bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study on simulation method of propeller cavitation
Authors: Wang Ru 
Published: July 2024

Abstract
With the continuous improvement of EEDI requirements and high fuel prices, people have higher and higher requirements for the efficiency of merchant propellers, but the efficiency of propellers is limited by cavitation, so it is one of the difficult problems in engineering to quickly evaluate the cavitation performance of propellers. The cavitation performance of the propeller in a viscous uniform flow field is calculated and studied by using computational fluid dynamics commercial software, and the cavitation distribution on the blade surface of the DTMB 4381 propeller under specific working conditions is simulated to study the influence mechanism of cavitation performance. It provides an important guiding direction for the optimal design of propellers and the reduction of ship energy consumption.
Research on three-dimensional dynamic design and decision system of airport
Authors: Shen Peng , Zhang Yan 
Published: July 2024

Abstract
Due to the lack of elevation information, the traditional airport design system based on the AutoCAD 2D platform is often unable to make a comprehensive and objective analysis and judgment when the designer evaluates the advantages and disadvantages of the design scheme. Therefore, this paper focuses on the development of airport 3D dynamic design and decision systems based on the Cesium framework. Firstly, based on the 3D geographic information platform, a multi-type 3D digital map loading and display module will be developed to realize the combination of 3D geographic information and the airfield model. Then, according to the 3D model data and the characteristics of the platform development, the method of calculating the earth volume of clearance processing, the evaluation algorithm of airport clearance obstacle limit surface, the optimization analysis method of tower position, and the analysis method of navigation station and flight track visibility are constructed.
Multi-objective topology optimization simulation study based on motor
Authors: Li Qiding , Huang Zhengping , Li Ketian , Wang Xiying , Yu Cheng , Li Bing 
Published: July 2024

Abstract
OptiStruct software is applied for structural optimization of the motor structure to solve the problem of resonance in a specific operating frequency range and reduce the motor operating noise. Comparing the experimental and simulated modal frequencies, a modified multi-objective topology optimization model is proposed to ensure the validity of the model, and a new optimization function is constructed to optimize the multi-objective topology of the motor housing. The results show that the geometric reconfiguration of the motor housing concerning the optimized model force transmission path achieves the effectiveness of reducing resonance noise. The multi-objective topology optimization model provides a feasible way to solve the motor-specific frequency resonance and operation noise problems.
A novel tuning range search method for single soliton state micro-comb generation
Authors: Ma Xi , Zhao Yi , Zhao Weigang , Tan Qinggui 
Published: July 2024

Abstract
Mode-locked single-soliton state micro-comb (SSC) can be achieved in a miniaturized highly compact platform by pumping the micro-resonator with a single-frequency continuous wave laser. However, a significant challenge lies in effectively exploring the tuning range of pump power, spanning over hundreds of mW, as well as the detuning range and tuning speed in the range of hundreds of MHz and the order of MHz/s to GHz/s respectively. Here, we delve into a simulation model, considering thermal effects to ensure that the simulation accurately reflects the power drop and thermal detuning observed in the experimental process. Our simulation model, featuring precision scanning and iterative parameter demonstrations, has proven instrumental in accurately exploring the optimal tuning range for the existence of SSC to a few MHz. This provides a foundation for controlled and predictable SSC generation experiments.
Design of petal-shaped probe for pulse eddy current testing based on finite element simulation
Authors: Zhou Haiting , Zhu Chenxi , Hu Qin , Mu Yanchun 
Published: July 2024

Abstract
This paper employs finite element simulation techniques to devise an innovative array-type petal-shaped pulsed eddy current testing probe and investigates its distinctions related to conventional cylindrical probes. Within the simulation framework, a thorough comparative analysis of the detection capabilities of the two probe types is conducted, encompassing an examination of the eddy current regions formed, the impact of lift-off effects and sensitivity to circular corrosion defects. The simulation outcomes reveal that the probe designed in this study exhibits a more extensive detection range and superior detection performance in comparison to its counterparts. This observation holds significance for defect detection in coated materials and the refinement of pulsed eddy current testing probe designs.
Target threat assessment model based on PCA-BP neural network
Authors: Shang Jielin , Chen Tong , Dou Jie 
Published: July 2024

Abstract
In this paper, the air threat received in modern naval warfare is taken as the background, and based on the threat assessment index, PCA and BP neural networks are used to establish a target threat assessment model. Through simulation and analysis, the threat values of different incoming targets are derived, and the data results of this model and other models are compared. It is concluded that the results of this model are basically consistent with the original values, and the error is significantly smaller than that of the other models, which realizes the real-time dynamic detection of threatening targets, and provides a strong support for the subsequent combat program.
Performance of PSG in retrieving exoplanet parameters from JWST data simulated with several tools
Authors: Barbier H. , López E. , Mayorga D. , Cherres D. 
Published: July 2024

Abstract
Our research explores different methods of utilizing several known tools (petitRADTRANS, PandExo, and PSG) to simulate spectral data acquired by the James Webb Space Telescope (JWST). We specifically focus on how the choice of method affects the performance of the Planetary Spectrum Generator (PSG) in retrieving exoplanet parameters from such data. To investigate this, we consider the case of exoplanet TOI-1266 c being observed by JWST’s Near Infrared Spectrograph in the Bright Object Time Series mode. Our results show that differences between PSG and PandExo regarding their noise simulation and method of adjusting spectral resolution lead to significant differences in retrieval quality. When utilizing PandExo to simulate noise for non-binned down spectral data, we recommend ensuring that the resolution of the theoretical forward model spectrum input into PandExo closely matches the resolution of the output spectral data, within the same order of magnitude.
About the 3D virtualization of the Millikan oil drop experiment
Authors: Ronquillo Daniel 
Published: July 2024

Abstract
Robert Millikan’s experiment, conducted in 1909, aimed to measure the charge of the electron. This experiment holds immense significance in the fields of electricity and magnetism and is widely performed by physics and chemistry students worldwide. While there are simplified two-dimensional recreations of this experiment available, obtaining measurements that closely resemble reality through a three-dimensional recreation is not easily accessible. This article delves into the development process of this three-dimensional recreation, achieved through reverse engineering of the original experiment using the Unity video game engine. Additionally, it incorporates laboratory equipment, user manuals, and a student guide to ensure a comprehensive experience. By comparing the data obtained from the virtual recreation with that of the real experiment, it was observed that the precision of the virtual experiment resulted in a mere 7% error. Consequently, the virtual experiment can be used as a substitute for the real experiment when circumstances prevent its execution, without compromising the hands-on experience.
Sokoban percolation on the Bethe lattice
Authors: Bonomo Ofek Lauber , Shitrit Itamar , Reuveni Shlomi 
Published: July 2024

Abstract
‘With persistence, a drop of water hollows out the stone’ goes the ancient Greek proverb. Yet, canonical percolation models do not account for interactions between a moving tracer and its environment. Recently, we have introduced the Sokoban model, which differs from this convention by allowing a tracer to push single obstacles that block its path. To test how this newfound ability affects percolation, we hereby consider a Bethe lattice on which obstacles are scattered randomly and ask for the probability that the Sokoban percolates through this lattice, i.e. escapes to infinity. We present an exact solution to this problem and determine the escape probability as a function of obstacle density. Similar to regular percolation, we show that the escape probability undergoes a second-order phase transition. We exactly determine the critical obstacle density at which this transition occurs and show that it is higher than that of a tracer without obstacle-pushing abilities. Our findings assert that pushing facilitates percolation on the Bethe lattice, as intuitively expected. This result, however, sharply contrasts with our previous findings on the 2D square lattice, where the Sokoban cannot escape even at obstacle densities well below the regular percolation threshold. This indicates that the presence of a regular percolation transition does not guarantee a percolation transition for a pushy tracer. The stark contrast between the Bethe and 2D lattices also highlights the significant impact of network topology on the effects of obstacle pushing and underscores the necessity for a more comprehensive understanding of percolation phenomena in systems with tracer-media interactions.
Corrigendum to “Airfoil friction drag reduction based on grid-type and super-dense array plasma actuators”
Authors: FANG 方 Ziqi 子淇 , ZONG 宗 Haohua 豪华 , WU 吴 Yun 云 , LIANG 梁 Hua 华 , SU 苏 Zhi 志 
Published: July 2024

Abstract
Figure 4(b) of the paper [Plasma Sci. Technol.
26 (2024) 025503] has been corrected. This modification does not affect the result presented in the paper.
Regulating electronic structure of anionic oxygen by Ti4+ doping to stabilize layered Li-rich oxide cathodes for Li-ion batteries
Authors: Zhu Xinyu , Hao Luqi , Li Yongjian , Chen Lai , Huang Qing , Lu Yun , Li Ning , Su Yuefeng 
Published: July 2024

Abstract
Layered Li-rich oxide cathodes enable to activate lattice oxygen anions redox in the charge compensation process and provide superior high specific capacity over 250 mAh g−1 due to their unique configuration, and thus attracting great attentions as promising cathode candidates for Li-ion batteries. However, how to better stabilize the bulk lattice oxygen framework and surface structure, and slow down the release of oxygen, is still major bottleneck to develop high performance Li-rich materials. Transition metal ions with outer d0 electronic configuration have distortable configuration, which can accommodate the local structure and chemical environment of the material, and then improve structural stability. Herein this work, the d0 transition metal Ti4+ is used as doping element to improve the chemical and structural stability, capacity retention and lithium ion diffusion kinetics of Li-rich material. The role of Ti in the material modification is revealed through synchrotron-based soft x-ray absorption spectroscopy, XRD, XPS and electrochemical tests. The improvement in structural stability can be attributed to that Ti doping can adjust the hybridization of O2p and TM3d to regulate the local electronic structure of both bulk lattice oxygen and surface oxygen vacancies. It is hoped that this work should shed light on the development of high-performance cathode materials for Li-ion Batteries.
Agreement between the National Cancer Institute’s Diet History Questionnaire II and III in a preconception cohort
Authors: Julián-Serrano Sachelly , Koenig Martha R , Wang Tanran R , Wesselink Amelia K , Hatch Elizabeth , Wise Lauren A , Tucker Katherine L 
Published: July 2024

Abstract
Food frequency questionnaires require updating over time due to population changes in diet, posing analytical challenges in consistently measuring diet in prospective studies. We compared reliability and agreement between nutrients in versions of the National Cancer Institute’s web-based Diet History Questionnaire (DHQ, III vs II) in an ongoing North American preconception study. We invited 51 consecutively enrolled US female participants aged 21-45 years to complete both DHQ versions within a 2-week period in a randomized order. We compared 30 nutrients from both DHQ versions and calculated within-person reliability using intraclass correlation coefficients (ICCs). Bland–Altman plots and 95% limits of agreement (LOA) were generated to assess nutrient agreement between DHQ versions. We observed highest reliability in percent energy from carbohydrates and cholesterol (ICCs, 0.88; 95% IC, 0.80-0.93) and lowest for percent energy from protein and vitamin D (ICCs, 0.56; 95% IC, 0.34-0.72). At the group level, all nutrients had most observations within the LOA. Bland–Altman plots showed assessment differences between DHQs for protein, fat, monounsaturated fat, and vitamin D. The remaining nutrients showed good agreement and good to moderate reliability. Some nutrients may require adjustment and calibration analysis before using them interchangeably across DHQ versions.
Invited commentary: deep learning—methods to amplify epidemiologic data collection and analyses
Authors: Quistberg D Alex , Mooney Stephen J , Tasdizen Tolga , Arbelaez Pablo , Nguyen Quynh C 
Published: July 2024

Abstract
Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorithms, that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 2023;192(11):1904-1916) presented a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high-dimensional data. The tools for implementing deep learning methods are not as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, health care providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiologic principles of assessing bias, study design, interpretation, and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.
Invited commentary: policies that support working parents and gender health equity—needed research and methodological challenges
Authors: Schnake-Mahl Alina , Jahn Jaquelyn 
Published: July 2024

Abstract
In a recent issue of the Journal, Platt et al (Am J Epidemiol. 2024;193(10):1362-1371) shed new light on the potential for supportive employment benefits, including family leave, flexible work hours, and employer-provided or subsidized childcare, to mitigate the risk of depression among full-time working mothers. The authors used a longitudinal study design and rigorous methods to carefully consider potential sources of bias, and, more broadly, their article underscores the importance of employment benefits as a social determinant of mental health for working mothers. In this commentary, we discuss some of the policy context surrounding employer benefits that support parenting, particularly around paid versus unpaid family leave laws and ordinances. We consider the ways in which the policy context affects larger structural inequities and the potential implications for internal and external validity.
This article is part of a Special Collection on Mental Health.
A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation
Authors: Tang Xiangru , Dai Howard , Knight Elizabeth , Wu Fang , Li Yunyang , Li Tianxiao , Gerstein Mark 
Published: July 2024

Abstract
Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.
GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity
Authors: Jiang Feng , Guo Yuzhi , Ma Hehuan , Na Saiyang , Zhong Wenliang , Han Yi , Wang Tao , Huang Junzhou 
Published: July 2024

AbstractThe interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR–epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR–epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR–epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR–epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.
A survey of experimental and computational identification of small proteins
Authors: Beals Joshua , Hu Haiyan , Li Xiaoman 
Published: July 2024

Abstract
Small proteins (SPs) are typically characterized as eukaryotic proteins shorter than 100 amino acids and prokaryotic proteins shorter than 50 amino acids. Historically, they were disregarded because of the arbitrary size thresholds to define proteins. However, recent research has revealed the existence of many SPs and their crucial roles. Despite this, the identification of SPs and the elucidation of their functions are still in their infancy. To pave the way for future SP studies, we briefly introduce the limitations and advancements in experimental techniques for SP identification. We then provide an overview of available computational tools for SP identification, their constraints, and their evaluation. Additionally, we highlight existing resources for SP research. This survey aims to initiate further exploration into SPs and encourage the development of more sophisticated computational tools for SP identification in prokaryotes and microbiomes.
Malignant paraganglioma of the prostate invading the bladder and bilateral seminal vesicles: a case report
Authors: Tao Jian , Ma Haiyan , Zeng Xianchun 
Published: July 2024

Abstract
Malignant paraganglioma (PGL) of the prostate is extremely rare, with only 3 cases reported in the English literature to date. In this article, we present a case of malignant prostatic PGL invading the bladder and bilateral seminal vesicles, in which the patient had a history of long-term haematuria and normal serum prostate specific antigen (PSA) level, and was misdiagnosed as a bladder tumour invading the prostate preoperatively. As this case belongs to functional tumour, there is a risk of developing hypertensive crisis during diagnostic biopsy or radical resection. The CT manifestations of prostatic PGL are characteristic, but its imaging features are rarely described due to the rarity of the tumour site. Meanwhile, improving the comprehensive understanding of CT, MRI, functional imaging, and clinical features of prostate PGL is conducive to make the correct diagnosis before surgery and ensure the safety of surgical treatment.