bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative outcomes of autologous cultured melanocytes transplantation and non‐cultured epidermal cell suspension transplantation in piebaldism patients: A retrospective study
Authors: Jin Rong , Hong Weisong , Ye Zhubiao , Fu Lifang , Hu Wenting , Xu Aie 
Published: January 2024

AbstractPurposeTo compare the efficacy and safety of autologous cultured melanocytes transplantation (CMT) and non‐cultured epidermal cell suspension transplantation (NCES) in the treatment of piebaldism.Patients and methodsA retrospective study was conducted on 30 anatomically based lesions from nine piebaldism patients who underwent either CMT (n = 7) or NCES (n = 23) between 2018 and 2020. The extent of repigmentation and colour matching was evaluated in all recipient sites using a digital imaging analysis system. In addition, adverse effects have also been assessed by follow‐up results.ResultsMore than 75% repigmentation was achieved in 100% (7/7) and 60.9% (14/23) of the 30 lesions with the CMT and NCES, respectively. There were significant differences between the two methods in terms of repigmentation. The majority of patients had colour mismatches, and there was no discernible difference between the two surgical techniques. Adverse reactions rarely occurred.ConclusionThe present study suggested that autologous CMT may provide better repigmentation in piebaldism patients than NCES with no significant side effects.
A Study on the Machinability of Environmentally Friendly Turning of Titanium Grade 2 Alloy
Authors: Cagan Suleyman Cinar , Buldum Berat Baris 
Published: January 2024

Abstract
This study focuses on environmentally responsible and sustainable manufacturing, where heat removal is critical. Various cooling and lubrication technologies are employed in manufacturing strategies, and although minimum quantity lubrication (MQL) systems are advantageous, they are not widely adopted in the industry. Recently, cutting fluids reinforced with nanoparticles has gained attention. The study investigates the machinability of Ti gr. 2 alloys with different cutting parameters and lubrication/cooling systems. Cutting fluids are essential in heat removal and irregularity prevention, reducing friction in the cutting zone, lowering cutting forces and vibrations, and improving tribological conditions during machining operations. Cutting forces increase with the cut and feed rate depth, resulting in higher energy consumption. With increasing feed rate, surface quality decreases, but MQL and NMQL (nanoparticle-reinforced MQL) perform better than dry machining. Scanning electron microscope (SEM) images indicate that cutting tools in dry environments experience more flank wear, whereas MQL and NMQL result in crater wear. High feed rates in MQL/NMQL environments cause sudden and fractured chip formation, which affects worker safety and health. Ti gr. 2 alloys have potential applications in various industrial components, and the NMQL, MQL, and dry systems used in the study are non-hazardous and environmentally friendly.
Biofuels and E-Fuels Used in a Multi-Fuel-Engine as Solution for Rapid Reduction of Greenhouse Gas Impacts of NRMM Engines
Authors: Thees Matthias , Günthner Michael , Fuhrmeister Jonas 
Published: January 2024

Abstract
In the non-road mobile machinery (NRMM) sector, long operating hours combined with specific high-load operating conditions place high performance requirements on the propulsion system.
The established propulsion systems in the field of NRMM meet these requirements through diesel engines powered by liquid fuels with high energy density — a requirement which cannot be met by current battery electric drive systems. However, in order to reduce fossil CO2 emissions as fast as possible, fuels produced from regenerative sources must be introduced for these purposes, which also have to be compatible with existing vehicle fleets.
These requirements were addressed in the project presented here through the development of a multi-fuel engine concept, which is suitable for the fully flexible use of pure rapeseed oil in any blending ratio with diesel fuel. In addition to the use of rapeseed oil/diesel blends, also the use of rapeseed oil/e-fuel blends (using hydrated vegetable oil – HVO – and oxymethylene ether – OME) has been investigated. For existing vehicles, such concepts could also provide the opportunity of retrofit solutions. The flexible use of vegetable oil, diesel fuel and e-fuels makes the customer independent of current fuel availability. As a prototype, a John Deere 4045 engine was optimized for the use of vegetable oil. In addition to the development of a DPF regeneration concept suitable for vegetable oil, the project focused on the potential for emission reduction and the impact on engine efficiency.
Multi-Fidelity Neural Network Regression for Efficient Training of Energy-Assisted Diesel Engine Control System
Authors: Nejadmalayeri Ari , Narayanan Sai Ranjeet , Yang Suo , Sun Zongxuan , Sapra Harsh Darshan , Hessel Randy , Kokjohn Sage , Kim Kenneth S. , Kweon Chol-Bum M. 
Published: January 2024

Abstract
Low cetane number fuels increase the probability of misfire in diesel engines. Utilizing an ignition assistant, along with a reliable engine-control system can enhance combustion and mitigate misfire. Such a control system requires significant pressure data for training the controller software. For engines operating with low cetane number fuel over a wide range of conditions, traditional training data collection based on experimental data alone is time-consuming and expensive. Our aim is to build a purely data-driven model for predicting average cylinder pressure, while varying cetane number, main injection timing, and ignition assistant power. The parametric space is filled using sparse experimental data, and numerous URANS (Unsteady Reynolds-Averaged Navier-Stokes) simulation results simultaneously. The experimental data has relatively high-fidelity, but is costly to acquire, while the URANS results have lower fidelity, but are much more cost effective to generate. An existing neural-network-based methodology for multi-fidelity regression of bi-fidelity problems is utilized, where two separate artificial neural networks — one for each level of fidelity — are used. The effectiveness of this approach is demonstrated by predicting the in-cylinder pressure profiles at various operating conditions and engine control parameters, which are in good agreement with the experimental data.
Understanding Diesel-Pilot Assisted Methane Combustion in a Compression Ignition Engine
Authors: Sirna Amanda , Hassan Amir , Ristow Hadlich Rodrigo , Loprete Jason , Trelles Juan Pablo , Van Dam Noah , Mack J. Hunter , Assanis Dimitris 
Published: January 2024

Abstract
Developing a sustainable alternative to diesel and gasoline is critical to mitigating climate change effects. Methane, the primary component of natural gas, presents itself as a successor to petroleum-based fuels. Compared to other hydrocarbons, methane (CH4) has the highest hydrogen to carbon ratio which means when combusted in an engine, fewer carbon-based emissions occur. Natural gas currently has an extensive distribution infrastructure. This makes it a realistic intermediary option as a fuel source until the zero carbon alternatives are understood. This work explores combustion of methane in a single cylinder compression ignition (CI) research engine. A small amount of diesel was used to promote ignition of the methane charge. An exploration of equivalence ratios and injection timings was conducted to seek stable operation and maximum brake torque (MBT). Trends of engine performance metrics, a heat release analysis, and an emission characterization are presented in this manuscript. Performance of methane with diesel pilot injection with blend ratios between 47–51% methane by energy content and 88–90% by volume are compared to a baseline single injection diesel case and a split injection (pilot and main) diesel case. The purpose of the diesel pilot in the methane case is to function as a “spark” for the methane, due to methane’s longer ignition delay time. Key findings include that a dual fuel (diesel pilot + methane), dual injection strategy with a pilot injection event 5 degrees before top dead center (bTDC) maintained the most favorable balance between combustion performance and engine-out emissions. The implications of this study will help to better understand optimal strategies for maximizing the usefulness of methane as a primary fuel source in future dual fuel engines.
Hot-Surface Pilot Ignition: A Novel Sustainable Aviation Fuel Agnostic Combustion Strategy
Authors: Sapra Harsh , Hessel Randy , Miganakallu Niranjan , Stafford Jacob , Amezcua Eri , Rothamer David , Kim Kenneth , Kweon C. M. , Kokjohn Sage 
Published: January 2024

Abstract
A novel hot-surface pilot ignition (HSPI) strategy is investigated using computational fluid dynamics (CFD) simulations for fuel-agnostic operation with varying cetane number (CN) sustainable aviation fuels (SAFs) in high-altitude conditions. HSPI employs a single-plume pilot injector behind a high-temperature ignition assistant (IA) such that a small pilot jet ignites on contact with the hot IA tip surface and then proceeds to ignite the main jets. The CFD simulations combine an energy source, a turbulence-chemistry interaction model, and a surrogate jet-fuel reaction mechanism to capture Ignition Assisted combustion of different CN fuels. The simulation is validated for IA combustion of a CN 30 SAF blend against engine measurements for a pilot-main injection strategy. The simulation is also extensively validated in previous research for different CN fuels, IA temperatures, and single injection events. After validation, the numerical simulation is adopted to investigate the HSPI strategy for fuels with CNs ranging from 15 to 50. Simulation results show that HSPI can effectively ignite all the tested CN fuels and keep the combustion phasing and ignition delays close to constant while SAFs with CNs lower than 30 misfired without HSPI. The simulations also show the potential of HSPI in achieving 99.99% combustion efficiency for difficult-to-ignite SAFs compared to the 95.3% combustion efficiency without HSPI.
Data-Driven Detection and Prediction of Spray Collapse Characteristics for Multi-Component Fuel Mixtures
Authors: Zhao Fengnian , Zhou Ziming , Liu Wensong , Hung David L. S. 
Published: January 2024

Abstract
Flash boiling spray improves the quality of fuel atomization while also increasing the occurrence of spray collapse. The spray collapse phenomenon significantly increases the likelihood of unintended spray-wall impingement, thus reducing combustion stability. Therefore, this study focuses on the spray collapse states under flash boiling conditions and implements machine learning methods to enhance spray analysis including collapse pattern recognition and prediction. In this study, the macroscopic spray cross-patterns of multiple-component fuel mixtures under different fuel temperatures and pressures were first captured by a high-speed planar laser Mie-scattering imaging system. Then, a supervised residual network (ResNet) classification model was applied for spray collapse pattern recognition. The well-trained ResNet model automatically classified the spray collapse patterns into three categories including non-collapse state, transition state, and collapse state with an accuracy of over 99%. This data-driven model provides a novel idea for the identification of spray collapse states. Based on these recognition results, a decision tree classifier (DTC) was built to evaluate the importance of test and fuel parameters that influence the spray collapse behavior. The results indicated that the superheated index modified by the ratio of ambient pressure to bubble point pressure was capable of determining the spray collapse states. With such trained DTC model and the modified superheated index, it is anticipated that the spray collapse states of multi-component fuels could be predicted with high accuracy even under untested conditions.
Experimental Study of the Impact of Ethanol Content on Partially Premixed Combustion With Ethanol-Gasoline Blends
Authors: Gainey Brian , Gandolfo John , Lawler Benjamin 
Published: January 2024

Abstract
As ethanol production increases and the demand for gasoline in spark ignition engines decreases over the next few decades, the use of these fuels in compression ignition systems is expected to increase. This raises the question of how much ethanol-in-gasoline is the right blend for compression ignition. This work experimentally studied E10, E30, E50, E75, and E100 in a split injection partially premixed compression ignition strategy. It was found that, separately, the cool flame reactivity of E10 and the high cooling potential of E75 and E100 enable heat release rate control in partially premixed combustion. However, E30 and E50 did not show the same control authority. E30 performed the worst due to having a small amount of cool flame reactivity and a low cooling potential. It was found that NOx emissions were not significantly impacted by ethanol content, but the smoke emissions were. E100 produced near zero smoke, but all of the ethanol-gasoline blends produced some smoke. When 20% of the fuel was injected during the middle of the compression stroke, E10, E30, E50, and E75 all produced low, but non-zero smoke emissions. When the second injection was moved near top dead center, the smoke emissions of E75 stayed low but the smoke emissions of E10, E30, and E50 all rose to a filter smoke number greater than 0.3. Overall, these results indicate that low- and high-ethanol content gasolines are both viable paths forward for partially premixed combustion systems, but medium-ethanol content gasolines should be avoided.
Performance and Emissions of an SI Engine Fueled With DME-Propane Blends
Authors: Hassan Hafiz Ahmad , Ankobea-Ansah King , Wickman David , De Ojeda William , Hall Carrie 
Published: January 2024

Abstract
Dimethyl Ether (DME) is an alternative fuel that can be produced renewably and has the potential for lower CO and NOx emissions than conventional petroleum-based fuels. Blending DME with another gaseous fuel such as propane, which has a lower knock tendency than gasoline, can allow this fuel to be leveraged on SI engines. In this study, the use of DME-propane blends on a spark ignition (SI) engine was studied via computer simulations in order to understand the impact on engine performance and emissions and to identify the knock limitations of using such fuel blends. A 2L Hyundai SI engine was modeled in GT Power and the model was validated by comparing it with computational fluid dynamics (CFD) simulation results. Starting from pure propane, DME was added incrementally until knock was observed in the engine. Results indicate that it is feasible to run propane with DME percentages up to 35% before severe knock impacts were observed. The BTE was higher, but the BSFC also increased for DME-propane blends as compared to gasoline. An increase in NOx emissions was detected along with a significant decrease in CO emissions. CO2 emissions declined for propane as compared to gasoline but increased with the addition of DME.
Evaluation of the Performance and Exhaust Emissions of a 4 Cylinder CI Engine Operating With Dimethyl Ether (DME) and Propane Blends
Authors: Ankobea-Ansah King L. , Hassan Hafiz Ahmad , Wickman David D. , De Ojeda William , Hall Carrie M. 
Published: January 2024

Abstract
In response to stringent emissions regulations and the need for higher efficiency engines, the utilization of DME and propane fuel blends in compression ignition (CI) engines has gained interest in the automotive industry. In this study, a range of DME-propane blends are explored in a CI combustion strategy at high injection pressures. A GT-Power model of a 2.2 L Hyundai CI engine was developed to facilitate evaluation of the impacts of variations of DME and propane blends at a light and medium engine speed-torque-load operating condition; specifically at 1500 rpm, 50 Nm and 2.84 bar brake mean effective pressure (BMEP); and 2000 rpm, 150 Nm, 8.53 bar BMEP speed-torque-load combination. The GT-Power model was validated using Computational Fluid Dynamics (CFD) simulations. The results indicate that high diesel-like efficiencies can be achieved with a 100% DME mass substitution and up to 50% propane-DME blends could be implemented without a significant penalty on engine performance indicators. Significant brake specific nitrogen oxides (BSNOx) reductions were also observed along with reductions in carbon dioxide (CO2) and soot when leveraging these fuel blends.
An undergraduate service-learning project to teach immunology concepts while increasing healthcare equity by enhancing access to stem cell donors among underrepresented populations
Authors: Sorisho Stephanie , Matias-Gomez Laura , Baithey Melissa A. , Cechova Vladimira , Madrid Fredrick Kyle , Rholl Drew A. 
Published: January 2024

ABSTRACT

Active learning has been shown to increase STEM student engagement and decrease the achievement gap among underrepresented students. As a parallel to the lack of equity in STEM education, BIPOC patients who require a life-saving hematopoietic cell transplantation (HCT) are much less likely—sometimes less than half as likely—than individuals of White-European descent to find a suitable donor when using the National Marrow Donation Program (NMDP). The Be the Match (BtM) Registry has made significant improvements in the likelihood of matching underrepresented patients, but the disparity persists. This activity uses a service-learning project to teach undergraduate students about stem cell donation and to add potential stem cell donors to the BtM Registry. A small data set of pre-/post-surveys from one cohort shows learning gains on the topic of HCT. The approach is flexible and scalable, and students overwhelmingly reported the project as a great use of class time and very rewarding.
Supplemental Material: High-Mg andesites and Nb-enriched basalts in the Dulate arc, East Junggar (NW China): Evidence of ocean ridge subduction
Authors: Qin Zhen , Wu Tao , al. et 
Published: January 2024

<div>Incompatible elements (K<sub>2</sub>O, Rb, Th, and La) versus Zr plots of the volcanic rocks of Kaxiweng Formation, in order to distinguish the alteration effects. </div>
DFT + U investigation on high pressure properties of monoclinic CuO
Authors: Jaf Zainab N. , Miran Hussein A. , Rahman M. Mahbubur , Amri Amun , Jiang Zhong-Tao 
Published: January 2024

This contribution demonstrates density functional theory calculations on the optoelectronic properties of monoclinic CuO under high pressures up to 15 GPa. In this account, the experimentally measured indirect band gap energy value of 1.41 eV has theoretically been predicted by tuning the Hubbard parameter ( U) values for the partial electronic states. The values of the Hubbard parameter correspond to UCu-3 d = 8 eV and UO-2 p = 4.5 eV. The electronic and optical characteristics were examined under applied pressure and the results reveal development in the band gap values with increasing pressure from 1.41 to 2.01 eV. Total and projected density of states for the adopted pressures have been provided and displayed that the major contribution in the valance and conduction bands comes from O-2 p and Cu-3 d electronic states, respectively. The plotted reflectivity data suggest high optical reflectivity magnitudes relatively in the ultraviolet area. The investigated systems under variant pressures manifest rather an excellent absorption coefficient in the ultraviolet area, pushing them to be employed in solar cell technologies. Our analyzed results of the wavelength-dependent electrical conductivity of the investigated materials confirm the absorptivity behavior in the ultraviolet and small part of visible region of electromagnetic waves (EMW). Finally and most importantly, our obtained results of loss functions for the entire surveyed systems reveal a slight energy loss in a range of EMW, including ultraviolet and visible regions.
Comparison of home detection algorithms using smartphone GPS data
Authors: Verma Rajat , Mittal Shagun , Lei Zengxiang , Chen Xiaowei , Ukkusuri Satish V. 
Published: January 2024

AbstractEstimation of people’s home locations using location-based services data from smartphones is a common task in human mobility assessment. However, commonly used home detection algorithms (HDAs) are often arbitrary and unexamined. In this study, we review existing HDAs and examine five HDAs using eight high-quality mobile phone geolocation datasets. These include four commonly used HDAs as well as an HDA proposed in this work. To make quantitative comparisons, we propose three novel metrics to assess the quality of detected home locations and test them on eight datasets across four U.S. cities. We find that all three metrics show a consistent rank of HDAs’ performances, with the proposed HDA outperforming the others. We infer that the temporal and spatial continuity of the geolocation data points matters more than the overall size of the data for accurate home detection. We also find that HDAs with high (and similar) performance metrics tend to create results with better consistency and closer to common expectations. Further, the performance deteriorates with decreasing data quality of the devices, though the patterns of relative performance persist. Finally, we show how the differences in home detection can lead to substantial differences in subsequent inferences using two case studies—(i) hurricane evacuation estimation, and (ii) correlation of mobility patterns with socioeconomic status. Our work contributes to improving the transparency of large-scale human mobility assessment applications.
Cost-aware Service Placement and Scheduling in the Edge-Cloud Continuum
Authors: Rac Samuel , Brorsson Mats 
Published: January 2024

The edge to data center computing continuum is the aggregation of computing resources located anywhere between the network edge (e.g., close to 5G antennas), and servers in traditional data centers. Kubernetes is the de facto standard for the orchestration of services in data center environments, where it is very efficient. It, however, fails to give the same performance when including edge resources. At the edge, resources are more limited, and networking conditions are changing over time.
In this article, we present a methodology that lowers the costs of running applications in the edge-to-cloud computing continuum. This methodology can adapt to changing environments, e.g., moving end-users. We are also monitoring some Key Performance Indicators of the applications to ensure that cost optimizations do not negatively impact their Quality of Service. In addition, to ensure that performances are optimal even when users are moving, we introduce a background process that periodically checks if a better location is available for the service and, if so, moves the service. To demonstrate the performance of our scheduling approach, we evaluate it using a vehicle cooperative perception use case, a representative 5G application. With this use case, we can demonstrate that our scheduling approach can robustly lower the cost in different scenarios, while other approaches that are already available fail in either being adaptive to changing environments or will have poor cost-effectiveness in some scenarios.