bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time-Optimal Trajectory Optimization of Serial Robotic Manipulator With Kinematic and Dynamic Limits Based On Improved Particle Swarm Optimization
Authors: Yang Yu , Xu Hongze , Li Shaohua , Zhang Lingling , Yao Xiuming 
Published: July 2021

Abstract
Effective motion control can achieve accurate and fast positioning and movement of industrial robotics and improve industrial productivity significantly. Time-optimal trajectory optimization (TO) is a great concern in the motion control of robotics and can improve motion efficiency by providing high-speed and reasonable motion references to the motion controller. In this study, a new time-optimal TO strategy, polynomial interpolation function (PIF) combined with improved particle swarm optimization (PSO) considering kinematic and dynamic limits, successfully optimizes the movement time of the PUMA 560 serial manipulator along a randomly assigned path. The 4-3-4 PIF is first used to generate the smooth and 3-order continuous movement trajectories of six joints in joint space. The PSO with cosine decreasing weight (CDW-PSO) algorithm further reduces the trajectories movement time considering the limits of the angular displacement, angular velocity, angular acceleration, angular jerk, and joint torque. Experimental results show that the CDW-PSO algorithm achieves a better convergence rate of 23 and a better fitness value of 2.46 compared with the PSO with constant weight and linearly decreasing weight algorithms. The CDW-PSO optimized movement time is reduced by 83.6% compared to the manually setting movement time value of 15. The proposed time-optimal TO strategy can be conducted easily and directly search for global optimal solutions without approximation of the limits. The optimized trajectories could be incorporated in the motion controller of the actual manipulators due to considering the kinematic and dynamic limits.
Number of Doses of Measles-Mumps-Rubeola Vaccine Applied in Brazil Before and During The COVID-19 Pandemic
Authors: Silva Tércia Moreira Ribeiro da , de Sá Ana Carolina Micheletti Gomide Nogueira , Vieira Ed Wilson Rodrigues , Prates Elton Junio Sady , Beinner Mark Anthony , Matozinhos Fernanda Penido 
Published: July 2021

Abstract
Background

Due to the social isolation measures adopted in an attempt to mitigate the risk of transmission of SARS-CoV-2, there has been a reduction in vaccination coverage of children and adolescents in several countries and regions of the world.
Objective

Analyze the number of doses of vaccine against Measles-Mumps-Rubeola (MMR) applied before and after the beginning of mitigation measures due to COVID-19 pandemic in Brazil.
Methods

Ecological study, with data from the National Immunization Program. The Mann-Whitney test evaluated differences between the median number of MMR vaccine doses applied in Brazilian regions, states and municipalities during April/2019 to March/2020 (before the beginning of mitigation measures) and April/2020 to September/2020 (after the start of mitigation measures). Spatial analysis identified clusters with a high percentage of reduction in the median of applied doses, and interpreted according to the Global Moran’s Index (I). All analyzes considered the significance level of 5%.
Results

There was a reduction in the median of doses applied in the Regions: North (-33.03%), Northeast (-43.49%) and South (-39.01%). In States: Acre (-48.46%), Amazonas (-28.96%), Roraima (-61.91%), Paraíba (-41.58%), Sergipe (-47.52%), Rio de Janeiro (-59.31%) and Santa Catarina (-49.32) (p < 0.05). High-high type spatial clusters (reduction between 34.00 to 90.00%) were formed in the five regions of Brazil (Moran’s I = 0.055; p = 0.01).
Conclusion

A reduction in the number of MMR vaccine doses was evidenced as a possible effect by the restrictive actions of COVID-19 in Brazil.
Recurrence Models based on Extreme Value Theory : Impacts on Probabilistic Seismic Hazard Assessments and Comparison with the Standard Approach
Authors: Dutfoy Anne , Senfaute Gloria 
Published: July 2021

Abstract
Probabilistic Seismic Hazard Analyses (PSHA) require that at least the mean activity rate be known, as well as the distribution of magnitudes. Within the Gutenberg-Richter assumption, the magnitudes follow an exponential distribution which is upperly truncated to a maximum possible magnitude denoted mmax. This parameter is often fixed from expert judgement under tectonics considerations, due to a lack of universal method.In this paper, we present two alternatives to the exponential distribution of the magnitudes, based on the extreme value theory and that don't require to fix a priori the value of mmax: the first model is based on a generalized Pareto distribution (GPD) to model the tail distribution of the magnitudes; the second model, the Randomized Gutenberg-Richter model, is a variation on the usual exponential distribution where mmax is randomized and follows a distribution defined from an extreme value analysis.We use the maximum likelihood estimates taking into account the time varying level of completeness of the catalog and the uncertainty in the magnitude value itself. We apply both modelizations to the Alps region. Then, we integrate the resulting magnitude-frequency relations into a probabilistic seismic hazard calculation to evaluate their impacts on the seismic hazard levels. These extreme-value-based recurrence models introduce a reduction of the seismic hazard level compared to the common Gutenberg-Richter model conventionally used for PSHA calculations. This decrease is significant mainly for long periods.
A Non-ergodic Spectral Acceleration Ground Motion Model for California Developed with Random Vibration Theory
Authors: Lavrentiadis Grigorios , Abrahamson Norman A. 
Published: July 2021

Abstract
A new approach for creating a non-ergodic PSA ground-motion model (GMM) is presented which account for the magnitude dependence of the non-ergodic effects. In this approach, the average PSA scaling is controlled by an ergodic PSA GMM, and the non-ergodic effects are captured with non-ergodic PSA factors, which are the adjustment that needs to be applied to an ergodic PSA GMM to incorporate the non-ergodic effects. The non-ergodic PSA factors are based on EAS non-ergodic effects and are converted to PSA through Random Vibration Theory (RVT). The advantage of this approach is that it better captures the non-ergodic source, path, and site effects through the small magnitude earthquakes. Due to the linear properties of Fourier Transform, the EAS non-ergodic effects of the small events can be applied directly to the large magnitude events. This is not the case for PSA, as response spectrum is controlled by a range of frequencies, making PSA non-ergodic effects depended on the spectral shape which is magnitude dependent. Two PSA non-ergodic GMMs are derived using the ASK14 (Abrahamson et al., 2014) and CY14 (Chiou and Youngs, 2014) GMMs as backbone models, respectively. The non-ergodic EAS effects are estimated with the LAK21 (Lavrentiadis et al., In press) GMM. The RVT calculations are performed with the V75 (Vanmarcke, 1975) peak factor model, the Da0.05−0.85 estimate of AS96 (Abrahamson and Silva, 1996) for the ground-motion duration, and BT15 (Boore and Thompson, 2015) oscillator-duration model. The California subset of the NGAWest2 database (Ancheta et al., 2014) is used for both models. The total aleatory standard deviation of the two non-ergodic PSA GMMs is approximately 30 to 35% smaller than the total aleatory standard deviation of the corresponding ergodic PSA GMMs. This reduction has a significant impact on hazard calculations at large return periods. In remote areas, far from stations and past events, the reduction of aleatory variability is accompanied by an increase of epistemic uncertainty.
Evaporative Flux Method of Leaf Hydraulic Conductance Estimation: Sources of Uncertainty and Reporting Format Recommendation
Authors: Wang Xiaoxiao , Zhao Jinfang , Huang Jianliang , Peng Shaobing , Xiong Dongliang 
Published: July 2021

Abstract
The accurate estimation of leaf hydraulic conductance (Kleaf) is important for revealing leaf physiology characteristics and function. However, there are some uncertain influencing factors in Kleaf measurement by using evaporation flux method (EFM), a widely used method. In this study, we investigated the potential impacts of plant sampling method, measurement setup, environmental factors, recording instrument, and transpiration steady status identification on Kleaf estimation. Our results indicated that the sampling and rehydration time, the small gravity pressure on leaf, and degassing treatment had limited effects on Kleaf values. Transpiration rate (E) was significantly affected by multiple environmental factors including airflow around leaf, light intensity, and leaf temperature. Kleaf values decreased by 40% from 1000 to 500 µmol m-2 s-1 light intensities and by 15.1% from 27 to 37 oC. In addition, the accurate flow rate (F) steady state identification and the leaf water potential measurement were important for Kleaf estimation. Based on the analysis of influencing factors, we provided a format for reporting the details of the EFM-based Kleaf measurement methods and metadata that future studies could interpret the results in method issue.
Erosive Effect of Acidic Beverages On Dental Filling Materials Surface (In Vitro Study)
Authors: Morozova Yuliya , Novotná Barbora , Holík Pavel , Voborná Iva , Zapletalová Jana 
Published: July 2021

Abstract
Erosive tooth wear, that is characterized like irreversible loss of hard dental tissues due to influence of external and internal acids of nonbacterial origin, is one of the most common damage of a tooth surface. Erosive factors also affect the surface of dental reconstruction materials that ideally should be resistant against them. Dental filling materials designed for this purpose must be able to withstand these effects in a comparable or even better way than enamel. In our in vitro study we investigated using the nanoindentation the hardness of currently available dental filling materials (dental composite resin, self curing and dual cures glassionomer cement, dental amalgam) before and after erosive attacks (exposure to Coca Cola for 5 min and 14 days) and consequently compare it with them of human dental enamel. Furtermore we analised the surface topography of the samples before and after acidic exposure by confocal laser scanning microscope. In our experiments the filling materials were affected by the acidic environment only minimally in comparison with hard dental tissues. We confirmed the fact that dental amalgam is still the most mechanically and chemically resistant filling material. Both GIC materials showed the worst resistance against acidic conditions and higher surface roughness even before exposure. We also declared excellent withstanding of resin composite materials against acidic conditions that was even better than natural enamel. Furthermore by CLSM was detected almost unaltered surface of these filling materials. This fact as well as adhesive properties of resin composites allow to assume that this material could be the most suitable material for dental erosion reconstruction.
A Resource Flow Based Branch-and-bound Algorithm to Solve Fuzzy Stochastic Resource-constrained Project Scheduling Problem
Authors: Alipouri Yaghoub 
Published: July 2021

Abstract
In this paper a resource flow based branch-and-bound procedure is designed to solve the well-known resource constrained project scheduling problem under the mixed uncertainty of fuzziness and randomness (FS-RCPSP). The objective is to minimize the expected makespan of the project subject to precedence and resource constraints. The proposed branch-and-bound can be employed to obtain optimal solutions and also can be truncated in order to find promising near optimal solutions. The depth-first strategy is utilized for constructing the search tree and earliest start time (EST) concept is adopted for selecting a node for further branching while traversing the tree down to the leaves. The performance of developed branch-and-bound is benchmarked against CPLEX and SADESP across an extensive set of 960 problems. The results returned by the proposed algorithm show experimentally its effectiveness to solve the FS-RCPSP.
Binge Eating Among Older Women: Prevalence Rates and Health Correlates Across Three Independent Samples
Authors: Wilfred Salome Adelia , Becker Carolyn Black , Kanzler Kathryn E , Musi Nicolas , Espinoza Sara E , Kilpela Lisa Smith 
Published: July 2021

Abstract
BackgroundEmerging research indicates that older women struggle with binge eating (BE; consuming unusually large amounts of food in one siting while feeling a loss of control) more frequently than once thought. Yet, health correlates of BE in older adult populations are poorly understood. The original goal of the study was to investigate BE prevalence, frequency, and health correlates in a sample of older adult women. Following surprising findings in this first study, we then aimed to replicate findings in two additional samples of older adult women. MethodUsing self-reported frequencies of BE from three separate samples of older women with very different demographics, we compared BE prevalence, frequency, and health correlates among older women. Study 1 (N = 185) includes data collected online (86% White; 59% overweight/obese status). Study 2 (N = 64) was conducted in person at a local food pantry (65% Hispanic; 47% household income < $10,000/year). Study 3 (N = 100) comprises data collected online (72% White; 50% Masters/Doctoral Degree). ResultsPer DSM-5 frequency criterion of BE at least weekly, we found prevalence rates ranging from 19%-26% across the three samples. Correlates of BE frequency included elevated negative mood, worry, BMI, and less nutritious food consumption. ConclusionsAcross three very different samples in terms of race/ethnicity, education, food security status, measurements, and sampling methodology, we found fairly consistent rates of self-reported BE at least weekly (19-26%). BE is related to negative outcomes among older women, supporting the need for more research in this population.
The New SUMPOT to Predict Postoperative Complications Using an Artificial Neural Network
Authors: Chelazzi Cosimo , Villa Gianluca , Manno Andrea , Ranfagni Viola , Gemmi Eleonora , Romagnoli Stefano 
Published: July 2021

Abstract
An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality inhigh-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial NeuralNetwork technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohortof 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units,high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, anda testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of theaccuracy in detecting those patients who will develop postoperative complications.A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperativecomplications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classificationaccuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified).The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications,suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirmits applicability in routine clinical practice.
Isolation and Molecular Charcteristic of a Recombinant Feline Calicivirus from Qingdao, China
Authors: Liu Yongxiang , Hu Xiaoliang , Qin Lide 
Published: July 2021

Abstract
Feline calicivirus (FCV) is a highly contagious pathogen that can cause seriously upper respiratory tract and oral disease in feline. Despite widespread vaccination, the prevalence of FCV remains high. In this study, the FCV qd/2019/china was isolated from domestic feline oropharyngeal swab which was collected in qingdao, China. The virus was purified with plaque assay and identified with PCR and IFA method. the capsid amino acid ,VP1, of qd/2019/china [1] showed sequence identity with other isolate ranging from 83.9% (ym3/2001/jp) to 91.1% (CH-JL4) .Sequence analysis of the capsid amino acid revealed that qd/2019/china was closely related to CH-JL4 and clustered with CH-JL4 in the phylogenetic tree. Phylogenetic analysis indicated that complete genome of qd/2019/china and CH-JL4 was also classified into the same cluster. While the recombination analysis with Simplot indicate that the qd/2019/china originated from the recombination of CH-JL4 and HRB-SS, and the region 3821–5301 nt originated from HRB-SS. Further, the region 3821–5301 nt belong to protease -polymerase (PP) of HRB-SS. Here we isolate a new FCV qd/2019/china, which may be a recombinant virus, These results were beneficial for understanding the evolution of FCV.
Golgi apparatus-targeted AIEgens for effective photodynamic therapy through the crosstalk of Golgi apparatus and mitochondria
Authors: Liu Minglun , Chen Yuncong , Guo Yan , Yuan Hao , Yao Shankun , Jin Suxing , Fan Huanhuan , Wang Chengjun , He Weijiang , Guo Zijian 
Published: July 2021

Abstract
Golgi apparatus (GA) oxidative stress induced by in situ reactive oxygen species (ROS) could severely damage the morphology and function of GA, which may open up a new avenue for effective photodynamic therapy (PDT). However, due to the lack of effective design strategy, photosensitizers (PSs) with specific GA targeting ability have not been reported. Herein, we report aggregation-induced emission luminogen (AIEgen) based PSs that can effectively target to GA with Pearson correlation coefficient (PCC) up to 0.98 and singlet oxygen generation rate up to 77.8%. GA fragmentation and cleavage of GA proteins (p115/GM130) were observed upon light irradiation. Meanwhile, the apoptotic pathway was activated through a crosstalk between GA oxidative stress and mitochondria in HeLa cells. Finally, TPE-PyT-CPS can effectively inhibited tumour growth in vivo with negligible adverse effect. This work provided a promising design strategy for the development of PSs with specific GA targeting ability, which is of great importance for precise and effective PDT.
Mapping and Spatial Pattern Analysis of COVID -19 in Central Iran Using the Local Indicators of Spatial Association (LISA)
Authors: Jesri Nahid , Saghafipour Abedin , Koopaei Alireza , Farzinnia Babak , Jooshin Moharram Karami , Abolkheirian Samaneh , Sarvi Mahsa 
Published: July 2021

Abstract
Background: Using geographical analysis to identify geographical factors affecting the prevalence of COVID-19 infection can effect on public health policies to control of the virus. The aim of this study was to determine the spatial analysis of COVID-19 regions in Qom Province, using the local indicators of spatial association (LISA). In a descriptive-analytical study, the total number of individuals infected with COVID-19 in Qom Province, from February 19, to September 2020, were included in the study. The spatial distribution in urban areas was determined using the Moran coefficient in GIS. in addition, the spatial autocorrelation of the coronavirus in the different urban districts of the province was calculated using LISA method. Results: The prevalence of COVID -19 in Qom province was estimated to be 356.75 per 100,000 populations. The pattern of spatial distribution of the prevalence of COVID -19 in Qom was clustered. The District 3 (Imam Khomeini St.), and District 6 (Imamzadeh Ebrahim St.), were set in HH category of LISA as two foci of COVID-19 in Qom province. Conclusions: Based on LISA, District 1 (Bajak) of urban districts was set in LH category. It means this district is located in a low value area surrounded by high values. One of the most important geographical factors affecting the incidence of coronavirus is based on spatial distribution model, distance and spatial proximity. So, health policy makers, should impose more restrictions on the observance of health protocols to control of the coronavirus.
Simple PDMS/AIN emitter as a passive daytime radiative cooling design
Authors: Mabchour  , mourad benlattar 
Published: July 2021

Abstract
Radiative cooling is a passive cooling purpose where a surface naturally cools by radiating the mid-infrared heat radiation to the cold outer space through the atmospheric window . Daytime passive radiative cooling technologies can be simply provided by using a multi-layer design that emits strongly in the transparency atmospheric window, while presents high reflectance in the solar spectrum . In this study, we propose a polydimethylsiloxane foil ) coated aluminum nitride (AIN) deposed onto silver (Ag) coated glass as a radiative cooler for enhancing both daytime and nighttime radiative cooling performances. The spectral selectivity of the proposed device was obtained using matrix method. Numerical results show that our proposed design can reflect more than 96 % in the solar spectrum, while its average emissivity in the atmospheric window can reach more than 90 %.In the absence of wind speed, the proposed device can achieve a net cooling power of under direct sunlight, cooling to a below the ambient air temperature. At nighttime, the proposed device temperature can drop by below the ambient, leading to a net cooling power of . Therefore, the proposed radiative design can fundamentally enable new methods for exploiting solar energy harvesting and energy conservation.