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The recent financial crisis led to the expansion of deposit-insurance coverage in many countries. We develop a structural model of the banking market in which banks act as financial intermediaries between consumers who have funds and businesses that seek loans, and explore the implications of such policies for banks and depositors. Our results indicate the policy could erode market discipline and increase banks’ moral hazard. As a result, banks extend their lending to riskier loans than they would have in the absence of the policy. We find this policy may even harm consumers. Moreover, market competition magnifies the lack of market discipline and induces additional moral hazard for excessive risk taking. Counterfactuals indicate banks may reduce their deposit interest rates by 2.7% in a duopoly market and almost triple their risk caps under the new policy. The estimated losses of depositors’ welfare are equivalent to at least a 3.27% drop in deposit interest rates. Data are available at https://doi.org/10.1287/mksc.2016.1009 .

Microentrepreneurs in emerging markets often rely on informal lenders for their routine borrowing needs. This paper investigates informal lenders’ and microentrepreneurs’ incentives to participate in a lender–borrower relationship in a market in which repayments are neither law protected nor asset secured. We consider a borrower who seeks a short-term loan, invests in a project, and repays in full using her project earnings if the project is successful. If the project fails, the borrower uses her outside option to repay over a period of time. The analysis uncovers an interesting effect of the borrower’s outside option on the loan rate offered by the lender—the loan rate first increases and then decreases with the borrower’s outside option. An important policy implication is that an increase in the outside options of the poor microentrepreneurs might actually reduce their surplus. Finally, we find that lenders in emerging markets may be more likely to engage in informal lending compared to those in developed or poorer markets.

Composite insulators have gradually become the preferred approach for electrical insulation in power systems, especially in polluted areas. Composite insulators consist of three main components: the shed, rod, and end fitting. Insulators withstand mechanical stresses via rods that are composed of glass-fiber-reinforced epoxy (GFRE). However, regardless of the high tensile strength of GFRE rods, in real-life operation, abnormal fractures have frequently been reported all over the world, which substantially increase the risk of major accidents in power systems. Fractural accidents mainly consist of brittle and decay-like fractures, which exhibit rather different morphologies at the cross sections. Brittle fracture has been effectively eliminated, while the mechanism of decay-like fracture has still not been clearly revealed. In this study, surface discharge tests were applied to investigate the discharge influence on the degradation of GFRE. The test successfully simulated the composition variation of the rods in real-life composite insulators with decay-like fractures. Moreover, it confirmed that the distinction between the characteristics of brittle fracture and decay-like fracture stems from epoxy degradation due to hydrolysis and carbonization. In addition, the respective influences of the resin type, glass fiber type, and acid liquid immersion on the degradation process were probed, and the degradation mechanism proposed in this research was verified. Based on the results, measures for preventing the development of decay-like fractures in real-life operations were determined.

Silicone rubber composites filled with nano-silica are currently widely used as high voltage insulating materials in power transmission and substation systems. We present a systematic study on the dielectric and mechanical performance of silicone rubber filled with surface modified and unmodified fumed nano-silica. The results indicate that the different interfaces between the silicone rubber and the two types of nano-silica introduce changes in their dielectric response when electrically stressed by a sinusoidal excitation in the frequency range of 10−4–1 Hz. The responses of pure silicone rubber and the composite filled with modified silica can be characterized by a paralleled combination of Maxwell-Wagner-Sillars interface polarization and DC conduction. In contrast, the silicone rubber composite with the unmodified nano-silica exhibits a quasi-DC (Q-DC) transport process. The mechanical properties of the composites (represented by their stress-strain characteristics) reveal an improvement in the mechanical strength with increasing filler content. Moreover, the strain level of the composite with a modified filler is improved.

Laser welding of thick plates has been used in automobile and shipping industry owing to its focusing heat input, little welding deformation, and high productivity. Using narrow-gap technique can decrease the filling volumes of wire and increase the welding efficiency. However, its process is more complicated since it introduces filler wire to narrow-gap weld configurations. Usually the welding path, planning mainly depends on man’s experiences. Human factors have big influences on the quality of the joint. The aim of this paper is to study the interactions between welding parameters and the geometry of single bead using statistical methods. Then, the founded models were employed to plan the welding path, the number of filling passes, and layers in the multi-pass narrow nap joining. These results will provide supports for automatic multi-pass laser welding. The welding system includes a 10-kW Nd:YAG laser system, a KUKA robot, a Fronius wire feeder, and a 20-mm-thick high-strength ship steel with narrow gap. Laser power, welding speed, and wire feed rate are input variables. The responses include the transversal area of the added metals, bead width, bead height, and the ratio of bead width to height. Verification experiments indicate that all these models can forecast the responses within the factor domain. Transversal section micrographs of the joints show that this method can get welding joint with less defects.

The mechanical system dynamics software,ADAMS,is used to establish multi-body dynamics system model for a truck front suspension and steering system. Through the simulation test of wheel travel, front wheel alignment parameters changing along with the wheel travel was obtained.

Generative AI tools such as ChatGPT have recently gained significant attention in higher education. This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions. Our study examines ChatGPT policies implemented at universities around the world, including their existence, content, and issuance dates. Specifically, we analyzed the top 500 universities according to the 2022 QS World University Rankings. Our findings indicate that there is significant variation in university policies. Less than one-third of the universities included in the study had implemented ChatGPT policies. Of the universities with ChatGPT policies, approximately 67 percent embraced ChatGPT in teaching and learning, more than twice the number of universities that banned it. The majority of the universities that ban the use of ChatGPT in assessments allow individual instructors to deviate from this restrictive policy. Our empirical analysis identifies several factors that are significantly and positively correlated with a university's likelihood of having a ChatGPT policy, including the university's academic reputation score, being in an English-speaking country, and the general public attitudes toward ChatGPT. In addition, we found that a university's likelihood of having a ban policy is positively associated with faculty student ratio, citations, and the English-speaking country dummy, while negatively associated with the number of peer universities within the same country that have banned ChatGPT. We discuss the challenges faced by universities based our empirical findings.

Interruption generally has a negative effect on performance by affecting working memory (WM). However, the neural mechanism of interruption has yet to be understood clearly, and previous studies have largely ignored the role of fatigue state. To address these issues, the present study explores the behavioral and electrophysiological effects of interruption on WM performance using electroencephalography (EEG) data. The moderating effect of fatigue is also explored. The participants performed spatial 2-back tasks with math task interruption, suspension interruption, and non-interruption under different fatigue states. The results show that interruption led to increased alpha activity and P300 amplitude, indicating inhibitory control to interference from irrelevant information. Analysis of P200 amplitude revealed that interruption affected attentional reallocation when resuming the primary task. Increased theta power indicated an increased demand for information maintenance during the interruption. A speeding-up effect was discovered after interruption; however, fatigue impaired cognitive ability and further exacerbated the negative effects of interruption on WM and behavioral performance. These findings contribute to a better understanding of cognitive activity during the interruption and of the interaction with fatigue, and provide further support for the theory of memory for goals (MFG).

Within the 21st century, in the Maritime Silk Road, wave energy, a clean renewable source, is drawing more interest, especially in areas with power shortages. This paper investigates wave energy in ships, particularly in a hybrid electric bulk carrier, by designing a system that supplements the existing power setup with oscillating buoy wave energy converters. The system includes diesel generators (DGs), a wave energy generation system, heterogeneous energy storage (consisting of battery storage (BS) and thermal storage (TS)), a combined cooling heat and power (CCHP) unit, and a power-to-thermal conversion (PtC) unit. To ensure safe and reliable navigation despite uncertainties in wave energy output, onboard power loads, and outdoor temperature, a robust coordination method is adopted. This method employs a two-stage robust optimization (RO) strategy to coordinate the various onboard units across different time scales, minimizing operational costs while satisfying all operational constraints, even in the worst-case scenarios. By applying constraint linearization, the robust coordination model is formulated as a mixed-integer linear programming (MILP) problem and solved using an efficient solver. Finally, the effectiveness of the proposed method is validated through case studies and comparisons with existing ship operation benchmarks, demonstrating significant reductions in operational costs and robust performance under various uncertain conditions. Notably, the simulation results for the Singapore–Trincomalee route show an 18.4% reduction in carbon emissions compared to conventional systems.

Background: In safety-critical environments, human error is a leading cause of accidents, with the loss of situation awareness (SA) being a key contributing factor. Accurate SA assessment is essential for minimizing such risks and ensuring operational safety. Traditional SA measurement methods have limitations in dynamic real-world settings, while physiological signals, particularly EEG, offer a non-invasive, real-time alternative for continuous SA monitoring. However, the reliability of SA measurement based on physiological signals depends on the accuracy of SA labeling. Objective: This study aims to design an effective SA measurement paradigm specific to high-speed train driving, investigate more accurate physiological signal-based SA labeling methods, and explore the relationships between SA levels and key physiological metrics based on the developed framework. Methods: This study recruited 19 male high-speed train driver trainees and developed an SA measurement paradigm specific to high-speed train driving. A method combining subjective SA ratings and task performance was introduced to generate accurate SA labels. Results: The results of statistical analysis confirmed the effectiveness of this paradigm in inducing SA level changes, revealing significant relationships between SA levels and key physiological metrics, including eye movement patterns, ECG features (e.g., heart rate variability), and EEG power spectral density across theta, alpha, and beta bands. Conclusions: This study supports the use of multimodal physiological signals for SA assessment and provides a theoretical foundation for future applications of SA monitoring in railway operations, contributing to enhanced operational safety.