Incremental Particle Swarm Optimization

AbstractBy simulating the population size of the human evolution, a PSO algorithm with increment of particle size (IPPSO) was proposed. Without changing the PSO operations, IPPSO can obtain better solutions with less time cost by modifying the structure of traditional PSO. Experimental results show that IPPSO using logistic model is more efficient and requires less computation time than using linear function in solving more complex program problems

Targeting oncogenic miR-335 inhibits growth and invasion of malignant astrocytoma cells

<p>Abstract</p> <p>Background</p> <p>Astrocytomas are the most common and aggressive brain tumors characterized by their highly invasive growth. Gain of chromosome 7 with a hot spot at 7q32 appears to be the most prominent aberration in astrocytoma. Previously reports have shown that microRNA-335 (miR-335) resided on chromosome 7q32 is deregulated in many cancers; however, the biological function of miR-335 in astrocytoma has yet to be elucidated.</p> <p>Results</p> <p>We report that miR-335 acts as a tumor promoter in conferring tumorigenic features such as growth and invasion on malignant astrocytoma. The miR-335 level is highly elevated in C6 astrocytoma cells and human malignant astrocytomas. Ectopic expression of miR-335 in C6 cells dramatically enhances cell viability, colony-forming ability and invasiveness. Conversely, delivery of antagonist specific for miR-335 (antagomir-335) to C6 cells results in growth arrest, cell apoptosis, invasion repression and marked regression of astrocytoma xenografts. Further investigation reveals that miR-335 targets disheveled-associated activator of morphogenesis 1(Daam1) at posttranscriptional level. Moreover, silencing of endogenous Daam1 (siDaam1) could mimic the oncogenic effects of miR-335 and reverse the growth arrest, proapoptotic and invasion repression effects induced by antagomir-335. Notably, the oncogenic effects of miR-335 and siDAAM1 together with anti-tumor effects of antagomir-335 are also confirmed in human astrocytoma U87-MG cells.</p> <p>Conclusion</p> <p>These findings suggest an oncogenic role of miR-335 and shed new lights on the therapy of malignant astrocytomas by targeting miR-335.</p

Analysis of the Risk Factors for Elevated D-Dimer Level After Breast Cancer Surgery: A Multicenter Study Based on Nursing Follow-Up Data

D-dimer level is often used to assess the severity of trauma as well as the risk of thrombosis. This study investigated the risk factors for high postoperative D-dimer level. This study included a total of 2706 patients undergoing breast cancer surgery to examine the associations between various clinicopathological factors and variation in D-dimer levels. After adjusting for other factors, T stage, neoadjuvant chemotherapy, blood loss, surgery type, diabetes, and elevated leukocyte and neutrophil counts were found to be significant risk factors for D-dimer variation. This study identified several factors associated with elevated D-dimer levels and consequent thrombosis after breast cancer surgery, which may aid in the development of more precise preventive measures and interventions as well as serve as a reference for future research

Nano-montmorillonite-doped lubricating grease exhibiting excellent insulating and tribological properties

Abstract Three types of nano-montmorillonite were doped as additives to afford lubricating greases. The physicochemical, insulating, and tribological performances of the obtained lubricating greases were investigated in detail. Furthermore, the tribological action mechanisms were analyzed by high magnification optical microscope, Raman spectroscopy, and energy dispersive X-ray spectroscope (EDS). The results show that the inorganic modification montmorillonite (IOMMT) can significantly increase the number of electron traps in the base grease, leading to excellent insulating performances. Moreover, IOMMT as a novel lubricant additive (1.5 wt% in grease) significantly enhances the friction reducing and anti-wear abilities for steel/steel contact that comprises a unique layered structure to prevent friction between the contact pairs and the protective tribofilm generated by physical adsorption and chemical reaction

Forest Canopy Height Mapping by Synergizing ICESat-2, Sentinel-1, Sentinel-2 and Topographic Information Based on Machine Learning Methods

Spaceborne LiDAR has been widely used to obtain forest canopy heights over large areas, but it is still a challenge to obtain spatio-continuous forest canopy heights with this technology. In order to make up for this deficiency and take advantage of the complementary for multi-source remote sensing data in forest canopy height mapping, a new method to estimate forest canopy height was proposed by synergizing the spaceborne LiDAR (ICESat-2) data, Synthetic Aperture Radar (SAR) data, multi-spectral images, and topographic data considering forest types. In this study, National Geographical Condition Monitoring (NGCM) data was used to extract the distributions of coniferous forest (CF), broadleaf forest (BF), and mixed forest (MF) in Hua’ nan forest area in Heilongjiang Province, China. Accordingly, the forest canopy height estimation models for whole forest (all forests together without distinguishing types, WF), CF, BF, and MF were established, respectively, by Radom Forest (RF) and Gradient Boosting Decision Tree (GBDT). The accuracy for established models and the forest canopy height obtained based on estimation models were validated consequently. The results showed that the forest canopy height estimation models considering forest types had better performance than the model grouping all types of forest together. Compared with GBDT, RF with optimal variables had better performance in forest canopy height estimation with Pearson’s correlation coefficient (R) and the root-mean-squared error (RMSE) values for CF, BF, and MF of 0.72, 0.59, 0.62, and 3.15, 3.37, 3.26 m, respectively. It has been validated that a synergy of ICESat-2 with other remote sensing data can make a crucial contribution to spatio-continuous forest canopy height mapping, especially for areas covered by different types of forest

Spatial and temporal variation of vegetation NPP and analysis of influencing factors in Heilongjiang Province, China

Vegetation net primary productivity (NPP) plays an important role in investigating carbon cycle and NPP provided by forest constitutes the main component of terrestrial vegetation NPP. Since late 1990s, a series of policies for protecting forest have been implemented in Heilongjiang Province, China, directly leading to the continuous change of forest area and growth environment. As a result, analysis of spatiotemporal characteristics of NPP after the implementation of protection policies was conducted in this study, and influencing factors leading to the distribution and spatiotemporal heterogeneity of NPP was also compared quantitatively, aiming to reveal the changing characteristics and influencing factors of NPP in a large area over a longer time span. In this study, multi-source data including MOD17A3 NPP product, meteorological data, topographic data and land-use data was selected, and then the spatiotemporal characteristics of NPP from 2001 to 2020 were analyzed through commonly-used spatiotemporal analysis indices and methods. Finally, influencing factors leading to the distribution and spatiotemporal heterogeneity of NPP in the study area were quantitatively compared and analyzed. Results show that the total amount of NPP in Heilongjiang Province has a fluctuating trend with an increasing rate of 23.47% over the past twenty years, and the increase/decrease scenarios of NPP usually can be found in forest regions with high forest coverage and urban regions experiencing dramatic urbanization processes, respectively. Meanwhile, this study also finds that there is no absolute dominant factor can be responsible for the difference in spatial distribution of NPP, but the conversion between forest and other land-use types are the main factors leading to the spatiotemporal heterogeneity of NPP in the study area. The conclusions in this study may provide guiding significance for formulating forestry protection policies in forest regions and designing land-use plans in the future, aiming to achieve the balance between ecological protection and urban development

Prevalence and associated factors of intention of COVID-19 vaccination among healthcare workers in China: application of the Health Belief Model

Healthcare workers (HCWs) are at an increased risk of coronavirus disease 2019 (COVID-19) and warrant COVID-19 vaccination to reduce nosocomial infections. This study investigated: (1) the prevalence of behavioral intention of COVID-19 vaccination (BICV) under eight scenarios combining vaccines’ effectiveness/safety/cost, plus two general scenarios of free/self-paid vaccination given governmental/hospital recommendations, (2) perceptions involving preferred timing of COVID-19 vaccination and impacts of various attributes on BICV, and (3) factors of BICV based on the Health Belief Model. An anonymous online cross-sectional survey was conducted among 2,254 full-time doctors/nurses in three Chinese provinces during 10/2020–11/2020. The prevalence of BICV was 75.1%/68.0% among nurses/doctors under the most optimum scenario of this study (free/80% effectiveness/rare mild side effects); it dropped to 64.6%/56.5% if it costed 600 Yuan (USD90). Similar prevalence was obtained (72.7%/71.2%) if the vaccination was recommended by the government/hospitals but dropped to <50% if effectiveness was 50% or mild side effects were common; 13.0% preferred to take up COVID-19 vaccination at the soonest (81.8% would wait and see). Scientific proof (completion of phase III clinical trials and approval from health authorities) was rated the highest in its impacts on vaccination decision, followed by vaccines’ performance, and then logistics. Multivariable logistic regression analyses showed that perceived severity, perceived barriers, cues to action, and self-efficacy (but neither perceived susceptibility nor perceived barriers) were significantly associated with the two BICV outcomes. The coverage of COVID-19 vaccination would be high only if the vaccines perform well. Health promotion may take the findings into account

Data-driven Fault Diagnosis for PEM Fuel Cell System Using Sensor Pre-Selection Method and Artificial Neural Network Model

International audienceFault diagnosis is a critical process for the reliability anddurability of proton exchange membrane fuel cells (PEMFCs). Due tothe complexity of internal transport processes inside the PEMFCs,developing an accurate model considering various failure mechanismsis extremely difficult. In this paper, a novel data-driven approachbased on sensor pre-selection and artificial neural network (ANN)are proposed. Firstly, the features of sensor data in time-domainand frequency-domain are extracted for sensitivity analysis. Thesensors with poor response to the changes of system states arefiltered out. Then experimental data monitored by the remainingsensors are utilized to establish the fault diagnosis model byusing the ANN model. Levenberg-Marquardt (LM) algorithm, resilientpropagation (RP) algorithm, and scaled conjugate gradient (SCG)algorithm are utilized in the training process, respectively. Theresults demonstrate that the diagnostic accuracy reaches 99.2% andthe recall reaches 98.3%. The effectiveness of the proposed methodis verified by comparing the diagnostic results in this work andthat by support vector machine (SVM) and logistic regression (LR).Besides, the high computational efficiency of the proposed methodsupports the possibility of online diagnosis. Meanwhile, timelyfault diagnosis can provide guidance for fault tolerant control ofthe PEMFCs system