bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of CIPP flipped lining method in the rehabilitation of old gas pipelines
Authors: Yun You , Wang Zhongyi , Wang Yuyan , Chen Hao , Wang xinxin 
Published: August 2024

Abstract

With the increase of urban gas pipeline operation time, more and more underground gas pipelines are facing various problems, and old gas pipelines urgently need to be updated and repaired. Realizing the comprehensive excavation, demolition, and replacement of old pipes with new ones poses pain points such as high construction difficulty, repair costs, and uncertainty of routing construction planning. Therefore, the implementation of trenchless repair and transformation of old gas pipelines has positive and practical significance. The CIPP flipped lining repair technology, as an emerging trenchless pipeline repair technology, provides an efficient and cost-effective solution for the repair of old gas pipelines. A case study on the application of CIPP flipped lining method was conducted for the rehabilitation project of Liangshen Road gas pipeline in Weituo Industrial Park, Chongqing. By comparing the characteristics of different trenchless pipeline repair techniques, the technical adaptability of CIPP flipped lining method was analyzed, and the construction parameter design, key process flow, and construction difficulties of using CIPP flipped lining to repair gas pipelines were explored and revealed, as well as the control of engineering quality impact. Practice has shown that CIPP flipped lining can provide a feasible technical choice for the preventive repair of urban gas pipelines. The repaired pipeline performance meets the operational requirements of urban gas pipelines, and has broad prospects and promotion value. It can provide reference for the non-excavation repair and utilization of old gas pipelines in the future.
Comparative Study of Turbulence Models in CFD for Transonic Flow Over the ONERA M6 Wing
Authors: Mazumder Md.Hasib Mahmud 
Published: August 2024

Abstract

Transonic flight has long been a complex area of study in aeronautics, primarily due to phenomena such as shock waves and turbulent boundary layer interactions that cause flow separation and instabilities. This study leverages Computational Fluid Dynamics (CFD) to analyze these effects using ANSYS-Fluent 2020R1. The ONERA M6 wing serves as the model for assessing the performance of five turbulence models: Spalart-Allmaras, k-ε Standard, k-ε Realizable, k-ω Standard, and k-ω SST. Under specific flow conditions (angle of attack, α = 3.06°, Mach number, M ∞ = 0.8395, and Reynolds number, Re = 11.72×10^6), the CFD simulations closely matched experimental data. The comparative analysis demonstrated that all five models produced results within a 5% margin of error compared to NASA's CFD results. Notably, the Spalart-Allmaras and k-ω SST models showed the highest accuracy in predicting shock formation and pressure distribution. This study confirms the robustness of ANSYS-Fluent in evaluating aerodynamic performance and supports its continued application in aerospace engineering for enhancing aircraft design efficiency and safety.
The effects of vulnerability-schema structure on suicidal ideation are enhanced by traumatic stress both directly and through depression
Authors: Luo Mengjie , Yu Yang , Zhao Mengxue , Feng Zhengzhi , Zhang Jingxuan 
Published: August 2024

Abstract

Objective
This study aimed to discover the complicated effects of impulsivity and traumatic stress on suicidal ideation mediated by negative cognitive processing bias and depression, through which we may detect the suicide signs as early as possible, to advance the preventive measures.
Methods
We recruited 1,798 undergraduates as participants. Self-rating Idea of Suicide Scale (SIOSS), Barratt’s Impulsiveness Scale (BIS), Negative Cognitive Processing Bias Questionnaire (NCPBQ), and PTSD Checklist Civilian Version (PCL-C) were applied as measuring tools. A moderated mediation latent path model was conducted, among which negative cognitive processing bias and depression were mediation variables and traumatic stress was moderation variable.
Results
Impulsivity was considered separately as three dimensions, namely, non-planning, motor and cognitive impulsivities. We found that all the models fitted well (RMSEA < 0.08, CFI > 0.90, TLI > 0.90, and SRMR < 0.08), in which traumatic stress moderated the direct effects of depression on suicidal ideation (p < 0.01, p < 0.001). For the model of non-planning and cognitive impulsivities, traumatic stress also moderated the effects of impulsivities on depression (p < 0.01) and of negative cognitive processing bias on suicidal ideation (p < 0.05). The simple indirect paths of “impulsivity→negative cognitive processing bias→suicidal ideation” and “impulsivity→depression→suicidal ideation” and the chain indirect path of “impulsivity→negative cognitive processing bias→depression→suicidal ideation” were all significant (p < 0.001) on different (i.e., high, medium, and low) levels of traumatic stress. Traumatic stress positively moderated the indirect effects (p < 0.001, p < 0.01, p < 0.05) except for the path of “motor impulsivity→negative cognitive processing bias→suicidal ideation”.
Conclusions
The present study confirmed that negative cognitive processing bias and impulsivity were closely correlated and worked together as trait-like vulnerability-schema structures in the development of suicidal ideation. For suicide prevention, we could monitor the dynamics of the vulnerability-schema structure and intervene in negative cognitive processing bias, which was easier and more practical to change compared with impulsivity. This might advance the time course of finding the suicide signs.
Deep Learning Model for Intrusion Detection Based on Weighted Cross Entropy Loss Function and OBiLSTM
Authors: Ding Yong , Xu Shuai , Peng Xinyong , Yang Changsong , Liang Hai , Li Zhenyu 
Published: August 2024

Abstract

Industrial Internet of Things has become an important tool to promote the transformation and upgrading of the manufacturing industry and enhance it's competitiveness. With the popularization of IIOT, the cyber security threats faced by industrial systems are also increasing, and intrusion detection techniques have emerged. However, due to the complex structure of IIOT and the variety of network traffic data generated, the Intrusion detection that has been studied accuracy is not high. To improve the accuracy of intrusion detection, we introduce an intrusion detection method for IIoT utilizing an Optimized Bidirectional Long Short-Term Memory network. We first enhance BiLSTM by substituting the inverse Long Short-term Memory network with a Gated Recurrent Unit, followed by designing a novel neural network, OBiLSTM. We also designed a weighting method for the Cross-entropy Loss function, which makes the detection accuracy further improved. Finally, We implemented the proposed intrusion detection prototype and tested it based on two datasets, UNSW-NB15 and NSL-KDD. The experimental outcomes indicate that our proposed prototype surpasses previous methods in terms of accuracy, recall, precision, and F1_score.
OptiFeat: Enhancing Feature Selection, A Hybrid Approach Combining Subject Matter Expertise and Recursive Feature Elimination Method
Authors: Vijayakumar G. , Bharathi R. K. 
Published: August 2024

Abstract

Optimizing the performance of Java Virtual Machines (JVMs)[1] is crucial for achieving efficient execution of Java applications. Feature selection plays a pivotal role in identifying the most relevant parameters for fine-tuning JVMs, thereby enhancing their overall efficiency. This paper presents a novel hybrid approach that integrates both subject matter expertise and Recursive Feature Elimination (RFE)[2] model to refine feature selection for JVM fine-tuning using machine learning techniques. Traditional feature selection methods often lack the ability to incorporate domain-specific knowledge, resulting in suboptimal selections[3]. In contrast, the hybrid approach leverages the expertise of JVM administrators or developers to guide the feature selection process. By integrating domain knowledge into the feature selection pipeline, ensure the inclusion of crucial JVM parameters that may not be captured by automated techniques alone. Furthermore, employed the RFE model, a powerful recursive feature elimination algorithm, to iteratively identify and eliminate irrelevant features from the initial feature set. This iterative process enhances the efficiency of feature selection by systematically pruning less influential parameters, thereby improving the overall performance of the JVM. To validate the effectiveness of the hybrid approach, conducted experiments using real-world JVM datasets and compare the performance of the method against existing feature selection techniques. The results demonstrate that the approach not only achieves superior performance in terms of JVM fine-tuning but also provides insights into the significance of domain expertise in optimizing JVM performance[4]. It contributes to the field of JVM optimization by proposing a novel hybrid approach that combines subject matter expertise with machine learning-based feature selection techniques. By leveraging both domain knowledge and automated algorithms, the approach offers a comprehensive solution for enhancing feature selection in JVM fine-tuning, ultimately leading to improved performance and efficiency in Java application execution.
DA-BAG: A Multi-Model Fusion Text Classification Method Combining BERT and GCN Using Self-Domain Adversarial Training
Authors: Shao Dangguo , Su Shun , Ma Lei , Yi Sanli , Lai Hua 
Published: August 2024

Abstract

Both pre-training-based methods and GNN-based methods are considered the most advanced techniques in natural language processing tasks, particularly in text classification tasks. However, traditional graph learning methods focus solely on structured information from text to graph, overlooking the hidden local information within the syntactic structure of the text. Conversely, large-scale pre-training model methods tend to overlook global semantic information, potentially introducing new noise and training biases when combined. To tackle these challenges, we introduce DA-BAG, a novel approach that co-trains BERT and graph convolution models. Utilizing a self-domain adversarial training method on a single dataset, DA-BAG extracts multi-domain distribution features across multiple models, enabling self-adversarial domain adaptation training without the need for additional data, thereby enhancing model generalization and robustness. Furthermore, by incorporating an attention mechanism in multiple models, DA-BAG effectively combines the structural semantics of the graph with the token-level semantics of the pre-trained model, leveraging hidden information within the text's syntactic structure. Additionally, a sequential multi-layer graph convolutional neural(GCN) connection structure based on a residual pre-activation variant is employed to stabilize the feature distribution of graph data and adjust the graph data structure accordingly. Extensive evaluations on 5 datasets(20NG, R8, R52, Ohsumed, MR) demonstrate that DA-BAG achieves state-of-the-art performance across a diverse range of datasets.
Magnetic iron-oxide nanoparticles in the brain connected to alcohol-associated liver disease
Authors: Kaub Leon , Milz Stefan , Barapatre Nirav , Büttner Andreas , Michalke Bernhard , Schmitz Christoph , Gilder Stuart A. 
Published: August 2024

Abstract

Magnetic iron-oxide nanoparticles in the form of magnetite (Fe3O4) are present in the human brain. They have been hypothesized to biomineralize in situ, as a result of dysfunctional iron homeostasis related to Alzheimer’s disease, or to enter the brain as airborne pollution particles. Regardless of their origin, magnetic iron-oxides pose a potential hazard to human health due to their high redox activity and surface charge. Here we report measurements on four post-mortem human brainstems, with one brainstem showing approximately 100 times higher magnetite concentrations than the other cases. This brainstem came from a subject with alcohol-associated liver disease (ALD) that manifested in liver cirrhosis and massive hepatic iron overload. Laser ablation – inductively coupled plasma – mass spectrometry showed the highest levels of trace metals (iron, copper and manganese) in the ALD brainstem. It is well established that a dysfunctional liver can result in the accumulation of trace metals in the brain. Our data indicate a similar pathway for magnetite particles, yet liver pathology has not been linked to magnetite occurrence in the brain so far. It may prove to be a crucial factor in understanding the high variation of magnetite concentrations found in human brains.
Coronal offset of C7 is associated with uneven joint degeneration between right and left hips after spinal fusion
Authors: kawai toshiyuki , Shimizu Takayoshi , Okuzu Yaichiro , Kuroda Yutaka , Morita Yugo , Otsuki Bungo , Goto Koji , Fujibayashi Shunsuke , Matsuda Shuichi 
Published: August 2024

Abstract

Introduction:
The aim of the study was to investigate the effects of coronal offset of C7 after spinal fusion on the discrepancy in joint space narrowing between the right and left hips.
Methods: We retrospectively reviewed data from patients who underwent lumbar spinal fusion from 2011 to 2018 at our institute. The rate of hip joint space narrowing after spinal fusion was measured in 190 patients (380 hips). We assessed the effects of the distance between the C7 plumb line and the central sacral vertical line (C7-CSVL) on the discrepancy in joint space narrowing between the right and left hips. Using multivariate regression models, we controlled for the effects of age, sex, body mass index, fusion length, and several spinopelvic alignment parameters (sacral slope, pelvic incidence, lumbar lordosis, pelvic incidence minus lumbar lordosis, and sagittal vertical axis) on the joint space narrowing rate.
Results: Multivariate regression showed that the C7-CSVL was associated with the discrepancy of the joint space narrowing rate between right and left, indicating that when C7 deviated to right, the joint space narrowing was larger in the right than left hip (standardized coefficient, 0.203; p=0.0005).
Conclusion: A larger C7-CSVL was associated with a larger right–left discrepancy in hip joint space narrowing after spinal fusion. These findings indicate that spinal coronal balance affects the distribution of joint degeneration in the right and left hips. Surgeons should understand the potential risk of uneven progression of degeneration between the right and left hips in patients with large coronal offset.
Level of Evidence: Level III
Continuous nuclear envelope surveillance is required for DNA double-strand break repair.
Authors: Machín Félix , Medina-Suárez Sara 
Published: August 2024

Abstract

Precise double-strand break (DSB) repair is paramount for genome stability. Homologous recombination (HR) is preferred to repair DSBs when a nearby sister chromatid ensures an error-free template. In Saccharomyces cerevisiae, this preference extends into anaphase and telophase (late mitosis; late-M), despite sister chromatids having been pulled apart. Previously, we identified the nuclear envelope (NE) protein Msc1 as important for late-M DSB repair. Here, we report that Msc1 faces the NE lumen, and its depletion leads to DSB-independent over-compartmentalization of the nucleus, which hampers approximation of sister loci after DSB. Depletion of Msc1 also leads to nuclear pore complex mislocation, a phenotype shared by the highly conserved NE healing complex ESCRT-III. Critically, we show that both Msc1 and ESCRT-III also ensure DSB repair in G2/M, and that there is synergism between Msc1 and the ESCRT-III subunit Snf7. These findings highlight the essential role of NE health in DSB repair.
Economic impact of TAVI pathway optimisation: the experience of the University Hospital of Parma.
Authors: Fanelli Simone , Lanza Gianluca , Pratici Lorenzo , Benatti Giorgio , Vignali Luigi 
Published: August 2024

Abstract

Background: Transcatheter Aortic Valve Implantation (TAVI) has emerged as a standard of care for patients suffering from symptomatic severe aortic stenosis. However, organisational analyses and investigations of this intervention’s economic impact are lacking. This study aims to assess the potential impact of implementing a process designed to optimise the TAVI pathway regarding organisational, human, material, and economic resources.
Methods: The research is conducted through a case study. The methodology consisted of three stages: 1. mapping of the current TAVI pathway; 2. identification of the organisational changes necessary to optimise it and implementation of the identified action with a multidisciplinary teamwork; 3. simulations of the organisational and economic impact of the optimisation process. Data related to costs, revenues, and activities were provided by the hospital’s Management Control office. The data analysed refer to a 12-month period. The TAVI pathway optimization presented in this analysis is aligned with the best practices described in the BENCHMARK study.
Results: The analysis of the current TAVI pathway in the studied hospital highlighted several critical points during the three phases of the course (pre-procedure, peri-procedure, and post-procedure). The working groups identified five areas for TAVI pathway improvement: patient and family education, coronary risk stratification, conduction disorders management, fast-track discharge eligibility, nurse-led rapid mobilisation and early discharge.
The organisational solutions highlighted by the working groups outline a new TAVI pathway capable of generating a significant impact not only from an organisational point of view, but also from an economic point of view. We estimated that in our cohort TAVI optimisation would have saved approximately 112 ICCU bed-days and have led to an average cost reduction of about €3,900 per patient.
Conclusions: This study showed a process of optimisation of the TAVI pathway highlighting the positive impacts for patients, caregivers, healthcare workers, and the hospital. A Clinical Valve Coordinator may help to manage the procedural programme for individual patients while maintaining seamless communication with the Heart Team throughout the patient care journey, making the process even more streamlined. The case study analysed can be useful to all hospitals wishing to undertake processes aimed at improving the TAVI pathway.
Quantum Deep Learning for Automatic Chronic Kidney Disease Identification and Classification with CT images
Authors: Hussain Sajid , Songhua Xu , Aslam Muhammad , Waqas Muhammad , Hussain Shahid 
Published: August 2024

Abstract

Chronic Kidney Disease (CKD) entails a progressive decline in renal function, often originating from comorbidities such as diabetes and hypertension. Early detection is crucial for managing progression and associated complications. Meanwhile, computed tomography (CT) serves as a vital tool for identifying kidney conditions. However, the accuracy and efficiency of radiological diagnoses are hampered by image quality, especially in early-stage disease. Despite extensive research on artificial intelligence (AI) based diagnostic models, high efficiency and optimal accuracy remains challenging. This study introduces a deep learning (DL) clinical diagnostic system aimed at enhancing the automatic identification and classification of CKD. Through an exploration of standard, advanced, and quantum DL algorithms in the CKD domain, it was selecting a hybrid quantum deep convolutional neural network (QDCNN) as our proposed model due to its high-quality performance. The model was optimized and refined through training with augmented and denoised datasets. This refined iteration yields impressive test performances in terms of accuracy: 99.98%, recall: 99.89%, precision: 99.84%, F1 score: 99.86%, specificity: 99.84%, Micro AUC: 99.99%, and testing time of 0.0641 seconds per image. Positioned to outperform existing methods, our proposed system demonstrates the potential to accurately identify kidney conditions at every stage, providing invaluable assistance to medical professionals and equipping them with an advanced level of accuracy, promptness, and reliability in diagnoses that is unparalleled in its excellence.
Explanation beyond Individual Features: Instance-wise Feature Grouping for EHR Predictive Analytics
Authors: Cheong Chin Wang , Yin Kejing , Cheung William K. , Tsang Ivor 
Published: August 2024

Abstract

Identifying relevant input features which contribute to the output of a clinical prediction model can enhance the model explainability. To allow the explainability to be more personalized, instance-wise feature selection (IWFS) methods can be adopted where features are selected specifically for each input instance. Existing IWFS methods often grapple with feature selection instability, and thus precarious interpretation. As relevant features among the instances in a dataset do overlap, feature grouping tricks have been proposed to regularize the selection, but often at the expense of sacrificing the downstream prediction accuracy. To this end, we propose a novel instance-wise feature grouping method called FlexGPC to achieve robust and stable selection by learning i) flexible representation for feature groups, and ii) flexible combination of feature groups implemented using neural networks. To evaluate the effectiveness of FlexGPC, we explore various feature group combination schemes and conduct extensive experiments for performance comparison using real-world electronic health records (EHR) data. Our experimental results show that FlexGPC outperforms all the SOTA baselines in terms of accuracy and feature selection stability for both downstream mortality and next-admission diagnosis prediction tasks. We also illustrate that computational phenotyping can be achieved at the same time, with the identified feature groups being the potential phenotypes.