With the improvement of people's living standards and the increasing emphasis on health, new and higher demands are also placed on the medical industry. The diagnosis and treatment methods combining artificial intelligence and medicine are bound to receive more and more attention and promotion. At present, drug design is stepping into the era of artificial intelligence, and academia and industry are actively applying artificial intelligence technology to all aspects of drug research and development, including drug screening, molecular characterization, drug activity and druggability optimization, chemical reaction design, etc. Relying on the big data information of millions of patients, the artificial intelligence system can quickly and accurately mine and screen out suitable drugs. Through computer simulations, artificial intelligence can make predictions about drug activity, safety, and side effects, and find the best drug that matches the disease. This technology will shorten the drug development cycle, reduce the cost of new drugs and improve the success rate of new drug development.

Skateparks, often called 'wheel parks,' are becoming increasingly common within communities worldwide. Despite this growth in parks, estimates show that the development of parks needs to catch up to users. Using a count data travel cost model, we estimate the adult user benefits associated with the Lauridsen Skatepark in Des Moines, Iowa – the largest in the United States. We estimate adult user benefits to be $61 per user per day and roughly $488,000 annually. This work contributes to the literature by being the first study to use econometric techniques to estimate skatepark user benefits. Second, we develop a simple and easy-to-apply method to assist municipalities in determining community skatepark needs. Third, our results support the literature showing that skateboarders are increasingly diverse.

Objective: The DSS was utilized to construct colitis model of mouse. The colitis mice were colonized with gut microbiota. The effects of gut microbial metabolites on colitis were studied. The mechanisms of gut microbial metabolites to improve intestinal immunity were also further explored. Methods: The male BALB/c mice were selected to construct colitis mouse model with DSS and colonized with gut microbiota. The content of short-chain fatty acids in intestinal metabolites of mice during modeling were detected with GC-MS. After the mice were sacrificed, the colon tissue was stained to observe the colitis in different groups of mice. The contents of IL-22 and IL-17 in colon tissue was determined with ELISA method. To study the mechanism of relieving colitis, qRT-PCR and western blotting were used to study the horizontal of Ffar2 gene and pSTAT3 and pAKT in colon tissue, respectively. The congenital lymphocytes were isolated and purified, and the migration ability of the congenital lymphocytes was examined by cell scratch plane migration test. Results: The colonization of the gut microbiota had significant effects on the contents of short-chain fatty acids in the intestinal metabolites of colitis mice, of which the effect on the content of acetic acid and butyric acid was more significant. The colonization of gut microbiota could effectively relieve colitis in mice and effectively promote the secretion of IL-22 in colon tissue. Studying the remission mechanism indicated that colonization of gut microbiota with colitis could effectively promote the expression of Ffar2 gene in colon tissue and increase the expression of pSTAT3 and pAKT protein. The migration ability of the lymphocyte was significantly upregulated in the model group compared with the other groups, demonstrating that DSS can effectively activate the lymphocyte; The migration of congenital lymphocyte in the experimental group was significantly alleviated than that in the model group, but it was up-regulated than that in the positive control group, and the colonic tissue of the positive control group was similar to that of the normal group. Conclusion: The short-chain fatty acids in the intestinal flora metabolites can promote the gene expression of the metabolite-sensitive receptor Ffar2. The effective combination of short-chain fatty acids and Ffar2 receptors can promote the phosphorylation of STAT3 and AKT proteins, effectively promote the secretion of IL-22 in intestinal ILC3 cells, alleviate colitis in mice, and thereby improve their intestinal immune function.

Innovation has received a great attention in the creative industries literature. We propose in this study a bibliometric method to examine the literature on innovation in creative industries (ICI). A file of 656 manuscripts published on ICI between 1998 and 2022 was retrieved from the Web of Science Core Collection for analysis. The results highlight the evolution of study volume, authors, affiliated institutions and countries, author networks, keyword co-occurrences, and keyword networks. The study also includes a thematic map that highlights four types of research: driving themes (e.g., digital technology, cultural innovation, performing arts, product innovation, innovation management); core and cross-cutting themes (e.g., creativity, digitization, technology, copyright); emerging themes (e.g., gender, artificial intelligence, sustainability); and specialized and peripheral themes (e.g., gender, blockchain, digital music). We finally conclude by proposing future perspectives and a research agenda in this area.

This paper explores how the medical expenditure risk affects the households’ portfolio choice across health status theoretically in a life cycle model and empirically using machine learning methods. Medical expenditure risk, as a background risk, has the potential to influence households’ financial decisions. A higher medical expenditure risk leads to a larger fluctuation and more uncertainty in households’ consumption and therefore utility. As a result, risk-free assets become more attractive. Our machine learning analysis provides evidence that aligns with the predictions of the theoretical life cycle model. Specifically, households with better health hold a larger proportion of stocks in their portfolios. Furthermore, when facing increased medical expenditure risk, households in good health demonstrate a greater willingness to invest in safe assets.

This paper describes two novel approaches to cost estimation of manufactured products where a data set of similar products have known manufactured costs. The methods use the notion of piecewise functions and are (1) clustering and (2) splines. Cost drivers are typically a mixture of categorical and numeric data which complicates cost estimation. Both clustering and splines approaches can accommodate this. Through four case studies, we compare our approaches with the often-used regression models. Our results show that clustering especially offers promise in improving the accuracy of cost estimation. While clustering and splines are slightly more complex to develop from both a user and a computational perspective, our approaches are packaged in an open-source software. This paper is the first known to adapt and apply these two well-known mathematical approaches to manufacturing cost estimation.

A high inflation period of seven years (1978-1985) in Israel, which turned into a hyperinflation, puzzled Israeli economists, who tried to understand its causes and mechanisms. As a result, they provided fourteen different explanations. Although all of the explanations were based on the same data, the researchers’ conclusions were either different or contradictory. This situation provides a virtual laboratory for the study of the ways and methods of economic thought; it is a classical Ceteris patribus situation. This laboratory further raises other subjects and questions. Are there principal differences between the economists' explanations? Is it possible to classify these differences? How do economic explanations for inflation differ from non-economic ones? In this article, I classified thirteen economic studies and one non-economic study into five clusters. This was the methodological tool used to inquire into the economic thought in this virtual laboratory.

The development of digital economy is becoming an important path to promote the kinetic energy of China's tax revenue development. Based on the panel data from 2011 to 2018, this paper uses Gini coefficient to analyze the regional differences of China's digital economy, and uses fixed effect model, spatial Durbin model, multi-period double difference model and other empirical methods to examine the tax effects of digital economy development. The results show that the regional disparity is the main reason for the regional disparity of digital economy development in China. The development of digital economy directly increases the tax amount of local digital industry, and at the same time it also radiates and drives the increase of local total tax revenue; The tax effect of digital economy has a negative spatial spillover, showing a "siphon effect" to neighboring areas; Mechanism analysis shows that the development of digital economy mainly promotes the increase of local tax revenue through growth effect.

There must be a better match between the supply of financial news graduates in colleges and the media's demand for financial news talents. Based on 300 questionnaires and in-depth interviews from three universities, namely Shanghai University of Finance and Economics, Nanjing University of Finance and Economics, and Jiangxi University of Finance and Economics, this paper uses the interview method to excavate the professional quality improvement path for majoring in financial news students. Results reveal that undergraduates majoring in financial news have problems such as low professional identity, insufficient recognition of the discipline system, poor sense of professional identity, common matching between job selection and majors, and worrying about the status quo of professional literacy. Therefore, the improvement of the professional quality of students majoring in financial news and the reform of the training program needs to drive the professional quality of students in multiple dimensions, change their professional cognitive problems, strengthen their professional identity, and achieve an effective match between supply and demand of financial media professionals.

This study aims to investigate the factors influencing consumers’ cognitive attitude towards the brands of products during their consumption willingness, behaviors and choices. Taking pork brands as an example, this study conducts a questionnaire by using a random sampling method in 165 farmers’ meat markets and 54 supermarkets in Tianhe District, Yuexiu District, Liwan District, the representative districts in Guangzhou city in China, and obtains 1,050 valid observations of the pork consumers. The empirical results via a logit regression show that the factors significantly enhance consumers’ brand cognitive attitude include younger age; lower perception of product safety; less cognition of green food, frailer awareness of fresh, cold, and frozen meats, as well as weaker ability to identify a specific product. Also, stronger consumers’ brand attitude results from larger amount of money spent in a single purchase, stronger cognition of nuisance-free food, and higher identification of different types of pork meats.

Triggered by the rising demand, a complex aircraft production network has been evolving during the past half century. In order to analysis the spatial-temporal evolution of this network, this paper establishes a novel and complex data by collecting 1774 major global aircraft manufacturers' production relationship from Jane's All the World' s Aircraft Development & Production (1965-2021), and visualizes the spatial temporal characteristics of the global aircraft production network by using Social Net Analysis method. The characteristics are summarized as follows: (1) the main aircraft firms from 48 counties form a complex production network centered on the United States, Europe and China; (2) The production network remains diffusion during 1965-2005, but it retracts after 2005, which documents evidences of de-globalization trend in the aircraft industry; (3)AVIC, Airbus, Boeing, Textron, Lockheed, Kawasaki, Mitsubishi, Sikorsky, Bell, and Embraer are all ranked high in the study period and play the role of leading firms in the production network and drive the cooperative connections of other companies.

The presence of electrical harmonics in low voltage networks causes many problems for customers, the most important of which is the decrease in the power quality beyond the permissible limits, which leads to a decrease in voltage for long periods, a decrease in power factor and a large losses in the electrical network due to the deformation of the electric waveform of voltage and current. The aim of this paper is to design an optimal passive filter and its optimal location to suppress the harmonic distortion in the distribution system that results from nonlinear loads. This filter will use single objective functions and multi objective functions that will minimize the proposed filter cost, the real power losses, total harmonic distortion "THD" and individual harmonic distortion "IHD". This will be done under the equality constraint of power balance and some inequality constraints such as filter parameters limits, quality factor limits, voltage limits, harmonic distortion limits. The optimal passive filter parameters and location will be calculated using the modern optimization techniques such jellyfish optimization technique. IEEE 33 bus radial system will be used for demonstrating results obtained by the proposed method.

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.

Parkinson’s Disease (PD) is a prevalent progressive neurodegenerative condition affecting millions globally. Research has found that individuals with PD have a reduced risk of certain cancers, such as colon, lung, and rectal cancers, but an increased risk of brain cancer. Therefore, there is an urgent need for the development of advanced PD diagnostic methods and for investigating the relationships between risk factors, such as lifestyle due to handedness associated with various types of cancers. Recent ad- vancements in magnetic resonance imaging have enhanced PD diagnosis, reducing misdiagnosis and facilitating more accurate disease progression monitoring. Nevertheless, challenges exist, particularly in the distinction of PD between left-handed and right-handed patients over time. This survey provides an overview of contemporary deep learning-based imag- ing analysis methodologies, encompassing both non-longitudinal and lon- gitudinal contexts. We also explore existing limitations and prospects for refinement to gain deeper insights. These insights are poised to inform the development of personalized treatment strategies for PD patients while elucidating the current disparities between deep learning models and their efficacious implementation in clinical practice.

Even though policy coordination is one of the oldest challenges that governments have to confront, as problems evolve and "New Public Management" concepts emerge, it has become even more essential. The current literature on policy coordination among government agencies, however, shows little regarding the way coordination is managed under centralized political systems. This study, based on the science and technology (S&T) policy documents issued by China's central government agencies between 1978 and 2020, provides a quantitative and dynamic analysis of the coordination of policies in China and presents a comprehensive overview of policy coordination paths and processes in centralized political systems. As a result, it provides a way that contributes to the analytical methods available for quantitatively analyzing policy documents. On the other hand, the key findings of the study show that, first, state council-administered ministries have taken the lead in coordinating policy while other types of organizations have collaborated in more subordinate capacities. Second, national themes that are democratic and driven by demand have been the core concern of coordination activities, such as social development, high-tech industrialization, and rural S&T. Third, policy coordination has evolved continuously and has mostly contributed to interpreting macrostrategies and implementing more specific implementation measures.

In the past, investing in housing has served as an engine of growth for many economies as it is widely recognized that poor housing conditions can have significant negative impacts on human health, education, and economic opportunities. To assess the housing-related quality of life, indicators such as housing quality, housing environment, and cost burdens can be applied. However, recent studies indicate that materials used for construction are critical. Permanent materials, as opposed to temporary materials, typically offer a range of benefits in terms of durability, low maintenance, improved energy efficiency, increased property value, and better safety. The ultimate aim is to identify the key drivers of housing conditions in Cambodia, with a particular focus on materials and water quality, and the legal status of housing. To find the empirical relationship between economic, socio-economic, and demographic variables on the one hand, and variables measuring housing and living conditions in Cambodia on the other, the analysis employs Ordinary Least Squares and Methods-of-Moments regression modeling. Results indicate that high employment rates and entrepreneurship increase home ownership, and improve the quality of drinking water available. Furthermore, in addition to employment and entrepreneurship, the higher-performing construction materials can also be empirically explained by a bigger labor force and variables capturing the wider macroeconomic environment.

Educational equity is the foundation of social equity, and education equalization is one of the serious issues to be solved by the government. This work considers one education equalization reform conducted by Suzhou Industrial Park of China, namely, the comprehensive "five to nine" (CFTN) housing purchase admission qualification adjustment policy, as an example and uses the resold housing transaction data to identify the effect of the equalization policy by the difference-in-differences (DID) method. Based on this natural experiment, the quantitative assessment results show that (1) the CFTN policy has a significantly positive effect on housing prices in nine-year consistent (NYC) school districts that belong to high-quality school districts; (2) the CFTN policy has no significant effect on housing prices in non-NYC consistent school districts that belong to ordinary school districts. These results uncover that the one-size-fits-all CFTN policy can only obtain significant housing price equilibrium effects in school districts above a certain level of education quality. For regions with backward education levels, the policy is ineffective. For these regions, continuously increasing high-quality educational resources and increasing educational service levels are essential solutions to reduce educational inequality.

Peripheral nerve injury is a serious and disabling disease prevalent in the world. It caused by trauma is often accompanied with soft tissue injuries, fractures, infections, etc., and can cause permanent damage. The treatment methods of peripheral nerve injury mainly include traditional microsurgical repair, neurotrophic drug treatment, as well as cuttingedge nerve conduit treatment, nerve stimulation, cell therapy, etc. However, more than 30% of patients with peripheral nerve injury still have poor recovery, including partial loss or complete loss of motor and/or sensory function, muscle atrophy，chronic pain and severe disability, among can lead to permanent disease. Phenolic compounds are secondary metabolites which is the most abundant in plants, consisting of an aromatic ring and one or more hydroxyl substituents, the main groups including flavonoids, phenolic acids, tannins, stilbene and lignans. A lot of studies have shown that natural phenolic compounds have various properties, such as antioxidant, anti-infective, anticancer, anti-inflammatory, etc., and have broad applications in the prevention of heart disease, cancer, diabetes, oxidative stress-related diseases, and neuroprotection prospect. This review discusses the potential applications and molecular mechanisms of natural phenolic compounds its polymer derivatives in the treatment of peripheral nerve injury.

The enactment of policies that bolster new research and development (R&D) institutions stands as a pivotal strategy to catalyze urban innovation and development. Adopting a strategic lens of innovation chain management and employing the Differences-in-Differences (DID) method to scrutinize panel data from 43 Chinese cities spanning 2008 to 2019, this study probes the efficacy and underlying mechanisms of policies designed to support nascent R&D institutions in facilitating urban innovation. Empirical findings reveal that policies in support of new R&D institutions have markedly enhanced the three integral stages of the urban innovation chain: research and development, transfer, and application. Furthermore, the innovation ambiance within cities and the innovative activities of enterprises emerge as significant mediators between support policies and the output of urban innovation across these stages. A regional heterogeneity analysis unveils that the impact of support policies on the output of urban innovation diverges across regions, with a notably more pronounced effect observed in the eastern region compared to central and western regions. An objective appraisal of the policy's impact on urban innovation not only aids in delving into the profound implementation effects of policy instruments but also furnishes policy-makers with decision-making references for optimizing the utilization of support policies for new R&D institutions to advance the edification of the national innovation system.

In order to avoid the real economy development lags behind and the deterioration of ecological problems in the process of traditional urbanization, China has been promoting city-industry deep integration, but the connection between city-industry integration and green economic growth, especially the spatial effect, has not been systematically explained. Based on the panel data from 2007 to 2018, this paper constructs an evaluation index system and uses the SEEA method to measure city-industry integration (CII) level and green economy growth (GEG) level. Then, by employing spatial Durbin model and intermediary effect model, it further systematically investigates the spatial impact of CII on GEG and the potential mechanism. The study found that: (1) On the whole, CII shows “slow-steady integration” trend, but regional heterogeneity was obvious and accompanied by “slow gap expansion”. GEG experienced “sharp increase” with “polarization” characteristic. (2) CII can directly promote regional GEG (with a marginal effect of 0.689), more effectively than traditional urbanization, and CII has obvious spatial spillover effects on GEG in both “local effect” and “neighboring effect”. (3) Interestingly, technological innovation and consumption structure upgrading are significant mediating mechanisms. (4) The direct positive effect of CII shows the regional imbalance characteristic. Finally, the corresponding policy implications are put forward.