Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students’ behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In this paper, we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRF). A Parallel fusion neural network model is designed to perform this task. We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews. The experimental results show that our proposed fusion model achieves better results compared to baseline models.     

Development of a stakeholder identification and analysis method for human factors integration in work system design interventions – Change Agent Infrastructure

In any work system design intervention—for example, a physical workplace re-design, a work process change, or an equipment upgrade—it is often emphasized how important it is to involve stakeholders in the process of analysis and design, to gain their perspectives as input to the development, and ensure their future acceptance of the solution. While the users of an artifact or workplace are most often regarded as being the most important stakeholders in a design intervention, in a work-system context there may be additional influential stakeholders who influence and negotiate the design intervention's outcomes, resource allocation, requirements, and implementation. Literature shows that it is uncommon for empirical ergonomics and human factors (EHF) research to apply and report the use of any structured stakeholder identification method at all, leading to ad-hoc selections of whom to consider important. Conversely, other research fields offer a plethora of stakeholder identification and analysis methods, few of which seem to have been adopted in the EHF context. This article presents the development of a structured method for identification, classification, and qualitative analysis of stakeholders in EHF-related work system design intervention. It describes the method's EHF-related theoretical underpinnings, lessons learned from four use cases, and the incremental development of the method that has resulted in the current method procedure and visualization aids. The method, called Change Agent Infrastructure (abbreviated CHAI), has a mainly macroergonomic purpose, set on increasing the understanding of sociotechnical interactions that create the conditions for work system design intervention, and facilitating participative efforts.     

Volatilization of fluorine in a CaO–SiO2–CaF2-based system with the substitution of Na2O with K2O

Cuspidine-based systems are used to control the crystallization in mold fluxes, which is enabled by CaF₂ additions. However, excess CaF₂ increases the corrosion of casting machines. Therefore, Na₂O and K₂O are added to the mold flux system to ensure an optimized crystallization and lubrication ability of the flux with the CaF₂ content. This study investigated the effect of substituting Na₂O with K₂O on the volatilization of fluorine in a CaO–SiO₂–CaF₂-based slag system at high temperatures. The substitution of Na₂O with K₂O was performed at 5 mol% intervals. The volatilization was observed by thermogravimetric analysis under several isothermal conditions. The mass loss was measured at a heating rate of 5, 10, and 20 K/min. As the temperature increased, the volatilization of the mixed samples increased. The activation energy was calculated using the Flynn–Wall–Ozawa and Kissinger–Akahira–Sunose methods. A kinetic analysis of the volatilization of fluorine was conducted using the calculated parameters and several known kinetic models. Consequently, the volatilization of the Na-rich sample was controlled by chemical reactions and that of the K-rich sample was identified to be controlled by a phase-boundary-controlled reaction. These results suggest that the addition of mixed alkali oxide promote the volatilization during the early stages of the reaction. From the post-experimental composition analyses, the remaining Na and K in the samples suggested a different mechanism for the Na and K volatilization. The volatilization of Na increased with time, whereas K volatilized easily during the beginning of the reaction.     

Quantification of Dendritic Spines Remodeling under Physiological Stimuli and in Pathological Conditions

Numerous brain diseases are associated with abnormalities in morphology and density of dendritic spines, small membranous protrusions whose structural geometry correlates with the strength of synaptic connections. Thus, the quantitative analysis of dendritic spines remodeling in microscopic images is one of the key elements towards understanding mechanisms of structural neuronal plasticity and bases of brain pathology. In the following article, we review experimental approaches designed to assess quantitative features of dendritic spines under physiological stimuli and in pathological conditions. We compare various methodological pipelines of biological models, sample preparation, data analysis, image acquisition, sample size, and statistical analysis. The methodology and results of relevant experiments are systematically summarized in a tabular form. In particular, we focus on quantitative data regarding the number of animals, cells, dendritic spines, types of studied parameters, size of observed changes, and their statistical significance.     

Fire safe,sustainable loose furnishing

The aim of this exploratory study has been to investigate the fire properties and environmental aspects of different upholstery material combinations, mainly for domestic applications. An analysis of the sustainability and circularity of selected textiles, along with lifecycle assessment, is used to qualitatively evaluate materials from an environmental perspective. The cone calorimeter was the primary tool used to screen 20 different material combinations from a fire performance perspective. It was found that textile covers of conventional fibres such as wool, cotton and polyester, can be improved by blending them with fire resistant speciality fibres. A new three‐dimensional web structure has been examined as an alternative padding material, showing preliminary promising fire properties with regard to ignition time, heat release rates and smoke production.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

人工智能赋能在线实验教学行为分析

教学行为分析作是教学质量分析的重要组成部分，也是教学引导与反馈机制的重要依据。文章阐述了人工智能（AI）的含义及发展历程，重点分析总结了教学行为分析方法及AI在教学行为分析上的应用。文章以东南大学电工电子在线实验为研究平台，探索分析了AI技术在实验教学行为分析上的可行性，梳理了基于专家系统的在线实验分析系统的设计思路，充分探讨了“智能”教育在实验教学中的深刻内涵。     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Optimal Design of Novel Electromagnetic-Ring Active Balancing Actuator with Radial Excitation

Imbalance vibration is a typical failure mode of rotational machines and has significant negative effects on the effii-ciency,accuracy,and service life of equipment.To automatically reduce the imbalance vibration during the opera-tional process,different types of active balancing actuators have been designed and widely applied in actual produc-tion.However,the existing electromagnetic-ring active balancing actuator is designed based on an axial excitation structure which can cause structural instability and has low electromagnetic driving efficiency.In this paper,a novel radial excitation structure and the working principle of an electromagnetic-ring active balancing actuator with a combined driving strategy are presented in detail.Then,based on a finite element model,the performance param-eters of the actuator are analyzed,and reasonable design parameters are obtained.Self-locking torque measurements and comparative static and dynamic experiments are performed to validate the self-locking torque and driving effi-ciency of the actuator.The results indicate that this novel active balancing actuator has sufficient self-locking torque,achieves normal step rotation at 2000 r/min,and reduces the driving voltage by 12.5％.The proposed novel balancing actuator using radial excitation and a combination of permanent magnets and soft-iron blocks has improved electro-magnetic efficiency and a more stable and compact structure.