bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of Borderline Fetal Growth with Progression with Fetal Growth Restriction
Authors: Bronner Baillie A. , Holod Monique , Schermerhorn Margaret , Sung Juliana , Mccormick Anna C. , Reyes Samantha De Los 
Published: October 2024

Objective This study aimed to evaluate if an estimated fetal weight (EFW) between 10 and 15th percentiles at the time of anatomy ultrasound, referred to as borderline fetal growth, is associated with progression to fetal growth restriction (FGR) on subsequent ultrasound, delivery of a small for gestational age (SGA) neonate, or neonatal intensive care (NICU) admission.
Study design We performed a secondary analysis using the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMom2b) data. The exposures were normotensive pregnancies with non-anomalous singleton gestations with normal growth, defined as EFW >15th percentile at the anatomy scan compared to borderline fetal growth fetuses defined as those with an EFW in the 10 to 15th percentiles. The primary outcome was FGR at subsequent ultrasound, defined as EFW or AC <10%. The secondary outcomes were NICU admission and SGA neonate. Univariable analyses were performed comparing maternal baseline demographic and clinical characteristics. Multivariable analysis was performed for the primary outcome with variables adjusted a priori for body mass index, smoking status, race/ethnicity, insurance status, and drug use.
Results In total, 4,883 patients met inclusion criteria with 114 in the borderline fetal growth group and 4,769 in the normal growth group. There were no significant differences in maternal demographic or medical characteristics. In adjusted multivariable analysis, patients with borderline growth had significantly higher odds of being diagnosed with FGR at their subsequent scan (adjusted odds ratio [aOR] = 6.68, confidence interval [CI]: 3.98–11.20) compared to those with normal growth. For secondary outcomes, patients with borderline fetal growth were significantly more likely to have SGA neonates (6.14 vs. 2.67%, p = 0.025). There was no difference in admissions to the NICU between groups.
Conclusion Diagnosis of borderline fetal growth at the time of the anatomy scan was associated with significantly increased odds of progression to FGR at subsequent scans and delivery of an SGA neonate.
Key Points
Efficacy and safety of endoscopic ultrasonography-guided ethanol injections of small pancreatic neuroendocrine neoplasms: a prospective multicenter study
Authors: Matsumoto Kazuyuki , Kato Hironari , Itoi Takao , Kitano Masayuki , Hara Kazuo , Kuwatani Masaki , Takenaka Mamoru , Ashida Reiko , Mukai Shuntaro , Okuno Nozomi , Kawakubo Kazumichi , Yamazaki Tatsuhiro , Sakurai Jun , Nakatsuka Yuki , Yoshida Michihiro , Otsuka Motoyuki 
Published: October 2024

AbstractEndoscopic ultrasonography (EUS)-guided ethanol injection (EI) has recently been introduced as one of the management strategies for pancreatic neuroendocrine neoplasms (PNENs); however, its role as a surgical alternative is unclear. We evaluated the efficacy and safety of EUS-EI in treating small PNENs through a prospective multicenter study.Patients with grade 1 tumors of ≤15 mm confirmed by pathology were included. The primary end point assessed efficacy and safety, measuring complete ablation using computed tomography at 1 and 6 months, prevention of adverse events (AEs) within 1 month, severe pancreatic fistula at 1 month, and incidence/worsening of diabetes mellitus (DM) at 6 months. The composite end point of EUS-EI was compared with that of historical results of a study based on surgical treatment.25 patients with PNENs, with a median tumor size of 10.1 mm, were treated using EUS-EI. The composite primary end point was achieved by 76.0% of patients (19/25; 95%CI 54.9%–90.6%), a proportion significantly higher than that of surgical treatment (P = 0.008). Regarding efficacy, 88.0% (22/25) of patients achieved complete ablation at 1 and 6 months (95%CI 68.8%–97.5%). Regarding safety, 96.0% (24/25) of patients had no severe AEs within 1 month (95%CI 79.7%–99.9%). No patients had severe pancreatic fistulas at 1 month, and 84.0% (21/25) had no incidence or exacerbation, or both, of DM at 6 months (95%CI 63.9%–95.5%).EUS-EI is safe and could be a potent treatment option for patients with small PNENs.
Artificial intelligence for low income countries
Authors: Khan Muhammad Salar , Umer Hamza , Faruqe Farhana 
Published: October 2024

AbstractThe global adoption rate of artificial intelligence (AI) is rising, indicating its transformative potential. However, this adoption is far from uniform, with low-income countries (LICs) trailing behind significantly. Despite needing AI for development, LICs face multiple challenges in harnessing its benefits, exacerbating existing global disparities in technology adoption. In spite of the potentially important role that AI can play in the development of LICs, AI literature overlooks these countries, with research predominantly focused on more advanced economies. This lack of inclusivity contradicts the principles of distributive justice and global equity, prompting us to explore the importance of AI for LICs, offer a theoretical grounding for AI catch-up, identify effective AI domains, and propose strategies to bridge the AI gap. Drawing insights from the leapfrogging and absorptive capacities literature, our position paper presents the feasibility of AI catch-up in LICs. One crucial finding is that there is no one-size-fits-all approach to achieving AI catch-up. LICs with strong foundations could favor leapfrogging strategies, while those lacking such foundations might find learning and acquisition prescriptions from absorptive capacity literature more relevant. The article also makes policy recommendations that advocate for the swift integration of AI into critical LIC domains such as health, education, energy, and governance. While LICs must address challenges related to digital infrastructure, human capital, institutional robustness, and effective policymaking, among others, we believe that advanced AI economies and relevant international organizations like UNESCO, OECD, USAID, and the World Bank can support LICs in AI catch-up through tech transfer, grants, and assistance. Overall, our work envisions global AI use that effectively bridges development and innovation disparities.
Multi-head attention-based variational autoencoders ensemble for remaining useful life prediction of aero-engines
Authors: Wang Yuxiao , Suo Chao , Zhao Yuyu 
Published: October 2024

Abstract
Accurate remaining useful life (RUL) prediction of aero-engines through condition monitoring (CM) data is of great significance for flight reliability and safety. Although deep learning (DL)-based approaches have been widely considered, individual DL models suffer from significant stochasticity and limited generalizability when predicting the RUL. To solve this issue, a novel multi-head attention-based variational autoencoders (MHAT-VAEs) ensemble model is proposed. Two distinct MHAT-VAEs are designed, employing linear and convolutional operations to capture global and temporal compressed representations of the CM data. Additionally, a dual-level ensemble strategy is introduced to adaptively fuse the outputs of the two base learners. A hyperparameter optimization method is also implemented to further enhance the efficiency and performance of the base learners. The effectiveness of the proposed method is validated using the C-MAPSS and N-CMAPSS datasets, with experimental results showing that it outperforms state-of-the-art approaches.
Performance enhancement of a magnetoactive elastomer actuator through a coupled magnetoelastic topology optimization scheme
Authors: Bergen Christian , Ounaies Zoubeida , von Lockette Paris 
Published: October 2024

Abstract
We have found that considering local magnetic fields, large deformations, and magnetoelastic coupling simultaneously significantly affect the resulting shape in magnetoelastic topology optimization in a uniaxial actuator case. In contrast to the work presented here, other works incorporate magnetoelastic formulations that include simplifying assumptions on the local field, and subsequent effects on the magnetization response of the material, or the absence of large deformation mechanics, or both. These assumptions were shown to produce solutions that differ substantively from cases where local fields and large deformations are addressed concurrently. Magnetoelastic topology optimization schemes are needed to optimize magnetoactive elastomer (MAE) devices. MAE devices are magnetic particle-filled polymer matrices designed for specific actuations and controlled remotely by an external magnetic field. They garner considerable research interest as an emerging technology for actuators in soft robots or in applications requiring untethered actuation. The material properties of MAEs are dependent on the volume fraction of particles in the elastomer matrix, where a high-volume fraction increases relative permeability (for soft magnetic particles) but also increases elastic modulus. For optimal actuation, a tradeoff between low stiffness and high magnetic response must be made by adjusting volume fraction and controlling material placement. Using a topology optimization scheme that considers both the magnetic and mechanical properties of the material, the shape and material composition of the device can be tuned to best achieve the desired actuation displacement. In this work, a two-dimensional density-based magnetoelastic multimaterial topology optimization scheme for soft magnetic material is implemented in COMSOL Multiphysics. The optimization scheme uses multiphysics coupling that considers local magnetic fields and large deformations at each iteration through a Maxwell stress tensor formulation. A simulated example is then considered to demonstrate the effectiveness and necessity of a coupled optimization. The effect of considering large deformations during optimization is also investigated. It was found that a coupled topology optimization scheme with large deformations produced shapes with modes of actuation not captured by schemes with simplifying assumptions, leading to better performance at lower material cost. Considering large deformations in the coupled scheme offered significantly better performance, with an increase of 81.3% in a side-by-side performance simulation when compared to uncoupled cases.
Combining underground and on-surface third-generation gravitational-wave interferometers
Authors: Iacovelli Francesco , Belgacem Enis , Maggiore Michele , Mancarella Michele , Muttoni Niccolò 
Published: October 2024

Abstract
Recently, detailed studies have been made to compare the performance of the European next generation GW observatory Einstein Telescope (ET) in a single-site triangular configuration with the performance of a configuration featuring two L-shaped detectors in different sites, still taken to have all other ET characteristics except for the geometry, in particular, underground and composed of a low-frequency interferometer working at cryogenic temperatures and a high-frequency interferometer working at room temperature.
Here we study a further possibility for a European network, made by a single L-shaped underground detector, like one of the detectors considered for the 2L version of ET, and a single third-generation 20-km L-shaped interferometer on the surface.
We compare the performances of such a network to those of the triangle and of the 2L-underground ET configurations. We then examine the performance of an intercontinental network made by a 40-km CE in the U.S., together with any of these European networks.
Survival of gas in subhalos and its impact on the 21 cm forest signals: insights from hydrodynamic simulations
Authors: Naruse Genki , Hasegawa Kenji , Kadota Kenji , Tashiro Hiroyuki , Ichiki Kiyotomo 
Published: October 2024

Abstract
Understanding the survival of gas within subhalos under various astrophysical processes is crucial for elucidating cosmic structure formation and evolution. We study the resilience of gas in subhalos, focusing on the impact of tidal and ram pressure stripping through hydrodynamic simulations. Our results uncover significant gas stripping primarily driven by ram pressure effects, which also profoundly influence the gas distribution within these subhalos. Notably, despite their vulnerability to ram pressure effects, the low-mass subhalos can play a pivotal role in influencing the observable characteristics of cosmic structures due to their large abundance.

Specifically, we explore the application of our findings to the 21 cm forest, showing how the survival dynamics of gas in subhalos can modulate the 21 cm optical depth, a key probe for detecting minihalos in the pre-reionization era.
Our previous study demonstrated that the 21 cm optical depth can be enhanced by the subhalos, but the effects of tidal and ram pressure stripping on the subhalo abundance have not been fully considered.
In this work, we further investigate the contribution of subhalos to the 21 cm optical depth with hydrodynamic simulations, particularly highlighting the trajectories and fates of subhalos within mass ranges of 104-6
M
⊙
h
-1 in a host halo of 107
M
⊙h-1, and subhalos within mass range of 104-5
M
⊙h-1 in a host halo of 106
M
⊙h-1.
Despite their susceptibility to ram pressure stripping, the contribution of abundant low-mass subhalos to the 21 cm optical depth is more significant than that of their massive counterparts primarily due to their greater abundance. We find that the 21 cm optical depth can be increased by a factor of approximately two due to the abundant low-mass subhalos. However, this enhancement is about twice as low as previously estimated in our earlier study, a discrepancy attributed to the effects of ram pressure stripping. Our work provides critical insights into the gas dynamics within subhalos in the early universe, highlighting their resilience against environmental stripping effects, and their impact on observable 21 cm signals.
Study on the characteristics of fluid exciting force on impeller of high-speed centrifugal pump under the condition of poiseuille inflow
Authors: Wang Hao , Li Yibin , Lei Mengxiang , Ren Haowei 
Published: October 2024

Abstract
The paper adopted the turbulence model of Wall-Model Large Eddy Simulation with improved algebraic form (WMLES S-Omega) to carry out the study on the fluid exciting force on impeller of high-speed centrifugal pump under the conditions of Poiseuille inflow and uniform inflow. The external characteristics and the vibration displacement of the shaft of high-speed centrifugal pump are tested. The results indicated that it is the high matching that the test and numerical external data of high-speed centrifugal pump under the condition of Poiseuille inflow. The test results of vibration displacement of shaft match the fluid exciting force better in terms of waveform and phase difference under the condition of Poiseuille inflow. The contribution degree of force and torque from the shroud inner surface in the X and Y direction is 76.3%, 80.7%, 79.5% and 85% of the resultant force and torque, and its force from the inner and outer surfaces of shroud and hub in the Z direction is 94.3%, and its torque from blades’ surfaces in the Z direction is 98.3%. The study results can provide some theoretical support for the research of fluid exciting force and hydraulic optimization design of centrifugal pump.
Construction of data base containing flow field information under different blade placement of pumping station in digital twin
Authors: Xu Wentao , Cheng Li , Jiang Hongying , Yan Hongqin , Pan Weifeng 
Published: October 2024

Abstract
The data base in digital twin technology includes various data such as the structure, performance, and operating status of the physical object. And the data base is the key point for the construction of the digital twin system. However, the data bases of different digital twin systems usually contain different information. In this paper, the flow state of the pumping station under different angles of four blades is selected as the research object. The research methods in this paper mainly include Latin hypercube sampling, numerical simulation and neural network prediction. The Latin hypercube sampling is used to select the typical Angle difference model. The numerical simulation is used to obtain flow field information. The neural network prediction method is used to construct a complete data base. The study incorporates an actual large-scale mixed-flow pump station to head, efficiency, and shaft power as the prediction targets. It utilizes the different angle difference data of ten sets of four blades as the training set, forty sets of remaining samples as the experiment set, and employs the backpropagation neural network method for constructing the prediction network. By changing the experiment set and test set, the method can efficiently complete the construction of data base including actual test and numerical simulation, which provides a solid foundation for the construction of digital twin platform.
Prediction of Pressure Fluctuation of Double-Suction Centrifugal Pump in Different Cavitation Conditions based on EMD-GRU
Authors: Du Yuxin , Lu Hongzhong , Yao Zhifeng , Tao Ran , Xiao Ruofu , Wang Fujun 
Published: October 2024

Abstract
The double-suction centrifugal pumps play a crucial role in various applications such as in irrigation-drainage projects, water diversion projects, and urban water supply and drainage projects. The pressure fluctuation is one of the crucial parameters for assessing the operational stability of pump. The pressure fluctuation exhibits distinct characteristics in various cavitation conditions. A pressure fluctuation prediction method based on Empirical Mode Decomposition (EMD) and Gated Recurrent Unit (GRU) is proposed in this paper. The model enables accurate forecasting of pressure fluctuation signals when pump operates at different cavitation conditions. The data were obtained from experimental measurements of a double-suction centrifugal pump. The original signal was decomposed into multiple sub-signals with varying energy levels using the EMD method. The neural network of GRU was employed for training and data prediction on individual sub-signals. The predicted results of each sub-signal were combined to form the final pressure fluctuation signals. The method based on EMD-GRU proves effective in predicting pressure fluctuation in three different cavitation conditions. The mean absolute error (MAE) in time-domain characteristics is below 6%, and the prediction error for the main frequency is below 1%. The prediction of low-frequency characteristics still requires improvement.
Observation of Superposition of Cavitation Instability in Liquid Rocket Inducer by Multi-point Pressure Measurement
Authors: Harada Sota , Nakura Yuto , Tamura Koki , Kawasaki Satoshi , Iga Yuka 
Published: October 2024

Abstract
The purpose of this study is to investigate the relationship between the growth and contraction processes of each blade cavity and cavitation instability, and to conduct multi-point pressure measurement tests. The experiments will be conducted using cavitation water flow tunnels installed at the Kakuda Space Center of the Japan Aerospace Exploration Agency (JAXA). In the multi-point pressure measurement experiment, a number of pressure sensors are installed in the inducer casing, and the cavity area can be estimated and the cavity length can be obtained by creating a pressure distribution between the blades during the depressurization experiments. This method makes it possible to continuously determine cavity length variation during inducer experiments and to investigate the cavity growth process. In addition, cavity length variation can be obtained with higher time resolution than in visualization experiment, allowing more detailed investigation of the unsteady cavity behaviour during cavitation instability. And two types of superposition cavitation instability were found in this study: one is Super-S R.C. and C.S., the other is Sync R.C. and C.S..
Prediction of cavitation using machine learning techniques on centrifugal pump
Authors: Stephen Christopher , Guguloth Vivek , Sivasailam Kumaraswamy , Gu Yandong , Parmar Richa , Banerjee Chandan 
Published: October 2024

Abstract
When the local static pressure drops below the fluid’s vapour pressure at that particular temperature, a dynamic process known as cavitation takes place in the liquid. Due to cavitation, the performance (total head and flow rate) of the pump will deteriorate, and hence the vibration and noise will develop. Both the performance and cavitation investigations are carried out for a low-specific-speed pump as per the testing standards. The results obtained during cavitation studies are extracted to train the machine learning algorithms. The flow rate, speed, torque, suction head, and delivery head are considered as input variables for machine learning, with total head, Net Positive Suction Head (NPSH), efficiency, and noise magnitude being output variables. The current studies employ four machine learning techniques: random forest, support vector machines, extreme gradient boosting, and linear regression. In the analysis, notable performance was observed for the random forest and extreme gradient boosting algorithms among others consistently demonstrated superior predictive capabilities for the output parameters of the pumps. For linear regression, random forest, and extreme gradient boosting, the coefficient correlation values are around one for all output parameters except for noise. Similar observation is depicted for coefficient of determination and normalized root mean squared error. These ensure the level of noise during cavitation is very dynamic. These findings will enable the pump user to incorporate AI techniques for the diagnosis of centrifugal pump operation with reference to the nominal flow rate (best efficiency flow). Through this technology, preventive and predictive maintenance of pumps can be adapted with less downtime in process industries, power plants, chemical industries, oil and gas refineries, and so on.
Analysis of Internal Leakage in Multistage Centrifugal Pumps under Conditions of Impeller Oscillation and Non-oscillation
Authors: Yang Zhengyin , Gu Yandong , Bian Junjie 
Published: October 2024

Abstract
Centrifugal pumps with multiple stages and floating impellers provide a convenient option for easy adjustment, boasting advantages such as easy head adjustment and efficient transfer of axial thrust from the impeller to the casing. These pumps utilize sealing gaskets to reduce leakage and control the axial movement of the impeller. Nonetheless, the influence of impeller oscillation and non-oscillation conditions on leakage losses and internal flow characteristics remains uncertain. To explore these phenomena, numerical simulation methods were employed, enabling the impeller to undergo both axial movement and rotation, with validation achieved through experimental verification of the numerical simulation results. Leakage in the front pump chamber exceeded that of the rear pump chamber significantly, whether oscillation was present or not. Consequently, the impeller’s immediate position during oscillation had a more pronounced impact on leakage in the front pump chamber than in the rear pump chamber. The overall leakage trend in the front pump chamber exhibited a negative correlation with the impeller’s instantaneous position, whereas it displayed a positive correlation with the overall leakage trend in the rear pump chamber. Impeller oscillation notably impacted the flow velocity and pressure in the front pump chamber, while exerting minimal effects on those parameters in the rear pump chamber. This research offers valuable insights on improving the efficiency and stability of multistage pumps.