Glioma is a highly challenging human malignancy and conventional drugs typically exhibit low blood-brain barrier (BBB) permeability as well as poor tumor targeting. To complicate matters further, recent advances in research on oncology have highlighted the dynamic and complex cellular networks within the immunosuppressive tumor microenvironment (TME) that complicate glioma treatment. Therefore, precise and efficient targeting of tumor tissue, whilst reversing immunosuppression, may provide an ideal strategy for the treatment of gliomas. Here, by using the "one-bead-one-component" combinatorial chemistry approach, we designed and screened a peptide that can specifically target brain glioma stem cells (GSCs), which was further engineered into glycopeptide-functionalized multifunctional micelles. We demonstrated that the micelles can carry DOX and effectively penetrate the BBB to achieve targeted killing of glioma cells. Meanwhile, mannose confers a unique tumor immune microenvironment modulating function to the micelles, which can activate the anti-tumor immune response function of tumor-associated macrophages and is expected to be further applied in vivo. This study highlights that glycosylation modification of targeted peptides specific to cancer stem cells (CSCs) may serve as an effective tool to improve the therapeutic outcome of brain tumor patients.

Access to vast datasets of visual and textual materials has become significantly easier. How to take advantage of the conveniently available data to support creative design activities remains a challenge. In the phase of idea generation, the visual analogy is considered an effective strategy to stimulate designers to create innovative ideas. Designers can read useful information off vague and incomplete conceptual visual representations, or stimuli, to reach potential visual analogies. In this paper, a computational framework is proposed to search and retrieve visual stimulation cues, which is expected to have the potential to help designers generate more creative ideas by avoiding visual fixation. The research problems include identifying and detecting visual similarities between visual representations from various categories and quantitatifying the visual similarity measures serving as a distance metric for visual stimuli search and retrieval. A deep neural network model is developed to learn a latent space that can discover visual relationships between multiple categories of sketches. In addition, a top cluster detection-based method is proposed to quantify visual similarity based on the overlapped magnitude in the latent space and then effectively rank categories. The QuickDraw sketch dataset is applied as a backend for evaluating the functionality of our proposed framework. Beyond visual stimuli retrieval, this research opens up new opportunities for utilizing extensively available visual data as creative materials to benefit design-by-analogy.

Design by analogy is a design ideation strategy to find inspiration from source domains to generate design concepts in target domains. Recently, many computational methods were proposed to measure similarities between source domains and target domains to build connections between them. However, most existing methods only explore either visual or semantic cues of the concepts in source and target domains but ignore the integration of both modalities. In fact, humans have remarkable visual reasoning ability to transfer knowledge learned from objects in familiar categories (source domains) to recognize objects from unfamiliar categories (target domains). In this paper, we propose a visual reasoning framework to support design by visual analogy. The challenge of this research is how computation methods can mimic the process of humans’ visual reasoning, which fuses visual and semantic knowledge. In the framework, the convolutional neural network (CNN) is applied to learn visual knowledge from objects in familiar categories. The hierarchy-based graph convolutional network (HGCN) is proposed to transfer learned visual knowledge from familiar categories to unfamiliar categories by their semantic distances. Finally, the unfamiliar objects can be reasoned and recognized based on the transferred visual knowledge. Extensive experimental results on one mechanical component benchmark dataset demonstrate the favorable performance of our proposed methods.

The goal of this research is to develop a computer-aided visual analogy support (CAVAS) framework to augment designers' visual analogical thinking by stimulating them by providing relevant visual cues from a variety of categories. Two steps are taken to reach this goal: developing a flexible computational framework to explore various visual cues, i.e., shapes or sketches, based on the relevant datasets and conducting human-based behavioral studies to validate such visual cue exploration tools. This article presents the results and insights obtained from the first step by addressing two research questions: How can the computational framework CAVAS be developed to provide designers in sketching with certain visual cues for stimulating their visual thinking process? How can a computation tool learn a latent space, which can capture the shape patterns of sketches? A visual cue exploration framework and a deep clustering model CAVAS-DL are proposed to learn a latent space of sketches that reveal shape patterns for multiple sketch categories and simultaneously cluster the sketches to preserve and provide category information as part of visual cues. The distance- and overlap-based similarities are introduced and analyzed to identify long- and short-distance analogies. Performance evaluations of our proposed methods are carried out with different configurations, and the visual presentations of the potential analogical cues are explored. The results have demonstrated the applicability of the CAVAS-DL model as the basis for the human-based validation studies in the next step.

The goal of this research is to develop a computer-aided visual analogy support (CAVAS) framework that can augment designers’ visual analogical thinking by providing relevant visual cues or sketches from a variety of categories and stimulating the designer to make more and better visual analogies at the ideation stage of design. The challenges of this research include what roles a computer tool should play in facilitating visual analogy of designers, what the relevant and meaningful visual analogies are at the sketching stage of design, and how the computer can capture such meaningful visual knowledge from various categories through analyzing the sketches drawn by the designers. A visual analogy support framework and a deep clustering model, called Cavas-DL, are proposed to learn a latent space of sketches that can reveal the shape patterns for multiple categories of sketches and at the same time cluster the sketches to preserve and provide category information as part of visual cues. The latent space learned serves as a visual information representation that captures the learned shape features from multiple sketch categories. The distance- and overlap-based similarities are introduced and analyzed to identify long- and short-distance analogies. Extensive evaluations of the performance of our proposed methods are carried out with different configurations, and the visual presentations of the potential analogical cues are explored. The evaluation results and the visual organizations of information have demonstrated the potential of the usefulness of the Cavas-DL model.

Visual analogy has been recognized as an important cognitive process in engineering design. Human free-hand sketches provide a useful data source for facilitating visual analogy. Although there has been research on the roles of sketching and the impact of visual analogy in design, little work has been done aiming to develop computational tools and methods to support visual analogy from sketches. In this paper, we propose a computational method to discover visual similarity between sketches, considering the following practical application: Given a sketch drawn by a designer that reflects the designer’s rough idea in mind, our goal is to identify the shape similar sketches that can stimulate the designer to make more and better visual analogies. The first challenge in doing so is how to discover the similar shape features embedded in sketches from various categories. To address this challenge, we propose a deep clustering model to learn a latent space which can reveal underlying shape features for multiple categories of sketches and cluster sketches simultaneously. An extensive evaluation of the clustering performance of our proposed method has been carried out in different configurations. The results have shown that the proposed method can discover sketches that have similar appearance, provide useful explanations of the visual relationship between different sketch categories, and has the potential to generate visual stimuli to enhance designers’ visual imageries.

When 32-ary amplitude phase shift keying (32APSK) modulation is used in the communication system, carrier recovery is one of the most important technology. The decision-directed (DD) phase locked loop (PLL) is widely used for carrier recovery. Based on the decision-directed (DD) phase locked loop (PLL) algorithm, the paper proposes a new fine carrier phase recovery method, which is based on the constellation classification and different nonlinear operation. Simulation results show that the performance of the proposed method is much better than the traditional one.

Design alternative evaluation in the early stages of engineering design plays an important role in determining the success of new product development, as it influences considerably the subsequent design activities. However, existing approaches to design alternative evaluation are overly reliant on experts' ambiguous and subjective judgments and qualitative descriptions. To reduce subjectivity and improve efficiency of the evaluation process, this paper proposes a quantitative evaluation approach through data-driven performance predictions. In this approach, the weights of performance characteristics are determined based on quantitative assessment of expert judgments, and the ranking of design alternatives is achieved by predicting performance values based on historical product design data. The experts' subjective and often vague judgments are captured quantitatively through a rough number based Decision Making Trial and Evaluation Laboratory (DEMATEL) method. In order to facilitate performance based quantitative ranking of alternatives at the early stages of design where no performance calculation is possible, a particle swarm optimization based support vector machine (PSO-SVM) is applied for historical data based performance prediction. The final ranking of alternatives given the predicted values of multiple performance characteristics is achieved through Visekriterijumska Optimizacija I kompromisno Resenje (VIKOR). A case study is carried out to demonstrate the validity of the proposed approach. (C) 2017 Elsevier Ltd. All rights reserved.

为了开拓设计思路、产生创新产品方案,将设计知识有效融入产品的概念设计过程中,提出一种融合设计过程与设计知识的产品概念设计方法.定义了基于产品—功能—结构的过程模型和知识模型,利用逐层映射行为、回溯映射行为、检索行为和存储行为支持设计过程和设计知识的融合.在产品域,通过基于组合权重的双层灰色关联分析法得到待改进产品的相似产品集;在功能域,使用功能相似度算法和功能操作方法得到新产品功能架构;在结构域,利用形态学矩阵生成多种概念方案,提出基于粗数的逼近理想解排序法,并定义了综合约束指数,对方案进行定性和定量评价、得到最优方案.利用所提方法获得了一种新型美容补水仪的概念方案,从而验证了该方法的可行性.

To measure the angular position of motors, robot joints, etc., an absolute magnetic rotary position sensor is developed, which possesses small-size, light, robust, and easy-to-integrate properties. The sensor consists of two inductors, i.e., a code disc and a signal processing part. Both of the two inductors are embedded with a big planar spiral copper coil and four smaller copper coils by employing microelectromechanical system (MEMS) technology, and there are two circles of regular copper sheets listing on the surface of the code disc. In addition, mathematical analyses of the inductor structure as well as the relationships of position and dimension among the coils and the copper sheet are addressed. Finite-element simulation results demonstrate that one group of the optimal sine and cosine signals can be generated by each inductor. Then, the absolute position measurement method is validated by a mathematical method. Finally, experiments are performed on the high-speed and the low-speed testing platforms to assess effectiveness and accuracy of the sensor.

Nowadays, the competition among modern manufacturing enterprises is actually relied on quality and efficiency of innovative design based on teamwork. The role of innovative design based on teamwork manufacturing sector is more important than ever. In the modern manufacturing context, in order to study teamwork innovative design quality and efficiency evaluation technical issues based on cognitive process, the human brain memory model of cognitive processes is studied. Based on analyzing the memory model of cognitive design process, creative design process cognitive models and innovative design team cognitive process model, the creative design cognitive processes in the individual and team level are elaborated. After analyzing the connotation of quality and efficiency, the creative design of the five quality dimensions and five rating scale and the relationship between team innovation design quality and the consistent of individuals design result are extracted. Then theory hypotheses are verified with experiment raw data. Finally experimental results obtained are as follows, in the aspect of design quality, the higher the design consistency among individual members design results are, the higher the design quality is.

Innovation in conceptual design period of products is an important indicator to show innovation capacity of one enterprise, and the improvement of relative technology is the base of it. By analyzing technical papers in a period, the relevant keywords can be summarized to make up a relevant technical keywords network, which may be very complex and include a large number of nodes and edges. This keywords network will show the key topics and hotspots in science and technology area currently or in the future. By building the keywords network which base on product conceptual design and analyzing the statistical result, some available techniques can be found to support product conceptual design. In this report an example of coal cutter is provided to discuss the feasibility and usability of this model.

Team collaboration for project implementation has become the main pattern in modern work. How to allocate tasks to the right team member is the key for project completion. The paper firstly reviewed the related methods and concepts of team collaboration and knowledge sharing. Then the model of knowledge was built. Based on knowledge model, this paper built the model of the task and the team member. According to the knowledge background and project experience of team members, the paper proposed an allocation model which automatically allocated tasks in group work. Finally, a prototype system was developed for practical validation.

Learning team has become an important foundation for collaborative work. In a team, according to the knowledge of members and task requirements, how to recommend learning resources to the appropriate team member is a key factor of success. This paper firstly reviewed related methods and concepts in knowledge management and recommendation. Then, it constructed different models for task, knowledge, team member and learning resource. The two strategies of resources recommendation were proposed. One was based on similarity measurement and another is based on knowledge background and experience of team members. Based on the two strategies, learning resources were recommended to team members. Finally, the prototype system was built for practical validation.

Based on the law of electromagnetic induction, a novel contactless absolute angular position sensor has been designed in the literature. It is mainly composed of three parts: two emitters, an annular encoder and a signal processing circuit. The emitter integrates a main coil and two groups of secondary windings, each of which comprises two series-wound coils with inverse enwinding directions. N and (N−1) copper sheets with regular shapes are arrayed uniformly in the outer and inner circles of the encoder which are corresponding to the two emitters respectively. The main coil stimulated by the external current can generate a magnetic field, which can be detected by two groups of secondary windings and a corresponding voltage signals will be exported. As the emitter rotated over any two neighboring copper sheets of the encoder, a period of sine and cosine signals will be outputted by two groups of secondary coils. The absolute angle position signals can be calculated based on the relationships between angles calculated by N and (N−1) periods of sine and cosine signals produced by the inner and the outer circles of emitters and the angles the emitter has rotated around the rotation axis. Experiment has been performed on a prototype of the angular position sensor and the results demonstrate the proposed scheme is feasible. Theoretic precision and testing error analysis of the sensor has been pursued at the end of the paper.

In the era of knowledge economy, organizations always face the challenge of technological progress and fierce competition. Team learning receives much more attention as a helpful way for organizations to deal with complex and changeable environment issue, and it has a great impact on organizational performance and organizational knowledge innovation. Team culture represents the values and philosophy of the team. For the influencing factors of team learning, few scholars study the influence of team culture on team learning. Therefore, empirical research method is used to explore the influence of team culture on team learning. Empirical study result showed that varies of dimensions of team culture affected team learning behavior and team spirit affected was the most effective dimension in team culture' s varies of dimensions.

A double sheet detection system with ultrasonic sensors is designed for providing scanning equipments with rapid and accurate double sheet detections and it's suitable to different kinds of paper. It is composed of the circuit to transmitting ultrasonic wave, the circuit to process ultrasonic wave received and the determination circuit. The system can give an alarm signal to the scanning equipments when there is double sheet to avoid paper jam or leaky scanning. Thanks to the high speed and high accuracy, the scanning trouble and resource wastes can be reduced, thus improving greatly the automatic extend for the scanning equipments.

Trust and reputation are now integral parts of the Semantic Web architecture. Computing trust for a stranger agent in such an open environment is a difficult job. Many trust evaluation algorithms has been proposed in literature. The EigenTrust algorithm provides global trust ranking mechanism for the agents in P2P social networks with certain limitations. The objectives of this paper are to propose a Scalable EigenTrust Architecture (SETA) to enhance its scalability, make it more secure, and augment its capabilities to make it more contexts specific.

Trust and reputation have been identified as an important component in the security infrastructure for e-commerce systems over the Web. Computing trust for an unknown agent in such an open environment is a demanding job. This work is an extension to our previous work [1] where a new algorithm has been added for local trust evaluation using Dempster-Shafer (Evidential) theory. We have suggested some new extensions to evidential theory for local trust evaluation.