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This paper examines the impact of forward guidance in monetary policy on the pass-through of interest rates in New Zealand by analyzing the degree of both the long-term and short-term pass-through.

We use dynamic OLS to estimate the long-term relationship between the official cash rate and the time-deposit rates and various lending rates of New Zealand banks. We use a standard error correction model to estimate the short-term dynamics.

The results show that implementing forward guidance improves the degree of long-term pass-through, especially for time-deposit interest rates and longer-term fixed mortgage rates. Furthermore, the markup for various lending rates decreased, and the degree of short-term pass-through increased slightly after the implementation.

Implementing forward guidance enhances the transmission of monetary policy. Commercial banks are able to respond more quickly to changes in monetary policy by adjusting their deposit and loan rates.

New Zealand is the first country to mandate inflation targeting. Since its birth in 1990, the 2% inflation target has become the norm for central banks all over the world, including the FED, European Central Bank, Bank of Japan, Bank of England, etc. To our knowledge, ours is the first paper to examine the impact of forward guidance on the interest rate pass-through.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

This paper aims to solve the problem that multiscale dispersion entropy (MDE) is prone to information loss in the process of coarse-grain, which makes it difficult to extract bearing fault information comprehensively.

A new fault diagnosis method of rolling bearing using refined composite multiscale peak-to-peak normalized dispersion entropy (RCMPNDE) and sparrow search algorithm optimized probabilistic neural network (SSA-PNN) is proposed. First, coarse-graining employs the peak-to-peak value calculation instead of the segmented mean calculation in the RCMDE algorithm, which can overcome the shortcomings of traditional coarse-graining and highlight the fault characteristics. Then, the influence of the selection of different parameters is reduced through the normalization operation, and the RCMPNDE is formed. Finally, the extracted feature parameters are combined with SSA-PNN for diagnosis recognition to construct the RCMPNDE-SSA-PNN fault diagnosis method.

The proposed RCMPNDE-SSA-PNN fault diagnosis method is tested on actual data sets and its outcomes have been compared to those generated by methods built upon MDE, RCMDE and PNN. The comparison results showed that the proposed method can extract the fault feature information of rolling bearings more accurately and improve the accuracy of fault classification. The recognition accuracy reached 98.5% under the conditions of this experiment.

The RCMPNDE-SSA-PNN method can obtain more accurate fault diagnosis accuracy and provide a new reliable diagnosis method for rolling bearing fault diagnosis.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0332/

In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty.

In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS).

The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS.

Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.

The high-load operation of the engine crankshaft causes severe wear and fatigue. This study aims to prepare in situ textures with effective density and study their wear mechanism on the surface of ductile cast iron, which optimizes the tribological properties of engine crankshafts and reduces wear.

A new method was proposed based on the hardness difference in graphite removal to form an in situ texture. The friction performance was evaluated using a combination of computational fluid dynamics and tribological testings. The influence of the texture characteristic parameters on the bearing capacity of the oil film was analyzed. The surface wear morphology was studied by scanning electron microscopy.

The texture density significantly affected the oil film bearing capacity. The surface texture can reduce the average friction coefficient (COF) by more than 35% owing to the oil film bearing and storage capacity. Specifically, the 13% texture density exhibited the lowest wear rate and COF under all three experimental conditions. The reduction in abrasive particles in the wear area of the textured surface indicates that the surface texture can improve the lubrication mechanism.

This study systematically explored the influence of the weight of each model parameter on tribological properties. Subsequently, focusing on the critical parameter (texture density), detailed tribological testings were carried out to reveal the specific effect of texture density on the wear mechanism under different working conditions, and the optimal texture density to achieve the optimal tribological performance was determined accordingly.

The process of conveyance of solid–liquid mixtures poses a significant challenge due to the considerable wear and tear experienced by critical components. This issue not only affects the lifespan of the system but also jeopardizes its safe operation. The purpose of this study is to numerically and experimentally investigate the erosion wear behavior of impeller steels (SS-410 and S-317) using Computational Fluid Dynamics (CFD) and Design of Experiments (DOE) techniques, aiming to address the significant challenges posed by wear in slurry transportation systems.

In this study, a robust two-phase solid-liquid model combining CFD with Discrete Phase Modeling (DPM) was applied to simulate the effects of coal-ash slurries on impeller steel. Additionally, an experimental evaluation was conducted using the DOE approach to analyze the impact of various parameters on impeller steel. This integrated methodology enabled a comprehensive analysis of erosion wear behavior and the influence of multiple factors on impeller durability by leveraging CFD for fluid flow dynamics and DPM to model particle interactions with the steel surface.

Simulation results highlight a strong link between particle size and the wear life of impeller steel. Through simulations and experiments on SS-410 and SS-317 under varied conditions, it’s evident that SS-410 outperforms SS-317 due to its higher hardness and density. This is supported by Taguchi’s method, with SS-410 showing a higher Signal-to-Noise ratio. Notably, particle size emerges as the most influential parameter compared to others.

Current research primarily focuses on either CFD or experimentation to predict pump impeller steel erosion wear, lacking relevant erosion mechanism insights and experimental data. This study bridges this gap by employing both CFD and DPM methods to comprehensively investigate particle effects on pump impeller steel and elucidate erosion mechanisms.

This study aims to investigate the impact of client size, client risk, client profitability, client complexity, audit reporting lag, client loss, audit firm size, and industry type on determining external auditor fees pre- and during the COVID-19 pandemic on the non-financial companies (53 industrial companies and 41 service companies) listed at the Amman Stock Exchange (ASE) for the period of 2017–2021. The initial number of observations started with 470 observations. The results of the regression analysis for the pooled sample revealed a significant positive impact for the factors of client size, client complexity, and audit firm size on external audit fees. The same results were found for the other two sub-samples. However, client loss and industry type indicated a significant negative impact on external audit fees, except for the pre-COVID-19 pandemic period for the loss and the COVID-19 pandemic period for the audit firm size. Moreover, client risk, audit report lag, and client profitability have an insignificant impact on external audit fees for all three samples. Furthermore, the study recommends the following: Allocate adequate resources for auditing and consider the influence of company size on audit fees, adjust audit budgets based on profitability and the potential complexity of financial statements, and analyze and communicate the level of complexity to auditors, especially for companies operating in multiple industries or with intricate structures.

This paper addresses the issue of internal spatial environmental pollution in non-blasting tunnel construction by proposing a comprehensive evaluation model. The model aims to provide a scientific basis for environmental pollution prevention in non-blasting tunnel construction, thereby facilitating green tunnel construction and sustainable development management.

The study firstly refines and constructs the evaluation index system from the perspective of pollution sources. A novel weight calculation method is introduced by integrating the analytic hierarchy process (AHP) with the ordered weighted averaging (OWA) operator, and a comprehensive evaluation model for internal environmental pollution in non-blasting tunnels is established by incorporating the grey clustering evaluation method. Finally, an empirical study is conducted using the Erbaoshan Tunnel as a case study to verify the feasibility and effectiveness of the model.

The study develops an evaluation system for internal environmental pollution in non-blasting tunnels and applies it to the Erbaoshan Tunnel. The results classify the pollution level as “general pollution,” confirming the rationality and applicability of the evaluation system and model while also identifying the primary pollution factors.

This study first developed a comprehensive evaluation system for environmental pollution in non-blasting tunnel construction from the pollution source perspective, making the system more comprehensive. Additionally, it innovatively combined AHP–OWA and gray clustering methods to scientifically assess pollution levels, providing valuable scientific guidance for the evaluation and management of non-blasting tunnels and similar underground projects.

This study aims to identify the level of sustainability consciousness among preservice teachers in China under the “Double Carbon” goal.

A sustainability consciousness questionnaire was distributed to 2,795 preservice teachers aged 17–26 years old from first-year undergraduate to third-year postgraduate students through an online questionnaire tool. The mean, standard deviation and correlation of their sustainability knowingness, attitude and behavior were analyzed using SPSS software.

First, preservice teachers scored high in sustainability knowingness, attitude and behavior. Second, across different household types, disciplines and university levels (double first-class and nondouble first-class), preservice teachers’ sustainability consciousness scores were comparable to each other. Finally, a negative correlation was found between sustainability knowingness and attitude as well as knowingness and behavior. However, the authors observed a positive correlation between sustainability attitude and behavior. Overall, the level of sustainability consciousness among Chinese preservice teachers is high and no significant differences exist between the sustainability consciousness of preservice teachers with different household types, in different disciplines and at different levels of universities. Influenced by the contextual differences of the relevant concepts in the questionnaire, there is a certain degree of structural differences in the preservice teachers’ level of sustainability consciousness across the dimensions.

This study contributes to the literature by investigating the current status of sustainability consciousness level among preservice teachers in China under the “Double Carbon” goal. The findings of this study provide universities and relevant departments with recommendations for education for sustainable development policies.

Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.