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A long-term and severe population decline of Rusty Blackbirds ( Euphagus carolinus ) has motivated biologists to search for possible causes of the decline. Several hypotheses have been forwarded, one of which is that habitat destruction on the overwintering grounds is responsible. Climate change is another possible explanation. We evaluated the population trend of Rusty Blackbirds in Arkansas by modeling their abundance recorded during Christmas Bird Counts conducted between 1965 and 2020. We used generalized additive modeling to evaluate population trends and explored the influence of weather, effort, habitat, and region on those trends. We found that counts of Rusty Blackbirds have increased by about 40 birds in Arkansas between 1965 and 2020; most of the increase occurred after 1995. We also found that proportion of forest land in each count circle’s county was inversely related to counts of Rusty Blackbirds but that temperature was a more important variable. During warmer years, fewer Rusty Blackbirds were counted. Rusty Blackbird geographic distribution also changed by decade; that change accounted for about 15% of the deviance in counts of Rusty Blackbirds. Finally, we observed a relationship between temperature and distribution; Rusty Blackbirds tended to overwinter in the northern portions of the state during warm years and more southerly portions of the state during cold years. Our analytical approach will be useful to anyone evaluating geographic shifts in populations that might be associated with climate change.

Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, and they have proven to be successful in many fields such as audio, image, and natural language processing. This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview of TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology, and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods, and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

Mobile Edge Computing (MEC) has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for wireless devices. In this paper, we investigate the optimal dynamic spectrum allocation-assisted multiuser computation offloading in MEC for overall latency minimization. Specifically, we first focus on a static multiuser computation offloading scenario and jointly optimize users' offloading decisions, transmission durations, and Edge Servers' (ESs) resource allocations. Owing to the nonconvexity of our joint optimization problem, we identify its layered structure and decompose it into two problems: a subproblem and a top problem. For the subproblem, we propose a bisection search-based algorithm to efficiently find the optimal users' offloading decisions and ESs’ resource allocations under a given transmission duration. Second, we use a linear search-based algorithm for solving the top problem to obtain the optimal transmission duration based on the result of the subproblem. Further, after solving the static scenario, we consider a dynamic scenario of multiuser computation offloading with time-varying channels and workload. To efficiently address this dynamic scenario, we propose a deep reinforcement learning-based online algorithm to determine the near-optimal transmission duration in a real-time manner. Numerical results are provided to validate our proposed algorithms for minimizing the overall latency in both static and dynamic offloading scenarios. We also demonstrate the advantages of our proposed algorithms compared to the conventional multiuser computation offloading schemes.

Land-use conversion affects the composition and assembly of plant-associated microbiomes, which in turn affects plant growth, development, and ecosystem functioning. However, agroforestry systems, as sustainable land types, have received little attention regarding the dynamics of different plant-associated microbes. In this study, we used high-throughput sequencing technology to analyze the assembly mechanisms and the driving factors of pine- and Panax notoginseng (P.n.)-associated microbiomes during the conversion of different pine forests (Pinus kesiya var. langbianensis and Pinus armandii) into P.n.-pine agroforestry systems. The results showed that the conversion of pure pine forest into P.n.-pine agroforestry systems significantly altered the diversity of pine-associated fungi rather than the community structure, and the community structure of P.n.-associated fungi rather than the diversity. Additionally, plant-associated fungi were more responsive to land-use change than bacteria. Main effect analysis revealed that compartment rather than genotype was the driving factor of pine- and P.n.-associated microbiomes, but P.n. cultivation also significantly affected the assembly of pine-associated microbiomes. In addition, there was a transfer of P.n. endophytes to pine trees in agroforestry systems and the beneficial microbiomes (Massilia, Marmoricola, Herbaspirillum, etc.) were enlarged in pine roots. Therefore, the diversity of the assembly mechanisms of P.n.- and pine-associated microbiomes played an important role in the P.n.--pine agroforestry systems and were the basis for the sustainable development of the P.n.--pine agroforestry systems.

Prediction of Chlorophyll-a (Chl-a) concentration is significant for marine ecology and environmental protection. This paper presents an integrated approach to forecast seasonal Chl-a concentration in coastal waters. Before modeling, feature construction procedures, such as simplification, combination, and normalization, are conducted to identify the potentially significant features. The feature extraction method based on Random Forest (RF) and eXtreme Gradient BOOSTing (XGBoost) is applied to select relevant variables. Then, we propose a Cluster-stacking-based approach which includes a station-oriented clustering model and a stacking-based regression model. The former model is used to divide the observation stations into several groups, thus partitions the study region into several sub-regions and the study dataset into several subsets according to the corresponding stations. In each subset, single regression models including K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Multi-Layer Perceptron regression (MLP) and XGBoost are established in level 0 space and integrated by RF in level 1 space via stacked generalization. We compare the performance of the Cluster-stacking model with that of Cluster-KNN, Cluster-SVR, Cluster-MLP, Cluster-XGBoost and the regression stacking model without cluster. The model evaluation shows that the Cluster-stacking-based approach outperforms others in forecasting Chl-a concentration with a coefficient of determination (R2) of 0.848 and a mean absolute error (MAE) of 0.665 ug/l.

The epicardial fat plays a key role in the development of many cardiovascular diseases. It is necessary and useful to precisely segment this fat from CT scans in clinical studies. However, it is not feasible to manually segment this fat in clinical practice, as the workload and cost for technicians or physicians is quite high. In this work, we propose a novel method for automatic segmentation and quantification of epicardial fat from CT scans accurately. In detail, dual U-Nets with the morphological processing layer is used for this goal. The first network is based on the U-Net framework to detect the pericardium, before segmenting its inside region. A morphological layer is concatenated as the following layer of the first network, to refine and obtain the ideal inside region of the pericardium. While the second network is also applied using U-Net as its backbone to find and segment the epicardial fat of the processed inside region from the pericardium using the first network. Our proposed method obtains the highest mean Dice similarity (91.19%), correlation coefficient (0.9304) compared to other state-of-art methods on a cardiac CT dataset with 20 patients. The results indicate our proposed method is effective for quantifying epicardial fat automatically.

The sensor-cloud is combined with cloud computing and wireless sensor networks,which extends the service ability of WSN by the support of cloud computing.It is one of the hot topics among the current researches.After comparing and surveying the mainstream system adequately,the characteristics and development of sensor-Cloud were summed up.Then,the direct influence of existing trust issues in the system for data protection and application on upper layer were found.The announced reasons explained that existing schemes were difficult to provide trusted sensor-cloud services.This was because that the capabilities of the underlying WSN nodes were too weak,and cloud computing model lacked the direct management of underlying nodes and data and so on.The trusted structures based on fog computing were given,and the key technologies of trusted evaluation,trusted data collection,and trusted storage were designed.Finally,the discussion pointed out new views for the researches in trusted sensor-cloud.

Currently, blockchain technology has been widely researched and is being applied to many other fields, such as financial transactions, E-government, logistics, and supply-chain system. It can be used to store and maintain transaction data through the de-centralized model. In this paper, we propose an autonomous resource request transaction framework based on blockchain in a social network, in which all kinds of resources in the social community can be traded through blockchain technology. When a user needs to acquire some resources from a community, the user may make a transaction with the members from the community through blockchain technology while the members autonomously negotiate each other to reach an agreement. The proposed framework provides an incentive mechanism to encourage community members to disseminate the resources through a smart contract.

Sensor-cloud has gradually developed into a research hotspot.A good trust evaluation mechanism can better address some security issues in this new structure,such as internal attacks.A good trust evaluation mechanism can also ensure the security and efficient transmission of information in this structure.After an extensive research on sensor-cloud and trust evaluation mechanisms,it was found that trust evaluation mechanisms in sensor-cloud can be divided into two categories,among entities and in entity.After comparing the advantages and shortcomings of existing trust evaluation mechanisms,a new fog-based trust evaluation mechanism was designed and future research directions of trust evaluation mechanisms were discussed in sensor-cloud.

Firstly,the previous work of fog computing was systematically analyzed and summarized.The background of fog computing and the comparison with cloud computing were introduced.Besides,based on the comparison with other computing style,the advantages and characteristics of fog computing were explained.In addition,the architecture of fog computing was described.Moreover,network management and resource scheduling of fog computing were discussed,where the related previous work were summarized and analyzed.At last,the applications of fog computing were described.Taking the intelligent driving and industrial Internet of things applications as examples,the key research issues of fog computing were proposed.

Through consulting lots of domestic and international literatures about sensor-cloud security,a series of security problems are found,such as reputation problem of service provider,the coupling problem of physical nodes,the leak of authority management,et al,which seriously hinders the further development of sensor-cloud.The secure problems occurring in sensor-cloud were analyzed,the current secure technologies were contrasted ,similarities and differences of various types solutions were discussed and summarized.After that,several future challenges of sensor-cloud security were concluded.Finally,a fog-based structure was proposed to solve the security problems,which would bring new ideas to the sensor-cloud security research.

Bioorganic fertilizers can alleviate (a) biotic stresses and sustainably increase crop yields. The effect of bioorganic fertilizers on the rhizosphere bacterial community of Panax notoginseng and soil metabolism remains unknown. Here, we tracked the changes in the soil physicochemical properties, bacterial microbiota responses, and soil metabolic functions after the addition of a bioorganic fertilizer in a P. notoginseng field. The application of a bioorganic fertilizer reduced the soil acidification, improved the organic matter, and increased the contents of the total/available soil nutrients. Soil amendment with a bioorganic fertilizer significantly affected the structure of the rhizosphere bacterial community, leading to the enrichment of specific bacterial consortia such as Rhodanobacter, Arthrobacter, Sphingomonas, Devosia, Pseudolabrys, Luteimonas, Lysobacter, Nitrosospira, and Nakamurella. Previously, many of these genera have been associated with nutrient cycling, plant productivity, and disease suppression. Metabolome analysis further highlighted that the bioorganic fertilizer treatment significantly reduced phenolic acids and flavonoids and enhanced organic acids, saccharides and alcohols, and amino acids. This result indicates a high survival of bacterial microbiota in the rhizosphere and an availability of nutrients for P. notoginseng growth. This work showed that the application of bioorganic fertilizers significantly improves soil health status, alters soil metabolic functions, and stimulates a specific subset of rhizosphere microbiota for nutrient cycling and disease protection in P. notoginseng.