In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

Sustainability issues have been seen as a promising paradigm for achieving a better future. Firms in the logistics service sector are still lacking clear value propositions on sustainable development. While many organizations publish their mission statements publicly as kinds of public information, reviewing mission statements is an appropriate means to evaluate an organization's strategy. This study focuses on the public information such as mission statements of the top 50 global 3-PL companies and the relevant sustainable development. A comprehensive content analysis identified four major content dimensions of mission statements relating to sustainability development. The dimensions are driving forces, approaches, responsibility to stakeholders, and competitive values. This paper offers a good methodological reference for researchers or practitioners managing the public information of organizations. Network analysis reveals that the location of companies has a limited effect on their mission and strategy as they all provide global service.

This paper presents an empirical study on how knowledge management practices and innovation sources affect product innovation performance, among the 152 manufacturers in the low- and high- tech industries in China. The results indicate that external innovation sources are positively correlated with innovation activities and new product performance. Intellectual Property (IP) and knowledge management practices (KMP) are positively correlated with innovation activities, and KMP is positively correlated with innovation sources. The dual effect of KMP shows its indispensable effect on the new product development for both high-tech and low-tech firms, but for low-tech firms, such effect is relatively weak. This empirical study shows that IP management is critical to high-tech but not low-tech firms. We also found that, for innovation activities, low-tech depends on the external sources of innovation whilst high-tech firms do not.

The global wine-making industry has faced challenges due to the increasing demands of consumers, particularly in emerging markets such as China, Brazil, India, and Russia. Controlling the quality during wine production is one of the key challenges faced by global winemakers to produce wine with appropriate sensorial properties tailored to specific markets. The wine production quality is constituted from a number of environmental factors such as climate, soil, and temperature, which affect the sensorial properties and the overall quality. This paper proposed a rule-based quality analytics system (RBQAS) to capture physicochemical data during wine production and to investigate the hidden patterns from the data for quality prediction. It consists of IoT for data capture on a real-time basis, followed by association rule mining to identify relationships between sensorial and physicochemical properties of wine.

The global wine-making industry has faced challenges due to the increasing demands of consumers, particularly in emerging markets such as China, Brazil, India, and Russia. Controlling the quality during wine production is one of the key challenges faced by global winemakers to produce wine with appropriate sensorial properties tailored to specific markets. The wine production quality is constituted from a number of environmental factors such as climate, soil, and temperature, which affect the sensorial properties and the overall quality. This paper proposed a rule-based quality analytics system (RBQAS) to capture physicochemical data during wine production and to investigate the hidden patterns from the data for quality prediction. It consists of IoT for data capture on a real-time basis, followed by association rule mining to identify relationships between sensorial and physicochemical properties of wine.