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This paper aims to investigate the impact of environmental risk on corporate governance through market reaction to bank loan announcements.

Using the establishment of environment court in China as a quasi-natural experiment, this paper adopt the difference-in-differences approach based on listed firms during 2003–2013 to explore the impact of environment court on corporate governance.

This paper find that the environment court would weaken the cumulative abnormal return of loan announcements. Then, this paper confirm that the potential reason is that environment court worsens the interest conflict between majority and minority shareholders. Further, cross-sectional analysis suggests that bank’s supervision, market competition and analyst coverage can alleviate the impact of environment court on corporate governance.

Environment courts intensify firms’ internal interest disputes, thus causing the decrease of corporate governance, which can be observed through the effect of bank loan announcements.

This paper provide reference for environmental policy formulation and implementation, firms’ decision-makings and improving the banking regulatory system.

This paper makes a contribution to the studies about the impact of environment court on firms’ decision-making and investors’ reaction, the impact of external factors on corporate governance and bank loan announcements effect.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

The purpose of this paper is to propose a physically plausible solution based on hybrid bounding volume (BV) hierarchy for real‐time collision detection (CD) and response between a deformable and a rigid object.

Hybrid BV can be used to build BV hierarchy for the deformable object. The overlapping tests based on separating axis theorem (SAT) can be used to deal with CD. The physics conception of restitution coefficient and other important forces can be used to more real collision response.

Many methods focus on a specific application, but none of them gives an approach to physically plausible, real‐time simulation of CD and response up to 10,000 of deforming primitives. The paper finds that hybrid BV AABB‐Sphere for deformable object could increase the efficiency for CD, and restitution coefficient and other important physical concepts could provide more real collision response.

The paper does not deal with all types of CD, such as CD for two deformable objects.

Using AABB‐Sphere hybrid BV to build hierarchical BV tree for deformable object, and OBB‐Sphere hybrid BV for rigid object. Owing to the variety of hybrid BV structures, during different phases of CD, different overlapping tests are used to accelerate CD effectively. Using important physics conceptions provides a good solution to simulate more real collision response.

The purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.

In this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.

The numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.

The paper provides a useful reference for further theoretical research and engineering application.

This study seeks to examine mature consumers' motives in the selection of apparel and footwear brands and reasons for patronizing department stores. Differences in brand‐choice motives are assessed among age cohort groups within the mature consumers segment as well as mature consumer segments defined by various socio‐demographic and lifestyle factors (i.e. gerontographic segmentation).

A USA nation‐wide random sample of 10,400 head of households was surveyed in regards to reasons for choosing apparel and footwear brands and department stores.

The results show that older consumers not only differ from their younger counterparts but are also heterogeneous when it comes to reasons for choosing specific brands and department stores. Specifically, price reductions and special sales drive the majority of mature consumers' brand selection, while advice or requests of spouse or other relatives and recommendations of salespeople are important factors in brand selection. Ease of returning products or getting refunds and frequency of items on sale are the main drivers of department store patronization.

Marketing managers should consider preference differences in age and gerontographic segments when creating marketing strategies to serve mature consumers.

Typically, mature consumers are aggregated into a single age segment (e.g. over 55). This study examines mature consumer reasons for selecting apparel and footwear brands and department stores based not just on age, but also on individual characteristics, specifically, gerontographic characteristics. Suggestions for marketing strategies designed to appeal to gerontographic segments are provided.

Shape memory polymers (SMPs) respond with a change in their shape against a specific stimulus by memorizing their original shape and are reformed after deformation most often by changing the temperature of the surrounding without additional mechanical efforts. In the coming years, these polymers indeed will be in limelight to manufacture textile materials which will retain their shape even after prolonged use under disturbed conditions. This study aims at defining shape memory materials and polymers as well as their technological characteristics and also highlights application in various fields of textiles.

The methodology used to explain these SMPs have been carried out starting with the discussion on their properties, their physical nature, types, viz., shape memory alloys (SMAs), shape memory ceramics, shape memory hybrid, magnetic shape memory alloy, shape memory composites, shape memory gels and SMP along with properties of each type. Other related details of these polymers, such as their advantages, structure and mechanism, shape memory functionality, thermally responsive SMPs and applications, have been detailed.

It has been observed that the SMPs are very important in the fields of wet and melt-spun fibers to offer novel and functional properties, cotton and wool fabric finishing, to produce SMP films, foams and laminated textiles, water vapor permeable and breathable SMP films, etc.

The field of SMPs is new, and very limited information is available to enable their smooth production and handling.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

Steel-reinforced concrete-filled steel tubular (SRCFST) columns have been increasingly popular in engineering practice for the columns' excellent seismic and fire performance. Significant design progress guidance has been made through continuous numerical and experimental research in recent years. This paper tested and analysed the residual loading capacity of SRCFST columns under axial loading after experiencing non-uniform ISO-834 standard fire.

The experimental research covered the main parameter of heating conditions, 1-side and 2-side fire, through two specimens. Two specimens were heated and loaded simultaneously in the furnace for 240 min. After cooling, the columns were moved to the hydraulic loading system and loaded to failure to determine the columns' residual capacity.

The experimental results indicated that the non-uniform heating area plays an essential role in the overall performance of SRCFST columns, the increasing heating area of columns results in lower residual loading capacity and stiffness. The SRCFST columns still had a high loading capacity after heating and loading in the fire.

The comparison of experimental data against design results showed that the design method generated a 16% safety margin for S2H4 and a 39% safety margin for S1H4.

This study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.

Based on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.

The findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.

The study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.