bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance and Emission Characteristics of a Marine Diesel Engine Fueled by Nano-Emulsified Biodiesel Produced From Waste Vegetable Oil
Authors: Ogunkunle Oyetola , Nkosi Nsizwazonke Cedrick , Laseinde Opeyeolu Timothy 
Published: September 2022

Abstract
This study presents an investigation of some performance parameters and emissions characteristics of a six-cylinder submarine diesel engine operated on blends of biodiesel produced from waste vegetable oil (WVO). A heterogeneous catalyst was developed from waste eggshells via calcination 900 oC for 3 hrs. Waste vegetable oil was purified via hydrolysis to degum and reduce the free fatty acid of the oil. Biodiesel was produced from the purified WVO using the heterogeneous catalyst and methanol as alcohol. Nanoparticles were prepared from coconut shells via calcination at 900 °C for 5 hrs. Nanoemulsified fuel samples, B5 (5% biodiesel + 95% diesel), B10 (10% biodiesel + 90% diesel), and B20 (20% biodiesel + 80% diesel) were prepared from the produced biodiesel together with a constant weight of 5g of nanoparticles and 5wt% water. Engine tests were carried out under different load conditions (20%, 40%, 60%, 80%, and 100%) using nano-emulsified biodiesel blend and pure diesel. Performance characteristics such as brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), and exhaust gas temperature (EGT) were determined. The emissions measured include CO, CO2, HC, NOx. The heterogeneous catalyst was found to have 62% CaO composition. An average yield of 90.5% was obtained from biodiesel production. The CO and HC emissions were maximum for pure diesel compared to the biodiesel blends. However, there was an increase in CO2 emissions as biodiesel content increased in the fuel blends. The NOx emissions were maximum for pure diesel, with B5 showing more NOx reduction. The results showed that all the fuel blends demonstrated more suitable qualities as a substitute fuel for a diesel engine. The blended fuels are similar to diesel performance, but B20 showed maximum BTE at full load conditions.
Thermodynamic Analysis of a Self-Sufficient and Nearly Emission Free Wastewater Treatment Plant
Authors: Erguvan Mustafa , MacPhee David W. , Amini Shahriar 
Published: September 2022

Abstract
Effluent standards in wastewater treatment plants have been very strict due to a dramatic increase in the amount of contaminants of household and industrial wastewater. For this reason, wastewater treatment plants consume a significant amount of power in order to treat wastewater throughout the world. Since most of the power in the United States is produced by petroleum products and natural gas, greenhouse gas emission has been a severe problem. In this study, a multigeneration energy system has been developed numerically to create an energetically self-sufficient wastewater treatment plants (WWTP) with zero emission. Since the WWTP part has already been investigated in another study [1], only the oxy-fuel power generation system, which is a combination of an oxy-fuel combustion cycle with a Rankine cycle, has been modeled. In the oxy-fuel combustor, the biogas generated by anaerobic digestion process is combusted with pure oxygen and recycled CO2 to produce hot exhaust gas. The system has been developed and modelled using Engineering Equation Solver and several parameters in the power cycle have been varied so as to investigate their effects on thermodynamic efficiencies and self-sufficiency ratio. The parameters in this sensitivity study are compression ratio, turbine inlet temperature, CO2 return ratio, recycled CO2 temperature, and temperature difference in the regenerator. Energy and exergy efficiencies of the parametric study have been calculated along with the self-sufficiency ratio. The maximum results for energy and exergy efficiencies shown to be 53.7% and 52.2%, and for self-sufficiency ratio to be 130.5%. The results show that this concept has a significant potential for future deployment.
A Proof-of-Concept Study of a Novel Elasto-Hydrodynamic Seal for CO2
Authors: Lyathakula Karthik Reddy , Cesmeci Sevki , Hassan Mohammad Fuad , Xu Hanping , Tang Jing 
Published: September 2022

Abstract
This paper deals with numerical studies of a novel Elasto-Hydrodynamic Seal (EHD), which has been developed for supercritical CO2 (sCO2) turbomachinery applications. Current sCO2 turbomachinery suffer from high leakage rates, which is creating a major roadblock for the full realization of sCO2 power technology. The high leakage rates not only penalize the efficiencies but also create environmental concerns due to greenhouse effects caused by increased CO2 discharge to the atmosphere. The proposed novel EHD seal needs to work at elevated pressures (12–35 MPa) and temperatures (350–700 °C) with low leakage and minimal wear. The unique mechanism of such a EHD seal provides a self-regulated constriction effect to restrict the flow without substantial material contact, thereby minimizing the leakage and wear. This work utilizes a physics-based modeling approach. The flow through the gradually narrowing seal clearance is modeled by the well-known Reynolds equation in EHD lubrication theory. Whereas the deformation of the seal is modeled by using the governing equations of three-dimensional solid mechanics. As for the solution methodology, COMSOL’s Thin-Film Flow and Solid Mechanics Modules were employed with their powerful capabilities. The numerical results were presented and discussed. It was observed that the Reynolds equation fully coupled with the surface deformation was able to capture the constriction effect successfully. It was also interesting to observe that the seal leakage followed a quadratic trend with increasing pressure differential, which can become advantageous for high-pressure applications such as sCO2 power generation technology.
Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction
Authors: Straw Isabel , Wu Honghan 
Published: September 2022

ObjectivesThe Indian Liver Patient Dataset (ILPD) is used extensively to create algorithms that predict liver disease. Given the existing research describing demographic inequities in liver disease diagnosis and management, these algorithms require scrutiny for potential biases. We address this overlooked issue by investigating ILPD models for sex bias.MethodsFollowing our literature review of ILPD papers, the models reported in existing studies are recreated and then interrogated for bias. We define four experiments, training on sex-unbalanced/balanced data, with and without feature selection. We build random forests (RFs), support vector machines (SVMs), Gaussian Naïve Bayes and logistic regression (LR) classifiers, running experiments 100 times, reporting average results with SD.ResultsWe reproduce published models achieving accuracies of >70% (LR 71.31% (2.37 SD) – SVM 79.40% (2.50 SD)) and demonstrate a previously unobserved performance disparity. Across all classifiers females suffer from a higher false negative rate (FNR). Presently, RF and LR classifiers are reported as the most effective models, yet in our experiments they demonstrate the greatest FNR disparity (RF; −21.02%; LR; −24.07%).DiscussionWe demonstrate a sex disparity that exists in published ILPD classifiers. In practice, the higher FNR for females would manifest as increased rates of missed diagnosis for female patients and a consequent lack of appropriate care. Our study demonstrates that evaluating biases in the initial stages of machine learning can provide insights into inequalities in current clinical practice, reveal pathophysiological differences between the male and females, and can mitigate the digitisation of inequalities into algorithmic systems.ConclusionOur findings are important to medical data scientists, clinicians and policy-makers involved in the implementation medical artificial intelligence systems. An awareness of the potential biases of these systems is essential in preventing the digital exacerbation of healthcare inequalities.
Establishing a colorectal cancer research database from routinely collected health data: the process and potential from a pilot study
Authors: Tamm Andres , Jones Helen JS , Perry William , Campbell Des , Carten Rachel , Davies Jim , Galdikas Algirdas , English Louise , Garbett Alex , Glampson Ben , Harris Steve , Khan Khurum , Little Stephanie , Malcomson Lee , Matharu Sheila , Mayer Erik , Mercuri Luca , Morris Eva JA , Muirhead Rebecca , Norris Ruth , O’Hara Catherine , Papadimitriou Dimitri , Peek Niels , Renehan Andrew , Roadknight Gail , Starling Naureen , Teare Marion , Turner Rachel , Várnai Kinga A , Wasan Harpreet , Woods Kerrie , Cunningham Chris 
Published: September 2022

ObjectiveColorectal cancer is a common cause of death and morbidity. A significant amount of data are routinely collected during patient treatment, but they are not generally available for research. The National Institute for Health Research Health Informatics Collaborative in the UK is developing infrastructure to enable routinely collected data to be used for collaborative, cross-centre research. This paper presents an overview of the process for collating colorectal cancer data and explores the potential of using this data source.MethodsClinical data were collected from three pilot Trusts, standardised and collated. Not all data were collected in a readily extractable format for research. Natural language processing (NLP) was used to extract relevant information from pseudonymised imaging and histopathology reports. Combining data from many sources allowed reconstruction of longitudinal histories for each patient that could be presented graphically.ResultsThree pilot Trusts submitted data, covering 12 903 patients with a diagnosis of colorectal cancer since 2012, with NLP implemented for 4150 patients. Timelines showing individual patient longitudinal history can be grouped into common treatment patterns, visually presenting clusters and outliers for analysis. Difficulties and gaps in data sources have been identified and addressed.DiscussionAlgorithms for analysing routinely collected data from a wide range of sites and sources have been developed and refined to provide a rich data set that will be used to better understand the natural history, treatment variation and optimal management of colorectal cancer.ConclusionThe data set has great potential to facilitate research into colorectal cancer.
Early detection of autism spectrum disorder in young children with machine learning using medical claims data
Authors: Chen Yu-Hsin , Chen Qiushi , Kong Lan , Liu Guodong 
Published: September 2022

ObjectivesEarly diagnosis and intervention are keys for improving long-term outcomes of children with autism spectrum disorder (ASD). However, existing screening tools have shown insufficient accuracy. Our objective is to predict the risk of ASD in young children between 18 months and 30 months based on their medical histories using real-world health claims data.MethodsUsing the MarketScan Health Claims Database 2005–2016, we identified 12 743 children with ASD and a random sample of 25 833 children without ASD as our study cohort. We developed logistic regression (LR) with least absolute shrinkage and selection operator and random forest (RF) models for predicting ASD diagnosis at ages of 18–30 months, using demographics, medical diagnoses and healthcare service procedures extracted from individual’s medical claims during early years postbirth as predictor variables.ResultsFor predicting ASD diagnosis at age of 24 months, the LR and RF models achieved the area under the receiver operating characteristic curve (AUROC) of 0.758 and 0.775, respectively. Prediction accuracy further increased with age. With predictor variables separated by outpatient and inpatient visits, the RF model for prediction at age of 24 months achieved an AUROC of 0.834, with 96.4% specificity and 20.5% positive predictive value at 40% sensitivity, representing a promising improvement over the existing screening tool in practice.ConclusionsOur study demonstrates the feasibility of using machine learning models and health claims data to identify children with ASD at a very young age. It is deemed a promising approach for monitoring ASD risk in the general children population and early detection of high-risk children for targeted screening.
1 A double-blind randomised control trial of high-volume image guided injections in achilles and patellar tendinopathy in a young active population
Authors: Barker-Davies Robert M , Baker Polly , Watson James , Goodall Duncan , Wheeler Patrick C , Nicol Alastair M , Fong Daniel TP , Lewis Mark P , Bennett Alexander N 
Published: September 2022

BackgroundChronic tendinopathy is a significant burden in physically active populations. High-volume image-guided injection (HVIGI) proposes to strip away associated neovascularity, disrupt painful nerve ingrowth and facilitate rehabilitation.MethodsThe objective was to investigate the efficacy of HVIGI with and without steroid relative to placebo. The study design was a three-arm block randomised control trial in a tertiary rehabilitation centre. Sixty-two participants were recruited between 25 May 2016 and 5 March 2020. Participants were men aged 18–55 with, Achilles or patellar tendinopathy of at least 6-months chronicity who had not improved with conservative management including shockwave, Ultrasound (US) evidence of neovascularisation, tendon thickening and echogenic changes.Participants were randomly assigned to control (3ml subcutaneous 0.5% Bupivacaine) or HVIGI (10 ml 0.5% Bupivacaine and 30 ml normal saline US guided between tendon and underlying fatpad) or HVIGI with steroid (0.25 ml 100 mg/ml hydrocortisone). Clinicians and assessors were blinded. All participants were supervised through a pain-guided progressive loading programme for 6-months post injection. The primary outcome measures were the Victoria institute of sport assessments for Achilles and patellar tendinopathy (VISA-A and VISA-P) and visual analogue scale (VAS)-pain at 6-months.ResultsVISA improved by 22.8 ((95% CI) 10.4 to 35.3, effect size (ES) 1.51) in control (n=21), 18.6 (9.1 to 28.0, ES 1.31) in HVIGI (n=21) and 18.5 (3.4 to 33.6, ES 0.88) in HVIGI with steroid (n=20) groups. VAS-pain improved by 15 ((IQR) -38.75 to 8, ES 0.39) in control, 13 in HVIGI (-34.0 to 3.75, ES 0.47) and 27 (-38.0 to-1.0, ES 0.54) in HVIGI with steroid groups. Main effects for time were significant (p<0.001) but not group (p≥0.48) with no group-time interaction (p=0.71). One participant was lost to follow-up from each group for whom multiple imputation was used. No adverse events occurred.ConclusionsData does not demonstrate superiority of HVIGI over control injection. Pain-guided progressive loading remains the mainstay of treatment.
Low LDL-C level and intracranial haemorrhage risk after ischaemic stroke: a prospective cohort study
Authors: Xu Jie , Chen Zimo , Wang Meng , Mo Jinglin , Jing Jing , Yalkun Gulbahram , Dai Liye , Meng Xia , Li Hao , Li Zixiao , Wang Yongjun 
Published: September 2022

Background and purposeThe Treat Stroke to Target trial has confirmed the benefit of targeting low-density lipoprotein cholesterol (LDL-C) of <1.8 mmol/L in patients who had an ischaemic stroke (IS). However, haemorrhagic risk brought by this target level (<1.8 mmol/L) or even lower level (<1.4 mmol/L) of LDL-C should also be concerned. In this study, we aimed to demonstrate whether low LDL-C could increase the intracranial haemorrhage risk following IS.MethodsPatients who had an IS from China Stroke Center Alliance programme with complete baseline information were prospectively enrolled. 793 572 patients who had an IS were categorised into 6 groups according to LDL-C level (<1.40 mmol/L, 1.40–1.79 mmol/L, 1.80–2.59 mmol/L, 2.60–2.99 mmol/L, 3.00–4.89 mmol/L, ≥4.90 mmol/L). The study outcome was defined as intracranial haemorrhage identified during hospitalisation. Logistic regression model was used to examine the association between different LDL-C levels and risk of intracranial haemorrhage.ResultsCompared with patients of LDL-C=1.80–2.59 mmol/L, both subgroups of LDL-C<1.40 mmol/L and LDL-C=1.40–1.79 mmol/L showed significantly higher risk of intracranial haemorrhage (OR=1.26, 95% CI=1.18 to 1.35; OR=1.22, 95% CI=1.14 to 1.30, respectively). Interaction effect was found to exist between the subgroups of intravenous thrombolytic therapy (p=0.04), rather than the subgroups of age, sex and body mass index. Moreover, the sensitivity analyses indicated that even patients who had an IS with minor stroke still suffered from the increased intracranial haemorrhage risk related to low LDL-C level.ConclusionsAmong patients who had an IS, the low LDL-C level (<1.4 mmol/L or <1.8 mmol/L) at baseline is associated with increased risk of intracranial haemorrhage during acute stage. While actively lowering LDL-C level for patients who had an IS, clinicians should also concern about the haemorrhagic risk associated with low LDL-C level.
Challenges in fish aging: the role of otolith preparation technique and experience level in aging lake whitefish (Coregonus clupeaformis)
Authors: McKeefry Madeline N. , Tucker Stefan R. , Ransom Andrew L. , Kroeff Timothy G. , Forsythe Patrick S. 
Published: September 2022

Lake whitefish ( Coregonus clupeaformis (Mitchill, 1818)) is an important commercial and recreational species in the Great Lakes. Precise age estimates are important for management, and two widely used techniques for otolith preparation are thin-section and crack-and-burn, which have not been compared for lake whitefish. Sagittal otoliths were collected from 92 lake whitefish in Green Bay and Lake Michigan and aged using thin-section and crack-and-burn techniques. Otoliths were aged independently by three individuals (two novices and one expert) to assess repeatability in estimated ages. Our investigation highlights the inherent difficulty of aging lake whitefish, where thin-section produced significantly older estimated ages (6–30 years) compared to crack-and-burn (5–26 years). Percent agreement of estimated ages between preparation techniques was low for all readers within ±0 years but increased when tolerance buffers were applied. Mean coefficient of variation values from both experience levels (>10.8%) exceeded the acceptable range reported in the literature (5%–7%); however, species longevity and nature of the structure must be considered when establishing target values. Variation in estimated ages is attributed to the experience level and interpretation of structural features. Species-specific training and establishing an objective framework to identify annuli will improve precision metrics.
Inference of neutrino nature and Majorana CP phases from $$\mathbf{0}{\nu \beta \beta }$$ decays with inverted mass ordering
Authors: Huang Guo-yuan , Nath Newton 
Published: September 2022

AbstractWhether the neutrino mass ordering is normal or inverted remains an experimentally open issue in neutrino physics. The knowledge of neutrino mass ordering has great importance for neutrinoless double-beta ($$ 0\nu \beta \beta $$

0
ν
β
β

) decay experiments, which can establish the nature of massive neutrinos, i.e., whether they are Dirac or Majorana fermions. Recently, the KamLAND-Zen 800 measurement has reached for the first time the parameter space of the inverted ordering with a vanishing lightest neutrino mass. By assuming the inverted ordering, we attempt to derive the physical information of the neutrino nature and Majorana CP phases from a negative or positive observation of $$ 0\nu \beta \beta $$

0
ν
β
β

decays in the near future. Moreover, the possibility of extracting the nuclear matrix element in the case of a positive observation is also examined.To avoid the ambiguity from unknown priors of neutrino masses, we adopt the maximum likelihood method instead of the Bayesian approach usually considered in previous works.
First record of the swallowtail moth Epiplema adamantina Inoue, 1998 (Lepidoptera: Uraniidae: Epipleminae) from western Himalaya, India
Authors: Lekhendra  , Arun Pratap Singh  
Published: September 2022

This record of Epiplema adamantina Inoue, 1998 from Mussoorie is the first confirmed record of this species from the Western Himalaya outside Nepal, which is ~800kmwest from vill. Bagmati, Mt. Phoolchauki in Eastern Nepal, the nearest known site record of this species. The species prefers ‘Monsoon season’ as its flight period in ‘Moist temperate oak forest zone’ of Western and Central Himalayas. The paper discusses distinguishing features of E. adamantina with its congeners E. morataria and E. arcuata found in the same region.
An Analytical Solution to the Perturbation Analysis of the Interaction between Downburst Outflows and Atmospheric Boundary Layer Winds
Authors: Moeini Masoud , Romanic Djordje 
Published: September 2022

Abstract
Downbursts are negatively buoyant downdrafts that emerge from a storm and spread outward upon hitting the surface. The produced outflow, however, is not spreading through a calm environment, but rather through an atmosphere characterized by larger-scale atmospheric boundary layer (ABL) winds. This interaction between ABL winds and downbursts forms an outflow that is more complex than an outflow created by an isolated downdraft. Here, we propose an analytical solution of the interaction between the ABL winds and an isolated downburst outflow. The model is applicable when the ratio of centerline downdraft velocity to the horizontal ABL velocity at the cloud base is larger than the nondimensional group (H/D)(r/H)1.1, where H is the cloud-base height, D is the diameter of the downdraft, and r is the distance from the centerline of isolated downdraft. Also, the solution is derived for a specific direction in the outflow when the ABL winds and the isolated downburst outflow are aligned and the vertical profiles of radial velocity are self-similar. The model is based on the use of impinging-jet dynamics, their spreading rates, and a universal renormalization group that describes numerous laboratory measurements of velocity profiles of impinging jets issuing into both quiescent and crossflowing backgrounds. The model assumes a “known” base state corresponding to an isolated downburst, and then derives its interaction with ABL winds by way of perturbation analysis. The radial and vertical profiles of horizontal velocity from our analytical model are compared against field observations of actual downbursts and other analytical models and physical simulations of downburst-like outflows.

Significance Statement
Downbursts are intense downdrafts that emerge from a thunderstorm and spread outward upon hitting the surface. Near-surface wind gusts in a downburst can be similar to those observed in an EF3-rated tornado (∼75 m s−1). A downburst outflow is not spreading through a calm environment, but rather through an atmosphere characterized by larger-scale atmospheric boundary layer (ABL) winds. This interaction between ABL winds and downbursts forms an outflow that is more complex than an outflow created by an isolated downdraft. However, most of the current analytical models of downbursts do not account for this interaction. Using experimental measurements of downburst-like impinging jets and mathematical rigor, our study derives an equation that quantifies this interaction between downburst and ABL winds.