A prevalent form of online healthcare comprises hybrid support services, allowing help seekers to pose health-related questions, assess answers from various healthcare experts and ordinary supporters, and vote on the usefulness of answers they find most satisfactory. However, the impact of healthcare experts' engagement on the subsequent supporters' performance within this hybrid service remains unclear. While concerns persist that experts' involvement may diminish subsequent supporters' performance due to a reduced likelihood of recognition, there is a plausible scenario in which it fosters learning and competition, thereby improving performance of subsequent supporters. Leveraging data from a Chinese online mental health platform, we utilize the mediation effects model to investigate the impact of healthcare experts' engagement on the performance of subsequent supporters. Within our model, we focus on the mediating effects of the effort and quality of answers from these subsequent supporters. Our research findings indicate that the engagement of counselors can directly enhance the social recognition obtained by subsequent supporters and can also indirectly boost this recognition by increasing the effort put into their answers. However, the quality of the subsequent supporters' answers does not play a significant mediating role in this relationship. These results fill a gap in the literature on online health services and expert effects, offering valuable insights for online health platforms aiming to enhance the performance of supporters by involving healthcare experts.

ABSTRACT A software ecosystem is a complex system that allows developers to cooperate with each other. Community is a universal and important topological property of networks. Detecting the communities of the software ecosystem is of great significance for analyzing its structural characteristics, discovering its hidden patterns, and predicting its behavior. Traditional community detection algorithms of complex networks are mostly for undirected networks. For the social network, the direction of information dissemination between developers cannot be ignored. In addition, the existing algorithms of community detection usually only consider direct influence between individuals while neglecting indirect relationships. To solve these problems, this paper presents a community detection method based on a two‐step information dissemination model for the software ecosystem. First, a two‐step information dissemination model is established to calculate the information gain of nodes. Second, a ranking method of developers' comprehensive influence is given through their influence vectors and information gains. Finally, communities are detected by taking the influential nodes as the cluster centers and the probability of information dissemination as the clustering direction. The proposed method is applied to community detection of typical software ecosystems in GitHub. The experimental results show that our method has good performance in the identification of community structure.

The recent development of artificial intelligence (AI) technology, especially the advance of deep neural network (DNN) technology, has revolutionized many fields. While DNN plays a central role in modern AI technology, it has rarely been used in genetic data analysis due to analytical and computational challenges brought by high-dimensional genetic data and an increasing number of samples. To facilitate the use of AI in genetic data analysis, we developed a C++ package, AIGen, based on two newly developed neural networks (i.e. kernel neural networks and functional neural networks) that are capable of modeling complex genotype-phenotype relationships (e.g. interactions) while providing robust performance against high-dimensional genetic data. Moreover, computationally efficient algorithms (e.g. a minimum norm quadratic unbiased estimation approach and batch training) are implemented in the package to accelerate the computation, making them computationally efficient for analyzing large-scale datasets with thousands or even millions of samples. By applying AIGen to the UK Biobank dataset, we demonstrate that it can efficiently analyze large-scale genetic data, attain improved accuracy, and maintain robust performance. Availability: AIGen is developed in C++ and its source code, along with reference libraries, is publicly accessible on GitHub at https://github.com/TingtHou/AIGen.

Heritability, the proportion of phenotypic variance explained by genome-wide single nucleotide polymorphisms (SNPs) in unrelated individuals, is an important measure of the genetic contribution to human diseases and plays a critical role in studying the genetic architecture of human diseases. Linear mixed model (LMM) has been widely used for SNP heritability estimation, where variance component parameters are commonly estimated by using a restricted maximum likelihood (REML) method. REML is an iterative optimization algorithm, which is computationally intensive when applied to large-scale datasets (e.g. UK Biobank). To facilitate the heritability analysis of large-scale genetic datasets, we develop a fast approach, minimum norm quadratic unbiased estimator (MINQUE) with batch training, to estimate variance components from LMM (LMM.MNQ.BCH). In LMM.MNQ.BCH, the parameters are estimated by MINQUE, which has a closed-form solution for fast computation and has no convergence issue. Batch training has also been adopted in LMM.MNQ.BCH to accelerate the computation for large-scale genetic datasets. Through simulations and real data analysis, we demonstrate that LMM.MNQ.BCH is much faster than two existing approaches, GCTA and BOLT-REML.

To address the vast multimedia traffic volume and requirements of user Quality of Experience (QoE) in the next generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing (MEC) can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named Learning based Cooperative Caching (LECC) strategy based on MEC architecture to reduce transmission cost while improving user QoE for future mobile networks. In LECC, we exploit a Transfer Learning (TL)-based approach for estimating content popularity, and then formulate the proactive caching optimization model. As the optimization problem is NP- hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate, decrease content transmission cost in comparison with known existing caching strategies.

Online product reviews play an important role in E-commerce websites because most customers read and rely on them when making purchases. For the sake of profit or reputation, review spammers deliberately write fake reviews to promote or demote target products, some even fraudulently work in groups to try and control the sentiment about a product. To detect such spammer groups, previous work exploits frequent itemset mining (FIM) to generate candidate spammer groups, which can only find tightly coupled groups, i.e. each reviewer in the group reviews every target product. In this paper, we present the loose spammer group detection problem, i.e. each group member is not required to review every target product. We solve this problem using bipartite graph projection. We propose a set of group spam indicators to measure the spamicity of a loose spammer group, and design a novel algorithm to identify highly suspicious loose spammer groups in a divide and conquer manner. Experimental results show that our method not only can find loose spammer groups with high precision and recall, but also can generate more meaningful candidate spammer groups than FIM, thus it can also be used as an alternative preprocessing tool for existing FIM-based approaches.

A new algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) is presented and applied to National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) data over China. Based on the assumptions that the surface bidirectional reflective property are not varying during one day and aerosol characteristics are constant in 0.1° × 0.1° window, we inverse the aerosol optical depth (AOD) and bidirectional reflectance distribution function (BRDF) parameters. Preliminary AOD validation with Aerosol Robotic Network (AERONET) data shows that the correlation coefficient, R2, is 0.79, the root-mean-square error, RMSE, is 0.13 and the uncertainty is Δτ= ±0.05 ± 0.20Δ. Comparing with MODIS AOD product, it is found that both the AOD results are consistent very well. The R2is 0.80 and RMSE is 0.10. The algorithm is flexible and appropriate for aerosol retrieval over both dark and bright land surface. It is potential to retrieve long term global AOD over land from NOAA AVHRR data since 1980s and to study aerosol climatology and global climate change well.

A new algorithm for Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS) is presented and applied to Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) data. Based on the assumptions that the surface bidirectional reflective property are not varying during one day and aerosol characteristics are constant in 2 × 2 window, we inverse the aerosol optical depth (AOD) and bidirectional reflectance distribution function (BRDF) parameters. Preliminary validation shows good accuracy. The correlation coefficient R2is 0.84, the root-mean-square error is about 0.05, and the uncertainty is found to be Δτ= ± 0.05 ± 0.15τ. Comparing with MODIS products, our inversion are consistent very well. The algorithm is flexible and appropriate for aerosol retrieval over both dark and bright land surface. It is potential to retrieve AOD with a high-frequency over land and to monitor aerosol's local spatio-temporal variation from the geostationary satellite data.

In this paper, a new algorithm, Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique (LABITS), is presented and applied to Advanced Very High Resolution Radiometer (AVHRR) data in North China. In this algorithm, we couple the Ross Thick-Li Sparse Bidirectional Reflectance Distribution Function (BRDF) model and the atmospheric radiative transfer model. Assuming that the surface bidirectional reflective property is unchanged during a short period, usually 2-4 days and aerosol characteristics has a high temporal variation but is consistent spatially, then we can obtain AOD and BRDF parameters jointly by numerical iterative technique. The data used to test our algorithm is Global Area Coverage (GAC) 4KM Level 1B from AVHRR/3 on board NOAA-18 and NOAA-19 from 8 July to 9 July, 2011 in North China (110°E-130°E, 25°N-45°N). Synchronous Aerosol Robotic Network (AERONET) level 1.5 data and field measured data during the Ministry Of Science and Technology Aerosol Project (MOSTap) in Beijing-Tianjin-Tangshan region in 2011 was adopted to validate our retrieved result. The correlation coefficient R is about 0.72. Also, in the area both retrieved AOD and MODIS aerosol product have an effective value, the consistency between them is quite good.

In this paper, an analytical strategy is presented to retrieve jointly aerosol optical depth (AOD) and surface reflectance (R) from geostationary satellites. The new algorithm is based on a parameterization of the atmospheric radiative transfer model. Taking AOD and R as unknown parameters and based on some reasonable assumptions of AOD's spatial consistence and R's temporal invariance, both parameters of each pixel can be derived. Applying this algorithm to data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations on board Meteosat Second Generation (MSG), we obtain regional maps of AOD and R from two adjacent observations. Preliminary validation results by comparing our retrieved AOD with Aerosol Robotic Network (AERONET) data show good accuracy, and retrieved R is also reasonable. This method is potential to be applied in instantaneously monitoring aerosol spatio-temporal variation using geostationary satellite sensors with only one single visible channel and high-frequency observations.

MODIS and MISR are two main satellites provide aerosol observations. However, AOD products generated from these two sensors by different retrieval algorithms are inconsistent. In this paper, a semi-empirical optical fusion method was proposed to produce consistent AOD with different derived AOD datasets form MODIS and MISR. Using the semi-empirical optical algorithm, new merged AOD data sets were generated over China for 2010. We used level 2 cloud screened quality assured AERONET measurements to evaluate the merged AOD results. Our results showed that the combination of MODIS and MISR with this method could produce a more consistent, reliable AOD data with great improvement in spatial AOD coverage.

The retrieval of aerosol properties over land is more complicated due to the relatively strong contribution of the land surface reflectance to the radiation measured at the top-of-atmosphere (TOA). Another problem caused by the anisotropic earth surface which is a second order effect, can create systematic biases in the aerosol retrieval. But the simple Lambertian surface assumption is still widely used in most aerosol retrieval algorithms for the single satellite view. In this paper, radiative transfer simulations with coupling surface-atmosphere are employed to assess how much uncertainties are introduced from the Lambertian surface assumption in satellite aerosol optical depth (AOD) retrieval. The result shows that it has great impacts on aerosol retrieval especially at lower aerosol loading. The uncertainties mainly depend on the anisotropy of the target. The more difference lies between the reflectance along observing direction and the average ' reflectance ρ̅t, ρ̅t, ρtof the whole BRDF surface, the larger error happens in aerosol retrieval.

Atmospheric aerosols play an important role in climate change research. It was found that different algorithms and instruments produce somewhat different results for aerosol optical depth (AOD) even if the same location at the same time is observed. Therefore, it is critical to integrate data from multiple platforms and techniques to derive a consistent AOD product. This paper introduced an approach to combine MODIS and MISR AOD data. One-month AOD data derived over Asian land with two different retrieval algorithms applied to MODIS and one retrieval algorithm applied to MISR are compared. Results show that the correlation coefficient between combined AOD product and AOD measured by CE318 is 0.70, and the root mean square error (RMSE) is 0.023. Moreover, it provided more details about the aerosols over land than either of the individual satellite measurements by mutually compensating for each other.

The algorithm is based on the assumption that TOA reflectance increase with the aerosol load as well as the surface reflectance at same time gradually changes on different days within 14 days. Then the surface reflectance is derived from FengYun-2D (FY2D) measurements every 1 hour as the second darkest of reflectance for each time of day to minimize the effect of geometry change and cloud. The “true surface reflectance” of each time was calculated from the composite reflectance and their weighs. The weigh of each time, contribution of the surface and aerosol background were determined using the prior information, and both of them were various in different time. The AOD retrieval based on a Look-Up Table (LUT) using composite background (CB) method and improved composite background (ICB) algorithm were compared with AERONET sites, it was found that the ICB provides larger coverage and higher accuracy AOD product compared with CB.

The determination of aerosol's effect contains much uncertainty. During quantification of aerosols via remote sensing, surface reflectance error of 0.01 could bring Aerosol Optical Depth (AOD) error of 0.1. In order to avoid this, simultaneous retrieval of AOD and surface properties would be a promising method. In this paper, we present a novel analytic solution to atmospheric radiative transfer equation and utilize this solution to retrieve AOD and surface albedo simultaneously from bi-temporal geostationary FY-2 remote sensing data.