Search results

This paper investigates the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets and explores the potential explanations.

In this paper, the authors use the panel data regressions and the dynamic tests of two-way Granger causality in the panel VAR model to examine the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets.

The authors find that the institutional ownership in the Chinese (the USA) stock market is significantly and positively (negatively) related to idiosyncratic volatility through various tests. This paper indicates that institutional investors in the USA are more prudent and risk-averse, while the Chinese institutional investors are not because of high risk-bearing capacity.

This paper deepens the authors’ understanding on the relationship between institutional ownership and idiosyncratic volatility and in the USA and the Chinese stock markets. This paper explains the opposite relationships between institutional ownership and idiosyncratic volatility in the stock markets in China and USA.

With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale.

In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas.

The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets.

It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

Polyethylene terephthalate (PET) as a fiber molding polymer is widely used in aerospace, electrical and electronic, clothing and other fields. The purpose of this study is to improve the thermal insulation performance of polyethylene terephthalate (PET), the SiO₂ aerogel/PET composites slices and fibers were prepared, and the effects of the SiO₂ aerogel on the morphology, structure, crystallization property and thermal conductivity of the SiO₂ aerogel/PET composites slices and their fibers were systematically investigated.

The mass ratio of purified terephthalic acid and ethylene glycol was selected as 1:1.5, which was premixed with Sb₂O₃ and the corresponding mass of SiO₂ aerogel, and SiO₂ aerogel/PET composites were prepared by direct esterification and in-situ polymerization. The SiO₂ aerogel/PET composite fibers were prepared by melt-spinning method.

The results showed that the SiO₂ aerogel was uniformly dispersed in the PET matrix. The thermal insulation coefficient of PET was significantly reduced by the addition of SiO₂ aerogel, and the thermal conductivity of the 1.0 Wt.% SiO₂ aerogel/PET composites was reduced by 75.74 mW/(m · K) compared to the pure PET. The thermal conductivity of the 0.8 Wt.% SiO₂ aerogel/PET composite fiber was reduced by 46.06% compared to the pure PET fiber. The crystallinity and flame-retardant coefficient of the SiO₂ aerogel/PET composite fibers showed an increasing trend with the addition of SiO₂ aerogel.

The SiO₂ aerogel/PET composite slices and their fibers have good thermal insulation properties and exhibit good potential for application in the field of thermal insulation, such as warm clothes. In today’s society where the energy crisis is becoming increasingly serious, improving the thermal insulation performance of PET to reduce energy loss will be of great significance to alleviate the energy crisis.

In this study, SiO₂ aerogel/PET composite slices and their fibers were prepared by an in situ polymerization process, which solved the problem of difficult dispersion of nanoparticles in the matrix and the thermal conductivity of PET significantly reduced.

This paper aims to study the effect of microcrystalline cellulose (MCC) on the mechanical property and shape memory property of water-borne epoxy (WEP).

In the present study, the MCC/WEP composites were successfully prepared by melt-blending, freeze-drying and hot-pressing. The mechanical property tests were performed using a tensile test instrument (Instron Corp, Norwood, Massachusetts, USA). dynamic mechanical analysis Q800 was performed to analyze the sample’s dynamic mechanics. The thermal–mechanical cycle tests performed on a thermal mechanical analysis (TMA) Q400 in dynamic TMA mode enabled to analysis of the shape memory properties of the MCC/WEP composites.

The results showed that the inclusion of 2 wt.% MCC led to significant improvements in tensile strength and modulus of the composites, with tensile strength increasing by 33.2% and modulus expanding by 65.0%. Although the inclusion of the MCC into WEP enhanced the shape memory property, the MCC/WEP composites still maintained good shape memory fixity and shape memory recovery ratio of more than 95.0%.

This study has a significant reference value for improving the mechanical properties of WEP and other water-borne shape memory polymers.

This study aims to explore how a Chinese lesson study (LS) supports a teacher's learning of transforming curriculum reform ideas into classroom practices.

Data analyzed in this study included lesson plans, three videotaped research lessons, three audio-taped group meetings, interviews, the teacher's reflection journals and other related materials. Clarke and Hollingsworth's interconnected model of professional growth (IMPG) was adopted as the theoretical and analytical framework for examining the teacher's learning process.

This study found that teachers can deepen their understanding of innovative curriculum ideas and implement them through an iterative cycle of planning, enactment and reflection in LS. Involving knowledgeable others in the process and reflecting on evidence-based evaluation of students' learning outcomes is crucial for changing the teacher's knowledge, beliefs, attitudes and teaching practices. Theoretical tools, such as learning trajectory, are useful for transforming curriculum reform ideas that teachers find abstract and overly broad into concrete and actionable lesson designs.

Theoretically, this study can broaden our knowledge about how the under-studied mechanism of Chinese LS can contribute to helping teachers to transform reform ideas into classroom practices. Practically, this study provides suggestions for researchers and educators to reflect on and improve the effectiveness of teachers' professional development programs in a reform context.

This paper aims to reveal the tribochemical reaction mechanism on the nano-cutting interface between HMX crystal and diamond tool.

Molecular dynamics simulation of HMX crystal nano-cutting by the reactive force field is carried out in this paper. The affinity of activated atoms and friction damage at the different interface have been well identified by comparing two cutting systems with diamond tool or indenter. The analyses of reaction kinetics, decomposition products and reaction pathways are performed to reveal the underlying atomistic origins of tribocatalytic reaction on the nano-cutting interface.

The HMX crystals only undergo damage and removal in the indenter cutting, while they appear to accelerate thermal decomposition in the diamond cutting. the C-O affinity is proved to be the intrinsic reason of the tribocatalytic reaction of the HMX-diamond cutting system. The reaction activation energy of the HMX crystals in the diamond cutting system is lower, resulting in a rapid increase in the decomposition degree. The free O atoms can induce the asymmetric ring-opening mode and change the decomposition pathways, which is the underlying atomistic origins of the thermal stability of the HMX-diamond cutting system.

This paper describes a method for analyzing the tribochemical behavior of HMX and diamond, which is beneficial to study the thermal stability in the nano-cutting of HMX.

MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.

D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.

Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.

Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.

Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.

The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.