Clustering and classification for dry bean feature imbalanced data.

The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the majority class and has a clustering function to improve the traditional machine learning classification accuracy and various performance indicators such as precision, recall, f1-score, and area under curve (AUC) for imbalanced data. The key idea is to use the advantages of borderline-synthetic minority oversampling technique (BLSMOTE) to generate new samples using samples on the boundary of minority class samples to reduce the impact of noise on model building, and the advantages of K-means clustering to divide data into different groups according to similarities or common features.

An empirical research based on spatial-temporal evolution of high-quality tourism development in Fujian Province of China.

The high-quality development of tourism is crucial to the sustainable development of regional economy. To evaluate high-quality tourism development, this paper has developed an index system with 6 second-level indicators and 24 third-level indicators and used methods of entropy-weight, AHP, and TOPSIS to empirically assess the high-quality tourism development of 9 cities in Fujian Province. According to the results, there are obvious regional differences in the development of high-quality tourism in Fujian Province.

A comparative study on the selection of new characters in Chinese primary school textbooks in mainland China and the Taiwan region.

The selection of new characters for Chinese textbooks is essential for primary school literacy teaching. This research used content and comparative analyses to study the selection of new characters for Chinese textbooks used in primary schools in mainland China and the Taiwan region. The results showed that there were more new characters in the Chinese primary school textbooks used in mainland China than in those used in the Taiwan region.

Digital transformation, innovation capabilities, and servitization as drivers of ESG performance in manufacturing SMEs.

This study investigates the relationship between digital transformation and Environmental, Social, and Governance (ESG) performance in the context of SMEs. Drawing upon Resource Orchestration Theory, this research proposes a theoretical model that examines the direct effect of digital transformation on ESG performance and the mediating roles of innovation capabilities and servitization level in this relationship. PLS-SEM and fsQCA were employed to analyze survey data from 215 SME executives.

Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review.

Background: Despite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialized for pregnancy care.

Objective: To identify and synthesize AI-augmented CDSS in pregnancy care, CDSS functionality, AI methodologies, and clinical implementation, we reported a systematic review based on empirical studies that examined AI-augmented CDSS in pregnancy care.

Methods: We retrieved studies that examined AI-augmented CDSS in pregnancy care using database queries involved with titles, abstracts, keywords, and MeSH (Medical Subject Headings) terms.

A digital resource copyright protection scheme based on blockchain cross-chain technology.

With the expansion of the digital publishing industry, copyright infringement of digital resources has become increasingly rampant. Blockchain technology offers a promising solution for copyright protection of digital resources. However, existing copyright protection methods based on single-chain or consortium blockchains have disadvantages such as low system processing efficiency, poor scalability, and a lack of alignment with real-world business needs.

Randomized algorithms for large-scale dictionary learning.

Dictionary learning is an important sparse representation algorithm which has been widely used in machine learning and artificial intelligence. However, for massive data in the big data era, classical dictionary learning algorithms are computationally expensive and even can be infeasible. To overcome this difficulty, we propose new dictionary learning methods based on randomized algorithms.

Simulating Two-Phase Seepage in Undisturbed Soil Based on Lattice Boltzmann Method and X-ray Computed Tomography Images.

The two-phase seepage fluid (i.e., air and water) behaviors in undisturbed granite residual soil (U-GRS) have not been comprehensively studied due to a lack of accurate and representative models of its internal pore structure.

Three-stage Dynamic Brain-cognitive Model of Understanding Action Intention Displayed by Human Body Movements.

The ability to comprehend the intention conveyed through human body movements is crucial for effective interpersonal interactions. If people can't understand the intention behind other individuals' isolated or interactive actions, their actions will become meaningless. Psychologists have investigated the cognitive processes and neural representations involved in understanding action intention, yet a cohesive theoretical explanation remains elusive.

Research on the impact of ESG performance on carbon emissions from the perspective of green credit.

Utilizing panel data from 30 Chinese provinces, this research examines the non-linear relationship between regional environmental, social, and governance (ESG) performance and carbon emissions (CE) from the viewpoint of green credit. The study reveals a single threshold effect between ESG performance and CE, with green credit acting as the threshold variable. When the amount of green credit in a region exceeds the threshold, the growth rate of CE in that region begins to decline with higher ESG scores.

The impact of clean energy demonstration province policies on carbon intensity in Chinese counties based on the multi-phase PSM-DID method.

Based on China's empirical data from 2000 to 2020 of 1875 county-level administrative units, combined with the multi-phase by the propensity score matching and difference-in-difference (PSM-DID) model, this paper studies the impact of clean energy demonstration province policies on the carbon intensity of pilot counties, and its further impact on carbon emissions and economic development level. The results showed that 1. from a county-level perspective, although the economic development level of the pilot areas of clean energy demonstration provinces has improved as the carbon emissions have also increased, what is more, the carbon intensity has also significantly improved in this process; 2.

The influence of the COVID-19 pandemic on the short- and long-term interactions in the agricultural market: Evidence from a connectedness network approach.

This study employs a bivariate GARCH model to examine the influence of the COVID-19 pandemic on the interactions of the commodities in the agricultural market via a connectedness network approach. Empirical results show that this pandemic alters the commodities' roles-the activators, net transmitters, and net receivers-in the volatility and return connectedness but not for the activators in the correlation connectedness. Moreover, this pandemic enhances the interactive degree of the unidirectional negative return spillovers and the bidirectional distinct-sign volatility spillovers but doesn't for the interactive degree of correlation.