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.

A large body of research has been dedicated to the study of relationships between social or cultural capital and educational outcomes in Western countries. However, few studies have examined these associations in a Chinese context, and even fewer have examined the effects of both forms of capital on educational outcomes simultaneously within a familial context in China. This study offers a reformulation of the associations between family social capital and family cultural capital on the educational outcomes of adolescents in both rural and urban China. Using the structural equation modelling approach and the China Education Panel Survey, this study sheds some new insights – the presence of significant compositional differences in both family social capital and family cultural capital between rural and urban Chinese adolescents, and differential effects of both forms of capital on educational outcomes were found. Family social capital presented larger positive effects on the academic effort and educational aspiration of rural adolescents while having no positive effects in facilitating the academic achievement of both rural and urban adolescents. Meanwhile, family cultural capital presented larger positive effects for urban adolescents on all educational outcomes as compared to their rural counterparts.

The policy of ethnic equality and solidarity aims to narrow the ethnic gap and develop harmoniously. At the level of higher education, it should promote the equity of ethnic education. This paper uses the administrative data of undergraduate students from 2008 to 2015 in a liberal arts university in southwest China to study the differences, causes and dynamic trends in the scores of college students from different nationalities. The results show that compared with Han students, the average GPA of ethnic minority students is 0.13 points lower (equivalent to 0.23 and 0.3 standard deviations of the scores of ethnic minority and Han students), the fourth grade scores are 0.28 standard deviations lower, and 7.3% lower. This difference is more significant in the western region, especially in Xinjiang, Yunnan, Hubei and other provinces inhabited by ethnic minorities; Kazak and uygur are the nationalities with the biggest gap with han students; The difference between girls is more significant than that between boys. The "peer effect" and cultural distance between roommates are important reasons for this difference. Further analysis shows that although the ethnic differences in academic performance are gradually expanding, the growth rate tends to converge. The above conclusions are helpful to clarify the object and time of making national higher education policy, and also have important reference significance for primary and secondary education.

Utilizing a 2021 nationally representative sample of 7,504 White-owned employer businesses (WOBs) in the United States of America (USA), the extent, and the determinants of WOB business registration are estimated. Business registration is employed as a proxy for business in/formality. Approximately one-fifth (22.5%) of all employer WOBs are unregistered or informal business concerns hidden from government purview. The primary determinants of employer WOB informality are business size (annual revenues under $500,000 and few paid employees), a business owner with less than a four-year college degree, and an upbringing in a lower- or middle-class environment, among other findings. Notably, this research reveals the white lie of the magnitude of White-owned employer business informality in the USA.

To investigate successful technology transfer, the potential path of innovations from the university research bench to the knowledge recipient is modelled. Universities exist in highly regulated environments and the initial path of decision-making is a hierarchical model and where decisions flow upward from manager to manager until a small number of candidate innovations for commercialization remain. These are then routed for further processing to the link connecting to the knowledge recipient, the Technology Transfer Office (TTO). In the TTO, a hierarchical decision-making model can be acceptable in terms of outcomes, but ambidextrous co-operative team structures are much superior in cases where staff have good insight and decision-making abilities. This report represents the first Structured Equation Model investigation of the management architecture of a TTO.

This study delves into the complex relationship between China's international trade and economic growth, specifically focusing on emission intensity and its environmental ramifications within the context of energy efficiency. The primary objective is to provide a comprehensive understanding of how China's international trade dynamics influence economic growth, emphasizing emission intensity and employing a model that considers the nonlinear impact of trade on output. Through this model, structural breakpoints are identified, revealing distinct outcomes for China amidst positive and negative shocks from 1972 to 2021. The study uncovers the dual nature of the energy sector, acting both as a source of environmental degradation and a potential avenue for positive change through increased efficiency. Complex interplays among economic activities, inflation, and investment on carbon intensity are revealed. Additionally, the varying effects of export volume on carbon emissions intensity across lag thresholds underscore the significance of global trade dynamics for environmental sustainability. The study concludes by emphasizing the importance of balancing economic prosperity with environmental responsibility, advocating for carefully balanced policies that foster sustainable growth. Recommendations include considering the dual nature of the energy sector and implementing policies promoting increased efficiency. Policymakers are urged to adopt growth models prioritizing environmental responsibility, balancing economic activities, inflation, and investment to mitigate carbon intensity while acknowledging the diverse effects of global trade dynamics on environmental sustainability.

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 article discusses the pressing need to integrate artificial intelligence (AI) into education to facilitate customizable, individualized, and on-demand learning pathways. At the same time, while AI has the potential to expand the learner base and improve learning outcomes, the development of NACE Competencies and durable skills – communication, critical thinking, creativity, leadership, adaptability, and emotional intelligence - must be purposefully integrated in curriculum design now more than ever. Recent studies have shown that AI-driven learning pathways can achieve outcomes more quickly, but this comes at the cost of the development of durable skills. Therefore, traditional student-to-student and student-to-teacher interactions must be prioritized. As such, this study proposes a balanced approach to curriculum design to ensure the best outcomes for learners, where durable skill development is prioritized alongside subject-specific skills and rote memorization. Additionally, the article highlights the need for a combination of Just in Time Training (JITT) approaches, facilitated by AI technology, to reach the implementation of durable skills. The article concludes by questioning how to develop human skills in an increasingly AI-driven education system and emphasizes the importance of curriculum design and traditional learning approaches in creating a cohesive learning experience that develops durable skills in students. It is necessary to recognize that AI-driven education cannot replace the development of human skills, and that traditional interactions play a crucial role in developing these skills.

At present, global warming poses the greatest threat to the planet's ecosystem. China is the greatest CO2 emitter, expected to account for roughly 33% of overall emissions in 2021, and this has caused authorities to express significant concern in the most latest Paris accord, when they decided to cut global emissions to a particular level. In comparison, 55% of China's power in 2021 appears from coal. As a result, investigating the ecological elements that affect China's CO2 output is essential. This analysis utilizes the autoregressive distributed lag (ARDL) technique to investigate the relationships concerning fossil fuels, renewables, and economic growth from 1990 to 2021. According to the data, using fossil fuels to generate energy and increase GDP significantly increases CO2 emissions. Yet, the use of renewable energy reduces CO2 emissions both in the long run and in the short term. Overall, the outcomes imply that using renewable energy sources is key to reaching sustainable environmental goals and deters the usage of fossil fuels in the energy mix. This study suggests that China take into account empirical findings and undertake long-term initiatives to reduce carbon emissions in favor of sustainable environmental outcomes.

This study investigates the impact of financial inclusion on environmental sustainability in 178 emerging and developing countries from 1996 to 2022. Employing a composite index derived through Principal Component Analysis (PCA) as a measure of financial inclusion and covering four aspects (access, depth, efficiency, stability), our analysis reveals negative outcomes. The findings indicate that enhancing financial inclusion is associated with a notable increase in CO2 per capita emissions as well as in Total Greenhouse Gas emissions. We demonstrate that controlling corruption improves environmental quality, yet this measure alone is insufficient to fully mitigate the impact of financial inclusion, as indicated by our moderation analysis. The same analysis, however, shows that fostering globalization through trade openness is an efficient tool to alleviate the positive effect of financial inclusion on the quality of the environment. The study employs various policies targeting the control of development levels, energy consumption, natural resource utilization, industry, and urban population dynamics to contextualize the influence of financial inclusion on environmental sustainability. Through econometric methods and a comprehensive examination of the specified time frame, our results provide insights into the complex interplay between financial inclusion and environmental outcomes in diverse socio-economic contexts. The research contributes to the discourse on sustainable development by highlighting the potential of certain factors as a catalyst for environmental improvement. Understanding these dynamics is crucial for policymakers, as it underlines the trade-off between integrating inclusive financial strategies and achieving environmentally sustainable development trajectories in emerging and developing nations. Moreover, shedding light on the underlying mechanisms, such as trade-offs, fills a significant gap in the literature.

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.

The paramount prerequisite for effective anti-cancer drugs is their ability to eradicate malignant cells while sparing non-cancer cells. The divergence in properties between malignant and non-cancer cells establishes a "therapeutic window," a critical consideration for achieving desirable treatment outcomes. Central to this is the imperative of a cancer drug's "selectivity and specificity." Taxanes, a pivotal class of successful anti-cancer drugs, continue to serve as the linchpin of cancer treatment due to their efficacy across a spectrum of cancer types. Operating as broad-spectrum chemotherapeutic agents, taxanes exert cytotoxic effects on proliferative cancer cells by binding to and stabilizing microtubules, disrupting mitosis, inducing mitotic catastrophe, and resulting in cell death. The distinct proliferative nature of cancer cells, as opposed to less proliferative non-cancer cells, affords taxanes a measure of specificity and selectivity. Nevertheless, sporadic yet recurring evidence suggests that taxanes also operate through non-mitotic mechanisms. Taxanes' binding and stabilization of microtubules lead to micronucleation and subsequent cell death, impacting both mitotic and non-mitotic cells. Recent discoveries indicate that the flexible and weakened nuclear envelope of malignant cells renders them sensitive to taxane-mediated micronucleation and cell death during various phases of the cell cycle. Conversely, non-cancerous cells typically exhibit a more robust and sturdy nuclear envelope, rendering them more tolerant to taxane-induced nuclear envelope fragmentation and subsequent micronucleation and cell death. The expression levels of nuclear envelope structural proteins, particularly Lamin A/C, emerge as indicators of taxane sensitivity. This evolving understanding underscores that nuclear envelope malleability, in conjunction with a high proliferation rate, is a pivotal determinant of taxane specificity and selectivity against malignant cells. These insights necessitate reconsidering oncological strategies to augment taxane efficacy, overcome resistance, and mitigate side effects.

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.

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.

Excessive volatility in financial markets can disrupt economic activity, affect investor and consumer confidence, and potentially lead to financial crises in an economy. Due to this backdrop, this study examined the link between asset class volatility and the output gap in Nigeria. The asset classes were categorized into stock, crude, gold, and bitcoin. The study adopted the GARCH and Bayesian VAR approach and found that all share index has an initial negative impulse with output gap while other asset classes have a positive impulse on output gap. The outcome of this study revealed to both policymakers and economists the potential risks and vulnerabilities of asset class volatility in the economy. Based on this result, recommendations are made amongst which is the strengthening of the Nigerian stock market to help with the inflationary pressures this is because the Nigerian stock market hurt the output gap also, the government should prioritize investing in crude, gold, and bitcoin to push the actual output to full capacity, which brings about employment.

Machine learning is playing an increasingly important role in the healthcare industry by transforming the way cancer is diagnosed and treated. By analyzing patient data, genomic data, and imaging data, machine learning algorithms can identify molecular signatures that distinguish cancer patients from healthy patients. Biomarkers that can accurately detect and diagnose cancer can be identified through analysis of these data sources. Additionally, personalized cancer therapies can be developed by identifying the most effective treatments based on individual patient characteristics and cancer type. Some of the machine learning techniques used for cancer biomarker discovery include deep learning and support vector machines, which can respectively identify complex patterns in data and classify data to identify relevant biomarkers. The benefits of using machine learning for cancer biomarker discovery are significant, including more precise and personalized treatments, improved patient outcomes, and the potential to transform cancer diagnosis and treatment. However, there are also challenges associated with using machine learning for cancer biomarker discovery, such as data collection and privacy issues, as well as the need for more powerful computational resources. This article explores the potential of machine learning in cancer biomarker discovery and argues that ongoing research in this field has the potential to revolutionize cancer diagnosis and treatment. Future research directions should focus on further developing machine learning algorithms and effective data collection and privacy protocols.

The expansion of urban infrastructure is an important indicator of agglomeration and a major factor in the deterioration of the urban environment. The investment in urban infrastructure is accompanied by the sealing of ground. The implementation of effective policies to reduce the practice of sealing ground is impeded by the existence of conflicting interests and fiscal disincentives. A significant challenge is the dearth of policy-relevant information. Conventional analysis considers urban outcome indicators in proportion to population and neglects non-linear distribution patterns, thus obscuring global regularities that contain important information. The study contributes to a more comprehensive understanding of urban imperviousness through the lens of urban scaling. The present study employs Germany as a case study to examine the extent to which the parameters of the scaling function differ between two types of cities. District Affiliated Cities (DACs) and Administrative City Districts (ACDs), which are afforded greater fiscal autonomy. The analysis indicates that the amount of sealed land exhibited a notable increase between 2006 and 2018 in the ACDs. Calibration of the scaling functions for cities with the actual coefficient of the DAC control estimate and a global orientation exponent (5/6) resulted in a 16% reduction of sealed ground. In conclusion, urban scaling laws offer a valuable tool for analyzing the complex dynamics of urban imperviousness and its control costs, thereby contributing to the formulation of more effective urban policies and planning.

Digitalization is nowadays one of the fastest developing processes. The adoption of digital technologies can provide innumerous opportunities for the organizations to evolve and gain competitive advantage by leveraging of technologies to respond to dynamic expectations and demands. Information about the country digitalization level is essential to decision makers in both public and private organizations. It can present insights on which areas need the most investment, and furthermore to gain feedback on the outcomes of these investments. For companies, it can create a certain predictability on which products and services will be required the most. To assess the evolution of this process in different countries, various indexes were proposed and employed by different corporations. The Digital Economy and Society Index (DESI) and ICT Development Index (IDI) are used by public organizations. Digitalization Index (DiGiX) and Cisco Digital Readiness Index (CISCO) are used by private companies. Comparing two sectors highlights the most common factors of digital evaluation. Indexes are analyzed thoroughly by their structure, coverage, weights, methodology and ranking. The result of the practical work is a equivalence table which shows the percentage of their similarity. Additionally, a new digitalization index is proposed, based on the result of the previous comparison, which can be applied to analyze both public and private sector of the country’s digitalization level.

The rise of privately issued digital currencies, which primarily serve as alternative investment assets poses a challenge to the traditional financial instruments traded in the financial market. This study examines the dynamic relationship between the major privately issued digital currency (Bitcoin) and two financial market securities in Nigeria. The paper employed Vector Autoregressive (VAR) model and presents three relevant findings. First, the impulse response function indicates the absence of a significant response of the Nigerian financial market to shocks emanating from the Bitcoin market, implying lower connectedness between the two markets. Secondly, the outcome of the variance decomposition reveals a lower contribution of Bitcoin to changes in stock prices and treasury bills, however, stock prices and treasury bills contributed higher impact to each other compared to the contribution of Bitcoin. Thirdly, a weak bi-directional causality between the Bitcoin and treasury bills was observed and a uni-directional causality running from treasury bills and stocks, implying the existence of portfolio rebalancing from the fixed income to the equities market. Despite the weak connection between digital currency and the financial market, the paper recommends that the Central Bank of Nigeria and the Securities and Exchange Commission should maintain monitoring the development of crypto exchanges and continue reviewing the existing policy restricting cryptocurrency transactions through banks to avoid its unsavoury effects.

The objective of this study is to explore the link between the financial sector, macroeconomic fluctuations, and sustainable energy production in South Asia. It evaluates existing research, identifies gaps, and suggests future directions. The study emphasizes the need to understand how economic activities influence the environment and how environmental factors impact economic and financial outcomes. It also examines financial market responses to understand the manifestation of economic and environmental factors, using data from Bangladesh, India, Pakistan, and Sri Lanka spanning 1987 to 2022. The results show that investment in green energy is essential for developing countries. The study suggests that the relationship between the region's stock market, GDP, carbon emissions, and green energy is complex, with a negative response to domestic credit indicating potential obstacles to investment, such as policy barriers or insufficient incentives. Mixed responses highlight the need for credit directed towards sustainable projects. Variations in responses across economic indicators may indicate policy challenges or structural issues, necessitating a reassessment of government policies to incentivize sustainable investments and promote green technology adoption. These results imply that policymakers should precisely assess the environmental ramifications of economic growth and stock market activities. This presents an opportunity to craft policies that guide investments toward cleaner technologies and industries, aligning economic growth with sustainable practices. Policymakers and financial institutions should explore ways to incentivize green energy investments and promote sustainable economic growth.