Computer intelligent simulation based on wireless sensor networks application in big data financial management

The advent of big data has revolutionized financial management by providing a more comprehensive and accurate data analysis platform. This transformation necessitates innovation and reform in traditional financial management practices to effectively address the challenges posed by big data. In this study, a simulation model is proposed that combines high-resolution imaging algorithms and computer technology to analyze the intricacies of big data financial management. By collecting and processing vast amounts of financial data, the simulation model employs high-resolution imaging algorithms for data analysis and pattern recognition. This enables the refinement and control of financial management through precise analysis and prediction. The results demonstrate that the simulation model, based on high-resolution imaging algorithms, yields more accurate financial data analysis and prediction, empowering companies to make well-informed decisions. The utilization of this method significantly enhances the efficiency and accuracy of financial management, reducing manual errors and decision deviations. Compared to traditional financial management methods, this approach promotes the refinement and control of financial management, thereby propelling the development of modern company management

Flamelet LES of pulverized coal combustion and NO formation characteristics in a supercritical CO2 boiler

In the present study, LESs of a modeled typical combustion zone of a 1000 MW S-CO2 coal-fired boiler using a hybrid flamelet/progress variable model are conducted for the first time. In the hybrid model, both the fuel-N from volatiles and char are considered, and two progress variables are used for major species and NO, respectively. The combustion and NO formation characteristics at different regions are qualitatively and quantitatively investigated. The results indicate that the mixture of primary air and secondary air, the high-temperature wall as well as the adjacent flame can promote the pulverized coal combustion (PCC) and NO formation. In addition, the effects of wall temperature and flue gas recirculation on PCC and NO formation are investigated. The results show that compared with the supercritical H2O boiler, a slight rise of 2.08% and 3.05% for temperature and NO production can be observed in the supercritical CO2 boiler due to a higher wall temperature; flue gas recirculation with a recirculation rate of 27% can effectively reduce the production of NO by 57.6% in the supercritical CO2 boiler

Effectiveness of rituximab in neuromyelitis optica: a meta-analysis

Abstract Background Neuromyelitis optica (NMO) is a severe inflammatory autoimmune disorder of the central nervous system and often results in paralysis or blindness. Rituximab (RTX) is a mouse–human chimeric monoclonal antibody specific for the CD20 antigen on B lymphocytes and used to treat many autoimmune diseases. Disability and relapses were measured using the Expanded Disability Status Scale (EDSS) and annualized relapse rate (ARR) ratio to evaluate the effectiveness of RTX. This review performed a meta-analysis of the efficacy of RTX in NMO. Methods We searched through the databases of PubMed, Embase, and Cochrane Library. We compiled 26 studies, in which 18 used ARR ratio, 22 used EDSS score, and 14 used both variables. Differences in the ARR ratio and EDSS score before and after RTX therapy were used as the main efficacy measures. Publication bias was evaluated after the consistency test, and a sensitivity analysis was performed with mean difference (MD) of the efficacy of RTX. Results A meta-analysis of 26 studies with 577 participants was conducted. Antibodies against aquaporin-4 autoantibody were recorded in 435 of 577 (75.39%) patients with NMO. RTX therapy resulted in a mean (WMD) − 1.56 (95% CI, − 1.82 to − 1.29) reduction in the mean ARR ratio and a mean (WMD) − 1.16 (95% CI, − 1.36 to − 0.96) reduction in the mean EDSS score. A total of 330 of 528 patients (62.9%) reached the relapse-free state. A total of 95 of 577 (16.46%) patients had adverse reactions. Conclusions RTX has acceptable tolerance, reduces the relapse frequency, and improves disability in most patients with NMO. Future studies should focus on reducing the health-care costs, improving the functional outcomes, and reducing the adverse effects associated with RTX treatment

Research on Grape-Planting Structure Perception Method Based on Unmanned Aerial Vehicle Multispectral Images in the Field

In order to accurately obtain the distribution of large-field grape-planting sites and their planting information in complex environments, the unmanned aerial vehicle (UAV) multispectral image semantic segmentation model based on improved DeepLabV3+ is used to solve the problem that large-field grapes in complex environments are affected by factors such as scattered planting sites and complex background environment of planting sites, which makes the identification of planting areas less accurate and more difficult to manage. In this paper, firstly, the standard deviation (SD) and interband correlation of UAV multispectral images were calculated to obtain the best band combinations for large-field grape images, and five preferred texture features and two preferred vegetation indices were screened using color space transformation and grayscale coevolution matrix. Then, supervised classification methods, such as maximum likelihood (ML), random forest (RF), and support vector machine (SVM), unsupervised classification methods, such as the Iterative Self-organizing Data Analysis Techniques Algorithm (ISO DATA) model and an improved DeepLabV3+ model, are used to evaluate the accuracy of each model in combination with the field visual translation results to obtain the best classification model. Finally, the effectiveness of the classification features on the best model is verified. The results showed that among the four machine learning methods, SVM obtained the best overall classification accuracy of the model; the DeepLabV3+ deep learning scheme based on spectral information + texture + vegetation index + digital surface model (DSM) obtained the best accuracy of overall accuracy (OA) and frequency weight intersection over union (FW-IOU) of 87.48% and 83.23%, respectively, and the grape plantation area relative error of extraction was 1.9%. This collection scheme provides a research basis for accurate interpretation of the planting structure of large-field grapes

Large-scale high-numerical-aperture super-oscillatory lens fabricated by direct laser writing lithography

In this study, direct laser writing (DLW) lithography is employed to fabricate a large-scale and high-numerical-aperture super-oscillatory lens (SOL), which is capable of achieving a sub-Abbe–Rayleigh diffraction limit focus in the optical far-field region by delicate interference. Large-diameter (600 μm), amplitude-modulated and phase-type SOLs with the smallest annular ring width of 1 μm are fabricated, and they have high quality. The dependence of DLW printing on the fabrication parameters including substrate materials, laser power, and scanning speed is well investigated. A standard procedure to manufacture high-quality binary amplitude SOLs is presented, which avoids direct printing patterns on metal films and reduces the surface roughness dramatically. Random displacements between squares constituting SOLs are discussed, and their influence on the focusing performance is studied by both numerical simulations and experiments. The optical performances of the SOLs fabricated by the DLW method are experimentally characterized, and a needle-like focus with a spot size of 0.42λ and a depth of focus of ∼6 μm are confirmed at a working distance of 100 μm for λ = 633 nm, thus giving an effective numerical aperture as high as 1.19 in air. As a complementary sub-micrometer fabrication method between traditional lithography and nanofabrication method, DLW is proved to be a promising approach to manufacture SOLs, presenting advantages of relatively high speed, low equipment volume, less complexity and sub-micrometer lateral resolution. Such SOLs can be very useful in high resolution bio-imaging on rough surfaces and in the related research fields.ASTAR (Agency for Sci., Tech. and Research, S’pore)MOE (Min. of Education, S’pore)Published versio

Columbianadin Suppresses Lipopolysaccharide (LPS)-Induced Inflammation and Apoptosis through the <i>NOD1</i> Pathway

Columbianadin (CBN) is one of the main bioactive constituents isolated from the root of Angelica pubescens. Although the anti-inflammatory activity of CBN has been reported, the underpinning mechanism of this remains unclear. In this study, we investigated the anti-inflammatory effect of CBN on lipopolysaccharide (LPS)-stimulated THP-1 cells and explored the possible underlying molecular mechanisms. The results showed that CBN suppressed LPS-mediated inflammatory response mainly through the inactivation of the NOD1 and NF- &#954; B p65 signaling pathways. Knockdown of NOD1 reduced the degree to which inflammatory cytokines decreased following CBN treatment, whereas forced expression of NOD1 and CBN treatment reduced NF- &#954; B p65 activation and the secretion of inflammatory cytokines. Furthermore, CBN significantly reduced cellular apoptosis by inhibiting the NOD1 pathway. Collectively, our results indicate that CBN suppressed the LPS-mediated inflammatory response by inhibiting NOD1/NF- &#954; B activation. Further investigations are required to determine the mechanisms of action of CBN in the inhibition of NOD signaling: However, CBN may be employed as a therapeutic agent for multiple inflammatory diseases