Cervical cancer is defined as a cancer arising in the cells of cervix that causes unusual vaginal bleeding, discharges, pain in the pelvic region, or pain during sexual activity. Cervical cancer is currently reported to be the fourth most prevalent malignancy among women globally. Surgery includes pelvic lymphadenectomy as well as radical hysterectomy, radiotherapy, as well as chemotherapy are the most common therapies for treating cervical cancer. Another approach includes targeted medication which affects the epidermal growth factor receptor (EGFR) and cyclooxygenase-2 (COX-2) for the curing cervical cancer. However, these therapies have the potential for risks and complications: surgery can result in bleeding and may cause organ damage surrounding the surgery, and clots may also start to form in the deep veins of the legs; radiotherapy can result in menopause, infertility, discomfort, or pain during intercourse; and chemotherapy can actually impact rapidly dividing cells along with cancer cells in the human body system. In this review, we will discuss about the use of several Randomised controlled trials (RCTs) for treating malnutrition in various oncology patients. In this review, we will discuss about the various therapeutic effects of natural products isolated from different microorganisms in treating cervical cancer.

The claim that “there is no third way” besides the economic models of capitalism and communism has faced a challenge from a new and growing body of research into a “third way” economic paradigm known as democratic planning. In this paper, we explore one of these democratic planning models–Robin Hahnel and Michael Albert’s model of a participatory economy, focusing in particular on its allocation mechanism–a non-market, non-command-planning procedure known as “participatory planning.” This procedure has recently been implemented in computer programs to explore its feasibility, the encouraging results of which have been published elsewhere and which we summarize here. But I present here for the first time the detailed algorithms and related pseudocode powering all of these computer programs for others to consider, examine, and build as their own programs. I also describe future directions for this avenue of research.

Bank deposit is closely related to systemic risks. In addition, considering that resident deposits in China have significant seasonal characteristics, this paper focuses on which component of deposits drives the systemic risk volatility, that is, it can supplement the existing forecast information. We use X-13ARIMA-SEATS to decompose deposit into three subsequences. The research findings show that the forecast effect of subsequence models is better than that of benchmark series. Most importantly, the model with trend component has the best forecast performance.

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.

I present a theoretical model and an empirical approach for jointly estimating the effectiveness of fiscal policy and the stochastic process of sovereign interest rate shocks. The theoretical model has features relevant to small open and emerging economies. Interest rate shocks affect the ability of firms to finance payroll expenses. This theoretical feature creates a propagation mechanism for interest rate shocks and affects government spending multipliers. This paper proposes a strategy for jointly estimating government spending multipliers and the interest rate shock process parameters.

The study investigates the impact of COVID-19 on African exchange rates, employing a panel dataset over the period 2016-2022 from 13 African economies and employing pooled OLS estimator. The findings reveal a noteworthy positive correlation between COVID-19 levels and exchange rates within the region. However, this association necessitates deeper exploration due to its multifaceted implications, including economic disruptions, government interventions, global market interconnections, regional stability perceptions, and investor behavior. Economic indicators, such as GDP, inflation rate, real interest rate, and unemployment rate, significantly influence exchange rates. While GDP positively impacts exchange rates through economic growth, investor confidence, and trade strength, controlled inflation fosters investor confidence and export competitiveness, positively affecting exchange rates. Moreover, a pronounced positive impact of the real interest rate indicates economic stability, attracting investment and contributing to currency appreciation. Conversely, high unemployment negatively affects exchange rates, signaling economic weaknesses and eroding investor confidence. These findings emphasize nuanced policy responses for managing health crisis-induced economic disruptions and suggest future research directions focusing on specific country-level analyses and multifaceted factors influencing currency valuations within African economies.

Entrepreneurs are important actors in economic activities and creators of social wealth. Excellent entrepreneurs contribute their wisdom to the accumulation of social wealth and the promotion of high-quality economic and social development. The business environment is the main manifestation of the soft power of cities and regional economic development, and a better business environment can effectively attract enterprises and promote their sustainable growth. Using data from Chinese A-share listed companies from 2009-2019 as a research sample, the following research conclusions were drawn: (1) A better business environment helps enhance entrepreneurship. (2) A better business environment promotes entrepreneurship by reducing rent-seeking expenses and corporate credit costs. (3) Compared to traditional enterprises, high-tech enterprises are better able to enjoy the benefits brought by business environment optimization and further enhance entrepreneurship. When competition is low, entrepreneurs face lower rent-seeking expenses, which is conducive to stimulating entrepreneurship. The businessenvironment can promote fairness and bring more equal financing opportunities for enterprises, which has a higher impact on entrepreneurship for the group facing higher financing constraints. This study meticulously analyzes the impact ofthebusiness environment on entrepreneurship, providing references for the next steps of optimizing the business environment and enhancing entrepreneurship.

We develop a four-party model involving banks, merchants, consumers, and platform firms to explain why large shopping malls can offer interest-free installment services to consumers. Using a collaboration between an online shopping platform firm and multiple banks to issue co-branded credit cards as a natural experiment, we find that consumers’ spending on the platform fulfills the requirements for merchants and banks to choose a four-way model. This study highlights the pivotal role of platform firms in the credit card market.

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.

Keeping in view the concept of agglomeration economies and the New Economic Geography (NEG) angle, this paper makes an attempt to examine the rural to urban population movement at the district level in India. The findings do not favour a strong positive association between levels of urbanization and migration rates.  However, there exists a cluster of districts which are able to attract migrants on a large scale in spite of being already urbanized. The work participation rate, share of services and construction work, and literacy rate all form parts of this positive nexus, indicating that opportunities exist with increased levels of urbanization which in turn prompt people to migrate. The positive spill-over effects of higher levels of urbanization are not limited to the urban spaces only as the adjoining rural areas also seem to have undergone   a significant transformation. The land use pattern and activities are changing and some of the developmental impact is evident. However, the regional variations in this respect are evident. There are sharp differences in the relationship between urbanization level and migration rates across regions. The nature of urbanization and its determinants and the outcome variables of urbanization and migration also unravel regional variations. Besides, there are many districts with higher levels of urbanization; yet, they are not able to attract migrants at a rapid pace. New investment opportunities can be created in these spaces to reduce the cost of growth and make employment generation process more effective, facilitating the rural population to take the benefits of agglomeration economies.

Adenosine alterations to RNA, which are largely determined by RNA modification writers (RMWs), are critical for cancer growth and progression. These RMWs can catalyze different types of adenosine modifications, such as N6-methyladenosine (m6A), N1-methyladenosine (m1A), alternative polyadenylation (APA), and adenosine-to-inosine (A-to-I) RNA editing. These modifications have profound effects on gene expression and function, such as immune response, cell development. Despite this, the clinical effects of RMW interactive genes on these cancers remain largely unclear. A comprehensive analysis of the clinical impact of these epigenetic regulators in pan-cancer requires further comprehensive exploration. Here, we systematically profiled the molecular and clinical characteristics of 26 RMWs across 33 cancer types using multi-omics datasets and validated the expression level of some RMWs in various cancer lines. Our findings indicated that a majority of RMWs exhibited high expression in diverse cancer types, and this expression was found to be significantly associated with poor patient outcomes. In the genetic alterations, the amplification and mutation of RMWs were the dominant alteration events. Consequently, the RNA Modification Writer Score (RMW score) was established as a means to assess the risk of RMWs in pan-cancer. We found that 27 of 33 cancers had significantly higher scores compared with normal tissues, and it was significantly correlated with prognosis. We also evaluated their impact on the tumor microenvironment and the response to immunotherapy and targeted therapy. These findings verified the important role of RMWs in different aspects of cancer biology, and provided biomarkers and personalized therapeutic targets for cancer.

Global distributions of net factor income from abroad (NFI) during 1990-2019 have witnessed that (1) the United States is the top one country accounting for 40% of surpluses of the global total, while a surge in China’s deficit with its GDP increase; (2) GDP growth in emerging economies has a price scissors with NFI deficits; (3) asymmetric NFI has covered up the severity of rich countries’ global arbitrages especially from emerging economies; (4) China’s economic power is exaggerated by the PPP-based GDP implemented by the World Bank. It concludes that (1) developing countries have paid for huge hidden cost for their emergence; (2) the statement of the United States suffering losses absolutely does not hold; (3) GDP is not a universal tool for measuring what matters. It suggests that (1) emerging economies countries should beware of the potential misleading of GDP on economic measurement and economic power comparison ; (2) GDP should be critiqued from the applicability perspective of economies’ types; (3) it is urgent to clarify some misjudgment and misleading concepts in the economic affairs surrounding the global value chain patterns; (4) the construction of national governance capacity in emerging economies should focus on “social infrastructure”, of which one of the important parts is an effective economic statistics system; (5) emerging economies should carry out the strategic layout of international economic statistics talents to enhance their soft powers.

I will show that if the propensity to consume from savings satisfies appropriate conditions, the debt-GDP ratio will not grow infinitely large and fiscal collapse will not occur. Using a basic macroeconomic model, with an overlapping generations model in mind, we show the following results: 1) The budget deficit including interest payments on the government bonds equals an increase in the savings from a period to the next period. 2) If the savings in the first period is positive, we need budget deficit to maintain full employment under constant prices or inflation in the later periods. 3) Under an appropriate assumption about the propensity to consume from savings, the debt-GDP ratio converges to a finite value. It does not diverge to infinity. The larger the propensity to consume from savings, the smaller the budget deficit required to achieve full employment. The larger the propensity to consume from savings, the less likely it is that the debt-GDP ratio will become large.

Motivated by the Blackorby-Schworm (1993) observation that market outcomes may differ from those originating in market-actor optimization, this paper claims that the number of banks in the market is larger than the number justified by bank profit maximization alone or in combination with bank depositor welfare maximization. This claim is made within the context of bilateral monopoly banks and intertemporal utility maximization by bank depositors. The basic policy implication towards bank population rationalization is a minimization of the deviation away from the optimal interest rate margin at every stage of the business cycle. It is meant to be an acyclical policy though the target of optimal bank population is attainable by active countercyclical policy as well. The nature of this policy issue makes the use of macroprudential measures imperative, jointly perhaps with a fiscal-monetary policy mix. A dynamic version of the model in a Cournot environment is akin to the modeling of Minsky's hypothesis of financial fragility.

This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as ’smart economics,’ this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics.  In the future, it’s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it’s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.

The Oswald hypothesis is that home ownership reduces mobility and through that channel results in poorer labor market outcomes. There has been only limited previous evidence on the Australian case. Here we use data from the first four Australian censuses of the twenty-first century, aggregated at the smallest geographical areas for which statistics are released. We propose testing the Oswald hypothesis by estimating the effect of home ownership on the full-time employment rate. Modelling the unemployment rate, as is often done in testing the hypothesis, produces implausible results with respect to the control variables, most likely due to the limitations of the official definition of unemployment. We find that, in modelling the full-time employment rate, the control variables for educational and demographic factors have the theoretically plausible effects, while variables capturing rates for both outright home ownership and with mortgage home ownership are positively related to full-time employment rates, with the effect of the former stronger than the latter. Our findings strongly contradict the Oswald hypothesis.

With the development of information technology and its application in environmental governance, the role of the internet in improving energy efficiency and reducing energy-saving potential (ESP) has attracted more attention. In this study, the slack-based model (SBM) and the unexpected model, along with the entropy method, were applied to measure China's energy-saving potential and internet development. Further, we empirically analyzed the direct effect, mediating effect, threshold effect, and regional heterogeneity of the internet on ESP. Our conclusion shows that there is a significant spatial correlation between internet penetration and ESP. Internet penetration has become an important tool for reducing ESP, but this effect shows regional heterogeneity. Human capital accumulation, financial development, and industrial upgrading are important influencing mechanisms, but indirect effects are weaker than direct effects. The impact of internet penetration on ESP is non-linear, and for improving human capital accumulation, financial development, and industrial upgrading, the role of internet popularization in energy conservation is more obvious.

College choice and choice of major are the most important decisions for future earnings. It is still unclear, however, what makes a greater difference—college or major—or whether a choice of college matters more for some majors, but not the others. Using cross-classified models and College Scorecard data, I show that a discipline is more consequential for future earnings than a college. The effect of STEM is substantial but is less pronounced at institutions with higher overall median earnings. The effect of college selectivity on earnings is more pronounced for non-STEM disciplines. Institutional characteristics—such as tuition, shares of graduates receiving different forms of financial aid, institutional size and location, and type of college—correlate with earnings of graduates. Racial and gender composition of an educational program correlate with expected earnings of its graduates even after control for other institutional and disciplinary characteristics. Models presented here provide a better understanding of the effect of college and major choices on future earnings.

The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.

This study employs empirical analysis using an econometric model that examines the interdependence among environmental degradation, exports, and economic development with energy use. It also provides an environmental Kuznets curve (EKC) for selected South Asian economies utilizing time-series data. The findings reveal a long-term, stable equilibrium link between energy demand and pollution. There exists a positive relationship between structural factors and pollution. Moreover, this study constructs a model of exports and pollution from an interdependent perspective. The three perspectives are tested: the scale and structure of energy consumption considering the twin constraints of export-trade and pollution, and the scale of pollution in export-trade constraint. These results show that the increase in energy use leads to higher CO2 emissions amidst export volume. However, in the presence of income, the scale of effect lowers a little. The analysis also supports the presence of Kuznets curve for south-Asian economies. The results imply substantial scope for development in the energy use and pollution structure within South Asia's current export trade process. This development can be attained by regulating energy use and enhancing system efficacy without necessitating changes to the scale effect or structural effect.