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Abstract In human life, respiration serves as a crucial physiological signal. Continuous real‐time respiration monitoring can provide valuable insights for the early detection and management of several respiratory diseases. High‐sensitivity, noninvasive, comfortable, and long‐term stable respiration devices are highly desirable. In spite of this, existing respiration sensors cannot provide continuous long‐term monitoring due to the erosion from moisture, fluctuations in body temperature, and many other environmental factors. This research developed a wearable thermal‐based respiration sensor made of cubic silicon carbide (3C‐SiC) using a microfabrication process. The results showed that as a result of the Joule heating effect in the robustness 3C‐SiC material, the sensor offered high sensitivity with the negative temperature coefficient of resistance of approximately 5,200ppmK‐1, an excellent response to respiration and long‐term stability monitoring. Furthermore, by incorporating a deep learning model, this fabricated sensor can develop advanced capabilities to distinguish between the four distinct breath patterns: slow, normal, fast, and deep breathing, and provide an impressive classification accuracy rate of ≈ 99.7%. The results of this research represent a significant step in developing wearable respiration sensors for personal healthcare systems.

Atmospheric visibility and cloud ceiling forecasts are essential for the safety and efficiency of flight operations and the aviation industry. Routine hourly aviation meteorological observations are recorded at every airport. However, forecasts of these two meteorological parameters using artificial intelligence techniques are limited. This research utilizes data from two study sites in Fiji, Nadi, and Nausori International Airport, and proposes a hybrid Iterative Input Selection – Long Short-Term Memory (IIS-LSTM) integrated model to forecast the consecutive hour’s visibility and ceiling parameters. The IIS algorithm acts as a feature selector from the global predictor matrix of predictor variables with its significant lagged inputs and the significant lagged inputs of the target variable, while the LSTM algorithm acts as the learning model and makes forecasts. The performance of the proposed hybrid IIS-LSTM model is evaluated using seven statistical score metrics and compared with four competing benchmark models. The evaluated results illustrate the superiority of the proposed hybrid IIS-LSTM integrated model and its advanced capability to generate accurate atmospheric visibility and cloud ceiling forecasts for the next consecutive hour compared to the benchmark models. The most important features selected were the second lagged input of visibility and first lagged input of rainfall to improve visibility forecasts while the first and the fifth lagged inputs of the total low cloud cover were paramount for accurate cloud ceiling forecasts. Considering the geography of the study sites, the overall efficacy of the IIS method is strongly advocated to screen most suitable model predictors and the subsequent integration of this input selection method with the LSTM predictive algorithm to attain enhanced performance of the hybrid IIS-LSTM forecast model. This objective model is therefore proposed to be an efficient and cost-effective predictive tool for atmospheric visibility and cloud ceiling forecasts, especially its applications in the aviation industry for aeronautical purposes.

SiC based pressure sensors show tremendous promise for harsh environment applications thanks to their excellent mechanical, electrical, thermal, and chemical properties. This paper presents the design, fabrication, and characterisation of a highly sensitive and robust 3C-SiC/Si pressure sensor. The sensor utilises a stress amplification structure consisting of four Si pillars built up from the 3C-SiC/Si membrane, supporting a series of released n-type 3C-SiC sensing elements. When pressure is applied to the diaphragm, the pillars act to locally concentrate and amplify strain in the 3C-SiC sensing elements, resulting in over 7 times higher stresses/strains in these sensing elements compared to a traditional structure. Additionally, the front side of the sensor is fully covered by a 3C-SiC thin film, which provides a strong chemical protective capability, allowing the sensor to operate in harsh chemically corrosive environments. The robust device utilises the full Wheatstone bridge to negate the effects of temperature. Experimental results show that the fabricated sensor is highly stable, repeatable, has a high sensitivity of 0.276 mV/V/kPa and a maximum non-linearity of 2.2 % in the 0–100 kPa region. The results indicate that this smart-structure pressure sensor is promising for applications that require highly precise pressure sensing in aggressively corrosive environments.

E-skin with physical sensing capability has attracted considerable interest towards practical applications in soft robotics, human–machine interfaces, and wearable health monitoring. However, the development of a multimodal sensing platform with multiple layers for e-skin sensing of temperature and pressure has faced challenges due to the typical use of bare or single sensing layers as well as the complication of integration of multifunctional sensing modules onto curved surfaces. Herein, we demonstrate a new platform technology with multiple sandwiched layers of highly oriented carbon nanotube (CNT) films and polyacrylonitrile (PAN) for integration of pressure and temperature sensory functionalities into a single platform that is thin, ultra-lightweight, flexible, and wearable. The key technology of in-situ deposition of sensor platform on objects or in robot interface makes this a unique method for the development of e-skins for robotic applications, offering a new approach to wearable electronics and portable health care.

Designing reliable and sensitive mechanical sensing technologies based on piezoresistive effect remains a challenge. Here, the authors propose an opto-electro-mechanical coupling strategy to enable giant piezoresistive effect in a highly doped 3C-SiC/Si heterojunction achieving a high GF of 58,000.

Abstract Semiconductor nanowires are widely considered as the building blocks that revolutionized many areas of nanosciences and nanotechnologies. The unique features in nanowires, including high electron transport, excellent mechanical robustness, large surface area, and capability to engineer their intrinsic properties, enable new classes of nanoelectromechanical systems (NEMS). Wide bandgap (WBG) semiconductors in the form of nanowires are a hot spot of research owing to the tremendous possibilities in NEMS, particularly for environmental monitoring and energy harvesting. This article presents a comprehensive overview of the recent progress on the growth, properties and applications of silicon carbide (SiC), group III‐nitrides, and diamond nanowires as the materials of choice for NEMS. It begins with a snapshot on material developments and fabrication technologies, covering both bottom‐up and top‐down approaches. A discussion on the mechanical, electrical, optical, and thermal properties is provided detailing the fundamental physics of WBG nanowires along with their potential for NEMS. A series of sensing and electronic devices particularly for environmental monitoring is reviewed, which further extend the capability in industrial applications. The article concludes with the merits and shortcomings of environmental monitoring applications based on these classes of nanowires, providing a roadmap for future development in this fast‐emerging research field.

Tuning the natural frequency of a resonator is an innovative approach for the implementation of mechanical resonators in a broad range of fields such as timing applications, filters or sensors. The conventional electrothermal technique is not favorable towards large tuning range because of its reliance on metallic heating elements. The use of metallic heaters could limit the tuning capability due to the mismatch in thermal expansion coefficients of materials forming the resonator. To solve this drawback, herein, the design, fabrication, and testing of a highly-doped SiC bridge resonator that excludes the use of metallic material as a heating element has been proposed. Instead, free-standing SiC structure functions as the mechanical resonant component as well as the heating element. Through the use of the Joule heating effect, a frequency tuning capability of almost ∆f/fo ≈ 80% has been demonstrated. The proposed device also exhibited a wide operating frequency range from 72.3 kHz to 14.5 kHz. Our SiC device enables the development of highly sensitive resonant-based sensors, especially in harsh environments.

A flexible pressure sensor with a rudimentary, ultra-low cost, and solvent-free fabrication process is presented in this paper. The sensor has a graphite-on-paper stacked paper structure, which deforms and restores its shape when pressure is applied and released, showing an exceptionally fast response and relaxation time of ≈0.4 ms with a sensitivity of −5%/Pa. Repeatability of the sensor over 1000 cycles indicates an excellent long-term stability. The sensor demonstrated fast and reliable human touch interface, and successfully integrated into a robot gripper to detect grasping forces, showing high promise for use in robotics, human interface, and touch devices.

Flow sensing in hostile environments is of increasing interest for applications in the automotive, aerospace, and chemical and resource industries. There are thermal and non-thermal approaches for high-temperature flow measurement. Compared to their non-thermal counterparts, thermal flow sensors have recently attracted a great deal of interest due to the ease of fabrication, lack of moving parts and higher sensitivity. In recent years, various thermal flow sensors have been developed to operate at temperatures above 500 °C. Microelectronic technologies such as silicon-on-insulator (SOI), and complementary metal-oxide semiconductor (CMOS) have been used to make thermal flow sensors. Thermal sensors with various heating and sensing materials such as metals, semiconductors, polymers and ceramics can be selected according to the targeted working temperature. The performance of these thermal flow sensors is evaluated based on parameters such as thermal response time, flow sensitivity. The data from thermal flow sensors reviewed in this paper indicate that the sensing principle is suitable for the operation under harsh environments. Finally, the paper discusses the packaging of the sensor, which is the most important aspect of any high-temperature sensing application. Other than the conventional wire-bonding, various novel packaging techniques have been developed for high-temperature application.

The main objective of this paper is to investigate the household willingness to pay (WTP) for improved solid waste management in Thu Dau Mot city, Binh Duong by using Dichotomons choice Contingent Valuation Method. A questionnaire was designed to draw a sample of 330 respondents for the study. The results show that mean household willingness to pay for solid waste management system in Thu Dau Mot is about 24 thousand VND/ month/ household, higher than the current fee (20 thousand VND month/ household).The paper used a logistic regression to establish the factor affecting to the household willingness to pay for solid waste management system. The logit results show that those with a higher income and more educated is willing to pay more for improvement in management of their solid waste. Also, more environmental aware the person the more they are willing to pay. Moreover, females are less likely to give a positive response to WTP compare to male.

This paper presents the design and development of a low cost and reliable maximal voluntary bite force sensor which can be manufactured in-house by using an acrylic laser cutting machine. The sensor has been designed for ease of fabrication, assembly, calibration, and safe use. The sensor is capable of use within an hour of commencing production, allowing for rapid prototyping/modifications and practical implementation. The measured data shows a good linear relationship between the applied force and the electrical resistance of the sensor. The output signal has low drift, excellent repeatability, and a large measurable range of 0 to 700 N. A high signal-to-noise response to human bite forces was observed, indicating the high potential of the proposed sensor for human bite force measurement.

The occurrence of geothermal spring not only enhances human health, but also plays a crucial role in generating community's revenue that boosts up the standard living of locals. An endeavor has been made to delineate the prospective areas of geothermal spring in Kon Tum province, Vietnam by the utilization of joint-geophysical techniques. The blended mode as mentioned is opted for its superiority in providing the rapidassesments of temperature in shallow temperature survey, comprehensive understanding the subsurface structure in electrical resistivity tomography in copporating with the validation in well log interpretation. The results found that the overlapping region of high temperature (35°C) region and low resistivity (200 Ωm) is observed in the east in the vicinity of Dlak Bla river, and it probably broadens to the west of the study area. The outcome of geophysical approaches strongly concur with the well log interpretation, revealing the presence of water-bearing zone encountered in fractured granite. The results also corroborated the existence of concealing fault F5 as a branch of the NE-SW fault F2 which is favorable for creating a route to form the geothermal spring. The output of this research could be considered as an intentive consultation for Kon Tum's planners and makers on expanding the investigation and exploitation. [ABSTRACT FROM AUTHOR]