ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

Variants of the low oxygen sensors EGLN1 and HIF-1AN associated with acute mountain sickness.

Two low oxygen sensors, Egl nine homolog 1 (EGLN1) and hypoxia-inducible factor 1-α inhibitor (HIF-1AN), play pivotal roles in the regulation of HIF-1α, and high altitude adaption may be involved in the pathology of acute mountain sickness (AMS). Here, we aimed to analyze single nucleotide polymorphisms (SNPs) in the untranslated regions of the EGLN1 and HIF-1AN genes and SNPs chosen from a genome-wide adaptation study of the Han Chinese population. To assess the association between EGLN1 and HIF-1AN SNPs and AMS in a Han Chinese population, a case-control study was performed including 190 patients and 190 controls. In total, thirteen SNPs were genotyped using the MassARRAY® MALDI-TOF system. Multiple genetic models were tested; The Akaike's information criterion (AIC) and Bayesian information criterion (BIC) values indicated that the dominant model may serve as the best-fit model for rs12406290 and rs2153364 of significant difference. However, these data were not significant after Bonferroni correction. No significant association was noted between AMS and rs12757362, rs1339894, rs1361384, rs2009873, rs2739513 or rs2486729 before and after Bonferroni correction. Further haplotype analyses indicated the presence of two blocks in EGLN1; one block consists of rs12406290-rs2153364, located upstream of the EGLN1 gene. Carriers of the "GG" haplotype of rs12406290-rs2153364 exhibited an increased risk of AMS after adjustments for age and smoking status. However, no significant association was observed among HIF-1AN 3'-untranslated region (3'-UTR) polymorphisms, haplotype and AMS. Our study indicates that variants in the EGLN1 5'-UTR influence the susceptibility to AMS in a Han Chinese population

Optimization of extraction condition for phytic acid from peanut meal by response surface methodology

Phytic acid (PA), a molecule with high commercial value, is one of the important component in peanutmeal. However, PA has not yet been isolated from peanut meal and played its role. This paper reportedthe extraction conditions of PA from peanut meal after removed protein. The independent variables werehydrochloric acid (HCl) concentration, solid to liquid ratio, extraction time and extraction temperature.Response surface methodology (RSM) was used to optimize the extraction conditions based on the extractionyield of PA. The results show that the second-order polynomial models derived from responseswell with the experimental (R2 = 0.9783). The optimal extraction condition was obtained with solid toliquid ratio of 1:16 (g:mL), HCl concentration of 0.02 mol/L, extraction time of 105 min, and extractiontemperature of 30 °C. At this condition, PA with higher purity were obtained. the extraction ratio was6.12%, and the content of PA was 182.7 mg/g dry PA extract. The experimental values under optimal conditionwere in good consistent with the predicted values. The PA extracted from peanut meal was verifiedqualitatively by IR spectra. The extraction technology of PA from peanut meal has a strong potential forrealized high-value utilization of peanut meal

An Optimization Method for the Remanufacturing Dynamic Facility Layout Problem with Uncertainties

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. Facility layout design, as the cornerstone of effective facility planning, is concerned about resource localization for a well-coordinated workflow that leads to lower material handling costs and reduced lead times. However, due to stochastic returns of used products/components and their uncontrollable quality conditions, the remanufacturing process exhibits a high level of uncertainty challenging the facility layout design for remanufacturing. This paper undertakes this problem and presents an optimization method for remanufacturing dynamic facility layout with variable process capacities, unequal processing cells, and intercell material handling. A dynamic multirow layout model is presented for layout optimization and a modified simulated annealing heuristic is proposed toward the determination of optimal layout schemes. The approach is demonstrated through a machine tool remanufacturing system

Consumer behavior, social influence, and smart grid implementation

To achieve the goals of German energy transition especially in renewable energy shares, the smart grid will play a key role in managing the demand able to match more volatile supply and optimizing the entire electricity system. Even though the system transformation is technically feasible, the successful transition cannot live without end users willing to transform their way of using energy. This thesis has explored possible roles of individual consumers in the smart grid implementation and in detail analyzed their influential factors. An online survey was conducted to capture preferences and behaviors of energy consumers during the time period of November 2013 to January 2014. The three roles of private electricity consumers - as consumers consuming electricity through appliances, as citizens holding attitudes towards smart grid applications, and as potential producers of electricity - are targeted. Constructs from the theory of planned behavior were tested by using a sample of 517 German citizens. Structural equation models of individual’s electricity saving behavior, their intention to participate in smart grid applications and investment behavior in solar panels were built. It was found that determinants of attitude, perceived norm, and perceived behavioral control together explain 32%-56% of the variance in the three behaviors. Attitude was found to be the most influential factor of individual electricity saving behavior, as well as of citizens’ intentions to participate in smart grid applications. For solar panel investment, it is perceived behavioral control that has the highest impact on the behavior. As the smart grid concept is not well understood by common people, education program and information campaigns are needed, in which social norm marketing is worth more attention, ascribable to the considerable impact caused by the diffusion of norms through social networks. To examine this social influence effect, empirically founded agent-based models for the above-mentioned three behaviors were created to estimate possible behavior changes brought by social norms at the aggregate level. Simulation results show that a reduction of total consumptions by 20% could be achieved in the virtual community due to behavior conformity induced by identified adopters. The potential impact of social norms on home generation and load shift are also promising

Recent advances in Fenton and Fenton-like reaction mediated nanoparticle in cancer therapy

Fenton and Fenton like reaction have been well clarified as efficient reactive oxygen species (ROS) sources in tumor, and have been widely developed into a cancer treatment method. Meanwhile, transition metal-based nanomaterials with Fenton or Fenton like reaction characteristics also have been well explored as therapeutic agents for the cancer therapy, mainly in chemo-dynamic and ferroptosis induced cancer therapy. Herein,to summarize recent advances in Fenton and Fenton like reaction mediated nanoparticles for cancer therapy, in this minireview, we first introduced the mechanisms of Fenton and Fenton like reaction and two therapeutic methods based on Fenton and Fenton like reaction, and then we introduced the well-designed nanoparticles with Fenton reaction or Fenton-like reaction characteristics for the cancer therapies. Finally its challenges and perspectives are discussed

Molecular Mechanisms of Metformin for Diabetes and Cancer Treatment

Metformin has been the first-line drug treatment for hyperglycemia and insulin resistance for over 50 years. However, the molecular basis of its therapeutic role remained incompletely understood. Recent advances demonstrate that metformin could exert its glucose-lowering effect by multiple mechanisms, including activation of 5′-AMP-activated protein kinase, decreasing production of cyclic AMP, suppressing mitochondrial complex I of the electron transport chain, targeting glycerophosphate dehydrogenase, and altering the gut microbiome. In addition, epidemiological and clinical observation studies suggest that metformin reduced cancer risk in patients with type 2 diabetes and improved survival outcome of human cancers. Experimental studies have shown that this drug can inhibit cancer cell viability, growth, and proliferation through inhibiting mTORC1 signaling and mitochondrial complex I, suggesting that it may be a promising drug candidate for malignancy. Here, we summarize recent progress in studies of metformin in type 2 diabetes and tumorigenesis, which provides novel insight on the therapeutic development of human diseases