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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.

With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a growing demand for developing a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method.

This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents.

By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment.

This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.

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 study a new halogen-free flame retardant that was prepared and characterised.

The phenyl phosphinic arid di-4-[1-(4-pheny phodphonic acid monophenyl ester-yl)-methyl-ethyl] phenyester dimelaminium (PDEPDM) was synthesised using phenylphosphonic dichloride, melamine, bisphenol A, triethylamine and dichloromethane via solvent-based reaction, that was added into the polyethylene to test flame performance. The chemical structures of PDEPDM were characterised by ¹H nuclear magnetic resonance spectroscopy, mass spectrometry and Fourier transform infrared spectrometer. The thermal stability, mechanical and flame properties, and morphology for the char layer of composite materials were separately investigated using thermogravimetric analysis, tensile and charpy impact tests, limiting oxygen index (LOI) and UL-94 HB flammability standard and scanning electron microscope.

The results showed that the PDEPDM had been prepared successfully. When the intumescent fame retardant was added into the PE, the LOI of composite material was improved.

The PDEPDM can be prepared successfully and can improve the flame resistance of composite material.

The PDEPDM has excellent flame-retardant properties and produce no toxic fumes when burnt in case of fire.

Under the optimal conditions, when the 32 per cent (Wt.%) PDEPDM was added into the PE, the LOI was 29.8, tensile strength and impact strength were 10.06 MPa and 16.77 kJ/m².

The purpose of this paper is to prepare a novel halogen-free intumescent flame retardant (IFR) BHPPODC (benzene hydroquinone phosphorous oxy dichloride cyanuric chloride) for application to epoxy resin (EP) and study their mechanical and flame-retardant performance.

The IFR was synthesised by phenylphosphonic dichloride, hydroquinone and cyanuric chloride via solvent reaction, and the structure was fully characterised by proton nuclear magnetic resonance (¹H-NMR), mass spectrometry (MS) and Fourier transform infrared (FT-IR) spectroscopy. The thermal stability, mechanical and flame properties and morphology of the char layer of the flame-retardant EP was investigated by using thermogravimetric analysis (TGA), tensile and Charpy impact tests, limiting oxygen index (LOI) and vertical burning test (UL-94) and scanning electron microscopy (SEM).

Results of the LOI indicated that the halogen-free flame retardant as an additive exhibits very good flame-retardant effects. The results showed that the addition of IFR improved the flame resistance properties of epoxies resin composites, and the residual char ratio at 800°C significantly increased.

The IFR can be prepared successfully and can improve the flame-retardant performance.

This contribution can provide a high flame retardant performance and has minimal impact on the mechanical performance of the BHPPODC/EP composition.

This study showed that flame-retardant BHPPODC has an effective flame effect under optimal conditions. When the 12 Wt.% IFR was added to the EP, the LOI was 29.1 and the UL-94 rank can reach V-0 rank, the tensile strength was 83.86 MPa and the impact strength was 8.82 kJ/m².

Artificial intelligence is gradually penetrating into human society. In the network era, the interaction between human and artificial intelligence, even between artificial intelligence, becomes more and more complex. Therefore, it is necessary to describe and intervene the evolution of crowd intelligence network dynamically. This paper aims to detect the abnormal agents at the early stage of intelligent evolution.

In this paper, differential evolution (DE) and K-means clustering are used to detect the crowd intelligence with abnormal evolutionary trend.

This study abstracts the evolution process of crowd intelligence into the solution process of DE and use K-means clustering to identify individuals who are not conducive to evolution in the early stage of intelligent evolution.

Experiments show that the method we proposed are able to find out individual intelligence without evolutionary trend as early as possible, even in the complex crowd intelligent interactive environment of practical application. As a result, it can avoid the waste of time and computing resources.

In this paper, DE and K-means clustering are combined to analyze the evolution of crowd intelligent interaction.

This paper aims to study a new halogen-free fame-retardant curing agent 1-aminoethylidenediphosphonate (AAEDP).

The AAEDP was synthesized by phosphoric acid, acetonitrile and ammonia. The chemical structures of AAEDP were characterized by proton nuclear magnetic resonance, mass spectrometry and Fourier transform infrared spectrometer. Thermal gravimetric analysis (TGA) and scanning electron microscope (SEM) would study the thermal properties and the char residues of AAEDP/EP. The thermal stability, mechanical and flame properties and morphology for the char layer of composite materials were separately investigated using TGA, tensile and charpy impact tests, limiting oxygen index (LOI), UL-94 HB flammability standard (UL-94) and SEM.

The results showed that the AAEDP had been prepared successfully. When the intumescent flame retardant was added into the EP, the LOI of composite material was improved.

The AAEDP can be prepared successfully and can improve the flame resistance of composite material.

The AAEDP has excellent flame-retardant properties and produce no toxic fumes when burnt in case of fire.

The results showed that the phosphorus content of AAEDP was 2.958 Wt.%; the impact and tensile strength of the composite material were 6.417 kJ m⁻² and 38.0 MPa, respectively; and the LOI and UL-94 were 29.7% and V-0 ranking, respectively. The TGA results indicated that the carbon residue ratio can be increased by 1000°C in air. The denser and more uniform structure of residual carbon prevents heat transfer and diffusion, restricts the production of combustible gas and reduces the rate of heat release.

The publishers of International Journal of Crowd Science wish to retract the article “Quality-time-complexity universal intelligence measurement”, by Wen Ji, Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, and Yiqiang Chen, which appeared in volume 2, issue 1, 2018. It has come to our attention that due to an error in the submission process the above article is an earlier version of the article published in International Journal of Crowd Science, volume 2, issue 2, 2018 (DOI: https://doi.org/10.1108/IJCS-04-2018-0007). The duplicate publication was the result of an inadvertent administrative error by the authors. The authors and publishers of the journal sincerely apologise to the readers.

Drawn from the push–pull perspective, this research aims to identify the determinants of Chinese technology's outward foreign direct investments (OFDI) into the Eurasian region.

The authors argue that contrary to the extant literature, technology-driven OFDI from emerging-market multinationals (EMNEs) do not always seek developed countries, and EMNEs' technology investments in emerging economies are rising indicating that there are factors in these economies that can prove attractive. The authors recognize the influence of the macroeconomic environment and the interaction of home and host-country institutional contexts that influence the location choice of EMNEs technology-driven OFDI into other emerging economies, mediated by the industry sector and firm's ownership structure. The authors test our hypotheses using a sample of 1,656 observations of Chinese MNEs' tech-investments in the Eurasian region from 2005 to 2019.

The study results indicate that bilateral diplomatic relations pave the way of the host-country institutional environment for Chinese MNEs uncovering the role of the Chinese government as an OFDI facilitator. This study also unveils a lower technology level of the Chinese MNEs' investments in the Eurasian region connoting an interest in market opportunities exploitation through their existing technologies – through its comparative advantage in the global markets – rather than strategic assets acquisition aiming at augmenting their technological capabilities. This trend is similar to that of other major foreign direct investment (FDI) source countries.

This research contributes to a better understanding of the characteristics and the location choice of technology investments from EMNEs into other emerging economies that have received scant attention in the literature. In addition, it extends the institutional theory by analyzing how home-country institutions, through bilateral diplomatic relations, may smooth the host country institutional environment for home-country MNEs' foreign investments and contributes as well to the debate on the applicability of the existing theoretical framework in the case of emerging-market MNEs.

The purpose of this study is to provide an in-depth understanding about the indoor-orbital electrical inspection robot, which is useful for motivating the further investigation on the inspection of electrical equipment. Currently, electric energy has a strong correlation with the economic development of the country. Intelligent substations play an important role in the transmission and distribution of the electricity; the maintenance of the substation has attracted intensive attention due to the requirement of reliability and safety. The indoor-orbital electrical inspection robot has increasingly become the main tool to realize the unmanned. Hence, a systematic review is conducted systematically reviewing the current technical status of the indoor-orbital electrical inspection robot and discuss the existed problems.

In this paper, the most essential achievements in the field of indoor-orbital electrical inspection robots were reported to present the current status, and the mechanical structures and key inspective technologies were also discussed.

Four recommendations are provided from the analyzed review, which have made constructive comments on the overall structural design, functionality, intelligence and future development direction of the indoor-orbital electrical inspection robot, respectively.

To the best of the authors’ knowledge, this is the first systematic review study on indoor-orbital electrical inspection robots; it fills the theoretical gap and proffers design ideas and directions for the development of the indoor-orbital electrical inspection robot.