bluuebunny/crossref_metadata_2025_split huggingface dataset:
Soot Particle Number Prediction in GDI Engines: A Comprehensive Grey-Box Machine Learning Approach
Authors: Ebrahimi Khashayar , Roggendorf Kevin , Kwon Hyuckmo , Kang Jinsuk 
Published: December 2024

Abstract
Particulate matter (PM) emissions, including soot, pose a significant environmental concern. Traditional emission control approaches, such as engine calibration, often struggle to maintain consistent performance across varying driving conditions, engine speeds, and loads. This inconsistency leads to variations in pollutant output, making it challenging for internal combustion engines to comply with stringent emission standards under real-world driving conditions. Physics-based soot models, grounded in complex mathematical equations, have been developed to capture the mechanisms of soot formation. However, these models often face accuracy limitations and require substantial computational resources, rendering them impractical for real-time emission control. In contrast, Machine Learning (ML) techniques offer a more versatile and efficient approach to soot prediction. This paper introduces a machine learning methodology for predicting Soot Particle Numbers (PN). The approach integrates both physics-based and black-box strategies to create a grey-box model for predicting soot emissions from a 1600 cc Gasoline Direct Injection (GDI) engine across diverse load and speed conditions. The physical model is based on a validated 1D engine model in GT-SUITE, while the black-box model is formulated using multiple ML approaches exclusively on measured data. Ultimately, the grey-box model is developed through ML techniques based on measured data and combustion-related parameters obtained from the 1D engine physical model in GT-SUITE. The grey-box model’s results are compared with those of the black-box model. The ML techniques employed in this study include Gaussian Process Regression (GPR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and an Ensemble of Regression Trees (ERT). The Minimum Redundancy Maximum Relevance (MRMR) and RreliefF feature selection methods are used to identify the most crucial features in soot PN prediction. The results indicate that the grey-box GPR exhibits superior performance for both single and multi-pulse injection strategies, achieving a coefficient of determination (R2) of 0.99.
Simultaneous High-Fidelity Rate of Injection Measurements in Each Hole of a Multi-Hole Diesel Injector
Authors: Perkins Chris , Thomasma Kevin , Agrawal Ajay K. , Bittle Joshua A. 
Published: December 2024

Abstract
The injection characteristics of multi-hole diesel fuel injectors play a pivotal role in determining engine performance including emissions. The rate of injection (ROI) of fuel through each hole, the injection duration, and the total amount of fuel injected are key parameters for engine development and modeling efforts. Historically, the total ROI of an injector has been measured using an apparatus that converts a pressure change to fuel mass flux from all holes. However, the momentum flux method relying on a force sensor affords the ability to measure the ROI of individual holes. In this work, a rigorously characterized ROI measurement system consisting of fast-response piezoelectric force transducers placed in front of each injector hole is utilized to analyze the behavior of four different multi-hole injectors: (a) new and used 8-hole injectors of the same model; the old injector was taken from a vehicle with more than 50,000 miles, and (b) a stock 7-hole injector and the same injector customized with different diameter holes varying from 100–300 μm. This study characterizes hole-to-hole variation of stock injectors and identifies effects of injector wear and non-uniform hole diameters on transient performance. By measuring ROI of each hole simultaneously, this study demonstrates how injector wear can be quantified, and how novel injector designs can be incorporated in modern engine designs. Results from this study provide valuable new information for engine design, analysis, and modeling.
Development of a Variational Autoencoder Model for Fuel Design
Authors: Yalamanchi Kiran K. , Pal Pinaki , Mohan Balaji , AlRamadan Abdullah S. , Badra Jihad A. , Pei Yuanjiang 
Published: December 2024

Abstract
In this work, a Variational Autoencoder (VAE) based modeling framework is developed with the overarching goal of fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space representation. The molecular structure is fed to the VAE encoder as SMILES string to extract the latent space representation, which is then decoded back to the SMILES string. Long Short-Term Memory (LSTM) networks are employed to encode and decode SMILES strings by virtue of their ability to capture the sequential nature and hierarchical dependencies inherent in molecular structures, thus enhancing the model’s capability to effectively learn and generate meaningful representations of chemical compounds. The complexities of the corresponding loss function and its impact on the model’s ability to accurately reconstruct inputs are thoroughly examined, focusing on balancing the accuracy of reconstruction with consistency of the latent space. This ensures that the latent space captures meaningful and independent features underlying the molecular structure. The impact of varying weightage (beta (β) parameter) of the latent space regularization term in the loss function on the resultant balance between reconstruction accuracy and validity is assessed. Two different strategies for beta variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing β from 0 to a threshold value of 0.25 over 75 epochs. The VAE can be readily coupled with Quantitative Structure-Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel fuels.
Greybody factor and quasinormal modes of scalar and Dirac field perturbation in Schwarzschild-de Sitter-like black hole in Bumblebee gravity model
Authors: Singh Yenshembam Priyobarta , Singh Telem Ibungochouba 
Published: December 2024

Abstract
In this paper, we study the scalar and Dirac fields perturbation of Schwarzschild-de Sitter-like black hole in bumblebee gravity model. The effective potentials, greybody factors and quasinormal modes of the black hole are investigated by using the Klein–Gordon equation and Dirac equation. To compute the greybody factors for scalar and Dirac fields, we use the general method of rigorous bound. We also investigate the quasinormal mode using the third order WKB approximation method and Pöschl–Teller fitting method. The impact of Lorentz invariance violation parameter L and cosmological constant

$$\varLambda $$

Λ

to the effective potential, greybody factors and quasinormal modes are analyzed for different modes. Increasing the parameters

$$\varLambda $$

Λ

and L lower the effective potentials and consequently increases the rigorous bound on the greybody factors. Our findings show that the oscillation frequency and the damping rate decrease with increasing L. We analyze the Hawking temperature, power spectrum and sparsity of Hawking radiation. Both the peak of power spectrum and total power emitted decrease with increasing L. The effect of L on the shadow radius is also discussed.
Algebraic Effects and Static Analysis for Safety-Critical Applications in Fuzion
Authors: Siebert Fridtjof , Lill Michael , Teufel Max 
Published: December 2024

This work-in-progress paper presents the introduction of algebraic effects to the Fuzion language and how algebraic effects can be used in the context of safety-critical systems.
Fuzion is a modern, general purpose programming language that unifies functional and object-oriented paradigms into a pure functional language. Algebraic effects are used to represent and manage non-functional aspects like I/O operations or mutable state. Static analysis is used extensively at several stages in the Fuzion toolchain to verify different correctness aspects of the application.
We start with a condensed overview of the Fuzion language to then describe how algebraic effects are used to represent non-functional aspects. The Fuzion toolchain will be explained and how static analysis is used to build and validate applications. Finally, it will be shown how algebraic effects can be used to model aspects relevant to safety-critical systems.
High Sensitivity Refractive Index and Temperature Sensor Based on D-Type Photonic Crystal Fiber Using Surface Plasmon Resonance
Authors: Yang ZiQin , Ruan Juan , Li Xin , Tang Rui 
Published: December 2024

An all-fiber refractive index and temperature sensor based on D-type photonic crystal fiber employing surface plasmon resonance is proposed. The sensor is constructed by a polishing surface and three layers of air holes. The coupling is enhanced by reducing the distance between the analyte and the fiber core through the polishing surface. The gold film coated on the polishing surface is applied to measure the refractive index. The results have shown that the refractive index sensitivity reaches up to 6125 nm RIU−1 in the measuring range of 1.37 ∼ 1.41 with a maximum sensitivity of 10400 nm RIU−1. The temperature measurement is realized by combining gold film and mixed liquid. The gold film is covered on the surface of the air hole in one layer and the mixed liquid is filled in the adjacent air hole in the same layer. The temperature sensitivity reaches up to 1.36 nm °C−1 in the measurement range of 0 ∼ 60 °C with a maximum temperature sensitivity of 2 nm °C−1. The proposed sensor is a good candidate for environmental monitoring and medical sensing applications due to its relatively high sensitivity.
Enhanced H2S Gas Sensing Performance of SnO2/BaSnO3 Heterostructures
Authors: Vu Thi-Nhai , Li Yun-Shen , Hsu Kuo-Chin , Hsiao Yu-Jen , Lu Yu-Sheng , Huang Ching-Chien , Fang Te-Hua 
Published: December 2024

In this study, tin oxide (SnO2) nanofibers were fabricated using electrospinning technology and composited with barium stannate (BaSnO3) perovskite material to produce a heterojunction film. X-ray diffractions results showed SnO2 was oriented at 550 °C with 4 h of calcination, and BaSnO3 was oriented at 700 °C with 4 h of calcination. SnO2 nanofibers are average deposits on the substrate without any bulking and curling. By employing the “point-coat” process and observing with scanning electron microscopy, a SnO2/BaSnO3 heterojunction thin film was successfully synthesized. After the gas detection, the sensitivity difference of the SnO2/BaSnO3 had enhanced to 40 ∼ 60% in 10 ppm of H2S at different temperatures compared with pure SnO2, and SnO2/BaSnO3 had maintained at 75 ∼ 76% sensitivity in 10 ppm of H2S at 255 °C.
Visual Neuroprostheses for Impaired Human Nervous System: State‐of‐the‐Art and Future Outlook
Authors: Mahadiuzzaman A. S. M. , Hoque Md Enamul , Alam Shafiul , Chawdhury Zarin Tasnim , Hasan Mohiuddin , Rashid Adib Bin 
Published: December 2024

Background: Visual neuroprostheses refer to devices or systems that restore the impaired visual system caused by severe degeneration or damage to the retina, optic nerve, or brain.Current Status of the Topic: Current clinical applications encompass a wide range of areas, including various technologies such as introducing engineered living tissues or microfabricated photoreceptor arrays and implantable electronic microsystems, referred to as visual prostheses solutions to restore vision. Among them, visual neuroprosthesis harbors the most promising advantage by artificially receiving image information from the outside world and delivering it to the natural visual system, enabling the subject to obtain a meaningful perception of the image.Problem Statement: Although the classification and the implementation of visual neuroprostheses have been discussed in several articles, very limited works have focused on the electrode‐based, up‐to‐date approaches incorporated into these devices.Aim of the Study: This review discusses four main types of visual neuroprosthesis approaches, including retinal approaches, lateral geniculate nucleus (LGN) approaches, optic nerve approaches, and cortical approaches, along with their constituent electrodes, metals, and stimulation patterns. Using existing neuroscience knowledge, the prospects of these devices are also highlighted.
Hierarchical Indexing for Retrieval-Augmented Opinion
Summarization
Authors: Hosking Tom , Tang Hao , Lapata Mirella 
Published: December 2024

Abstract
We propose a method for unsupervised abstractive opinion summarization, that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs). Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews. Then, we use a pretrained LLM to generate a readable summary that is grounded in these extracted evidential clusters. The modularity of our approach allows us to evaluate its efficacy at each stage. We show that HIRO learns an encoding space that is more semantically structured than prior work, and generates summaries that are more representative of the opinions in the input reviews. Human evaluation confirms that HIRO generates significantly more coherent, detailed, and accurate summaries.
Comparing the experiences of cancer survivors living with sleep disturbances between differing levels of psychological distress: a qualitative study
Authors: Chan Julia , Ng Danielle Wing Lam , Fielding Richard , Lam Wendy Wing Tak 
Published: December 2024

Abstract
Background
Psychological distress often co-occurs with sleep disturbances; but the specific mechanisms linking the two remain unclear. A qualitative study explored perceptions and factors associated with sleep disturbances in cancer survivors between patients with varying levels of psychological distress.

Methods
Thirty-three Cantonese speaking mixed type cancer survivors were recruited from a community cancer care program. Participants that scored > 5 on the Pittsburgh Sleep Quality Index and had non-clinical or borderline to clinical levels of psychological distress underwent semi-structured interviews. Interviews were analyzed using grounded theory.

Results
Common triggers of sleep disturbances included unresolved treatment side-effects, intrusive thoughts about cancer and fear of cancer recurrence or progression, poor sleep hygiene and a lack of routine. Those with higher levels of distress exhibited more worry about recovery after cancer. Further, they engaged in thought suppression and experienced meta-worry about negative emotions and worry of poor sleep impacting recovery and cancer progression. They commonly exhibited high sleep reactivity and were observed to have limited social support. In contrast, those with low distress adopted better adaptive mechanisms, including a changed commitment to prioritizing health and positive reappraisal of their recovery progress. Self-distraction was used to cope with sleep disturbances and they had fewer expectations of good sleep quality.

Conclusions
Findings provided insights into the suitability of interventions for patients with sleep disturbances. Interventions targeting maladaptive emotion-focused coping may be more effective in addressing sleep disturbances in cancer survivors with higher distress. Interventions adopting a stepped-care approach may be advantageous in managing sleep disturbances by catering for varying levels of distress.
The South African Competition Commission COVID-19 easing of competition rules for private healthcare to facilitate public-private interaction - a media content analysis
Authors: Abdullah Amina , Sefuthi Thatohatsi , Ramokgopa Mapato , Fonn Sharon , Nkonki Lungiswa 
Published: December 2024

Abstract
Background
Future emergencies from climate degradation or diseases are likely, prompting ongoing investment in emergency readiness and learning from country responses. South Africa’s healthcare system, divided into public and private sectors, required a coordinated, intersectoral response to the COVID-19 pandemic. A coordinated action that the South African government took was adapting competition regulations. The South African Department of Trade, Industry and Competition on 19 March 2020, published a block exemption (easing of competition rules) for healthcare to promote standardization of practices across the health sector and facilitate agreements between the National Department of Health and the private sector to ensure adequate service delivery to all South Africans.

Methods
We assessed how much coverage the block exemption for healthcare received in the media and if the scope included details of what the exemption meant, how stakeholders and the public could use it, and the resulting public sentiment. We conducted a media content analysis to select, screen and assess the media material. Online and social-media articles in the public domain from 19 March 2020 to 19 March 2021, in English were considered.

Results
We analysed 22 online media articles that matched our inclusion criteria. Twenty of these reflected a positive sentiment, and two were negative. Media reported on the COVID-19 block exemption in healthcare only in the first three months of our study period. The articles primarily communicated what the block exemption meant, focusing on allowing collaboration, the purpose of the exemption, the activities granted under the exemption and the actors to whom it applied. It’s estimated that these media articles could have been read by up to 432,003 people out of a total population of just under 43 million people over the age of 15 years.

Conclusion
In times of crisis, the media has a significant responsibility to provide accurate information to the public. However, the accuracy and reliability of such information depends on the quality of official sources on which the media relies. Our research shows that very few media reports were available about the block exemption for healthcare. While the government implemented the exemption, it did not communicate its purpose directly to the public. Our research highlights the need for better communication between policymakers and the media.
Inequalities in the prevalence recording of 205 chronic conditions recorded in primary and secondary care for 12 million patients in the English National Health Service
Authors: Wang Shaolin , Lau Yiu-Shing , Sutton Matt , Anderson Michael , Kypridemos Christodoulos , Head Anna , Barr Ben , Cookson Richard , Bentley Chris , Anselmi Laura 
Published: December 2024

Abstract
Background
Understanding the prevalence of diseases and where it is detected and recorded in healthcare settings is important for planning effective prevention and care provision. We examined inequalities in the prevalence of 205 chronic conditions and in the care setting where the related diagnoses were recorded in the English National Health Service.

Methods
We used data from the Clinical Practice Research Datalink Aurum linked with Hospital Episode Statistics for 12.8 million patients registered with 1406 general practices in 2018. We mapped diagnoses recorded in primary and secondary care in the previous 12 years. We used linear regressions to assess associations of ethnicity, deprivation, and general practice with a diagnosis being recorded in primary care only, secondary care only, or both settings.

Results
72.65% of patients had at least one diagnosis recorded in any care setting. Most diagnoses were reported only in primary care (62.56%) and a minority only in secondary care (15.24%) or in both settings (22.18%). Black (− 0.08 percentage points (pp)), Asian (− 0.08 pp), mixed (− 0.13 pp), and other ethnicity patients (− 0.31 pp) were less likely than White patients to have a condition recorded. Patients in most deprived areas were 0.27 pp more likely to have a condition recorded (+ 0.07 pp in secondary care only, + 0.10 pp in both primary and secondary care, and + 0.10 pp in primary care only). Differences in prevalence by ethnicity were driven by diagnostic recording in primary care. Higher recording of diagnoses in more deprived areas was consistent across care settings. There were large differences in prevalence and diagnostic recording between general practices after adjusting for patient characteristics.

Conclusions
Linked primary and secondary care records support the identification of disease prevalence more comprehensively. There are inequalities in the prevalence and setting of diagnostic recording by ethnicity, deprivation, and providers on average across conditions. Further research should examine inequalities for each specific condition and whether they reflect also differences in access or recording as well as disease burden. Improving recording where needed and making national linked records accessible for research are key to understanding and reducing inequalities in disease prevention and management.
Investigating pain-related medication use and contribution to polypharmacy in adults with intellectual disabilities: a systematic review
Authors: Pacitti Christine , Cairns Deborah , Ward Laura , Nicholl Barbara I. 
Published: December 2024

Abstract
Background
Adults with intellectual disability experience more pain than adults without and, despite a higher number of medications being prescribed, may be less likely to receive medication for pain. We conducted a systematic review of existing literature on medication for pain and painful conditions in adults with intellectual disability to explore if there is any association with polypharmacy, multimorbidity or demographic characteristics.

Methods
This systematic review followed PRISMA guidelines. Medline, Embase, PubMed, PsycINFO, Web of Science, CINAHL, Cochrane Library and Scopus were searched from January 2000 to 21st October 2024. We included original, peer-reviewed observational, qualitative or mixed-method studies published in English with data on medication for pain or painful conditions in adults with intellectual disability. Two independent reviewers performed study selection, data extraction, and quality assessment; disagreements were resolved by a third reviewer. Adapted Newcastle–Ottawa Scale or the Critical Appraisal Skills Programme for qualitative studies was used for quality assessment of included studies and findings were reported via narrative synthesis. PROSPERO registration: CRD42023415051.

Results
Twenty-seven of 26,170 articles met the eligibility criteria. Adults with intellectual disability were more likely to have simple analgesic medication than non-steroidal anti-inflammatory drugs, opioids or adjuvant pain medications than the general population. Psychotropic medications were more commonly prescribed in adults with intellectual disability than medication for pain or painful conditions. Adults with intellectual disability and caregivers reported under-recognition and most likely under-treatment of pain.

Conclusions
Adults with intellectual disability may receive less pharmacological management of pain with analgesics and medication for painful conditions despite the high prevalence of polypharmacy, suggesting pain is under-treated. Better assessment and pharmacological treatment of pain and painful conditions is a key future research priority to address this health inequality and improve quality of life for this vulnerable group of people.