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In infrared small target detection, the infrared patch image (IPI)-model-based methods produce better results than other popular approaches (such as max-mean, top-hat, and human visual system) but in some extreme cases it suffers from long processing times and inconsistent performance. In order to overcome these issues, we propose a novel approach of dividing the traditional target detection process into two steps: suppression of background noise and elimination of clutter. The workflow consists of four steps: after importing the images, the second step applies the alternating direction multiplier method to preliminarily remove the background. Comparatively to the IPI model, this step does not require sliding patches, resulting in a significant reduction in processing time. To eliminate residual noise and clutter, the interim results from morphological filtering are then processed in step 3 through an improved new top-hat transformation, using a threefold structuring element. The final step is thresholding segmentation, which uses an adaptive threshold algorithm. Compared with IPI and the new top-hat methods, as well as some other widely used methods, our approach was able to detect infrared targets more efficiently (90% less computational time) and consistently (no sudden performance drop).

Students’ evaluation of teaching, for instance, through feedback surveys, constitutes an integral mechanism for quality assurance and enhancement of teaching and learning in higher education. These surveys usually comprise both the Likert scale and free-text responses. Since the discrete Likert scale responses are easy to analyze, they feature more prominently in survey analyses. However, the free-text responses often contain richer, detailed, and nuanced information with actionable insights. Mining these insights is more challenging, as it requires a higher degree of processing by human experts, making the process time-consuming and resource intensive. Consequently, the free-text analyses are often restricted in scale, scope, and impact. To address these issues, we propose a novel automated analysis framework for extracting actionable information from free-text responses to open-ended questions in student feedback questionnaires. By leveraging state-of-the-art supervised machine learning techniques and unsupervised clustering methods, we implemented our framework as a case study to analyze a large-scale dataset of 4400 open-ended responses to the National Student Survey (NSS) at a UK university. These analyses then led to the identification, design, implementation, and evaluation of a series of teaching and learning interventions over a two-year period. The highly encouraging results demonstrate our approach’s validity and broad (national and international) application potential—covering tertiary education, commercial training, and apprenticeship programs, etc., where textual feedback is collected to enhance the quality of teaching and learning.

A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame from the sequence is an important problem to be solved first. A maximum standard deviation of the sensitive region method was proposed, which can identify a reasonable image frame automatically from an infrared image sequence; then, a stratagem of image composition was applied for enhancing the detection of deep defects in the key frame. Blob analysis had been adopted to acquire general information of defects such as their distributions and total number of defects. A region of interest of the defect was automatically located by its key frame combined with blob analysis. The defect information was obtained through image segmentation techniques. To obtain a robustness of results, a method of two steps of detection was proposed. The specimen of polyvinyl chloride with two artificial defects at different depths as an example was used to demonstrate how to operate the proposed method for an accurate result. At last, the proposed method was successfully adopted to examine the damage of carbon fiber-reinforced polymer. A comparative study between the proposed method and several state-of-the-art ones shows that the former is accurate and reliable and may provide a more useful and reliable tool for quality assurance in the industrial and manufacturing sectors.

The aim of this study is to evaluate the reliability of a crowd simulation model developed by the authors by reproducing Dyer et al.'s experiments (published in Philosophical Transactions in 2009) on human leadership and consensus decision making in a computer-based environment. The theoretical crowd model of the simulation environment is presented, and its results are compared and analysed against Dyer et al.'s original experiments. It is concluded that the simulation results are largely consistent with the experiments, which demonstrates the reliability of the crowd model. Furthermore, the simulation data also reveals several additional new findings, namely: 1) the phenomena of sacrificing accuracy to reach a quicker consensus decision found in ants colonies was also discovered in the simulation; 2) the ability of reaching consensus in groups has a direct impact on the time and accuracy of arriving at the target position; 3) the positions of the informed individuals or leaders in the crowd could have significant impact on the overall crowd movement; and 4) the simulation also confirmed Dyer et al.'s anecdotal evidence of the proportion of the leadership in large crowds and its effect on crowd movement. The potential applications of these findings are highlighted in the final discussion of this paper.

Fluoride-salt-cooled high-temperature reactor coupled with SCO2 Brayton cycle power generation system has the advantages of high inherent safety, high efficiency, compact structure, and waterless cooling. However, the cold end of the SCO2 Brayton cycle system operates near the critical point and is very sensitive to the cold end parameters, which seriously affects the performance of the cycle system. In addition, when the cold end parameters cross the critical point, the liquefied mass can seriously threaten the safety of the compressor and even the cycle system, therefore, the investigation of the characteristics of the cold end parameters during the dynamic process becomes the key to the safe and stable operation of the SCO2 Brayton cycle system. This paper took the SCO2 Brayton cycle system of a small fluoride-salt-cooled high-temperature reactor as the research object. Matlab/Simulink software was used as the simulation platform, and the Refprop interface was embedded to calculate physical properties. The one-dimensional dynamic simulation model was established for compressors, turbines, heat exchangers, reactors, and other thermal equipment through energy conservation and mass conservation. On this basis, the dynamic characteristics of the system under the disturbance of the cooling water flow of the SCO2 pre-cooler were studied, and the influence of the cold end parameters on the system performance during the variable load was explored. The results show that the disturbance of the cooling water flow of the SCO2 pre-cooler directly affects the inlet temperature of the main compressor, which in turn seriously affects the stable operation of the whole system, so it is necessary to impose certain control means to ensure the stability of the pre-cooler outlet temperature during the operation of the system. Due to the role of the thermal inertia of the reactor and the interaction between the reactor and the heat exchanger, the hot end of the system fluctuates obviously and then affects the cold end, so the reactor is the main reason for the longterm fluctuations of the cold end. In the process of load reduction, the cold end parameters of the system gradually approach the critical point, resulting in increasingly drastic changes in carbon dioxide density at the inlet of the main compressor, which leads to the sensitivity of the main compressor to the inlet temperature and inlet pressure gradually increases. At the low load state, the main compressor inlet temperature is disturbed by 1 ℃, the highest change of main compressor inlet density is 262 kg/m3, the highest change of main compressor outlet pressure is 7 MPa, and the highest change of main compressor efficiency is 6.82%. The main compressor inlet pressure is disturbed by 0.1 MPa, the highest change of main compressor inlet density is 228 kg/m3, the highest change of main compressor outlet pressure is 6.33 MPa, and the highest change of main compressor efficiency is 6.23%. However, the inlet temperature and inlet pressure of the main compressor have a small and almost negligible effect on the outlet temperature.