bluuebunny/crossref_metadata_2025_split huggingface dataset:
Virtual Pressure Sensor Based on Ion Current Measurements Using Artificial Neural Networks
Authors: Björnsson Ola , Tunestål Per 
Published: December 2024

Abstract
In-cylinder pressure measurements serve as the gold standard for combustion diagnostics in Internal Combustion Engines. However, the associated costs render it unfeasible to equip engines with in-cylinder pressure sensors. For spark-ignited engines, using the more affordable in-cylinder ion current measurements as an alternative to the traditional pressure sensors has shown promising results for combustion diagnostics. Still, it has not been widely adopted, most likely due to concerns regarding robustness.
The recent advancements in machine learning and big data have attracted significant attention. Given the strong correlation between ion current measurements and in-cylinder pressure, combined with the simplicity of collecting a large amount of data, ion current measurements are a good candidate for building machine learning models.
In this paper, we show the feasibility of utilizing Artificial Neural Networks (ANN) to estimate the in-cylinder pressure using the ion current measurement as an input, effectively creating a virtual pressure sensor. Additionally, we calculate different combustion metrics useful for combustion diagnostics and compare them with those calculated on the measured in-cylinder pressure trace. The following combustion metrics are considered: Gross Indicated Mean Effective Pressure (IMEPg), Peak Pressure Location (PPL), maximum pressure, and heat release-based measurements such as total heat release and CA50. All of which could be estimated with good accuracy.
Improving the Predictive Capability of Empirical Heat Transfer Correlations for Hydrogen Internal Combustion Engines
Authors: Parsons Dominic , Yuan Hao , Akehurst Sam , Esposito Stefania 
Published: December 2024

Abstract
Hydrogen internal combustion is widely considered as a viable technology to achieve near-zero tailpipe CO2 and NOx emissions for difficult to electrify applications due to the maturity of ICE technology and production facilities.
1D/0D modelling is a valuable tool for engine development due to its relatively low computational requirements, but hydrogen combustion models still require further development to enable accurate predictive capabilities across a larger operating range. A large factor in this is the gas to wall heat transfer, which is much higher for hydrogen combustion than for hydrocarbon fuels at equivalent conditions due to its higher flame temperatures and shorter quenching distance. For accurate prediction of in-cylinder temperatures, and therefore combustion rates and knock propensity, a well calibrated heat transfer model is essential.
This paper evaluates existing heat transfer models against previously published experimental cylinder pressure and heat flux data from a Cooperative Fuel Research (CFR) engine with hydrogen Port Fuel Injection (PFI) at compression ratios from 8.5 to 9.5 and lambda ranging from 1.4–2.2. A new heat transfer correlation is developed against this data, utilising a newly developed fluid properties correlation to better represent the change in viscosity and conductivity resulting from changes in hydrogen concentration. Recent developments in 0D turbulence models also contribute to the improvement of the characteristic velocity calculation, which is augmented with a combustion term to improve the accuracy of the model during combustion.
This model is tested against a second dataset from the CFR engine with lambda ranging from 1.0–4.0 and compression ratios of 9–13, showing improved performance for hydrogen combustion in comparison to previously published models. Whilst the new model provides more consistent results during the combustion phase for variations in lambda and compression ratio, it requires improvement in its prediction of heat loss during expansion, and further validation at higher engine speeds and different engine configurations.
Reducing NOx Emissions From Ammonia Combustion Through Staged Combustion
Authors: Nasim Md Nayer , Landis Joshua , Das Shubhra Kanti , Nawaz Behlol , Mack J. Hunter 
Published: December 2024

Abstract
While the absence of carbon makes ammonia (NH3) a promising alternative fuel, some challenges need to be addressed before commercial implementation in practical systems such as gas turbines or internal combustion engines. One potential obstacle is an increase in NOx and N2O emissions due to the fuel-bound nitrogen in ammonia. This study investigates the reduction of NOx emissions from NH3 combustion through staged combustion, incorporating features of exhaust gas recirculation, where two distinct ignition events occur at disparate conditions; a primary ignition that burns a rich fuel-air mixture prior to completing the combustion in a subsequent stage after the introduction of additional air. This process can be emulated in a constant volume combustion chamber (CVCC) by initially igniting a rich mixture, adding air to the combustion products, and then igniting a second time at a globally lean condition. This approach provides isolated insight into how changes in initial equivalence ratios affect the production of NOx emissions for both single-stage and 2-stage combustion. The experimental results indicate that a staged approach reduces NOx emissions, particularly in comparison to stoichiometric and lean ammonia combustion.
Characterization of Knocking on Spark Assisted Compression Ignition Mode in a Rapid Compression Machine
Authors: Jin Long , Yu Xiao , Reader Graham , Zheng Ming 
Published: December 2024

Abstract
Spark-assisted compression ignition (SACI) employs a direct spark event to control the combustion phasing of homogeneously charged compression ignition mode. However, the induced deflagration process results in elevated pressure and temperature within the unburned gas zone, leading to the end gas autoignition. In addition, the rapid heat release in the chamber potentially leads to the internal damage caused by conventional knocking, heavy knocking, or detonation knocking. This represents the primary challenge to improving power density and thermal efficiency under a high compression ratio and boosted engine. This study investigates knocking combustions and characterizes the oscillating pressure waves from the end gas autoignition using the DME/air mixtures in a rapid compression machine. The circumferential frequency of the vibration modes is computed with respect to the combustion chamber (Ø50mm). The first (11.5kHz) circumferential mode is the primary focus to explore the impact of end-gas autoignition on the individual wave intensity since they contain the majority of the acoustic energy from the decomposed harmonic waves. A detailed analysis of the ringing intensity, knock intensity, and duration of ratio on excitation to residence are performed to characterize the knocking behaviors under the HCCI and SACI combustion modes. The high-speed images are utilized to observe the end-gas autoignition onset timing and process, and identify transition of the fast flame propagation to detonation which exceeds 2km/s. The knocking suppression is also investigated by increasing the excessive air-fuel ratio up to lambda 2.
Large-Eddy Simulations of N-Heptane/Ammonia Dual-Fuel Spray Flames
Authors: Singh Harsimran , Pal Pinaki , Aggarwal Suresh Kumar 
Published: December 2024

Abstract
Diesel engines are extensively used in heavy-duty transportation, power generation, and marine vehicles due to their superior thermal efficiency and extended high-load operability compared to spark ignition (SI) engines. However, combustion in diesel engines is generally characterized by locally rich fuel-air mixtures and high combustion temperatures, causing significant amounts of soot and NOx emissions from these engines. Utilizing carbon-free alternative fuels and enhancing fuel efficiency represent promising strategies to mitigate greenhouse gas (GHG) and other emissions in the heavy-duty transportation sector. In this context, ammonia (NH3), as a hydrogen carrier, has received significant attention as a viable substitute for hydrocarbon fuels, due to its carbon-free composition, relatively high energy density, and well-established infrastructure. Many previous studies have considered combustion and emission characteristics of ammonia-hydrocarbon fuel blends in engines and simplified flames. However, detailed investigations on the effect of ammonia on the performance of hydrocarbon fuels under engine conditions are lacking. In the present study, we perform large-eddy simulations (LES) of the ignition and flame processes in a constant volume combustion reactor, where n-heptane is injected in an ammonia/air ambient mixture in a diesel-like environment. A detailed and validated reaction mechanism containing 302 species and 1981 reactions is employed. The ECN Spray H experimental data is used to validate the spray model under non-reacting and reacting conditions. Dual-fuel combustion is simulated using the well-stirred reactor (WSR) approach. Results are presented for two spray cases: (1) Single fuel (SF) case with n-heptane injected into a mixture of air and combustion products; (2) Dual-fuel (DF) case with the injection of n-heptane in a mixture of air, ammonia, and combustion products. It is observed that the presence of ammonia has a significant effect on the ignition and flame development processes. First, the ignition delay time increases significantly. Second, the ignition kernel size (both in physical and temperature-composition space), and its location and growth rate decrease noticeably. Third, the spray flame development and final quasi-steady flame structure are also modified. The outcome of this research would enable better understanding of ammonia-diesel dual-fuel spray flame behavior and development of optimized fuel injection strategies with respect to emissions.
Investigating the Influence of Evaporation and Mixing Regimes on Droplet Evaporation Rate at Elevated Pressure
Authors: Manin Julien , Crua Cyril 
Published: December 2024

Abstract
Understanding the evaporation of spray droplets under high-pressure conditions holds significant relevance across various applications, particularly in combustion systems such as rocket combustors, gas turbine engines, and compression-ignition engines. Operating at pressures above the fuel’s critical point, these systems undergo a transcritical change of state where the injected fuel transitions from a liquid-like to a gas-like state. This complex process involves diffusive evaporation and mixing, challenging the classical perspective of droplet evaporation governed solely by surface area. To shed light on the evaporation characteristics of fuel droplets under such transcritical conditions, we conducted experimental measurements in well-controlled, high-pressure, and high-temperature inert gaseous environments. High-resolution imaging experiments at high repetition rates enabled us to detect and characterize fuel droplets as function of time, until complete evaporation. By comparing the droplet population decays with time, which was expressed as a droplet population lifetime metric, the results confirm that higher temperatures led to faster evaporation. Using the droplet lifetimes, the evaporation rate constant was estimated, showing an expected increase with temperature. However, deviations from the expected linear behavior were observed, particularly in cases where the droplet evaporation regime was identified as diffusive, with evaporation rate constant estimates up to nearly 70% higher than predicted by established correlations such as the d2-law of evaporation. Our findings bridge a significant gap in understanding droplet evaporation under extreme conditions, revealing that the evaporation rate is strongly influenced by the evaporation regime.
Home self-management of type 2 diabetes with diabetes technologies in northern France: a focused ethnographic study protocol
Authors: Marcilly Romaric , Quindroit Paul , Lemaitre Madleen , Vambergue Anne , Avez Eric , Bubeck Arnaud , Hehn Coline , Beuscart Jean-Baptiste , Grimes Tamasine C 
Published: December 2024

IntroductionType 2 diabetes is a chronic condition associated with impaired glucose tolerance and a high prevalence of comorbidity, polypharmacy and medication safety incidents. Little is known about the patient work associated with using diabetes management technologies by patients and their informal caregivers at home. This study aims to apply a systems engineering approach to better understand this work.Methods and analysisThis is a qualitative focused ethnographic study using interview and photography. Adults, living independently at home, with type 2 diabetes who have been using insulin as part of their treatment regimen for a minimum of 6 months and who are using at least one diabetes management technology without support of a professional at home are eligible for inclusion. Participants will be recruited through advertisements on social media, in diabetes clinics and by contacting associations of persons living with diabetes and diabetes specialists. Participant consent will be taken, interviews will be undertaken in the participant’s home, audio-recorded and photographs securely saved. The Systems Engineering Initiative for Patient Safety (SEIPS) model will frame the data coding and we will develop new codes to accommodate data outside the SEIPS model. Results will be interpreted to produce a description of work processes, work system elements and interactions that support or jeopardise the achievement of safety. This protocol will follow the consolidated criteria for reporting qualitative research checklist for the reporting of qualitative research interviews.Ethical considerations and disseminationThis protocol was approved by the University of Lille’s Behavioural Sciences Ethics Committee. The study will comply with data protection legislation: the protocol has been declared by the Data Protection Officer of the University of Lille to the National Commission on Informatics and Liberty. We plan to disseminate our findings via presentations at relevant patient/public, professional, academic and scientific meetings, and publish in a peer-reviewed journal.
Relationship between anxiety and poor sleep quality in family caregivers of psychiatric patients based on history of sharp object injuries: a case-control study in Bitlis, Turkey
Authors: Önen Cihan , Göktaş Ahmet , Okutan Şerafettin 
Published: December 2024

ObjectivesThis study aims to investigate the relationship between anxiety levels and poor sleep quality among family caregivers of psychiatric patients, based on the history of sharp object injuries (SOI) inflicted by the patients.DesignA case-control study.Setting and dataThe data were collected through face-to-face interviews with family caregivers of patients at the Bitlis Province Community Mental Health Center and Bitlis State Hospital Psychiatry Service between December 2021 and May 2022.ParticipantsA total of 111 family caregivers participated in the study.Outcome measuresThe sleep quality of family caregivers was evaluated using the Pittsburgh Sleep Quality Index, while anxiety levels were assessed using the Beck Anxiety Inventory (BAI).ResultsAmong family caregivers, 52.3% had poor sleep quality, 24.3% experienced moderate anxiety and 31.5% experienced severe anxiety. An increase of one point on the anxiety scale (measured by BAI) raised the odds of poor sleep by 14%. The anxiety-poor sleep relationship was stronger in SOI-exposed caregivers (r=0.699) than in non-exposed ones (r=0.607). When age was controlled, the relationship strengthened among SOI-exposed individuals (r=0.722) but remained unchanged for non-exposed ones (r=0.608).ConclusionsThis study shows that each point increase on the anxiety scale is associated with a 14% increase in the odds of poor sleep among family caregivers of psychiatric patients. The relationship between anxiety level and poor sleep quality was further elevated among individuals exposed to SOI. Providing family caregivers of psychiatric patients, especially those exposed to SOI, with coping strategies and sleep hygiene training can improve their anxiety and sleep quality and assist in managing the care process.
Aspects of a novel nonlinear electrodynamics in flat spacetime and in a gravity-coupled scenario
Authors: Kar Anjan 
Published: December 2024

Abstract
A novel nonlinear electrodynamics (NLE) model with two dimensionful parameters is introduced and investigated. Our model obeys the Maxwellian limit and exhibits behaviour similar to the Born–Infeld Lagrangian in the weak field limit. It is shown that the electric field of a point charge in this model has a definite maximum value. Thus, the self-energy of the point charge is finite. The phenomenon of vacuum birefringence is found to occur in the presence of an external uniform electric field. Causality and unitarity conditions for all background electric fields hold, whereas, for magnetic fields, a restricted domain of validity is found. Moreover, a minimal coupling of Einstein’s General Relativity (GR) with this NLE results in solutions of regular black holes or naked singularities, depending on whether the source is a nonlinear magnetic monopole or an electric charge, respectively.
Definitive Evidence for the Identification and Function of Renin-Expressing Cholinergic Neurons in the Nucleus Ambiguus
Authors: Fekete Éva M. , Gomez Javier , Ghobrial Mina , Kaminski Kathren , Muskus Patricia C. , Boychuk Carie R. , Hantke Guixa Ana , Vazirabad Ibrahim , Xie Michelle , Ganiyu Azeez , Golosova Daria , Mathieu Natalia M. , Wang Yoko B. , Lu Ko-Ting , Wackman Kelsey K. , Brozoski Daniel T. , Mouradian Gary C. , Hodges Matthew R. , Segar Jeffrey L. , Grobe Justin L. , Sigmund Curt D. , Nakagawa Pablo 
Published: December 2024

BACKGROUND:
The importance of the brain renin-angiotensin system in cardiovascular function is well accepted. However, not knowing the precise source of renin in the brain has been a limitation toward a complete understanding of how the brain renin-angiotensin system operates.

METHODS:
Highly sensitive in situ hybridization techniques and conditional knockout mice were used to address the location and function of renin in the brainstem.

RESULTS:

We identified novel renin-expressing cholinergic neurons in the nucleus ambiguus (NuAm), a major vagal cardioinhibitory center in the brainstem. The expression of renin-angiotensin system genes was relatively abundant in the NuAm, implying that angiotensin II might mediate an important regulatory role in this nucleus and other regions with neural connectivity to the NuAm. Then, we generated conditional knockout mice lacking the classical renin isoform (Ren-a
ChAT-KO
), specifically in cholinergic neurons. Ablation of Ren-a in cholinergic neurons abrogated renin expression in the NuAm. Moreover, studies using radiotelemetry, heart rate variability analyses, and pharmacological approaches revealed that the parasympathetic nervous system is depressed in Ren-a
ChAT-KO
males while augmented in the Ren-a
ChAT-KO
females. Subsequently, transcriptomic approaches were used to infer putative genes and signaling pathways regulated by renin within the NuAm.

CONCLUSIONS:
This study revealed that renin in cholinergic neurons plays a fundamental role in preserving autonomic balance and cardiovascular homeostasis in a sex-dependent manner. These findings define the NuAm as an endogenous, local source of renin with biological function and serve as conclusive evidence for the presence and functionality of the brain renin-angiotensin system.
Neural Correlates of Different Randomization Tasks
Authors: Guseva Maja , Bogler Carsten , Allefeld Carsten , Ziya Ece Büşra , Haynes John-Dylan 
Published: December 2024

Abstract
In some cases, when we are making decisions, the available choices can appear to be equivalent. When this happens, our choices appear not to be constrained by external factors and, instead, we can believe to be selecting “randomly.” Furthermore, randomness is sometimes even explicitly required by task conditions such as in random sequence generation tasks. This is a challenging task that involves the coordination of multiple cognitive processes, which can include the inhibition of habitual choice patterns and monitoring of the running choice sequence. It has been shown that random choices are strongly influenced by the way they are instructed. This raises the question whether the brain mechanisms underlying random selection also differ between different task instructions. To assess this, we measured brain activity while participants were engaging in three different variations of a sequence generation task: On the basis of previous work, participants were instructed to either (1) “generate a random sequence of choices,” (2) “simulate a fair coin toss,” or (3) “choose freely.” Our results reveal a consistent frontoparietal activation pattern that is shared across all tasks. Specifically, increased activity was observed in bilateral inferior and right middle frontal gyrus, left pre-SMA, bilateral inferior parietal lobules, and portions of anterior insular cortex in both hemispheres. Activity in the mental coin toss condition was higher in right dorsolateral prefrontal cortex, left (pre-) SMA, a portion of right inferior frontal gyrus, bilateral superior parietal lobules, and bilateral anterior insula. In addition, our multivariate analysis revealed a distinct region in the right frontal pole to be predictive of the outcome of choices, but only when randomness was explicitly instructed. These results emphasize that different randomization tasks involve both shared and unique neural mechanisms. Thus, even seemingly similar randomization behavior can be produced by different neural pathways.
On the Compressive Power of Autoencoders With Linear and ReLU Activation Functions
Authors: Sun Liangjie , Wu Chenyao , Ching Wai-Ki , Akutsu Tatsuya 
Published: December 2024

Abstract
In this article, we mainly study the depth and width of autoencoders consisting of rectified linear unit (ReLU) activation functions. An autoencoder is a layered neural network consisting of an encoder, which compresses an input vector to a lower-dimensional vector, and a decoder, which transforms the low-dimensional vector back to the original input vector exactly (or approximately). In a previous study, Melkman et al. (2023) studied the depth and width of autoencoders using linear threshold activation functions with binary input and output vectors. We show that similar theoretical results hold if autoencoders using ReLU activation functions with real input and output vectors are used. Furthermore, we show that it is possible to compress input vectors to one-dimensional vectors using ReLU activation functions, although the size of compressed vectors is trivially Ω(log n) for autoencoders with linear threshold activation functions, where n is the number of input vectors. We also study the cases of linear activation functions. The results suggest that the compressive power of autoencoders using linear activation functions is considerably limited compared with those using ReLU activation functions.