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The purpose of this paper is to construct a proportion-integral-type (PI-type) memristor, which is different from that of the previous memristor emulator, but the constructing memristive chaotic circuit possesses line equilibrium, leading to the emergence of the initial conditions-related dynamical behaviors.

This paper presents a PI-type memristor emulator-based canonical Chua’s chaotic circuit. With the established mathematical model, the stability region for the line equilibrium is derived, which mainly consists of stable and unstable regions, leading to the emergence of bi-stability because of the appearance of a memristor. Initial conditions-related dynamical behaviors are investigated by some numerically simulated methods, such as phase plane orbit, bifurcation diagram, Lyapunov exponent spectrum, basin of the attraction and 0-1 test. Additionally, PSIM circuit simulations are executed and the seized results validate complex dynamical behaviors in the proposed memristive circuit.

The system exhibits the bi-stability phenomenon and demonstrates complex initial conditions-related bifurcation behaviors with the variation of system parameters, which leads to the occurrence of the hyperchaos, chaos, quasi-periodic and period behaviors in the proposed circuit.

These memristor emulators are simple and easy to physically fabricate, which have been increasingly used for experimentally demonstrating some interesting and striking dynamical behaviors in the memristor-based circuits and systems.

This paper aims to explore the effects of mineral diesel fuel carbon soot (MCS) and biodiesel carbon soot (BCS) on the lubrication of polyalphaolefin (PAO) and diesel fuels.

Two styles of carbon soot were prepared from the natural combustion of mineral diesel fuel oil (MDO) and biodiesel oil (BDO). Tribological tests were conducted on a high-frequency reciprocating rig. Friction surfaces were characterized using three-dimensional laser scanning confocal microscopy and Raman spectroscopy.

The addition of MCS and BCS to PAO could reduce friction in most cases. MCS had a negligible effect on the wear for contents not exceeding 1.0 per cent. By contrast, BCS exhibited a considerable negative influence on the wear resistance even at low contents. For diesel fuels, MCS reduced both friction and wear, whereas BCS substantially deteriorated the lubrication of BDO. MCS formed a Fe₃O₄/C composite lubricating film on the friction surface. BCS also entered the contact region, but it did not form an effective Fe₃O₄/C composite lubricating film.

This work compared MDO and BDO from a different perspective, i.e. the effects of their combustion carbon soot on the lubrication of lubricating oil and fuel oil. The significant negative effect of BCS on the lubrication of lubricating oil and BDO is a problem that could occur in the industrial application of BDO.

Labyrinth seals have been used extensively in industrial production. Better prediction of the performance of a labyrinth seal requires that these mechanisms be understood. This cannot be achieved except by investigating the flowfield details. Therefore, a total variation diminishing (TVD) finite volume scheme is applied to the Navier‐Stokes equations to obtain gas seal flowfield characteristics of axially staggered configuration in this paper. The calculation results here show the evolution process from unsteady flowfield characterization to steady flow pattern. Also, these new flowfield details may provide referable basis for understanding seal mechanisms.

The sandy environment is one of the typical environments in which helicopters operate. Air-sand two-phase flow in sandy environments may be an important factor affecting flight safety. Taking a typical example, this paper aims to investigate the aerodynamic and rotor trim characteristics of the UH-60 helicopter in sandy environments.

A computational study is conducted to simulate the air-sand flow over airfoils based on the Euler–Lagrange framework. The simulation uses the S-A turbulence model and the two-way momentum coupling methodology. Additionally, the trim characteristics of the UH-60 rotor are calculated based on the isolated rotor trim algorithm.

The simulation results show that air-sand flow significantly affects the aerodynamic characteristics of the SC1095 airfoil and the SC1094R8 airfoil. The presence of sand particles leads to a decrease in lift and an increase in drag. The calculation results of the UH-60 helicopter rotor indicate that the thrust decreases and the torque increases in the sandy environment. To maintain a steady forward flight in sandy environments, it is necessary to increase the collective pitch and the longitudinal cyclic pitch.

In this paper, the aerodynamic characteristics of airfoils and the trim characteristics in the air-sand flow of the UH-60 helicopter are discussed, which might be a new view to analyse the impact of sandy environments on helicopter safety and manoeuvring.

The purpose of this study is to propose and examine an integrated learning model for improving training effectiveness in workplace learning. Specifically, this study investigated the effect of achievement goal-setting intervention across three groups of new employees from a multinational medical company. During a three-day remote training program, the role of each achievement goal orientation (AGO) in goal setting intervention and their relations with trainees’ applied learning strategies were examined. This study proposed and validated an integrated training model for improving remote workplace learning effectiveness.

This study was based on two data sources, the pre- and posttests scores; time on task (deep learning: completing reflective practice) and time on content learning (surface learning: watching tutorials) retrieved from an adaptive learning platform. A total number of 133 participants were recruited in this study, and they were randomly assigned to three interventional groups. The intervention was grounded from the AGO theory and goal setting theory. A series of statistical analysis were conducted to examine the effect of each type of achievement goal setting as a prompt for new employees’ learning behavior and performance.

Results indicated that setting mastery goal at the beginning of the training program leads to productive learning outcomes. Compared with the groups being required to set performance goal (final rank) or not to set any goal for the training purpose, trainees’ who were assigned to set a mastery goal (final performance score) performed statistically significantly higher than the other groups. Additionally, learners who set mastery goal spent higher proportion of time on deep learning than learners from the other groups. The results proved mastery goal setting as an effective prompt for boosting workplace learning effectiveness.

Organizations and institutions can take setting mastery approach goals as a prompt at the beginning of the training to increase learning effectiveness. In this way, trainees are promoted to apply more deep learning strategies and achieve better learning outcomes while setting mastery goal for their training purpose.

To the best of the authors’ knowledge, this study was the first to combine the intervention of goal setting and types of AGOs into workplace learning. This study adds to previous research on goal setting theory and AGO theory for the practical application and proposes an effective model for learners’ adaptive remote learning. Findings of this study can be used to provide educational psychological insights for training and learning in both industrial and academic settings.

This paper aims to solve the problems molybdenum disulfide (MoS₂) nanosheets suffer from inadequate dispersion stability and form a weak lubricating film on the friction surface, which severely limits their application as lubricant additives.

MoS₂/C₆₀ nanocomposites were prepared by synthesizing molybdenum disulfide (MoS₂) nanosheets on the surface of hydrochloric acid-activated fullerenes (C₆₀) by in situ hydrothermal method. The composition, structure and morphology of MoS₂/C₆₀ nanocomposites were characterized. Through the high-frequency reciprocating tribology test, its potential as a lubricant additive was evaluated.

MoS₂/C₆₀ nanocomposites that were prepared showed good dispersion in dioctyl sebacate (DOS). When 0.5 Wt.% MoS₂/C₆₀ was added, the friction reduction performance and wear resistance improved by 54.5% and 62.7%, respectively.

MoS₂/C₆₀ composite nanoparticles were prepared by in-situ formation of MoS₂ nanosheets on the surface of C₆₀ activated by HCl through hydrothermal method and were used as potential lubricating oil additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0321/

The purpose of this paper is to confirm that the axisymmetric finite element and smoothed particle hydrodynamics (FE-SPH) adaptive coupling method is effective to solve explosion problem in concrete based on the experiments.

Axisymmetric FE-SPH adaptive coupling method is first presented to simulate dynamic deformation process of concrete under internal blast loading. Using calculation codes of FE-SPH coupling method, numerical model of explosion is approximated initially by finite element method (FEM), and distorted finite elements are automatically converted into meshless particles to simulate damage, splash of concrete by SPH method, when equivalent plastic strain of elements reaches a specified value.

In this paper, damage process and pressure curve of concrete around explosive are analyzed and buried depth of explosive in concrete influence on damage effect under internal blast loading are obtained. Numerical analyses show that FE-SPH coupling method integrates high computational efficiency of FEM and advantages of SPH method, such as natural simulation to damage, splash and other characteristics of explosion in concrete.

This work shows that FE-SPH coupling method has good performance to solve the explosion problem.

The high-efficient processing of mass data is a primary issue in building and maintaining security video surveillance system. This paper aims to focus on the architecture of security video surveillance system, which was based on Hadoop parallel processing technology in big data environment.

A hardware framework of security video surveillance network cascaded system (SVSNCS) was constructed on the basis of Internet of Things, network cascade technology and Hadoop platform. Then, the architecture model of SVSNCS was proposed using the Hadoop and big data processing platform.

Finally, we suggested the procedure of video processing according to the cascade network characteristics.

Our paper, which focused on the architecture of security video surveillance system in big data environment on the basis of Hadoop parallel processing technology, provided high-quality video surveillance services for security area.

This study aims to investigate the catalytic performance and tribological properties of MoS₂ powder.

In this work, the authors attempted to use MoS₂ nanoparticles (nano-MoS₂) as a catalyst to synthesize trimethylolpropane oleate (TMPTO) by esterification of trimethylolpropane and oleic acid. The small amount of highly dispersed nano-MoS₂ catalyst remaining in TMPTO needed not to be separated and could be used as a lubricant modifier directly to achieve the purpose of improving the lubricity performance of TMPTO.

The results demonstrated that nano-MoS₂ had good catalytic esterification ability and achieved in situ dispersion of about 0.191% nano-MoS₂ in TMPTO while catalyzing the synthesis of base oil. After high-speed centrifugal sedimentation treatment, the product TMPTO still retained about 0.008% of nano-MoS₂. The above-synthesized TMPTO has significantly better lubricity performance than commercially available TMPTO, in which the friction coefficient and wear rate could be reduced by 75%.

The results of this study provide an idea for the design of catalysts for ester oil synthesis.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.