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The purpose is to provide decision support for tourists recommending scenic spots and corresponding suggestions for the management of scenic spots.

Based on the Baidu index data generated, this paper analyzes the temporal and spatial characteristics of network attention of 5A scenic spots in Sichuan Province. The online comment data are used to build the assessment model of scenic spots based on network attention, and the comment information of tourists is mined and analyzed through statistical analysis. At the same time, the key attributes of scenic spots from the perspective of network attention are evaluated and analyzed by using the probabilistic linguistic term set. Finally, this paper further constructs a recommendation model based on the key attribute set of scenic spots.

This paper uses different types of tourism network information, integrates multi-types of data and methods, fully excavates the value information of tourism network information, constructs the research framework of “scenic spot assessment + scenic spot recommendation” from the perspective of network attention, analyzes the network attention characteristics of scenic spots, evaluates the performance of scenic spots, and implements scenic spot recommendation.

This paper integrates multi-source data and multidisciplinary theoretical methods to form a scenic spot research framework of “assessment + recommendation” from the perspective of network attention.

Using computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.

This paper proposes a binocular image classification model to solve ferrographic image classification problems.

This paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/

The purpose of this paper is to evaluate the role of encapsulation temperature on the preparation of silica-encapsulated waterborne aluminium pigments.

The waterborne aluminium pigments were prepared with H₂O₂ as anchoring agent and siloxane used as precursors in pH = 9.0 medium at different temperatures. The anchorage and compactness of silicon which on aluminium surface were characterized by optical microscopy, scanning electron microscopy and N₂ adsorption-desorption. The anticorrosion property was characterized by the volume of produced hydrogen as a function of time.

The effect of encapsulation temperature on anticorrosion property of aluminium pigments is reflected from the anchorage and the compactness of silica on aluminium surface. Furthermore, when encapsulation temperature is 45-50°C, the silica platelets uniformly anchored on the aluminium surface as a dense film, which show the best anticorrosion property. Lower and higher encapsulation temperatures cause the silica platelets to agglomerate rather than anchor on the aluminium surface, which is unfavourable for the anchorage and the formation of compact silica film. The use of product in waterborne coatings gives a higher glossiness than that of raw material.

Only pH = 9.0 medium was explored, and the other pH medium could result in different optimum temperatures.

The investigation results provide theoretical basis for obtaining excellent waterborne aluminium pigments.

The method of investigating corrosion resistance mechanism of aluminium pigments based on anchorage and compactness is novel.

Although proactive fault handling plans are widely spread, many unexpected data center outages still occurred. To rescue the jobs from faulty data centers, the authors propose a novel independent job rescheduling strategy for cloud resilience to reschedule the task from the faulty data center to other working-proper cloud data centers, by jointly considering job nature, timeline scenario and overall cloud performance.

A job parsing system and a priority assignment system are developed to identify the eligible time slots for the jobs and prioritize the jobs, respectively. A dynamic job rescheduling algorithm is proposed.

The simulation results show that our proposed approach has better cloud resiliency and load balancing performance than the HEFT series approaches.

This paper contributes to the cloud resilience by developing a novel job prioritizing, task rescheduling and timeline allocation method when facing faults.

A direct and unified approach is proposed toward simultaneously simulating large strain elastic behaviors of gellan gels with different gellan polymer concentrations. The purpose of this paper is to construct an elastic potential with certain parameters of direct physical meanings, based on well-designed invariants of Hencky’s logarithmic strain.

For each given value of the concentration, the values of the parameters incorporated may be determined in the sense of achieving accurate agreement with large strain uniaxial extension and compression data. By means of a new interpolating technique, each parameter as a function of the concentration is then obtained from a given set of parameter values for certain concentration values.

Then, the effects of gellan polymer concentrations on large strain elastic behaviors of gellan gels are studied in demonstrating how each parameter relies on the concentration. Plane-strain (simple shear) responses are also presented for gellan gels with different polymer concentrations.

A direct, unified approach was proposed toward achieving a simultaneous simulation of large elastic strain behaviors of gellan gels for different gellan polymer concentrations. Each parameter incorporated in the proposed elastic potential will be derived as a function of the polymer concentration in an explicit form, in the very sense of simultaneously simulating large strain data for different concentrations.

The purpose of this study is to find the multi-factor influence law of stress, strain rate and sulfate-reducing bacteria (SRB) on X70 pipeline steel in a simulated solution of sea mud and the order of influence of the three factors on X70 steel to develop a scientific basis for pipeline corrosion protection.

This paper studied the effects of stress, strain rate and SRB on the X70 pipeline steel corrosion behavior in simulated sea mud solution through orthogonal testing, electrochemical experiments and morphological observations.

The results of this study showed that stress proved to be the most relevant element for corrosion behavior, followed by SRB and strain rate. At high stresses (301 MPa and 576 MPa), stress dominated the corrosion behavior of X70 pipeline steel. However, at low stress (82 MPa), SRB played the most important role.

Subsea pipelines are in a very complex environmental regime that includes stress, strain rates and SRB, which often cause pipeline pitting and perforation. However, most scholars have only looked into the influence of single factors on metal corrosion. So, the single-factor experimental results of previous studies could hardly be applied to actual working conditions. There is an urgent need to understand the multi-factor influence law of stress, strain and SRB acting together on the pipeline corrosion behavior, especially to determine the dominant factor.

A research stream known as prospect theory describes how decision biases lead to results that differ from those predicted by classical utility theory (Kahneman and Tversky, 1979). Prospect theory hypothesizes that individuals will experience potential losses more intensely than potential gains, and will be more risk‐seeking in loss situations, while more risk‐avoiding in gain situations. This study includes 948 participants from the PRC and 318 students from the USA. All of our attempts to replicate these findings in the Peoples’ Republic of China have revealed a different pattern. Chinese subjects consistently demonstrated risk‐seeking preferences, both in gain and loss situations.

1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.

A novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.

The purpose of this paper is to test two kinds of rare earth complexes of Lanthanum Dialkyldithiophosphate (LaDDP) and Lanthanum Dialkylphosphate (LaDP) as lubricant additives in liquid paraffin for the untreated 60Si2Mn steel and laser‐cladding Ni35A coating on 60Si2Mn steel sliding pairs which are a potential substitute for Zinc Dialkldithiophosphate (ZnDDP).

Tribological properties were evaluated by an Optimol‐SRV oscillating friction and wear test. The morphologies of the worn surfaces were observed by a scanning electron microscope (SEM), and the chemical states of several typical elements on the worn surfaces were examined by means of X‐ray photoelectron spectroscopy (XPS).

Treated laser cladding coatings of steel can improve its hardness and strength and the coated steel possess higher load‐carrying capacity than that of 60Si2Mn; The rare earth complexes of LaDDP and LaDP possess good oil‐solubility, friction‐reducing and wear resistance properties. Those rare earth complexes as additives in liquid paraffin during the friction process can form a protective film containing rare earth oxide, sulfate and sulfur‐containing compound during the friction process.

The paper presents two kinds of potentially useful, environmentally‐friendly and highly efficient substitutes for the ZnDDP additives in lubricants.

Owing to their good friction‐reducing and wear resistance properties, LaDDP and LaDP are two optimum and promising industry lubrication additives.

This work is a new application of rare earth complex as lubricant additive in liquid paraffin, which provides a new direction for designing friction pairs and lubricant additive. The tribology experiments have been carried out through the variation of experiment conditions.

This study aimed to adapt existing text-mining techniques and propose a novel topic recognition approach for textual patient reviews.

The authors first transformed multilabel samples for adapting model training forms. Then, an improved method was proposed based on dynamic mixed sampling and transfer learning to improve the learning problem caused by imbalanced samples. Specifically, the training of our model was based on the framework of a convolutional neural network and self-trained Word2Vector on large-scale corpora.

Compared with the SVM and other CNN-based models, the CNN+ DMS + TL model proposed in this study has made significant improvement in F1 score.

The improved methods based on dynamic mixed sampling and transfer learning can adequately manage the learning problem caused by the skewed distribution of samples and achieve the effective and automatic topic recognition of textual patient reviews.

The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2021-0059.