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The purpose of this paper is to review the development of the processed feed industry in China through the lens of one of its most successful and earliest entrants, the Hope Group's “New Hope” enterprise. With the feed industry in China now facing a transition phase, the paper looks at the root causes of the problems facing China's feed industry, analyzes the transition strategy undertaken by New Hope and others, and discusses the opportunities for industry growth in the world's most populous country, where urbanization and standards of living are still on the rise and per capita use of feeds is still low by world standards (e.g. one‐fifth of that in the USA). The case also addresses the potential industry impact of mandates outlined in China's Twelfth Five‐Year Plan, such as the imperative to modernize agriculture and improve peasants’ economic well‐being.

The paper draws on expert knowledge of the CEO of New Hope Group's sister agribusiness company, West Hope Group, as well as New Hope Group annual reports and global economic data.

The study reveals future challenges to China's processed feed industry as being price of labor, raw materials, and energy rises; companies undergoing integration; and established companies shifting from product marketing to service marketing models.

This paper is useful for academics interested in industry perspectives, policymakers, practitioners, and business people considering investment in China.

How China's processed feed and breeding industries modernize will impact the economic well‐being of each link in the nation's agribusiness chain (from small producers to conglomerates), the cost and quality of animal‐based proteins, and the world's “food vs. fuel” conflicts.

The paper offers a rare insider's look at one of China's largest private companies and a global player in agribusiness.

Sargassum fusiforme is a popular edible seaweed in coastal cities of China that contains diverse nutrients including iodine. Cooking is an effective way to improve food safety, but it can alter both the contents of elements along with speciation and bioavailability. Three common cooking methods, the soaking, steaming and boiling, were evaluated for their effects on the protein structures, protein digestibility, iodine content and iodine bioavailability of S. fusiforme.

Fourier transform infrared spectroscopy was used to study the structural changes of protein, and an in vitro digestion/Caco-2 cell culture system was used to evaluate the digestibility of protein, bioaccessibility and bioavailability of iodine.

Boiling and steaming altered the protein secondary structure demonstrated by increased a-helix and random coil and decreased β-sheet, which improved the in vitro protein digestibility. Iodine content was reduced by cooking, with the highest loss observed after boiling, followed by soaking and steaming, while it was found that both bioaccessibility and cellular uptake of iodine were significantly elevated by boiling and steaming using an in vitro digestion/Caco-2 cell culture system. The presence of ascorbic acid, citric acid or tyrosine was beneficial for the iodine absorption, while oxalic acid and phytic acid hindered the iodine bioavailability.

The present finding suggested that cooking was conducive to the digestion and absorption of iodine in S. fusiforme. In addition, different dietary factors could have a certain impact on the absorption of iodine. Results of the study are essential for improving the application value of S. fusiforme to ensure reasonable consumption of seaweeds.

The purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy.

The proposed approach is based on the RBM. The authors employ user occupation information as a context to design a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations.

The experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features.

To improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.