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Commercial dental porcelain powder was deposited via slurry extrusion and laser densified to fabricate dental restorations in a multi‐material laser densification (MMLD) process.

A dental porcelain slurry was made from ball milled dental porcelain powders and extruded using the MMLD system. Extruded lines and rings were laser densified under different conditions in order to study how to build fully dense porcelain layers without warping and cracking during the MMLD process.

The geometric cross section of laser densified porcelain lines were dependent on laser processing parameters. Laser densified single ring showed no warping, and multiple layer body after laser densification showed cracks in the rings. The interface microstructure suggested good bonding between multiple layers. The mechanism to achieve single porcelain ring without warping and cracking is discussed. Alternate ways to build physical tooth layer by layer are proposed.

In the MMLD process, dental porcelain slurry was extruded from a human tooth computer file and laser densified to manufacture dental restorations based on solid freeform fabrication (SFF) principles. The understanding developed will pave the way for fabricating a physical dental restoration unit in the near future.

The purpose of this paper is to establish an effective risk assessment approach based on the conditional value-at-risk (CVaR) in the agricultural supply chain.

This study analyzes and assesses the risks of breeding, processing, transportation and warehousing in the agricultural supply chain. The ordered weighted averaging operator is used to sort risk control factors according to their importance and determine the main risk indicators of an enterprise. The CVaR model is utilized to establish the risk loss function, and an improved genetic algorithm is employed to identify the optimal risk control portfolios in the case of the smallest risk loss.

Based on the approach, the optimal combination of risk control to minimize risk losses is determined. Results show that the proportion of capital investment in risk control differs at three confidence levels, and a large amount of money needs to be invested in the production process at the source. Thus, any attempt to control the risks inherent in the agricultural supply chain must begin with the production process at the source.

Supply chain risk management has become increasingly important and significant to the operation and production of enterprises in recent years. The proposed method to assess the risk in the agricultural supply chain can benefit managers in making smart decisions to control total risk.

The implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.

A spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.

The proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.

The assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.

The spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

The purpose of this study is to test the mechanism of visionary leadership on subordinates' work withdrawal behavior through cognitive strain and psychological contract violation, and also to reveal the possible dark side of visionary leadership. The moderation effects of subordinates' facades of conformity and leader behavioral integrity in the cognition–affect dual-path process are also discussed.

This study conducted a three-wave longitudinal survey. The data were collected from 574 employees and their superiors in several Chinese enterprises. The authors used Mplus 7.4 and adopted a bootstrapping technique in the data analysis.

Visionary leadership has positive effects on cognitive strain and psychological contract violation; cognitive strain and psychological contract violation mediate the relationship between visionary leadership and work withdrawal behavior, respectively. Subordinates' facades of conformity and leader behavioral integrity moderate the positive effects of visionary leadership on cognitive strain and psychological contract violation, as well as the indirect effect of visionary leadership on subordinates' work withdrawal behavior through cognitive strain and psychological contract violation.

This study reveals the underlying mechanism of visionary leadership's negative impact on job outcome through the cognition and affective reaction of subordinates to visionary leadership, and broadens the scope of visionary leadership research. It also provides some practical suggestions on how to transmit the organizational vision effectively and reduce subordinates' work withdrawal behavior.

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.

Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.

By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.

This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.

The purpose of this paper is to analyze and solve abnormal variation of pipe‐to‐soil potentials of an oil‐transfer pipeline.

Pipe‐to‐soil potentials of an oil‐transfer pipeline varied abnormally at several locations. Visual detections find the pipeline is buried near an electric railway and there are several anodic ground beds nearby. Corrosion patterns of the pipeline and examination of the soil reveal no microbiological corrosion. The potential gradients indicate the pipeline might not be attacked by stray currents. However, whole day measurements of one pipeline pile show there are two kinds of stray currents influencing the pipeline: AC stray current and DC stray current.

The highest pipe‐to‐soil potential reaches 12.958 V when there are AC stray currents. In addition, the biggest and lowest DC pipe‐to‐soil potentials are 0.888 V and −5.90 V, respectively. Radiodetection pipeline current mapper measurement finds there is some bitumen coating breaking points on pipeline. These make the stray currents enter the pipeline and stray current corrosion happens easily. As a result, stray current corrosion happens.

The potential gradients cannot indicate stray current corrosion under all circumstance.

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them.

This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach.

The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information demand. The AISCN approach used heuristic algorithm to solve a composite objective function which takes all these factors into consideration, so that the optimized information-sharing pattern can be obtained.

This paper introduces a set of factors that can be used to describe the correlation between information-sharing pattern and performance of crowd network. By quantifying such correlation based on these factors, this paper proposes an adaptive information-sharing approach which can explore the optimized information-sharing pattern for a variety of crowd networks. As the approach is a data-driven approach that explores the information-sharing pattern based on the network’s performance on historical tasks and network’s characteristics, it is adaptive to the dynamic change (change of incoming tasks, change of network characteristics) of the target crowd network. To ensure the commonality of the information-sharing approach, the proposed approach is not designed for a specific optimization algorithm. In this way, during the implementation of the proposed approach, heuristic algorithms can be chosen according to the complexity of the target crowd network.

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.