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Optimizing the allocation of university resources to improve the efficiency of inputs and outputs is an important issue for the high-quality development of universities. In recent years, China has become an important growth pole for the development of global higher education. In particular, Chinese agricultural universities, with their distinctive disciplinary characteristics and outstanding professional advantages, have made important contributions to the sustainable development of agricultural education around the world. In contrast, academic research on the efficiency of resource allocation in Chinese agricultural universities is very limited. To fill this gap, this study was guided by econometrics and took high-level agricultural universities in China as the research object to measure the effects of financial investment, disciplinary differences, and development level on the level of resource allocation efficiency of universities. With the help of a data envelopment model (DEA) and a Malmquist index decomposition model, we found that the overall level of resource allocation efficiency in the sample universities was high, but there were great disparities in resource input–output effectiveness between universities. In many universities, marginal inputs exceeded marginal outputs, resulting in input redundancy and resource wastage. In addition, this study shows that for high-level agricultural universities, the regression of capital input technology is preventing a sustained increase in productivity, which places the total factor productivity of resource allocation in a diminishing state.

Low-level, in-utero exposure to toxic metals such as lead (Pb) and mercury (Hg) is widespread in the US and worldwide; and, individually, was found to be obesogenic in children. To address the literature gaps on the health effects of co-exposure to low-level toxic metals and the lack of intervention strategy, we aimed to investigate the association between in-utero co-exposure to Hg, Pb, cadmium (Cd) and childhood overweight or obesity (OWO) and whether adequate maternal micronutrients (selenium (Se) and folate) can be protective.This study included 1442 mother-child pairs from the Boston Birth Cohort, a predominantly urban, low-income, Black, and Hispanic population, who were enrolled at birth and followed prospectively up to age 15 years. Bayesian kernel machine regression (BKMR) was applied to estimate individual and joint effects of exposures to metals and micronutrients on childhood OWO while adjusting for pertinent covariables. Stratified analyses by maternal OWO and micronutrient status were performed to identify sensitive subgroups.In this sample of understudied US children, low-level in-utero co-exposure to Hg, Pb, and Cd was widespread. Besides individual positive associations of maternal Hg and Pb exposure with offspring OWO, BKMR clearly indicated a positive dose-response association between in-utero co-exposure to the three toxic metals and childhood OWO. Notably, the metal mixture-OWO association was more pronounced in children born to mothers with OWO; and in such a setting, the association was greatly attenuated if mothers had higher Se and folate levels.In this prospective cohort of US children at high-risk of toxic metal exposure and OWO, we demonstrated that among children born to mothers with OWO, low-level in-utero co-exposure to Hg, Pb, and Cd increased the risk of childhood OWO; and that adequate maternal Se and folate levels mitigated the risk of childhood OWO.NCT03228875.

Weatherizing residential buildings to make them more energy efficient offers great potential for reducing greenhouse gas emissions, conserving resources, and improving occupant comfort and well-being in the face of climate change. However, weatherization adoption rates remain low. Previous research suggests that social norms and energy-related interactions within a community have greater influence on households’ weatherization adoption decisions than financial incentives. Therefore, an increased understanding of how social factors can be leveraged to encourage more households to weatherize is needed. This paper describes a hybrid simulation model on households’ weatherization adoption decisions in an urban community. The simulation model integrates a building energy simulation model with an agent-based model that is embedded in a social network to represent the interactions, adaptations, and decision making of individual households over time. The model is demonstrated for several experimental scenarios for a residential block in the City of Des Moines, Iowa. Results indicate that social connectedness, community leaders, and delivery mechanisms for weatherization assistance all have a significant impact on household weatherization adoption. These results provide insight into the potential of policy interventions to increase weatherization adoption.

Soil infrared spectroscopy has great potential for estimating soil properties, but reference soil measurements are typically required in combination with multivariate statistical models to estimate soil properties. User-friendly predictive tools based on open-source statistical environment remain one of the main limitations to enable technology diffusion to non-specialist users. Our aim is to build capacity for an automated machine learning routine for rapid and robust prediction of soil health indicators using lab acquired soil infrared spectra. This intelligent system runs on R statistical environment and includes (1) a diverse soil spectral library comprising main physiographic regions from the USA Midwest region under diverse land uses and various sampling depths, (2) a classification process to detect potential outliers in newly acquired spectra using supervised machine learning techniques, and (3) a multi-model optimized prediction process based on linear and non-linear statistical procedures (partial least squares, support vector machines, and neural network). This prediction system works at the intersection of soil and data science and high-performance computing to enable efficient parallel processing of spectral data on multi-core coprocessors. Using artificial intelligence to automate soil infrared spectroscopy is a fundamental demand that will make this technique an effective routine in soil laboratories to estimate soil health.

Background: Implementation researchers have sought ways to use simulations to support the core components of implementation, which typically include assessing the need for change, designing implementation strategies, executing the strategies, and evaluating outcomes. The goal of this article is to explain how agent-based modeling could fulfill this role. Methods: We describe agent-based modeling with respect to other simulation methods that have been used in implementation science, using non-technical language that is broadly accessible. We then provide a stepwise procedure for developing agent-based models of implementation processes. We use, as a case study to illustrate the procedure, the implementation of evidence-based smoking cessation practices for persons with serious mental illness (SMI) in community mental health clinics. Results: For our case study, we present descriptions of the motivating research questions, specific models used to answer these questions, and a summary of the insights that can be obtained from the models. In the first example, we use a simple form of agent-based modeling to simulate the observed smoking behaviors of persons with SMI in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with SMI). In the second example, we illustrate how a more complex agent-based approach that includes interactions between patients, providers, and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. Conclusion: In this article, we explain how agent-based models can be used to address implementation science research questions and provide a procedure for setting up simulation models. Through our examples, we show how what-if scenarios can be examined in the implementation process, which are particularly useful in implementation frameworks with adaptive components. Plain Language Summary: The goal of this paper is to explain how agent-based modeling could be used as a supplementary tool to support the components of complex implementation processes. Such models have not yet been widely used in implementation science, partly because they are not straightforward to develop. To promote the use of agent-based modeling we provide a stepwise procedure using non-technical language and emphasizing the relationships between the model and implementation processes. We used two detailed examples to demonstrate our proposed approach. In the first example, we simulate the observed smoking behaviors of persons with serious mental illness in a recently completed trial (IDEAL, Comprehensive Cardiovascular Risk Reduction Trial in Persons with Serious Mental Illness). In the second example, we illustrate how agent-based models that include interactions between patients, providers and site administrators can be used to provide guidance for an implementation intervention that includes training and organizational strategies. This example is based in part on an ongoing project focused on scaling up evidence-based tobacco smoking cessation practices in community mental health clinics in Maryland. For this example, we show how the visual user interface of an agent-based model can be in the form of a dashboard with levers for simulating what-if scenarios that can be used to guide implementation decisions. In summary, this paper shows how agent-based models can provide insights into the processes in complex interventions, and guide implementation decisions for improving delivery of evidence-based practices in community mental health clinics.

Background Maternal smoking affects more than half a million pregnancies each year in the US and is known to result in fetal growth restriction as measured by lower birthweight and its associated long-term consequences. Maternal smoking also has been linked to altered fetal DNA methylation (DNAm). However, what remains largely unexplored is whether these DNAm alterations are merely markers of smoking exposure or if they also have implications for health outcomes. This study tested the hypothesis that fetal DNAm mediates the effect of maternal smoking on newborn birthweight. Methods This study included mother–newborn pairs from a US predominantly urban, low-income multi-ethnic birth cohort. DNAm in cord blood were determined using the Illumina Infinium MethylationEPIC BeadChip. After standard quality control and normalization procedures, an epigenome-wide association study (EWAS) of maternal smoking was performed using linear regression models, controlling for maternal age, education, race, parity, pre-pregnancy body mass index, alcohol consumption, gestational age, maternal pregestational/gestational diabetes, child sex, cord blood cell compositions and batch effects. To quantify the degree to which cord DNAm mediates the smoking-birthweight association, the VanderWeele-Vansteelandt approach for single mediator and structural equational model for multiple mediators were used, adjusting for pertinent covariates. Results The study included 954 mother–newborn pairs. Among mothers, 165 (17.3%) ever smoked before or during pregnancy. Newborns with smoking exposure had on average 258 g lower birthweight than newborns without exposure (P GFI1, AHRR, CYP1A1, and CNTNAP2; 8 of those, located in the first three genes, were Bonferroni significantly associated with newborn birthweight and mediated the smoking-birthweight association. The combined mediation effect of the three genes explained 67.8% of the smoking-birthweight association. Conclusions Our study not only lends further support that maternal smoking alters fetal DNAm in a multiethnic population, but also suggests that fetal DNAm substantially mediates the maternal smoking–birthweight association. Our findings, if further validated, indicate that DNAm modification is likely an important pathway by which maternal smoking impairs fetal growth and, perhaps, even long-term health outcomes.

Supplemental material, sj-pdf-1-irp-10.1177_26334895211010664 for Agent-based modeling for implementation research: An application to tobacco smoking cessation for persons with serious mental illness by Wanyu Huang, Chia-Hsiu Chang, Elizabeth A Stuart, Gail L Daumit, Nae-Yuh Wang, Emma E McGinty, Faith B Dickerson and Takeru Igusa in Implementation Research and Practice

Additional file 1: Table 1. Genome-wide DNA methylation association study identified 38 CpG sites significantly associated with maternal smoking during pregnancy in 954 mother–newborn pairs from the Boston Birth Cohort. Additional file 1: Table 2. Genome-wide DNA methylation association study with maternal smoking during pregnancy: Comparison of significant CpGs among total sample, Blacks only, and non-Blacks only. Additional file 1: Table 3. Methylation mediation effect of each of the 38 single CpG sites on the maternal smoking-newborn birthweight association in 954 mother–newborn pairs from the Boston Birth Cohort. Additional file 1: Table 4. Single CpG, single gene score, and combined multiple gene mediation analysis on the maternal smoking—newborn birthweight association, among 504 male newborns in the Boston Birth Cohort. Additional file 1: Table 5. Single CpG, single gene score, and combined multiple gene mediation analysis on the maternal smoking—newborn birthweight association, among 450 female newborns in the Boston Birth Cohort. Additional file 1: Table 6. Single CpG, single gene score, and combined multiple gene mediation analysis on the maternal smoking—newborn birthweight association, among 679 Black newborns in the Boston Birth Cohort. Additional file 1: Table 7. Single CpG, single gene score, and combined multiple gene mediation analysis on the maternal smoking—newborn birthweight association, among 275 non-Black newborns in the Boston Birth Cohort. Additional file 1: Table 8. Characteristics of mother–newborn pairs included (N = 954) versus excluded (N = 7555) from this current study. Additional file 1: Figure 1. Illustration of the relationship between maternal smoking categories (0 = never, 1 = quitter, 2 = current) and GFI1 gene score (Panel A), AHRR cg05575921 (Panel B), and CYP1A1 gene score (Panel C); and between smoking categories (0 = never, 1 = quitter, 2 = current) and birthweight (Panel D), in 954 mother–newborn pairs from the Boston Birth Cohort. Additional file 1: Figure 2. Manhattan and Q-Q plots for the EWAS analyses in cord blood in associations with maternal smoking, in Black (Fig 2A) and non-Black subset (Figure 2B). Grey dotted line represents the epigenome-wide significance cut-off after Bonferroni correction, and the red dotted line represents the epigenome-wide significance cut-off with FDR< 0.05; Genes in red represent known genes associated with smoking as identified in previous studies. Additional file 1: Figure 3. An illustration of our sequential analytical methods to dissect the interplay of maternal smoking (X), cord blood DNA methylation (M: mediator), and newborn birthweight (Y), where C’ represents the direct effect of X on Y. Additional file 1: Figure 4. Distribution of cotinine/hydroxy-cotinine by smoking status: Panel A: cord plasma cotinine/hydroxy-cotinine by maternal smoking status; Panel B: maternal plasma cotinine/hydroxy-cotinine by maternal smoking status. Additional file 1: Figure 5. Flowchart of Study Participants.

Supplemental material, sj-pdf-1-irp-10.1177_26334895211010664 for Agent-based modeling for implementation research: An application to tobacco smoking cessation for persons with serious mental illness by Wanyu Huang, Chia-Hsiu Chang, Elizabeth A Stuart, Gail L Daumit, Nae-Yuh Wang, Emma E McGinty, Faith B Dickerson and Takeru Igusa in Implementation Research and Practice

Fine particulate matter (PM2.5) and ozone (O3) air pollutants are known risk factors for asthma exacerbation. We studied the association of these air pollutants with pediatric asthma exacerbation in the Philadelphia metropolitan region, and evaluated potential effect modification by children's characteristics (e.g., race/ethnicity, atopic conditions) and environmental factors (e.g., neighborhood tree canopy, meteorological factors, aeroallergens). We conducted a time-stratified case-crossover study of 54,632 pediatric (age ≤18 years) asthma exacerbation cases occurring from 2011 to 2014, identified through electronic health records (EHR) of the Children's Hospital of Philadelphia (CHOP) health system. We applied conditional logistic regression to estimate associations between air pollution and asthma exacerbation, using daily census-tract level pollutant concentrations estimated from the EPA Fused Air Quality Surface Using Downscaling (FAQSD) files. The associations were estimated within warm (Apr-Sep) and cold (Oct-Mar) months for unlagged exposure and for cumulative effects up to 5 days after exposure, with adjustment for temperature, relative humidity, and holidays. We found small increases in odds of asthma exacerbation with higher pollutant concentrations, with positive associations (OR, comparing concentrations of 75th to 25th percentile) observed for PM2.5 during both warm (1.03, 95% CI: 0.98-1.08) and cold months (1.05, 95% CI: 1.02-1.07), and for O3 during cold months (1.08, 95% CI: 1.02-1.14). The exposure-response relationship with PM2.5 during the cold months was essentially linear, whereas thresholds of effect were observed for the other associations at low-medium pollutant concentrations. Results were robust to multi-pollutant modeling and adjustment for additional covariates. We found no effect modification by most children's characteristics, while effect sizes were higher on days with detected tree and grass pollens during warm months. Our results suggest that even small decreases in pollutant concentrations could potentially reduce risk of childhood asthma exacerbation - an important finding, given the high burden of childhood asthma and known disparities in asthma control.

the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the `new' and `comment' mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content. A series of experiments tested on public datasets demonstrate that: (1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; (2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.

CPU cache misses and branch mispredictions waste CPU cycles and affect program performance. Such software inefficiencies could be neither eliminated by existing compilers nor avoided by developers. In this paper, we propose a novel approach, named PERDICE, to automatically discover such performance bugs by leveraging concolic execution. PERDICE adopts a new path exploration algorithm to discover such software inefficiencies. In particular, we measure performance losses in the granularity of program locations (e.g., instructions, source code lines) instead of paths to avoid getting stuck into the code without software inefficiencies. Moreover, when scoring test inputs, our new approach prefers the test inputs incurring increments in performance losses. This strategy allows PERDICE to avoid getting stuck into the software inefficiencies that have been found. We have implemented PERDICE for both PC (X86 instructions) and Android smartphones (ARM instructions). The experimental results with real-world desktop software and Android native code show that PERDICE outperforms the other four popular algorithms and PROFs (a multi-path performance profiler) in terms of the speed to discover software inefficiencies and the severity (i.e, amount of wasted CPU cycles) of inefficiencies.

We design an induction current acquisition device based on ARM in order to realize high resolution and high sampling rate of acquisition for the induction current in wire-loop. Considering its characteristics of fast attenuation and small signal amplitude, we use the method of multi-path fusion for noise suppression. In the paper, the design is carried out from three aspects of analog circuit and device selection, independent power supply structure and the electromagnetic interference suppression of high frequency. DMA and ping-pong buffer, as a new data transmission technology, solves real time storage problem of massive data. The performance parameters of ARM acquisition device are tested. The comparison test of ARM acquisition device and cRIO acquisition device is performed at different time constants. The results show that it has 120dB dynamic range, 47kHz bandwidth, 96kHz sampling rate, 5μV the smallest resolution, and its average error value is not more than 4%, which proves the high accuracy and stability of the device.

Energy conservation in residential buildings has been a topic of interest in recent years because of their high levels of energy consumption. Weatherization is set of approaches that can be used to make buildings more energy-efficient, thereby helping residents lower their energy bills and improving environmental sustainability. However, there are two significant challenges associated with weatherization adoption: high upfront investment costs with a long payback period, and minimal awareness of weatherization and its benefits. This paper proposes an agent-based model that will allow researchers to explore residents' socially-motivated energy conservation decisions by providing a realistic social context via a multilayer social network and incorporating opinion dynamics based on the Susceptible-Exposed-Infected-Recovered epidemic model. Several experimental scenarios are run to demonstrate the model's potential to help policymakers determine how to encourage residential weatherization adoption.

The electricity market in the U.S. is changing rapidly from a utility-scale centralized generation-distribution model to a more distributed and customer-sited energy model. Increasingly, residential consumers are showing interest in solar-based electricity, which has resulted in increased adoption of distributed solar on the rooftops of owner-occupied residences. However, limited accessibility of rooftop photovoltaic (PV) has led to equity concerns among policymakers. Also, utility companies face a decline in revenues as more residents adopt rooftop PV. In response to these issues, utility companies must consider providing alternative renewable energy options to their customers and incorporate consumer adoption modeling in their expansion planning approach. Agent-based modeling enables energy consumers' socially-motivated adoption decisions to be realistically captured. This paper describes an agent-based model that demonstrates the value of incorporating consumer-adoption modeling in a utility company's expansion planning approach.

Aiming to solve a series of problems in photo collection over citizen’s license, this paper proposes Portrait Extraction Algorithm over our face based on facial detection technology and state-of-the-art image segmentation algorithm. Considering an input image where the foreground stands a man with unfixed size and its background is all sorts of complicated background, firstly we use Haar&Adaboost facial detection algorithm as a preprocessing method so as to divide the image into different sub-systems, and we get a fix-sized image of human face. Then we use GrabCut and closed-form algorithm to segment the preprocessed image and output an image which satisfies our requirements (i.e. the fixed size and fixed background). Up to now the GrabCut and closed-form algorithm has been realized, both of which have its own advantages and shortages.

This article studies full-waveform associated identification method of airborne time-domain electromagnetic method (ATEM) 3-d anomalies based on multiple linear regression analysis method. By using convolution algorithm, full-waveform theoretical responses are computed to derive sample library including switch-off-time period responses and off-time period responses. Extract full-waveform attributes from theoretical responses to derive linear regression equations which are used to identify the geological parameters. In order to improve the precision ulteriorly, we optimize the identification method by separating the sample library into different groups and identify the parameter respectively. Performance of full-waveform associated identification method with field data of wire-loop test experiments with ATEM system in Daedao of Changchun proves that the full-waveform associated identification method is feasible practically.

Dynamic symbolic execution is an important automated testing technique. Firstly, we introduce the traditional symbolic execution and dynamic symbolic execution technology, and then review the research status of Python application testing with dynamic symbolic execution technology. Then we compare and analyze the dynamic symbolic execution tools with the existing Python programs and Architectures, performance, and supported data types. Finally, the development direction of this field prospects. It is helpful for researchers to understand the progress of dynamic symbolic execution technology in current Python program and lay a foundation for future research work.

Objective: The aim of this study was to research the impact of air pollution on the number of outpatient and emergency room visits to pediatric department in a hospital Beijing at January 2013. Met...

The aim of this study was to investigate the impact of air pollution on the number of pediatric outpatient and emergency room visits in January 2013.Health-related data was from a major hospital in Haidian,Beijing while related data on air pollution was from the Chinese Research Academy of Environmental Sciences. Data on weather was from the China Weather Website. Number of hospital outpatient visits per day during the haze period was compared to those from the previous year to get the increase of percentage. Concentration-response functions were estimated using generalized additive modeling with natural log link function, while multiple structural change test was used to determine the changing point in CRFs. Constrained piecewise linear function was used to estimate the risks for different segments of CRFs.The peak of NO(X), SO₂, PM(2.5) and PM(10) reached 672.63 µ g/m³, 146.20 µ g/m³, 312.19 µ g/m³ and 995.00 µ g/m³, around 28 December 2012 and 10 January 2013. The peaks of visits to the pediatric department were observed at the some days, showing 47.75% and 34.14% of increase in the visits to the pediatric department. In the low or intermediate ranges of concentration, all the four air pollutants were significantly associated with pediatric outpatient visits, in the form of C- and S- shape of concentration-response function. However, in the range of high concentration, only PM(10) and SO₂were significantly associated with the number of visits to the pediatric emergency room, appeared as the J-shape form. Discernable thresholds were found in S- and J- shapes which were located at 200 µg/m³, 20 µg/m³, 40 µg/m³ and 100 µ g/m³ for NO(X), SO₂, PM(2.5) and PM(10), respectively.Air pollution during the haze period was associated with the increase of pediatric outpatient and emergency room visits. There were different sensitive groups within the pediatric cases. Their susceptibility showed a decreasing trend from C-, S-, to J- shape related group.

As an important supplement and development of traditional methods, the meshfree method has received a great deal of attention in the field of engineering calculation, and has been successfully used to solve many problems which traditional methods have difficulty in solving. However, the application of meshfree method is relatively less in the area of geophysics. In this paper, we apply the meshfree method to the numerical simulation of geophysical electromagnetic prospecting, taking the 2D magnetotelluric as an example and deduce the corresponding meshfree radial point interpolation method (RPIM) equivalent linear equations in detail. The high-efficiency and accurate solutions of large-scale sparse linear equations are solved by the quasi-minimal residual method based on Krylov subspace. The optimal values of the shape parameters are given by numerical experiments. The correctness of the meshfree method is verified by a layered model. The root mean square error of the calculation results is no more than 0.35%, its accuracy is superior to the finite element method. We also compare the meshfree solution with FEM solution by calculating an inclined vein body model, and the calculation results are in good agreement. A continuously changing fault model and undulating terrain model which traditional methods have difficulty in simulating are respectively calculated, the sectional profiles of the apparent resistivity accurately reflect the trend of the anomalies. The meshfree method does not require the complicated mesh generation, and the physical parameters are loaded at a series of points, thus it is especially suitable for the calculation of the complex geological models. With the rapid development of computational science, the meshfree techniques will certainly become a new robust numerical simulation method in geophysical electromagnetic prospecting.

Study on diffusion behaviors, such as contagion of virus, adoption of production, and information propagation, is one of the hottest topics of social network research. The conventional models usually focus on a particular factor that influences the contagion process. A novel model of diffusion based on multiple behavior evidence fusion is proposed on the analysis of prevalent social network data set, which can simulate the information propagation process with the fitting goodness of 0.79. In this model, impact factors, i.e. the influence between neighborhood, and the activity of user, are taken into account together and fused into combined evidence mass function, which can be used to infer users' forwarding behavior by D-S theory reasoning. Series of experiments tested on the published Enron email data set show that the multiple evidence fusion based diffusion model (MEFDM) is reasonable and feasible in demonstrating the cascading of information in the social network.

This work surveyed residents of an economically disadvantaged community on their attitudes toward weatherization and their energy use behaviors. To support urban leaders making decisions to mitigate the effects of large-scale climate change, data-driven simulation models are being developed. To ensure that these models are equitable, the needs of all citizens must be included, especially those most vulnerable to the impacts of climate change. The results of this survey indicate that residents are taking steps to weatherize and conserve energy, but they are hindered by a lack of resources and knowledge of available assistance programs. These results are being applied to agent-based models (ABM) to model emergent community behavior. These models will be used to design decision support systems for city stakeholders.