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The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

The purpose of this paper is to provide a detailed overview of the China Academic Library and Information system (CALIS) document supply service platform (CDSSP) – its historical development, network structure and future development plans – and discuss how its members make use of and benefit from its various components.

The authors provide a first-person account based on their professional positions at the CALIS Administrative Center.

CDSSP comprises five application systems including a unified authentication system, Saas-based interlibrary loan (ILL) and document delivery (DD) service system, ILL central scheduling and settlement system, File Transfer Protocol (FTP) service system and a service integration interface system. These systems work together to meet the needs of member libraries, other information service institutions, and their end users. CDSSP is widely used by more than 1,100 libraries based on a cloud service strategy. Each year more than 100,000 ILL and DD transactions are processed by this platform.

The development of CDSSP makes it becomes true for CALIS to provide one stop information retrieval and supply service. At the same time, it promotes the resource sharing among member libraries to a great degree.

This paper aims to describe the China Academic Library and Information System (CALIS), an academic library consortium which promotes resource sharing among Chinese academic libraries.

The authors use a case study approach based on their professional experience at the CALIS Administrative Center in Beijing.

CALIS has a bright future and it is hoped that CALIS ILL and document delivery services will be expanded to all kinds of academic libraries in China, and help them meet their users ' information needs.

The paper provides insights into the current state of interlending in China.

The purpose of this paper is to review CALIS-ETD full text service in CALIS-ETD project, Phase 3 and put forward the prospect of this project.

The paper involves: the introduction and assessment of the CALIS-ETD full text service in CALIS-ETD Phase 3; analysis of the issues encountered; and recommendations for the coming new phase.

The thesis full text service is a systematic engineering, involving resources, systems, copyright management, service policies and, etc. Among them, the copyright management is the key issue in the thesis full text service in China.

The CALIS-ETD full text service supplies the users a more convenient and formal way to reach the thesis literatures.

The CALIS-ETD full text service in the period of its Phase 3 is the first nationwide thesis service in China. It is the very important milestone of this service in China.

This paper aims to report on the methodology and findings of the China Academic Library and Information System (CALIS) Model Member Libraries (MML) plan which evaluated the success of interlibrary loan and document delivery (ILL/DD) services among 71 CALIS member libraries.

CALIS constructed an evaluation instrument consisting of a set of primary and secondary performance indicators, which were used to assess participating libraries in a number of areas such as the number of bibliographic holdings contributed to the union catalog, the number of qualified professional staff dedicated to ILL/DD, the amount of training provided for library staff and end-users, request volume, fill rate, turnaround time and user satisfaction.

CALIS has implemented the evaluation with the purpose of better understanding ILL/DD service among its member libraries. The evaluation has achieved the expected targets and improved service performance as originally planned. The performance measures provide a basis for the MML to compare their ILL/DD service performance with another similar institution.

According to the evaluation, the major objectives of MML on ILL/DD service are summarized.

This paper aims to understand the magnetohydrodynamics (MHD) mechanism in the molten pool under different modes of magnetic field. The comparison focuses on the Lorenz force excitation and its effect on the melt flow and solidification parameters, intending to obtain practical references for the design of magnetic field-assisted laser directed energy deposition (L-DED) equipment.

A three-dimensional transient multi-physical model, coupled with MHD and thermodynamic, was established. The dimension and microstructure of the molten pool under a 0T magnetic field was used as a benchmark for accuracy verification. The interaction between the melt flow and the Lorenz force is compared under a static magnetic field in the X-, Y- and Z-directions, and also an oscillating and alternating magnetic field.

The numerical results indicate that the chaotic fluctuation of melt flow trends to stable under the magnetostatic field, while a periodically oscillating melt flow could be obtained by applying a nonstatic magnetic field. The Y and Z directional applied magnetostatic field shows the effective damping effect, while the two nonstatic magnetic fields discussed in this paper have almost the same effect on melt flow. Since the heat transfer inside the molten pool is dominated by convection, the application of a magnetic field has a limited effect on the temperature gradient and solidification rate at the solidification interface due to the convection mode of melt flow is still Marangoni convection.

This work provided a deeper understanding of the interaction mechanism between the magnetic field and melt flow inside the molten pool, and provided practical references for magnetic field-assisted L-DED equipment design.

The purpose of this paper is to present a method which enables a robot to learn both motion skills and stiffness profiles from humans through kinesthetic human-robot cooperation.

Admittance control is applied to allow robot-compliant behaviors when following the reference trajectories. By extending the dynamical movement primitives (DMP) model, a new concept of DMP and stiffness primitives is introduced to encode a kinesthetic demonstration as a combination of trajectories and stiffness profiles, which are subsequently transferred to the robot. Electromyographic signals are extracted from a human’s upper limbs to obtain target stiffness profiles. By monitoring vibrations of the end-effector velocities, a stability observer is developed. The virtual damping coefficient of admittance controller is adjusted accordingly to eliminate the vibrations.

The performance of the proposed methods is evaluated experimentally. The result shows that the robot can perform tasks in a variable stiffness mode as like the human dose in the teaching phase.

DMP has been widely used as a teaching by demonstration method to represent movements of humans and robots. The proposed method extends the DMP framework to allow a robot to learn not only motion skills but also stiffness profiles. Additionally, the authors proposed a stability observer to eliminate vibrations when the robot is disturbed by environment.

This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.

First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.

Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.

The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

The emergence of social media as a corporate disclosure channel has caused significant changes in the production and dissemination of corporate information. This review identifies important themes in recent research on the impact of social media on the corporate information environment and provides suggestions for further explorations of this new but fast-growing area of research. Specifically, we first review the evolution of Internet-based corporate disclosure and related regulations, and then focus on three recent streams of research: 1) companies’ use of social media; 2) information produced by non-corporate users and its impact on capital markets; and 3) the credibility of corporate information on social media platforms.

This paper aims at understanding the current situation of research support services offered by academic libraries in world-leading universities and providing useful implications and insights for other academic libraries.

Of the top 100 universities listed in the QS World University Rankings in 2017, 76 libraries were selected as samples and a website investigation was conducted to explore the research support services. The statistical method and visualization software was used to generalize the key services, and the text analysis and case analysis were applied to reveal the corresponding implementation.

Research support service has become one of the significant services of academic libraries in the context of e-research and data-intensive research. The research support services can be generally divided into seven aspects, as follows: research data management (62, 81.58 per cent), open access (64, 84.21 per cent), scholarly publishing (59, 77.63 per cent), research impact measurement (32, 42.11 per cent), research guides (47, 61.84 per cent), research consultation (59, 77.63 per cent) and research tools recommendation (38, 50.00 per cent).

This paper makes a comprehensive investigation of research support services in academic libraries of top-ranking universities worldwide. The findings will help academic libraries improve research support services; thus, advancing the work of researchers and promoting scientific discovery.