This paper empirically investigates the relation between firm performance in corporate social responsibility (CSR) and the need and likelihood of external financing to test the predictions of agency and stakeholder theories. Empirical results from Logit, Linear Probability Model, OLS and Firm fixed effects regressions indicate that CSR is negatively related to the likelihood and level of external financing. Further analysis indicates that CSR has a negative and significant effect on both net equity issued (NEI) and net debt issued (NDI), the two components of external financing. Overall, the empirical results support the predictions of agency theory.

There is a considerable amount of debate on the impact of capital liberalization on economic performance. Using Three-Stage Least Squares (3SLS) estimation technique introduced by Zellner and Theil (1962), we synthesize studies on the determinants of governance and capital flows. We find evidence of a revolving door relationship. Foreign aid has a negative impact on governance and, thereby reduces capital inflows since bad governance hinders capital inflows. The need to fill the gap created by private capital outflows encourages inflow of foreign aid, which in turn harms governance. Therefore, capital liberalization could grease a revolving door and undermine economic development in the aid receiving countries.

Investment funds are increasingly focusing on ESG issues, seeking to contribute to improving environmental, social, and governance concerns. ESG-focused investing involves promoting good ESG performance and may result in a decrease in expected financial returns. This may lead to a conflict between solely profit-focused shareholders and ESG-focused investors, who may respond accordingly. As such, a firm's ESG commitment may trigger several events that can affect the stock price. A firm's ESG commitment may increase its stock price; however, the end of such commitment may also positively affect the stock price. Hedge funds may consider this in their activism which may reshape the way hedge funds target firms and influence their actions accordingly.

 In the past, researchers investigated the immune response acting against mycobacterial antigens in tuberculosis (TB). Several ELISA tests have been conducted for the diagnosis of tuberculosis. Other researchers manipulated 16 kDa antigen from Mycobacterium Tuberculosis to develop a tuberculosis diagnostic test while some others studied the subspecies of M. tuberculosis. In the present study, we explored the possibilities of detecting 16 kDa protein derived from M. tuberculosis, on an interdigitated electrode (IDE). Measurements with Electrochemical impedance spectroscopy and current-volt systems were demonstrated using antibody as the probe.  Further, the involvement of gold nanoparticle for the signal-enhancement and high-performance are evidenced. The specificity in the serum containing samples is used to show the clinical relevancy. 16 kDa antigen used in this experiment is highly reliable for TB diagnosis as it largely expresses and can be implemented in other sensing system.

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 investigates the likelihood of time to bank failures in the US between 2001 and April 2023, based on data collected from the Federal Deposit Insurance Corporation's report on "Bank Failures in Brief - Summary 2001 through 2023". The dataset includes 564 instances of bank failures and several variables that may be related to the likelihood of such events, such as asset amount, deposit amount, ADR, deposit level, asset level, inflation rate, short-term interest rates, bank reserves, and GDP growth rate. We explore the efficacy of machine learning survival models in predicting bank failures and compare the performance of different models. Our findings shed light on the factors that may influence the probability of bank failures with a time perspective and provide insights for improving risk management practices in the banking industry.

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.

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.

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.

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

Influenced by traditional notions of solidarity, when a province’s planning can be highly aligned with that of the central government, the province is perceived to be more collective and thus able to receive financial or resource favors from the central government. This consistency, as is often the case, reflected in doing the same thing as the central government. This situation may lead governors to ignore local economic performance and thus reduce regional innovation efficiency, as in the case of China’s Great Leap Forward. Likewise, it is possible to get better resources (energy or capital) by demonstrating managerial submissiveness, thus improving the regional innovation efficiency. Therefore, to verify the relationship between imitative governance and regional innovation efficiency, we collected relevant data from 31 major provincial administrative units in China, calculated the degree of imitative governance between provincial government work reports and central government work reports through text similarity, as well as utilized the SBM-DEA model to evaluate regional innovation efficiency. Meanwhile, we provide a new explanation of the phenomenon from the perspective of resource inclination. Finally, the empirical results show that imitative governance promotes local innovation efficiency and is moderated by resource inclination.

Nanozyme with biomimetic enzyme activity has the advantages of good catalytic performance, high stability, and is not easily affected by temperature. However, the application of tumor microenvironment (TME) in the tumor is limited due to its low catalytic activity. Herein, a multifunctional nanozyme based on CuCoP with multivalent metal ions (Cu+/Cu2+ and Co2+/ Co3+) provides an innovative strategy for constructing acid-responsive cancer therapy. CuCoP has excellent photothermal properties (photothermal conversion efficiency of 66.9%) as well as glutathione (GSH) peroxidase activity and high photo-enhanced peroxidase-like (POD) enzyme activity in weak acid TME. CuCoP can consume the overexpressed GSH, thus alleviating the antioxidant capacity of the tumor, and reduces Cu2+/ Co3+ to Cu+/ Co 2+. Subsequently, the generated Cu+/Co2+ will react with endogenous H2O2 to generate cytotoxic hydroxyl radical (•OH), which have high catalytic efficiency in weakly acidic TME. Crucially, the synergistic effect of PTT and the cascade reaction of bimetallic atoms with GSH and H2O2 can effectively reduce antioxidant capacity, inhibit tumors and trigger effective immune cell death (ICD) process, which caused a strong immune response and inhibit tumor recurrence and metastasis. As a novel bimetallic sulfide nanoenzyme triggered by TME, CuCoP has great research value in cancer therapy.

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.

This study examined the determinants of the exchange rate in a mono-resource economy, during the period of currency and oil price fall in Nigeria. The aim of the study is to ascertain the potentiality of economic diversification (non-oil export) in saving the falling value of the domestic currency, as well as, its stability. The study employed the use of monthly data from the Central Bank of Nigeria and Nigeria National Petroleum Corporation from January 2008 to December 2020 using the ARDL and NARDL models to achieve the stated objectives. In the literature, several empirical studies analyzed the relationship between exchange rates, oil price, and oil export and came to the logical conclusion that a rise in the price of oil would bring about a rise in the exchange rate, all things being equal while the reverse is the case. To add to the existing body of knowledge this study did not only look at the relationship between the variables but also examined the determinants of the exchange rate in a mono-resource economy and ascertained the potentiality of economic diversification in bringing succor to the falling exchange rate in the midst of falling oil price. The NARDL result corroborates the findings of previous studies on the relationship between exchange rate, oil price, and the performance of non-oil export. It maintains that positive changes in oil prices would lead to the appreciation of domestic currency while negative changes in oil prices would lead to the depreciation of the domestic currency. With this analysis, the study recommends the diversification of the economy and revitalization of the non-oil sector. Since import proves to have a negative impact on the exchange rate, the revitalization of the non-oil sector stimulates domestic consumption and thus reduces import bills which raise the demand for the dollar and weakens the naira.