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With the continuous increase in Web services, efficient identification of Web services that meet developers’ needs and understanding their relationships remains a challenge. Previous research has improved recommendation effectiveness by using correlations between Web services through graph neural networks (GNNs), while it has not fully leveraged service descriptions, limiting the depth and diversity of learning. To this end, a Web services recommendation method called LLMSARec, based on Large Language Model and semantic alignment, is proposed. This study aims to extract potential semantic information from services and learn deeper relationships between services.

This method consists of two core modules: profile generation and maximizing mutual information. The profile generation module uses LLM to analyze the descriptions of services, infer and construct service profiles. Concurrently, it uses LLM as text encoders to encode inferred service profiles for enhanced service representation learning. The maximizing mutual information model aims to align the semantic features of the services text inferred by LLM with structural semantic features of the services captured by GNNs, thus achieving a more comprehensive representation of services. The aligned representation serves as an input for the model to identify services with superior matching accuracy, thereby enhancing the service recommendation capability.

Experimental comparisons and analyses were conducted on the Programmable Web platform data set, and the results demonstrated that the effectiveness of Web service recommendations can be significantly improved by using LLMSARec.

In this study, the authors propose a Web service recommendation approach based on Large Language Model and semantic alignment. By extracting latent semantic information from services and effectively aligning semantic features with structural features, new representations can be generated to significantly enhance recommendation accuracy.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Social recommendation has been recognized as a kind of e-commerce with large potential, but how social recommendations influence consumer decisions is still unclear. This paper aims to investigate how recommendations from different social ties influence consumers’ purchase intentions through both behavior and brain activity.

Utilizing behavioral (N = 70) and electroencephalogram (EEG) (N = 49) experiments, this study explored participants’ behavior and brain responses after being recommended by different social ties. The data were analyzed using statistical inference and event-related potential (ERP) analysis.

Behavioral results show that social tie strength positively impacts purchase intention, which can be fitted by a logarithmic model. Moreover, recommender-to-customer similarity and product affect mediate the effect of tie strength on purchase intention serially. EEG findings show that recommendations from weak tie strength elicit larger N100, N200 and P300 amplitudes than those from strong tie strength. These results imply that weak tie strength may motivate individuals to recruit more mental resources in social recommendation, including unconscious processing of consumer attention and conscious processing of cognitive conflict and negative emotion.

This study considers the effects of continuous social ties on purchase intention and models them mathematically, exploring the intrinsic mechanisms by which strong and weak ties influence purchase intentions through recommender-to-customer similarity and product affect, contributing to the applications of the stimulus-organism-response (SOR) model in the field of social recommendation. Furthermore, our study adopting EEG techniques bridges the gap of relying solely on self-report by providing an avenue to obtain relatively objective findings about the consumers’ early-occurred (unconscious) attentional responses and late-occurred (conscious) cognitive and emotional responses in purchase decisions.

Microblogs are communication platforms for companies and consumers that challenge companies' brand marketing strategies. This paper provides a theoretical basis for expanding microblog applications and a practical basis for improving the effectiveness of brand marketing.

The authors use factor analysis to extract the factors of microblog user influence and construct a structural equation model to reveal the interaction mechanism of the influencing factors. Additionally, the authors clarify the promotion and enhancement effects of these factors.

Microblog user influence can be converted into richness, interaction and value factors. The richness factor significantly affects the latter two, whereas the interaction factor does not affect the value factor.

First, the sample used is limited to media industry practitioners. To increase generalizability, diverse groups should be included in future studies. Second, this model's theoretical explanatory ability can be further developed by adding other meaningful factors beyond the existing ones.

This study analyzes the factors of microblog user influence in China and validates the relevant elements. As a result, it improves the influence research on social media users and benefits the practice of information recommendation and microblog marketing.

This paper aims to analyze brand loyalty and electronic word-of-mouth (eWOM) antecedents in restaurants: (1) utilitarian and hedonic benefits, (2) brand satisfaction and (3) brand love. It also provides valuable knowledge through the comparison between positive and negative restaurant experiences.

A survey was carried out of restaurant satisfied and dissatisfied consumers. Structural equation modeling (SEM) and multi-group analysis (MGA) were performed to examine the cause-and-effect relationship in both groups.

The results show the relevance of benefits, brand satisfaction and brand love as causes for brand loyalty and e-WOM. Also, these relationships are significantly stronger for dissatisfied consumers than for satisfied ones.

The outcome of the research provides new insights to develop a conceptual stimulus-organism-response (S-O-R) model of consumers’ restaurant behavior by drawing comparisons across satisfied and dissatisfied ones.

This study aims to develop an electrochemical sensor for the detection of benzophenone (BP) as an alternative to conventional techniques that are known, expensive, complex and less sensitive.

The developed sensor is a platinum electrode modified with a plasticized polymer film based on ß-cyclodextrin, using PVC as the polymer, PEG as the plasticizer and ß-CD as the ionophore. This sensor is characterized by various techniques, such as optical microscopy, scanning electron microscopy and cyclic voltammetry. This latter is also used for analyzing kinetic processes at the electrode/electrolyte interface and to evaluate the selectivity and sensitivity of the sensor.

The results highlight the performance of our sensor. In fact, it exhibits a linear response extending from 10⁻¹⁹ to 10⁻¹³ M, with a correlation coefficient of 0.9836. What is more, it has an excellent detection limit of 10⁻¹⁹ M and a good sensitivity of 21.24 µA/M.

The results of this investigation demonstrated that the developed sensor is an analytical tool of choice for the monitoring of BP in the aqueous phase. The suggested sensor is fast, simple, reproducible and inexpensive.

This study intended to bridge this gap by investigating the perspectives of international students regarding Taiwan’s bilingual education policy and its impact on their willingness to enroll in graduate programs in Taiwan. Additionally, the comparisons among international students from diverse backgrounds were examined.

Employing a qualitative research design, the study included nine participants from three countries, with each country contributing three students. Three sessions of semi-structured interviews were conducted, supplemented by the analysis of 15 documents from 8 organizations and universities.

Results indicated predominantly negative views toward Taiwan’s bilingual education policy, with little impact reported by two participants and none by others. Furthermore, themes derived from document analysis deviated from participants' viewpoints.

A focus on students from southern Asia, which was the major source of international students in Taiwan, becomes critical. In the same vein, little literature has been found concerning graduate students' perceptions toward bilingual education policy, which should be thoroughly explored as well.

In the era of the digital economy, higher demands are placed on versatile talents, and the cultivation of students with innovative and entrepreneurial abilities has become an important issue for the further development of higher education, thus leading to extensive and in-depth research by many scholars. The study summarizes the characteristics and patterns of dual-innovation education at different stages of development, hoping to provide a systematic model for the development of dual-innovation education in China and make up for the shortcomings.

This paper uses Citespace software to visualize and analyze the relevant literature in CNKI and Web of Science databases from a bibliometric perspective, focusing on quantitative analysis in terms of article trends, topic clustering, keyword co-linear networks and topic time evolution, etc., to summarize and sort out the development of innovation and entrepreneurship education research at home and abroad.

The study found that the external characteristics of the literature published in the field of bi-innovation education in China and abroad are slightly different, mainly in that foreign publishers are more closely connected and have formed a more stable ecosystem. In terms of research hotspots, China is still in a critical period of reforming its curriculum and teaching model, and research on the integration of specialization and creative education is in full swing, while foreign countries focus more on the cultivation of students' entrepreneurial awareness and the enhancement of individual effectiveness. In terms of cutting-edge analysis, the main research directions in China are “creative education”, “new engineering”, “integration of industry and education” and “rural revitalization”.

Innovation and entrepreneurship education in China is still in its infancy, and most of the studies lack an overall overview and comparison of foreign studies. Based on the econometric analysis of domestic and foreign literature, this paper proposes a path for domestic innovation and entrepreneurship education reform that can make China's future education reform more effective.

This study aims to break through the limitations of previous studies that have focused too much on the individual-level effects of humble leadership. Based on the affective events theory (AET), this study provides to construct an individual-team multilevel model of humble leadership focusing on the followers’ affective reaction and attribution of intentionality.

On the basis of subordinates’ attribution of humble leadership, it is believed that there are actually two motivations for humble leadership: true intention (serve the organizational collective interest) and pseudo intention (serve the leader’s self-interest), to which subordinates have different affective reactions, causing different leadership effectiveness. Thus, this study conducted an extensive review based on the qualitative method and proposed an integrated multilevel model of leader humility on individual and team outputs.

Followers’ attribution of intentionality moderates the relationship between humble leadership and followers’ affective reaction, which also determines followers’ performance (task performance, interpersonal deviant behavior and leader–member exchange); the interaction between team leaders’ humble leadership and collective attribution of intentionality influences team outputs (team outputs, organizational deviant behavior and team–member exchange) through team affective reaction; team humble leadership affects individual outputs through affective reaction and team affective climate plays a moderating role between affective reaction and individual outputs.

This study explores the individual-team multilevel outputs of humble leadership based on the AET theory, which is relatively rare in the current field. This study attempts to incorporate leaders’ motivation (such as attributions of intentionality) into the humble leadership research, by confirming that humble leadership affects affective reaction, which further influences individual-team multilevel outputs.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.