Oil spill clean-up is a long-standing challenge for researchers to prevent serious environmental pollution. A new kind of oil-absorbent based on silicon-containing polymers (e.g., poly(dimethylsiloxane) (PDMS)) with high absorption capacity and excellent reusability was prepared and used for oil-water separation. The PDMS-based oil absorbents have highly interconnected pores with swellable skeletons, combining the advantages of porous materials and gels. On the other hand, polymer/silica composites have been extensively studied as high-performance functional coatings since, as an organic/inorganic composite material, they are expected to combine polymer flexibility and ease of processing with mechanical properties. Polymer composites with increased impact resistance and tensile strength without decreasing the flexibility of the polymer matrix can be achieved by incorporating silica nanoparticles, nanosand, or sand particles into the polymeric matrices. Therefore, polymer/silica composites have attracted great interest in many industries. Some potential applications, including high-performance coatings, electronics and optical applications, membranes, sensors, materials for metal uptake, etc., were comprehensively reviewed. In the first part of the review, we will cover the recent progress of oil absorbents based on silicon-containing polymers (PDMS). In the later details of the review, we will discuss the recent developments of functional materials based on polymer/silica composites, sand, and nanosand systems.

In the current competitive global marketplace, innovation is key for high-tech firms to thrive. Open innovation offers a promising approach, but its effectiveness remains unclear. Therefore, this research explored the connection between open innovation, knowledge management capability, and innovation performance within high-tech firms. We used a mediation approach to highlight the central role of knowledge management capability in the relationship between open innovation and innovation performance. We used a survey questionnaire approach to collect data from the 462 employees of high-tech firms on open innovation, knowledge management capability, and innovation performance using a convenient sampling technique. We used partial least square structural equations modeling through PLS-SEM statistics. Results indicated that open innovation has a direct, positive and significant connection with innovation performance. Similarly, the current research serves as a pioneering exploration into mediation analysis, highlighting the mediating role of knowledge management capability that influences the relationship between open innovation and innovation performance. Empirical studies offer valuable insights for leaders of high-tech firms, guiding them to identify effective knowledge management practices and determine the ideal extent of open innovation to boost innovation performance. The current study reveals novel insights into the benefits of knowledge management capability in enhancing open innovation efforts within firms. This research provides valuable implications and future research directions.

This study aims to examine the influence of employee and entrepreneur competencies on work efficiency and performance of export companies at the Nong Khai border checkpoint. The research conducted is a quantitative survey. The population for this study includes employees and entrepreneurs from the cross-border export service industry, exporters, and freight forwarder agents operating at the Nong Khai border checkpoint. A non-probability sampling method was employed to select participants. The sample size was Cochran estimated using Cochran’s formula. A structured questionnaire was used to collect data from 385 logistics employees and entrepreneurs selected through purposive sampling. The questionnaires were distributed to employees and entrepreneurs from the export entrepreneurial industry, cross-border export service providers, exporters, and freight forwarder agents at the Nong Khai border checkpoint. The findings revealed that employee and entrepreneur competencies have a direct influence on the work efficiency and performance of export companies. The study concludes that enhancing the competencies of employees and entrepreneurs positively impacts work efficiency and the overall export performance of the company. The research suggests that entrepreneurs should prioritize training and competency development for employees to further improve work efficiency.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

This article aims to examine the impact of fiscal decentralization on the performance of local government expenditure in Vietnam. By using a dataset including 63 provinces from 2012 to 2021, the research shows the more expenditure-based fiscal decentralization occurs, the better is the performance of local expenditure. Moreover, the level of provincial literacy and the size of the private sector have positive impacts on the local expenditure index, while the opposite effect can be seen in the case of the ratios of local citizens to total citizens of the country. Besides this, the study also provides some recommendations which are strictly related to the mechanism of fiscal decentralization to improve local expenditure performance of Vietnamese provinces, such as more effective decentralization of budget expenditures to local government, improving the vertical budget imbalance at local budget level, increasing local government budget autonomy, and establishing stronger mechanisms to control public spending.

Interest in the impact of environmental innovations on firms’ financial performance has surged over the past two decades, but studies show inconsistent results. This paper addresses these divergences by analyzing 74 studies from 1996 to 2022, encompassing 4,390,754 firm-year observations. We developed a probability-based meta-analysis approach to synthesize existing knowledge and found a generally positive impact of environmental innovations on financial performance, with a probability range of 0.85 to 0.97. Manufacturing firms benefit more from environmental innovations than firms in other industries, and survey-based studies report a more favorable relationship than those using secondary data. This study contributes to existing knowledge by providing a comprehensive aggregation of data, supporting the resource-based view (RBV) and the Porter hypothesis. The findings suggest significant policy implications, highlighting the need for tailored incentives and information-sharing mechanisms, and underscore the importance of diverse data sources in research to ensure robust results.