The Impact of Power on Information Sharing in E-Government

In the digital era, information sharing is of utter importance to improving the quality and benefits of government services. At present, there is a lot of information housed by and distributed among different government agencies, which poses significant challenges and barriers to information sharing and dissemination. This paper presents a research model that examines some crucial factors, including administrative power, trust, perceived risk, and power games, that may affect information sharing in e-Government. The administrative power can be classified as coercive power and coordinated power. Trust, perceived risk, and power games are introduced as moderators of the power on information sharing in the research model. Results of our empirical study indicate that coercive power and coordinated power positively affect information sharing, and such effects are moderated by trust among employees and power ames. The research and practical implications of this tudy are also discussed

A Context-Dependent Sentiment Analysis of Online Product Reviews based on Dependency Relationships

Consumers often view online consumer product review as a main channel for obtaining product quality information. Existing studies on product review sentiment analysis usually focus on identifying sentiments of individual reviews as a whole, which may not be effective and helpful for consumers when purchase decisions depend on specific features of products. This study proposes a new feature-level sentiment analysis approach for online product reviews. The proposed method uses an extended PageRank algorithm to extract product features and construct expandable context-dependent sentiment lexicons. Moreover, consumers’ sentiment inclinations toward product features expressed in each review can be derived based on term dependency relationships. The empirical evaluation using consumer reviews of two different products shows a higher level of effectiveness of the proposed method for sentiment analysis in comparison to two existing methods. This study provides new research and practical insights on the analysis of online consumer product reviews

Optimization of “Deoxidation Alloying” Batching Scheme

In this paper, a mathematical model was established to predict the deoxidation alloying and to optimize the type and quantity of input alloys. Firstly, the GCA method was used to obtain the main factors affecting the alloy yield of carbon and manganese based on the historical data. Secondly, the alloy yield was predicted by the stepwise MRA, the BP neural network and the regression SVM models, respectively. The conclusion is that the regression SVM model has the highest prediction accuracy and the maximum deviation between the test set prediction result and the real value was only 0.0682 and 0.0554. Thirdly, in order to reduce the manufacturer's production cost, the genetic algorithm was used to calculate the production cost mathematical programming model. Finally, sensitivity analysis was performed on the prediction model and the cost optimization model. The unit price of 20% of the alloy raw materials was increased by 20%, and the total cost change rate was 0.7155%, the lowest was -0.4297%, which proved that the mathematical model established presented strong robustness and could be certain reference value for the current production of iron and steel enterprises

Research Article Performance Monitoring and Analysis of the Photovoltaic Power Generation System Based on the PCI Data Acquisition Card

Abstract: In order to analyze the performance monitoring of the photovoltaic power generation system and achieve the optimal control between the energy storage and consumption, the paper has built a multifunctional performance monitoring system based on the virtual instrument technology. The voltage, current, power, environmental temperature and light intensity are collected via the 1716L-PCI data acquisition card and displayed in real time. After the analysis of the collected data, the system explores the performance of the photovoltaic power generation system. Meanwhile, in order to improve energy use efficiency, the system has set different control modes, including automatic mode, manual mode and custom mode, to discuss the optimal control between the load and the storage energy. The experiment results show that the system has flexible control ability, feasible analysis results and pratical value

Polyethylene Glycol-grafted poly alpha-lipoic acid-dexamethasone nanoparticles for osteoarthritis

Osteoarthritis (OA) is a chronic inflammatory disease that causes synovial hyperplasia, cartilage destruction, and the formation of bone spurs. Macrophages play an indispensable role in the pathogenesis of OA by producing proinflammatory cytokines. To achieve the effect of arthritis, hormones can effectively inhibit the progression of inflammation by inhibiting the secretion of inflammatory cytokines by macrophages in traditional therapy. However, the drug is quickly cleared from the joint space, and the high injection site infection rate and low local drug concentration make the clinical efficacy of corticosteroids greatly reduced. We described the design and preparation of Polyethylene Glycol-grafted Poly Alpha-lipoic Acid-dexamethasone Nanoparticles (NPDXM/PPLA), elucidated the mechanism of action of NPDXM/PPLA in the treatment of OA in mice, and provided an experimental basis for investigating the treatment of OA with polymer nanoparticles loaded with dexamethasone. Flow cytometry and confocal laser scanning microscopy were used to confirm that NPDXM/PPLA was well absorbed and released by macrophages, and it was discovered that NPDXM/PPLA could efficiently reduce the proliferation of activated macrophages (RAW 264.7 cells). Enzyme-linked immunosorbent assay revealed that NPDXM/PPLA could efficiently reduce the expression of proinflammatory cytokines IL-1β, IL-6, and TNF-α. The knee bone structure of OA mice was investigated by MicroCT, and it was discovered that intraarticular injection of NPDXM/PPLA effectively alleviated the bone damage of the articular cartilage. Therefore, NPDXM/PPLA is a potential therapeutic nanomedicine for the treatment of OA

Forest Fire Prevention Early Warning Method Based on Fuzzy Bayesian Network

In the environment of large forest, the factors causing fire are nonlinear and uncertain. If the data collected by the sensor is simply analyzed and compared, the false alarm rate will be higher. How to combine the data of several sensors for effective fire warning is a difficult point. In order to improve the accuracy of prediction, aiming at the shortcomings of traditional forest fire prevention early warning system, we propose a forest fire prevention early warning method based on fuzzy Bayesian network. Firstly, we combine the fuzzy control system and the Bayesian network in series, and pre-process the collected sensor data. The pre-processed data is sent to the previously trained Bayesian network for processing. Then the calculated open fire probability, smoldering fire probability, and no fire probability are used as input data of fuzzy control system, and fuzzy inference is performed. Finally, we de-fuzzify the results of fuzzy reasoning and get the probability of fire. Simulation results show that our method can effectively combine the data collected by multiple sensors, quickly and accurately determine fire occurrence probability, improve the accuracy of forest fire prevention warning, and reduce the false positive rate

A Classification Model for Detection of Chinese Phishing E-Business Websites

There has been an increasing number of fake e-Business websites created and used, which have resulted in rising financial loss for online consumers and businesses. Therefore, developing effective approaches to detecting phishing websites is essential to mitigating the possibility of being victimized by those sites and minimizing financial loss and risks. In this research, we propose a novel classification model for automatically detecting Chinese phishing e-Business websites. By extending previous research and incorporating unique characteristics of Chinese e-Business websites, our model consists of feature vectors of both the URL and content of a Website. We have trained and evaluated the proposed model with roughly 900 Chinese e-Business websites using four different classification algorithms. Results show that among those four algorithms, the Sequential Minimal Optimization (SMO) algorithm performs the best. To examine the impact of individual features in the model on detection accuracy, we further conducted a sensitivity analysis to identify the most influential features, which helps make the classification model more parsimonious. The findings of this research provide several research and practical insights into the development of anti-phishing solutions

Effects of Alkaline Hydrogen Peroxide Pre-Extraction on Bamboo Lignin Chemistry and Other Bamboo Chemical Components

This paper aimed to investigate the reasons for improved properties of bamboo alkaline peroxide mechanical pulp (APMP), such as relatively high brightness and low post-colour number, by alkali hydrogen peroxide pre-extraction (AHPP) treatment. It was found that AHPP could affect the dissolution of 1% sodium hydroxide extractives, benzene-ethanol extractives, and acid-soluble lignin. The results of Fourier transform infrared (FTIR) and 13C-nuclear magnetic resonance spectroscopy (13C-NMR) illustrated that carboxyl, syringyl, guaiacyl, acetyl, and methoxyl groups of the milled wood lignin (MWL) in bamboo were degraded slightly after AHPP treatment. However, some lignin-like structures, the links of α-O-4, β-O-4, and some carbohydrates such as xylan, α-glucose, β-glucose, α-mannose, and β-mannose in lignin-carbohydrate complexes (LCCs) were degraded noticeably. The analysis of UV spectrophotometry indicated that AHPP treatment was conductive to the degradation of some chromophores in MWL. The degradation of MWL and LCC under simulated AHPP conditions showed consistency with the above results

Purification Effect of Oysters Based on the Analysis of Environmental Parameters

With the development of coastal industries, marine pollution has intensified, and more and more shellfish in marine areas have been polluted to varying degrees. People's health can be compromised when eating contaminated shellfish. In the actual purification process of the shellfish purification plant project, there is no in-depth exploration of the parameters of the purification pond. Based on supplementing the basic research for the development of the oyster purification industry at different temperatures, salinities, pH, ultraviolet lamp power, irradiation time, and water flow rate, we find the best environmental conditions are a temperature of 30 °C, a pH of 8, a salinity of 32%, an ultraviolet lamp illumination power of 9w, an ultraviolet lamp irradiation time of 12h, and a water body flow rate of 0.5m/s. We supplement basic research related to bioengineering in China. We hope that our results can provide reference for further research and contribute to the sustainable development of the oyster purification industry. Create economic benefits for society