bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intelligent planning process with adaptive quick response system for low volume manufacturing
Authors: Dcoutho John , Rahman Akhlaqur , Eisenbart Boris , Kulkarni Ambarish 
Published: September 2022

Abstract

The automotive manufacturing industry in recent years has seen a paradigm shift in production. Increased customer individualization demands and shorter product life cycles have become the norm in the market. Traditionally, production planning methods in this sector are based on high volumes; thus, production lines used to be relatively rigid. With the current demand for individualized low-volume production, the line must be altered frequently, leading to increased downtime and additional cost. This shift in automotive manufacturing requires production planning to cater to faster, cost-effective adoption to changing low-volume individualized demands. This research discusses a novel Intelligent Planning Process (IPP) to address low-volume individualized manufacturing. The IPP model harnesses transformative technologies such as extended reality (xR) to facilitate faster and more adaptive planning. Further, artificial intelligence is embedded through xR models using various response nodes (e.g., quick response) This provides a critical advantage in developing and evaluating multiple production layouts with considerably reduced efforts. A case studyon positioning preloaded planning data to the real world with quickresponse nodes resulted in one-fourth of the time required by manualinteractive positioning of physical assets. In addition, real-timecontrol and synchronous optimization were other intangible outcomes.
Epidemiology of hand, foot, and mouth disease and genetic characteristics of Coxsackievirus A10 in Taiyuan City, Shanxi Province from 2016–2020
Authors: Wang Jitao , Liu Hongyan , Cao Zijun , Xu Jihong , Guo Jiane , Zhao Lifeng , Wang Rui , Xu Yang , Gao Ruihong , Gao Li , Zuo Zhihong , Xiao Jinbo , Lu Huanhuan , Zhang Yong 
Published: September 2022

Abstract

Background:In recent years, the prevalence of hand, foot, and mouth disease (HFMD)caused by enteroviruses other than enterovirus A71 (EV-A71) and coxsackievirus A16 (CVA16) has gradually increased. The pathogen spectrum of HFMD also continues to change. In this study, we analyzed the epidemiological characteristics of HFMD and the genetic characteristics of the coxsackievirus A10 (CVA10)virus identified in Taiyuan City, Shanxi Province.Methods:The epidemiological data from 2016–2020 on HFMD in Taiyuan City was collected from the China Information System For Disease Control and Prevention. The throat swab specimens of 2701 HFMD cases were tested, theVP1regions of CVA10 RNA were amplified using RT-PCR, and phylogenetic analysis of CVA10 was performed. The study results were analyzed using Pearson's Chi-Square Test.Results:Children aged 1–5 years accounted for the majority (81.65%) of all HFMD cases, and the average annual incidence is higher in boys than in girls. Of the 2,701 specimens collected, 1,439 (53.28%) were positive for human enterovirus (EV). The positivity rates of EV-A71, CVA16, and other EVs were 15.22% (219/1439), 28.77% (414/1439), and 56.01% (806/1439), respectively. The proportion of specimens positive for other EVs was significantly higher than that of EV-A71 and CVA16. CVA10 is one of the important viruses of other EVs. A total of 52 CVA10 strains were used for phylogenetic analysis based on theVP1region. Of the 52 CVA10 strains, 31 were from this study, and 21 were downloaded from GenBank. All CVA10 sequences could be assigned to seven genotypes (A, B, C, D, E, F, and G), and genotype C was further divided into C1 and C2 subtypes. Of the 31 CVA10 strains identified in this study, only one belonged to subtype C1 and the remaining 30 belonged toC2.Conclusions:This study emphasized the importance of strengthening the surveillance of the HFMD in order to understand the mechanisms of pathogen variation and evolution, so as to provide a scientific basis for HFMD prevention, control, and vaccine development.
Face masks modulate non-verbal communication in selfies, but do not affect beauty ratings
Authors: Jacomuzzi Alessandra Cecilia , Bruno Nicola 
Published: September 2022

Abstract
Widespread use of face masks during the COVID-19 pandemic has stimulated interest in their effect on interpersonal communication. We asked whether face masks modulate posing during selfie-taking and whether they affect the attractiveness of the poser. We collected a dataset of selfie pairs (one with and one without the mask) from a population of university community members. Results replicated well-known posing biases in selfies, including a preference for displaying the left cheek and a sex-related difference in facial prominence. However, these biases were modulated by face masks, which caused a marked increase of frontal poses in comparison to three-quarter poses (for both left- and right-cheek face rotations), and a weaker but still significant increase in facial prominence of both male and female takers. In contrast, beauty ratings did not differ between mask and no-mask selfies by the same individuals. These results support models of selfies as means of non-verbal communication, whereas do not support recent reports that face masks enhance facial beauty. (word count = 170)
Analyses of Flight Time During Solar Proton Events and Solar Flares
Authors: xu x h , Wang Yi , Wei Fengsi , feng xueshang , bo m h , tang h w , wang d s , bian l , Wang Boyi , Zuo Pingbing , Jiang Chaowei , Xu Xiaojun , Zhou Zilu , li z , Zou Peng , wang l d , gu y x , chen y l , zhang w y , sun p 
Published: September 2022

Abstract
Analyzing the effects of space weather on aviation is a new and developing topic. It has been commonly accepted that the flight time of the polar flights may increase during solar proton events because the flights have to change their route to avoid the high-energy particles. However, apart from such phenomenon, researches related to the flight time during space weather events is very rare. Based on the analyses of 39 representative international air routes around westerlies, it is found that 97.44% (94.87%) of the commercial airplanes on the westbound (eastbound) air routes reveal shorter (longer) flight time during solar proton events compared to those during quiet periods, and the averaged magnitude of change in flight time is ~10 min or 0.21%-4.17% of the total flight durations. Comparative investigations reassure the certainty of such phenomenon that the directional differences in flight time are still incontrovertible regardless of over-land routes (China-Europe) or over-sea routes (China-Western America). Further analyses suggest that the solar proton events associated atmospheric heating will change the flight durations by weakening certain atmospheric circulations, such as the polar jet stream. While the polar jet stream will not be obviously altered during solar flares so that the directional differences in flight time are not found. Besides the conventional space weather effects already known, this paper is the first report that indicates a distinct new scenario of how the solar proton events affect flight time. These analyses are also important for aviation since our discoveries could help the airways optimize the air routes to save passenger time costs, reduce fuel costs and even contribute to the global warming issues.
Modeling the Time-Dependent Transmission Rate Using Gaussian Pulses for Analyzing the Covid-19 Outbreaks in the World
Authors: Setianto Setianto , Hidayat Darmawan 
Published: September 2022

Abstract

In this work, an SEIR epidemic model with time-dependent transmission rate parameters for the multiple waves of COVID-19 infection was investigated. It is assumed that the transmission rate is determined by the superposition of the Gaussian pulses. The interaction of these dynamics is represented by recursive equations. Analysis of the overall dynamics of disease spread is determined by the effective reproduction numberRe(t)produced throughout the infection period. As a result, the development of the epidemic over time has been successfully studied and the phenomenon of multiple waves of COVID-19 infection in the world has been explained.
Designing and Evaluating a Big Data Analytics Approach for predicting students’ success factors
Authors: Fahd Kiran , Miah Shah J. 
Published: September 2022

Abstract
Reducing student attrition in tertiary education plays a significant role in the core mission and financial well-being of an educational institution. The availability of big data source from Learning Management System (LMS) can be analysed to help with the attrition issues. This study aims to use an integrated Design science research (DSR) methodology to develop and evaluate a Big Data Analytical Solution (BDAS) embedded in Educational Decision Support System as an educational artefact. The BDAS as DSR artefact harnesses the outcome of the application of Artificial Intelligence(AI) based approaches on the dataset collected from student interaction with LMS to train a predictive model to predict potential students at risk. Identifying students at risk helps to take timely intervention in the learning process to improve student academic progress to increase the retention rate. To evaluate the performance of the predictive model, we compare the accuracy of the collection of representational Artificial Intelligence algorithms in the literature. The BDAS aims not to replace any existing practice but to support educators to implement a variety of pedagogical practices to improve students’ academic performance.
Multi-scenario LUCC modeling based on a new LMCM model: A case study of Hefei, China
Authors: He Yecheng , Wu Weicheng , Ke Xinxin , Song Yifei , Zhou Cuimin , Li Wenjing , Li Yuan , Jing Rong , Song Peixia , Fu Linqian , Mao Chunlian , Xie Meng , Xie Xinyuan 
Published: September 2022

Abstract

Detection of land use and land cover change (LUCC) and its future projection have become a critical issue for rational management of land resources. For this purpose, land use mapping in 2010, 2015 and 2020 in Hefei were conducted by an integrated classification approach based on spring Landsat images and digital elevation model (DEM) data, and dynamic LUCC of 2010-2015 and 2015-2020 were characterized. To predict land use change, a new comprehensive hybrid model consisting of Celluar Automata (CA) and Markov chain (M), Logistic Regression (LR) and Multi-Critical Evaluation (MCE), namely Logistic-MCE-CA-Markov (LMCM) model, was proposed to avoid the disadvantages of the previous models such as CA-Markov (CM), Logistic-CA-Markov (LCM) and MCE-CA-Markov (MCM). This new hybrid model LMCM used the fully standardized logistic regression coefficients as importance of the driving factors to represent their impact weight on each land use type. The CM, LCM, MCM and LMCM models were applied to estimate the land use pattern of 2020 based on the states of 2010 and 2015 of the study area, and we noted that the LMCM model performed better than other three versus the classified map of 2020 with a higher accuracy, that is, 1.72-5.4%, 2.14-6.63% and 2.78-9.33% higher than CM, LCM and MCM models respectively. We believed hence that the newly proposed LMCM hybrid model was capable of achieving more reliable prediction of LUCC and was employed to predict the land use and land cover (LULC) situation of 2025 within four scenarios, i.e., business as usual (BAU), economic development (ED), ecological protection (EP), and comprehensive development (CD). The results show that the LUCC modeling using the LMCM model with ED or CD scenario would be pertinent for a socioeconomic development in the study area and the approaches may be extended for such study in other regions.
Comparison of Different Nutritional Screening Tools in Pediatric Oncology
Authors: Xavier Camila Machado , Freitas Josiane De Paula , Miola Thais Manfrinato , Firmino Aline Do Vale 
Published: September 2022

Abstract

Background Malnutrition in pediatric oncology inpatients can cause several complications, and the early identification of inpatients at nutritional risk with screening tool is crucial. Objective The aim of this study was to evaluate StrongKids and SGNA in order to analyze their performance and efficacy in pediatric inpatients undergoing oncological treatment and compare with objective assessment. Materials and Methods This was a cross-sectional study with pediatric oncology inpatients, of both sexes, admitted to A.C. Camargo Cancer Center. Data collection was conducted during the first hospital admission appointment, when StrongKids and SGNA were applied, and anthropometric data were collected. Results Sixty inpatients with an average age of 8.49 years old were evaluated, and 21,6% inpatients were diagnosed with malnutrition. SGNA and StrongKids shows a sensitivity of 92.3% and 84.6%, respectively. In the comparison of both tools, an adequate accuracy of 81.67% was detected. In addition, a significant kappa (κ) value was obtained (p = 0.021) for the evaluation comparing SGNA and the objective assessment, showing reasonable concordance (κ = 0.235). Conclusion Both nutritional screening tools showed a good performance with respect to sensitivity, compared with the objective assessment. Neither method showed a satisfactory accuracy when applied to pediatric oncology inpatients.
Validity and Test-Retest Reliability of the Spanish Version of the international Hip Outcome Tool (iHOT-12Sv)
Authors: González-de-la-Flor Ángel , López-de-Uralde-Villanueva Ibai , Valera-Calero Juan Antonio , Almazán-Polo Jaime , López-Marcos José Javier , Fernández-de-las-Peñas César , García-Fernández Pablo , Plaza-Manzano Gustavo 
Published: September 2022

Abstract
Objective: To develop a Spanish version of the international Hip Outcome Tool questionnaire (iHOT-12Sv) for assessing the psychometric characteristics (internal consistency, convergent validity, test-retest reliability and floor and celing effects) of this version in physically active patients with hip pain.
Methods: After conducting the translation and transcultural adaptation, a consecutive sample of patients with labral tear injury and/or femoroacetabular impingement (Pincer or Cam type) were recruited in a Spanish Hospital. Patients completed the iHOT-12Sv and the Spanish version of the iHOT-33 and the Hip Outcome Score (HOS). Internal consistency was calculated using Cronbach's alpha, convergent validity was evaluated using Spearman correlation coefficients (Rho) with iHOT-33 and HOS, test-retest reliability was examined using the Intraclass Correlation Coefficient (ICC), Standard Error of Measurement (SEM) and Minimal Detectable Changes (MDC) and floor and ceiling effects as the percentage of patients who obtained the minimum and the maximum score.
Results: One hundred fifty-three patients (64.7% males) participated in this study. Cronbach’s alpha of 0.92 revealed the excellent internal consistency. In addition, the iHOT-12Sv demonstrated strong to very strong correlations with the HOS (Rho ranged from 0.741 to 0.827; p<0.001) and the iHOT-33 (Rho=0.932; p<0.001) respectively, acceptable test-retest reliability (ICC=0.86 to 0.94); SEM=6.21 and MDC=17.22) and no floor or ceiling effects were found.
Conclusions: The iHOT-12Sv can be used as a valid and reliable tool for clinical evaluation of physically active patients with hip pathology. However, the full version is preferable for research purposes aiming to assess changes in hip function.
Enhanced Modulation Recognition by Time-series Data Augmentation Based Spatiotemporal Multi-channel Framework
Authors: Pi Shuang , Zhang Shuanggen , Guo Bochi , Yan Wei 
Published: September 2022

Abstract
Automatic modulation recognition with deep learning has great prospective owing to computing power and big data. However, modulation recognition accuracy depends highly extent on massive volume of data and model applicability. Here, to overcome difficulties such as small sample dataset, manual extraction of features and low accuracy, we proposed an efficient recognition method that combined time-series data augmentation with spatiotemporal multi-channel learning framework. The results showed that the method provided positive indicators on the order of 93.5 percent for ten modulation signal types, which can be improved by at least 15 percent. Especially for QAM16 and QAM64 signals, the average recognition accuracy is increased by nearly 50 percent at SNRs as low as -2 dB, revealing remarkable recognition performance. Effectiveness of the proposed method provides an attractive approach for signal modulation recognition for wired or wireless communication fields.
A study of LCCA-based wastewater treatment technologies in the context of an Indian scenario
Authors: Rathore Purva , Parde Divyesh , Killedar Deepak J. 
Published: September 2022

Abstract
With ever-increasing population, volumes of wastewater treatment are a major concern in our country. The choice of an appropriate wastewater treatment technology in India needs to be focused. The Activated Sludge Process (ASP), Sequencing Batch Reactor (SBR), Upflow Anaerobic Sludge Blanket (UASB), Moving Bed Biofilm Reactor (MBBR), Soil Bio Technology (SBT), Biological Filtration and Oxygenated Reactor (BioFOR), Submerged Aerated Fixed Film (SAFF), Phytorid, Waste stabilization ponds (WSP), Membrane Bioreactor (MBR) and Trickling Filter (TF) employing the Life Cycle Cost Assessment (LCCA) tool, are all financially examined in this study. This study shows the present value approach, which involves discounting the expenses for a 20-year economic life and is used to calculate life cycle costs. The published literature is used to determine the price of sewage treatment per million litres per day (MLD) of wastewater treatment technology. Additionally, this analysis compares the life cycle costs of the 11 technologies stated earlier to determine the most likely candidate for an economical technology by factoring in capital, annual operation and maintenance, energy, salvage, and replacement expenses. The uncertainty of land cost and discount rate with its effect on LCCs is studied by varying both over a range. The present study evaluates the Net Savings and Savings Investment Ratio of technologies with a base case. The LCCA demonstrates the ascending order of technologies as WSP < PHYTORID < UASB < SBT < BioFOR < TF < SAFF < ASP < SBR < MBR < MBBR. This study suggests that more research on the payback periods of various wastewater treatment methods be conducted.
Nomogram for predicting preoperative axillary lymph node status in male breast carcinoma: SEER population-based study
Authors: Chen Wenxin , Wang Hebing , Yang Binglin , Zhang Maoquan , Chen Jicheng 
Published: September 2022

Abstract
Background: Sentinel lymph node (SLN) has been recommended as a replacement for axillary lymph node dissection (ALND) in male breast carcinoma (MBC) with clinical axillary lymph node-negative as in women. However, the morbidity after SLN may also have short-term or long-term complications. It is imperative to develop a model to evaluate the risk of lymph node metastasis to avoid unnecessary operations.
Patients and Methods: A retrospective review of the clinical and pathology records were performed in patients diagnosed with MBC between 2010 and 2018 from Surveillance, Epidemiology, and End Results (SEER) database. The cohort was divided into training and validation cohorts. A logistic regression model was used to construct the nomogram in the training cohort and then verified in the validation cohort.ROC curve, C-index, and calibration were used to assess the predictive ability of the nomogram.
Results: Overall, 2,610 patients diagnosed with MBC were enrolled, of which 1,740 were in the training cohort and 870 were in the validation cohort. Logistic regression analysis indicated ,age at diagnosis, tumor location, tumor stage, pathological type, and histologic grade were significantly related to axillary lymph node metastasis(ALNM). The AUC of the nomogram was 0.846 (95% CI, 0.825 -0.867) and C-index was 0.848 (95%CI, 0.807-0.889), demonstrating a good prediction performance. The calibration curve for the nomogram was plotted and the slope was close to 1. The performance of the nomogram was further validated in the validation cohort, with an AUC of 0.848 (95% CI 0.819 - 0.877).
Conclusion: We successfully established a nomogram to predict LANM, especially for those who were of advanced age at diagnosis,had small tumor size, low malignancy, and clinical axillary lymph node-negative to avoid unnecessary axillary operation —thus improving the quality of life of the patients without adverse effect on their survival rates.
A Farmer &amp; Consumer Friendly Traceable and Trustable Agro-Info DApp Using Block-Chain Technology
Authors: Adithi G , Dakshayini M 
Published: September 2022

Abstract
Agricultural supply chain (ASC) is a distributed syatem involves all the activities in the transfer of various agricultural commodities from farmers to end consumers and plays a vital role in impacting the nation’s economic growth. However, the agriculture industry in India currently does not have an established mechanism that validates and keeps track of the distribution of seeds and food crops(Agro-Information System(AIS)). As a result, farmers do not obtain proper information about the origin of the seeds that they would buy and sow. Also, the consumers do not obtain any information about the origin of the food crops and the products they would be consuming. In the existing ASC, tracing the history of seeds, their quality, contaminated crops, etc. is very time-consuming and resource-consuming. The data are also easily prone to modification, which largely affects security. With the present way in which the AIS is spread across the country, the number of diseases arising from low-quality food crops is also increasing and these diseases spread rapidly among people. The hampered quality of crops may be due to the usage of low-quality seeds, pesticides and the process followed. This is another issue that impacts the quality of food and further leads to food spoilage. Hence, tracking and identifying the origin of seeds and crops is of high concern in these cases. These issues can be well handled by the usage of blockchain technology. The primary objective of this paper is to make ASCs transparent and flexible systems using the Hyperledger fabric framework of blockchain technology. The proposed system enables the authenticated farmer community to obtain all the information required to select good-quality seeds and consumers to obtain quality food products by tracking every movement of the ASC right from seed selection to delivery of food products to the consumer. Implementation of the proposed system using the Hyperledger fabric framework of Blockchain technology has proven the effective tracking of the entire supply movement across the ASC in a minimum time of 0.015 sec with 100 percent transparency and authentication, helping people to obtain good-quality seeds and food products.