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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.

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.

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.

Electric vehicles are well known for a silent and smooth drive; however, their presence on the road is difficult to identify for road users who may be subjected to certain incidences. Although electric vehicles are free from exhaust emission gases, the wear particles coming out from disc brakes are still unresolved issues. Therefore, the purpose of the present paper is to introduce a smart eco-friendly braking system that uses signal processing and integrated technologies to eventually build a comprehensive driver assistance system.

The parameters obstacle identification, driver drowsiness, driver alcohol situation and heart rate were all taken into account. A contactless brake blending system has been designed while upgrading a rapid response. The implemented state flow rule-based decision strategy validated with the outcomes of a novel experimental setup.

The drowsiness state of drivers was successfully identified for the proposed control map and set up vindicated with the improvement in stopping time, atmospheric environment and increase in vehicle active safety regime.

The present study adopted a unique approach and obtained a brake blending system for improved braking performance as well as overall safety enhancement with rapid control of the vehicle.

In recent times, the construction industry is being influenced by technological innovations when delivering a better, more effective and efficient desired project, cyber-physical systems (CPSs) offer a coupling of the physical and engineered systems by monitoring, coordinating, controlling and integrating their operations. This study aims to examine the level of awareness of professionals and usage of CPSs for construction projects in Nigerian construction industry.

The target population for this study was the professionals in the construction industry consisting Architects, Quantity Surveyors, Engineers and Builders. Data collection was through the use of a structured questionnaire administered to the target population. The data was analyzed by using statistical tools.

This study concluded that the construction professionals in the Nigerian construction industry are mostly aware about the heating, ventilation and air conditioning (HVAC) systems, global positioning system, microphone, speakers and camera as the most widely used CPSs in construction industry. HVAC systems was also found to be the mostly adopted technologies in the construction industry.

This study recommended that platforms that increase the awareness and encourage the usage of CPSs in construction industry should be encouraged by stakeholders concerned with management of construction projects. Such include electronic construction and adoption of blockchain technology.

The study’s objective is to measure the importance consumers attach to AI-based attributes, namely, chatbots, face recognition, virtual fitting room, smart parking and cashier-free station in retail stores. The study also examines the specific purpose of using these attributes for shopping.

A conjoint experiment was conducted using fractional factorial design. Consumers were given 14 profiles (AI attributes and its levels) to rank according to their visiting preferences.

The results revealed that the retail chatbot was considered the most important attribute, followed by face recognition, virtual fitting room, smart parking system and cashier-free station. Moreover, consumers prefer to use chatbots for in-store shopping assistance over alerts and updates, customer support and feedback. Similarly, consumers wish a face recognition facility for greetings while entering the store over other services. In addition, cluster analyses revealed that customer groups significantly differ in their preferences for AI-based attributes.

The study guides retail managers to invest in AI technologies to provide consumers with a technology-oriented shopping experience.

Our results provide an insight into the receptivity of AI technologies that consumers would like to experience in their favorite retail stores. The present study contributes to the literature by investigating consumer preferences for various AI technologies and their specific uses for shopping.

The fourth industrial revolution and digital transformation have caused paradigm changes in the procedures of goods production and services through disruptive technologies, and they have formed new methods for business models. Health and medicine fields have been under the effect of these technology advancements. The concept of smart hospital is formed according to these technological transformations. The aim of this research, other than explanation of smart hospital components, is to present a model for evaluating a hospital readiness for becoming a smart hospital.

This research is an applied one, and has been carried out in three phases and according to design science research. Based on the previous studies, in the first phase, the components and technologies effecting a smart hospital are recognized. In the second phase, the extracted components are prioritized using type-2 fuzzy analytic hierarchical process based on the opinion of experts; later, the readiness model is designed. In the third phase, the presented model would be tested in a hospital.

The research results showed that the technologies of internet of things, robotics, artificial intelligence, radio-frequency identification as well as augmented and virtual reality had the most prominence in a smart hospital.

The innovation and originality of the forthcoming research is to explain the concept of smart hospital, to rank its components and to provide a model for evaluating the readiness of smart hospital. Contribution of this research in terms of theory explains the concept of smart hospital and in terms of application presents a model for assessing the readiness of smart hospitals.

The purpose of this study is to investigate the application of non-destructive testing methods in measuring bearing oil film thickness to ensure that bearings are in a normal lubrication state. The oil film thickness is a crucial parameter reflecting the lubrication status of bearings, directly influencing the operational state of bearing transmission systems. However, it is challenging to accurately measure the oil film thickness under traditional disassembly conditions due to factors such as bearing structure and working conditions. Therefore, there is an urgent need for a nondestructive testing method to measure the oil film thickness and its status.

This paper introduces methods for optically, electrically and acoustically measuring the oil film thickness and status of bearings. It discusses the adaptability and measurement accuracy of different bearing oil film measurement methods and the impact of varying measurement conditions on accuracy. In addition, it compares the application scenarios of other techniques and the influence of the environment on detection results.

Ultrasonic measurement stands out due to its widespread adaptability, making it suitable for oil film thickness detection in various states and monitoring continuous changes in oil film thickness. Different methods can be selected depending on the measurement environment to compensate for measurement accuracy and enhance detection effectiveness.

This paper reviews the basic principles and latest applications of optical, electrical and acoustic measurement of oil film thickness and status. It analyzes applicable measurement methods for oil film under different conditions. It discusses the future trends of detection methods, providing possible solutions for bearing oil film thickness detection in complex engineering environments.

In the context of fixed-wing aircraft wing assembly, there is a need for a rapid and precise measurement technique to determine the center distance between two double-hole components. This paper aims to propose an optical-based spatial point distance measurement technique using the spatial triangulation method. The purpose of this paper is to design a specialized measurement system, specifically a spherically mounted retroreflector nest (SMR nest), equipped with two laser displacement sensors and a rotary encoder as the core to achieve accurate distance measurements between the double holes.

To develop an efficient and accurate measurement system, the paper uses a combination of laser displacement sensors and a rotary encoder within the SMR nest. The system is designed, implemented and tested to meet the requirements of precise distance measurement. Software and hardware components have been developed and integrated for validation.

The optical-based distance measurement system achieves high precision at 0.04 mm and repeatability at 0.02 mm within a range of 412.084 mm to 1,590.591 mm. These results validate its suitability for efficient assembly processes, eliminating repetitive errors in aircraft wing assembly.

This paper proposes an optical-based spatial point distance measurement technique, as well as a unique design of a SMR nest and the introduction of two novel calibration techniques, all of which are validated by the developed software and hardware platform.