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Complex product development will inevitably face the design planning of the multi-coupled activities, and overlapping these activities could potentially reduce product development time, but there is a risk of the additional cost. Although the downstream task information dependence to the upstream task is already considered in the current researches, but the design process overall iteration caused by the information interdependence between activities is hardly discussed; especially the impact on the design process’ overall iteration from the valid information accumulation process. Secondly, most studies only focus on the single overlapping process of two activities, rarely take multi-segment and multi-ply overlapping process of multi coupled activities into account; especially the inherent link between product development time and cost which originates from the overlapping process of multi coupled activities. For the purpose of solving the above problems, as to the insufficiency of the accumulated valid information in overlapping process, the function of the valid information evolution (VIE) degree is constructed. Stochastic process theory is used to describe the design information exchange and the valid information accumulation in the overlapping segment, and then the planning models of the single overlapping segment are built. On these bases, by analyzing overlapping processes and overlapping features of multi-coupling activities, multi-segment and multi-ply overlapping planning models are built; by sorting overlapping processes and analyzing the construction of these planning models, two conclusions are obtained: (1) As to multi-segment and multi-ply overlapping of multi coupled activities, the total decrement of the task set development time is the sum of the time decrement caused by basic overlapping segments, and minus the sum of the time increment caused by multiple overlapping segments; (2) the total increment of development cost is the sum of the cost increment caused by all overlapping process. And then, based on overlapping degree analysis of these planning models, by the VIE degree function, the four lemmas theory proofs are represented, and two propositions are finally proved: (1) The multi-ply overlapping of the multi coupled activities will weaken the basic overlapping effect on the development cycle time reduction (2) Overlapping the multi coupled activities will decrease product development cycle, but increase product development cost. And there is trade-off between development time and cost. And so, two methods are given to slacken and eliminate multi-ply overlapping effects. At last, an example about a vehicle upper subsystem design illustrates the application of the proposed models; compared with a sequential execution pattern, the decreasing of development cycle (22%) and the increasing of development cost (3%) show the validity of the method in the example. The proposed research not only lays a theoretical foundation for correctly planning complex product development process, but also provides specific and effective operation methods for overlapping multi coupled activities.

Aiming at enterprises without commercial project management systems (PMS) in a product data management (PDM) environment, and using a cloud computing platform, this research analyses the business process and function of complex product project management in PDM, and proposes a PMS-based organizational structure for such a project. This model consists of a task view, user view, role view, and product view. In addition, it designs the function structure, E-R model and logical model of a PMS database, and also presents an architecture based on the Baidu cloud platform, describes the functions of the Baidu App Engine (BAE), establishes the overall PMS software architecture. Finally, it realizes a revised product design project by using EasyUI, J2EE and other related technologies. Practice shows that the PMS designed for PDM has availability, scalability, reliability and security with the help of the Baidu cloud computing platform. It can provide a reference for small- and medium-sized enterprises seeking to implement information systems with high efficiency and at low cost in the age of big data.

Zhiguo Chen,1 Shenghai Qiu,2 Xiaofei Lu31Department of Orthopedics, Linyi People’s Hospital, Linyi City, Shandong Province, People’s Republic of China; 2Department of Orthopedics, People’s Hospital of Taiyuan, Taiyuan City, Shanxi Province, People’s Republic of China; 3Department of General Surgery, Jinan Central Hospital affiliated to Shandong University, JinanCity, Shandong Province, People’s Republic of ChinaAim: To investigate the expression of hepatoma-derived growth factor (HDGF) in osteosarcoma (OS) and the correlation with clinicopathologic factors, prognosis, and tumor progression.Method: HDGF expression in OS tissues was detected by immunohistochemistry. The correlation between HDGF and clinicopathologic factors was analyzed by chi-square test, and the association between HDGF expression and the overall survival rates was evaluated by univariate analysis using Kaplan–Meier method. HDGF concentration in cell medium or cell lysates was detected by enzyme-linked immunosorbent assay method. The effect of extrinsic and intrinsic HDGF on OS cell proliferation was detected by MTT assay after recombinant HDGF stimulation or HDGF knockdown, respectively.Results: Proportion of HDGF high expression was 18.69% (20/107) in OS. HDGF high expression was significantly associated with larger tumor size (P=0.004). With experiments in vitro, we demonstrated that human recombinant HDGF could activate AKT and MAPK signaling pathway, resulting in OS cell proliferation. By knocking down HDGF expression, we proved that intrinsic HDGF was required in OS proliferation.Conclusion: High HDGF expression was significantly associated with larger OS tumor size and could promote OS cell proliferation, indicating that HDGF could be an effective biomarker and a potential drug target in OS treatment.Keywords: hepatoma-derived growth factor, osteosarcoma, tumor size, proliferation, overall survival rate

To alliance manufacturing enterprises, there are some problems in resources such as different kinds, ununiform standards, high repeatability etc. Under cloud manufacturing service platform, using cluster analysis method to classify intelligent resources of alliance enterprise was studied. The same or similar resources are divided into one resources cluster. The uniqueness attribution of resources is solved. According to the cloud user needs, resources in a cluster not in all resources set are searched and matched. Using the evaluation method of manufacturing dynamic capacity, optimal resources is provided. It can improve use ratio of the resources. Finally an instance was verified.