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The wireless body area network (WBAN) has emerged as a promising networking paradigm thanks to embedded systems, integrated circuit technologies, and wireless communications advancements. WBAN has the ability to send real-time biomedical data to remote medical personnel for clinical diagnostics through intelligent medical sensors in or around the patient's body. Moreover, WBANs have played an increasingly important role in modern medical systems over the past decade as part of the Internet of Things (IoT). In addition to their conveniences, WBANs present us with the data confidentiality challenge and protecting patient's privacy. The system requires a robust security mechanism to protect against threats because of the massive production of delay-sensitive data. This article presents a quantum key distribution (QKD) protocol-based authentication scheme for secure communication within WBAN infrastructure to prevent attacks and provide services free of security risks. To the best of our knowledge, we proposed a privacy preserving quantum authentication scheme for secure data sharing in WBANs to enhance security by encrypting medical data and safeguarding patients identities. Simulations conducted using Mathematica evaluate the proposed quantum authentication protocol both theoretically and practically, showing strong encryption performance against recent schemes. Results include computational cost of 6.832 ms, communication costs of 588 bits, and energy consumption of 432.98 μ J indicating the proposed quantum authentication protocol model exhibits superior reliability, feasibility, and efficiency in safeguarding WBAN data during transmission and storage. [ABSTRACT FROM AUTHOR]

Modern spacecraft and launch vehicle design is more oriented towards reducing system-level design and assembly complexities. In order to maintain high overall system performance while reducing these complexities, the use of smart materials and smart structural components is a well-known practice and is currently of rising interest to space systems' designers. The paper discusses a concept of smart space structures, in particular, a carbon fiber composites structure embedded with Optical Fiber Sensors (OFS) for spacecraft and launch vehicle applications. This study highlights the operational requirements for such tank and the smart features enabled by the optical fiber sensors. For the latter aspect, a quantitative comparison between Fiber Bragg Grating sensors (FBGs) and Distributed Optical Fiber Sensors (DOFS) based on Optical Frequency Domain Reflectometry (OFDR) is presented to state their core performance parameters, such as the sensitivity, sensing range, dynamic measurement capability, and spatial resolution. The increased performance and reliability in harsh environments associated with fiber optic sensors come with a reduction in size, mass, and power consumption compared to the conventional electronic sensors. Optical fiber sensors embedded in carbon fiber structures have proven their capability in providing accurate real-time measurements of temperature and monitoring structural integrity while detecting precisely possible points of rupture and failure as discussed and demonstrated in the literature review. The applications of fiber optic sensing in smart propellant tanks may extend to detecting fluid leakage, also providing increased precision in propellant gauging through temperature mapping, and can be used in on-ground qualification, pre-flight testing, as well as in-orbit operation, condition, and structural health monitoring. The article presents a statement for an optimal FOS embedding approach in composite pressure vessels and discusses the related placement and orientation method for the fiber optic sensors, coupled with a one component simplified analytical stress-strain transfer model deriving the stress component along the maximum principal direction (i.e., σ Max P r i n c i p a l ). The novel approach is believed to serve the optimal employment of embedded FOS in composite structures, e.g., pressure vessels and light-weight structures in spacecraft, among other applications. The simplified model is believed to pave the way for effective data interpretation and processing, utilizing the available limited computational resources on-board the spacecraft. • Smart propellant tank embedding fiber optic sensors for structural health monitoring. • The opto-mechanical model defines the local stress in the maximum principal strain direction. • Quasi-distributed optic fiber sensors employing arrays of FBGs for spacecraft monitoring. • Distributed fiber optic sensing based on OFDR for spacecraft applications. [ABSTRACT FROM AUTHOR]

This work suggests an all-digital temperature sensor with a high sampling rate that is based on a time-to-digital converter (TDC). Two on-chip voltage-controlled oscillators (VCOs) are used in the design of the sensor core, which senses temperatures between − 4 0 ∘ C and 200 ∘ C. For digital code conversion, the outputs of the VCO are fed into two asynchronous counters. In both low- and high- resolution modes, the error following two-point calibration is observed between − 1. 0 8 ∘ C and + 1. 0 6 ∘ C. The sensor's ability to function in both high- and low-resolution modes based on conversion time is an important feature. At a sampling frequency of 0.19 MHz, the maximum resolution achieved is 0.18 ∘ C. Additionally, the sensor has control logic built in to turn off the sensing as soon as the conversion is complete. At 90-nm process, 1.1 V supply voltage and 27 ∘ C, the proposed sensor occupies 0. 0 4 4 mm 2 and consumes 8 1 7. 5 μ W. [ABSTRACT FROM AUTHOR]

In general, defects are crucial in designing the different properties of two-dimensional materials. Therefore large variations in the electric and optical characteristics of two-dimensional layered molybdenum disulphide might be attributed to defects. This study presents the design of a temperature and nitrogen sensor based on few-layer molybdenum disulfide sheets (FLMS), which was developed from bulk MoS2 (BMS) through an exfoliation approach. The produced sulfur defect, molybdenum defect, line defect, and plane defect were characterized by scanning transmission electron microscopy (STEM), which substantially impacts the sensing characteristics of the resulting FLMS. Our theoretical analysis validates that the sulfur vacancies of the MoS2 lattice improve sensing performance by promoting effective charge transfer and surface interactions with target analytes. The FLMS-based sensor showed a high sensitivity for detecting nitrogen gas with a detection limit (LOD) of ~ 0.18 ppm. Additionally, temperature-detecting capabilities were assessed over various temperatures, showing outstanding stability and repeatability. To the best of our knowledge, this material is the first of its kind, demonstrating visible N2 gas sensing with chromic behaviour. [ABSTRACT FROM AUTHOR]

Umami is essential in food which has garnered attention due to its potential benefits, mainly produced by umami amino acids, nucleotides and peptides. Expanding the mechanism analysis and compiling the available detection information of umami compounds facilitate advance this area. This review initially extracted research focus areas of umami by bibliometric analysis. Then, the specific role of umami contributors in preventing and treating metabolic diseases were introduced. Umami compounds exhibit outstanding performance in various health benefits, including antihypertensive, gut health promotion, enhanced appetite and suppression of obesity, and so on, marking their new perspectives in bioactivity development. Emphasis was placed on elucidating the presentation mechanisms with core metabolism pathways contributing to the umami tastes. The potential umami presentation mechanism and metabolism pathways for key umami compounds were then proposed based on their structural properties. Additionally, current developments in the intelligent detection of umami compounds were reviewed. Intelligent sensors based on umami receptors combined with multivariate statistics provide a more accessible and more intuitive strategy for distinguishing and predicting umami compounds and providing recommendations for further advancements. This review could deepen the understanding of controlling umami taste in food matrices and foster the development of the related industry. [ABSTRACT FROM AUTHOR]

AbstractEdible polymers are safe for consumption by humans and use in society. Shellac is a derivative of Lac, which has been used for a long time in many different industries due to its remarkable properties, which include film-forming, water resistance, thermoplastic, adhering, bonding, and simple solubility in spirit and aqueous alkali solvents. A paradigm shift has been observed in the variety of applications and uses of shellac due to the growing desire for natural products, particularly in the food, pharmaceutical, 3D printing, stealth technology, green electronics, and intelligent sensor industries. It is commonly used as an enteric coating in the pharmaceutical sector and as a preservative coating or confectioners glaze in the food industry. Being a natural and eco-friendly substance, shellac has a great deal of potential for more sustainable technologies. Due to the film’s strength and polymerization, shellac, however, has limitations. The low stability and mechanical brittleness of shellac, which result from self-polymerization reactions, limit its application even though it has great potential as an environmentally benign protective covering. This review offers a comprehensive understanding of lac, encompassing its characteristics, its uses, and its prospects for the future. This study aims to investigate a new method that may mitigate some of the disadvantages of shellac coating whilst improving the material’s stability and mechanical properties. [ABSTRACT FROM AUTHOR]

Textile‐based electromechanical sensors are increasingly used as wearable sensors for various applications, such as health monitoring and human‐machine interfaces. These sensors are becoming increasingly popular as they offer a comfortable and conformable sensing platform and possess properties that can be tuned by selecting different fiber materials, yarn‐spinning techniques, or fabric fabrication methods. Although it is still in its early stages, recent attempts have been made to introduce auxeticity to textile sensors to enhance their sensitivity. Having a negative Poisson's ratio, i.e., undergoing expansion laterally when subjected to tensile forces and contraction laterally under compressive forces, makes them distinct from conventional sensors with positive Poisson's ratio. This unique feature has demonstrated great potential in enhancing the performance of electromechanical sensors. This review presents an overview of electromechanical sensors based on auxetic textiles (textiles made from auxetic materials and/or non‐auxetic materials but with auxetic structures), specifically focusing on how the unique auxetic deformation impacts sensing performance. Sensors based on different working mechanisms, including piezoelectric, triboelectric, piezoresistive, and piezocapacitive, are covered. It is envisioned that incorporating auxeticity and electromechanical sensing capabilities into textiles will significantly advance wearable technology, leading to new sensors for health monitoring, fitness tracking, and smart clothing. [ABSTRACT FROM AUTHOR]

Electronic skin is increasingly receiving tremendous attention for its potential applications in medical rehabilitation and human‐machine interaction. However, the trade‐off between detection range and sensitivity of e‐skin has not been well addressed, although various strategies have been proposed. Interlocked microridges between the epidermis and dermis can effectively transfer stress to mechanoreceptors, allowing human skin to exhibit excellent sensitivity even upon both subtle and large external stimuli. Herein, inspired by human skin, a novel bionic e‐skin is developed in which interlocked microridges are introduced between the sensitive layer and interdigitated electrode. Thanks to the interlocked microridges, excellent compression capability and remarkable change of contact area between sensitive layer and interdigitated electrode can be achieved and the e‐skin exhibits an ultrahigh sensitivity (≈1502.5 kPa−1), excellent durability (10 000 cycles), a short response time (10 ms) as well as a wide detection range (≈160 kPa). Moreover, due to the effective transmission of external stress from a sensitive layer to an interdigitated electrode, such bionic e‐skin has ability to detect a wide range of human vital signs and vibrations caused by sound waves. Such facile preparation of bionic interlocked microridges opens a new pathway to achieve high‐performance e‐skins and extend their application prospects in future wearable intelligent systems. [ABSTRACT FROM AUTHOR]

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts on railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data are transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events, like impact detection, that require a rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine-learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications. [ABSTRACT FROM AUTHOR]

The integration of advanced technologies is revolutionizing classrooms, significantly enhancing their intelligence, interactivity, and personalization. Central to this transformation are sensor technologies, which play pivotal roles. While numerous surveys summarize research progress in classrooms, few studies focus on the integration of sensor and AI technologies in developing smart classrooms. This systematic review classifies sensors used in smart classrooms and explores their current applications from both hardware and software perspectives. It delineates how different sensors enhance educational outcomes and the crucial role AI technologies play. The review highlights how sensor technology improves the physical classroom environment, monitors physiological and behavioral data, and is widely used to boost student engagements, manage attendance, and provide personalized learning experiences. Additionally, it shows that combining sensor software algorithms with AI technology not only enhances the data processing and analysis efficiency but also expands sensor capabilities, enriching their role in smart classrooms. The article also addresses challenges such as data privacy protection, cost, and algorithm optimization associated with emerging sensor technologies, proposing future research directions to advance educational sensor technologies. [ABSTRACT FROM AUTHOR]

Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge graphs and graph representational learning methods was proposed to identify targeted performance, decipher hidden information, and discover new designs. Unlike process-parameter-based machine learning methods, it used the relationship as semantic features to improve prediction precision (up to 0.81). Based on the proposed framework, a strain sensor was designed and tested, demonstrating a wide strain range (300%) and closely matching predicted performance. This predicted sensor performance outperforms similar materials. Overall, the present work is favorable to design constraints and paves the way for the long-awaited implementation of text-mining-based knowledge management for sensor systems, which will facilitate the intelligent sensor design process. [ABSTRACT FROM AUTHOR]

This study explores how smart irrigation systems can be improved by introducing advanced technologies and reducing water consumption to fight climate change. This study aims to identify the factors affecting the success of smart irrigation systems based on the Internet of Things with the mediation of optimal consumption management. This is an applied research employing mixed methodologies (qualitative-quantitative). Initially, the semi-structured interviews were carried out to obtain the effective success factors of the smart irrigation system. Analysis in the qualitative section has been conducted using the Maxqda software. For the quantitative study, a 25-statement questionnaire was applied according to the selected codes obtained with a fivepoint Likert scale to validate the obtained model. The questionnaire was distributed online in January 2023, and analysis was conducted using SmartPLS3 software. The technical aspects of the questionnaire were assessed using validity and reliability criteria, which can guarantee the precision of the research findings. Construct and content validity were employed in this study to examine the questionnaire's validity. The researchers employed Cronbach's alpha coefficient to evaluate the reliability of the measurement instrument. Besides, it can be said that the research instrument has acceptable reliability because obtained Cronbach's alpha for all variables was greater than 0.70 and the questionnaire's overall alpha was determined to be 0.810. Furthermore, the t-statistics for all research hypotheses is higher than 1.96 at the 95% confidence level. In this sense, the hypotheses of the research were confirmed. The Internet of Things and will change the nature of activities and services in the market. In fact, for emerging markets, the untapped and growing investment opportunities and the economic leap against the Internet of Things are so enticing and facilitating that it is impossible to resist. and neglecting it will cause irreparable damage to agility. Therefore, the success of the Internet of Things for emerging markets depends on the preconditions of integration of activities carried out by decision-making organizations, facilitation of regulations, and communication and interaction between relevant sectors, which should be considered a priority through cross-sectoral research. [ABSTRACT FROM AUTHOR]

Traffic control through video/image processing is a trending research topic within Intelligent Transportation Systems. The number of moving vehicles, the density of vehicles at the junctions, traffic anomalies, and traffic flow directly influence real-time traffic congestion. The primary goal of this work is to design an architecture that is simple and fast enough to use in real-time heterogeneous traffic scenes. The proposed methodology uses user-defined zones for determining vehicle occupancy and count based on traffic surveillance video. The angular integral projection function accompanies the background subtraction method for better localization. Traffic anomaly detection is implemented by incorporating intelligent sensor technology. Also, the skipping of frames at fixed intervals has increased the processing speed with minimal data loss. The compatibility with any Internet Protocol camera further distinguishes this approach from other state-of-the-art methods. The proposed algorithm has been tested on a publicly available traffic surveillance video dataset and real-time surveillance feeds. Experimental results from the real-time intersection scenes show an overall accuracy of 97.14% and an average processing speed of 92.91%. The accuracy of the detection module in the custom-made dataset is 98.73%. [ABSTRACT FROM AUTHOR]

Markers are the backbone of various cross-domain augmented reality (AR) applications available to the research community. However, the use of markers may limit anywhere augmentation. As smart sensors are being deployed across the large spectrum of consumer electronic (CE) products, it is becoming inevitable to rely upon natural gestures to render and interact with such CE products. It provides limitless options for augmented reality applications. This paper focuses on the use of the human palm as the natural target to render 3D virtual objects and interact with the virtual objects in a typical AR set-up. While printed markers are comparatively easier to detect for camera pose estimation, palm detection can be challenging as a replacement for physical markers. To mitigate this, we have used a two-stage palm detection model that helps to track multiple palms and the related key-points in real-time. The detected key-points help to calculate the camera pose before rendering the 3D objects. After successfully rendering the virtual objects, we use intuitive, one-handed (uni-manual) natural gestures to interact with them. A finite state machine (FSM) has been proposed to detect the change in gestures during interactions. We have validated the proposed interaction framework using a few well-known 3D virtual objects that are often used to demonstrate scientific concepts to students in various grades. Our framework has been found to perform better as compared to SOTA methods. Average precision of 96.5% (82.9% SSD+Mobilenet) and FPS of 58.27 (37.93 SSD+Mobilenet) have been achieved. Also, to widen the scope of the work, we have used a versatile gesture dataset and tested it with neural network-based models to detect gestures. The approach fits perfectly into the proposed AR pipeline at 46.83 FPS to work in real-time. This reveals that the proposed method has good potential to mitigate some of the challenges faced by the research community in the interactive AR space. [ABSTRACT FROM AUTHOR]

Electrical devices are integral to daily life, but limited battery life remains a significant issue. A proposed solution is to convert dissipated energy from human motion into electricity using piezoelectric materials. This study investigates lead–zirconate–titanate (PZT) piezoelectric materials in bimorph configuration, conducts performance tests to understand their characteristics and determine the optimal load resistance, and develops an energy-harvesting prototype. Performance tests adjusted input parameters and varied load resistance and input magnitude to optimize power gained from the PZT bimorph. A suitable human movement for the application of the bimorph is a mouse-clicking motion by fingers. A prototype was created by integrating the bimorph into a computer mouse to capture energy from clicks. The results showed that the deformation rate of the PZTs, input magnitude, and resistance load were key factors in optimization. The bimorph configuration produced 0.34 mW of power and 5.5 V at an optimum load of 5072 Ω, requiring less effort to generate electricity. For the computer mouse energy harvester case, it yielded a total average power of approximately 38.4 μW per click with a click frequency of 4 Hz. This power could be used to support IoT devices such as human sensors (e.g., CO2, temperature, and pulse sensors) and smart home sensors, enabling comprehensive health and environmental monitoring. In conclusion, input specifications, magnitude, and load resistance are essential for optimizing piezoelectric energy harvesters. [ABSTRACT FROM AUTHOR]

Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology's complexity, costs, fabrication and implementation challenges often hinder engineers' exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication. [ABSTRACT FROM AUTHOR]

This article demonstrates the integration of sensor fusion for pose estimation and data collection in tennis balls, aiming to create a smaller, less intrusive form factor for use in progressive learning during tennis practice. The study outlines the design and implementation of the Bosch BNO055 smart sensor, which features built-in managed sensor fusion capabilities. The article also discusses deriving additional data using various mathematical and simulation methods to present relevant orientation information from the sensor in Unity. Embedded within a Vermont practice foam tennis ball, the final prototype product communicates with Unity on a laptop via Bluetooth. The Unity interface effectively visualizes the ball's rotation, the resultant acceleration direction, rotations per minute (RPM), and the orientation relative to gravity. The system successfully demonstrates accurate RPM measurement, provides real-time visualization of ball spin and offers a pathway for innovative applications in tennis training technology. [ABSTRACT FROM AUTHOR]

Medication adherence is an essential aspect of healthcare for patients and is important for achieving medical objectives. However, the lack of standard techniques for measuring adherence is a global concern, making it challenging to accurately monitor and measure patient medication regimens. The use of sensor technology for medication adherence monitoring has received much attention lately since it makes it possible to continuously observe patients' medication adherence behavior. Sensor devices or smart wearables utilize state-of-the-art machine learning (ML) methods to analyze intricate data patterns and provide predictions accurately. The key aim of this work is to develop a sensor-based hand gesture recognition model to predict medication activities. In this research, a smart sensor device-based hand gesture prediction model is developed to recognize medication intake activities. The device includes a tri-axial gyroscope, geometric, and accelerometer sensors to sense and gather data from hand gestures. A smartphone application gathers hand gesture data from the sensor device, which is then stored in the cloud database in a.csv format. These data are collected, processed, and classified to recognize the medication intake activity using the proposed novel neural network model called Sea Horse Optimization–Deep Neural Network (SHO-DNN). The SHO technique is implemented to update the biases and weights and the number of hidden layers in the DNN model. By updating these parameters, the DNN model is improved in classifying the samples of hand gestures to identify the medication activities. The research model demonstrates impressive performance, with an accuracy of 98.59%, sensitivity of 97.82%, precision of 98.69%, and an F1 score of 98.48%. Hence, the proposed model outperformed the most available models in all the aforementioned aspects. The results indicate that this model is a promising approach for medication adherence monitoring in healthcare applications, instilling confidence in its effectiveness. [ABSTRACT FROM AUTHOR]

With the rise of demand for smart wearable and flexible electronic devices in the modern world, high‐performance hybrid mechanical energy harvesters (HMEHs), which can easily convert biomechanical energy into electricity for powering a variety of portable electronic gadgets and operating smart sensors, have gained extensive interest. Herein, propose piezo/ferroelectric and dielectric niobium (Nb)‐doped bismuth titanate (Bi4Ti3‐xNbxO12, NBTO) plates, are proposed and synthesized by a molten‐salt synthesis technique, and they are further embedded into the poly(vinylidene fluoride‐co‐hexafluoropropylene) (NBTO/PVDF‐HFP) flexible composite film (CF) to construct a flexible HMEH. The prepared NBTO/PVDF‐HFP CFs reveal good piezo/ferroelectricity, β‐phase fraction, and dielectric properties, which can improve the electrical output performance of the HMEH. The 2 wt.% NBTO/PVDF‐HFP CF‐based HMEH exhibits high and stable electrical performance of ≈175 V, ≈5.8 µA, ≈76 µC m−2, and ≈2.02 W m−2, respectively. Furthermore, the durability and mechanical robustness analysis of the HMEH is conducted for several days. The real‐time applications of the HMEH are demonstrated by harvesting the biomechanical energy obtained from daily human activities and powering various portable electronics. Also, the HMEH integrated with an Arduino microcontroller unit is employed as a smart sensor switch for implementing smart home/building stair‐sensing applications and sending emergency e‐mail alerts. [ABSTRACT FROM AUTHOR]

Smart responsive materials that can alter their function in response to environmental changes are attractive for their potential applications in intelligent devices and products. Herein, a smart material that exhibits reversible changes in multiple properties upon variations in humidity or temperature is created. The material spontaneously transits between hydrated and dehydrated states in response to fluctuations in the surrounding humidity or temperature. Consisting of a mixture of poly(propylene glycol) (PPG) with urea linkages (PPGurea) and ionic liquid [EMIM][TFSI], the transition is attributed to a series of synergetic interactions among various chemical components and groups, including ether‐cation coordination, water‐anion complex, urea‐urea bidentate hydrogen bonds, and cation–anion electrostatic interactions. In the hydrated state, with a very small amount (4–5 wt%) of spontaneously absorbed moisture content, the smart material is soft, transparent, and conductive, and possesses rapid self‐healing ability. Upon dehydration, the material transits into a phase‐separated system with PPG‐rich and IL‐rich phases, resulting in opacity, severely reduced ionic conductivity, yet significantly enhanced stiffness, strength, and toughness. The drastic change in multiple properties makes it an intelligent material well‐suited for various smart applications such as sensors, 3D printed optoelectronics and smart windows, which can automatically alter their functions to adapt to environmental changes. [ABSTRACT FROM AUTHOR]

Sensors have become more versatile and sophisticated in recent years to fulfill the increasing demands for human health applications. Physiological information such as electrocardiogram, pulse rate, and respiration are essential indications of personal health, often collected as vitals, which are typically collected from medical‐grade electrocardiogram (ECG) machines. In‐textile sensors are a fast‐growing sub‐category of wearable sensors embedded in smart textiles to acquire physiological information and movement index and provide harmful chemical warnings without compromising the comfortable nature of clothing. Recent literature has shown that integrating new materials has greatly improved the stability, specificity, and selectivity of in‐textile sensors. For example, polyvinylidene fluoride nanofiber produced a highly stretchable sensor to measure ECG readings during movement without losing data quality. This review discusses a group of nanomaterial‐based in‐textile sensors for consumer use in the home, workplace, and healthcare environments. This review will focus on exploring and analyzing the latest developments in these nanomaterial‐based e‐textiles due to their ability to be more easily integrated for daily use and their great potential for medical applications. Future work will be necessary to incorporate recycled materials, improve the method of powering these sensors, and ultimately refine the designs to be appropriate for more sustainable use. [ABSTRACT FROM AUTHOR]

Anisotropic bilayer hydrogel actuators are high‐performance materials engineered to exhibit unique and programmable mechanical properties, including varying stiffness and directional bending capabilities, by integrating two hydrogel layers with distinct responses to stimuli. However, programming and constructing these bilayer hydrogels remains challenging due to their lack of mechanical robustness, rapid responsiveness, and dual‐actuation capabilities, which hinder their practical applications and further development. Hence, developing a double‐actuating bilayer hydrogel with a temperature‐responsive and auxiliary layer could address these challenges. Herein, an anisotropic hydrogel actuator is developed using a simple and economical casting method, in which a unique multiasymmetric bilayer structure locked by an interfacial is fabricated. The as‐prepared hydrogels demonstrate exceptional temperature‐responsive bending abilities, achieving a 360 °C angle in just 8 s, and exhibit adaptive, complex shape transformation capabilities tailored to specific needs (e.g., two dimensional (2D) letters, leaves, flower, and butterfly hydrogel). Furthermore, the hydrogels possess excellent shape memory, mechanical strength, and conductivity. Additionally, gripper and humidity alarm prototypes made from the hydrogel are also successfully developed, illustrating that this approach opens new avenues for designing and producing smart hydrogels with practical applications in sensors, smart humidity alarms, and on‐demand smart grippers and actuators. [ABSTRACT FROM AUTHOR]

4H‐SiC is a key enabler for realizing integrated electronics operating in harsh environments, which exhibit very high temperatures. Through advances in 4H‐SiC process technology, different sensor and circuit types have been demonstrated to operate stable at temperatures as high as 800 °C, paving the way toward harsh‐environment immune smart sensors. In this work, for the first time the operation of ion‐implanted 4H‐SiC Hall sensors realized in a wafer scale Bipolar‐CMOS‐DMOS technology is demonstrated at a wide operation temperature range spanning room temperature up to 500 °C in addition to short‐term operation up to 600 °C. The temperature‐dependent sensor characteristics of 15–22 samples are evaluated in terms of sensitivity and noise. The small inter‐device variations reflect the stability of the used process for very high temperature Hall sensors. The noise‐limited detectivity is further evaluated, revealing a best value of 950 nT/Hz$\sqrt{\mathrm{Hz}}$ and a mean detectivity of 1 µT/Hz$\sqrt{\mathrm{Hz}}$ at 500 °C. This is the best value reported up to date for very high temperature Hall sensors, besides being the first demonstration of ion‐implanted wide‐bandgap Hall sensors. Overall, the results reflect the potential of the demonstrated Hall sensors for the next generation of integrated magnetic field sensors in harsh environments. [ABSTRACT FROM AUTHOR]

This paper introduces a proof‐of‐concept spatial decision support system (SDSS) that assists decision‐makers to generate equitably distributed sensor networks and evaluate their placement with reference to specific population‐based coverage criteria. Our approach centres equity in infrastructure distribution; we focus on the decision‐making process required to achieve the best possible sensor coverage of the geographical area for selected vulnerable populations and visualise trade‐offs in coverage inherent in infrastructure distribution. The development of the tool brings together expertise from quantitative geography, urban planning, data science and software engineering, and its technical development is underpinned and shaped by interviews with decision‐makers and their iterative feedback. Through this, we ask: how can decision support tools help with the work of building equitable infrastructure? As well as a technical application, our approach develops a conceptual framework for evaluating sensor network purpose and distribution before actual placement. The unique combination of distribution algorithms, user interface and decision‐maker input, developed by an interdisciplinary team, offers a novel approach to sensor network conceptualisation and generation. Our research contributes to the understanding of the distribution of essential infrastructure and can be repurposed for any sensor type and geographical location to promote equity in infrastructure distribution. [ABSTRACT FROM AUTHOR]

Personal protective equipment (PPE) has been universally recognized for its role in protecting workers from injuries and illnesses. Smart PPE integrates Internet of Things (IoT) technologies to enable continuous monitoring of workers and their surrounding environment, preventing undesirable events, facilitating rapid emergency response, and informing rescuers of potential hazards. This work presents a smart PPE system with a sensor node architecture designed to monitor workers and their surroundings. The sensor node is equipped with various sensors and communication capabilities, enabling the monitoring of specific gases (VOC, CO2, CO, O2), particulate matter (PM), temperature, humidity, positional information, audio signals, and body gestures. The system utilizes artificial intelligence algorithms to recognize patterns in worker activity that could lead to risky situations. Gas tests were conducted in a special chamber, positioning capabilities were tested indoors and outdoors, and the remaining sensors were tested in a simulated laboratory environment. This paper presents the sensor node architecture and the results of tests on target risky scenarios. The sensor node performed well in all situations, correctly signaling all cases that could lead to risky situations. [ABSTRACT FROM AUTHOR]

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This approach can be utilized to improve a user's rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques. [ABSTRACT FROM AUTHOR]

As a significant technological innovation in the fields of medicine and geriatric care, smart care wheelchairs offer a novel approach to providing high-quality care services and improving the quality of care. The aim of this review article is to examine the development, applications and prospects of smart nursing wheelchairs, with particular emphasis on their assistive nursing functions, multiple-sensor fusion technology, and human–machine interaction interfaces. First, we describe the assistive functions of nursing wheelchairs, including position changing, transferring, bathing, and toileting, which significantly reduce the workload of nursing staff and improve the quality of care. Second, we summarized the existing multiple-sensor fusion technology for smart nursing wheelchairs, including LiDAR, RGB-D, ultrasonic sensors, etc. These technologies give wheelchairs autonomy and safety, better meeting patients' needs. We also discussed the human–machine interaction interfaces of intelligent care wheelchairs, such as voice recognition, touch screens, and remote controls. These interfaces allow users to operate and control the wheelchair more easily, improving usability and maneuverability. Finally, we emphasized the importance of multifunctional-integrated care wheelchairs that integrate assistive care, navigation, and human–machine interaction functions into a comprehensive care solution for users. We are looking forward to the future and assume that smart nursing wheelchairs will play an increasingly important role in medicine and geriatric care. By integrating advanced technologies such as enhanced artificial intelligence, intelligent sensors, and remote monitoring, we expect to further improve patients' quality of care and quality of life. [ABSTRACT FROM AUTHOR]

Enhancing road safety by monitoring a driver's physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, the system can detect early signs of drowsiness and sudden medical emergencies, such as heart attacks, thereby preventing accidents. This is particularly vital for fully self-driving (FSD) systems, as it ensures control is not transferred to an impaired driver. The proposed system employs a 60 GHz frequency-modulated continuous wave (FMCW) radar placed behind the driver's seat. This article mainly describes how advanced signal processing methods, including the Huber–Kalman filtering algorithm, are applied to mitigate the impact of respiration on heart rate detection. Additionally, the autocorrelation algorithm enables fast detection of vital signs. Intensive experiments demonstrate the system's effectiveness in accurately monitoring HRV, highlighting its potential to enhance safety and reliability in both traditional and autonomous driving environments. [ABSTRACT FROM AUTHOR]

[Display omitted] 4D printing combines the typical 3D printing with "smart materials", allowing 3D printed materials to undergo a structural change over time. Since its original concept was first introduced in 2013, 4D printing became an innovative research that has received more attention from scientists in different fields. This review summarizes the progress achieved in 4D printing technologies and their associated materials. First, the technology and process of 4D printing are overviewed, and then the structure and properties of smart materials utilized in 4D printing are analyzed in depth, including metamaterials, shape memory materials, hydrogels, and self-healing polymers. We systematically illustrate the morphing mechanisms of the 4D printed smart materials, and then critically discuss the stimuli that can trigger transformation in the 4D printed smart materials, including heat, light, moisture, pH, electric current, and magnetic field. For 4D printed smart materials, all the changes programmed in the materials follow a mathematical model that allows scientists to predict and design the desired behaviors of the structures, using parameters such as the material distribution and the spatial gradients of the metric tensor. We finally conclude with the discussion of future challenges and opportunities for this ever-growing technology. Overall, 4D printing can create dynamic structures programmed to be responsive to external stimuli in the environment, widening its use in a myriad of applications such as rapid prototyping, electronics, biomedicine, soft robotics, self-assembly structures, smart sensors, and dynamic actuators. [ABSTRACT FROM AUTHOR]

Human physical activity monitoring plays a crucial role in promoting personalized health management. In this work, inspired by an ancient Chinese belt, a belt-type wearable sensor (BWS) based on a triboelectric nanogenerator (TENG) is presented to monitor daily movements and collect the body motion mechanical energy. The developed BWS consists of a soft silicone sheet and systematically connected sensing units made from triboelectric polymer materials including polytetrafluoroethylene (PTFE) and polyamide (PA). A parameter study of the sensing units is firstly conducted to optimize the structure of BWS. The experimental studies indicate that the parameter-optimized BWS unit achieves a maximum output voltage of 47 V and a maximum current of 0.17 μA. A BWS with five sensing units is manufactured to record body movements, and it is able to distinguish different physical activities including stillness, walking, running, jumping, normal breathing, cessation of breathing, and deep breathing. In addition, the developed BWS successfully powers electronic devices including a smartphone, digital watch, and LED lights. We hope this work provides a new strategy for the development of wearable self-powered intelligent devices. [ABSTRACT FROM AUTHOR]

The rapid advancements in Agriculture 4.0 have led to the development of the continuous monitoring of the soil parameters and recommend crops based on soil fertility to improve crop yield. Accordingly, the soil parameters, such as pH, nitrogen, phosphorous, potassium, and soil moisture are exploited for irrigation control, followed by the crop recommendation of the agricultural field. The smart irrigation control is performed utilizing the Interactive guide optimizer-Deep Convolutional Neural Network (Interactive guide optimizer-DCNN), which supports the decision-making regarding the soil nutrients. Specifically, the Interactive guide optimizer-DCNN classifier is designed to replace the standard ADAM algorithm through the modeled interactive guide optimizer, which exhibits alertness and guiding characters from the nature-inspired dog and cat population. In addition, the data is down-sampled to reduce redundancy and preserve important information to improve computing performance. The designed model attains an accuracy of 93.11 % in predicting the minerals, pH value, and soil moisture thereby, exhibiting a higher recommendation accuracy of 97.12% when the model training is fixed at 90%. Further, the developed model attained the F-score, specificity, sensitivity, and accuracy values of 90.30%, 92.12%, 89.56%, and 86.36% with k-fold 10 in predicting the minerals that revealed the efficacy of the model. [ABSTRACT FROM AUTHOR]

This paper presents here a novel electrical sensor for detecting alcohol concentration in the exhale of an individual. We highlight fabrication of the sensor followed by subsequent assemblage into a handheld unit. The prototype yields a remarkable sensitivity along with a linear range of ~ 250–800 ppm. Additionally, we demonstrate the implementation of it in the helmet of a biker—which is further validated by in situ results related to monitoring exhale activity of few subjects. Through proper tuning with the ignition control system, the prototype can act as a protective gear for drunken drivers. Once the concentration of alcohol is sensed above a permissible level, the ignition system can be momentarily stopped; thereby averting plausible accidents. We believe that this sensor can be upgraded to more advanced protocol via a little tweaking with Internet of Things. [ABSTRACT FROM AUTHOR]

Considering the harsh environment of the port, automated straddle carriers, characterized by their large size, tall frame, and high center of gravity, may experience instability during steering and transportation due to inaccurate state estimation. Thus, this paper explores state estimation techniques for automated straddle carriers utilizing smart sensors which are capable of data measurement and processing. First, using the steering principles and lateral characteristics of automated straddle carriers, a dynamic linear model is established based on Newton's second law of motion. Then, in order to enhance the reliability and flexibility of state estimation, a distributed smart sensor network structure is introduced. In addition, considering the challenge of unknown-but-bounded noise and the precision demands of the considered automated straddle carrier, a modified distributed set-membership estimation algorithm is proposed and is derived sufficient conditions for the existence of the estimation set for the considered automated straddle carriers. Finally, the effectiveness and superiority of the proposed method are demonstrated by performance analyses. [ABSTRACT FROM AUTHOR]

The goal of this research article is to investigate the effects of using two-dimensional functionally graded materials on the performance of piezoelectric sensors/actuators when subjected to simultaneous complex loading conditions. The considered disc-shaped sensors/actuators have nonuniform thicknesses and undergo asymmetric hygro-thermo-electro-mechanical loading. A power-law model is used to grade the materials radially, whereas the cosine function, which includes two independent parameters, describes the pattern along the circumferential direction. Comparing the results obtained by using the finite element method with those of one-dimensional graded structures leads to promising outcomes. For example, the radial displacement exhibits vital changes that varied between - 13 and 31 % . This is beneficial for such structures in terms of enhancing their sensing/actuating abilities. Also, the tangential stress can be reduced substantially by about 39.5 % through the proper selection of the corresponding material parameters. In addition, this reduction of the tangential stress has a positive effect on the von Mises stress that can be decreased by nearly 33 % . Accordingly, the structure would have improved durability and sustain higher loads. These findings would revolutionize the manufacturing of smart structures and enhance their behaviors under severe conditions. [ABSTRACT FROM AUTHOR]

Nucleic acids have the ability to generate advanced nanostructures in a controlled manner and can interact with target sequences or molecules with high affinity and selectivity. For this reason, they have applications in a variety of nanotechnology applications, from highly specific sensors to smart nanomachines and even in other applications such as enantioselective catalysis or drug delivery systems. However, a common disadvantage is the use of water as the ubiquitous solvent. The use of nucleic acids in non-aqueous solvents offers the opportunity to create a completely new toolbox with unprecedented degrees of freedom. Ionic liquids (ILs) and deep eutectic solvents (DESs) are the most promising alternative solvents due to their unique electrolyte and solvent roles, as well as their ability to maintain the stability and functionality of nucleic acids. This review aims to be a comprehensive, critical, and accessible evaluation of how much this goal has been achieved and what are the most critical parameters for accomplishing a breakthrough. [ABSTRACT FROM AUTHOR]

Predicting risky driving behaviour in real time is crucial for road safety, allowing for early intervention to prevent crashes. Drivers use cognitive processes like attention, perception, and decision-making to navigate changing road conditions. These processes are prone to errors due to cognitive overload or external distractions, leading to risky behaviour. By predicting such behaviour, safety systems can intervene promptly, reducing crash risks and enhancing road safety. The European Horizon2020 project "i-DREAMS" aimed to address these issues by defining, developing, testing, and validating a "Safety Tolerance Zone (STZ)" to ensure drivers operate within safe boundaries. Within the i-DREAMS framework, three levels of driving behaviour were established: "Normal" driving, "Dangerous" driving, and "Avoidable Accident" driving. Many current risk prediction models lack robustness and interpretability, with common black-box approaches providing limited insight into the factors contributing to risky behaviour. This study aims to address these issues by developing a framework that uses large-scale data from intelligent sensor systems and machine learning techniques to create models that are both robust and interpretable. Specifically, four classification models: Ridge Classifier (RC), Support Vector Machines (SVM), Random Forests (RF), and extreme Gradient Boosting (XGBoost) - were developed. These models were utilized to categorize driving behaviour into three defined levels of "Safety Tolerance Zone (STZ)". Also, to improve interpretability, SHAP (SHapley Additive exPlanations) values were employed, providing a way to identify the key features driving individual predictions. The results revealed that the RF and XGBoost models achieved high accuracy, reaching 95% in prediction accuracy. By identifying the factors that influence risky driving behaviour, this framework offers valuable insights for guiding safety interventions, ultimately contributing to enhanced road safety. [ABSTRACT FROM AUTHOR]

Human vision system remains alert for dangers and adopts high‐speed and low‐power coding methods to convert the image information to spike signals. To meet the demand for danger alert in machine vision, it is important to design intelligent sensors to integrate the functions of image perception and high‐efficiency coding for high‐priority analysis. Inspired by the human visual system, a MoS2 phototransistor is introduced on SiNx substrate enabling simultaneous image perception and time‐to‐first‐spike (TTFS) coding. The device demonstrates exceptional performance in encoding 3‐bit grayscale images, achieving a low mean squared error of 0.008 and a high structural similarity index of 0.9784. Spiking neural networks (SNN) with TTFS coding achieve high recognition accuracy (98.86%) while reducing spike count by 75%. The device array also perceives motion direction and object states by converting data temporally. This work establishes a hardware foundation to promote the performance of SNNs in efficiently identifying crucial information. [ABSTRACT FROM AUTHOR]

Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut's Elitex fiber outperformed Shieldex's fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm. [ABSTRACT FROM AUTHOR]

Grasping and object manipulation have been considered key domains of Cyber-Physical Systems (CPS) since the beginning of automation, as they are the most common interactions between systems, or a system and its environment. As the demand for automation is spreading to increasingly complex fields of industry, smart tools with sensors and internal decision-making become necessities. CPS, such as robots and smart autonomous machinery, have been introduced in the meat industry in recent decades; however, the natural diversity of animals, potential anatomical disorders and soft, slippery animal tissues require the use of a wide range of sensors, software and intelligent tools. This paper presents the development of a smart robotic gripper for deployment in the meat industry. A comprehensive review of the available robotic grippers employed in the sector is presented along with the relevant recent research projects. Based on the identified needs, a new mechatronic design and early development process of the smart gripper is described. The integrated force sensing method based on strain measurement and magnetic encoders is described, including the adjacent laboratory and on-site tests. Furthermore, a combined slip detection system is presented, which relies on an optical flow-based image processing algorithm using the video feed of a built-in endoscopic camera. Basic user tests and application assessments are presented. [ABSTRACT FROM AUTHOR]

Electronic textiles (e-textiles) are a branch of wearable technology based on integrating smart systems into textile materials creating different possibilities, transforming industries, and improving individuals' quality of life. E-textiles hold vast potential, particularly for use in personal protective equipment (PPE) by embedding sensors and smart technologies into garments, thus significantly enhancing safety and performance. Although this branch of research has been active for several decades now, only a few products have made it to the market. Achieving durability, reliability, user acceptance, sustainability, and integration into current manufacturing processes remains challenging. High levels of reliability and user acceptance are critical for technical textiles, such as those used in PPE. While studies address washing reliability and field tests, they often overlook end user preferences regarding smart textiles. This paper presents a narrow fabric-based e-textile system co-developed by engineers, garment and textiles' manufacturers, and firefighters. It highlights material choices and integration methods, and evaluates the system's reliability, sustainability, and user experience, providing comprehensive insights into developing and analyzing e-textile products, particularly in the PPE field. [ABSTRACT FROM AUTHOR]

The automatic detection of smoke by analyzing the video stream acquired by traditional surveillance cameras is becoming a more and more interesting problem for the scientific community thanks to the necessity to prevent fires at the very early stages. The adoption of a smart visual sensor, namely a computer vision algorithm running in real time, allows one to overcome the limitations of standard physical sensors. Nevertheless, this is a very challenging problem, due to the strong similarity of the smoke with other environmental elements like clouds, fog and dust. In addition to this challenge, data available for training deep neural networks is limited and not fully representative of real environments. Within this context, in this paper we propose a new method for smoke detection based on the combination of motion and appearance analysis with a modern convolutional neural network (CNN). Moreover, we propose a new dataset, called the MIVIA Smoke Detection Dataset (MIVIA-SDD), publicly available for research purposes; it consists of 129 videos covering about 28 h of recordings. The proposed hybrid method, trained and evaluated on the proposed dataset, demonstrated to be very effective by achieving a 94% smoke recognition rate and, at the same time, a substantially lower false positive rate if compared with fully deep learning-based approaches (14% vs. 100%). Therefore, the proposed combination of motion and appearance analysis with deep learning CNNs can be further investigated to improve the precision of fire detection approaches. [ABSTRACT FROM AUTHOR]

Objective: This study introduces a novel methodology combining rapid stretch compound training with blood flow restriction (BFR) to investigate post activation performance enhancement (PAPE) in basketball players, a field that has been predominantly explored for lower limbs. We aimed to assess the efficacy of this combined approach on upper limb muscle performance in athletes. Methods: We employed a randomized, self-controlled crossover trial with ten male basketball players. The bench press throw (BPT) served as the primary metric, with players undergoing four interventions post-baseline: (1) STR—plyometric training; (2) BFR—blood flow restriction; (3) COMB—STR integrated with BFR; and (4) CON—control. Innovatively, we utilized an intelligent tracking sensor to precisely measure peak power (PP), peak velocity (PV), mean power (MP), and mean velocity (MV) at 4, 8, and 12 min post-intervention, providing a detailed temporal analysis of PAPE. Results: The COMB intervention demonstrated superior PAPE effects at 4 min, significantly outperforming STR and BFR alone and the control group across all measured indices (p < 0.05). Notably, the COMB group maintained these improvements for PV, PP, and H up to 12 min post-intervention, suggesting a prolonged effect. Conclusion: (1) The COMB stimulation has been shown to successfully induce PAPE more effectively than STR and BFR modality alone. (2) It appears that the optimal effects of PAPE are achieved within 4 min of exercising under this COMB. By the 12 min mark, only the COMB group continued to show significant improvements in PV, PP, and H compared to both the baseline and the CON group, while the effects in the STR and BFR groups further diminished. This suggests that although the PAPE effect is maintained over time, its optimal performance may peak at the 4 min mark and then gradually weaken as time progresses. [ABSTRACT FROM AUTHOR]

This work demonstrates that the packaging material commonly called "bubble wraps" (Aeroplast) can be leveraged to serve as a probe to visible sunlight exposure. This probe relies on a newly developed layered metal selenide photocatalyst with the general formula (DMAH)2xMnxSn3–xSe6 (DMSe‐1) (x = 1.3–1.7; DMAH+ = dimethylammonium), featuring a narrow bandgap of 0.76 eV, in addition to an indicator dye and a reducing agent. The photochemically sensitive probe is introduced into the air‐filled compartments of bubble wraps and undergoes photocatalytic degradation, resulting in a chromatic response to sunlight exposure. The probe's sensitivity to variable irradiation doses can be adjusted by varying the amount of the photocatalyst. The color intensity correlates with the absorbed irradiation dose, allowing for qualitative assessment by the naked eye or quantitative measurement using the RGB color system. The results obtained from the new probe agree with those obtained from standard sunlight pyranometers (r = 0.98), with an average error of <15%. This suggests that beyond their use as protective coatings, bubble wraps can be successfully repurposed as visible light sensors. Furthermore, this study describes the initial use of metal chalcogenides as visible light probes, potentially paving the way for the development of innovative light‐sensitive materials. [ABSTRACT FROM AUTHOR]

Achieving convenient, sensitive, low‐cost, and non‐contact detection of trace addictive drugs is a challenging problem. Herein, a novel ionically conductive metal‐organic framework (IC‐MOFs) is designed through a controlled interface assembly strategy. The active metal anions are incorporated into the layered MOFs with a porous structure, forming charge carriers that served as effective adsorption/binding sites for N‐methylphenethylamine (MPEA), a crucial simulator of addictive drugs. The in situ integrated Zn3(HHTP)2‐MOF sensor device designed in this study demonstrated real‐time detection of sub‐ppb level MPEA at room temperature, with an exceptionally low theoretical detection limit of 20 ppt. The overall sensing performance of this sensor surpassed all previously reported chemical sensors for detecting methamphetamine. The Zn3(HHTP)2‐MOF sensor exhibited outstanding selectivity, rapid response time (ca. 5 s), excellent long‐term stability, amenable miniaturization, and device consistency. The device successfully passed dual 85 test (500 h at 85 °C and 85% of humidity), which is rarely report previously. Density functional theoretical calculations (DFT) and spectral characterization confirmed that the prominent selectivity of Zn3(HHTP)2‐MOF toward MPEA is attributed to the strong binding ability. The general and straightforward strategy provides brand‐new route to exploiting smart sensors for drug prevention and surveillance. [ABSTRACT FROM AUTHOR]

Here, the implementation of a smartphone‐based portable molecularly imprinted photonic crystal hydrogel (MIPCH) colorimetric sensor for the visual and label‐free detection of bisphenol A (BPA) in water samples is reported. The sensor is prepared by photopolymerizing BPA‐added hydrogel precursor solution within the voids of a photonic crystal opal film, followed by the extraction of BPA molecules. Rebinding of the BPA analyte to the MIPCH causes the hydrogel to swell, leading to a significant redshift in the diffracting spectrum. The MIPCH sensor exhibits a low limit of detection of 0.96 × 10−15m. The sensor also shows linear response in the femtomolar range, rapid responsiveness (≤6 min), selective detection of BPA in complex matrices, long‐term stability, and reproducibility. Images of the sensor responses are used to train a deep‐learning‐based regression model on a smartphone to predict the BPA concentration quantitatively. Integration of the regression model with the developed sensor provides an accurate and portable smart sensor platform well‐suited for real‐time and on‐field monitoring of BPA. [ABSTRACT FROM AUTHOR]

Tactile sensing provides robots the ability of object recognition, fine operation, natural interaction, etc. However, in the actual scenario, robotic tactile recognition of similar objects still faces difficulties such as low efficiency and accuracy, resulting from a lack of high-performance sensors and intelligent recognition algorithms. In this paper, a flexible sensor combining a pyramidal microstructure with a gradient conformal ionic gel coating was demonstrated, exhibiting excellent signal-to-noise ratio (48 dB), low detection limit (1 Pa), high sensitivity (92.96 kPa−1), fast response time (55 ms), and outstanding stability over 15,000 compression-release cycles. Furthermore, a Pressure-Slip Dual-Branch Convolutional Neural Network (PSNet) architecture was proposed to separately extract hardness and texture features and perform feature fusion. In tactile experiments on different kinds of leaves, a recognition rate of 97.16% was achieved, and surpassed that of human hands recognition (72.5%). These researches showed the great potential in a broad application in bionic robots, intelligent prostheses, and precise human–computer interaction. [ABSTRACT FROM AUTHOR]

Multimodal haptic perception is essential for enhancing perceptual experiences in augmented reality applications. To date, several artificial tactile interfaces have been developed to perceive pressure and precontact signals, while simultaneously detecting object type and softness with quantified modulus still remains challenging. Here, inspired by the campaniform sensilla on insect antennae, we proposed a hemispherical bimodal intelligent tactile sensor (BITS) array using the triboelectric effect. The system is capable of softness identification, modulus quantification, and material type recognition. In principle, due to the varied deformability of materials, the BITS generates unique triboelectric output fingerprints when in contact with the tested object. Furthermore, owing to the different electron affinities, the BITS array can accurately recognize material type (99.4% accuracy), facilitating softness recognition (100% accuracy) and modulus quantification. It is promising that the BITS based on the triboelectric effect has the potential to be miniaturized to provide real-time accurate haptic information as an artificial antenna toward applications of human-machine integration. [ABSTRACT FROM AUTHOR]

Intelligent wearable sensors play a crucial role in the detection of toxic gases and monitoring physiological signals, thereby effectively ensuring environmental and personal health safety. Nonetheless, achieving the requirements for antibacterial properties, comfortable wear, and multifunctional detection remains a major challenge. In this study, a novel Def‐ZIF‐8/PPY/BC‐based flexible sensor is developed by in situ growth of zeolitic imidazolate frameworks‐8 (ZIF‐8) and polypyrrole (PPY) on bacterial cellulose (BC), followed by water immersion. The Def‐ZIF‐8/PPY/BC‐based flexible sensor demonstrates effective dual‐sensitivity responses to nitrogen dioxide (NO2) toxic gas and stress‐strain behaviors at room temperature. The structural characterization and theoretical calculations affirm that the innovative water treatment method successfully introduces defects into ZIF‐8, resulting in a significant 2.57‐fold improvement in response intensity to 80 ppm NO2. Stress‐strain sensing performance analysis reveals that the Def‐ZIF‐8/PPY/BC‐based flexible sensor exhibited high sensitivity (6.44 kPa−1), rapid response and recovery times (0.7/0.4 s), and exceptional cyclic stability (8000 cycles). Further, the inhibitory effect of ZIF‐8 on common bacterial strains contributes to the exceptional antibacterial properties (antibacterial rate exceeding 99%) of the Def‐ZIF‐8/PPY/BC based on the flexible sensor. This study offers a significant advancement in metal‐organic framework defect engineering and provides an effective strategy for developing multifunctional wearable sensors. [ABSTRACT FROM AUTHOR]

Bluetooth sensors in intelligent transportation systems possess extensive coverage and access to a large number of identity (ID) data, but they cannot distinguish between vehicles and persons. This study aims to classify and differentiate raw data collected from Bluetooth sensors positioned between various origin–destination (i–j) points into vehicles and persons and to determine their distribution ratios. To reduce data noise, two different filtering algorithms are proposed. The first algorithm employs time series simplification based on Simple Moving Average (SMA) and threshold models, which are tools of statistical analysis. The second algorithm is rule-based, using speed data of Bluetooth devices derived from sensor data to provide a simplification algorithm. The study area was the Historic Peninsula Traffic Cord Region of Istanbul, utilizing data from 39 sensors in the region. As a result of time-based filtering, the ratio of person ID addresses for Bluetooth devices participating in circulation in the region was found to be 65.57% (397,799 person IDs), while the ratio of vehicle ID addresses was 34.43% (208,941 vehicle IDs). In contrast, the rule-based algorithm based on speed data found that the ratio of vehicle ID addresses was 35.82% (389,392 vehicle IDs), while the ratio of person ID addresses was 64.17% (217,348 person IDs). The Jaccard similarity coefficient was utilized to identify similarities in the data obtained from the applied filtering approaches, yielding a coefficient (J) of 0.628. The identity addresses of the vehicles common throughout the two date sets which are obtained represent the sampling size for traffic measurements. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare. However, these intelligent sensors face many security threats, particularly from malware attacks. Identifying and classifying malware is crucial for preventing such attacks. As the number of sensors and their applications grow, malware targeting sensors proliferates. Processing massive malware samples is challenging due to limited bandwidth and resources in IoT environments. Therefore, compressing malware samples before transmission and classification can improve efficiency. Additionally, sharing malware samples between classification participants poses security risks, necessitating methods that prevent sample exploitation. Moreover, the complex network environments also necessitate robust classification methods. To address these challenges, this paper proposes CSMC (Compressed Sensing Malware Classification), an efficient malware classification method based on compressed sensing. This method compresses malware samples before sharing and classification, thus facilitating more effective sharing and processing. By introducing deep learning, the method can extract malware family features during compression, which classical methods cannot achieve. Furthermore, the irreversibility of the method enhances security by preventing classification participants from exploiting malware samples. Experimental results demonstrate that for malware targeting Windows and Android operating systems, CSMC outperforms many existing methods based on compressed sensing and machine or deep learning. Additionally, experiments on sample reconstruction and noise demonstrate CSMC's capabilities in terms of security and robustness. [ABSTRACT FROM AUTHOR]