Strategically designing heterostructures incorporating highentropy metal particles and carbon fibers can enhance the defect density within carbon fibers, which enhances the boosted polarization relaxation effects within composite materials. Traditional synthesis methods primarily rely on hydrothermal processes and laser-assisted synthesis, which are complex and yield low amounts of raw materials. Moreover, the controllable design of metal-carbon materials with magneto-electric synergistic effects based on natural fiber substrates remains highly challenging. Therefore, there is an urgent need to develop a green, pollution-free, low-cost, and simple synthetic strategy.

Abstract

Purpose – Our study investigates how board co-option influences solvency risk in Australian and New Zealand banks. Board governance is considered one of the most critical variables impacting bank risk management practices and policies.

Design/methodology/approach – Our sample consists of commercial banks from both countries and data from 2011 to 2021. The results obtained were based on fixed-effect, 2SLS and GMM Models. Our results are robust to the other two measures of Board Co-option, Tenure-Weighted Co-Option and Residual Co-option, showing the applicability of our econometric model.

Findings – Results reveal that an increased proportion of co-opted directors on the board is associated with a notably reduced Z-Score ratio value, signifying an elevated level of solvency risk for banks. The evidence is consistent with the notion that co-opted directors bring about less effective board governance, escalating agency problems and enhancing solvency risk.

Research limitations/implications – The banks in these two countries must carefully establish a risk management framework under the Basel Accords to avoid risks like solvency risk. The regulators in the financial services industry may also devise mechanisms and regulate the banks under the second pillar of Basel-II and III, “Supervisory Review Process,” to avoid solvency risk management issues. Future researchers and scholars can extend the limits of future studies from two countries to various geographic locations, such as Europe, China and Southeast Asian regions.

Practical implications – Our study establishes the fact that banks in Australia and New Zealand are more exposed to solvency risk due to increasing board co-option phenomena at the board level.

Social implications – The unique measure of board co-option reveals the significance of board governance for bank risk management. To properly develop and implement bank risk management policies, the appointment and performance of board members must be actively monitored in Australian and New Zealand banks through a sensitive measure of board co-option.

Originality/value – Our study provides fresh insight and adds to the body of knowledge. It is a pioneering effort and a point of reference for forthcoming researchers, as there are either limited or no other such studies available in the literature to the best of our knowledge in terms of the relationship between Board co-option and solvency risk. A few previous studies are limited to US firms only.

Knowing the correct phase connectivity information plays a significant role in maintaining high-quality power and reliable electricity supply to end-consumers. However, managing the consumer-phase connectivity of a low-voltage distribution network is often costly, prone to human errors, and time-intensive, as it involves either installing expensive high-precision devices or employing field-based methods. Besides, the ever-increasing electricity demand and the proliferation of behind-the-meter resources have also increased the complexity of leveraging the phase connectivity problem. To overcome the above challenges, this paper develops a data-driven model to identify the phase connectivity of end-consumers using advanced metering infrastructure voltage and current measurements. Initially, a preprocessing method that employs linear interpolation and singular value decomposition is adopted to improve the quality of the smart meter data. Then, using Kirchoff’s current law and correlation analysis, a discrete convolution optimization model is built to uniquely identify the phase to which each end-consumer is connected. The data sets utilized are obtained by performing power flow simulations on a modified IEEE-906 test system using OpenDSS software. The robustness of the model is tested against data set size, missing smart meter data, measurement errors, and the influence of prosumers. The results show that the method proposed correctly identifies the phase connections of end-consumers with an accuracy of about 98%.

A speech study by a research team from The University of Texas at El Paso has identified an underappreciated aspect of speech in English and Spanish speakers that could lead to improvements in artificial intelligence spoken dialogue systems.