Prediction of User Temporal Interactions with Online Course Platforms Using Deep Learning Algorithms

The analysis of learning interactions during online studying is a necessary task for designing online courses and sequencing key interactions, which enables online learning platforms to provide users with more efficient and personalized service. However, the research on predicting the interaction itself is not sufficient and the temporal information of interaction sequences hasn’t been fully investigated. To fill in this gap, based on the interaction data collected from Massive Open Online Courses (MOOCs), this paper aims to simultaneously predict a user’s next interaction and the occurrence time to that interaction. Three different neural network models: the long short-term memory, the recurrent marked temporal point process, and the event recurrent point process, are applied on the MOOC interaction dataset. It concludes that taking the correlation between the user action and its occurrence time into consideration can greatly improve the model performance, and that the prediction results are conducive to exploring dropout rates or online learning habits and performances

A novel delay time modelling method for incorporating reuse actions in three-state single-component systems

This paper presents a new delay time modelling method for reusing single-component systems with two defective states and one failure state. It assumes that a component may be reused for the purposes of resource, economic and environmental sustainability. The possibility of reusing industrial components is not generally considered in maintenance models, which represents a knowledge gap in the literature, especially in the delay time related models. To address this gap, this paper proposes a method based on the delay time modelling method to investigate different scenarios of component reusability and uses real-world systems in the mining industry to illustrate its applicability. The paper then derives the expected cost rate, obtains lower and upper bounds of the expected total cost, considers the improving learning rate of correctly classifying defective components and incorporates the environmental impact of disposed components in optimization of the inspection interval. Results discuss when the reuse action may provide economic benefits even when the reused item may have different reliability than new one

Prediction of user temporal interactions with online course platforms using deep learning algorithms

The analysis of learning interactions during online studying is a necessary task for designing online courses and sequencing key interactions, which enables online learning platforms to provide users with more efficient and personalized service. However, the research on predicting the interaction itself is not sufficient and the temporal information of interaction sequences hasn't been fully investigated. To fill in this gap, based on the interaction data collected from Massive Open Online Courses (MOOCs), this paper aims to simultaneously predict a user's next interaction and the occurrence time to that interaction. Three different neural network models: the long short-term memory, the recurrent marked temporal point process, and the event recurrent point process, are applied on the MOOC interaction dataset. It concludes that taking the correlation between the user action and its occurrence time into consideration can greatly improve the model performance, and that the prediction results are conducive to exploring dropout rates or online learning habits and performances

Fully Nonstationary Spatially Variable Ground Motion Simulations Based on a Time-Varying Power Spectrum Model

By analyzing the evolutionary spectrum method for multivariate nonstationary stochastic processes, a simulation method for fully nonstationary spatially variable ground motion is proposed based on the Kameda time-varying power spectrum model. This method can properly simulate nonstationary spatially variable ground motion based on a target response spectrum. Two numerical examples, in which the Kameda time-varying power spectra are calculated for different conditions, are presented to demonstrate the capabilities of the proposed method. In the first example, the nonstationary spatially variable ground motion that satisfies the time-frequency characteristics and response characteristics of the original ground motion is simulated by identifying the parameters of the given time-varying power spectrum. In the second example, the ground motion that satisfies the design response spectra is simulated by defining the parameters of the time-varying power spectrum directly. The results demonstrate that the method can effectively simulate nonstationary spatially variable ground motion, which implies that the proposed method can be used in engineering applications

Functional requirements elicitation approach for the design and integration of robotic system for automation

Implementing robotic systems in industrial production enables manufacturing automation, boosting process engineering efficiency. However, new issues regarding process and product safety are raised due to the introduction of robots. Ensuring operation reliability and product quality in the design phase becomes tricky. In this paper, we aim at reliable design for robotics systems applied in production by investigating safety relations between the automation system and the manufacturing process. Functional modelling is a modern concept to translate the overall system goal into properties, based on which a function-centric procedure is proposed for identifying functional requirements specification on the robot-enabled production during the design phase. To apply the procedure in safety analysis, a form for function-centric hazard identification approach (F-CHIA) is presented based on the previous work, which provides a systematic way to conclude functional safety requirements. A specific case study on the pick-place task in the slaughterhouse using an autonomous manipulator is presented in this paper as an example, showing good applicability and reasonability of the proposed approach

Improvement of and Parameter Identification for the Bimodal Time-Varying Modified Kanai-Tajimi Power Spectral Model

Based on the Kanai-Tajimi power spectrum filtering method proposed by Du Xiuli et al., a genetic algorithm and a quadratic optimization identification technique are employed to improve the bimodal time-varying modified Kanai-Tajimi power spectral model and the parameter identification method proposed by Vlachos et al. Additionally, a method for modeling time-varying power spectrum parameters for ground motion is proposed. The 8244 Orion and Chi-Chi earthquake accelerograms are selected as examples for time-varying power spectral model parameter identification and ground motion simulations to verify the feasibility and effectiveness of the improved bimodal time-varying modified Kanai-Tajimi power spectral model. The results of this study provide important references for designing ground motion inputs for seismic analyses of major engineering structures

Building Digital Twin of Mobile Robotics Testbed Using Centralized Localization System

The digital twin (DT), recognized as a crucial tool for laboratory automation and smart factories, can visualize, simulate, and control its counterpart as digitalization advances. To ensure system validation, precise state synchronization is required between the DT and the physical twin (PT). More specifically, the real-time position is regarded as the vital metric when mobile robots are implemented. In this paper, we propose a DT system for the mobile robot testbed with the aid of a centralized, integrated localization system. The integrated localization system is realized via the robot's onboard sensor, images from the monitor camera, and an Extended Kalman Filter (EKF), which enables the PT's real-time state to be estimated and transmitted to the DT controller. Additionally, the DT system integrates 3D simulation functions and provides a port for physical robot control. The proposed approach offers a state synchronization solution that does not rely on a single robot's sophisticated sensors or localization ability, indicating the potential for integrating robots with different technological capabilities

Decoding Risk Management: The Crucial Means-End Aspect of Countermeasures and Hazards

This paper explores the relationship between countermeasures and hazards in contemporary risk management frameworks. The fundamental premise posits hazards as undesirable ends, with countermeasures serving as the means to prevent or mitigate adverse situations. Focusing on foundational principles within socio-technical systems, the paper explores the aggregation and decomposition of countermeasure-hazard relations in a means-end manner. Action theories, grounded in this dimension, offer significant potential for informed decision-making in design, operation, and maintenance tasks. Through a case study on the function-centered hazard identification approach (F-CHIA), the paper illustrates practical applications in robotics safety, deriving risk mitigation strategies within design specifications. Ultimately, it underscores the critical role of the means-end dimension in effective risk mitigation strategies, contributing to advancements in riskmanagement

Properties of Concrete Reinforced with a Basalt Fiber Microwave-Absorbing Shielding Layer

The purpose of this study was to propose a highly efficient, durable, and environmentally friendly method for the rapid removal of ice and snow. A microwave-absorbing functionality layer was placed between a conductive metal mesh and magnetite sand shielding layer, and ordinary cement concrete (OC). Microwave heating, mechanical strength determination, and indoor and outdoor de-icing tests were performed on the cement concrete specimens with the shielding layer. Basalt fibers were added to the absorbing functionality layer, and the formed specimens were tested for strength and durability. The microstructure was observed using SEM experiments. The results show that the temperature rise of microwave-absorbing cement concrete with a magnetite sand shielding layer (MCMS) and microwave-absorbing cement concrete with a conductive metal mesh shielding layer (MCMM) increased by approximately 17.2% and 27.1%, respectively, compared to that of microwave-absorbing concrete (MAC). After freeze–thaw cycles, the compressive strength and flexural strength of microwave-absorbing concrete with basalt fiber (MAB) increased by 4.35% and 7.90% compared to those of MAC, respectively. The compressive strength and flexural strength of microwave-absorbing concrete with a magnetite sand shielding layer and basalt fiber (MAMB) increased by 8.07% and 6.57%, respectively, compared to those of MCMS. Compared to specimens without basalt fiber, the wear rate per unit area of MAMB decreased by 8.8%, and the wear rate of MAB decreased by 9.4%. The water absorption rate of MAMB specimens decreased by 13.1% and 12.0% under the conditions of 20 and 40 microwave freeze–thaw cycles, respectively, compared to that of MCMS. The water absorption rate of MAB specimens decreased by 9.9% and 8.3% under the conditions of 20 and 40 microwave freeze–thaw cycles, respectively, compared to that of MAC. SEM analysis showed that the addition of basalt fibers improved the compactness and stability of the cement concrete structure as a whole. This study provides valuable references for the promotion and application of microwave de-icing technology

Psychological Status, Compliance, Serum Brain-Derived Neurotrophic Factor, and Nerve Growth Factor Levels of Patients with Depression after Augmented Mindfulness-Based Cognitive Therapy

Objective. Mindfulness-based cognitive therapy (MBCT) is a cost-effective psychosocial program that prevents relapse/recurrence in major depression. The present study aimed to analyze the effects of augmented MBCT along with standard treatment dominated by pharmacotherapy on psychological state, compliance, brain-derived neurotrophic factor (BDNF), and nerve growth factor (NGF) expression levels in patients with depression. Methods. A total of 160 eligible patients with depression in The First Affiliated Hospital of Zhengzhou University were included in this study. The study randomly assigned the patients to the experimental group (n = 80) and control group (n = 80). All participants were assessed with the questionnaires including the 17-item Hamilton Depression Rating Scale (HAMD-17), Rosenberg Self-esteem Scale (RSES), Self-Acceptance Questionnaire (SAQ), and Stigma Scale (Scale of Stigma in People with Mental Illness, SSPM). The serum levels of BDNF and NGF were detected by enzyme-linked immunosorbent assay (ELISA). Results. After 8 weeks of treatment, the experimental group showed significant lower HAMD-17 score, higher RSES, and SAQ score, as well as lower SSPM score compared with the control group (P<0.01). Furthermore, ELISA revealed that the serum levels of BDNF and NGF remarkably increased in the experimental group after treatment (P<0.001). Conclusions. Our data showed that augmented MBCT combined with pharmacotherapy contributed to improvement on patients’ psychological state, compliance, and disease recurrence