RMSE-ELM: Recursive Model based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

Extreme learning machine (ELM) as an emerging branch of shallow networks has shown its excellent generalization and fast learning speed. However, for blended data, the robustness of ELM is weak because its weights and biases of hidden nodes are set randomly. Moreover, the noisy data exert a negative effect. To solve this problem, a new framework called RMSE-ELM is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different groups concurrently, then employs selective ensemble to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the results have shown that RMSE-ELM significantly improves robustness with slightly computational time compared with representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.Comment: Accepted for publication in Mathematical Problems in Engineering, 09/22/201

Secure Hot Path Crowdsourcing with Local Differential Privacy under Fog Computing Architecture

Crowdsourcing plays an essential role in the Internet of Things (IoT) for data collection, where a group of workers is equipped with Internet-connected geolocated devices to collect sensor data for marketing or research purpose. In this paper, we consider crowdsourcing these worker's hot travel path. Each worker is required to report his real-time location information, which is sensitive and has to be protected. Encryption-based methods are the most direct way to protect the location, but not suitable for resource-limited devices. Besides, local differential privacy is a strong privacy concept and has been deployed in many software systems. However, the local differential privacy technology needs a large number of participants to ensure the accuracy of the estimation, which is not always the case for crowdsourcing. To solve this problem, we proposed a trie-based iterative statistic method, which combines additive secret sharing and local differential privacy technologies. The proposed method has excellent performance even with a limited number of participants without the need of complex computation. Specifically, the proposed method contains three main components: iterative statistics, adaptive sampling, and secure reporting. We theoretically analyze the effectiveness of the proposed method and perform extensive experiments to show that the proposed method not only provides a strict privacy guarantee, but also significantly improves the performance from the previous existing solutions.Comment: This paper appears in IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2020.303933

Investigation and protection of fishery resources in the middle of Bohai Sea

In May and October 2017, 12 stations were set up in the Central Bohai Sea for fishery resources investigation. The results show that there are many dominant species in this area, and the inshore fishery resources are higher than those in the open sea because of the abundant nutrients from land, the high density of zooplankton and the food of swimming animals. In order to effectively protect the fishery resources in the Central Bohai Sea, this paper puts forward some suggestions, such as strengthening the protection propaganda, scientific and reasonable fishing, and strengthening the management of marine environment

Detection and Genetic Analysis of Porcine Bocavirus

Porcine Bocavirus (PBoV) has been reported to be associated with postweaning multisystemic wasting syndrome and pneumonia in pigs. In this study, a survey was conducted to evaluate the prevalence of PBoV in slaughter pigs, sick pigs, asymptomatic pigs and classical swine fever virus (CSFV) eradication plan herds in five provinces of China (Henan, Liaoning, Shandong, Hebei and Tianjin) by means of PCR targeting NS1 gene of PBoV. Among the total of 403 tissue samples, 11.41% were positive for PBoV. The positive rates of spleen (20.75%) and inguinal lymph node (27.18%) are higher than those of other organs. PCR products of twenty PBoV positive samples from slaughter pigs were sequenced for phylogenetic analysis. The result revealed that PBoV could be divided into 6 groups (PBoV-a~PBoV-f). All PBoV sequenced in this study belong to PBoV-a–PBoV-d with 90.1% to 99% nucleotide identities. Our results exhibited significant genetic diversity of PBoV and suggested a complex prevalence of PBoV in Chinese swine herds. Whether this diversity of PBoV has a significance to pig production or even public health remains to be further studied

Photocatalytic nitrogen reduction to ammonia: Insights into the role of defect engineering in photocatalysts

Engineering of defects in semiconductors provides an effective protocol for improving photocatalytic N2 conversion efficiency. This review focuses on the state-of-the-art progress in defect engineering of photocatalysts for the N2 reduction toward ammonia. The basic principles and mechanisms of thermal catalyzed and photon-induced N2 reduction are first concisely recapped, including relevant properties of the N2 molecule, reaction pathways, and NH3 quantification methods. Subsequently, defect classification, synthesis strategies, and identification techniques are compendiously summarized. Advances of in situ characterization techniques for monitoring defect state during the N2 reduction process are also described. Especially, various surface defect strategies and their critical roles in improving the N2 photoreduction performance are highlighted, including surface vacancies (i.e., anionic vacancies and cationic vacancies), heteroatom doping (i.e., metal element doping and nonmetal element doping), and atomically defined surface sites. Finally, future opportunities and challenges as well as perspectives on further development of defect-engineered photocatalysts for the nitrogen reduction to ammonia are presented. It is expected that this review can provide a profound guidance for more specialized design of defect-engineered catalysts with high activity and stability for nitrogen photochemical fixation

Zonal Soil Type Determines Soil Microbial Responses to Maize Cropping and Fertilization.

Soil types heavily influence ecological dynamics. It remains controversial to what extent soil types shape microbial responses to land management changes, largely due to lack of in-depth comparison across various soil types. Here, we collected samples from three major zonal soil types spanning from cold temperate to subtropical climate zones. We examined bacterial and fungal community structures, as well as microbial functional genes. Different soil types had distinct microbial biomass levels and community compositions. Five years of maize cropping (growing corn or maize) changed the bacterial community composition of the Ultisol soil type and the fungal composition of the Mollisol soil type but had little effect on the microbial composition of the Inceptisol soil type. Meanwhile, 5 years of fertilization resulted in soil acidification. Microbial compositions of the Mollisol and Ultisol, but not the Inceptisol, were changed and correlated (P < 0.05) with soil pH. These results demonstrated the critical role of soil type in determining microbial responses to land management changes. We also found that soil nitrification potentials correlated with the total abundance of nitrifiers and that soil heterotrophic respiration correlated with the total abundance of carbon degradation genes, suggesting that changes in microbial community structure had altered ecosystem processes. IMPORTANCE Microbial communities are essential drivers of soil functional processes such as nitrification and heterotrophic respiration. Although there is initial evidence revealing the importance of soil type in shaping microbial communities, there has been no in-depth, comprehensive survey to robustly establish it as a major determinant of microbial community composition, functional gene structure, or ecosystem functioning. We examined bacterial and fungal community structures using Illumina sequencing, microbial functional genes using GeoChip, microbial biomass using phospholipid fatty acid analysis, as well as functional processes of soil nitrification potential and CO2 efflux. We demonstrated the critical role of soil type in determining microbial responses to land use changes at the continental level. Our findings underscore the inherent difficulty in generalizing ecosystem responses across landscapes and suggest that assessments of community feedback must take soil types into consideration. Author Video: An author video summary of this article is available

Effect of L-Arginine or L-Lysine on the Quality of Duck Meat Patties during Freeze-thaw Cycles

In this study, the effects of L-arginine or L-lysine on the quality of duck meat patties during repeated freeze-thaw cycles were studied to provide a theoretical basis for the application of L-arginine or L-lysine as cryoprotectant in meat products. L-arginine or L-lysine was added in the marinating process of duck meat patties, and the prepared duck meat patties was treated with freeze-thaw cycles. The texture, cooking loss, color, pH, total volatile base nitrogen (TVB-N), thiobarbituric reactive substances (TBARS), low-field nuclear magnetic resonance, and microstructure were measured to evaluate the quality of duck meat patties. The results showed that with the increase of freeze-thaw cycles, the hardness, springiness, cohesiveness, chewiness, a* value, pH and P21 of duck meat patties in the blank group decreased significantly (P<0.05), while the cooking loss, TVB-N value and TBARS value increased significantly (P<0.05). After five freeze-thaw cycles, L-arginine or L-lysine significantly inhibited the deterioration of duck meat patties quality (P<0.05), and the cooking loss of duck meat patties in L-arginine group was 13.23% and 6.93% higher than those in blank group and sodium tripolyphosphate (STP) group, respectively (P<0.05). In addition, after five freeze-thaw cycles, the TVB-N value and TBARS value of L-arginine group were 41.92% and 63.47% lower than those of blank group (P<0.05), respectively, which were the lowest among the four groups. Therefore, the L-arginine or L-lysine treatment could effectively inhibit spoilage, the oxidation of fat, improve water retention, and maintain good quality characteristics of duck meat patties