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This study aims to analyze the risk profile of banks whose managers sit on Federal Reserve district bank boards in 2023. In particular, to analyze the impact tha Federal Reserve bank directors have on their own banks.

Use a matched sample approach to perform univariate analysis and multiple regression methodology to study whether banks whose managers sit on Federal Reserve Bank boards differ in risk profile from banks whose managers do not sit on Federal Reserve district boards.

There is limited evidence that banks managed by Fed directors have different capital ratios and leverage ratios relative to non-Fed director banks. There does appear to be a slight difference in the growth of Held-to-Maturity (HTM) Securities between the two samples. Specifically, banks managed by a Fed director saw their HTM portfolio grow over the study period, while banks managed by non-Fed directors reduced their HTM securities. Overall, the results suggest that bank directors on Federal Reserve district boards do so with no apparent detriment to the banks that they manage.

Results of this study suggest that stakeholder director relationships are not associated with higher risk-taking at director banks. This study is unique in that, rather than looking at how director ties might influence the firm that they are on the board of, the focus here is how the firm (the Fed district, in this case) might influence director affiliations. Limitations include a small sample size (70 banks, including the matched sample), and data over a short time horizon. Additional measures of risk can also be analyzed in future research.

While there has been much speculation in the industry and in the press regarding the conflict of interest involving bank directors on Fed district boards, this research suggests there is little evidence of any risk differential involving these directors and their specialties to the Fed.

This study involves a unique approach to corporate governance analysis, whereby any conflict of interest that might exist between directors and the firm is studied from an alternate angle – in particular, whether the association with a regulator’s board impacts the director firm’s risk. Furthermore, with the recent events in the banking industry involving the collapse of several banks, including Silicon Valley, the notion that bank management participating on the boards of directors of their own regulator seemed a worthwhile question as to whether this diminished the safety and soundness of the banks that they run.

The transmission of monetary policy has received considerable attention due to the sizable enlargement of the Federal Reserve’s balance sheet and consequently of the large reserve balances held by the Depository Institutions. This paper aims to investigate whether changes in the quantity of the reserve balances during the so-called normalization period and the COVID-19 crisis put significant pressure on short-term interest rates and specifically on the Effective Federal Funds rate (EFFR).

Under the new monetary policy regime, with two newly administered interest rates, the authors use the spread of the Federal Funds rates and the Interest on Reserve Balances (as a measure of the price of liquidity. With the means of various models such as the structural vector autoregression, the authors investigate, for two different subsample periods, the effectiveness of the monetary policy and the creation of (any) liquidity effects.

The results showed that when the Fed decreases its balance sheet size, during the normalization period, significant liquidity effects are present meaning that the authorities could influence the stage of the short-term interest rates under the new monetary policy regime. However, this relationship appears to weaken considerably as the level of reserve balances, particularly in response to the COVID-19 pandemic, increases substantially. The authors enriched the findings by highlighting the role of the benchmark repo rate. During the COVID-19 period, and in light of abundant reserve balances, the repo rate reacts more vigorously to a reduction in reserves, whereas an increase in the repo rate seems to exert a strong positive influence on the EFFR.

The findings are very important for the efficiency of the monetary transmission mechanism. An expanded balance sheet is still considered an arcane concept in regard to the structure and its effects on monetary policy implementation. This is one of the only few studies that investigates the effect of the abundant reserve balances on the short-term interest rates for two different in nature subsample periods. It shows as well the interplay between short-term interest rates, secured and unsecured.

This paper follows the call for more future-oriented practices within organisations, particularly in relation to how they respond to growing concerns about Earth’s sustainability and life on the Planet. This study aims to explore how the data produced by major scientific projects in the Space sector can support future-oriented accountability practices by enabling both a projection and an imagination of a more or less distant future, thereby feeding into accountability practices.

We rely upon a multiple interpretative case study analysis and interview-based data from three main organisations in the Earth observation (EO) value chain: an International Space Company, a Research Centre of Energy Transition and a European Private Equity Firm.

We find that future-oriented accountability practices can be fed by a creative assemblage of scientific data provided by Space sector’s programmes with different sources of knowledge and information. These data are embedded into a broader accountability system, connecting different actors through a “value chain”: from the data providers, gathering data from Space, to the primary users, working on data modelling and analysis, to the end users, such as local authorities, public and private organisations. The predictive data and expertise exchanged throughout the value chain feed into future-oriented accountability efforts across different time-space contexts, as a projected and imagined, more or less distant, future informs the actions and accounts in the present.

This research extends the literature on the time dimension of accountability. We show how a creative assemblage of scientific data with different sources of knowledge and information –such as those provided by Space sector’s programmes and EO data – enable organisations to both project the present into (a more or less distant) future and imagine this future differently while taking responsibility, and accounting for, what could be done and desired in response to it. We also contribute to the limited literature on accountability in the Space sector by examining the intricate accountability dynamics underpinning the relationships among the different actors in the EO data value chain.

This paper aims to analyse the persistence of the S&P500 and DAX 30 stock indices as well as of the Fed’s Effective Federal Funds rate and of the European Central Bank’s Marginal Lending Facility rate, and the long-run linkages between stock prices and interest rates in the USA and Europe, respectively.

The methodology is based on the concepts of fractional integration and cointegration.

Using monthly data from January 1999 to December 2022, the results can be summarised as follows. All series examined are non-stationary: stock prices are found to be I(1) while interest rates display orders of integration substantially above 1, which implies a rejection of the hypothesis of mean reversion in all cases examined.

This paper uses an appropriate econometric framework to obtain new, reliable empirical evidence. All four series are highly persistent, and mean reversion does not occur in any single case. Moreover, the fractional cointegration analysis suggests that stock prices and interest rates are not linked in the long run.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

This study aims to investigate the impact of terrorism on financial inclusion that is achieved through automated teller machine penetration and bank branch expansion.

Eight countries that are the most terrorized countries in the world were analysed using the panel fixed effect regression model and the generalized linear model.

The results provide evidence that terrorism reduces the level of financial inclusion in countries experiencing terrorism, but the presence of strong legal institutions, accountability governance institutions and political stability governance institutions mitigate the adverse effect of terrorism on financial inclusion.

A growing literature has shown that terrorism affects the economy, yet little is known about its impact on financial inclusion.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

While the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.

In the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.

An empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.

Compared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

The need to enhance student support is evident in higher education (HE) curricula. In addition to the complications created by the COVID-19 pandemic, the current strategies used in academia are criticised for their lack of appropriate student support in HE. The study focused on the themes under Section 4 of the National Student Survey (NSS): availability to contact tutors, receiving good advice and guidance and availability of good advice. The study aimed to provide recommendations for enhancing academic support by developing drivers that need implementation during course delivery.

A documental analysis and a qualitative survey were adopted for this study. A documental analysis of 334 mid-module reviews (MMRs) from levels three to six students in the built environment (BE) discipline. Critical themes identified from the MMRs were fed forward in developing a questionnaire for academics. A sample of 23 academics, including a Head of school, a Principal lecturer, Subject leads and Lecturers, participated in the questionnaire survey. Content analysis is adopted through questionnaire data to develop drivers to enhance academic support in BE. These drivers are then modelled by interpretive structural modelling (ISM) to identify their correlation to NSS Section 4 themes. A level partition analysis establishes how influential they are in enhancing academic support.

The study identified nine drivers, where two drivers were categorised as fundamental, two as significant, four as important, and one insignificant in enhancing academic support in HE. Module leaders’/tutors’ improving awareness and detailing how academic support is provided were identified as fundamental. Differentiating roles in giving advice and the importance of one-to-one meetings were identified as significant. A level partitioning diagram was developed from the nine drivers to illustrate how these drivers need to be implemented to promote the best practices in academic support in HE.

The identified drivers and their categories can be used to set prioritised guidelines for academics and other educational institutions to improve students’ overall satisfaction.

Novelty from the study will be the developed drivers and the level partitioning diagram to assist academics and academic institutions in successfully integrating academic support into HE curricula.

Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.

To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.

The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.

This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.