Pseudomonas aeruginosa is a common pathogen, and its presence in medical environments and water bodies has attracted widespread attention. Traditional detection methods are usually time-consuming and cumbersome, so it is necessary to develop a rapid and sensitive detection technology. DNase can specifically recognize and cut DNA molecules complementary to its substrate sequence. The researchers took advantage of this property to design various DNase-based sensors for detecting the presence of Pseudomonas aeruginosa. These sensors usually use DNase as a recognition element to identify target strains by hybridizing with specific DNA sequences. When the target strain is present, DNase is activated and begins to catalyze the cleavage reaction, producing a detectable signal. This DNase-based sensor has the advantages of rapidity, high sensitivity, and high specificity. In addition, the researchers also explored combining DNase with nanomaterials, fluorescent dyes, etc. to further improve the performance of the sensor. These improvements have improved the detection ability of the sensor in complex samples, laying the foundation for practical applications. With the continuous improvement of technology, these sensors are expected to be widely used in medical, environmental monitoring and other fields, and provide more efficient and convenient solutions for bacterial detection. This study reviewed the research progress of DNase-based sensors for the rapid detection of Pseudomonas aeruginosa.

Influenced by traditional notions of solidarity, when a province’s planning can be highly aligned with that of the central government, the province is perceived to be more collective and thus able to receive financial or resource favors from the central government. This consistency, as is often the case, reflected in doing the same thing as the central government. This situation may lead governors to ignore local economic performance and thus reduce regional innovation efficiency, as in the case of China’s Great Leap Forward. Likewise, it is possible to get better resources (energy or capital) by demonstrating managerial submissiveness, thus improving the regional innovation efficiency. Therefore, to verify the relationship between imitative governance and regional innovation efficiency, we collected relevant data from 31 major provincial administrative units in China, calculated the degree of imitative governance between provincial government work reports and central government work reports through text similarity, as well as utilized the SBM-DEA model to evaluate regional innovation efficiency. Meanwhile, we provide a new explanation of the phenomenon from the perspective of resource inclination. Finally, the empirical results show that imitative governance promotes local innovation efficiency and is moderated by resource inclination.

Nanozyme with biomimetic enzyme activity has the advantages of good catalytic performance, high stability, and is not easily affected by temperature. However, the application of tumor microenvironment (TME) in the tumor is limited due to its low catalytic activity. Herein, a multifunctional nanozyme based on CuCoP with multivalent metal ions (Cu+/Cu2+ and Co2+/ Co3+) provides an innovative strategy for constructing acid-responsive cancer therapy. CuCoP has excellent photothermal properties (photothermal conversion efficiency of 66.9%) as well as glutathione (GSH) peroxidase activity and high photo-enhanced peroxidase-like (POD) enzyme activity in weak acid TME. CuCoP can consume the overexpressed GSH, thus alleviating the antioxidant capacity of the tumor, and reduces Cu2+/ Co3+ to Cu+/ Co 2+. Subsequently, the generated Cu+/Co2+ will react with endogenous H2O2 to generate cytotoxic hydroxyl radical (•OH), which have high catalytic efficiency in weakly acidic TME. Crucially, the synergistic effect of PTT and the cascade reaction of bimetallic atoms with GSH and H2O2 can effectively reduce antioxidant capacity, inhibit tumors and trigger effective immune cell death (ICD) process, which caused a strong immune response and inhibit tumor recurrence and metastasis. As a novel bimetallic sulfide nanoenzyme triggered by TME, CuCoP has great research value in cancer therapy.

The Weibull distribution is extensively useful in the field of finance, insurance and natural disasters. Recently, It has been considered as one of the most frequently used statistical distributions in modelling and analyzing stock pricing movement and uncertain prediction in financial and investment data sets, such as insurance claims distribution. It is well known that the Bayes estimators of the two-parameter Weibull distribution do not have a compact form and the closed-form expression of the Bayes estimators cannot be obtained. In this paper and the Bayesian setting, it is assumed that the scale parameter of the Weibull model has a gamma prior under the assumption that its shape parameter is known. A simulation study is performed using random claims amount to compare the performance of the Bayesian approach with traditional maximum likelihood estimators in terms of Root Mean Square Errors (RMSE) and Mean Absolute Error (MAE) for different sample sizes, with specific values of the scale parameter and shape parameters. The results have been compared with the estimated result via the maximum likelihood method. The result revealed that the Bayesian approach behaves similarly to the maximum likelihood method when the sample size is small. Nevertheless, in all cases for both methods, the RMSE and MAE decrease as the sample size increases. Finally, applications of the proposed model to the insurance claim data set have been presented.

This study examined the determinants of the exchange rate in a mono-resource economy, during the period of currency and oil price fall in Nigeria. The aim of the study is to ascertain the potentiality of economic diversification (non-oil export) in saving the falling value of the domestic currency, as well as, its stability. The study employed the use of monthly data from the Central Bank of Nigeria and Nigeria National Petroleum Corporation from January 2008 to December 2020 using the ARDL and NARDL models to achieve the stated objectives. In the literature, several empirical studies analyzed the relationship between exchange rates, oil price, and oil export and came to the logical conclusion that a rise in the price of oil would bring about a rise in the exchange rate, all things being equal while the reverse is the case. To add to the existing body of knowledge this study did not only look at the relationship between the variables but also examined the determinants of the exchange rate in a mono-resource economy and ascertained the potentiality of economic diversification in bringing succor to the falling exchange rate in the midst of falling oil price. The NARDL result corroborates the findings of previous studies on the relationship between exchange rate, oil price, and the performance of non-oil export. It maintains that positive changes in oil prices would lead to the appreciation of domestic currency while negative changes in oil prices would lead to the depreciation of the domestic currency. With this analysis, the study recommends the diversification of the economy and revitalization of the non-oil sector. Since import proves to have a negative impact on the exchange rate, the revitalization of the non-oil sector stimulates domestic consumption and thus reduces import bills which raise the demand for the dollar and weakens the naira.

Cloud computing is one of the fastest growing domains of the IT industry, however, the growth rates for each of the models on a global and local (Russian) scale have not been generalized, despite the obvious value of such analysis. Modern business and industry in Russia is going through another period of instability and at the same time is forced to follow the trend of digitalization. That is why a balanced scientific analysis and forecast of the development of cloud technologies is needed, as the main factor in improving business efficiency. The purpose of the study: is to review the main models of cloud services for modern business and industry, describing their capabilities separately and the prospects for the market as a whole. The materials for the study were analytical reports of leading consulting companies and materials from specialized web-sites. Result: The article considers cloud technologies as the main global technological trend, describes popular models of cloud services, their opportunities for business and industry. Russian and global leaders in the provision of cloud services are also described, the growth rates of these markets for each of the models are indicated. The most popular and promising cloud service models in 2022 are: UCaaS, CaaS, SaaS, IaaS, RPAaaS, PaaS. The cloud technology market will show strong growth until 2030, generating both hybrid and absolutely new service models. The private business, industrial and public sectors will increasingly move to cloud services. The main trends in the development of cloud technologies until 2030 are: hybrid cloud, artificial intelligence (AI) and cloud computing, deployment of cloud services in the planetary orbit. The results obtained are very valuable for analysts and industry experts. The above trend analysis provides valuable predictive data to research economists, investors, business and industry leaders and other stakeholders on how the cloud services market will grow in the next 5-7 years. The results of the study also have a general theoretical nature, substantiating the importance of further forecasting the development of this market (especially using mathematical methods).