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.

The present paper empirically examines the validity of the environmental Kuznets curve (EKC) hypothesis, which posits an inverted U-shaped relationship between CO2 emissions and economic growth, in Egypt, India, Mexico, Pakistan, Thailand, and Turkey for the period 1970-2020. The EKC validity is highly debated due to varying results across countries. This study is significant to addresses this controversy by estimating the EKC's robustness while considering the influence of financial development, globalization, and government expenditure. We employ two cointegration techniques, the autoregressive distributed lag (ARDL) and vector error correction model (VECM), also considering potential structural breaks in the data. Out key findings indicate that the EKC hypothesis is supported in India, Pakistan, and Turkey, with carbon emissions eventually declining after reaching a peak. The hypothesis is not supported in Egypt and Thailand, where emissions rise monotonically with economic growth. In Mexico, the results are mixed, with ARDL suggesting a U-shaped relationship and VECM supporting the EKC. From our empirical analysis of the EKC hypothesis, we derive the following policy implications: 1) countries should adopt comprehensive policy strategies that integrate these goals and fully consider the long-term environmental and societal impacts of their decisions; 2) countries should invest heavily in research and development to accelerate the creation of new technologies that can effectively reduce carbon emissions; and 3) countries must actively raise awareness about the environmental repercussions of economic growth.

Alternative bio actively chemicals may be found in natural goods and traditional herb medications, but only a few plant-information formulations have been rigorously studied and verified for their potential as medicinal therapies. The study of plant-derived elements' immunomodulation capabilities and their ability as provoke the immune system as combat various elemental disorders like cancer is, nonetheless, a promising area in current therapeutics information on plant-derived chemicals. This research showed how network pharmacology may be applied as define and validate natural individual elements or more complicated preparations as prospective cancer therapies information on their various aim capabilities in this research. We give a summary of the present state of understanding on network pharmacology, with a particular emphasis on various technical methods and their implications for cancer treatment.

The workforce participation rate, and hence the level of employment, for women in Pakistan is among the lowest in South Asia – standing at 25 percent in 2023. Conventional explanations attribute this to poor skills and cultural norms of families and society at large. Empirical work has established that low levels of education, and community attitudes regarding gender roles, hinder women's labor force participation. Ethnographic work suggests that household rules – family culture – on who makes decisions about opportunities for married women to work outside of the home for pay – profoundly impact female employment. This is due to the limited availability of data pertaining to explicit assessments of familial traditions that are pertinent to women's participation in the workforce beyond their domestic sphere. Drawing on data from the Pakistan Social and Living Standards Measurement Survey (PSLM): 2005-2006 – which provides information, from married women, on who in the family decides if they can work – we address this shortcoming in the literature. We find that in families where the decision lies clearly, and exclusively, in the hands of males, married women are 18-19 percent less likely to be employed than if the family culture is for married women – alone – to make this decision. Moreover, this is a much larger deterrent to work than poor education and residential location.

The dawn of the Artificial Intelligence (AI) era presents a plethora of new possibilities for analyzing regional economic development. The present article provides an in-depth exploration of the methods employed in this field, highlighting the immense opportunities that AI offers while also addressing potential challenges. The role of AI is crucial in complex data handling, enabling efficient analyses of intricate regional economic patterns. This capacity is paramount in shaping economic policies and strategies that are reflective of each region's unique needs and potential. The article firstly explores various AI methods used in economic analysis, including but not limited to machine learning, deep learning, and natural language processing. It delves into the application of these methods in discerning development trends, predicting economic shifts, and identifying strategic economic drivers unique to various regions. Subsequently, the potential of AI to transform regional economic analysis is discussed, encompassing its capability to process large and complex datasets, its power to predict future trends based on past and present data, and its ability to aid in strategic decision-making. However, this new era of AI-driven economic analysis is not without challenges. The latter part of this article thus confronts the issues related to data privacy, ethical use of AI, and the necessity of interdisciplinary skills in AI and economics. This exploration contributes to a broader understanding of how AI is transforming the landscape of regional economic development analysis, illuminating both its present use and future implications. By understanding these dynamics, we can better harness the potential of AI to advance economic prosperity in various regions around the globe.

The paper addresses the alternative policy options available to address the question of lives versus livelihood in an SIRD model augmented with a macroeconomic structure. An important contribution of the paper lies in designing the policy of lockdown dependent on the extent of the constraint on the health facilities. The paper supplements the literature with a less stringent version of the lockdown policy, viz. soft lockdown policy which is shown to be more attractive from a public policy standpoint and has actually been practised in many countries across the globe during the recent pandemic. Finally, the optimal policy derived on the basis of the level of lockdown and adjustment of the binding constraint on health facilities depends on the objective of policy makers contingent on the relative weights of lives versus livelihood.

This paper evaluates the use of macro-level insurance to finance post-disaster recovery and reinstatement by investigating the case of the National Disaster Insurance policy implemented by Sri Lanka. The national scheme provided cover for all properties in the country against hazards such as flooding and provided compensation for those affected. Analysis of secondary data obtained from the organisations that managed the scheme showed that the scheme has delivered a much higher return compared to the total insurance premium paid, suggesting that the scheme has delivered a net positive benefit compared to the cost of the premium and can be considered a viable option. However, the existing secondary data did not reveal the values of other costs and indirect benefits associated with the scheme to compute a meaningful benefit-to-cost ratio. Further rigorous, evidence-based cost-benefit analysis is required to assess the cost-effectiveness of a scheme of this nature. A cost-benefit analysis approach based on an analytical hierarchy process is proposed as a possible solution to assess the cost-effectiveness of the scheme, which could be used by government organisations as an alternative, where analytical and research resources may be limited, to evaluate a range of disaster recovery financing options using stakeholder opinion.

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.

This study considers the effects of disclosure conditions on firms' investment decisions when facing an identical competitor in multiple markets. Assuming that there are congestion costs between multiple investments to reduce marginal cost for each market, this study focuses on the cases where the disclosure conditions may differ by market. Like previous studies on single Cournot competition, these results show that firms invest more in the observable investment markets than in the unobservable markets under symmetric disclosure conditions. However, firms invest more in the unobservable market if there are asymmetric disclosure conditions.

The aim of this article is to model a negotiation between shareholders in high-technology-risk industries and local residents on the safety investments to be implemented. The methodology used is a Nash bargaining model, with a DE curve representing shareholders' dividend demands and an NS curve representing the safety demands of local residents' associations. The model is used to determine the level of safety investment required. One of the main results is that the estimation of a higher accident risk is accompanied by both a higher safety investment and a higher dividend payout. The most obvious implication of this result is that it is undoubtedly necessary to give greater weight to local residents in investment decision-making, in order to improve the well-being of all stakeholders.

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.

In light of the events of 2020 and 2022, this study aims to examine the co-movements between the capital markets of the Netherlands (AEX), France (CAC 40), Germany (DAX 30), the United Kingdom (FTSE 100), Italy (FTSE MIB), Spain (IBEX 35), Russia (IMOEX), and spot prices of crude oil (WTI), silver (XAG), gold (XAU), and platinum (XPT) from January 1, 2018 to December 31, 2022. The purpose of this analysis is to answer the following research question: (i) Did the events of 2020 and 2022 increase the shocks between stock markets and WTI, XAG, XAU, and XPT prices? The findings indicate that time series do not follow a normal distribution and are stationary. In response to the question of investigation, we found that during the Tranquil period, it was possible to verify the existence of 28 causal relationships (out of 110 possibilities). During the stress period, there was a very significant increase in the number of causal relationships between the market pairs under analysis (62 causal relationships out of 110 possibilities), including a relative increase in the influence of commodities on capital markets and capital markets on commodities. These findings show that during the events of 2020 and 2022, capital markets and commodities significantly accentuated their co-movements among themselves, indicating that alternative markets such as WTI, XAG, XAU, and XPT do not provide safe-haven properties. These results have implications for portfolio diversification during times of global economic uncertainty.

The extensive, interdisciplinary nature of construction supply chains make them prone to inefficiencies at organisational interfaces. Inefficiencies are accentuated by the project-centric delivery paradigm, and complex logistics systems between multiple stakeholders. They manifest as a multitude of concurrent activities, processes, and systems both on and off-site. Transportation is the largest component of the logistics domain. Transport operations are inherently fragmented, intrinsic to every business, while vehicle ownership and deployment is typically externalised. Differentiated waste removal and materials delivery further disintegrate the already fragmented construction supply chain. Inefficiencies from the insularity of the planning process across segmental boundaries aggregate incrementally, with impacts visible at the macro level. Re-configuration of activities, resources and actors are acknowledged strategies for optimising logistics and transportation function. This paper investigates the impact of three integration strategies on a manufactured construction products supply chain efficiency. These strategies include vertical integration of distribution, integrated planning for transport operations, and integration of reverse logistics into operations. Sustainability impacts are evaluated according to domestically determined monetary parameters in order to benchmark performance at the business and national scale.

Gold has been traditionally well recognized as a safe heaven for financial markets. Lately, Bitcoin has been gradually considered as a popular alternative. Since the outbreak of COVID-19 in early 2020, it has become even more necessary and critical to examine the diversification capability of them to hedge financial risks associated with an unexpected crisis comparable to the pandemic. This paper hence employs the wavelet analysis, complemented by the multivariate DCC-GARCH approach, to measure the coherence of the gold and Bitcoin prices with six representative stock market indices, three for developed economies and three for emerging economies, all of which are heavily affected by the pandemic. To have a more balanced and comprehensive analysis, two-year data are used, spanning from 12th April 2019 to 15th April 2021, which covers approximately one year before and one year after the announcement of the COVID-19 pandemic. The results suggest that the returns of both gold and Bitcoin are generally not strongly correlated with the market returns of all six indices, particularly for short-term investment horizons. That is, investors in all six indices can benefit through gold, as well as Bitcoin, in terms of hedging. Meanwhile, compared with Bitcoin, gold shows to be less correlated with the indices, particularly for long-term investment horizons. The findings hence suggest that gold and Bitcoin offer diversification benefits to investors in the market indices during a crisis such as the COVID-19 pandemic, especially for short-term investment horizons. The study also reminds policymakers thinking beyond the pandemic about the future of the earth, including air pollution and health, for sustainable development of the whole world.

Cancer, a global health menace, continues to pose significant challenges in terms of incidence and mortality, necessitating innovative therapeutic strategies. Despite existing treatments, the limitations persist, prompting a quest for novel approaches. The emergence of immunotherapy marked a transformative era in solid tumor treatments, yet its efficacy is constrained by adverse effects. Concurrently, the integration of advanced technologies into cancer treatment explores the vast potential residing at the molecular level through gene analysis and manipulation. This review articulates the role of state-of-the-art genome editing technology, notably clustered regularly interspaced short palindromic repeats (CRISPR-Cas9), in overcoming the constraints of immunotherapy for cancers. Unveiling the intricacies of CRISPR-Cas9-mediated genome editing, the review introduces the formidable CRISPR toolbox. A spotlight is cast on the transformative impact of CRISPR-induced double-strand breaks (DSBs) on cancer immunotherapy, encompassing knockout and knock-in strategies. The utilization of CRISPR/Cas9 technology in pre-clinical cancer research has demonstrated notable success; however, its transition to the clinical setting remains in the nascent stages of development. This review aims to elucidate the fundamental aspects of CRISPR technology and offer a comprehensive survey of its existing applications while outlining its prospective role in the realm of cancer therapies. Through an exploration of CRISPR's mechanisms, current applications, and anticipated future potentials, this review provides valuable insights into the evolving landscape of CRISPR-based cancer treatment strategies.

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.

Overall development of a country largely depends on the economic policy instruments particularly fiscal and monetary policy to streamline the development and continue the developmental progress. These two policies have significant effects on long-term growth. It is noticed that policy adoption and reforms in both fiscal and monetary policies undertaken by Southeast Asian nations during the 1960s through 1990s have contributed to their advancement. This paper discusses the strategies for flourishing as emerging economies. Examples from Singapore, Thailand and Vietnam are highlighted in this study. It is found that prudent fiscal and monetary policy, effective discounting and interest rate; modernized tax system and most importantly policy regime are the contributing factors of these emerging economies. However, in spite of high-income growth and development because of supportive these policy initiatives, administrative and politico-economic constraints challenged the path of economies. Long-term development strategies are suggested to sustain the growth and continue the development pace.

Understanding the economic trends and demographic dynamics of a country is crucial for policymakers and researchers to formulate effective strategies and policies. This study aims to examine the GDP trends and aging index dynamics in Portugal from 2011 to 2021, with a focus on regional disparities and their implications for demographic challenges. The findings highlight the relationship between regional GDP and the aging index, indicating that regions with higher GDP values tend to have a relatively younger population, while regions with lower GDP values have a relatively older population. The study contributes to the existing literature on population aging by providing insights into the demographic, economic, and social implications of an aging society. It provides valuable insights into the demographic, economic, and social implications of population aging in Portugal. The findings contribute to the existing literature and can inform policymakers and stakeholders in developing targeted strategies and policies to address the specific challenges faced by each region.

Cancer treatment faces multiple challenges, including tumor heterogeneity, drug resistance, microenvironment influence, treatment side effects, and treatment cost. The heterogeneity of the tumor makes the effect of the same treatment vary in different patients, so the development of personalized treatment strategies is crucial. In addition, resistance of tumor cells to therapeutic drugs is a major challenge, and new strategies to overcome resistance are needed. As a cutting-edge field of science and technology, nanotechnology has brought great potential and opportunities for tumor treatment. Nanoparticle drug delivery systems improve drug efficacy and reduce side effects through precise targeted delivery and controlled release. Cell-membrane coated nanoparticles show great promise in tumor therapy. Nanoparticles coated with cell membranes have good biocompatibility, can reduce the obstacles of immune rejection and cell uptake, improve the accumulation and retention of drugs in tumor tissues, and have good drug delivery ability, drug stability and control release ability. This review discusses advances in the use of cell-membrane coated nanoparticles to target tumor drugs.

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.