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Human Activity Recognition (HAR) deals with the automatic recognition of physical activities and plays a crucial role in healthcare and sports where wearable sensors and intelligent computational techniques are used. We propose a HAR algorithm that uses the smartphones accelerometer data for human activity recognition. In particular, we present a recurrent convolutional neural network-based HAR algorithm that combines a Convolutional Neural Network (CNN) to extract temporal features from the sensor data, a Fuzzy C-Means (FCM) clustering algorithm to cluster the features extracted by the CNN, and a Long Short-Term Memory (LSTM) network to learn the temporal dependencies between the features. We evaluate the proposed methodology on two distinct datasets: the MotionSense dataset and the WISDM dataset. We evaluate the proposed CNN-FCM-LSTM model on the publicly available MotionSense dataset to classify ten activity types: 1) walking upstairs, 2) walking downstairs, 3) jogging, 4) sitting, 5) standing, 6) level ground walking, 7) jumping jacks, 8) brushing teeth, 9) writing, and 10) eating. Next, we evaluate the model's performance on the WISDM dataset to assess its ability to generalize to unseen data. On the MotionSense test dataset, CNN-FCM-LSTM achieves a classification accuracy of 99.69%, a sensitivity of 99.62%, a specificity of 99.63%, and a false positive rate per hour (FPR/h) of 0.37%. Meanwhile, it achieves a classification accuracy of 97.27% on the WISDM dataset. The CNN-FCM-LSTM model's capability to classify a diverse range of activities within a single architecture is noteworthy. The results suggest that the proposed CNN-FCM-LSTM model using smartphone inputs is more accurate, reliable, and robust in detecting and classifying activities than the state-of-the-art models. It should be noted that activity recognition technology has the potential to aid in studying the underpinnings of physical activity, designing more effective training regimens, and simulating the rigors of competition in sports. [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]

Summary: Internet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS). [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]

Internet of Things (IoT) facilitates connectivity in businesses and smart homes by integrating embedded technology, wireless sensor networks and data aggregation. Regular monitoring of energy usage in IoT networks is crucial due to the high energy consumption and delays in transmitting data to the Base Station (BS) by the sensor nodes. The most significant challenges in IoT include energy depletion and transmission delays. In this research, the proposed Differential Evolution with Sailfish Optimization (DESFO) model addresses large network handling, achieves maximum convergence rates, and reduces energy consumption. The Differential Evolution (DE) mutation and crossover operators enhance exploration capabilities, while SFO adaptive movement strategies improve the exploitation of the search space. Together, they achieve high convergence rates, prevent falling into local optima, provide iterative control and manage high-dimensional networks effectively. The DESFO method exhibits superior performance when compared to the existing methods, Firefly Optimization and Aquila Optimization (FF-AO), Fixed-Parameter Tractable Approximation Clustering (FPTAC), and Cluster based Reliable Data Aggregation-Sunflower Optimization (CRDA-SFO). The proposed DESFO method yields impressive results, achieving a Packet Delivery Ratio (PDR) of 96.12% at 250 nodes, a Delay of 3ms at 250node, Energy consumption of 12J at 250 respectively. [ABSTRACT FROM AUTHOR]

Water pollution is the most important factor affecting the environment. Appropriate monitoring is a big challenge to make sustainable growth by maintaining it for society. In recent times, water monitoring has turned into a smart monitoring system for water pollution (SMS-wp), with the advances on the Internet of things (IoT), machine learning (ML), and the improvement in current sensors. River Ganga is one of the major sources of water for drinking, irrigation, and industries in the northern part of India. Day by day, Ganga River is getting polluted, due to anthropogenic activities, such as the construction of dams, extensive use of fertilizers in agriculture, and untreated discharges from industries. Contamination in the river water is adversely affecting human health and river biota. Therefore, to improve the river ecosystem and to check infections and diseases, water quality assessment is very much important. The main aim of this study is to determine the Water Quality Index (WQI) of the River Ganges at the upper part of the Indo-Gangetic plain, just downstream of the Himalayan foothill using the last 3 years of data (2017–2019). Trend analysis for River Ganga water at considered locations is also a part of this study. Trend analysis is presenting the water quality of river Ganga in the coming years up to 2025. Twelve physicochemical parameters (TDS, chlorides, alkalinity, DO, temperature, COD, BOD, pH, magnesium, hardness, total coliform, and calcium) were analyzed to determine the water quality of River Ganga. As a result, WQI for next 5 years (from 2020 to 2025) is forecasted as an increment of 17.34% at Haridwar, 4.16% at Roorkee, and 21.63% at Dehradun. Results of the study indicated that WQI values just downstream of the Himalayan foothills in the upper reaches of the Gangetic plain are increasing every year. The authors have concentrated on how the advances in sensor innovation, the Internet of things, and machine learning techniques make water pollution monitoring a genuinely brilliant checking framework. Finally, the system of robust strategies for ML, denoising techniques, and advancement of appropriate guidelines for wireless sensor networks (WSNs) have been recommended. [ABSTRACT FROM AUTHOR]

The state of the driver of being extremely tired or sleepy through the operation of the vehicle is called driver drowsiness. Different factors caused this state such as alcohol, lack of sleep, and the side effect of some medication. The drowsiness of drivers is a serious safety lead to accidents or fatalities on external and internal roads. The increased number of road accidents resulted from drowsy driving. A special smart, reliable, and accurate system, Using Python language 3.6 for Windows, was designed to build an alert system for drivers in detecting drowsiness driver. This system is crucial in reducing accidents road by the ability to concentrate, react quickly, and produce sound decisions through driving. This system implements a real-time detector that can monitor the states of drivers through driving. Smart cameras with 16-megapixel were used to ensure that capturing photos have a high quality. These cameras were used in gathering the driver's dataset in different alertness states, including both alert states and drowsy. The collected dataset is processed by extracting all relevant features such as head movement, yawning, and eye closure, which were used in identifying the driver's drowsiness. Python's libraries such as TensorFlow, OpenCV, Keras, and Pygame are used for extracting all the above features. Viola-Jones algorithm is used in face eye region detecting and extracting from the image of the face in the proposed system. A Support Vector Machine (SVM) algorithm was used in classifying between drowsy and non-drowsy drivers. The system is tested and evaluated in the real world, to ensure that the system is reliable and robust; it has high performance and accuracy, and the accuracy is about 99.1%. This system can be used in manufacturing vehicles. [ABSTRACT FROM AUTHOR]

Remote sensing technology is a vital component of disaster management, poised to revolutionize how we safeguard lives and property through enhanced prediction, mitigation, and recovery efforts. Disaster management hinges on continuous monitoring of various environments, from urban areas to forests and farms. Data from these observations are relayed to servers, where sophisticated processing algorithms forecast impending disasters. Remote sensing technology operates through a layered framework. The sensing layer acquires raw data, the network layer facilitates data transmission, and the data processing layer extracts meaningful insights. The application layer then leverages these insights to make informed decisions. Elevating the intelligence of remote sensing technology necessitates advancements across these layers. This paper delves into disaster management concepts and highlights the pivotal role played by remote sensing technology. It offers a comprehensive exploration of each layer within the remote sensing technology framework, detailing foundational principles, tools, and methodologies for enhancing intelligence. Addressing challenges inherent to this technology, the paper also presents future-oriented solutions. Furthermore, it examines the influence of wireless network infrastructure, alongside emerging technologies like the Internet of Things, cloud computing, virtual machines, and low-power wireless networks, in nurturing the evolution and sustainability of remote sensing technology. [ABSTRACT FROM AUTHOR]

The development of a wearable, easy-to-fabricate, and stable intelligent minisystem is highly desired for the closed-loop management of diabetes. Conventional systems always suffer from large size, high cost, low stability, or complex fabrication. Here, we show for the first time a wearable, rapidly manufacturable, stability-enhancing microneedle patch for diabetes management. The patch consists of a graphene composite ink-printed sensor on hollow microneedles, a polyethylene glycol (PEG)-functionalized electroosmotic micropump integrated with the microneedles, and a printed circuit board for precise and intelligent control of the sensor and pump to detect interstitial glucose and deliver insulin through the hollow channels. Via synthesizing and printing the graphene composite ink, the sensor fabrication process is fast and the sensing electrodes are stable. The PEG functionalization enables the micropump a significantly higher stability in delivering insulin, extending its lifetime from days to weeks. The patch successfully demonstrated excellent blood glucose control in diabetic rats. This work may introduce a new paradigm for building new closed-loop systems and shows great promise for widespread use in patients with diabetes. [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]

The decision-making process in Industrial Wireless Sensor Networks heavily relies on the information provided by smart sensors. Ensuring the trustworthiness of these sensors is essential to prolong the lifetime of the network. Additionally, dependable data transmission by sensor nodes is crucial for effective decision-making. Trust management approaches play a vital role in safeguarding industrial sensor networks from internal threats, enhancing security, dependability, and network resilience. However, existing trust management schemes often focus solely on communication behaviour to calculate trust values, potentially leading to incorrect decisions amidst prevalent malicious attacks. Moreover, these schemes often fail to meet the resource and dependability requirements of IWSNs. To address these limitations, this paper proposes a novel hybrid Trust Management Scheme called the Multi-layered Assessment System for Trustworthiness Enhancement and Reliability (MASTER). The MASTER scheme employs a clustering approach within a hybrid architecture to reduce communication overhead, effectively detecting and mitigating various adversarial attacks such as Sybil, Blackhole, Ballot stuffing, and On–off attacks with minimal overheads. This multifactor trust scheme integrates both communication-based trust and data-based trust during trust estimation, aiming to improve the lifetime of industrial sensor networks. Furthermore, the proposed MASTER scheme utilizes a flexible weighting scheme that assigns more weight to recent interactions during both direct and recommendation (indirect) trust evaluation. This approach ensures robust and precise trust values tailored to the specific network scenario. To efficiently process and glean insights from dispersed data, machine learning algorithms are employed, offering a suitable solution. Experimental results demonstrate the superior performance of the MASTER scheme in several key metrics compared to recent trust models. For instance, when 30% of malicious Sensor Nodes (SNs) exist in a network comprising 500 sensor nodes, the MASTER scheme achieves a malicious behaviour detection rate of 97%, surpassing the rates of other models. Even after the occurrence of malicious SNs exceeding 30%, the False Negative Rate (FNR) in the MASTER scheme remains lower than other models due to adaptive trust functions employed at each level. With 50% malicious SNs in the network, the MASTER scheme achieves a malicious behaviour detection accuracy of 91%, outperforming alternative models. Moreover, the average energy consumption of SNs in the MASTER scheme is significantly lower compared to other schemes, owing to its elimination of unnecessary transactions through clustered topology utilization. Specifically, with 30% and 50% malicious SNs in the network, the MASTER scheme achieves throughput rates of 150 kbps and 108 kbps, respectively, demonstrating its efficiency in challenging network scenarios. Overall, the proposed MASTER scheme offers a comprehensive solution for enhancing security, trustworthiness, and collaboration among sensor nodes in IWSNs, while achieving superior performance in various metrics compared to existing trust models. [ABSTRACT FROM AUTHOR]

The growing elderly population in smart home environments necessitates increased remote medical support and frequent doctor visits. To address this need, wearable sensor technology plays a crucial role in designing effective healthcare systems for the elderly, facilitating human–machine interaction. However, wearable technology has not been implemented accurately in monitoring various vital healthcare parameters of elders because of inaccurate monitoring. In addition, healthcare providers encounter issues regarding the acceptability of healthcare parameter monitoring and secure data communication within the context of elderly care in smart home environments. Therefore, this research is dedicated to investigating the accuracy of wearable sensors in monitoring healthcare parameters and ensuring secure data transmission. An architectural framework is introduced, outlining the critical components of a comprehensive system, including Sensing, Data storage, and Data communication (SDD) for the monitoring process. These vital components highlight the system's functionality and introduce elements for monitoring and tracking various healthcare parameters through wearable sensors. The collected data is subsequently communicated to healthcare providers to enhance the well-being of elderly individuals. The SDD taxonomy guides the implementation of wearable sensor technology through environmental and body sensors. The proposed system demonstrates the accuracy enhancement of healthcare parameter monitoring and tracking through smart sensors. This study evaluates state-of-the-art articles on monitoring and tracking healthcare parameters through wearable sensors. In conclusion, this study underscores the importance of delineating the SSD taxonomy by classifying the system's major components, contributing to the analysis and resolution of existing challenges. It emphasizes the efficiency of remote monitoring techniques in enhancing healthcare services for the elderly in smart home environments. [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 study introduces a novel wearable device that combines dual‐functional modules for energy storage and sensing. The device features an ionic gel serving as both the battery electrolyte and sensor dielectric layer, along with VO2‐nanoneedles electrodes. The solid‐state zinc‐ion battery module demonstrates a specific capacity of 286 mAh g−1, delivering consistent and long‐lasting power output even under variable pressure conditions. Moreover, the iontronic pressure sensor module exhibits high sensitivity, achieving 2.3 × 104 kPa−1, enabling precise detection of fingertip pulses and identification of respiratory patterns. The device's flexibility and structural resilience allow it to endure complex deformations, ensuring continuous operation in challenging environments. These results highlight the potential of the all‐in‐one device for wearable smart healthcare monitoring and management. [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]

Nowadays, additive manufacturing technologies have impacted different engineering sectors. Three- and four-dimensional printing techniques are increasingly used in soft and flexible electronics thanks to the possibility of working contemporarily with several materials on various substrates. The materials portfolio is wide, as well as printing processes. Shape memory polymers, together with composites, have gained great success in the electronic field and are becoming increasingly popular for fabricating pH, temperature, humidity, and stress sensors that are integrated into wearable, stretchable, and flexible devices, as well as for the fabrication of communication devices, such as antennas. Here, we report an overview of the state of the art about the application of 4D printing technologies and smart materials in electronics. [ABSTRACT FROM AUTHOR]

Chlorpyrifos is one of the most widely used broad-spectrum insecticides in agriculture. Given its potential toxicity and residue in food (e.g., tea), establishing a rapid and reliable method for the determination of chlorpyrifos residue is crucial. In this study, a strategy combining surface-enhanced Raman spectroscopy (SERS) and intelligent variable selection models for detecting chlorpyrifos residue in tea was established. First, gold nanostars were fabricated as a SERS sensor for measuring the SERS spectra. Second, the raw SERS spectra were preprocessed to facilitate the quantitative analysis. Third, a partial least squares model and four outstanding intelligent variable selection models, Monte Carlo-based uninformative variable elimination, competitive adaptive reweighted sampling, iteratively retaining informative variables, and variable iterative space shrinkage approach, were developed for detecting chlorpyrifos residue in a comparative study. The repeatability and reproducibility tests demonstrated the excellent stability of the proposed strategy. Furthermore, the sensitivity of the proposed strategy was assessed by estimating limit of detection values of the various models. Finally, two-tailed paired t-tests confirmed that the accuracy of the proposed strategy was equivalent to that of gas chromatography–mass spectrometry. Hence, the proposed method provides a promising strategy for detecting chlorpyrifos residue in tea. [ABSTRACT FROM AUTHOR]

The Smart Autonomous Vehicular System (AVS) is designed to combine technologies such as sensors, cameras, radars, and machine learning algorithms in cars. The implementation of Smart AVS in smart cars has the potential to revolutionize the automotive industry and transform the way we think about transportation. In this paper, the implementation of Smart AVS in smart cars includes two steps. Firstly, the architecture is designed using Microsoft Threat Modelling tool. Secondly, with the use of Engineering Software, smart cars are constructed and simulated to verify and validate algorithms related to autonomous driving, path planning, and other intelligent functionalities. Simulating these algorithms in a controlled virtual environment helps to identify and address issues before implementation on physical vehicles. The main advantages of using the proposed model are early detection of vulnerabilities, realistic simulation of sensor inputs, communication protocol testing, cloud integration validation, user interface, and consumer experience, and validation of compliance with security standards. [ABSTRACT FROM AUTHOR]

The efficient operation of smart farms relies on the precise monitoring of farm environmental information, necessitating the deployment of a large number of wireless sensors. These sensors must be integrated with their specific locations within the fields to ensure data accuracy. Therefore, efficiently and rapidly determining the positions of sensor nodes presents a significant challenge. To address this issue, this paper proposes a hybrid optimization DV-Hop localization algorithm based on the chaotic crested porcupine optimizer. The algorithm leverages the received signal strength indicator, combined with node hierarchical values, to achieve graded processing of the minimum number of hops. Polynomial fitting methods are employed to reduce the estimation distance error from the beacon nodes to unknown nodes. Finally, the chaotic optimization crested porcupine optimizer is designed for intelligent optimization. Simulation experiments verify the proposed algorithm's localization performance across different monitoring areas, varying beacon node ratios, and assorted communication radii. The simulation results demonstrate that the proposed algorithm effectively enhances node localization accuracy and significantly reduces localization errors compared to the results for other algorithms. In future work, we plan to consider the impact of algorithm complexity on the lifespan of wireless sensor networks and to further evaluate the algorithm in a pH monitoring system for farmland. [ABSTRACT FROM AUTHOR]

Featured Application: A possible application of the machine learning-based data imputation framework presented in this paper is deployment in condition-based maintenance (CBM) systems for advanced manufacturing organizations. Missing data is a commonly encountered issue in the smart sensor networks that enable CBM, and our adaptive imputation framework could be beneficial to addressing this problem. In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems is the problem of connectivity, where a dropped Internet connection can lead to the loss of important condition data from a machine. Such gaps in the data, which we call irregular truncated signals, can lead to incorrect assumptions about the status of a machine and other flawed decision-making processes. This paper presents an adaptive data imputation framework based on machine learning (ML) algorithms to assess whether the missing data in a signal is missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) and automatically select an appropriate ML-based data imputation model to deal with the missing data. Our results demonstrate the potential for applying ML algorithms to the challenge of irregularly truncated signals, as well as the capability of our adaptive framework to intelligently solve this issue. [ABSTRACT FROM AUTHOR]