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In a coaxial ultrasonic flow sensor (UFS), wall thickness is a vital parameter of the measurement tube, especially those with small inner diameters. The paper aims to investigate the influence of wall thickness on the transient signal characteristics in an UFS.

First, the problem was researched experimentally using a series of measurement tubes with different wall thicknesses. Second, a finite element method–based model in the time domain was established to validate the experimental results and further discussion. Finally, the plane wave assumption and oblique incident theory were used to analyze the wave propagation in the tube, and an idea of wave packet superposition was proposed to reveal the mechanism of the influence of wall thickness.

Both experimental and simulated results showed that the signal amplitude decreased periodically as the wall thickness increased, and the corresponding waveform varied dramatically. Based on the analysis of wave propagation in the measurement tube, a formula concerning the phase difference between wave packets was derived to characterize the signal variation.

This paper provides a new and explicit explanation of the influence of wall thickness on the transient signal in a co-axial UFS. Both experimental and simulated results were presented, and the mechanism was clearly described.

With the development of artificial intelligence, proximity sensors show their great potential in intelligent perception. This paper aims to propose a new planar capacitive sensor for the proximity sensing of a conductor.

Different from traditional structures, the proposed sensor is characterized by sawtooth-structured electrodes. A series of numerical simulations have been carried out to study the impact of different geometrical parameters such as the width of the main trunk, the width of the sawtooth and the number of sawtooths. In addition, the impact of the lateral offset of the approaching graphite block is investigated.

It is found that sensitivity is improved with the increase of the main trunk with, sawtooth width and sawtooth number while a larger lateral offset leads to a decrease in sensitivity. The performance of the proposed planar capacitive proximity sensor is also compared with two conventional planar capacitive sensors. The results show that the proposed planar capacitive sensor is obviously more sensitive than the two conventional planar capacitive sensors.

In this paper, a new planar capacitive sensor is proposed for the proximity sensing of a conductor. The results show that the capacitive sensor with the novel structure is obviously more sensitive than the traditional structures in the detection of the proximity conductor.

To solve the problem of low sensitivity of traditional capacitive proximity sensor, this paper aims to propose a novel capacitive sensor for detection of an approaching conductor.

Five capacitive proximity sensors with different structures are designed and the performance is compared with the traditional capacitive sensor. The impacts of geometrical parameters on the performance of the proposed capacitive sensor are studied. Furthermore, the sensitivity of the proposed capacitive sensor to an approaching conductor with different sizes is discussed. Also, how the designed capacitive sensor is sensitive to the lateral placement of the approaching object is analyzed.

Several capacitive proximity sensor structures have been designed and analyzed. It is found that the capacitive sensor with the top small ring-bottom large ring structure shows stronger electric field distribution around the top electrode and higher sensitivity to the approaching conductor than other sensors. Through further analysis of the proposed sensor, the results demonstrate that proposed capacitive sensor is effective for proximity object detection.

This paper proposes a novel capacitive proximity sensor with top small ring-bottom large ring structure. Compared with the traditional capacitive sensor, the proposed capacitive sensor is more sensitive to the approaching object. This would be helpful for the accurate detection of the approaching object. Also, the top and bottom electrodes are much smaller.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

There is an urgent need to develop climate-smart agrosystems capable of mitigating climate change and adapting to its effects. Conventional agricultural practices prevail in Mauritius, whereby synthetic chemical fertilizers, pesticides and insecticides are used. It should be noted that Mauritius remains a net-food importing developing country of staple food such as cereals and products, roots and tubers, pulses, oil crops, vegetables, fruits and meat (FAO, 2011). In Mauritius, the agricultural sector faces extreme weather conditions like drought or heavy rainfall. Moreover, to increase the crop yields, farmers tend to use 2.5 times the prescribed amount of fertilizers in their fields. These excess fertilizers are washed away during heavy rainfall and contaminate lakes and river waters. By using smart irrigation and fertilization system, a better management of soil water reserves for improved agricultural production can be implemented. Soil Nitrogen, Phosphorus and Potassium (NPK) content, humidity, pH, conductivity and moisture data can be monitored through the cloud platform. The data will be processed at the level of the cloud and an appropriate mix of NPK and irrigation will be used to optimise the growth of the crops. Machine learning algorithms will be used for the control of the land drainage, fertilization and irrigation systems and real time data will be available through a mobile application for the whole system. This will contribute towards the Sustainable Development Goals (SDGs): 2 (Zero Hunger), 11 (Sustainable cities and communities), 12 (Responsible consumption and production) and 15 (Life on Land). With this project, the yield of crops will be boosted, thus reducing the hunger rate (SDG 2). On top of that, this will encourage farmers to collect the waters and reduce fertilizer consumption thereafter sustaining the quality of the soil on which they are cultivating the crops, thereby increasing their yields (SDG 15).

Safety and security (S&S) are critical concerns in South Africa, especially in Cape Town, one of the country’s most crime-ridden cities. The University of Cape Town (UCT), situated on a large, open campus, has experienced increased malefaction. Facilities management (FM) services at universities bear the primary responsibility for providing S&S to their communities. To comprehensively understand and address the community’s demands regarding S&S, the current study was conducted to investigate the challenges specific to open universities. This study aims to determine whether implementing community-based FM (CbFM) principles and using technological innovations could offer a more effective and sustainable solution.

The study adopted interpretivist overarching case study methodology, which is ontologically based. A mixed-method approach was used to incorporate the strengths and limitations of the weaknesses of both methods. The data collection took the form of an online survey of the university community and semi-structured interviews with university executive management to obtain data from the single case study of UCT. Descriptive statistics were used to analyze the quantitative data, and thematic analysis was used to identify emergent themes from the qualitative data.

The study presents an overall view of the provision of S&S at UCT, the unique challenges faced by management and the main S&S issues affecting the community. Moreover, the study reveals that UCT has implemented community participation processes in the past with limited success. This is because the strategies implemented constitute a narrow perspective of community participation. Therefore, a much smarter and more inclusive perspective using technological innovation is required for successful community participation to occur and to be successfully used in providing S&S toward achieving future-proofing facilities.

This research has demonstrated the influence of CbFM and innovative technologies on the S&S of the open campus. Hence, future-proof facilities can be achieved when FM actively engages university communities in managing campuses through technological innovation.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

This paper aims to report the study of a fluid buoy system that includes wave effects, with particular emphasis on validating the numerical results with experimental data.

A fluid–solid coupled algorithm is proposed to describe the motion of a rigid buoy under the effects of waves. The Navier–Stokes equations are solved with the open-source finite volume package Code Saturne, in which a free-surface capture technique and equations of motion for the solid are implemented. An ad hoc experiment on a laboratory scale is built. A buoy is placed into a tank partially filled with water; the tank is mounted into a shake table and subjected to controlled motion that promotes waves. The experiment allows for recording the evolution of the free surface at the control points using the ultrasonic sensors and the movement of the buoy by tracking the markers by postprocessing the recorded videos. The numerical results are validated by comparison with the experimental data.

The implemented free-surface technique, developed within the framework of the finite-volume method, is validated. The best-obtained agreement is for small amplitudes compatible with the waves evolving under deep-water conditions. Second, the algorithm proposed to describe rigid-body motion, including wave analysis, is validated. The numerical body motion and wave pattern satisfactorily matched the experimental data. The complete 3D proposed model can realistically describe buoy motions under the effects of stationary waves.

The novel aspects of this study encompass the implementation of a fluid–structure interaction strategy to describe rigid-body motion, including wave effects in a finite-volume context, and the reported free-surface and buoy position measurements from experiments. To the best of the authors’ knowledge, the numerical strategy, the validation of the computed results and the experimental data are all original contributions of this work.

The asymmetry of the velocity profile caused by geometric deformation, complex turbulent motion and other factors must be considered to effectively use the flowmeter on any section. This study aims to better capture the flow field information and establish a model to predict the profile velocity, we take the classical double elbow as the research object and propose to divide the flow field into three categories with certain common characteristics.

The deep learning method is used to establish the model of multipath linear velocity fitting profile average velocity. A total of 480 groups of data are taken for training and validation, with ten integer velocity flow fields from 1 m/s to 10 m/s. Finally, accuracy research with relative error as standard is carried out.

The numerical experiment yielded the following promising results: the maximum relative error is approximately 1%, and in the majority of cases, the relative error is significantly lower than 1%. These results demonstrate that it surpasses the classical optimization algorithm Equal Tab (5%) and the traditional artificial neural network (3%) in the same scenario. In contrast with the previous research on a fixed profile, we focus on all the velocity profiles of a certain length for the first time, which can expand the application scope of a multipath ultrasonic flowmeter and promote the research on flow measurement in any section.

This work proposes to divide the flow field of double elbow into three categories with certain common characteristics to better capture the flow field information and establish a model to predict the profile velocity.

This study aims to conduct text mining and semantic network analysis of muscle-supportive and posture-corrective wearable robots for the elderly to understand key terms related to the topic and to identify considerations for developing these types of clothing.

The authors searched and identified the key terms wearable robot, muscle-supportive, posture correction and elderly using the text-mining software Textom to extract terms as well as the network analysis software UCINET 6 to process and visualize the relationships among the terms. The authors compared and analyzed the term frequency (TF), the TF-inverse document frequency and the degree centrality of the terms, and the authors visualized and summarized the terms using NetDraw.

The key terms and their relationships in 3–4 groups were identified: wearable robot, muscle-supportive, posture correction and elderly. The authors identified the aspects of designing muscle-supportive and posture-corrective wearable robots for the elderly.

This study contributes to the field of muscle-supportive clothing and wearable robotics by deriving insights into what people are discussing and interested in, and by offering recommendations when developing these types of clothing for the elderly.