bluuebunny/crossref_metadata_2025_split huggingface dataset:
In-process evaluation of layer defects and surface topography on material extruded parts through a novel point cloud functional analysis
Authors: Lafirenza Marco , Guerra Maria Grazia , Lavecchia Fulvio , Galantucci Luigi Maria 
Published: December 2024

Abstract
Material extrusion (MEX) is one of the most used additive manufacturing technologies, thanks to its appealing in various industrial fields. Despite its numerous advantages, process quality remains an issue when compared to other, more established technologies. In-process monitoring has become fundamental to meet the tight requirements of precision industries. In this work, a novel MEX monitoring methodology based on point-cloud functional analysis was tested and discussed. Using a blue-laser line profilometer integrated into a consumer 3D printer, the point cloud of each layer was obtained immediately after its completion and subsequently analysed. Functional analysis tools were employed to characterize the surface topography and assess the distribution of material and voids to detect defects. Common defective conditions in the form of excess or lack of deposited material, referred to as overfill and underfill, respectively, were induced by varying printing parameters and the specimen’s cross-section. After slicing the layer surface according to specified height thresholds, functional parameters such as the projected percentage area (PPA), void and material volume percentage and void and material mean thickness were computed and analysed. Results indicated that the PPA values effectively depicted the overall surface conditions and served as a suitable initial step for enabling corrective actions through the contour extraction of defective regions. Meanwhile, the percentage volume and mean thickness of voids and material, referred to as functional parameters, provided more detailed insights into the morphology of the layer surface and the detected defects.
Application and optimal design of high-temperature near-bit instrument in deep shale gas in southern sichuan
Authors: Xiao Meng , Zhang Shengliang , Liu Zeping 
Published: December 2024

Abstract
The exploration of deep shale gas in southern Sichuan faces a challenging operational environment characterized by elevated temperatures, high pressures, and intense vibrations. These conditions cause the heightened failure rate among steerable instruments and low drilling efficiency. In response to these challenges, this study introduces an advanced high-temperature near-bit geosteering drilling system, which can withstand temperature 175°C and pressure 140MPa while adapting to diverse drilling fluid environments. Field tests have been successfully carried out in south Sichuan, effectively improving drilling efficiency. Furthermore, this research identifies deficiencies observed during the Beijing Petroleum Machinery near-drill bit field test and presents multiple optimization strategies aimed at mitigating instrument failures resulting from downhole vibrations. These efforts are directed towards reducing instrument failure rates while offering crucial technical support for the efficient development of deep shale gas resources.
Study on hydrogen and helium chromatographic analysis system
Authors: Zhang Xinghua , Liu Shibin , Guo Zhenhua 
Published: December 2024

Abstract
Rare gas helium is widely used in industrial and high-tech areas, is the national important strategic resources, to lead the scientific and technological innovation has the increasingly important role, helium production capacity in China is relatively scarce, helium gas resources long-term dependence on imports. In order to strengthen the helium resource guarantee capacity of our country and ensure the advantageous position in the international development competition. In recent years, helium detection and analysis in real-time while drilling at wellsite has been gaining interest in the oil and gas industry, the detection of non-hydrocarbon gases such as helium has become the focus of current exploration. In order to meet the demand of hydrogen and helium exploration and development, a set of hydrogen-helium chromatography analyzers is developed. A separation method of helium and hydrogen combined with the pre-analysis column and the main analysis column is proposed to realize the efficient separation of hydrogen and helium, the wide spectrum TCD (Thermal Conductivity Detector) is used as the analyzer to achieve the high efficiency detection of hydrogen and helium. Field application shows that the system can detect hydrogen and helium efficiently within 30 seconds analysis cycle while drilling, which provides a technical means for the detection of hydrogen and helium in the exploration process. It provides a good technical reserve for the future large-scale development and utilization of natural hydrogen and helium.
The study of water-based drilling fluid ion concentration monitoring calibration methods
Authors: Cao Yang , Wu Haosheng , Jia Haijun 
Published: December 2024

Abstract
Drilling fluids are essential components in the exploration for hydrocarbons. The variations in the ionic content of these fluids frequently indicate changes in the underground situation, and the determination of the ionic content is important for the timely detection of changes in the drilling fluids and the prediction of changes in the formations. However, the current ion concentration measurement methods have shortcomings such as insufficient timeliness, significant influence from filtrate pH and temperature, and cumbersome and complex operating procedures that cannot achieve automated measurement. In response to the issues mentioned earlier, where the performance of ion-selective electrodes is influenced by the presence of various ions, PH values, and temperatures in the medium, we propose an automatic measurement method for the ionic content of water-based drilling fluids based on ion-selective electrodes. This method involves the theoretical study of the measuring principle of ideal ion-selective electrodes and the experimental analysis of the measurement errors caused by multiple ions cross-sensitivity, resulting in the development of a multi-linear regression model for ionic content correction. We verify the significance of this model and the relative errors before and after correction, thereby enhancing the accuracy of ion-selective electrodes. Additionally, we conduct theoretical and experimental research on the impact of media PH value changes on the results of ion-selective electrodes, develop a PH compensation model for ion-selective electrodes, and validate it through experiments. Similarly, we investigate the effect of media temperature changes on the results of ion-selective electrodes, develop a temperature compensation model for ion-selective electrodes, and experimentally verify it. The accuracy of ion-selective electrodes is found to meet the requirements of routine drilling operations, with total errors typically less than 10%. This ensures the provision of reliable data support for safe and efficient drilling.
TST-Refrac: A Novel Transformer Variant for Prediction of Production of Re-fractured Oil Wells
Authors: Li Diquan , Jia Jing , Wang Lichang , Yu Hongguang , Jing Jianglu 
Published: December 2024

Abstract
The accurate prediction of post-fracturing production for re-fractured wells is crucial for selecting suitable targets for re-fracturing. Given that the post-fracturing production of re-fractured wells is essentially time series data, this study aims to provide an accurate deep neural network approach for production prediction of re-fractured wells. Traditional deep neural networks, such as Recurrent Neural Networks (RNN) and Long Short-Term Memory Networks (LSTM), are not adept at capturing long-range dependencies. To address this limitation, a new Transformer Variant for the Prediction of Production of Re-fractured Oil Wells is proposed, based on the time window method and the classic Transformer architecture. Experimental results demonstrate that the performance of the Transformer shows significant improvement compared to RNN and LSTM. Utilizing production data from Block W in the J Basin, a random sample of nine wells was selected for model fitting and prediction. The time series Transformer model exhibited the lowest Root Mean Square Error. The successful implementation of the proposed Transformer model for time series demonstrates its capability to effectively capture long-range dependencies, enabling more precise predictions than traditional deep learning methods.
Optimal configuration design and attitude measurement method of redundant accelerometer for steering drilling tools
Authors: Rao Zhihua , Zhang Weiguo , xiao Menglin , Liu Yongfeng , Lei Hong , Chen Bin , Jin Yong 
Published: December 2024

Abstract
In order to improve the accuracy and reliability of attitude parameters measurement of the rotary steerable drilling tool, a slanting configuration method and measurement method of double accelerometer were proposed. The measurement equation of the redundant accelerometer in the drill coordinate system is established. According to the installation position of the accelerometer and sensitive direction, the trouble-free and uniaxial failure in both cases, in order to improve the component of gravity and angular velocity calculation precision as the objective, design measure performance evaluation indexes, the optimal configuration determines the redundant accelerometer. It uses the stability equation constraints gravity no trace adaptive Kalman filter algorithm, Accurate angular velocity measurement, the tool face Angle, and healthy inclination. Simulation and experimental results show that the optimal configuration of the redundant accelerometer is reasonable, and the attitude estimation algorithm is effective.
3D printed β-TCP scaffolds loaded with SVVYGLR peptide for promoting revascularization and osteoinduction
Authors: Gan Lin , Zhao Chaoqian , Chen Haojie , Li Yucai , Pan Zhen , Li Yan , Wang Xiao , Wang Jielin , Yu Jiangming , Ye Xiaojian 
Published: December 2024

Abstract
It is crucial for the successful transplantation of large segmental bone defects to achieve rapid vascularization within bone scaffolds. However, there are certain limitations including uncontrolled angiogenesis and inadequate vascular function. Therefore, there is an urgent need to develop bone scaffolds with functional vascular networks. In our study, porous β-tricalcium phosphate (β-TCP) scaffolds with varying pore sizes were prepared by 3D printing technology, loaded with osteopontin derived peptide Ser-Val-Val-Tyr-Gly-Leu-Arg (SVVYGLR) to induce osteoinduction and angiogenesis. In vitro, the proliferation and migration behaviors of human umbilical vein endothelial cell on scaffolds were assessed by Cell Counting Kit-8, confocal laser scanning microscopy and scanning electron microscopy. And the osteogenic ability of bone marrow mesenchymal stem cells was assessed using alkaline phosphatase staining and Alizarin Red S staining. The messenger ribonucleic acid (mRNA) expression levels of cell adhesion molecule (CD31), vascular endothelial growth factor and hypoxia inducible factor‐1α in each group were detected by quantitative real-time fluorescence polymerase chain reaction (PCR) analysis. In vivo, cube scaffolds were subcutaneously implanted on the right hips of Sprague-Dawley (SD) rats for 6 weeks. Hematoxylin and Eosin staining, Masson’s trichrome staining, and immunohistochemical analysis of osteocalcin and CD31 were performed on slices for every sample with three sections to explore the effect of SVVYGLR-loaded scaffolds on angiogenesis and osteogenic induction for bone reconstruction. The results indicate that 3D printed β-TCP scaffolds loaded with the SVVYGLR peptide offer superior revascularization and osteoinduction to the scaffolds without the SVVYGLR in situ. Moreover, scaffolds with a pore size of 400 µm demonstrate higher effectiveness compared to those with a 150 µm pore size. The distinct hollow channel scaffolds and the specific SVVYGLR peptide substantially improve cell adhesion, spreading, and proliferation, as well as promote angiogenesis and bone formation. Furthermore, scaffolds with a pore size of 400 µm may exhibit greater efficacy compared to those with a pore size of 150 µm. The results of this study provide an idea for the development of practical applications for tissue-engineered bone scaffolds.
Predicting Australian energy demand variability using weather data and machine learning
Authors: Richardson Doug , Hobeichi Sanaa , Sweet Lily-belle , Rey-Costa Elona , Abramowitz Gab , Pitman Andrew J 
Published: December 2024

Abstract
Managing energy systems requires understanding the variability of energy demand, which on daily timescales is driven primarily by the weather. Historical records of demand typically cover 1–2 decades which may be too short to capture the range of possible demand, particularly for a climate with high interannual variability such as that of Australia. Predicting demand using long records of weather data opens the possibility of more robustly estimating true demand variability. We estimate daily energy demand between 2010 and 2019 for Australian states in the National Electricity Market using machine learning with reanalysis weather variables as predictors. We assess the performance of these models and examine their behaviour to identify which weather variables are most important for predicting demand. We then use the models to estimate demand for the period 1959–2022. We use this 64-year record to quantify how the probability of high demand days can change compared to individual 10-year periods and when conditioned by the phase of the El Ni

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o Southern Oscillation (ENSO). Energy demand can be accurately predicted with weather, with median errors of 2%–4% on years omitted from the training. We show that the probability of extreme demand over different 10-year periods can vary from half to twice as likely, depending on the decade. When further conditioned on ENSO phase, the probabilities can be up to 7 times higher than when using the 64-year period, implying a risk of overestimating weather-related energy demand if shorter records are used. We conclude that machine learning methods can accurately predict energy demand using only weather data, enabling us to estimate demand variability over longer time horizons than is possible with demand observations. These longer records are important when attempting to quantify tail risks of demand, and so can help to inform the design of energy systems.
High-resolution simulations of gas-liquid Couette-type sealing gap flows
Authors: Vorspohl J , Burchardt L , Meinke M , Brümmer A , Schröder W 
Published: December 2024

Abstract
The two-phase flow in sealing gaps of twin-screw compressors is investigated using highly resolved coupled multiphase simulations. Oil-injected rotary-type positive displacement compressors (RPDC) such as twin-screw machines are used for the compression of refrigerants in HVAC (Heating, Ventilation, and Air Conditioning) systems. The efficiency of such machines is largely determined by the inevitable two-phase surge and gap flows. The simulation of flows in such compressors is still a challenge due to the complexity of the two-phase flow in the narrow gaps (<0.3mm) with organic working fluids and moving boundaries. Due to the rotation of the screws relative to the housing, gaps are present between the rotors and the end plates of the housing and between the tips of the rotors and the cylindrical housing walls. Oil injection can increase the efficiency of screw compressors or expanders by more than 10%, since oil enhances the sealing of the gaps where leakage occurs. On the other hand, additional momentum losses are caused by the two-phase gap flows. The oil injection, however, increases the hydraulic losses which depend on the geometry of the gap, the liquid, and the kinematics of the established multiphase flow in the gap. In this work, a highly efficient, high-resolution numerical method is used for the simulation of the gas-liquid multiphase flow in the screw compressor gap between the rotor tip and the stationary housing. The Lattice-Boltzmann method (LBM) defines the basis of the method used in this work, forming an in-house developed framework in which each fluid phase is solved by a separate solution algorithm. To capture the motion of the liquid-gas phase boundary, a level-set method is used. In the present work, a generic gap flow is considered. A Couette-type flow configuration, where the sealant flow in the gap is driven by the moving inner wall, is investigated. The Reynolds number based on the height of the channel is Re = 20000. Finally, we discuss the impact of bubble-laden sealing gaps on the momentum losses of the system.
ET-PROTACs: modeling ternary complex interactions using cross-modal learning and ternary attention for accurate PROTAC-induced degradation prediction
Authors: Cai Lijun , Yue Guanyu , Chen Yifan , Wang Li , Yao Xiaojun , Zou Quan , Fu Xiangzheng , Cao Dongsheng 
Published: December 2024

Abstract

Motivation
Accurately predicting the degradation capabilities of proteolysis-targeting chimeras (PROTACs) for given target proteins and E3 ligases is important for PROTAC design. The distinctive ternary structure of PROTACs presents a challenge to traditional drug–target interaction prediction methods, necessitating more innovative approaches. While current state-of-the-art (SOTA) methods using graph neural networks (GNNs) can discern the molecular structure of PROTACs and proteins, thus enabling the efficient prediction of PROTACs’ degradation capabilities, they rely heavily on limited crystal structure data of the POI-PROTAC-E3 ternary complex. This reliance underutilizes rich PROTAC experimental data and neglects intricate interaction relationships within ternary complexes.

Results
In this study, we propose a model based on cross-modal strategy and ternary attention technology, ET-PROTACs, to predict the targeted degradation capabilities of PROTACs. Our model capitalizes on the strengths of cross-modal methods by using equivariant GNN graph neural networks to process the graph structure and spatial coordinates of PROTAC molecules concurrently while utilizing sequence-based methods to learn the protein sequence information. This integration of cross-modal information is cohesively harnessed and channeled into a ternary attention mechanism, specially tailored for the unique structure of PROTACs, enabling the congruent modeling of both PROTAC and protein modalities. Experimental results demonstrate that the ET-PROTACs model outperforms existing SOTA methods. Moreover, visualizing attention scores illuminates crucial residues and atoms pivotal in specific POI-PROTAC-E3 interactions, thus offering invaluable insights and guidance for future pharmaceutical research.

Availability and implementation
The codes of our model are available at https://github.com/GuanyuYue/ET-PROTACs
New insights into the mechanisms of aspirin-exacerbated respiratory disease
Authors: Laidlaw Tanya M. 
Published: December 2024

Purpose of review
Aspirin-exacerbated respiratory disease (AERD), a syndrome characterized clinically by asthma, chronic rhinosinusitis with nasal polyposis, and respiratory reactions to aspirin and other cyclooxygenase-1 inhibitors, is an inflammatory condition of the respiratory tract that is often severe and challenging to treat. There have been several recent advances in our understanding of the underlying pathology of the disease. These have been paralleled by welcome advances in the availability of targeted treatment options for patients with AERD.

Recent findings
Spurred in part by results from trials of targeted biologic therapies, along with single cell genomics, there is now clear evidence that the chronic respiratory inflammation in AERD is driven by combination of local tissue factors. These include abnormalities in effector cell populations, with increased accumulation and activation of mast cells and plasma cells in the nasal polyp, along with notable epithelial barrier dysregulation. The key mediators now identified include high levels of both type 2 inflammatory cytokines (IL-4, IL-5, IL-13) and cytokines involved in broader inflammatory pathways (IL-33, TSLP, IL-6, oncostatin M), as well as the overproduction of cysteinyl leukotrienes, and the underproduction of prostaglandin E2.

Summary
This review covers the latest insights into the immunopathogenesis of and targeted treatment of AERD, including the roles of lipids, effector cells, and inflammatory cytokines, and discusses unanswered questions regarding its pathogenesis and potential future therapies.
Update on aspirin exacerbated respiratory disease with chronic rhinosinusitis
Authors: Gandre Jason R. , Ledford Dennis K. 
Published: December 2024

Purpose of review
This review provides the current understanding on the mechanism, diagnosis, and treatment of aspirin exacerbated respiratory disease (AERD) with chronic rhinosinusitis (CRS).

Recent findings
Updates focus on the current understanding of type 2 inflammation as a disease driver, alterations in gene expression in nasal polyps, and use of biologics in treating aspirin exacerbated respiratory disease. Recent findings include altered expression of GATA binding protein 3 (GATA3), interleukin (IL)-4, IL-5, and IL-17 in nasal polyps supports the current understanding that type 2 inflammation predominantly drives the pathophysiology of AERD with CRS. From a clinical standpoint, biologics offer an effective treatment option to address type 2 inflammation. Biologics should not be favored over endoscopic sinus surgery and aspirin desensitization with daily aspirin therapy (unless contraindication are present) due to high associated cost and failure to achieve remission.

Summary
This review outlines the current approach for diagnosis and treatment of aspirin exacerbated respiratory disease with a focus on desensitization protocols, the importance of endoscopic sinus surgery, the role of biologics, and the use of leukotriene modulators.
Lung regeneration and lung bioengineering
Authors: Cohen Edmond 
Published: December 2024

Purpose of review
Chronic obstructive pulmonary disease affects more than 65 million people worldwide. Lung transplantation is the only definitive treatment. However, donor availability is limited in meeting the demand.

Recent findings
Lung regeneration is a new therapeutic strategy that uses the patient's stem cells to replace dysfunctional tissue and restore functional lung tissue rather than alleviate symptoms. Organoids are a new promising target for human lung regeneration. The AEP cells are isolated from human lung tissue for growth. The 3D organ-like structures conserve the alveolar progenitor's capacity to proliferate and differentiate into various epithelial cell types.
Bioengineered organs, from a patient's cells, allow for customized biocompatible organs-on-demand without the need for immunosuppressive therapy. The concept involves the creation of a form of 3D tissue scaffold, to be populated by cells of the desired tissue to be transplanted into the patient, allowing for function as closely to the native organ as possible.

Summary
The lung's ability to regenerate extensively after injury suggests that this capability could be promoted in diseases in which loss of lung tissue occurs. Lung bioengineering offers the potential to drastically extend life expectancy in patients with end-stage lung disease. If lung reengineering were successful, it would revolutionize the world of transplantation.