bluuebunny/crossref_metadata_2025_split huggingface dataset:
Habitability and Hygrothermal Comfort Analysis of Social Housing in Ecuador, Temperate Continental Climatic Zone
Authors: Calle Juan Carlos Vázquez , Calle Doris Alexandra Alvear , Farfán Julio Pintado 
Published: December 2021

Abstract
Housing sector plays an essential role in the sustainable development of the countries; however, there are significant problems in terms of its ability to access which promotes the occurrence of solutions that guide the housing deficit reduction as well as economic savings and natural resources preservation. One key strategic would be the use of social housing as a mechanism to ensure a decent house accessed by vulnerable population. The general housing design requires satisfying characteristics of indoor environmental comfort, arising the need outlook technical solutions. This research aims to propose functional and hygrothermal improvements for social housing located in a temperate continental climate zone in The Sierra region of Ecuador. The methodology was focused on energy simulations using Design Builder software and comparing the results with the application of passive bioclimatic recommendations. In addition, based on the quantitative housing deficit this research achieves qualitative contributions in order to improve the living conditions of low-income population whose get access to social housing in Ecuador. An experimental case study, located in Azogues a canton of the province of Cañar, has been selected for the simulation analysis which belongs to a housing governmental program named Manuela Espejo promoted by the Ministry of Urban Development and Housing and special designed for people with disabilities. The research has an applicative nature approach, with a sectional scope and explanatory depth, based on primary and secondary information sources; data collection techniques were social perception surveys used to determine comfort levels of the social housing users. The results show, based on the energy simulations carried out, improvements in terms of habitability conditions and an architectural proposal compiles passive bioclimatic recommendations focused on the design of social housing in Ecuador. Therefore, this research contributes to the implementation of new social housing plans that not only would help to reduce the housing deficit, but also contribute to improving the housing indoor environmental comfort perceived by the users.
Sustainability Assessment Methods in Existing Buildings: Comparative Analysis in Heritage Assets Signed for Public Use in Girón, Ecuador
Authors: Calle Marco Andrés Reinoso , Ullauri María del Cisne Aguirre 
Published: December 2021

Abstract
This paper compares sustainability assessment methods applicable to existing buildings. It also uses the most influential international certifications (BREEAM, LEED and VERDE) and their guidelines to evaluate their application in three heritage buildings in the Girón canton (Ecuador), which allows us to establish that the LEDD BO&M certification is the one with the greatest technical feasibility and professional suitability. Thus, it is known that, with small adaptations to the initial mechanisms, the sustainability of a heritage building could be certified, especially when it is intended for public use, as is the case of the study universe, where through the recommended interventions it is possible to overcome the range of compliance positioned between 8 and 27% up to 80 and 90%. In turn, this is feasible on the basis of guidelines, categories and a valuation system applicable to the sustainable valuation of a heritage building in the context of the study and its peers.
Wind Energy Collection System Based on Phase Change System and Deformable Materials
Authors: Ma Long , Cao Maoqing , Wang Wei , Yin Yanli , Meng Yang , Geng QiChen 
Published: December 2021

Abstract
The purpose of this paper is to design and discover a new system, which can realize the collection, transformation and storage of energy from wind energy in the environment ^ temperature/physical deformation → material phase change → energy storage, so as to achieve the effect of green energy saving. At the same time, it can provide theoretical support and design basis for the design of high-rise Garden residential suite in cold region. Wind energy is collected through the narrow channel effect. With the help of deformation induced ferrite phase transformation technology and memory alloy materials, the collected energy is stored in the new colloid/graphene battery equipment. With the help of the relevant content research parameters, a systematic digital model is formed, and the basic database is established to simulate the external environment of high-rise Garden residential buildings in cold areas. Through the design of a new wind energy collection system and embedded in the simulation environment, the wind energy utilization in cold, low sunshine, low wind speed or unstable wind speed areas is realized, and the green energy conservation, environmental protection and sustainable development in such areas are realized. Then, the feasibility and effectiveness of the system are demonstrated according to the experimental results, and a systematic theory is formed.
A distributed localization method for mobile nodes
Authors: Liu Chao , Luo Qinghua , Yan Xiaozhen , Shao Yang , Yang Kexin , Ju Chunyu 
Published: December 2021

Abstract
In Wireless Sensor Networks(WSNs), the location services are the basis of many application scenarios. However, for the range-based localization method, the localization accuracy and the system robustness of the distributed localization system are difficult to guarantee, due to the uncertainty of the distance estimation and position calculation are affected by the node state and communication uncertainty. In this paper, we propose the distributed localization method based on anchor node selection and Particle Filter optimization. In this method, we analyze the uncertainty of error propagation in the Least-squares method and find that there is a proportional relation between localization error and uncertainty propagation. According to this relationship, we propose the corresponding optimization criterion methods of anchor nodes. To optimize the initial localization results, we present the distributed localization method based on anchor node optimal selection and Particle Filter. Simulation results show that the methods we proposed could effectively improve the localization accuracy of the mobile nodes and the robustness of the system.
Research on torsional vibration monitoring system of ship power shafting
Authors: Xue Shufei , Wu Shuochen , Tang Qing , Liu Shulin , Liu Bai 
Published: December 2021

Abstract
In this paper, the real-time monitoring technology of ship power system torsional vibration is studied. The photoelectric non-contact measurement method is used to measure the torsional vibration intensity of shafting, and the analysis service network management platform is established to realize the functions of real-time monitoring of shafting torsional vibration and upload and collect alarm data, through the front-end system, the torsional vibration strength and other parameters of the shafting are collected and transmitted to the background. The data of the background system is used to calculate and analyze, and the status of the shafting is alarmed. The system realizes continuous monitoring and data recording of torsional vibration index of ship power system, and ensures the operation performance and safe operation of ship power system. It provides theoretical and technical support for the future development of new technologies and related research, such as durability simulation and durability virtual test of ship power critical parts.
A gas turbine thermal performance prediction method based on dynamic neural network
Authors: Hao Yansong , Jin Yunfeng , Liu Chao , Hao Jiangang , Huang Haizhou , Jiang Dongxiang 
Published: December 2021

Abstract
In order to ensure safety and reliability of energy transportation, it is necessary to understand and predict the performance of the gas turbine components. A prediction frame of the gas turbine compressor isentropic efficiency is established using the neural time series theory based on the Dynamic Neural Network. In order to obtain appropriate parameters for the network, a validation set is introduced to generalize the model. The compressor isentropic efficiency can be predicted based on the suggested model which provides an effective technical mean for the early warning of gas turbine performance. The experiment verified that the performance calculation model and the isentropic entropy efficiency prediction model based on the neural time series are effective.
Remaining Useful Life Prediction of Aero-engines by Appropriate Utilization of Multi-sensor Signals
Authors: Zhang Bin , Huang Ning , Luo Jiufei , Zhang Yi 
Published: December 2021

Abstract
This paper presents the prediction of remaining useful life (RUL) with appropriate fusion of multi-sensor signals for the aero-engine, which is the heart of an aircraft. With the rapid development of information technology, health condition of one aero-engine is usually monitored with multiple sensors. To properly utilize these multi-sensor condition information for degradation modeling and RUL prediction is one of the key challenges for condition-based maintenance of the whole aircraft. Thus this paper proposes one statistical method based on health indicator (HI) construction and empirical parametric model for aero-engines RUL prediction. The method is validated with run-to-failure data sets of an aircraft gas turbine engine test-bed developed by NASA. Results show that the proposed method can effectively fuse multi-sensor signals to describe the degradation and predict RUL of the aero-engine.
RETRACTED: Research on 3D reconstruction method of wear particle dynamic image based on multi contour space mapping
Authors: Wang Han , Zuo Hongfu , Liu Zhenzhen , Fei Hang , Liu Yan , Jiang Zhiqiang 
Published: December 2021

Abstract
Aiming at the problems of current image monitoring methods of lubrication oil wear particles, this paper designs and builds a dynamic monitoring system for oil wear particles based on microfluidic microscopic images. A contour-based 3D reconstruction method of debris particle images is proposed. The image sequences of rotating wear particles tracked by a single target are used as data, and the contour of the wear particle is extracted and the data is stored. The minimum area external rectangle method is used to correct the rotation of the particle images for the problem of deflection. And an algorithm based on cylindrical coordinate space conversion is used to convert the discrete contour point data into three-dimensional space. Complete the 3D model reconstruction of microfluidic wear particles. The ability to analyse wear particles in oil online monitoring technology is improved, which also shows new ideas for wear status monitoring and fault diagnosis technology.
Exploiting graph neural network with one-shot learning for fault diagnosis of mechanical equipment
Authors: Yang Shuai , Chen Xu , Bai Yun 
Published: December 2021

Abstract
For the classification of mechanical fault diagnosis, a graph neural network (GNN) method with one-shot learning is proposed. Convolutional Neural Network (CNN) is used to extract the feature vectors and One-Hot coding from images of Fault diagnosis of mechanical equipment. Inputting feature vectors and One-Hot coding into GNN, according to the Adjacency Matrix between vertices in the Graph, and is used for classification and inference. The method with one-shot learning is used for fault diagnosis classification. Through the fault classification for the industrial robot RV reducer and public data set CWRU pictures, the effectiveness of the method is verified. Five categories are used for fault diagnosis and classification in RV Reducer of the industrial robots. 80 categories are used in the public data set CWRU, and 55 categories are used as the training set. GNN is employed to spread the label information from the supervised sample of the unlabeled query data. The large-scale dataset can then be used as baseline classes to learn transferable knowledge for classifying novelties with one-shot samples. The one-shot learning with graph neural network GNN significantly improves the classification accuracy. The results show that the proposed method is superior to other similar methods and has a substantial potential for improvement in Fault diagnosis of mechanical equipment.
Evidence for spatial and temporal variation in mating system ofTolpis macrorhiza(Asteraceae), a species endemic to Madeira
Authors: Crawford Daniel J , Menezes De Sequeira Miguel , Mort Mark E , Kerbs Benjamin , Kelly John K 
Published: December 2021

AbstractThere have been many studies of morphological and genetic variation in island plant radiations, but few have shown how the mating system has shaped the patterns of variation. In this study, outcrossing rates and paternity in eight populations of the Madeiran endemic Tolpis macrorhiza were estimated using genome-wide RADseq genotyping. The species is believed to have evolved early in the geological history of the island, and we here examine mating system evolution during colonization and establishment of populations across Madeira. The mating system is highly outcrossing in seven populations and mixed mating in one. Some maternal plants in highly outcrossing populations were inbred, suggesting that mating system varies temporally as well as spatially. This mating system may provide flexibility for establishment of new populations in the dynamic landscapes of oceanic islands while maintaining genetic diversity within populations. Multiple paternity is prevalent in populations, indicating that compatible mates are not limited to a few sires. Our analyses of T. macrorhiza were enabled by several methodological advances included in the v.3 release of the BORICE estimation program. These include SNP filtering programs to generate valid likelihoods and post-processing scripts to partition mating system variation among populations and among maternal plants within populations.
Multisystem Inflammatory Syndrome in Children—United States, February 2020–July 2021
Authors: Miller Allison D , Zambrano Laura D , Yousaf Anna R , Abrams Joseph Y , Meng Lu , Wu Michael J , Melgar Michael , Oster Matthew E , Godfred Cato Shana E , Belay Ermias D , Campbell Angela P ,   MIS-C Surveillance Authorship Group, Anyalechi Gloria E , Bowen Anna , Do Tuyen , Gastañaduy Paul A , Lindsey Katherine , St. Cyr Sancta B , Kaur Ramandeep , Pompa Xandy Peterson , Le Marchand Chloe E , Singson Jason Robert C , O’Brien Shannon C , Schmitz Ann M , Torres Díaz Carola I , Elbedewy Walaa M , Tobin-D’Angelo Melissa J , Reid Heather D , Fricchione Marielle J , Hallyburton Sara J , Richardson Gillian , Hand Julie P , Leach Dylan H , Burkholder Cole P , Lim Sarah , Thomas Deepam , Gowie Donna L , Dufort Elizabeth M , Lee Ellen H , Falodun Ayotola A , Dewart Courtney M , Colles Zachary J , Wallace Jennifer L , Johnson LaKita D , Herring Kristina L , Liptack Andrea R 
Published: December 2021

Abstract

Background
Multisystem inflammatory syndrome in children (MIS-C) is a severe hyperinflammatory condition in persons aged &lt;21 years associated with antecedent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our objective was to describe MIS-C cases reported to Centers for Disease Control and Prevention’s (CDC’s) national surveillance since the coronavirus disease 2019 (COVID-19) pandemic began.

Methods
We included patients meeting the MIS-C case definition with onset date from 19 February 2020 through 31 July 2021, using CDC’s MIS-C case report form, which collects information on demographics, clinical presentation, and laboratory results. Trends over time across 3 MIS-C pandemic waves were assessed using Cochran-Armitage test for categorical and Jonckheere-Terpstra test for continuous variables.

Results
Of 4901 reported cases, 4470 met inclusion criteria. Median patient age increased over time (P &lt; .001), with a median of 9 years (interquartile range, 5–13 years) during the most recent (third) wave. Male predominance also increased (62% in third wave, P &lt; .001). A significant (P &lt; .001) increase in severe hematologic and gastrointestinal involvement was observed across the study period. Frequency of several cardiovascular complications (ie, cardiac dysfunction, myocarditis, and shock/vasopressor receipt) and renal failure declined (P &lt; .001). Provision of critical care including mechanical ventilation (P &lt; .001) and extracorporeal membrane oxygenation (ECMO; P = .046) decreased, as did duration of hospitalization and mortality (each P &lt; .001).

Conclusions
Over the first 3 pandemic waves of MIS-C in the United States, cardiovascular complications and clinical outcomes including length of hospitalization, receipt of ECMO, and death decreased over time. These data serve as a baseline for monitoring future trends associated with SARS-CoV-2 B.1.617.2 (Delta) or other variants and increased COVID-19 vaccination among children.
Host Predictors of Broadly Cross-Reactive Antibodies Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants of Concern Differ Between Infection and Vaccination
Authors: Tang Li , Cherry Sean , Tuomanen Elaine I , Kirkpatrick Roubidoux Ericka , Lin Chun Yang , Allison Kim J , Gowen Ashleigh , Freiden Pamela , Allen E Kaitlynn , Su Yin , Gaur Aditya H , Estepp Jeremie H , McGargill Maureen A , Krammer Florian , Thomas Paul G , Schultz-Cherry Stacey , Wolf Joshua ,   St. Jude Investigative Team, Mori Tomi , Hijano Diego R , Hakim Hana , Dallas Ronald H , Cortez Valerie , Vazquez-Pagan Ana , Webby Richard J , Fabrizio Thomas , Russell-Bell Jamie , Brice David C , Castellaw Ashley , Bajracharya Resha , Clark Brandi L , Van de Velde Lee Ann , Awad Walid , Wilson Taylor L , Kirk Allison M , Hodges Jason , Sparks James , WIttman David E , Hayden Randall T , Hoffman James 
Published: December 2021

Abstract

Background
Following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or vaccination there is significant variability between individuals in protective antibody levels against SARS-CoV-2, and within individuals against different virus variants. However, host demographic or clinical characteristics that predict variability in cross-reactive antibody levels are not well-described. These data could inform clinicians, researchers, and policymakers on the populations most likely to require vaccine booster shots.

Methods
In an institutional review board–approved prospective observational cohort study of staff at St. Jude Children’s Research Hospital, we identified participants with plasma samples collected after SARS-CoV-2 infection, after mRNA vaccination, and after vaccination following infection, and quantitated immunoglobulin G (IgG) levels by enzyme-linked immunosorbent assay to the spike receptor binding domain (RBD) from 5 important SARS-CoV-2 variants (Wuhan Hu-1, B.1.1.7, B.1.351, P.1, and B.1.617.2). We used regression models to identify factors that contributed to cross-reactive IgG against 1 or multiple viral variants.

Results
Following infection, a minority of the cohort generated cross-reactive antibodies, IgG antibodies that bound all tested variants. Those who did had increased disease severity, poor metabolic health, and were of a particular ancestry. Vaccination increased the levels of cross-reactive IgG levels in all populations, including immunocompromised, elderly, and persons with poor metabolic health. Younger people with a healthy weight mounted the highest responses.

Conclusions
Our findings provide important new information on individual antibody responses to infection/vaccination that could inform clinicians on populations that may require follow-on immunization.
Psychometric Properties of Instruments Measuring Dyadic Communication and Environment in Dementia Care: A Systematic Review
Authors: Kim Sohyun , Liu Wen 
Published: December 2021

AbstractBackground and ObjectivesA valid and reliable assessment of dementia dyadic communication and environment is essential to understand and facilitate social interaction and quality care. This review described the characteristics and evaluated psychometric properties of instruments that assess dyadic communication and environment between persons living with dementia and their caregivers.Research Design and MethodsA systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Literature published until June 30, 2021, was searched. Ten psychometric properties and the ratio of sample size to the number of items were evaluated using the Psychometric Assessment for Self-report and Observational Tool.ResultsA total of 3,708 scholarly records was identified, and 24 eligible instruments from 48 scholarly records were evaluated. Twenty-two instruments assessed dyadic communication, and 2 assessed both dyadic communication and environment. Eighteen instruments were developed to assess task-related communication and 15 for paid (professional) caregivers. All instruments were scored as low psychometric quality (score range = 0–7). Behavioral Observation Scoring System was scored the highest (total score = 7), followed by Dyadic Dementia Coding System, Grid for observation of physical and verbal behaviors of caregiver and resident, and Trouble-Indicating Behaviors and Repair (total score = 6). These instruments had low psychometric evidence for internal consistency, content validity, and structural validity.Discussion and ImplicationsExisting instruments are in the early stages of development and validation in dementia population. Further testing is needed in diverse communication types in paid and unpaid dementia caregiver populations.
Considerations for development of child abuse and neglect phenotype with implications for reduction of racial bias: a qualitative study
Authors: Landau Aviv Y , Blanchard Ashley , Cato Kenrick , Atkins Nia , Salazar Stephanie , Patton Desmond U , Topaz Maxim 
Published: December 2021

Abstract

Objective
The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect.

Materials and Methods
We conducted a qualitative study using in-depth interviews with 20 pediatric clinicians working in a single pediatric ED to gain insights about generating an EHR-based phenotype to identify children at risk for abuse and neglect.

Results
Three central themes emerged from the interviews: (1) Challenges in diagnosing child abuse and neglect, (2) Health Discipline Differences in Documentation Styles in EHR, and (3) Identification of potential racial bias through documentation.

Discussion
Our findings highlight important considerations for generating a phenotype for child abuse and neglect using EHR data. First, information-related challenges include lack of proper previous visit history due to limited information exchanges and scattered documentation within EHRs. Second, there are differences in documentation styles by health disciplines, and clinicians tend to document abuse in different document types within EHRs. Finally, documentation can help identify potential racial bias in suspicion of child abuse and neglect by revealing potential discrepancies in quality of care, and in the language used to document abuse and neglect.

Conclusions
Our findings highlight challenges in building an EHR-based risk phenotype for child abuse and neglect. Further research is needed to validate these findings and integrate them into creation of an EHR-based risk phenotype.