtained with invasive techniques that require sample removal. Here, we demonstrate the use of pump-probe microscopy to nondestructively visualize the three-dimensional structure and degradation progress of CdS pigments in oil paints. CdS pigments, reproduced following historical synthesis methods, were reproduced as oil paints and artificially aged by exposure to high relative humidity and light. The degradation of CdS to CdSO4·xH2O was confirmed by both FTIR (Fourier-transform infrared) and XPS (x-ray photoelectron spectroscopy) experiments. During the degradation process, optical pump-probe microscopy was applied to track the degradation progress in single grains, and volumetric imaging revealed early CdS degradation of small particles and on the surface of large particles. This indicates that the particle dimension influences the extent and evolution of degradation of historical CdS. In addition, the pump-probe signal decrease in degraded CdS is observable before visible changes to the eye, demonstrating that pump-probe microscopy is a promising tool to detect early-stage degradation in artworks.
The Quebec Semantic Memory Battery: Development, Standardization, and Psychometric Assessment of a Semantic Memory Battery in French
Authors: Monetta Laura , Boland Angela , Macoir Joël , Sheppard Christine L , Taler Vanessa 
Published: April 2024

Abstract

Objective
People with aphasia often experience semantic memory (SM) impairment. To improve diagnostic outcomes, SM tasks should recruit various sensory input channels (oral, written, and pictographic), permitting accessible, complete evaluation. There is a need for SM batteries for French-speaking Quebecers that use multiple input channels. The present study, therefore, describes the development of a novel French-language semantic battery: la Batterie québécoise de la mémoire sémantique (BQMS), the assessment of the BQMS’s psychometric properties, and the establishment of normative data for the BQMS.

Method
We first developed eight SM tasks. Following a pilot validation study, we determined the BQMS’s reliability and validity, to ensure consistent, accurate detection of SM impairment. Among French-speaking Quebecers with cerebrovascular aphasia (n = 10), people with the semantic variant of Primary Progressive Aphasia (n = 4), and healthy controls (n = 14), we examined its convergent validity, concurrent validity, test–retest reliability, and internal consistency. Finally, we established normative data for the BQMS by calculating cut-off scores per task that indicate SM impairment (in 93 cognitively healthy French-speaking Quebecers), stratified by sociodemographic variables associated with performance.

Results
The BQMS shows high concurrent, discriminant, and convergent validity, as well as good test–retest reliability and internal consistency. The cut-off score indicating SM impairment ranged from the 2nd to 25th percentiles (stratified by task, age, and sex).

Conclusions
The BQMS’s psychometric properties indicate that it could be a valuable clinical tool for detecting SM impairment. Our normative data will help clinicians detect such impairments.
A Case Report of Autoimmune Encephalitis after Anti-SARS-CoV-2 Vaccination: The Role of Cognitive Impairments in the Diagnostic Process
Authors: Di Tella Marialaura , Nahi Ylenia Camassa , Paglia Gabriella , Geminiani Giuliano Carlo 
Published: April 2024

Abstract

Objective
Autoimmune encephalitis includes a heterogeneous group of rare and complex diseases, usually presenting with severe and disabling symptoms, such as behavioral changes, cognitive deficits, and seizures.

Method
This report presents the case of a 26-year-old man who was diagnosed with autoimmune encephalitis following SARS-CoV-2 vaccination (&lt;40 days). Symptoms first appeared in February 2022 with a temporal seizure, associated with confusion and memory loss. Psychiatric manifestations such as disorientation and altered thought contents emerged soon after.

Results
Neuroimaging testing showed signs of hypometabolism in occipital, prefrontal, and temporal regions, whereas an extensive neuropsychological assessment revealed the presence of multiple alterations in memory, executive, and visuoconstructive processes.

Conclusions
In this case, a combination of neuroimaging testing, psychiatric evaluation, and neuropsychological assessment provided evidence for a diagnosis of autoimmune encephalitis post-vaccination. Early recognition is essential in order to prevent clinical progression; avoid intractable epilepsy, brain atrophy, and cognitive impairment; and improve prognosis.
Impact of Family Group Conference Referrals at Pre-Proceedings Stage on Child Outcomes: A Randomised Controlled Trial
Authors: Taylor Sarah , Blackshaw Emily , Dorsett Richard , Lawrence Hannah , Stern Daniel , Gilbert Lizzie , Raghoo Nilesh 
Published: April 2024

Abstract
Previous evidence on the impact of Family Group Conferencing in the UK cannot establish causality. This article presents results from the first UK randomised controlled trial of Family Group Conferences. All families entering pre-proceedings between September 2020 and May 2022 in twenty-one local authorities in England were eligible for inclusion in the trial. In total, 1,511 families were randomised and child outcomes were collected from administrative data until up to twenty-one months post-referral. Half of the families were randomly allocated to be referred for a Family Group Conference, on top of usual practice during pre-proceedings. The (pre-registered) primary outcome is the child’s looked-after status twelve months after the pre-proceedings letter. This was 8.6 percentage points lower among referred families (36.2 percent vs 44.8 percent). Children were also less likely to have care proceedings issued (59.2 percent vs 71.7 percent) and spent less time in care (87 vs. 115 days). However, there was no significant difference in whether a child’s living arrangement remained the same or changed in the months after the decision. These findings have implications for the support given to families during pre-proceedings; they provide evidence in favour of Family Group Conferences as a way to reduce the chances of children going into care.
Abortion Pills: Killing or Letting Die?
Authors: Hershenov David 
Published: April 2024

Abstract
Christian pro-lifers often respond to Thomson’s defense of abortion that the violinist is allowed to die while the embryo is killed. Boonin and McMahan counter that this distinction does not provide an objection to extraction abortions that disconnect embryos and allow them to die. I disagree. I first argue that letting die and killing are not to be distinguished by differences between acts and omissions, moral and immoral motives, intentional or unintentional deaths, and causing or not causing a pathology. I offer a taxonomy in which hysterotomies turn out to be killings. I then argue that if either the placenta is considered an organ of the embryo or the embryo is construed as a part of the pregnant woman, then abortion pills kill rather than allow death. So, assuming that killing people is morally worse than letting them die, extraction abortions can be condemned to the same degree as other abortions.
Cortical-cerebellar circuits changes in preschool ASD children by multimodal MRI
Authors: Yi Ting , Ji Changquan , Wei Weian , Wu Guangchung , Jin Ke , Jiang Guihua 
Published: April 2024

Abstract

Objective
To investigate the alterations in cortical-cerebellar circuits and assess their diagnostic potential in preschool children with autism spectrum disorder using multimodal magnetic resonance imaging.

Methods
We utilized diffusion basis spectrum imaging approaches, namely DBSI_20 and DBSI_combine, alongside 3D structural imaging to examine 31 autism spectrum disorder diagnosed patients and 30 healthy controls. The participants’ brains were segmented into 120 anatomical regions for this analysis, and a multimodal strategy was adopted to assess the brain networks using a multi-kernel support vector machine for classification.

Results
The results revealed consensus connections in the cortical-cerebellar and subcortical-cerebellar circuits, notably in the thalamus and basal ganglia. These connections were predominantly positive in the frontoparietal and subcortical pathways, whereas negative consensus connections were mainly observed in frontotemporal and subcortical pathways. Among the models tested, DBSI_20 showed the highest accuracy rate of 86.88%. In addition, further analysis indicated that combining the 3 models resulted in the most effective performance.

Conclusion
The connectivity network analysis of the multimodal brain data identified significant abnormalities in the cortical-cerebellar circuits in autism spectrum disorder patients. The DBSI_20 model not only provided the highest accuracy but also demonstrated efficiency, suggesting its potential for clinical application in autism spectrum disorder diagnosis.