bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic Algorithm Based Optimization Technique for Savonius-Style Wind Turbine
Authors: Paul Diplina , Banerjee Abhisek 
Published: December 2021

Abstract
In this article, authors have studied genetic algorithm-based optimization technique to optimize rotor profile for elliptic shaped Savonius-style wind turbine with an aim to maximize the coefficient of performance. Genetic algorithm has been used to optimize design variables having distinct values and discontinuous and nondifferentiable objective functions. Optimization procedure using genetic algorithm uses the following steps: initialization, assessment, assortment, crossover and lastly alteration. Once the genetic algorithm is initialized, then the evaluation process trails, where each parametric value is evaluated based on the fitness function stated as objective function. Then the GA operators i.e assortment, cross over and alteration are applied. At the end of GA operation procedure, a new set of values of design parameter is generated. This procedure is endlessly iterated until the convergence criteria is met. Then the optimized and non-optimized profiles are studied using numerical simulation. Initially a two-dimensional numerical model is developed and validated against experimental results. The two-dimensional analysis is conducted using k-ω shear stress transport model. Unsteady Reynold’s Averaged Navier Stoke’s equations have been solved to simulate the flow field of a Savonius-style rotor. This analysis has been executed using finite volume approach in Fluent 17.2 version. Grid independence study is performed to curtail the effect of grid size on the flow field portrayals. The optimization technique implemented on the Savonius-style wind turbine, generated design parameters that were able to yield a coefficient of performance value of 0.398. The coefficient of torque and coefficient of performance values are studied for both optimized and non-optimized profile as a function of tip speed ratio. Numerical simulation predicted a maximum gain of 41% for coefficient of performance at TSR = 1.0 over for optimized profile over the non-optimized profile.
Application of Machine Learning Based Surrogate Model for Prediction of Sectional Temperature of Radially Cooled Gas Turbine Blades
Authors: Shrivastava Rishabh , Tamar Nisha , Grover Amit , Das Debdulal 
Published: December 2021

Abstract
Accurate thermal prediction of gas turbine blades is essential to ensure successful operation throughout the design life. Large Gas turbines operate in different conditions based on customer requirements, due to which turbine blades are subjected to variations in thermal loading conditions. Simulating this behavior using conventional finite element modeling involves detailed and time-consuming analyses for calculation of blade temperature, which can be further utilized to assess cyclic and creep life. This paper deals with developing and utilizing machine learning based surrogate models to predict the sectional temperature (output) of a radially cooled blade. The surrogate models are developed to predict the output using turbine inlet temperature, hot gas mass flow, cooling air temperature and cooling air mass flow as input to the machine learning (ML) model. All thermal parameters for ML model have been obtained from CFD based 3D thermal calculations. A comparative study is presented between linear regression, decision tree, random forest, and gradient boost ML models, to select the model with the least mean absolute error. Additionally, hyperparameter optimization is performed using grid search to minimize the error. The results show that the linear regression-based model outputs the least mean absolute error of 6.5°C and the highest dependence of the output is on the turbine inlet temperature, followed by the cooling air temperature. The findings show a good agreement between the predicted output of the surrogate model and multi-dimensional physics based thermal calculations, while offering a considerate reduction in analysis time.
Investigation on Flat-Fan Spray Characterization in High-Speed Air Coflow for Gas Turbine Online Water Washing Application
Authors: Kumar Kiran , Chaudhari Vasudev , Sahu Srikrishna , Devi Ravindra G. 
Published: December 2021

Abstract
Fouling in compressor blades due to dirt deposition is a major issue in land-based gas turbines as it impedes the compressor performance and degrades the overall engine efficiency. The online water washing approach is an effective alternate for early-stage compressor blade cleaning and to optimize the time span between offline washing and peak availability. In such case, typically a series of flat-fan nozzles are used at the engine bell mouth to inject water sprays into the inflowing air. However, optimizing the injector operating conditions is not a straightforward task mainly due to the tradeoff between blade cleaning effectiveness and material erosion. In this context, the knowledge on spray characteristics prior to blade impingement play a vital role, and the experimental spray characterization is crucial not only to understand the basic process but also to validate numerical models and simulations. The present paper investigates spray characteristics in a single flat-fan nozzle operated in the presence of a coflowing air within a wind-tunnel. A parametric investigation is carried out using different air flow velocity in the tunnel and inlet water temperature, while the liquid flow rate was maintained constant. The spray cone angle and liquid breakup length are measured using back-lit photography. The high-speed shadowgraphy technique is used for capturing the droplet images downstream of the injector exit. The images are processed following depth-of-filed correction to measure droplet size distribution. Droplet velocity is measured by the particle tracking velocimetry (PTV) technique. As both droplet size and velocity are known, the cross-stream evolution of local droplet mass and momentum flux are obtained at different axial locations which form the basis for studying the effectiveness of the blade cleaning process due to droplet impingement on a coupon coated with foulant of known mass.
Harmonic Analysis of the High Speed Direct Coupled SCO2 Turbo-Generator
Authors: Patel Ashutosh , Biradar Vijay , Kumar Pramod 
Published: December 2021

Abstract
The rotordynamics and harmonic characteristics of the rotor assembly designed for 40-kW high-speed sCO2 direct-coupled turbo-generator pair have been evaluated numerically using finite element solver “ANSYS Mechanical”. First, the shaft geometry and dimensions have been optimized using lumped mass-inertia-based AxSTREAM RotorDynamics module followed by the bearing selection analysis using SKF SimPro expert to ensure enough separation margin from the nearby critical speeds. Equivalent 2D geometry has been used with an FEA-based ANSYS general axisymmetric model to reduce the computation time. The effect of the damping on the forces transmitting to the bearings and shaft deflection at the critical speeds are analyzed by performing harmonic analysis under various damped and undamped conditions (ζ = 0, 0.005, 0.01, and 0.02).
Coaches’ Reflections of Using a Charity-Driven Framework to Foster Youth Athletes’ Psychosocial Outcomes
Authors: Bean Corliss , Nienhuis Carl , Proulx Jason , Cash Tiara , Aknin Lara , Whillans Ashley V. 
Published: December 2021

When structured appropriately, sport can promote psychosocial development in youth athletes. However, few frameworks exist that allow coaches to intentionally support youth’s psychosocial development through their sport programming. The Play Better framework represents one intentional approach that incorporates prosocial behavior where youth earn donations toward charitable causes for reaching process-based goals. Given the potential benefit that explicit strategies have for yielding positive developmental outcomes, there is a need for research to explore the role of intentionality in enhancing quality sport delivery. The purpose of this study was to understand coaches’ perceptions of using the framework within their coaching practices. Twenty-three soccer coaches (83% male) participated in a one-on-one semistructured interview analyzed inductively. Results indicated that coaches perceived the Play Better framework to (a) help enact their coaching philosophies; (b) enable youth choice, while supporting sport-skill development and enjoyment; (c) facilitate intentional approaches to life skills development and transfer; and (d) foster professional and personal development. This research provides initial evidence of the benefit of using an intentional framework, like Play Better, for athletes and coaches. Future research is needed to understand athlete and parent perspectives of utilizing the framework. Findings help inform future coach training resources and best practices.
Effectiveness of mRNA-1273 against delta, mu, and other emerging variants of SARS-CoV-2: test negative case-control study
Authors: Bruxvoort Katia J , Sy Lina S , Qian Lei , Ackerson Bradley K , Luo Yi , Lee Gina S , Tian Yun , Florea Ana , Aragones Michael , Tubert Julia E , Takhar Harpreet S , Ku Jennifer H , Paila Yamuna D , Talarico Carla A , Tseng Hung Fu 
Published: December 2021

AbstractObjectivesTo evaluate the effectiveness of the mRNA-1273 vaccine against SARS-CoV-2 variants and assess its effectiveness against the delta variant by time since vaccination.DesignTest negative case-control study.SettingKaiser Permanente Southern California (KPSC), an integrated healthcare system.ParticipantsAdult KPSC members with a SARS-CoV-2 positive test sent for whole genome sequencing or a negative test from 1 March 2021 to 27 July 2021.InterventionsTwo dose or one dose vaccination with mRNA-1273 (Moderna covid-19 vaccine) ≥14 days before specimen collection versus no covid-19 vaccination.Main outcome measuresOutcomes included infection with SARS-CoV-2 and hospital admission with covid-19. In pre-specified analyses for each variant type, test positive cases were matched 1:5 to test negative controls on age, sex, race/ethnicity, and specimen collection date. Conditional logistic regression was used to compare odds of vaccination among cases versus controls, with adjustment for confounders. Vaccine effectiveness was calculated as (1–odds ratio)×100%.ResultsThe study included 8153 cases and their matched controls. Two dose vaccine effectiveness was 86.7% (95% confidence interval 84.3% to 88.7%) against infection with the delta variant, 98.4% (96.9% to 99.1%) against alpha, 90.4% (73.9% to 96.5%) against mu, 96-98% against other identified variants, and 79.9% (76.9% to 82.5%) against unidentified variants (that is, specimens that failed sequencing). Vaccine effectiveness against hospital admission with the delta variant was 97.5% (92.7% to 99.2%). Vaccine effectiveness against infection with the delta variant declined from 94.1% (90.5% to 96.3%) 14-60 days after vaccination to 80.0% (70.2% to 86.6%) 151-180 days after vaccination. Waning was less pronounced for non-delta variants. Vaccine effectiveness against delta infection was lower among people aged ≥65 years (75.2%, 59.6% to 84.8%) than those aged 18-64 years (87.9%, 85.5% to 89.9%). One dose vaccine effectiveness was 77.0% (60.7% to 86.5%) against infection with delta.ConclusionsTwo doses of mRNA-1273 were highly effective against all SARS-CoV-2 variants, especially against hospital admission with covid-19. However, vaccine effectiveness against infection with the delta variant moderately declined with increasing time since vaccination.
Improved outcomes following the implementation of a decompensated cirrhosis discharge bundle
Authors: Smethurst Katherine , Gallacher Jennifer , Jopson Laura , Majiyagbe Titilope , Johnson Amy , Copeman Philip , Mansour Dina , McPherson Stuart 
Published: December 2021

IntroductionMortality from liver disease is increasing and management of decompensated cirrhosis (DC) is inconsistent across the UK. Patients with DC have complex medical needs when discharged from hospital and early readmissions are common. Our aims were: (1) to develop a Decompensated Cirrhosis Discharge Bundle (DCDB) to optimise ongoing care and (2) evaluate the impact of the DCDB.MethodsA baseline review of the management of patients with DC was conducted in Newcastle in 2017. The DCCB was developed and implemented in 2018. Impact of the DCDB was evaluated in two cycles, first a paper version (November 2018–October 2019) and then an electronic version (November 2020–March 2021). Key clinical data were collected from the time of discharge.ResultsOverall, 192 patients (62% male; median age 55; median model for end-stage liver disease 17; 72% alcohol related) were reviewed in three cycles. At baseline, management was suboptimal, particularly ascites/diuretic management and provision of follow-up for alcohol misuse and 12% of patients had a potentially avoidable readmission within 30 days. After DCDB introduction, care improved across most domains, particularly electrolyte monitoring (p=0.012) and provision of community alcohol follow-up (p=0.026). Potentially preventable readmissions fell to 5% (p=0.055).ConclusionsUse of a care bundle for patients with DC can standardise care and improve patient management. If used more widely this could improve outcomes and reduce variability in care for patients with DC.
Neuropathic pain in the IMI-APPROACH knee osteoarthritis cohort: prevalence and phenotyping
Authors: van Helvoort Eefje Martine , Welsing Paco M J , Jansen Mylène P , Gielis Willem Paul , Loef Marieke , Kloppenburg Margreet , Blanco Francisco , Haugen Ida K , Berenbaum Francis , Bay-Jensen Anne-C , Ladel Christoph , Lalande Agnes , Larkin Jonathan , Loughlin John , Mobasheri Ali , Weinans Harrie , Lafeber Floris , Eijkelkamp Niels , Mastbergen Simon 
Published: December 2021

ObjectivesOsteoarthritis (OA) patients with a neuropathic pain (NP) component may represent a specific phenotype. This study compares joint damage, pain and functional disability between knee OA patients with a likely NP component, and those without a likely NP component.MethodsBaseline data from the Innovative Medicines Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway knee OA cohort study were used. Patients with a painDETECT score ≥19 (with likely NP component, n=24) were matched on a 1:2 ratio to patients with a painDETECT score ≤12 (without likely NP component), and similar knee and general pain (Knee Injury and Osteoarthritis Outcome Score pain and Short Form 36 pain). Pain, physical function and radiographic joint damage of multiple joints were determined and compared between OA patients with and without a likely NP component.ResultsOA patients with painDETECT scores ≥19 had statistically significant less radiographic joint damage (p≤0.04 for Knee Images Digital Analysis parameters and Kellgren and Lawrence grade), but an impaired physical function (p<0.003 for all tests) compared with patients with a painDETECT score ≤12. In addition, more severe pain was found in joints other than the index knee (p≤0.001 for hips and hands), while joint damage throughout the body was not different.ConclusionsOA patients with a likely NP component, as determined with the painDETECT questionnaire, may represent a specific OA phenotype, where local and overall joint damage is not the main cause of pain and disability. Patients with this NP component will likely not benefit from general pain medication and/or disease-modifying OA drug (DMOAD) therapy. Reserved inclusion of these patients in DMOAD trials is advised in the quest for successful OA treatments.Trial registration numberThe study is registered under clinicaltrials.gov nr: NCT03883568.
The GAMBIT Universal Model Machine: from Lagrangians to likelihoods
Authors: Bloor Sanjay , Gonzalo Tomás E. , Scott Pat , Chang Christopher , Raklev Are , Camargo-Molina José Eliel , Kvellestad Anders , Renk Janina J. , Athron Peter , Balázs Csaba 
Published: December 2021

AbstractWe introduce the Universal Model Machine (), a tool for automatically generating code for the global fitting software framework , based on Lagrangian-level inputs. accepts models written symbolically in and formats, and can use either tool along with and to generate model, collider, dark matter, decay and spectrum code, as well as interfaces to corresponding versions of , , and (C
"Image missing"). In this paper we describe the features, methods, usage, pathways, assumptions and current limitations of . We also give a fully worked example, consisting of the addition of a Majorana fermion simplified dark matter model with a scalar mediator to via , and carry out a corresponding fit.
Anomaly-free Abelian gauge symmetries with Dirac seesaws
Authors: Bernal Nicolás , Restrepo Diego 
Published: December 2021

AbstractWe perform a systematic analysis of Standard Model extensions with an additional anomaly-free gauge U(1) symmetry, to generate tree-level Dirac neutrino masses. An anomaly-free symmetry demands nontrivial conditions on the charges of the unavoidable new states. An intensive scan was performed, looking for solutions generating neutrino masses by the type-I and type-II tree-level Dirac seesaw mechanism, via operators with dimension 5 and 6, that correspond to active or dark symmetries. Special attention was paid to the cases featuring no extra massless chiral fermions or multicomponent dark matter with unconditional stability.
Erasure-Resilient Sublinear-Time Graph Algorithms
Authors: Levi Amit , Pallavoor Ramesh Krishnan S. , Raskhodnikova Sofya , Varma Nithin 
Published: December 2021

We investigate sublinear-time algorithms that take partially erased graphs represented by adjacency lists as input. Our algorithms make degree and neighbor queries to the input graph and work with a specified fraction of adversarial erasures in adjacency entries. We focus on two computational tasks: testing if a graph is connected or ε-far from connected and estimating the average degree. For testing connectedness, we discover a threshold phenomenon: when the fraction of erasures is less than ε, this property can be tested efficiently (in time independent of the size of the graph); when the fraction of erasures is at least ε, then a number of queries linear in the size of the graph representation is required. Our erasure-resilient algorithm (for the special case with no erasures) is an improvement over the previously known algorithm for connectedness in the standard property testing model and has optimal dependence on the proximity parameter ε. For estimating the average degree, our results provide an “interpolation” between the query complexity for this computational task in the model with no erasures in two different settings: with only degree queries, investigated by Feige (SIAM J. Comput. ‘06), and with degree queries and neighbor queries, investigated by Goldreich and Ron (Random Struct. Algorithms ‘08) and Eden et al. (ICALP ‘17). We conclude with a discussion of our model and open questions raised by our work.
Survival Outcomes among Human Epidermal Growth Factor Receptor 2- (HER2-) Positive Breast Cancer Patients at Kenyatta National Hospital
Authors: Tuwei Gloria , Degu Amsalu 
Published: December 2021

Introduction. HER2-positive breast cancer is associated with poor outcomes and higher mortality rates than other breast cancer subtypes. The advent of trastuzumab has significantly changed the natural history of HER2-positive breast cancer. However, it is not an affordable treatment option in sub-Saharan African countries. Because of the expense, most patients in our setting do not receive trastuzumab for the optimal control of their disease. Additionally, there is a lack of comprehensive data about the survival outcomes of HER2-positive breast cancer patients in our setting. The present study was aimed at determining the survival outcomes among HER2-positive breast cancer patients at the Oncology Department of Kenyatta National Hospital. Methods. A hospital-based retrospective cohort design was used to evaluate the survival outcomes among patients with HER2-positive breast cancer treated from 1st January 2015 to 31st December 2019 at Kenyatta National Hospital. A total of 50 eligible HER2-positive breast cancer patients were included in the study. In the predesigned data abstraction tool, data were collected by reviewing the medical records of the patients. The data were entered and analyzed using the Statistical Package for the Social Sciences version 27 software. The mean survival time was estimated using Kaplan-Meier survival analysis. Results. The mean age was

45.44
±
12.218

years, with a majority (80%) of the patients being below 60 years. Most patients (64%) had advanced-stage disease. The median follow-up time for patients with curative stages of breast cancer was 41 months, while the median follow-up time for those with the advanced incurable disease was 8.5 months. The 4-year survival rate was 62.5% for those curable-stage HER2-positive breast cancer compared to 5.6% for those with metastatic disease at presentation. Conclusion. The 4-year survival rate for both early-stage and advanced-stage HER2-positive breast cancer in our setting is suboptimal when compared to existing outcome data from health care systems where trastuzumab is more widely available.
The Quest to Define and Target Cellular Senescence in Cancer
Authors: Wang Boshi , Demaria Marco 
Published: December 2021

Abstract
Cellular senescence represents a double-edged sword in cancer and its therapy. On one side, senescence-associated growth arrest and immunomodulatory properties exert potent antimalignant functions. On the other side, senescence bypass and secretory phenotype are associated with tumor progression and relapse. Recent studies have demonstrated the enormous potential to combine pro- to antisenescence interventions as a new anticancer approach. However, the heterogeneity of senescence-associated features makes definition and targeting of therapy-induced senescent cells a challenging task. Here, we describe these challenges and discuss how to exploit senescence-associated features to improve treatment efficacy and tolerability.
Coiled-Coil Domain: Uncoiling Tumor Suppression by BRCA1
Authors: Mishra Arun P. , Sahu Sounak , Sharan Shyam K. 
Published: December 2021

Abstract
The coiled-coil domain of BRCA1 is essential for its interaction with partner and localizer of BRCA2 (PALB2). In mice, loss of this interaction is known to result in Fanconi anemia–associated phenotypes. In a study published in this issue of Cancer Research, Pulver and colleagues from the Jonkers lab have generated a mouse model with a leucine to proline change in codon 1363 in the coiled-coil domain of BRCA1 (Brca1LP), which disrupts its binding with PALB2. Unlike the previously reported viable coiled-coil defective mice, homozygous Brca1LP/LP mutant mice die during embryogenesis. The authors examined the role of the BRCA1/PALB2 interaction on mammary tumorigenesis and reported increased incidence of mammary tumors that are carcinosarcomas or sarcomatoids, unlike the adenocarcinomas that are characteristic mammary tumor types associated with loss of Brca1 and Trp53 in mice. The findings reveal the relevance of the coiled-coil domain in mammary tumor suppression by BRCA1.
See related article by Pulver et al., p. 6171