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.

Deposit insurance systems are designed to balance the benefits of preventing bank runs and protecting ordinary savers against the costs of reduced market discipline and potential burdens on taxpayers. Design flaws of deposit insurance make the benefits too low and the costs too high. This paper presents an example in which solvent banks can effectively manage runs, depositors discipline banks to a reasonable extent, and taxpayers have a fair deal. It has three key features: the bank’s authority to activate deposit insurance early, a coinsurance scheme that transfer money from those who run on solvent banks to those who stay put, and a shareholder position for taxpayers. Early activation of deposit insurance prevents fire sales of assets and provides opportunities to verify the bank’s solvency. The coinsurance scheme weakens the incentive to run and strengthens the incentive to hold on to their accounts. As shareholders, taxpayers receive dividends in normal times in exchange for large payouts in catastrophic events.

This study provides insight into CEO compensation dynamics in the public sector and private sector publicly listed firms in New Zealand. This research uses descriptive statistics, OLS regression, and the difference-in-difference method to analyze the compensation-performance relationship for the period 2005 to 2012. Our findings show that CEOs in the private sector publicly listed firms are receiving higher remuneration benefits. Our results suggest that firm sales and past compensation are the most important determinants of CEO cash-based as well as total compensation. Firms with a larger board size and the presence of a formal remuneration committee are likely to provide higher cash compensation than those without.

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.

This paper presents an analytical framework to assess the probability of achieving nationally determined contributions (NDC). The prediction model based on the Kaya identity is used to simulate the pathway of carbon emission until the target year. Applying the modified STIRPAT framework (named CO-STIRPAT) to data observed in South Korea shows that the probability that the predicted pathway with existing climate technology will stay above the NDC target pathway is significantly high. The result suggests that it is necessary to design a climate policy to improve energy intensity and carbon intensity by accelerating the advance in climate technology.

Information leakage in the stock market has been widely proven. Information disclosure is sometimes uneven, and there is significant information asymmetry between ordinary investors and professional institutional investors. In this paper, Regression Discontinuity design (RDD) model is first employed to analyze the information leakage issues. Based on the daily closing stock prices of 15 capital service listed companies, we analyze the difference between the market reaction time and the disclosure time of two stamp tax policies. We found that the sample policies information may leaked to the market about two days earlier. This paper provides a new method analyzing information leakage.

This paper investigates the impacts of COVID-19 on women’s employment and gender disparity with a longitudinal dataset spanning the pandemic. We exploit the regional intensities of social vulnerability and temporal variation to implement the difference-in-differences (DID) estimation. The results indicate that the pandemic and its associated lockdowns generate a significant and negative impact on women’s employment but not on men’s employment. Moreover, a counterfactual analysis using pre-pandemic data further supports the causal nature of the documented relationships. The evidence suggests that economic downturns caused by public health emergencies, unlike previous economic recessions, have a greater impact on women, and differentiated policies should be designed.

In reality firms most often face negatively sloped demand curves. Then, for a given level of consumers’ surplus, levies on prices yield higher fiscal revenues than specific duties. Therefore, according to the prevailing view, the switch from unit to ad valorem taxation is supposed to generate more welfare; some even speak of an associated Pareto-improvement. However, this is not true because taxing prices merely transfers profits to the Treasury, while total rent remains unaffected. Since excise duties diminish the welfare gain in comparison with untaxed trade, an appropriately designed income tax allows all parties to benefit. Sales should be taxed only exceptionally.

We quantify financial returns to backers in reward-based crowdfunding projects on Kickstarter and show that such investments provide profitable opportunities in addition to non-monetary benefits. The average unconditional annualized return is 11.5% and the average return on successful projects is 30%. Hence, backing money near the end of a campaign, when the probability of success is already high, is a profitable strategy. The most attractive is the Design category, where successful projects yield 73%, on average. Short-term projects are more profitable than long-term ones. Financial return is an important type of extrinsic motivation in reward-based crowdfunding, which has generally been neglected in academic literature. Reward-based crowdfunding outperforms other forms of crowdfunding and other common alternative investments.

According to economic theory and common sense, the issuance of consumption coupons on one hand stimulated the increasing of residents' consumption, but on the other pushed up the price level. In this paper, using the theory of mechanism design, the game model was built to analyze the relationship between the effect of consumer coupons during the epidemic and the number of merchants participated. The results show that the smaller the merchants’ number, the weaker the coupons’ effect, vice versa. Then, using KNN regression method, combined with the monthly year-on-year data of CPI from 1998 to 2020, this paper analyzes the changes of CPI forecast value and actual value in the first 10 months of 2020, finding that the actual value of CPI is higher than the predicted one during the first two months of 2020, but lower from March 2020 on, which further verifies the conclusion of game analysis.

Cardiovascular disease is still a disease with high incidence rate and mortality. Although advanced technology continues to increase our understanding of cardiovascular disease, its diagnosis and treatment still have limitations. As an emerging interdisciplinary method, nanotechnology has shown enormous clinical application potential. Nanomaterials have unique physical and chemical properties, which help to improve the sensitivity and specificity of biosensor technology and molecular imaging technology in the diagnosis of cardiovascular diseases. This paper first summarizes the versatility of nanomaterials, the physicochemical adjustability of biomolecular engineering, the design strategy of nanoparticles in cardio cerebral Vascular disease, the application of nanomaterials in the diagnosis and treatment of common cardiovascular diseases, and the use of nanomaterials can significantly improve the diagnostic sensitivity, specificity and therapeutic effect. Subsequently, the article summarized various nanomaterials. Finally, the article demonstrated the potential of the antioxidant/anti-inflammatory and photoelectric/photothermal properties of nanomaterials to be directly applied to the treatment of cardiovascular diseases.

Foreign direct investment (FDI) is widely viewed as a key driving force behind China’s exceptional growth performance in the last four decades. This paper investigates several questions posed by China’s success in capturing gains from FDI. What explains that success? Can other countries replicate it, or is it unique to China? What lessons are from China for other countries? China indeed has advantages in attracting FDI such as huge market and cheap labor, the well-designed policy and strategy, however, seem to play more important role in the successful story. Our empirical estimates support on four hypotheses: FDI and GDP growth in China positively interact each other; FDI is helpful to China’s technological progress, FDI promotes China’s industrial development, and FDI stimulates China’s manufactured exports.

We offer an extensive analysis of the significance of information within the realm of Gaussian quadratic economies. We build upon the seminal papers of Morris and Shin (2002, 2007) and consider a signal game of incomplete information. Particularly, we question the suitability of partial transparency portrayed by fragmented information in addition to the private signal in terms of welfare effects. We can summarize our findings in two main points. First, fragmented information, in conjunction with a private signal, can reduce the reliance on public signals. Second, a conflicting effect arises between increasing full disclosure and increasing the precision of fragmented (semi-public) information when examining different complex scenarios, involving for example endogenous private information or imperfect correlated signals. For a critical threshold, an optimal communication strategy designed by fragmented information should be implemented whenever that kind of information is acquired at a high precision.

Metadherin (MTDH), also known as LYRIC or AEG-1, is an oncogene that enhances tumor progression, metastasis, drug resistance, and immune escape in various cancers by modulating multiple oncogenic pathways, including NF-κB, PI3K/AKT, Wnt/β-catenin, MAPK, and AMPK. Due to the unknown of the complete structure of MTDH, the deep mechanisms of MTDH and selective inhibitors targeting MTDH remain to be explored. The Protein-Protein interaction (PPI) with the Staphylococcal nuclease domain containing 1 (SND1) is a crucial mechanism underlying the function of MTDH. Current studies have demonstrated that inhibitors, including antisense oligonucleotides, peptides, and small molecules targeting MTDH or MTDH-SND1 interactions, provide novel strategies to inhibit the oncogenetic effects of MTDH. This review summarizes and discusses the structure, function, and regulation of MTDH in cancers, providing the potential therapeutic perspectives of MTDH or MTDH-SND1 PPI for drug discovery.

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.

Income and population growth are key determinants of housing demand, while land use regulations are designed to affect housing supply. Previous studies of housing price determinants focus either on specific regulations in particular cities/regions, or on selective subsets of major cities and regulations. This study examines the impact of land use regulations on housing prices from 1989 to 2006 in an unusually large sample of 250 major US cities. Aside from factors that are commonly associated with housing demand (income, population growth and density), housing prices are found to be associated with local cost-increasing land use regulations (approval delays) and with statewide regulations. Since statewide regulations factor prominently into the results, specific examples of the impact of different types of land use regulations are provided for 5 cities in the state of Washington. The estimated increase in housing prices associated with regulations is, on average (over 250 cities), substantially larger than the effects of income and population growth. While the estimated dollar costs associated with regulations may be sizable at times, the results are remarkably consistent with previous studies that were based on smaller cross sections.

As prices have grown at their fastest pace in recent times, inflation has become a key concern for the macro-policy environment. In many jurisdictions, consumer prices have typically formed the basis for price stability policies. Notwithstanding, producer prices remain an important channel and must be closely watched. We utilise data on Ghana and investigate the causal links between consumer and producer inflation and assess the necessity to include producer inflation target in the monetary policy rule. Our VECM and Granger causality analyses show that consumer and producer prices exhibit very stable long-term relationship and short-term gaps between the two tend to normalise over time. The relationship between consumer and producer prices has not been a one-sided lag structure, even though producer prices lead more than lag consumer prices. We conclude that Bank of Ghana’s monetary policy design that does not distinguish between consumer and producer inflation is less problematic at the moment.

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.

The digital economy industry plays a transformative role in reshaping the global production networks, the local division of production, and the integration and innovation of business forms. However, to what extent the industry enables the high-quality development and upgrading of urban economies is unclear. Based on the census data of digital economy enterprises in Hangzhou in 2008, 2013 and 2018, this paper verifies the characteristics of the digital economy industry and its enabling mechanism for high-quality urban economic development. It is found that: firstly, the digital economy industry has three main forms for the economy, namely, the sharing, scale and scope economy. Secondly, the industry has four major mechanisms to improve the quality of urban economies. That is, it can restructure the global industrial division of labor and value chains, promote the transformation of scale economy from the supply side to the demand side, enable the softening and servitization of industrial structure, and generate the scale of economies. This paper thus contributes to understanding the evolution of enterprise organizational structure and industrial competitiveness and the endogenous technological change under the digital economy.

This study examines the role of news sentiments in the GCC equity markets’ connectedness. We collected news titles for the period from 22nd June 2006 until 31st December 2020 from Gulf News, which is the most widely read English newspaper in the Arab World. We filter these news titles using a carefully designed list of keywords that capture public sentiment on matters related to financial markets. Next, we classify the news titles to compute the geographically distinguished sentiment indexes that allow for a detailed analysis of the source of news sentiment spillovers to compare the impact of domestic versus regional sentiments on the equity markets of GCC countries. Our quantile regression results reveal that equity markets in the GCC are most sensitive to news sentiments when underperforming. Moreover, our results from the connectedness approach suggest that the UAE equity markets are most influenced by domestic sentiments, whilst the KSA equity market is most influenced by regional sentiments from the other GCC countries. Mixed results are found for other countries. The time-varying component of this study also shows that the influence of news spillovers intensified during the major crises events, including the COVID-19 outbreak.