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We investigate the interconnectedness between the financial sectors and new energy companies in China from the perspective of the multilayer network, and analyze the static and time-varying characteristics of the multilayer network at system and company levels, respectively.

We employ the multilayer network containing the realized volatility (RV here after) layer, the realized skewness (RS here after) layer and the realized kurtosis (RK here after) layer. The three realized indicators adopted to construct the multilayer network are generated by the intraday trading data from 2012 to 2022.

(1) Different layers have different characteristics, and can provide supplementary information. (2) Banks tend to play the role of risk transmitters on the whole, while the insurances and new energy companies tend to play the role of risk receivers on average. (3) The connectedness strength of financial sectors and new energy companies varies over time, and climbs sharply during the major crisis events. The roles of financial sectors and new energy companies may change from risk transmitters to risk receivers, and vice versa.

We adopt three realized indicators to construct the three-layer network, which provides a more comprehensive perspective for understanding the connectedness between the financial sectors and new energy companies in China.

This paper studies the effects of lottery preference on stock market participation at the macro level.

The authors use the abnormal search volume intensity for lottery-related keywords from the Baidu search engine to capture retail investors' lottery preference. To measure stock market participation, they use five different macro-level measures from various angles. They perform the time series regression analysis in their empirical study.

First, the validation tests show that the lottery preference index in this study is reasonable. Further, the authors find that lottery preference increases people's propensity to enter and trade in the stock market. Besides, they find that the effect on trading behavior is asymmetric, that is, high lottery preference has a more significant impact on trading behavior than low lottery preference. However, lottery preference has no significant effect on the stockholding.

This paper contributes to the growing literature that examines the determinants of stock market participation and the role of lottery/gambling preference in the financial market. It also provides direct and novel evidence for Statman's (2002) conclusions about the similarity of lottery players and stock traders.

This paper aims to propose a method for finding the maximum rotational speed of an inclined turntable at which the stability of the bearing oil film is maintained.

The finite difference method was used to solve the Reynolds equation. Variation of bearing capacity of a tilted hydrostatic turret over time was determined. The combined effect of tilt and rotational speed of the turret on the oil film stability was also analyzed.

When the turntable is operated at low speeds with only small angle of tilt, stability of the oil film is maintained. At lower rotational speeds, a smaller angle of tilt improves the bearing capacity and ensures stability of the oil film. Whereas, higher rotational speeds can have a considerable influence on the bearing capacity.

The results demonstrate that the inclination or tilt of the turntable significantly affects the stability of the oil film.

Recycled aggregate self-compacting concrete (RASCC) has the potential for sustainable resource utilization and has been widely applied. Predicting the compressive strength (CS) of RASCC is challenging due to its complex composite nature and nonlinear behavior.

This study comprehensively evaluated commonly used machine learning (ML) techniques, including artificial neural networks (ANN), random trees (RT), bagging and random forests (RF) for predicting the CS of RASCC. The results indicate that RF and ANN models typically have advantages with higher R2 values, lower root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE) values.

The combination of ML and Shapley additive explanation (SHAP) interpretable algorithms provides physical rationality, allowing engineers to adjust the proportion based on parameter analysis to predict and design RASCC. The sensitivity analysis of the ML model indicates that ANN’s interpretation ability is weaker than tree-based algorithms (RT, BG and RF). ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

ML regression technology has high accuracy, good interpretability and great potential for predicting the CS of RASCC.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

Corporate site visits increase labor investment efficiency.

Our empirical model for the baseline analysis follows those of Jung et al. (2014) and Ghaly et al. (2020).

We show that corporate site visits are associated with significantly higher labor investment efficiency; more specifically, site visits reduce both over-hiring and under-hiring of employees. The effect of site visits on labor investment efficiency is more pronounced for firms with higher labor adjustment costs, greater financial constraints, weaker corporate governance and lower financial reporting quality. We also find that site visits mitigate labor cost stickiness.

First, while the literature has suggested how the presence of institutional investors and analysts may affect labor investment decisions, we focus on institutional investors and analysts’ activities and interactions with firm executives. We provide direct evidence that institutional investors and analysts may use corporate site visits to improve labor investment efficiency. Second, our study adds to a line of recent studies on how corporate site visits reduce information asymmetry and agency conflicts. We show that corporate site visits allow institutional investors and analysts to influence labor investment efficiency. We also provide new evidence that corporate site visits reduce labor cost stickiness.