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The aim of this paper is to achieve a reasonable microclimate between clothing and the human body and optimize the custom dress pattern.

An interactive design method of 3D modeling, virtual try-on and heat transfer simulation are used. First, a 3D dress is designed with nonuniform rational B-splines curves and tried on virtually. After that, the heat transfer in the body-air-clothing microclimate and temperature distributions on the clothing surface are obtained. Based on the heat transfer in the body-air-clothing system, we design a method to improve the thermal comfort by optimizing the garment pattern digitally. Then, this paper utilized two heat transfer validating indexes to quantify the improvement of thermal comfort, and evaluate the modified model of dress.

The microclimate under the clothing is varied with the air gap distance, and the heat transfer on the area of the clavicle, bust point and front abandon are higher than other parts due to the narrow air gaps. In view of thermal comfort, the pattern optimization changes the distance ease and reforms the air circulating efficiency. The mean heat transfer and its standard variance are changing by about 10% and more than 20%. Thus, the heat transfer evaluation indexes are suitable to represent the heat transfer and thermal comfort in the microclimate system.

It can be concluded that the methodology proposed in this paper has the advantage of interactive design, 3D visualization and local heat transfer simulation. This technology meets the need of personalized customization and well-considered garment and has broad application prospects.

This study demonstrates that modifying the distance ease on body key girths based on heat transfer is a reliable way to improve thermal comfort. This method meets the consumers’ demand of the comfort of body-fit clothing under the condition of daily activities.

• 3D air gap distributions.

• Heat transfer varies with air gap distance.

• Thermal comfort can be improved by optimizing garment pattern.

3D air gap distributions.

Heat transfer varies with air gap distance.

Thermal comfort can be improved by optimizing garment pattern.

The shared development concept is crucial for the construction of a socialist political economy with Chinese characteristics. The paper aims to discuss this issue.

This is because shared development constitutes the logic thread of the socialist political economy with Chinese characteristics and the core for the formation and development of its whole system.

China’s modernization is well underway and is following a unique path with its own characteristics, whereby shared development is undoubtedly one of its core values.

In the new era, the development path under the concept of shared development of socialism with Chinese characteristics must adhere to the all-round development of human beings, promote social equity and justice via development, and embrace inclusive growth, specifically, pro-poor growth.

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.

This study aims to examine the effects of formal and informal institutional factors (i.e. marketization and guanxi culture) on interorganizational conflicts (IOCs) and their interaction effects.

Drawing on IOC literature and an institution-based view, the authors use a sample of 12,022 Chinese firms from the World Bank’s Investment Climate Survey. A zero-inflated negative binomial regression was used to analyze the data.

The results suggest that guanxi culture has U-shaped effects, but marketization does not negatively affect IOCs. Furthermore, a low level of marketization weakens the U-shaped effect of guanxi culture on IOCs. A moderate level of guanxi culture can enable marketization to reduce IOCs.

This study provides a better understanding of the management of IOCs. Managers should fully understand the differential effects of the institutional environment in different regions and their interactions by adopting different response strategies.

This study enriches the literature on IOCs’ antecedents and contextual factors by examining the institutions’ direct and interaction effects on IOCs.

Taking Huangshan City as an example, the purpose of this paper is to explore the relationship between gross products of agriculture (GPA), its main composition as well as their influential intermediate inputs, followed by some suggestion, in order to optimize agricultural industrial structure and distribute the ratio of various inputs in agriculture, farming, forestry, animal husbandry and fishery, so as to improve GPA of Huangshan City.

This paper is characterized by an exploratory research by using the grey incidence analysis based on the grey systems theory initiated by Chinese professor Julong Deng. The original data processed in the model are quoted from the Statistical Yearbook of Huangshan City.

This paper draws some important conclusions. First, in terms of large, intermediate inputs in animal husbandry production and fishery production are two important aspects which affect the growth potentiality of agricultural gross products in Huangshan City. This also reflects that the development potentialities of fishery and animal husbandry are larger. Second, the level of agricultural modernization is relatively low in Huangshan City, the growth of agricultural output relies on a large number of inputs such as fertilizers, pesticides, but it also reflects the agriculture production system has huge potentiality to increase in Huangshan City from the side. Third, agriculture producer services as a whole is still relatively weak in Huangshan City. It is difficult to adapt to the development of modern agriculture and agricultural modernization. Finally, the results show that intermediate inputs have different influence on farming, forestry, animal husbandry and fishery.

Suggestion are also proposed to the related policy makers as follows: to vigorously develop animal husbandry and fishery in order to optimize agricultural industrial structure of Huangshan City, to improve agricultural modernization level, to speed up the service support system construction of agriculture production, to optimize the allocation of inputs in farming, forestry, animal husbandry and fishery reasonably.

Artificial intelligence (AI) has a large number of applications at the industry and user levels. However, AI's uniqueness neglect is becoming an obstacle in the further application of AI. Based on the theory of innovation resistance, this paper aims to explore the effect of AI's uniqueness neglect on consumer resistance to AI.

The authors tested four hypothesis across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI's uniqueness neglect leads to consumer resistance to AI; Studies 2 focused on the role of human–AI interaction trust as an underlying driver of resistance to medical AI. Study 3–4 provided process evidence by way of a measured moderator, testing whether participants with a greater sense of non-verbal human–AI communication are more reluctant to have consumer resistance to AI.

The authors found that AI's uniqueness neglect increased users' resistance to AI. This occurs because the uniqueness neglect of AI hinders the formation of interaction trust between users and AI. The study also found that increasing the gaze behavior of AI and increasing the physical distance in the interaction can alleviate the effect of AI's uniqueness neglect on consumer resistance to AI.

This paper explored the effect of AI's uniqueness neglect on consumer resistance to AI and uncovered human–AI interaction trust as a mediator for this effect and gaze behavior and physical distance as moderators for this effect.

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.

The purpose of the study is to clarify the quality of home delivery logistics services from the perspectives of customers and provide insight to aid the prioritization of service quality improvements and guide managerial strategic planning.

The study used a three-dimensional model that integrated Kano model, goal difficulty (GD) and importance–performance analysis (IPA) for investigating service quality aspects emphasized by customers and determine which attributes should be prioritized according to an enterprise's resource and capability constraints. Data were collected through questionnaires administered to the customers and managers of five primary home delivery logistics service enterprises and six small to medium-sized enterprises in Taiwan. Improving the quality of home delivery logistics services has become of increased interest for enterprises.

The three most important attributes, ranked in order of priority for improvement, were the protection of customers' personal information, delivery of products without damage and reasonable compensation standards for product damage. The study concludes that enterprises should prioritize the improvement of these attributes. Implications, detailed explanations and directions for further investigations are also proposed.

The study discusses the importance and relevant satisfaction levels of service quality attributes from the perspective of customers while also considering the limitations of companies' resources and capabilities. The results indicate that the method can be used to identify service quality attributes of home delivery logistics and formulate strategies for enhancing customer satisfaction.

The purpose of this paper is to draw from organizational ecology and institutional theory, the authors suggest that there will be a curvilinear relationship between legal system uncertainty and foreign direct investment (FDI) attraction in Southeast Asia. The authors extend theory by arguing that this is because uncertainty will provide opportunities for FDI that seek this form of operating environment, leveraging legal system uncertainty as a basis for competitive advantage.

The authors test and find support for the hypotheses using FDI data from nine Southeast Asian countries for the years 1995-2005.

In this paper, the authors hypothesize and find that the relationship between legal system uncertainty and FDI attraction is curvilinear in nature, such that FDI attraction decreases with legal system uncertainty down to an inflection point, but then increases beyond this point; and that the relationship between legal system uncertainty and FDI attraction is moderated by government intervention in the host country economy, such that the strength of this relationship is greater when government intervention is high rather than when it is low. Implications of the findings and suggestions for future inquiry are presented.

Conventional wisdom suggests that legal system uncertainty will negatively affect FDI attraction. However, to date, research on the effects of legal system uncertainty on FDI attraction in emerging markets has received very little attention. The aim of this research study is to shed new light on how, under certain conditions, legal system uncertainty will attract FDI in Southeast Asia.