bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving the school food environment for the prevention of childhood obesity: What works and what doesn't
Authors: Pineda Elisa , Bascunan Josefina , Sassi Franco 
Published: January 2021

SummaryThe food environment has a significant influence on dietary choices, and interventions designed to modify the food environment could contribute to the prevention of childhood obesity. Many interventions have been implemented at the school level, but effectiveness in addressing childhood obesity remains unclear.We undertook a systematic review, a meta‐analysis, and meta‐regression analyses to assess the effectiveness of interventions on the food environment within and around schools to improve dietary intake and prevent childhood obesity. Estimates were pooled in a random‐effects meta‐analysis with stratification by anthropometric or dietary intake outcome. Risk of bias was formally assessed. One hundred papers were included. Interventions had a significant and meaningful effect on adiposity (body mass index [BMI] z score, standard mean difference: −0.12, 95% confidence interval: 0.15, 0.10) and fruit consumption (portions per day, standard mean difference: +0.19, 95% confidence interval: 0.16, 0.22) but not on vegetable intake. Risk of bias assessment indicated that n = 43 (81%) of non‐randomized controlled studies presented a high risk of bias in the study design by not accounting for a control. Attrition bias (n = 34, 79%) and low protection of potential contamination (n = 41, 95%) presented the highest risk of bias for randomized controlled trials.Changes in the school food environment could improve children's dietary behavior and BMI, but policy actions are needed to improve surrounding school food environments to sustain healthy dietary intake and BMI.
AtPFA‐DSP3, an atypical dual‐specificity protein tyrosine phosphatase, affects salt stress response by modulating MPK3 and MPK6 activity
Authors: Xin Jing , Li Chuanling , Ning Kexin , Qin Yuan , Shang Jian‐Xiu , Sun Yu 
Published: January 2021

AbstractProtein phosphorylation, especially serine/threonine and tyrosine phosphorylation, plays significant roles in signalling during plant growth and development as well as plant responses to biotic or abiotic stresses. Dual‐specificity protein tyrosine phosphatases dephosphorylate components of these signalling pathways. Here, we report that an atypical dual‐specificity protein tyrosine phosphatase, AtPFA‐DSP3 (DSP3), negatively affects the response of plants to high‐salt conditions. A DSP3 loss‐of‐function mutant showed reduced sensitivity to salt treatment. DSP3 was primarily localized in nuclei and was degraded during salt treatment. Compared to wild type, the level of ROS was lower in the dsp3 mutant and higher in plants ectopically expressing DSP3, indicating that higher DSP3 level was associated with increased ROS production. DSP3 interacted with and dephosphorylated MPK3 and MPK6. Genetic analyses of a dsp3mpk3 double mutant revealed that DSP3's effect on salt stress depends on MPK3. Moreover, the phosphatase activity of DSP3 was required for its role in salt signalling. These results indicate that DSP3 is a negative regulator of salt responses in Arabidopsis by directly modulating the accumulation of phosphorylated MPK3 and MPK6.
Atypical aplasia cutis in association with Xia Gibbs syndrome
Authors: Ellis Carter , Pai Gurpur Shashidhar , Wine Lee Lara 
Published: January 2021

AbstractXia Gibbs syndrome is a genetic disorder first defined in 2014 characterized by hypotonia, intellectual disability, global developmental delay, and dysmorphic facial features. While many additional features may be present, there are few reports of dermatologic findings. We report a case of atypical aplasia cutis in a female infant who was found to have Xia Gibbs syndrome. This case highlights consideration of cutaneous manifestations of Xia Gibbs syndrome which may aid in diagnosis.
Exposure of Human Skin Models to KrCl Excimer Lamps: The Impact of Optical Filtering†
Authors: Buonanno Manuela , Welch David , Brenner David J. 
Published: January 2021

AbstractFar‐UVC radiation is a promising technology that is potentially both effective at killing airborne microbes such as coronaviruses and influenza, and being minimally hazardous to the skin and eyes. Our previous studies on health risks from far‐UVC have employed a krypton‐chloride (KrCl) excimer lamp, emitting principally at 222 nm, supplemented with an optical filter to remove longer wavelength emissions inherent to these lamps. This study explores KrCl lamp health hazards by comparing filtered and unfiltered KrCl lamps using effective spectral irradiance calculations and experimental skin exposures. Analysis of effective irradiances showed a notable increase in allowable exposure when using a filter. Induction of DNA dimers (CPD and 6‐4PP) was measured in human skin models exposed to a range of radiant exposures up to 500 mJ cm−2. Compared to sham‐exposed tissues, the unfiltered KrCl lamps induced a statistically significant increase in the yield of both DNA lesions at all the radiant exposures studied. Conversely, filtered KrCl lamps do not induce increased levels of dimers at the current daily TLV exposure limit for 222 nm (23 mJ cm−2). This work supports the use of filters for far‐UVC KrCl excimer lamps when used to limit disease transmission in occupied locations.
Should leisure‐time sedentary behavior be replaced with sleep or physical activity for prevention of diabetes?
Authors: Aadahl Mette , Andreasen Anne H. , Petersen Christina B. , Gupta Nidhi , Holtermann Andreas , Lau Cathrine J. 
Published: January 2021

The aim was to examine the effects of replacing self‐reported leisure‐time sedentary behavior with sleep, light‐to‐moderate physical activity, or vigorous physical activity on incident diabetes among Danish adults using isotemporal substitution modeling. Participants ≥25 years from the Danish Capital Region Health Survey 2007 (N = 69 800, response rate 52.3%), 2010 (N = 95 150, response rate 52.3%), and 2013 (N = 95 150, response rate 43.5%) were included. Information on daily sleep duration, leisure‐time sedentary behavior, and movement behaviors was collected by questionnaire. Information on incident diabetes was obtained from National registers. Analyses included Cox proportional hazards regression models and isotemporal substitution analyses, with time (in years) from baseline to incident diabetes or censoring December 31, 2017. Potential confounders, sex, age, BMI, ethnicity, education, smoking, inflammatory joint disease, perceived stress, physical and mental component scale and work status, were included. Out of N = 87 339 in the final study sample, n = 3007 had incident diabetes during a mean follow‐up time of 7.4 years. Adults with incident diabetes included more men, higher mean age, and higher BMI, compared to respondents without incident diabetes. Theoretically substituting 30 minutes of leisure‐time sedentary behavior with light‐to‐moderate PA (HR: 0.96; 95% CI: 0.94; 0.98) or with vigorous PA (HR: 0.82; 95% CI: 0.72; 0.94) decreased the risk of incident diabetes. We found no change in incident diabetes risk of substituting sedentary time with sleep (HR: 1.00; 95% CI: 0.97; 1.02). Substituting 30 minutes per day of leisure‐time sedentary behavior with light‐to‐moderate or vigorous PA may significantly reduce the risk of incident diabetes among adults.
Non‐uniform displacement within ruptured Achilles tendon during isometric contraction
Authors: M. Khair Ra’ad , Stenroth Lauri , Péter Annamária , Cronin Neil J. , Reito Aleksi , Paloneva Juha , Finni Taija 
Published: January 2021

The purpose of this study was investigate tendon displacement patterns in non‐surgically treated patients 14 months after acute Achilles tendon rupture (ATR) and to classify patients into groups based on their Achilles tendon (AT) displacement patterns. Twenty patients were tested. Sagittal images of AT were acquired using B‐mode ultrasonography during ramp contractions at a torque level corresponding to 30% of the maximal isometric plantarflexion torque of the uninjured limb. A speckle tracking algorithm was used to track proximal‐distal movement of the tendon tissue at 6 antero‐posterior locations. Two‐way repeated measures ANOVA for peak tendon displacement was performed. K‐means clustering was used to classify patients according to AT displacement patterns. The difference in peak relative displacement across locations was larger in the uninjured (1.29 ± 0.87 mm) than the injured limb (0.69 ± 0.68 mm), with a mean difference (95% CI) of 0.60 mm (0.14‐1.05 mm, P < .001) between limbs. For the uninjured limb, cluster analysis formed 3 groups, while 2 groups were formed for the injured limb. The three distinct patterns of AT displacement during isometric plantarflexion in the uninjured limb may arise from subject‐specific anatomical variations of AT sub‐tendons, while the two patterns in the injured limb may reflect differential recovery after ATR with non‐surgical treatment. Subject‐specific tendon characteristics are a vital determinant of stress distribution across the tendon. Changes in stress distribution may lead to variation in the location and magnitude of peak displacement within the free AT. Quantifying internal tendon displacement patterns after ATR provides new insights into AT recovery.
Analyzing the association between emotions and socioeconomic characteristics of census tracts via user‐generated content
Authors: Yu Xinkai , Fei Teng , Li Xiang 
Published: January 2021

AbstractEnvironmental and socioeconomic characteristics of places continuously influence human emotions. To understand the quality of human life, human emotions must be effectively evaluated and their connections with socioeconomic characteristics must be determined. Social computing tools can extract emotional information from massive user‐generated content (UGC); thus, we proposed an approach that correlates residents' emotions with the socioeconomic characteristics of place based on census tracts from New York State. First, demographic and economic characteristic data from census tracts were obtained and clustered; then, an emotion recognition technique extracted human emotions quantitatively from the UGC. Applying the Apriori algorithm and logistic regression analysis showed that the socioeconomic characteristics that may significantly influence residents’ emotions include family situation, race, and economic status. Local policymakers can use the proposed approach to compare the quality of human life in different regions over a large scale via UGC and identify socioeconomic characteristics to formulate policies to improve residents’ well‐being and welfare.
RATS: A Robotic Arm Training System Designed for Rats
Authors: Zhang Haohan , Luna Tatiana , Yang Lillian , Martin John , Agrawal Sunil 
Published: January 2021

AbstractThis paper presents a novel robotic system to characterize and retrain reaching in rats. This robot is intended to be a research platform for the rehabilitation of forelimb movements in rats. In this paper, we focus on the design of this robotic system. We present the design requirements, mathematical models, and details of the physical device. A parallel mechanism with a special alignment of the component chains is used to accommodate observed reaching motions of a rat’s forelimb. Additionally, we demonstrate the use of this robot to record forelimb trajectories. Three healthy rats were used to record repeated reaching motions while the robot applied nearly zero force. We believe that this robotic system can be used in future training studies with rats who have impaired arm motions due to a neurological insult.
StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction
Authors: Jiang Haoliang , Nie Zhenguo , Yeo Roselyn , Farimani Amir Barati , Kara Levent Burak 
Published: January 2021

AbstractUsing deep learning to analyze mechanical stress distributions is gaining interest with the demand for fast stress analysis. Deep learning approaches have achieved excellent outcomes when utilized to speed up stress computation and learn the physical nature without prior knowledge of underlying equations. However, most studies restrict the variation of geometry or boundary conditions, making it difficult to generalize the methods to unseen configurations. We propose a conditional generative adversarial network (cGAN) model called StressGAN for predicting 2D von Mises stress distributions in solid structures. The StressGAN model learns to generate stress distributions conditioned by geometries, loads, and boundary conditions through a two-player minimax game between two neural networks with no prior knowledge. By evaluating the generative network on two stress distribution datasets under multiple metrics, we demonstrate that our model can predict more accurate stress distributions than a baseline convolutional neural-network model, given various and complex cases of geometries, loads, and boundary conditions.
Modeling of Natural Gas Composition Effect on Low NOx Burners Operation in Heavy Duty Gas Turbine
Authors: Romano Serena , Meloni Roberto , Riccio Giovanni , Nassini Pier Carlo , Andreini Antonio 
Published: January 2021

Abstract
This paper addresses the impact of natural gas composition on both the operability and emissions of lean premixed gas turbine combustion system. This is an issue of growing interest due to the challenge for gas turbine manufacturers in developing fuel-flexible combustors capable of operating with variable fuel gases while producing very low emissions at the same time. Natural gas contains primarily methane (CH4) but also notable quantities of higher order hydrocarbons such as ethane (C2H6) can also be present. A deep understanding of natural gas combustion is important to obtain the highest combustion efficiency with minimal environmental impact. For this purpose, Large Eddy Simulations of an annular combustor sector equipped with a partially premixed burner are carried out for two different natural gas compositions with and without including the effect of flame strain rate and heat loss resulting in a more adequate description of flame shape, thermal field, and extinction phenomena. Promising results, in terms of NOx, compared against available experimental data, are obtained including these effects on the flame brush modeling, enhancing the fuel-dependency under nonadiabatic condition.
Durable Abrasive Tip Design for Single Crystal Turbine Blades
Authors: Nagy Douglas , Tollett Robert 
Published: January 2021

Abstract
In order to create and maintain peak efficiency in turbine stages, it is useful to minimize hot air leakage over blade tips. For unshrouded blades, this means creating minimal clearance over the airfoil tip with rub-tolerate zero-gap materials systems. In this study, a refractory abrasive tip material (ABT) was developed and applied to a plain turbine blade tip to work in conjunction with an abradable coating on the tip-shoe. Process development efforts resulted in abrasive material that could be as much as 2 mm thick. Further, the abrasive media included imbedded grits that would remain in back-up until such time as they were exposed and were pressed into service. Application methods of both furnace brazing and induction brazing were explored as were pre- and post-consolidation net shaping. Evaluation testing included compatibility for the application process onto single-crystal blades. Performance testing included rub-rig testing and long-term oxidation testing. Service testing included commercial engine operation for 14,000 h followed by metallurgical reevaluation. Service performance was mechanically successful, although some material transfer from the tip-shoe was observed which decreased the abrasive nature of the tip. Metallurgically, some intergranular oxidation was observed, but the grits themselves were well retained and were sufficiently refractory to avoid microstructural or oxidation degradation. A production implementation of the coating by an induction heating process is shown.
Two-Stage Robust Tracking Controller for Linear Systems With Known Uncertainty Using Filtered Basis Functions
Authors: Ramani Keval S. , Okwudire Chinedum E. 
Published: January 2021

Abstract
There is growing interest in the use of the filtered basis functions (FBF) approach to track linear systems, especially nonminimum phase (NMP) plants, because of the distinct advantages it presents as compared to other popular methods in the literature. The FBF approach expresses the control input to the plant as a linear combination of basis functions. The basis functions are forward filtered through the plant dynamics and the coefficients of the linear combination are selected such that the tracking error is minimized. This paper proposes a two-stage robust filtered basis functions approach for tracking control of linear systems in the presence of known uncertainty. In the first stage, the nominal model for filtering the basis functions is selected such that a Frobenius norm metric which considers the known uncertainty is minimized. In the second stage, an optimal set of basis functions is selected such that the effect of uncertainty is minimized for the nominal model selected in the first stage. Experiments on a 3D printer, demonstrate up to 7 times improvement in tracking performance using the proposed method as compared to the standard FBF approach.
Optimal Tuning of Single-Axis Satellite Attitude Control Parameters Using Genetic Algorithm
Authors: Khalili Nasser , Ghorbanpour Amin 
Published: January 2021

Abstract
This paper studies the optimization of control parameters for single-axis attitude control of a rigid satellite using thrusters. It is desired to tune the control parameters to minimize the number of thruster firings. The motion task is defined as an attitude pointing maneuver from arbitrary initial condition to the rest. To this end, a control loop with pulse-width pulse-frequency modulator is suggested. The control actuators are pairs of identical non-ideal thrusters which each one produces torque in one direction around the control axis. A novel approach is proposed to model the dynamics of thruster with a design parameter which shapes the response of the actuator. Nine parameters of the control loop, e.g. feedback gains, modulator parameters, and thruster dynamics, are selected as decision variables. Genetic algorithm is used to find the optimal values of the variables such that the firing is minimized. It is shown that firing minimization requires a sluggish thruster. Moreover, to study the effect of deviation from optimal value on number of thruster firings and fuel consumption, a sensitivity analysis is performed. Based on sensitivity analysis, an optimal range is suggested for each parameter where both number of firing and fuel consumption are minimized.
Traffic Prediction for Merging Coordination Control in Mixed Traffic Scenarios
Authors: Shao Yunli , Rios-Torres Jackeline 
Published: January 2021

Abstract
Connected and autonomous vehicles (CAVs) have the potential to bring in safety, mobility, and energy benefits to transportation. The control decisions of CAVs are usually determined for a look-ahead horizon based on previewed traffic information. This requires an effective prediction of future traffic conditions and its integration with the CAV control framework. However, the short-term traffic prediction using information from connectivity is a challenging research topic, especially for mixed traffic scenarios. This work focuses on the development of a traffic prediction framework for a merging coordination controller. The previously developed merging controller coordinates the merging sequence and travel speed of CAVs to maximize the energy efficiency and overall mobility. In mixed traffic scenarios, the controller receives information regarding the position of all the vehicles traveling inside a control zone and controls the desired speed of all CAVs. The controller has no control on the human-driven vehicles. The merging controller does not have direct information or an explicit prediction on the behaviors of human-driven vehicles. Aiming to improve the performance of the merging controller in various mixed traffic conditions, a traffic prediction algorithm is developed and evaluated in this work. The performance of this traffic prediction algorithm is investigated for various penetration rates of connectivity for a single-lane secondary road merging to a single-lane primary road. The results show that compared to a constant speed assumption of human-driven vehicles, the proposed traffic prediction algorithm is able to reduce the prediction error of the arrival time of human-driven vehicles at the merging zone by more than 50%.
Prioritized Foraging Strategies for an Ant Colony-Inspired Swarm System
Authors: Iyer Hari R. , Kumar Manish , Ramakrishnan Subramanian 
Published: January 2021

Abstract
We investigate ant-colony-inspired foraging strategies for enhancing the efficiency of a swarm of artificial agents engaged in a search-and-retrieval application. First, we extend a mathematical model of ant foraging to account for the evolution of the information collected during search-and-retrieval over time. We then use the extended model to numerically investigate the efficiency of search-and-retrieval under the two distinct cases of non-depleting information and depleting information at the sources. In the former case, we obtain optimal ranges of parameter values of the ant foraging model that enhance efficiency. In the latter case, we find that appropriately designed deposition functions in the model can induce self-organization in the swarm and therefore prioritize the collection of quickly depleting information. The ability to prioritize is highly desirable in a swarm for search-and-retrieval applications and, to our knowledge, induced emergent behavior resulting in prioritization capabilities has not been reported in swarms inspired by ant foraging. The results are expected to be broadly significant for swarm robotics as well as in applications such as the Travelling Salesman Problem (TSP) with time-varying profit.
A Control-Oriented Dynamical Model of Deposited Droplet Volume in Electrohydrodynamic Jet Printing
Authors: Spiegel Isaac A. , van de Laar Tom , Oomen Tom , Barton Kira 
Published: January 2021

Abstract
Electrohydrodynamic jet printing (e-jet printing) is a nascent additive manufacturing process most notable for extremely high resolution printing and having a vast portfolio of printable materials. These capabilities make e-jet printing promising for applications such as custom electronics and biotechnology fabrication. However, reliably fulfilling e-jet printing’s potential for high resolution requires delicate control of the volume deposited by each jet. Such control is made difficult by a lack of models that both capture the dynamics of volume deposition and are compatible with the control schemes relevant to e-jet printing. This work delivers such a model. Specifically, this work introduces a definition of “droplet volume” as a dynamically evolving variable rather than a static variable, and uses this definition along with analysis of high speed microscope videos to develop a hybrid dynamical system model of droplet volume evolution. This model is validated with experimental data, which involves the contribution of a novel technique for extracting consistent droplet volume measurements from videos.