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The purpose of this study is to investigate the accumulation process of transfer film formation and dissipation and its effect on friction coefficients in non asbestos organic friction materials with various lubricant FeS₂ contents.

In total, 2.5%, 5% and 10% FeS₂ were added as lubricating components to the friction materials. Friction tests composed of two stages were conducted for these friction materials, and the friction surfaces of the counterpart discs were examined using scanning electron microscopy.

The transfer film formation reduced the friction coefficients, and the transfer film dissipation influenced the recovery of the friction coefficients. The effect of a high content of FeS₂ was to promote the transfer film formation at high temperatures and to hinder the transfer film dissipation at low temperatures, thus resulting in a decrease in the friction coefficients at high temperatures together with recovery retardation at low temperatures.

FeS₂ contributed to the transfer film formation at high temperatures in the fade test but hindered the transfer film removal in the recovery test, resulting in the retardation of friction coefficient recovery. The mechanism by which the FeS₂ lubricant component affected the transfer film formation and dissipation was analyzed and attributed to the different levels of FeS₂ pyrolysis at different temperature levels.

This study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.

A systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.

The review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.

This study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.

The purpose of this study is to examine the impact of mandatory corporate social responsibility (CSR) spending on firm value in the Indian context.

Using firm-level data over the period 2012–2017, this study uses the difference-in-differences (DID) technique combined with matching to control for potential endogeneity of the decision to comply with the CSR Act since the Act in its current form is applicable as a comply-or-explain obligation.

The results of this study suggest that mandatory CSR spending has a positive and statistically significant impact on firm value. These results remain robust to alternative econometric techniques such as regression discontinuity design (RDD) and randomization inference test as well as to alternative empirical specifications. Furthermore, the study demonstrates that the positive effect of CSR spending on firm value is more pronounced for firms with higher information asymmetry problem and lower institutional holdings.

This study explicitly considers the “comply-or-explain” flexibility option, in terms of spending on CSR, provided to Indian firms for the initial two to three years and investigates whether spending on CSR helps firms enhance their firm value. The study also finds that the positive effect of CSR spending on firm value is more pronounced for firms with higher information asymmetry problems and lower institutional holdings.