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Time to event outcomes is commonly encountered in epidemiologic research. Multiple papers have discussed the inadequacy of using the hazard ratio as a causal effect measure due to its noncollapsibility and the time-varying nature. In this paper, we further clarified that the hazard ratio might be used as a conditional causal effect measure, but it is generally not a valid marginal effect measure, even under randomized design. We proposed to use the restricted mean survival time (RMST) difference as a causal effect measure, since it essentially measures the mean difference over a specified time horizon and has a simple interpretation as the area under survival curves. For observational studies, propensity score adjustment can be implemented with RMST estimation to remove observed confounding bias. We proposed a propensity score stratified RMST estimation strategy, which performs well in our simulation evaluation and is relatively easy to implement for epidemiologists in practice. Our stratified RMST estimation includes two different versions of implementation, depending on whether researchers want to involve regression modeling adjustment, which provides a powerful tool to examine the marginal causal effect with observational survival data.

Clostridium botulinum (CB) is a Gram-positive anaerobic bacterium and a significant cause of food spoilage. Foodborne botulism occurs worldwide every year and even lead to death from respiratory distress in severe cases after eating botulism-contaminated food. The pathogenicity of CB lies in its ability to produce a potent neurotoxin, “botulinum toxin (BTX)”, for which eight different subtypes have already been isolated so far. Botulinum toxin type A (BTX-A) is widely used to treat critical clinical issues due to its good affinity and tolerability. Studies have shown that BTX-A injections effectively treat myofascial pain, inflammatory pain, and neuropathic pain. The current article mainly reviews the latest research progress using BTX-A in pain treatment during two years.

Food safety is related to all aspects of human life, and the quality of food has a profound impact on people's lives and health. Acrylamide is a chemical pollutant produced by the Maillard reaction in baked goods such as potato chips, bread, and cookies. Studies have shown that acrylamide has neurotoxicity, carcinogenicity, reproductive toxicity, genotoxicity, and hepatosplenic toxicity in humans. However, the carcinogenicity of acrylamide in humans is still controversial, and it is unclear whether there is a significant correlation between human exposure to acrylamide and cancer incidence. This review aims to explore the latest research on the human health hazards of acrylamide and prevent or reduce the hazards of acrylamide to humans by detection methods, risk assessment, and mitigation measures to ensure people's food safety.

Background Residential exposure to greenness is associated with better birth outcomes, but it remains unknown whether this is explained by maternal characteristics associated with both place of residence and birth outcomes. We examined whether changes in residential greenness are associated with preterm birth (PTB) and birthweight. Methods We examined cross-sectional associations between maternal exposure to residential greenness [normalized difference vegetation index (NDVI)] and PTB ( Results Each 0.1-unit change in NDVI was associated with 0.98 [95% confidence interval (CI): 0.97, 0.99] times lower odds of PTB and a 9.0 (95% CI: 8.1, 9.9)-gram increase in birthweight after adjusting for individual and neighbourhood covariates. When we controlled for time-invariant maternal unmeasured confounders; these associations were close to null [odds ratio (OR): 1.00 (95% CI: 0.98, 1.01); β: -0.3 (95% CI: -2.0, 3.6)]. We did not find a relationship between greenness and birth outcomes among women who did not move between pregnancies, but for whom greenness changed within their residential location (as in an intervention). Conclusions Residential greenness does not predict birth outcomes, after controlling for time-invariant maternal characteristics, using longitudinal evidence. Future research should explore residential selection factors, spatial and individual heterogeneity and experimental study designs.

Climate change, food security, and environmental sustainability are pressing issues faced by today’s global population. As production demands increase and climate threatens crop productivity, agricultural research develops innovative technologies to meet these challenges. Strategies include biodiverse cropping arrangements, new crop introductions, and genetic modification of crop varieties that are resilient to climatic and environmental stressors. Geography in particular is equipped to address a critical question in this pursuit—when and where can crop system innovations be introduced? This manuscript presents a case study of the geographic scaling potential utilizing common bean, delivers an open access Google Earth Engine geovisualization application for mapping the fundamental climate niche of any crop, and discusses food security and legume biodiversity in Sub-Saharan Africa. The application is temporally agile, allowing variable growing season selections and the production of ‘living maps’ that are continually producible as new data become available. This is an essential communication tool for the future, as practitioners can evaluate the potential geographic range for newly-developed, experimental, and underrepresented crop varieties for facilitating sustainable and innovative agroecological solutions.

In the context of top tier major development, innovative assessment management is one of the important ways to improve the quality and ability of major development leaders and strengthen teams. It is beneficial to use HeXie management model to study the assessment management of major development leaders in colleges and universities. Based on HeXie management model, this paper uses data analysis, literature analysis and other methods and explores the assessment management of major development leaders and puts forward corresponding opinions and suggestions.

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole, which provides a unified framework to develop and reproduce recommendation algorithms for research purpose. In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We implement the RecBole library based on PyTorch, which is one of the most popular deep learning frameworks. Our library is featured in many aspects, including general and extensible data structures, comprehensive benchmark models and datasets, efficient GPU-accelerated execution, and extensive and standard evaluation protocols. We provide a series of auxiliary functions, tools, and scripts to facilitate the use of this library, such as automatic parameter tuning and break-point resume. Such a framework is useful to standardize the implementation and evaluation of recommender systems. The project and documents are released at https://recbole.io/., Comment: 12 pages, 4 figures

Hydropower dam information such as construction and completion timings is often incomplete and missing in existing dam databases, and the hydropower dam’s adjacency impact distance, which is important to the surrounding environment, is also lacking for many dams. In this study, we developed a new remote sensing approach to specifically determine the timings and to assess the influencing distance on land use and land cover at the above and below dam areas. We established the new remote sensing method by identifying levels shifts in trajectories of Normalized Difference Vegetation Index (NDVI) indicators and by identifying the change point in entropy coefficient of variation (CV) variations to allow an auto-acquisition of the information above at the water basin level. We used three geospatial datasets including 1) a 30-year Landsat time series, 2) an annual Landsat Normalized Difference Vegetation Index (NDVI) composite, and 3) digital elevation model (DEM) data. We applied the proposed method to the Mekong River Basin (MRB) in Southeast Asia, where hydropower dam constructions have increased significantly since the 1990s. Results suggested that we were able to obtain the desired information for 67 Mekong hydropower dams successfully. Pearson correlation tests were used to validate timing results against official records, and the correlation coefficients were found to be 0.96 and 0.90, respectively, for construction and completion timing determination. We discovered that the buffer radius of a Mekong dam’s adjacency impact on land use and land cover was usually 4.0-km and 2.5-km in the above and below dam area. The data determined from this study may fill important information gaps in existing dam databases, and the approach developed in this case may be generalized to the other watersheds of the world, where hydropower dams exist. However, essential dam information is either incomplete or unavailable.

Recent proliferation of hydro-dams was one of the nature-based solutions to meet the increasing demand for energy and food in the Lower Mekong River Basin (LMRB). While construction of these hydro-dams generated some hydropower and facilitated expansion of irrigated lands, it also significantly altered the basin-wide hydrology and subsequently impacted wetland ecosystems. Unintended adverse consequences of ecosystem services from lakes and wetlands offset the intended gains in hydroelectricity and irrigated agriculture. The trade-offs between gains in energy and food production and losses in aquatic ecosystem services were perceived to be significant but knowledge of the magnitude, spatial extent, and type of ecosystem services change is lacking and, therefore, the question whether the hydro-dam is an optimized solution or a potential ecological problem remains unanswered. In this study, as the first step to answer this question and using the Tonlé Sap Lake as an example, we quantified one of the impacts of hydro-dams on lake ecosystem's phenology in terms of open water area, a critical ecological characteristic that affects lake systems' fish production, biodiversity, and livelihoods of the local communities. We used the MODIS-NDVI time series, forecast function and the Mann-Kendall trend test method to first quantify the open water area, analyzed its changes over time, and then performed correlation analysis with climate variables to disentangle dam impacts. The results showed reduced hydro-periods, diminishing lake seasonality and a declining trend in Tonlé Sap Lake open water area over the past 15 years. These changes were insignificantly related to climatic influence during the same period. It is concluded that basin-wide hydro-dam construction and associated agricultural irrigation were deemed to be the primary cause of these ecological changes. Further analyses of changes in the lake's ecosystem services, including provision and cultural services, need to be carried out in order to have a holistic understanding of the trade-offs brought by the hydro-dam proliferation as a solution to the emerging energy and food demand in the LMRB.

This paper proposes an optimal dispatch model of an integrated energy system (IES), with carbon trading and flexible loads (FLs) considered. First, the carbon trading is introduced into the dispatch model, taking into account the carbon emissions of the entire energy supply chain. Next, flexible loads are divided into three categories, i.e. curtailable, transferable and shiftable ones, which are respectively modeled in detail. On this basis, electric network constraints, natural gas network constraints, together with internal constraints of energy hubs (EHs) are integrated into the low-carbon economic dispatch model with an objective of minimizing the operation costs including the carbon trading cost, flexible loads dispatching cost and system energy cost. Finally, an integrated energy system including 9 energy hubs is employed to demonstrate the proposed model, together with detailed analysis about the impacts of the carbon trading price and FLs on the IES.

Carbon emission trading can effectively eliminate the externality of pollution through a market-oriented way. Accordingly, carbon emission quotas have typical properties of financial products, and can significantly affect the profits of a microgrid by participating in a joint market of electricity and carbon as a single entity. Due to the fluctuation of the carbon price and the interdependence between carbon price and electricity price, a microgrid should inevitably take risk aversion into consideration in developing optimal bidding strategies. Given this background, the Copula theory is employed to model the joint probability distribution of the electricity price and carbon price first. Then, gas turbines, distributed generation units, demand side resources and electric vehicles (EVs) are all integrated into a microgrid, and comprehensive uncertainties of the microgrid are modeled by Copula and conditional value at risk (CVaR). To minimize the CVaR under the expected profit, an optimal bidding strategy for a microgrid considering risk interdependence between the electricity and carbon prices is addressed. Finally, based on stochastic simulations some case studies are carried out and compared under different scenarios, and the effectiveness and robustness of the proposed method are thus demonstrated.

The coordination of electricity and natural gas systems has ever been intensifying due to a growing share of natural gas-fired generating units in electricity production. Accordingly, concerns of the interrelationship between the coupled electricity and natural gas markets are emerging. Given this background, an Integrated Energy System (IES) with gas turbines, combined heat and power systems (CHP), distributed generation units, power-to-gas (P2G) facilities and multi-energy storage systems (ESSs) is first described. The operation strategy for the IES is investigated in a low-carbon context. Then, the Copula method is employed to model the joint probability distribution of electricity and natural gas prices. An optimal operation strategy of the IES is proposed to minimize the total operating cost, with the interdependence structure of electricity and natural gas prices well taken into account. Finally, a solution approach and case studies are presented, and the effectiveness of the proposed method demonstrated.

An ecosystem is a system formed by the interaction of a community of organisms with their environment within a geographic unit or region, and ecosystem sustainability is an ecosystem’s capacity to endure and maintain its functions and services indefinitely. Although some literature is emerging on the development of sustainability science frameworks, there is little information available on framing ecosystem sustainability. Part of the challenge is that it requires the full integration of natural and human systems with a quantitative nexus among all fundamental elements of an ecosystem. In this chapter, we first review the key elements of ecosystem sustainability, and then provide a review of remote sensing capabilities to provide spatio-temporal dynamics of key indicators of an ecosystem that are critical to its sustainability. While traditional sustainability science tends to focus on four pillars of sustainability—social, environmental, economic, and cultural—ecosystem sustainability will need to be placed in a global context. This chapter focuses on the environmental dimension of sustainability science with a review of remote sensing capabilities for such applications.

Currently, only trastuzumab, ramucirumab, and apatinib effectively treat gastric cancer. Thus, additional novel targets are required for this disease.We investigated the immunohistochemical and fluorescence in situ hybridization expression of MET, ROS1, and ALK in four gastric cell lines and a cohort of 98 gastric cancer patients. Crizotinib response was studied in vitro and in vivo.Crizotinib potently inhibited in vitro cell growth in only one cell line, which also showed MET amplification. A positive correlation between crizotinib sensitivity and MET overexpression was observed (P = 0.045) in the histoculture drug response assay. Meanwhile, patient-derived tumor xenograft mouse models transplanted with tissues with higher MET protein expression displayed a highly selective sensitivity to crizotinib. In the 98 patients, MET overexpression was found in 42 (42.9 %) and MET was amplified in 4 (4.1 %). ROS1 and ALK overexpression were found in 25 (25.5 %) and 0 patients, respectively. However, none of the patients screened harbored ALK or ROS1 rearrangements. No significant association was found between overall survival and MET or ROS1 status. We also observed a stage IV gastric cancer patient with MET amplification who experienced tumor shrinkage and clinical benefit after 3 weeks of crizotinib as fourth-line treatment.Crizotinib may induce clinically relevant anticancer effects in MET-overexpressed or MET-amplified gastric cancer patients.

Agent based simulation method has become a prominent approach in computational modeling and analysis of public emergency management in social science research. The group emotions evolution, information diffusion, and collective behavior selection make extreme incidents studies a complex system problem, which requires new methods for incidents management and strategy evaluation. This paper studies the group emotion evolution and intervention strategy effectiveness using agent based simulation method. By employing a computational experimentation methodology, we construct the group emotion evolution as a complex system and test the effects of three strategies. In addition, the events-chain model is proposed to model the accumulation influence of the temporal successive events. Each strategy is examined through three simulation experiments, including two make-up scenarios and a real case study. We show how various strategies could impact the group emotion evolution in terms of the complex emergence and emotion accumulation influence in extreme events. This paper also provides an effective method of how to use agent-based simulation for the study of complex collective behavior evolution problem in extreme incidents, emergency, and security study domains.

Terrorist organizations are not completely independent. However, they frequently interact with each other. Detecting the terrorist organizations cooperation behavior is quite helpful to develop effective counter-terrorism strategy. Computational intelligence techniques used to design effective data analysis method offer a way for monitoring covert terrorism activities. In this paper, we propose a cooperation behavior analysis solution based on Adaboost classifier to identify. The traditional social network method is extended by employing computational intelligence technique to generate automated analysis capacity. Variant network properties are combined by using Adaboost classification to improve the robustness of changes identification of cooperation behaviors. Finally, the method is applied to an open source dataset. The result indicates that Adaboost classifier can take advantage of multi-properties and is more robust than only using single property. It shows the potential of developing an effective method by using computational intelligence techniques in the field of public security.

This paper presents the system architecture of a driverless robot car, designed to participate in the Carolo-Cup, a competition regarding automated model vehicles. We describe an implementation that completed the different tasks in this competition on our EyeBot platform in detail. EyeBot has one RGB camera as well as three infrared distance sensors, and it’s powered by Raspberry Pi 3B. We developed the lane detection algorithm using the OpenCV library and completed the traffic sign recognition task based on SVM which can be used offline. Experimental and simulation results recorded in real-time are also reported. The test result showed that our programs can run at high speed to achieve stable motion control in real time and complete all the tasks in the competition. The highlight of our work is that the whole system can run on a platform with limited computing resources.

Terrorist organizations are not completely independent, however, they frequently interact with each other. Emerging researches have studied the consequence and influential factors of these relationships. But there have rarely been given precise empirical analysis on its interactive strength and effectiveness. Social Network Analysis (SNA) is considered as an effective method to quantify and analyze relationship between different social actors. In this paper, our focus is the cooperation relations between terrorist organizations. Based on the terrorist incident dataset from 1970 to 2014, this paper establishes the cooperation network of terrorist organizations and studies its structural characteristic. The results have shown that both the states of cohesion and separated can be found in the cooperation network. The relationship between organizations is more closely in the central part of the network, and the organizations within the same subgroups have obvious similarities in political goals and activity regions. Additionally, some important organizations in the operational cooperation have been identified. The removing of these organizations will have a significant impact on the cooperation network structure.

Behavior analysis and events detection of insurgent organization are important issues in intelligence information research and public security management. Due to the covertness of insurgent activities, the relationships between organization and events are hard to detect. Using open source data provides an effective way to obtain information of organizational activities. One important issue in this field is mining hidden frequency pattern with noisy or incomplete information. In this paper, we study the activities of insurgent organization and related event with news data mined from insurgent website. A covert behavior mining method is proposed to process the open source data and analyze news release behaviors. The method can mine frequency pattern of news releasing in the case of losing part of news information. Three methods are employed to detect the events based on the processed data. The performance of the proposed method is evaluated through real data. With the results, we show how this method can be used to improve the events detection and surveillance of insurgent organizational activities.

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques.

To evaluate the national military development situation, is not only to systematically grasp their own strength and security situation of the country, but also to compare and analyze the degree of the countries' future military construction. Based on the net assessment method, this paper established the evaluation index system of national military development, used the application of statistical methods to calculate the assessed value of national military development. Finally, based on the method proposed in this paper, it evaluates the priority degree of China and Japan's military development to 2020, comparatively analyzes the calculated assessment findings of the method. The relevant results are in line with the current situation, simultaneously they have guiding significances to explore the evaluation system of military development.

With the explosion development of multimedia information on the Internet and online sharing community, social network and Community multimedia data have received plenty of attention. In order to analyze and mining the structure and characteristics of online network communities with their large number of multimedia data automatically, we look into community photos as an basic data source for community social network construction and community organization structure analysis. By constructing the social network with community photo collection through face recognition algorithms, social network analysis method is used to analysis the community properties and individual characteristics. The effectiveness of different face recognition algorithms on community network construction is also calculated by three defined variables. Experimental analysis results of a real community album demonstrate the community social network characteristics and the influence brought by face recognition algorithms. The analysis method will provide new approach for using the online multimedia information to understanding the community organization structure and characteristics.

e15076 Background: Gastric cancer is among the leading causes of cancer deaths. Currently, only trastuzumab, ramucirumab, and lapatinib effectively treat gastric cancer. Thus, additional novel targ...