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Based on the recent advances of hybrid stress finite elements, a series of alternative stress assumptions for these elements are investigated. Several new element models are proposed by using different concepts for the stress interpolation. Under a unified formulation presented in this paper for Hellinger—Reissner principle based hybrid stress element models, the element series 5β‐family for plane stress and 18β‐family for three‐dimensional problems are discussed. The extra incompatible displacements sometimes also added are not introduced in this unified formulation. A number of popular benchmark elastic problems are examined for both two element families. In each family, the element model presented in this paper using normalized transformed higher order stress trials usually gives better predictions than the others.

The purpose of this paper is to prepare new modified polypropylene (PP) with phenolic microspheres (PFMs). Furthermore, the crystallinity and mechanical properties of PP modified by fillers (silicon dioxide [SiO₂] and light calcium carbonate [CaCO₃], respectively) have also been investigated and compared.

For effective toughening, three different fillers were added into the PP matrix. PP composites were prepared through melt blending with double-screw extruder and injection moulding machine.

It was found that with the addition of 3 Wt.% PFM, the impact strength was maximum in all PP composites and increased by 1.4 times compared to pure PP. Scanning electron microscopy (SEM) and polarised optical microscopy (POM) analysis confirmed that 3 Wt.% PFM, 3 Wt.% SiO₂ and 2 Wt.% CaCO₃ were optimal to add in PP and PFM to give the best compatibility with PP.

PFM particles not only are tougher and less brittle and can offer other advantages such as enhanced machinability, but also are important organic materials and have a good compatibility with polymer for reinforcing polymer properties.

The method developed provided a simple and practical solution to improving the toughness of PP.

There will be thermoplastic plastics with higher toughness in domestic, packaging and automotive applications, particularly at lower temperatures.

The PP modified by tiny amounts of fillers in this work had high toughness, which can be applied as an efficient material widely used in domestic, packaging and automotive applications.

Faeyee Electronics was an electronics company that manufactured and sold smartphones including XS and XT types. The management of Faeyee wanted to pursue the maximum contribution margin as much as possible, especially since they were faced with limited resources. It was necessary to apply analytical tools and cost accounting concepts to study this case including cost-volume-profit analysis, learning curve analysis, regression analysis, definitions of competitive products, constrained non-linear optimization, and contribution margin.

The case uses business analytics tools and cost accounting concepts, including regression models and constrained optimization approaches, to study how to maximize business outcomes, such as contribution margin and profits when limited business resources are available. The company and individuals are disguised.

This case can be used in any junior-, senior-, masters- or MBA-level managerial accounting course. Students need to have at least one course of introduction to statistics or instructors review required statistics concepts or techniques before assigning this case. Students are exposed to the challenges of deriving learning curve models, using regression analysis to study collected data and allocating limited resources to maximize contribution margin.

The purpose of this paper is to find a new method to reinforce high-density polyethylene (HDPE) with polyacrylonitrile fibers (PAN). Furthermore, the crystallinity, viscoelasticity and thermal properties of HDPE composites have also been investigated and compared.

For effective reinforcing, samples with different content fillers were prepared. HDPE composites were prepared by melt blending with double-screw extruder prior to cutting into particles and the samples for testing were made using an injection molding machine.

With the addition of 9 Wt.% PAN fibers, it was found that the tensile strength and flexural modulus got the maximum value in all HDPE composites and increased by 1.2 times than pure HDPE. The shore hardness, storage modulus and vicat softening point of the composites improved continuously with the increase in the proportion of the fibers. The thermal stability and processability of composites did not change rapidly with the addition of PAN fibers. The degree of crystallinity increased with the addition of PAN fibers. In general, the composites achieve the best comprehensive mechanical properties with the fiber content of 9 Wt.%.

The fibers improve the strength of the polyethylene and enhance its ability to resist deformation.

The modified HDPE by PAN fibers in this study have high tensile strength and resistance to deformation and can be used as an efficient material in engineering, packaging and automotive applications.

The purpose of this study is to solve the problems of poor stability and high energy consumption of the dynamic window algorithm (DWA) for the mobile robots, a novel enhanced dynamic window algorithm is proposed in this paper.

The novel algorithm takes the distance function as the weight of the target-oriented coefficient, and a new evaluation function is presented to optimize the azimuth angle.

The jitter of the mobile robot caused by the drastic change of angular velocity is reduced when the robot is closer to the target point. The simulation results show that the proposed algorithm effectively optimizes the stability of the mobile robot during operation with lower angular velocity dispersion and less energy consumption, but with a slightly higher running time than DWA.

A novel enhanced dynamic window algorithm is proposed and verified. According to the experimental result, the proposed algorithm can reduce the energy consumption of the robot and improves the efficiency of the robot.

This paper aims to propose a novel statistic energy analysis method with fuzzy parameters to study the dynamic and acoustic responses of coupled system with fuzzy parameters, which can expand the applied range of statistic energy analysis method in engineering to some extent.

On the basis of the property of membership level, the uncertain fuzzy parameters are expressed as the interval forms. Interval mathematics and interval expansion principle are adopted to solve the problem with interval parameters. At last, two numerical examples, which include a two-plate coupled system and a single-partition sound-insulation system, are carried out to demonstrate the feasibility and validity of the presented method.

Interval mathematics and interval expansion principle are adopted to solve the problem with interval parameters.

By integrating the interval analysis, optimization technique and Taylor expansion method, two non-probabilistic, set-theoretical statistical energy analyses are proposed for predicting the dynamical and acoustical response of the complex coupled system with uncertain parameters in high-frequency domain.

Interaction content is created during online learning interaction for the exchanged information to convey experience and share knowledge. Prior studies have mainly focused on the quantity of online learning interaction content (OLIC) from the perspective of types or frequency, resulting in a limited analysis of the quality of OLIC. Domain concepts as the highest form of interaction are shown as entities or things that are particularly relevant to the educational domain of an online course. The purpose of this paper is to explore a new method to evaluate the quality of OLIC using domain concepts.

This paper proposes a novel approach to automatically evaluate the quality of OLIC regarding relevance, completeness and usefulness. A sample of OLIC corpus is classified and evaluated based on domain concepts and textual features.

Experimental results show that random forest classifiers not only outperform logistic regression and support vector machines but also their performance is improved by considering the quality dimensions of relevance and completeness. In addition, domain concepts contribute to improving the performance of evaluating OLIC.

This paper adopts a limited sample to train the classification models. It has great benefits in monitoring students’ knowledge performance, supporting teachers’ decision-making and even enhancing the efficiency of school management.

This study extends the research of domain concepts in quality evaluation, especially in the online learning domain. It also has great potential for other domains.

Advances in information technology now permit the recording of massive and diverse process data, thereby making data-driven evaluations possible. This study discusses whether teachers’ information literacy can be evaluated based on their online information behaviors on online learning and teaching platforms (OLTPs).

First, to evaluate teachers’ information literacy, the process data were combined from teachers on OLTP to describe nine third-level indicators from the richness, diversity, usefulness and timeliness analysis dimensions. Second, propensity score matching (PSM) and difference tests were used to analyze the differences between the performance groups with reduced selection bias. Third, to effectively predict the information literacy score of each teacher, four sets of input variables were used for prediction using supervised learning models.

The results show that the high-performance group performs better than the low-performance group in 6 indicators. In addition, information-based teaching and behavioral research data can best reflect the level of information literacy. In the future, greater in-depth explorations are needed with richer online information behavioral data and a more effective evaluation model to increase evaluation accuracy.

The evaluation based on online information behaviors has concrete application scenarios, positively correlated results and prediction interpretability. Therefore, information literacy evaluations based on behaviors have great potential and favorable prospects.

The purpose of this paper is to investigate the relationship between the keyhole geometry and acoustic signatures from the backside of a workpiece. It lays a solid foundation for monitoring the penetration state in variable polarity keyhole plasma arc welding.

The experiment system is conducted on 6-mm-thick aluminum alloy plates based on a dual-sensor system including a sound sensor and a charge coupled device (CCD) camera. The first step is to extract the keyhole boundary from the acquired keyhole images based on median filtering and edge extraction. The second step is to process the acquired acoustic signal to obtain some typical time domain features. Finally, a prediction model based on the extreme learning machine (ELM) technique is built to recognize different keyhole geometries through the acoustic signatures and then identify the welding penetration status according to the recognition results.

The keyhole geometry and acoustic features after processing can be closely related to dynamic change information of keyhole. These acoustic features can predict the keyhole geometry accurately based on the ELM model. Meanwhile, the predict results also can identify different welding penetration status.

This paper tries to make a foundation work to achieve the monitoring of keyhole condition and penetration status through image and acoustic signals. A useful model, ELM, is built based on these features for predicting the keyhole geometry. Compared with back-propagating neural network and support vector machine, this proposed model is faster and has better generalization performance in the case studied in this paper.

Based on the typical service supply chain (SSC) structure, the authors construct the model of e-tailing SSC to explore the coordination relationship in the supply chain, and big data analysis provides realistic possibilities for the creation of coordination mechanisms.

At the present stage, the e-commerce companies have not yet established a mature SSC system and have not achieved good synergy with other members of the supply chain, the shortage of goods and the greater pressure of express logistics companies coexist. In the case of uncertain online shopping market demand, the authors employ newsboy model, applied in the operations research, to analyze the synergistic mechanism of SSC model.

By analyzing the e-tailing SSC coordination mechanism and adjusting relevant parameters, the authors find that the synergy mechanism can be implemented and optimized. Through numerical example analysis, the authors confirmed the feasibility of the above analysis.

Big data analysis provides a kind of reality for the establishment of online SSC coordination mechanism. The establishment of an online supply chain coordination mechanism can effectively promote the efficient allocation of supplies and better meet consumers' needs.