Explore the vast range of titles available.

This paper constructs a model of the particle swarm algorithm, compares and analyzes the performance of the particle swarm algorithm under the two parameters of and in detail, and solves the constrained optimization problem by the particle swarm algorithm. On the basis of the local optimal value to find the global optimal value, the particle swarm algorithm is improved with reference to the particle’s motion state and behavior. Based on the particle swarm algorithm, a digital enterprise management system is constructed to plan enterprise management operations and optimize efficiency. Finally, we compare the performance of different algorithms in enterprise management risk prediction, analyze the correlation between the management system and enterprise management efficiency, and compare the management efficiency of different enterprises to explore the effect of the particle swarm algorithm in digital enterprise management. The results show that the predictive classification effect of the particle swarm algorithm model reaches more than 95% correct rate, and the management system of the particle swarm algorithm presents significance at 1% and 5% significance level for enterprise management efficiency, respectively.

In this paper, we first study the ARMA model and its derivative models, smooth the non-stationary time series by order difference, and estimate the coefficients of the ARMA model by the method of moment estimation. Then, hypothesis testing is conducted based on serial correlation of the relationship between corporate business administration and organizational effectiveness. In the process of hypothesis testing, the empirical likelihood method is used to test the serial correlation of the residual terms under weak hypotheses, which is then extended to apply to the test case where the residual terms contain finite and infinite variances. Finally, based on the model, we assessed the level of business administration and organizational effectiveness of Enterprise H, explored the correlation between the two, and analyzed the linkage effect between business administration and organizational effectiveness. The results show that there is a significant positive correlation between the dimensions of business administration and organizational effectiveness of enterprises, r=0.754-0.864, P<0.01. The correlation coefficients between the two fluctuate between 0.4 and 0.8 for a long period of time, with significant time-varying characteristics.

Photocathode with superior catalytic activity, long-term stability, and fast mass/electron transfer is highly desirable but challenging for dye-sensitized solar cell (DSC). Herein, the ZIF-67 grown on carbon cloth is successfully transformed into CoSe 2 embedded in N-doped carbon nanocage (CoSe 2 /N-C) via a growth-carbonization-selenization process. The carbon cloth supported CoSe 2 /N-C, as photocathode of DSC, demonstrates a good long-term stability and high photovoltaic efficiency (8.40%), outperforming Pt. The good efficiency can be attributed to the high catalytic activity of CoSe 2 , fast mass transfer of porous 3D structure, and good electron transport derived from the intimate contact between CoSe 2 and highly conductive carbon cloth. The high stability would be ascribed to N-doped carbon coating that perfectly prevents CoSe 2 from decomposition. This work will pave the way to develop highly efficient and stable Pt-free photocathode for DSC.

There have been conflicting reports regarding the function of miR-20a in a variety of cancer types and we previously found it to be dysregulated in sporadic versus familial papillary thyroid cancer. In this study, we studied the expression of miR-20a in normal, benign and malignant thyroid samples, and its effect on thyroid cancer cells in vitro and in vivo. The expression of miR-20a in normal, benign and malignant thyroid tissue was determined by quantitative RT-PCR. Thyroid cancer cells were transfected with miR-20a and the effect on cellular proliferation, tumor spheroid formation, and invasion was evaluated. Target genes of miR-20 were determined by genome-wide mRNA expression analysis with miR-20a overexpression in thyroid cancer cells and target prediction database. Target genes were validated by quantitative PCR and immunoblotting, and luciferase assays. MiR-20a expression was significantly higher in anaplastic thyroid cancer than in differentiated thyroid cancer, and benign and normal thyroid tissues. MiR-20a significantly inhibited thyroid cancer cell proliferation in vitro (p<0.01) and in vivo (p<0.01), tumor spheroid formation (p<0.05) and invasion (p<0.05) in multiple thyroid cancer cell lines. We found that LIMK1 was a target of miR-20a in thyroid cancer cell lines and direct knockdown of LIMK1 recapitulated the effect of miR-20a in thyroid cancer cells. To our knowledge, this is the first study to demonstrate that miR-20a plays a role as a tumor suppressor in thyroid cancer cells and targets LIMK1. Our findings suggest the upregulated expression of miR-20a in anaplastic thyroid cancer counteracts thyroid cancer progression and may have therapeutic potential.

In the field of AGV, a path planning algorithm is always a heated area. However, traditional path planning algorithms have many disadvantages. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constraint A* algorithm and the following dynamic window approach algorithm. The kinematical constraint A* algorithm can plan the global path. Firstly, the node optimization can reduce the number of child nodes. Secondly, improving the heuristic function can increase efficiency of path planning. Thirdly, the secondary redundancy can reduce the number of redundant nodes. Finally, the B spline curve can make the global path conform to the dynamic characteristics of AGV. The following DWA algorithm can be dynamic path planning and allow the AGV to avoidance moving obstacle. The optimization heuristic function of the local path is closer to the global optimal path. The simulation results show that, compared with the fusion algorithm of traditional A* algorithm and traditional DWA algorithm, the fusion algorithm reduces the length of path by 3.6%, time of path by 6.7% and the number of turns of final path by 25%.

Background: The pandemic of coronavirus disease 2019 (COVID-19) resulted in grave morbidity and mortality worldwide. There is currently no effective drug to cure COVID-19. Based on analyses of available data, we deduced that excessive prostaglandin E 2 (PGE 2 ) produced by cyclooxygenase-2 was a key pathological event of COVID-19. Methods: A prospective clinical study was conducted in one hospital for COVID-19 treatment with Celebrex to suppress the excessive PGE 2 production. A total of 44 COVID-19 cases were enrolled, 37 cases in the experimental group received Celebrex as adjuvant (full dose: 0.2 g, bid ; half dose: 0.2 g, qd ) for 7–14 days, and the dosage and duration was adjusted for individuals, while seven cases in the control group received the standard therapy. The clinical outcomes were evaluated by measuring the urine PGE 2 levels, lab tests, CT scans, vital signs, and other clinical data. The urine PGE 2 levels were measured by mass spectrometry. The study was registered and can be accessed at http://www.chictr.org.cn/showproj.aspx?proj=50474 . Results: The concentrations of PGE 2 in urine samples of COVID-19 patients were significantly higher than those of PGE 2 in urine samples of healthy individuals (mean value: 170 ng/ml vs 18.8 ng/ml, p < 0.01) and positively correlated with the progression of COVID-19. Among those 37 experimental cases, there were 10 cases with age over 60 years (27%, 10/37) and 13 cases (35%, 13/37) with preexisting conditions including cancer, atherosclerosis, and diabetes. Twenty-five cases had full dose, 11 cases with half dose of Celebrex, and one case with ibuprofen. The remission rates in midterm were 100%, 82%, and 57% of the full dose, half dose, and control group, respectively, and the discharged rate was 100% at the endpoint with Celebrex treatment. Celebrex significantly reduced the PGE 2 levels and promoted recovery of ordinary and severe COVID-19. Furthermore, more complications, severity, and death rate were widely observed and reported in the COVID-19 group of elders and with comorbidities; however, this phenomenon did not appear in this particular Celebrex adjunctive treatment study. Conclusion: This clinical study indicates that Celebrex adjuvant treatment promotes the recovery of all types of COVID-19 and further reduces the mortality rate of elderly and those with comorbidities.

Transdifferentiation of other cell type into human neuronal cells (hNCs) provides a platform for neural disease modeling, drug screening and potential cell-based therapies. Among all of the cell donor sources, human urine cells (hUCs) are convenient to obtain without invasive harvest procedure. Here, we report a novel approach for the transdifferentiation of hUCs into hNCs. Our study demonstrated that a combination of seven small molecules (CAYTFVB) cocktail induced transdifferentiation of hUCs into hNCs. These chemical-induced neuronal cells (CiNCs) exhibited typical neuron-like morphology and expressed mature neuronal markers. The neuronal-like morphology revealed in day 1, and the Tuj1-positive CiNCs reached to about 58% in day 5 and 38.36% Tuj1+/MAP2+ double positive cells in day 12. Partial electrophysiological properties of CiNCs was obtained using patch clamp. Most of the CiNCs generated using our protocol were glutamatergic neuron populations, whereas motor neurons, GABAergic or dopaminergic neurons were merely detected. hUCs derived from different donors were converted into CiNCs in this work. This method may provide a feasible and noninvasive approach for reprogramming hNCs from hUCs for disease models and drug screening.

This paper presents a fusion algorithm based on the enhanced RRT* TEB algorithm. The enhanced RRT* algorithm is utilized for generating an optimal global path. Firstly, proposing an adaptive sampling function and extending node bias to accelerate global path generation and mitigate local optimality. Secondly, eliminating path redundancy to minimize path length. Thirdly, imposing constraints on the turning angle of the path to enhance path smoothness. Conducting kinematic modeling of the mobile robot and optimizing the TEB algorithm to align the trajectory with the mobile robot's kinematics. The integration of these two algorithms culminates in the development of a fusion algorithm. Simulation and experimental results demonstrate that, in contrast to the traditional RRT* algorithm, the enhanced RRT* algorithm achieves a 5.8% reduction in path length and a 62.5% decrease in the number of turning points. Utilizing the fusion algorithm for path planning, the mobile robot generates a superior, seamlessly smooth global path, adept at circumventing obstacles. Furthermore, the local trajectory meticulously conforms to the kinematic constraints of the mobile robot.

Gastrodia elata (G. elata) tuber is a valuable herbal medicine used to treat many diseases. The procedure of establishing a reasonable and feasible quality assessment method for G. elata tuber is important to ensure its clinical safety and efficacy. In this research, an effective and comprehensive evaluation method for assessing the quality of G. elata has been developed, based on the analysis of high performance liquid chromatography (HPLC) fingerprint, combined with the quantitative analysis of multi-components by single marker (QAMS) method. The contents of the seven components, including gastrodin, p-hydroxybenzyl alcohol, p-hydroxy benzaldehyde, parishin A, parishin B, parishin C, and parishin E were determined, simultaneously, using gastrodin as the reference standard. The results demonstrated that there was no significant difference between the QAMS method and the traditional external standard method (ESM) (p > 0.05, RSD < 4.79%), suggesting that QAMS was a reliable and convenient method for the content determination of multiple components, especially when there is a shortage of reference substances. In conclusion, this strategy could be beneficial for simplifying the processes in the quality control of G. elata tuber and giving references to promote the quality standards of herbal medicines.

Designing high-performance nonprecious electrocatalysts to replace Pt for the oxygen reduction reaction (ORR) has been a key challenge for advancing fuel cell technologies. Here, we report a systematic study of 15 different AB₂O₄/C spinel nanoparticles with well-controlled octahedral morphology. The 3 most active ORR electrocatalysts were MnCo₂O₄/C, CoMn₂O₄/C, and CoFe₂O₄/C. CoMn₂O₄/C exhibited a half-wave potential of 0.89 V in 1 M KOH, equal to the benchmark activity of Pt/C, which was ascribed to charge transfer between Co and Mn, as evidenced by X-ray absorption spectroscopy. Scanning transmission electron microscopy (STEM) provided atomic-scale, spatially resolved images, and high-energy-resolution electron-loss near-edge structure (ELNES) enabled fingerprinting the local chemical environment around the active sites. The most active MnCo₂O₄/C was shown to have a unique Co-Mn core–shell structure. ELNES spectra indicate that the Co in the core is predominantly Co2.7+ while in the shell, it is mainly Co2+. Broader Mn ELNES spectra indicate less-ordered nearest oxygen neighbors. Co in the shell occupies mainly tetrahedral sites, which are likely candidates as the active sites for the ORR. Such microscopic-level investigation probes the heterogeneous electronic structure at the single-nanoparticle level, and may provide a more rational basis for the design of electrocatalysts for alkaline fuel cells.