bluuebunny/crossref_metadata_2025_split huggingface dataset:
Frequency of human platelet antigens (HPA) in the Greek population as deduced from the first registry of HPA‐typed blood donors
Authors: Kaltsounis Georgios , Boulomiti Evangelia , Papadopoulou Dimitroula , Stoimenis Dimitrios , Girtovitis Fotios , Hasapopoulou‐Matamis Eleni 
Published: September 2024

AbstractBackground and ObjectivesHuman platelet antigens (HPA) play a central role in foetal and neonatal alloimmune thrombocytopenia (FNAIT), post‐transfusion purpura and some cases of platelet therapy refractoriness. The frequency distribution of HPA had not been studied in the Greek population before we started to create a registry of HPA‐typed apheresis platelet donors. The aim of this study was the determination of the frequency of various HPA in the Greek population, through the establishment of a registry of typed donors.Materials and MethodsHere, we report on the first 1000 platelet donors of Greek origin who gave informed consent and were genotyped for 12 pairs of antithetical HPA by Single Specific Primer‐Polymerase Chain Reaction (SSP‐PCR), including HPA‐1, HPA‐3, HPA‐5 and HPA‐15. Antigen frequencies are reported, and allele frequencies were calculated and compared with other European and non‐European populations. Tested donors cover all ABO and Rhesus D antigen spectrum.ResultsAntigen and allele frequencies are very similar to other White populations. The frequency of HPA‐1bb is 2.9% in our study, and the frequency of HPA‐2b, HPA‐4b, HPA‐9b and HPA‐15b is also slightly higher than in other literature reports, while the frequency of HPA‐15b was found higher than that of HPA‐15a.ConclusionWe report antigen and allele frequencies for a large array of clinically significant HPA for the first time in the Greek population. Frequencies are consistent with other European populations. This registry of HPA‐typed platelet donors, available to donate on demand, is an important asset for the treatment of FNAIT cases in Greece.
Assessing the feasibility of using municipal wastewater for aiding algal oil production: From a spatial coupling perspective
Authors: Chiu Yiwen , Brown Tyler , Lucett Erin , Worrell Kate 
Published: September 2024

AbstractCompeting over limited freshwater resources has been a major challenge in sustaining algal oil production. To tackle this issue, municipal wastewater (MWW) is proposed to aid algal oil production but with unknown feasibility. This study aims to investigate the change in algal oil production in response to MWW distribution. Using California as a case, the result shows that the oil production is only 34% of what was proposed in a former study, with 29% to 74% of wastewater being derived to meet 4% to 9% of refinery water demand. There is no substantial increase in oil production beyond a MWW distribution distance longer than 24 km. The analytical framework developed in this study requires minimal data in climate and topographic inputs and can be applied at a global level to identify the most promising areas where algal oil production can be sustained by municipal wastewater.
ОПТИМИЗАЦИЯ ПРОТОКОЛА ВЫДЕЛЕНИЯ ДНК ИЗ ГЕРБАРНОГО МАТЕРИАЛА РАСТЕНИЙ
Authors: Исламова С.С. , Нұртаза А.С. , Саматова И.Н. , Какимжанова А.А. 
Published: September 2024

В настоящее время количество гербарного материала свидетельствует о богатстве растительного мира. Данные, зафиксированные в гербариях, не устаревая со временем, остаются актуальными и служат основой для изучения биоразнообразия. За последние десятилетия использование ДНК-технологий в изучении гербарных материалов стало значимым прорывом, благодаря разработке новых методов выделения. Это позволяет проводить комплексные исследования по систематике, филогении и генетике растений. Однако, использование гербарного материала представляет собой сложный процесс, который зависит от методов сбора и обработки растений, проводимых при подготовке гербария. Важно учитывать, как физические, так и химические изменения, возраст гербарных образцов, которые играют значительную роль в сохранении ДНК.
В качестве объектов исследований использовали гербарные растительные материалы Ribes janczewskii, Ribes nigrum и Ribes aureum. Для оптимизации изучены четыре протокола выделения ДНК из гербарного материала, которые различались между собой составом лизирующего буфера. Образцы из сухого растительного материала выделены по протоколу 1 (контроль), в котором использовали набор реактивов Kit (Omega BIO-TEK E.Z.N.A. Plant DNA Kit).
В результате сравнительного анализа оптимальным среди четырех протоколов выделения ДНК из гербарного растительного материала является протокол 2 (состав лизирующего буфера CTAB с 2% цетилтриметиламмония бромида, 20 мМ ЭДТА, 100 мМ Трис-HCl, 1,4 мМ NaCl). Хотя концентрация ДНК в образцах находилась в оптимальном диапазоне от 270 до 621 нг/мкл, соотношение А260/А230 указывало на возможное присутствие загрязнений полифенолами. В связи с этим, изучено воздействие 1% PVP на лизирующий буфер с целью улучшения чистоты ДНК. Также, проведена двойная экстракция хлороформом, после которой все образцы ДНК были выделены успешно.
Таким образом, оптимизирован эффективный метод выделения ДНК из гербарных растений. Использование лизирующего буфера CTAB с добавлением 2% цетилтриметиламмония бромида, 20 мМ ЭДТА, 100 мМ Трис-HCl, 1,4 мМ NaCl и 1% PVP позволило получить высококачественную ДНК из сложных образцов. Двойная очистка хлороформом позволила устранить белковые загрязнения ДНК без применения токсичного β-меркаптоэтанола.
Научное исследование проводится в рамках программы BR18574125 «Изучение современного состояния видового разнообразия сосудистых растений Казахстана с использованием современных методов ботаники, молекулярной генетики и биоинформатики» при финансовой поддержке Министерства науки и высшего образования Республики Казахстан.
Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation
Authors: Mai Rene E. , Sears Katherine , Roessling Grace , Julius Agung , Mishra Sandipan 
Published: September 2024

Abstract
We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle’s lateral state.
Combining Multi-Indirect Features Extraction and Optimized Gaussian Process Regression Algorithm for Online State of Health Estimation of Lithium-Ion Batteries
Authors: Lin Chunsong , Tuo Xianguo , Wu Longxing , Zhang Guiyu , Lyu Zhiqiang , Zeng Xiangling 
Published: September 2024

Abstract
With the wide application of lithium batteries (LIBs) in electrified transportation and smart grids, especially in the pure electric vehicle industry, the accurate health maintenance monitoring of LIBs has emerged as critical to safe battery operation. Although many data-driven methods with state of health (SOH) estimation for LIBs have been proposed, the problems of industrial application and computational cost still need to be improved further. In contrast, this article carried out a low-complexity SOH estimation method for LIBs. Specifically, the seven health indicators are extracted firstly to characterize battery health status from voltage, current, temperature, and other data that can be obtained online. Then, the optimized Gaussian process regression (GPR) algorithm is proposed with proper computational cost. Ultimately, by combining a multi-indirect features extraction and optimized GPR algorithm, the online SOH estimation for LIBs was established and verified with NASA experiment data. The experimental results show that the maximum MAPE of SOH estimation from the proposed method is 1.4496 and the minimum MAPE only reaches 0.5635. More importantly, the optimized GPR for SOH estimation can achieve a maximum 65.37% improvement under multiple evaluation criteria compared to traditional GPR. The method proposed in this article is helpful for realizing online SOH estimation in battery management systems.
Full-Size Experimental Investigations on Planetary Gear Journal Bearings in High-Power Wind Turbines
Authors: Chen Qi , Zhang Kai , Zhang Yabin , Ding Qiangming , Zhu Yuhang , Feng Kai 
Published: September 2024

Abstract
To satisfy the large-scale and high-power demands of wind turbines, planetary gear journal bearings (PGJBs) have been applied in large wind turbine gearboxes (WTGs), as an alternative to traditional rolling bearings, due to their higher reliability and smaller size. To simulate the actual lubrication behaviors of PGJBs and investigate their hydrodynamic performance, a full-size test rig for PGJBs was built in this article. A multiparameter detection system coupled with the ultrasonic testing method was developed. Four ultrasonic piezoelectric elements, eight thermistors, two pressure transducer, and one torque sensor were used to obtain the film thickness, oil temperature, oil pressure, and friction torque data of the test PGJB. The rated condition experiment was conducted to investigate the variation of measured lubrication performance with the operating time. Three-dimensional predictions of oil film pressure, temperature, and thickness were presented to analyze the steady-state lubrication characteristic at the rated condition. Moreover, a series of steady-state experiments were carried out to simulate the normal operations of the test PGJB at different conditions, and the measured results were verified by the numerical predictions. The influence of rotational speed, input load, oil supply temperature, and oil supply flow on the hydrodynamic performance of the test PGJB was explored.
Numerical Analysis of the Thermal Elastohydrodynamic Lubrication Line Contact Problem Under Intermittent Motion
Authors: Li Haiping , Zhang Mingyu , Sun Haoyang , Wang Jing 
Published: September 2024

Abstract
A mathematical model of time-varying thermal elastohydrodynamic lubrication (EHL) is developed using a sleeve chain as the object of study. The effects of thermal effect, load, speed, rest time, and equivalent radius of curvature on its EHL are investigated using theoretical simulations. The results show that during intermittent motion, a portion of oil is entrapped in the contact zone at the end of the deceleration phase, after which this entrapped oil gradually moves out of the contact zone with the onset of speed, and a lower film of oil at the lubrication inlet passes through the center of the contact zone. In simple sliding intermittent motion, the temperature rise plays a crucial role in the variation of the oil film, particularly during the motion phases, and is an unavoidable factor. An increase in stop time reduces the film thickness and increases the following friction coefficient in the acceleration stage. An increase in the equivalent radius of curvature increases the film thickness and thermal rise in the contact zone as well as decreases the friction coefficient. Since the sleeve chain is one of the most commonly used mechanism in industry, high priority should be given to the lubrication design of this structure.
Analysis of Carbon Dioxide Injectivity and Dynamic Storage Capacity in Shale Reservoirs
Authors: Zhang Yiming , Wang John 
Published: September 2024

Abstract
Carbon dioxide could be stored in unconventional shale reservoirs in a supercritical state due to available pore volume, infrastructure, and injectivity. However, there is a lack of knowledge about the injectivity and storage capacity in shale reservoirs. In this paper, a two-dimensional dual-porosity, dual-permeability model was built to investigate CO2 injectivity and dynamic storage capacity spatially and temporally. Parametric studies are conducted to evaluate the effect of matrix permeability, fracture conductivity, fracture half-length, operating conditions, and near-wellbore connectivity on storage factors. Systematic and comprehensive numerical experiments are carried out using random sampling to generate a probability distribution of CO2 storage factors and replacement ratio. Results showed the parameters with the most impact to least impact on injectivity and storage factor: matrix permeability, near-wellbore connectivity, bottomhole pressure, fracture half-length, and fracture conductivity. The methodology in this study provides a foundation to examine how CO2 storage factors change spatially and temporally in and outside the stimulated reservoir volume. The new understanding can be applied to optimize field development, well spacing, and infill drilling to increase economic storage.
Efficiency-Driven Supervised Learning Regressors in Power Modeling and Optimization of Vertical Axis Wind Turbines
Authors: Dorosti Ehsan , Shabani Amir , Vijayaraghavan Krishna 
Published: September 2024

Abstract
Of all the clean energy sources, wind power stands as the most widely available and employed form. Besides the Horizontal Axis Wind Turbines (HAWT), Vertical Axis Wind Turbines (VAWT) are attracting significant attention. This study focuses on optimizing a 12-kW lift-based 3-blade VAWT by introducing an optimal diffuser to enhance performance using two optimization procedures. To do that, a nonsymmetric diffuser in the shape of NACA4405 is introduced to the VAWT which is simulated using Computational Fluid Dynamics (CFD) and validated with experimental data. To optimize the power coefficient (Cp) of the VAWT, adjustments will be made to the position and orientation of both the upper and lower walls of diffuser. Two optimization methods were employed: one involves direct optimization, where a Genetic Algorithm (GA) is integrated with CFD. The second method utilizes Machine Learning (ML) models, such as Gaussian Process Regression (GPR), Support Vector Regression (SVR), and AdaBoost, fitted to the data set extracted from CFD. These ML models then replace CFD and are used in optimization. Results show that optimal diffuser can contribute to 27% increase in Cp. When employing GPR and AdaBoost, the optimal Cp values closely match those from direct optimization while optimization using ML models reduces computational costs by nearly 78% fewer simulation runs.
Numerical Optimization and Analysis of 3D-Printed Porous Structures for Open Volumetric Receivers
Authors: Broeske Robin Tim , Schwarzbözl Peter 
Published: September 2024

Abstract
Solar tower power plants with open volumetric receivers using air as the heat transfer fluid present an alternative to existing molten salt or steam receiver systems, offering high robustness, high availability, and effective storage capabilities. As the central component of this receiver type, volumetric porous structures are responsible for the absorption of the concentrated solar irradiation and the heat transfer to the air flow. Manufacturing techniques such as 3D-printing make it possible to purposefully design the porous structures down to sub-millimeter scales.
A new numerical optimization approach has been developed which allows the derivation of new porous structures precisely tailored to the available manufacturing techniques. Potential absorber designs are first characterized via three-dimensional CFD and raytracing simulations. The resulting effective properties are then used by a 1D LTNE homogeneous model to evaluate the absorber performance. The variation of the absorber geometry is controlled by an optimization method, both an exact and a heuristic method have been implemented within the new approach. A key feature of the design process is the explicit incorporation of the limitations and capabilities of the associated manufacturing technologies. This allows to directly ensure the manufacturability of the optimized absorber designs. The approach has been applied to derive optimized absorber designs which were then created via 3D-printing and experimentally evaluated confirming their potential as solar absorbers.
Physical Versus Data-Driven Modeling of Thermionic Device Performance Over the Full Range of Power Generation Operating Conditions
Authors: Juette Elizabeth D. , Carey Van P. , Fleurial Jean Pierre 
Published: September 2024

Abstract
Real thermionic device performance asymptotically approaches ideal behavior in the saturated and Boltzmann regimes at high and low voltages. The maximum power point occurs between the two ideal regimes in a transition region where the output current density is a fraction of the ideal output current due to space charge in the interelectrode gap. The operating mode of a power-generating thermionic device is determined by the operating conditions of the device and the device specifications. These conditions and specifications include the electrode temperatures, operating voltage, device geometry, and the electrode material properties.
The focus of this paper is to develop a physics-based model for the transition region between the two regimes and to incorporate data from real devices. We based the modeling on classical models of current transport between electrodes. The saturated regime is characterized by the Child-Langmuir law for thermionic emission and Langmuir space-charge theory is implemented for the charge-affected regime to characterize the transition behavior in real diodes. We modify the conventional solution for the interelectrode motive to include an electron reflection probability parameter at the collector and establish a general functional form for the current flux variation with voltage in the transition region. We then utilize machine learning tools to interpret the behavior of real data in the context of the developed framework, and to develop a performance predictive tool that leverages the advantages of a physics-inspired machine learning methodology.
The model is demonstrated to fit performance data for four different real, small gap, vacuum thermionic devices to within 12%. We used the framework and the functional form suggested in tandem with experimental data to develop a performance predictive model for the power-generating operating regimes which reflects the underlying physics and is consistent with performance trends in the data for real devices. The sensitivity of the device performance to changes in work function, electron reflection probability, and interelectrode gap width is also discussed.
Helium Gas Brayton Cycle and Rankine Cycle Combined System With PCM Thermal Storage for Efficient CSP System
Authors: Li Sheng , Gamil Ahmed , Li Peiwen 
Published: September 2024

Abstract
For a Concentrating Solar Power (CSP) plant to achieve high thermal efficiency and maintain operation during nighttime, high temperature heating and the integration of thermal energy storage in the system is important, which becomes even more critical for the cost competitiveness of the CSP against solar PV technologies. To achieve the goal, the current work proposes a concentrating solar thermal power plant to have combined thermal cycles — a Brayton cycle (helium gas) at high temperatures (800–850 °C) alongside a Rankine cycle (water). The water in the Rankine cycle takes the energy from the helium gas cooling process in Brayton cycle to generate steam. To get a high concentrating solar receiving efficiency and high temperature in the solar receiver, a novel jet impingement heat transfer approach has been proposed for helium gas to be heated. For compact and large quantity (16 hours) thermal energy storage at temperatures ≥ 800 °C, NaCl has been selected as a suitable Phase Change Material (PCM) due to its appropriate melting point (801°C), high heat of fusion (481.8kJ/kg), and large reserves and relatively low cost among many high temperature PCMs. The feasibility of the proposed CSP system is studied through basic thermodynamic analysis to the combined cycles, examination of solar receiver heat transfer of jet impingement, and calculation of PCM thermal energy storage capacity. A thermal efficiency of 43.8–45.2% was found achievable for the thermal power system. The solar receiving efficiency can approach 89.2%. If the total solar energy in a field going to the solar receiver can achieve 90%, then the energy from solar field can have 35.2–36.3% being converted to electrical energy in such a CSP power plant that can produce electrical power 24 hours a day.
An Experimental Study on Performance Degradation and Recovery Protocols of Module Stack Proton Exchange Membrane Fuel Cell Under Dynamic Load
Authors: Nghiem Quan Thien Phan , Nguyen Huu Linh , Kim Younghyeon , Yu Sangseok 
Published: September 2024

Abstract
In this paper, the performance degradation of three-cell proton exchange membrane fuel cell (PEMFC) stacks was investigated in dynamic working conditions. A customized current cycle based on the World Harmonized Vehicle Cycle was developed as a test cycle. Long-term experiments, consisting of equal-duration test periods, were conducted at stack temperatures of 60°C and 70°C. The test cycle was repeated in each test period, followed by a recovery stage. To investigate the responses of PEMFC stacks during tests, the polarization curves were measured at the beginning and end of each test period, as well as after the recovery stages. Based on the degradation assessment, the more severe working temperature was selected as the test condition for assessing the recovery efficiency of the shutdown and the voltage cycling method. The experimental results indicated that the temperature of 70°C resulted in a higher deterioration of stack voltage than the case of 60°C. Specifically, the stack voltage measured at 0.5 A/cm2 in the case of 60°C and 70°C dropped by 1.75% and 3.59%, respectively, after 100 hours. Regarding the recovery effectiveness, the shutdown method was more efficient than the voltage cycling method as time progressed. At 0.8 A/cm2, for instance, the recovery efficiency of the shutdown method maintained at 77.25%, whereas the figure of the voltage cycling method decreased to 53.46% after 100 hours. The ohmic region had the highest recovery efficiency compared to the other regions with the shutdown approach. This study emphasized the recovery capability of the shutdown method and motivated further works to shorten its duration for practical conditions.
Thermodynamic Optimization of Low-Cost Thermal Energy Storage Systems Using Reclaimed Minerals
Authors: Engel Tihamer , Sephton Thomas , Baghaei Lakeh Reza 
Published: September 2024

Abstract
Renewable energy sources, such as solar and wind, hold immense potential in transitioning towards a sustainable energy future. However, their intermittent nature poses challenges to grid stability. Energy storage solutions, particularly Thermal Energy Storage (TES) systems, have emerged as a promising approach to address this issue and enhance the integration of renewables. This study delves into the development of a TES system that utilizes repurposed minerals as storage material, integrated with a low-temperature zero liquid discharge thermal desalination system. The integration of TES with desalination processes holds significant promise in mitigating the economic and environmental consequences of ocean or brackish water desalination. By employing TES, the study aims to improve the overall efficiency of the integrated system, contributing to the sustainability of both energy generation and water production. This research employs a comprehensive thermodynamic analysis, encompassing optimization and sensitivity analyses of the TES system utilizing only sensible heat. A detailed MATLAB model relying on the first law of thermodynamics provides valuable insights into the transient behavior of a 4,000–15,000 kg storage system under various charge and discharge scenarios.