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OBJECTIVE: Real-time continuous glucose monitoring (CGM) is effective for diabetes management in cases of type 1 diabetes and adults with type 2 diabetes (T2D) but has not been assessed in adolescents and young adults (AYAs) with T2D. The objective of this pilot interventional study was to assess the feasibility and acceptability of real-time CGM use in AYAs with T2D. METHODS: Adolescents and young adults (13-21 years old) with T2D for six months or more and hemoglobin A1c (A1c) greater than 7%, on any Food and Drug Administration-approved treatment regimen, were included. After a blinded run-in period, participants were given access to a real-time CGM system for 12 weeks. The use and acceptability of the real-time CGM were evaluated by sensor usage, surveys, and focus group qualitative data. RESULTS: Participants (n = 9) median age was 19.1 (interquartile range [IQR] 16.8-20.5) years, 78% were female, 100% were people of color, and 67% were publicly insured. Baseline A1c was 11.9% (standard deviation ±2.8%), with median diabetes duration of 2.5 (IQR 1.4-6) years, and 67% were using insulin. Seven participants completed the study and demonstrated statistically significant improvement in diabetes-related quality of life, with the mean Pediatric Quality of Life inventory (PedsQL) diabetes score increasing from 70 to 75 after using CGM (P = .026). Focus group results supported survey results that CGM use among AYAs with T2D is feasible, can improve quality of life, and has the potential to modify behavior. CONCLUSION: Real-time CGM is feasible and acceptable for AYAs with T2D and may improve the quality of life of patients with diabetes. Larger randomized controlled trials are needed to assess the effects on glycemic control and healthy lifestyle changes.

BackgroundPeople with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.MethodsThe application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.ResultsThe mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.ConclusionThere is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

BACKGROUND: The chicken body louse is an obligate ectoparasite of domestic chickens. Chicken body lice feed on feathers, and infestation with this louse is linked to decreases in egg production, hen weight, and feed conversion efficiency. However, it is unknown how chicken body lice impact egg-laying chickens in cage-free environments. Welfare and behavior metrics were collected from flocks of egg-laying chickens either infested with chicken body lice or left uninfested. METHODS: In two trials, two flocks of cage-free commercial egg-laying chickens were infested with chicken body lice or maintained as uninfested controls. At three timepoints, behavior and welfare of all chickens was measured. On-animal sensors were used to quantify pecking, preening, and dustbathing behavior. Other animal-based welfare metrics included recording comb wounds and skin lesions. RESULTS: Birds infested with chicken body lice exhibited significantly more preening behaviors than uninfested birds, even at low louse levels. Moderate or severe skin lesions were detected on birds that were moderately infested with chicken body lice while skin lesions were never detected on uninfested birds. CONCLUSIONS: The welfare of chickens was impacted by the chicken body louse, a chewing louse that primarily feather feeds. Evidence of skin lesions on infested birds suggests that lice may cause more damage to birds than previously thought, and further evaluation of louse economic damage is necessary.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Human Activity Recognition (HAR) is a highly promising research area meant to automatically identify and interpret human behaviour using data received from sensors in various contexts. The potential uses of HAR are many, among them health care, sports coaching or monitoring the elderly or disabled. Nonetheless, there are numerous hurdles to be circumvented for HAR's precision and usefulness to be improved. One of the challenges is that there is no uniformity in data collection and annotation making it difficult to compare findings among different studies. Furthermore, more comprehensive datasets are necessary so as to include a wider range of human activities in different contexts while complex activities, which consist of multiple sub‐activities, are still a challenge for recognition systems. Researchers have proposed new frontiers such as multi‐modal sensor data fusion and deep learning approaches for enhancing HAR accuracy while addressing these issues. Also, we are seeing more non‐traditional applications such as robotics and virtual reality/augmented world going forward with their use cases of HAR. This article offers an extensive review on the recent advances in HAR and highlights the major challenges facing this field as well as future opportunities for further researches. [ABSTRACT FROM AUTHOR]

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A novel method for discovery of adulteration in edible oil is proposed based on concept of refractive index and electronic sensors. The research work focusses on two distinct methodologies like employing datasets and implementing a fumigation technique that integrates real-time hardware for testing Edible oil Impurities. In the first method, the dataset taken into consideration contains spectral data collected using Advanced ATR-MIR Spectroscopy for pure oil and various levels of adulteration with Vegetable oil. Each and every edible oil has a certain value of refractive index. When such oils are contemned in a change adding adulterants, the value of its refractive indices also changes. This value of refractive index serves as a feature for testing the oil and helps us in detecting the adulteration. If Oil is adulterated with vegetable oils, the refractive index will be lower and with animal fats, the refractive index will be higher than that of pure Oil. While in Fumigation Method a hardware module is develop in which adulterated & pure oil samples are heated at 40–50 °C for 4.66 min and the volatiles that are generated by varying gas concentrations are forcefully passed through to the MEMS Gas Sensor-MISC-2714 and Multichannel Gas sensor. The conductance of the sensors changes according to the gases sensed by the sensors contributes to features extraction. The conductance value serves as a feature for the classifier to determine whether the sample is highly, moderately, or lowly contaminated. Thus, in proposed methods we use different algorithms based on machine learning like KNN, Random Forest, CATBOOST and XGBOOST to accurately reveal the adulteration. Amongst all the applied algorithm Random Forest (RF) Classifier & XGBOOST algorithm outperform well and gives 100% accuracy. The proposed work is used for identifying food adulteration in edible food products which helps us to feed Society with high-quality food. [ABSTRACT FROM AUTHOR]

This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback in sport and cite similar reviews, that focus on other aspects of the field. With its focus on machine learning models that use signals from simple sensors, this review covers a very focused area that has not yet been covered by any other review. As described in problem definition, we use well-defined inclusion criteria, we have reviewed 72 papers. They present existing solutions, that use machine learning to extract useful information from data collected using various sensors in sports. To be included, papers had to use machine learning methods using data collected from sensors during sports, had to focus on sports-related applications and the result of machine learning had to be some information that can be used in real-time. We have found that the field is rapidly developing as 46 of the 72 included papers were from the last four years. Furthermore, we have found that the field is moving from using classical machine learning techniques to using deep learning. We analyze which data is used as input for machine learning, and we find that the most commonly used sensor is the accelerometer, closely followed by the gyroscope. The most common sensor platform is using a single wearable sensor, however, the studies that used deep learning, use multiple wearable sensors most often. Dataset sizes of sports papers are relatively small compared to other fields, but datasets are on average slightly larger in studies that use deep learning than in those that do not. We analyze the most common preprocessing methods and find that low-pass filtering and feature extraction are commonly used. We compare different machine learning models and the results of the studies that have tested multiple models on the same data, where we find that deep learning proved to be better than classical machine learning. Most studies show classification accuracy of over 90%, showing that machine learning is a useful tool for the researched problems. We end the review by researching how far the machine learning methods were implemented. Twenty of the included papers used their machine learning models in applications beyond a research paper and provided some sort of feedback back to athletes or coaches. After completing the review of the field, we propose a solution – a plan for future research. The proposed solution is to use a combination of best practices from the included paper and methods that we found are not yet implemented in the field of sports. We further elaborate, where we see the current state of the field. We conclude the article with short summary of the findings. [ABSTRACT FROM AUTHOR]

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management. [ABSTRACT FROM AUTHOR]

Nanoclays are layered structures in the nanoscale range with widespread application due to their unique properties such as swelling, cation exchange capacity, and ease of functionalisation using metals, metal oxides, and organic compounds such as carbon paste, polymers, and other biomolecules that form nanoclay composites. Nanoclay functionalisation with silver (Ag), zinc oxide (ZnO), and bimetallic silver--gold (Ag--Au) using hydrophilic and hydrophobic clays is here evaluated and discussed. The composites' synthesis and morphological, crystallinity, and electroactive properties in comparison with pure nanoclay are also assessed. The layered structure and crystallinity of all these nanoclay composites were slightly changed. The clumped layered structures on the surface of the nanocomposites had dispersed white spots that indicated possible surface modification. The nano-films of the composites' electroactivity were comparatively high, as seen from the increase in current in the cyclic voltammetry characterisation voltammograms and the differential pulse voltammograms of the pharmaceutical detection. Efavirenz, nevirapine, and zidovudine detection was improved by modification of the nanocomposite with human serum albumin (HSA), as shown by the higher current, thus indicating improved conductivity of the composites compared to the pure nanoclays. Applying HAS-modified nanocomposites in the analysis of efavirenz, nevirapine, and zidovudine on a glassy carbon electrode (GCE) showed good linearity and acceptable detection limits comparable to those of previous studies. Therefore, it has potential for application in pharmaceutical quality control and environmental monitoring. [ABSTRACT FROM AUTHOR]

The measurement of hydrogen concentration in fuel cell systems is an important prerequisite for the development of a control strategy to enhance system performance, reduce purge losses and minimize fuel cell aging effects. In this perspective paper, the working principles of hydrogen sensors are analyzed and their requirements for hydrogen control in fuel cell systems are critically discussed. The wide measurement range, absence of oxygen, high humidity and limited space turn out to be most limiting. A perspective on the development of hydrogen sensors based on palladium as a gas-sensitive metal and based on the organic magnetic field effect in organic light-emitting devices is presented. The design of a test chamber, where the sensor response can easily be analyzed under fuel cell-like conditions is proposed. This allows the generation of practical knowledge for further sensor development. The presented sensors could be integrated into the end plate to measure the hydrogen concentration at the anode in- and outlet. Further miniaturization is necessary to integrate them into the flow field of the fuel cell to avoid fuel starvation in each single cell. Compressed sensing methods are used for more efficient data analysis. By using a dynamical sensor model, control algorithms are applied with high frequency to control the hydrogen concentration, the purge process, and the recirculation pump. [ABSTRACT FROM AUTHOR]

Human activity recognition is a critical task for various applications across healthcare, sports, security, gaming, and other fields. This paper presents BodyFlow, a comprehensive library that seamlessly integrates human pose estimation and multiple-person estimation and tracking, along with activity recognition modules. BodyFlow enables users to effortlessly identify common activities and 2D/3D body joints from input sources such as videos, image sets, or webcams. Additionally, the library can simultaneously process inertial sensor data, offering users the flexibility to choose their preferred input, thus facilitating multimodal human activity recognition. BodyFlow incorporates state-of-the-art algorithms for 2D and 3D pose estimation and three distinct models for human activity recognition. [ABSTRACT FROM AUTHOR]

Structural composite materials have gained significant appeal because of their ability to be customized for specific mechanical qualities for various applications, including avionics, wind turbines, transportation, and medical equipment. Therefore, there is a growing demand for effective and non-invasive structural health monitoring (SHM) devices to supervise the integrity of materials. This work introduces a novel sensor design, consisting of three spiral resonators optimized to operate at distinct frequencies and excited by a feeding strip line, capable of performing non-destructive structural strain monitoring via frequency coding. The initial discussion focuses on the analytical modeling of the sensor, which is based on a circuital approach. A numerical test case is developed to operate across the frequency range of 100 to 400 MHz, selected to achieve a balance between penetration depth and the sensitivity of the system. The encouraging findings from electromagnetic full-wave simulations have been confirmed by experimental measurements conducted on printed circuit board (PCB) prototypes embedded in a fiberglass-based composite sample. The sensor shows exceptional sensitivity and cost-effectiveness, and may be easily integrated into composite layers due to its minimal cabling requirements and extremely small profile. The particular frequency-coded configuration enables the suggested sensor to accurately detect and distinguish various structural deformations based on their severity and location. [ABSTRACT FROM AUTHOR]

In this paper, a signal conditioner intended for use in variable impedance sensors is presented. First, an inductive linear displacement sensor design is described, and the signal conditioner discrete realization is presented. Second, based on this system's requirements, the integrated conditioner is proposed. The conditioner comprises an amplifier, a tunable band-pass filter, and a precision high-frequency AC-DC converter. It is designed in a low-cost AMS 0.35 µm CMOS process. The presented conditioner measures the sensor's impedance magnitude by using a simplified variation of the sensor voltage and current vector measurement. It can be used for the real-time measurement of fast sensors, having small output impedance. The post-layout simulation results show that the integrated conditioner has an inductance measurement range from 10 nH to 550 nH with a nonlinearity of 1.2%. The operating frequency in this case was 8 MHz, but the circuit can be easily adjusted to different operating frequencies (due to the tunable filter). The designed IC area is 500 × 330 μm2, and the total power consumption is 93.8 mW. [ABSTRACT FROM AUTHOR]

Biotechnological processes are essential for developing economies that aim to stand out in future markets. The use of bioreactors is one of the most important unit operations of biotechnological processes, and real-time monitoring of bioreactors is essential to ensure precise bioprocess control. This review presents different types of bioreactors, sensors, and applications in other sectors. Bioreactors, controlled systems for cultivating microorganisms and cells, are essential tools in various fields, from scientific research to industrial production. The use of a variety of sensors is critical for accurate, real-time monitoring, early problem detection, reproducibility, cost reduction, and increased efficiency. These benefits are being realized in numerous applications, including biofuel production, bioremediation and leaching processes, tissue engineering, and drug manufacturing. Innovations in bioreactor technology are expanding opportunities for a more sustainable and healthier future. By developing new types of bioreactors, integrating advanced sensors, and exploring promising applications, bioreactors are playing a key role in addressing global challenges and sustainably advancing science and technology. [ABSTRACT FROM AUTHOR]

In this review article, the main techniques for spectroscopic studies of gases in field conditions are considered. The issues related to the study of gas emissions from soils and the determination of their concentrations are analysed. The main types of spectroscopy used in portable devices for soil gas analysis, along with their design features and sampling approaches, are provided. Various studies aimed at optimising the operation of devices for analysing gases emitted from the soil, taking into account agronomic, agrochemical, and ecological specifics, are also presented. The effect of using different types of lasers and reflecting elements on the accuracy of optical measurements and the sensitivity to various substances in the gases is analysed. [ABSTRACT FROM AUTHOR]

Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m2) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO2 content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system's productivity and usefulness compared to the older manual monitoring and operating methods. [ABSTRACT FROM AUTHOR]

A dominant event determining the course of heart failure (HF) includes the disruption of the delicate sodium (Na+) and water balance leading to (Na+) and water retention and edema formation. Although incomplete decongestion adversely affects outcomes, it is unknown whether interventions directly targeting (Na+), such as strict dietary (Na+) restriction, intravenous hypertonic saline, and diuretics, reverse this effect. As a result, it is imperative to implement (Na+)-targeting interventions in selected HF patients with established congestion on top of quadruple therapy with angiotensin receptor neprilysin inhibitor, β-adrenergic receptor blocker, mineralocorticoid receptor antagonist, and sodium glucose cotransporter 2 inhibitor, which dramatically improves outcomes. The limited effectiveness of (Na+)-targeting treatments may be partly due to the fact that the current metrics of HF severity have a limited capacity of foreseeing and averting episodes of congestion and guiding (Na+)-targeting treatments, which often leads to dysnatremias, adversely affecting outcomes. Recent evidence suggests that spot urinary sodium measurements may be used as a guide to monitor (Na+)-targeting interventions both in chronic and acute HF. Further, the classical (2)-compartment model of (Na+) storage has been displaced by the (3)-compartment model emphasizing the non-osmotic accumulation of (Na+), chiefly in the skin. 23(Na+) magnetic resonance imaging (MRI) enables the accurate and reliable quantification of tissue (Na+). Another promising approach enabling tissue (Na+) monitoring is based on wearable devices employing ion-selective electrodes for electrolyte detection, including (Na+) and (Cl–). Undoubtably, further studies using 23(Na+)-MRI technology and wearable sensors are required to learn more about the clinical significance of tissue (Na+) storage and (Na+)-related mechanisms of morbidity and mortality in HF. [ABSTRACT FROM AUTHOR]

This research focused on the helicopter turboshaft engine dynamic model, identifying task solving in unsteady and transient modes (engine starting and acceleration) based on sensor data. It is known that about 85% of helicopter turboshaft engines operate in steady-state modes, while only around 15% operate in unsteady and transient modes. Therefore, developing dynamic multi-mode models that account for engine behavior during these modes is a critical scientific and practical task. The dynamic model for starting and acceleration modes has been further developed using on-board parameters recorded by sensors (gas-generator rotor r.p.m., free turbine rotor speed, gas temperature in front of the compressor turbine, fuel consumption) to achieve a 99.88% accuracy in identifying the dynamics of these parameters. An improved Elman recurrent neural network with dynamic stack memory was introduced, enhancing the robustness and increasing the performance by 2.7 times compared to traditional Elman networks. A theorem was proposed and proven, demonstrating that the total execution time for N Push and Pop operations in the dynamic stack memory does not exceed a certain value O(N). The training algorithm for the Elman network was improved using time delay considerations and Butterworth filter preprocessing, reducing the loss function from 2.5 to 0.12% over 120 epochs. The gradient diagram showed a decrease over time, indicating the model's approach to the minimum loss function, with optimal settings ensuring the stable training. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) testing is used for the continuous evaluation of structural integrity and the monitoring of damage evolution in structural components and materials. During operation, the environmental and loading conditions of metal structures can result in corrosion and surface wear damage. The early detection of surface degradation flaws is crucial, as they can serve as local stress concentration points, leading to crack initiation and failure. In this work, the effectiveness of AE in monitoring corrosion and surface wear flaw formation was experimentally evaluated. AE sensors were installed on steel test plates during the artificial induction of corrosion and surface wear in order to detect and record the generated AE signals. Corrosion-related AE signals typically exhibit low amplitude, count, and energy values. The direct detection of active corrosion can be challenging in noisy environments, but it can be carried out under certain conditions using dedicated AE sensor groups. Surface-wear-related AE signals exhibit high amplitude, energy, and count values, with long duration values that are associated with wear and grinding conditions. It was found that AE sensors can be utilised to detect corrosion and surface degradation events. The effectiveness of the AE method in detecting surface degradation in noisy environments can be improved by implementing a filtering methodology. This will limit the recording of noise-related signals that can mask out actual surface degradation AE events. [ABSTRACT FROM AUTHOR]

Object detection and classification in autonomous vehicles are crucial for ensuring safe and efficient navigation through complex environments. This paper addresses the need for robust detection and classification algorithms tailored specifically for Indian roads, which present unique challenges such as diverse traffic patterns, erratic driving behaviors, and varied weather conditions. Despite significant progress in object detection and classification for autonomous vehicles, existing methods often struggle to generalize effectively to the conditions encountered on Indian roads. This paper proposes a novel approach utilizing the YOLOv8 deep learning model, designed to be lightweight, scalable, and efficient for real-time implementation using onboard cameras. Experimental evaluations were conducted using real-life scenarios encompassing diverse weather and traffic conditions. Videos captured in various environments were utilized to assess the model's performance, with particular emphasis on its accuracy and precision across 35 distinct object classes. The experiments demonstrate a precision of 0.65 for the detection of multiple classes, indicating the model's efficacy in handling a wide range of objects. Moreover, real-time testing revealed an average accuracy exceeding 70% across all scenarios, with a peak accuracy of 95% achieved in optimal conditions. The parameters considered in the evaluation process encompassed not only traditional metrics but also factors pertinent to Indian road conditions, such as low lighting, occlusions, and unpredictable traffic patterns. The proposed method exhibits superiority over existing approaches by offering a balanced trade-off between model complexity and performance. By leveraging the YOLOv8 architecture, this solution achieved high accuracy while minimizing computational resources, making it well suited for deployment in autonomous vehicles operating on Indian roads. [ABSTRACT FROM AUTHOR]

Spermidine is an essential biomarker related to antiaging. Although the detection of spermidine levels is in high demand in life science fields, easy‐to‐use analytical tools without sample purification have not yet been fully established. Herein, we propose an organic field‐effect transistor‐based chemical sensor for quantifying the spermidine concentration in commercial cosmetics. An extended‐gate structure was employed for organic field‐effect transistor (OFET)‐based chemical sensing in aqueous media. A coordination‐bond‐based sensing system was introduced into the OFET device to visualize the spermidine detection information through changes in the transistor characteristics. The extended‐gate‐type OFET has shown quantitative responses to spermidine, which indicates sufficient detectability (i. e. the limit of detection for spermidine: 2.3 μM) considering actual concentrations in cosmetics. The applicability of the OFET‐based chemical sensor for cosmetic analysis was validated by instrumental analysis using high‐performance liquid chromatography. The estimated recovery rates for spermidine in cosmetic ingredient products (108–111 %) suggest the feasibility of cosmetic analysis based on the OFET‐based chemical sensor. [ABSTRACT FROM AUTHOR]

This study reviews major sensing technologies for the Lunar environment and its resources, as they have been developed since the times of Apollo missions. Selected technologies of sensors and instruments for the chemical, isotopic, and structural analysis of Lunar rocks and regoliths, as well as of the Lunar exosphere environment, are presented in a critical review towards an optimised and information-rich framework. Special focus is given on the activated Lunar regolith, and especially the Lunar dust, describing the development of Lunar simulants as the only accessible materials for experimentation and testing of the above technologies. New technologies are also highlighted, such as the development of the OxR microfluidic and spectroscopy integrated device, which, in its small scale aims to detect reactive oxygen species in the Lunar regolith, while in its larger format it is used to release oxygen gas from the Lunar dust and regolith, enabling astronaut respiration and fuel production on the Moon. It is finally suggested that miniaturisation of instruments and sensors, together with the standardisation of output information and characterisation protocols through holistic informational frameworks, will enhance the dynamic expansion and further integration and interoperation of sensors and devices, aiming to an efficient, safer, and resilient utilisation on the Moon and the establishment of sustainable settlements in the near future. [ABSTRACT FROM AUTHOR]

This paper presents a novel optoelectronic transimpedance amplifier (OTA) for short-range LiDAR sensors used in 180 nm CMOS technology, which consists of a main transimpedance amplifier (m-TIA) with an on-chip P+/N-well/Deep N-well avalanche photodiode (P+/NW/DNW APD) and a replica TIA with another on-chip APD, not only to acquire circuit symmetry but to also obtain concurrent automatic gain control (AGC) function within a narrow single pulse-width duration. In particular, for concurrent AGC operations, 3-bit PMOS switches with series resistors are added in parallel with the passive feedback resistor in the m-TIA. Then, the PMOS switches can be turned on or off in accordance with the DC output voltage amplitudes of the replica TIA. The post-layout simulations reveal that the OTA extends the dynamic range up to 74.8 dB (i.e., 1 µApp~5.5 mApp) and achieves a 67 dBΩ transimpedance gain, an 830 MHz bandwidth, a 16 pA/ √ Hz noise current spectral density, a −31 dBm optical sensitivity for a 10−12 bit error rate, and a 6 mW power dissipation from a single 1.8 V supply. The chip occupies a core area of 200 × 120 µm2. [ABSTRACT FROM AUTHOR]

E-skin is a bionic device with flexible and intelligent sensing ability that can mimic the touch, temperature, pressure, and other sensing functions of human skin. Because of its flexibility, breathability, biocompatibility, and other characteristics, it is widely used in health management, personalized medicine, disease prevention, and other pan-health fields. With the proposal of new sensing principles, the development of advanced functional materials, the development of microfabrication technology, and the integration of artificial intelligence and algorithms, e-skin has developed rapidly. This paper focuses on the characteristics, fundamentals, new principles, key technologies, and their specific applications in health management, exercise monitoring, emotion and heart monitoring, etc. that advanced e-skin needs to have in the healthcare field. In addition, its significance in infant and child care, elderly care, and assistive devices for the disabled is analyzed. Finally, the current challenges and future directions of the field are discussed. It is expected that this review will generate great interest and inspiration for the development and improvement of novel e-skins and advanced health monitoring systems. [ABSTRACT FROM AUTHOR]

Real-time monitoring of changes in packaged food is crucial to ensure safety and alleviate environmental issues. Accordingly, the development of indicators and sensors for smart packaging has long been anticipated, especially for gases related to food deterioration and microbial growth. However, the characteristics of indicators and sensors used in food packaging cannot be adjusted according to the specific food type, making it essential to select and apply suitable indicators and sensors for each type of food. In this review, the principles and characteristics of gas indicators and sensors for oxygen, carbon dioxide, and ammonia that are commercialized or in the development phase were summarized, and their application status and prospects were assessed. Indicators and sensors for smart packaging are applied in forms such as films, labels, sachets, and devices. Their detection methods include redox reactions, analyte binding, enzyme reactions, pH changes, electron transfer, conformational changes, and electrode reactions. In this work, 9 types of indicators and sensors for oxygen, carbon dioxide, and ammonia were evaluated based on their detection and indication methods, materials, sensitivity, detection range, limit of detection, and advantages and disadvantages in food applications. We anticipate our review will propose criteria for selecting the optimal indicators and sensors for specific foods. Furthermore, this review examines the current application status and future prospects of these indicators and sensors. [ABSTRACT FROM AUTHOR]

This study presents an air quality monitoring system that employs the Arduino Uno microcontroller. The system is augmented with a moving average filter and data fusion techniques from BME680 and CCS811 sensors, which are designed to process and combine data from these sensors. The system was tested and analyzed empirically across a range of residential environments in order to validate its efficacy. The findings indicated that the typical IAQ level in a bedroom was approximately 20 units. However, this level increased significantly, reaching 140 units, within minutes after the introduction of a 17% perfume spray. In contrast, the use of an aromatic diffuser resulted in a smaller increase in IAQ to 40 units, which returned to normal levels after ventilation. Moreover, the analysis demonstrated that the kitchen and bathroom exhibited inferior air quality in comparison to the bedroom. This was evidenced by elevated VOC and humidity levels, which were observed to be 10–20% higher due to the combined effects of household activities and inadequate ventilation. This study makes a significant contribution to the field of air quality monitoring by proposing a solution that employs sensor technology and data processing methods to enhance the quality of life within residential spaces. [ABSTRACT FROM AUTHOR]

The economic cost of repairing corrosion-affected reinforced concrete structures (RCSs) means that reliable and accurate assessment and early detection methods must be sought after. Conventional techniques, such as visual inspections, or measuring either cover layer resistivity or the corrosion potential, are methods that require accessibility and involve personnel having to travel to take in situ measurements. Monitoring by embedded sensors is a much more efficient approach that allows early detection by remote sensing. This work presents the implementation of a new measurement protocol regarding the existing monitoring system called INESSCOM (Integrated Sensor Network for Smart Corrosion Monitoring). Along with the corrosion intensity measurement in embedded sensors, it also proposes monitoring the double layer capacity of the sensors' responses. It aims to determine, along with the rebars' corrosion rate, the triggering agent of the corrosion process. This study was carried out using three reinforced concrete scaled columns that were exposed to different environments. The results demonstrate with this new protocol that the remote INESSCOM monitoring system can establish the corrosion rate and identify the precursor agent of corrosion (carbonation or chlorides), even when the recorded corrosion rates are similar. [ABSTRACT FROM AUTHOR]

In recent years, electrochemical sensors have garnered significant attention in the environmental field due to their potential to offer effective solutions for addressing the environmental challenges our planet faces, particularly in proactive environmental preservation. A particular interest has been focused on the development of electrochemical sensors due to their restricted sample volume and the potential for system miniaturization, enabling portability. Taking these aspects into consideration, this review centers on the utilization of electrochemical electrodes to acquire relevant chemical information pertaining to environmental issues. Furthermore, it will analyze how the use of nanotechnology has provided new substrate alternatives for the determination of chemically significant species in the environment. Finally, the challenges and future prospects in this field are discussed. [ABSTRACT FROM AUTHOR]

Screen‐printed electrochemical sensing platforms are ubiquitous within the field of electrochemistry where they provide benefits of being disposable, cost‐effective, reproducible, easily customisable, portable and allow one to transfer the laboratory approach into the field. In this review, we introduce the concept of screen‐printed electrodes, we summarise positive and negative aspects before moving into the current highlights of using traditional screen‐printed carbon electrodes within the field of electroanalysis. We then look to cover metallic and bulk modified varieties, geometric changes (micro, microband and associated arrays), electrode activation and finally the physical length of screen‐printed electrodes, providing insights for future research. [ABSTRACT FROM AUTHOR]

The 5G‐enabled sensor network systems make it possible to connect cyber and real 'things' in many ways. Even so, the flow of data between 5G‐enabled sensor devices brings big data environment problems, such as huge amounts of data, duplicated data, a lack of scalability etc. Some of these problems are as follows. It is hard to keep an eye on 5G‐enabled sensor systems in a 'big data' environment. Cyberattacks that could put the safety of the sensor network systems at risk are hard to find, which make the situation even more complicated. The security challenges of 5G‐enabled Sensor Network Systems are studied and analyzed due to some constraints associated with the sensor nodes. The proposed advanced algorithm for securing the 5G‐enabled sensor systems is a Multidimensional big data environment using artificial intelligence/machine learning (AI/ML). Using a structure that depends on both geographical and temporal data, an improved clear point selection operation may get important information from multidimensional time series data that is spread across a wide range of sensor nodes. Therefore, the actions of the 5G‐enabled sensor network can be shown accurately and a complete model of its underlying data structure is built to analysis attacking, pattern on 5G‐enabled Sensor Network Systems using the AI/ML Algorithm. [ABSTRACT FROM AUTHOR]

The aim of the present study was to analyze changes in young swimmers' in-water force, performance, kinematics, and anthropometrics during one full competitive season. Twenty-five swimmers (11 girls and 14 boys, 12.04 ± 0.16 years) were assessed over four distinct time points throughout a competitive season. The in-water force of both hands (D, dominant; ND, non-dominant) was retrieved during two bouts of 25 m front crawl allowing the estimation of the symmetry index. The velocity (v25) was calculated from the time to complete the 25 m and considered the performance outcome, while the stroke rate, stroke length, and the stroke index were used as kinematic variables. For anthropometric variables, body mass, stature, arm span and the hand surface area were measured. The in-water force (16-24%) and performance (8%) improved over the competitive season with significant changes in the first macrocycle. The stroke index was the only kinematic variable that changed between M1 and M4 (12.7%), accompanied by a higher asymmetric motion later in the season. A time effect was found in the stature (p < 0.001, ηp² = 0.71), the arm span (p < 0.001, ηp² = 0.79), and the hand surface area (D = p < 0.001, ηp² = 0.63; ND = p < 0.001, ηp² = 0.666). Swimming performance showed associations with in-water force, stroke efficiency and anthropometric features in all time points of the season. Thus, the natural anthropometric growth experienced over the season may translate into a more efficient swimming pattern with greater in-water forces that can enhance performance. [ABSTRACT FROM AUTHOR]

"Biomimicry" is an acronym used to describe how people looked at nature for inspiration to tackle a variety of problems. The modern problems of fastincreasing urbanization, land degradation, climate change, pandemics, loss of biodiversity, and widespread use of pesticides and fertilizers seriously threaten our food supply chain. There is a growing consumer demand for nutrient-dense, flavourful plant-based cuisine with minimal environmental impact. Moreover, a considerable portion of food roughly 24% is lost before it reaches consumers, partly as a result of poor quality and protracted supply chains. Researching new methods of producing food is essential since, by 2050, there will be more than 9.7 billion people on the planet, 70% of whom will reside in cities. Vertical farming (VF), which relieves pressure on conventional agricultural land by using vertical space instead of horizontal expansion, is growing in popularity as a solution to these problems. Because VF incorporates soil-less growth techniques, it is well-suited for urban environments. This strategy may help to produce more premium products, such as fruits, vegetables, flowers, and herbs. It may also help to produce cosmetics and medications made from plants. Vertical farming, is becoming more favoured as an alternative to traditional agriculture, and provides avenues for enhancing sustainable food production given the growing challenges of climate change and population growth. [ABSTRACT FROM AUTHOR]

The light field (LF) imaging technique can capture 3D scene information in 4D by recording both 2D intensity and 2D direction of incoming light rays. Due to this capability, LF has shown a great interest in virtual reality (VR) and augmented reality (AR) for enhanced immersion, improved depth perception and reconstruction of realistic 3D environments. This paper presents a comprehensive review of LF imaging technology and other approaches used for VR content creation. The applications of LF technology beyond VR and AR are also discussed. The challenges and limitations of other approaches for VR content creation are examined. State‐of‐the‐art research has focused on how VR experiences benefit from LF technology and identified the challenges to creating comfortable, immersive and realistic VR content such as (1) image size and resolution, (2) processing speed, (3) precise calibration and (4) depth reconstruction. Recommendations that can be considered for creating immersive VR content are provided to enhance user experience. These recommendations aim to contribute to developing more comfortable and realistic VR content, extending the potential applications of LF imaging technology in diverse fields. [ABSTRACT FROM AUTHOR]

Real-time monitoring of structural health status is very important for supersonic aircraft, since the long-term operation in harsh service environments, such as high temperatures, can cause potential damage. Structural health monitoring (SHM) based on lamb wave has been approved to be a critical technique for the reliable operation of supersonic aircraft, but is limited by the operating temperature of conventional piezoelectric sensors, which makes it difficult to meet the high-temperature monitoring requirements. The BiFeO 3 –BaTiO 3 ceramics exhibit excellent piezoelectric properties and high Curie temperature (T c), making them promising for high-temperature SHM of supersonic aircraft. Here, we fabricated a piezoelectric sensor using 0.67BiFeO 3 -0.33BaTiO 3 -0.25%molMnO 2 (BF33BTMN-025) ceramic, which exhibits a piezoelectric charge constant (d 33) of 135 pC/N at room temperature and a T c of 448 °C. The BF33BTMN-025 ceramic sensor operates effectively at temperatures up to 200 °C and maintains stable damage monitoring sensitivity as well. Compared with Pb(Zr, Ti)O 3 (PZT), BaTiO 3 (BTO) and other piezoelectric sensors commonly used in SHM field, the BF33BTMN-025 ceramic sensor has a higher operating temperature and a comparable sensitivity. Our work demonstrates that the BF33BTMN-025 ceramic sensor can be employed as piezoelectric sensor for high-temperature SHM, which has a wide application potential in the field of real-time monitoring of supersonic aircraft. [ABSTRACT FROM AUTHOR]

Over the past years, transport mode recognition has become a large field of research. However, flight as a type of transportation has been mostly overlooked. A system for flight detection might be useful for context-aware applications, but more importantly, it can be used to automatically manage airplane mode on smartphones. Smartphones transmit radio frequency signals which could potentially interfere with aircraft systems, and it is therefore important that devices enable airplane mode to avoid this problem. This paper proposes flyDetect, a method for automatic flight mode detection and an embodiment in the form of an app that demonstrates the viability of the method. Thus, the system uses the accelerometer and barometer in an Android smartphone, can detect the start and end of a flight, and notify other apps or systems on the device when this happens. Our evaluation shows that flyDetect meets the requirements set for the solution, and the results are very promising. [ABSTRACT FROM AUTHOR]

In the paper, a new design of a quasi-isotropic antenna for high-power electromagnetic (EM) field measurement is presented, along with its investigation into suitability. The measuring probe is intended for assessing pulsed microwaves, which cannot be measured by available meters due to the high value of electric field strength and short pulse duration. The measurement of such a strong field is required according to guidelines for protecting people against microwave fields, especially those emitted by radars. The proposed probe is based on the concept of dipole–diode detection. To enable high-power measurement, the receiving antenna is electrically "small," allowing diode detection within the diode square-law characteristics range. Additionally, the shortened dipole length minimizes the spatial integration error, which can be significant in the case of microwave measurement. To obtain the desired antenna polarization, a new dipole geometry was proposed. Fulfilling the requirement of measuring all incident EM field polarizations, the receiving antenna was based on three dipoles arranged within a specific "magic" angular arrangement, ensuring a suitable quasi-isotropic radiation pattern. The proposed probe can operate in a frequency range from 1 GHz to 12 GHz. [ABSTRACT FROM AUTHOR]

Simple, instantaneous, contactless, multiple-point metamaterial-inspired microwave sensors, composed of multi-band, low-profile metamaterial-inspired antennas, were developed to detect and identify meningioma tumors, the most common primary brain tumors. Based on a typical meningioma tumor size of 5–20 mm, a higher operating frequency, where the wavelength is similar or smaller than the tumor target, is crucial. The sensors, designed for the microwave Ku band range (12–18 GHz), where the electromagnetic property values of tumors are available, were implemented in this study. A seven-layered head phantom, including the meningioma tumors, was defined using actual electromagnetic parametric values in the frequency range of interest to mimic the actual human head. The reflection coefficients can be recorded and analyzed instantaneously, reducing high electromagnetic radiation consumption. It has been shown that a single-band detection point is not adequate to classify the nonlinear tumor and head model parameters. On the other hand, dual-band and tri-band metamaterial-inspired antennas, with additional detecting points, create a continuous function solution for the nonlinear problem by adding extra observation points using multiple-band excitation. The point mapping values can be used to enhance the tumor detection capability. Two-point mapping showed a consistent trend between the S11 value order and the tumor size, while three-point mapping can also be used to demonstrate the correlation between the S11 value order and the tumor size. This proposed multi-detection point technique can be applied to a sensor for other nonlinear property targets. Moreover, a set of antennas with different polarizations, orientations, and arrangements in a network could help to obtain the highest sensitivity and accuracy of the whole system. [ABSTRACT FROM AUTHOR]

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1–110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy (BLE) mesh networks provide flexible and reliable communication among low-power sensor-enabled Internet of Things (IoT) devices, enabling them to communicate in a flexible and robust manner. Nonetheless, the majority of existing BLE-based mesh protocols operate as flooding-based piconet or scatternet overlays on top of existing Bluetooth star topologies. In contrast, the Ad hoc On-Demand Distance Vector (AODV) protocol used primarily in wireless ad hoc networks (WAHNs) is forwarding-based and therefore more efficient, with lower overheads. However, the packet delivery ratio (PDR) and link recovery time for AODV performs worse compared to flooding-based BLE protocols when encountering link disruptions. We propose the Multipath Optimized AODV (M-O-AODV) protocol to address these issues, with improved PDR and link robustness compared with other forwarding-based protocols. In addition, M-O-AODV achieved a PDR of 88%, comparable to the PDR of 92% for flooding-based BLE, unlike protocols such as Reverse-AODV (R-AODV). Also, M-O-AODV was able to perform link recovery within 3700 ms in the case of node failures, compared with other forwarding-based protocols that require 4800 ms to 6000 ms. Consequently, M-O-AODV-based BLE mesh networks are more efficient for wireless sensor-enabled IoT environments. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) play a vital role in modern research and applications due to their potential to gather data from various environments. Because sensor nodes (SNs) within WSNs have limited battery life, those in close proximity to the sink often experience rapid power depletion, leading to the emergence of hotspot issues. To address this, the concept of a mobile sink (MS) has emerged as a potential solution, effectively mitigating power usage in SNs and thereby extending the network's overall lifespan. Furthermore, many sensor-based applications necessitate specific data collection timeframes, underscoring the necessity of effective strategies. Leveraging rendezvous points (RPs) to enhance network efficiency becomes imperative in enabling the MS to efficiently collect data from all SNs within designated time periods. A sophisticated cost function is employed to strategically determine RPs, considering multiple factors that influence the efficacy of each RP. This process culminates in the selection of RPs, optimizing for the longest path with minimal delays. Through the proposed hybrid mobile vehicle (HMV) method, compared against the prevailing MOOVor method, significant enhancements are observed in terms of sensor coverage and reduced hop count within the network. [ABSTRACT FROM AUTHOR]

A nanocomposite of CaO:MgAl2O4 was synthesized through a straightforward and cost-effective sol–gel method. The investigation of the novel CaO:MgAl2O4 nanocomposite encompassed an examination of its morphological and structural alterations, as well as an exploration of its photocatalytic activities and electrochemical characteristics. XRD analysis revealed a nanocomposite size of 24.15 nm. The band gap, determined through UV studies, was found to be 3.83 eV, and scanning electron microscopy (SEM) illustrated flake-like morphological changes in the CaO:MgAl2O4 samples. TEM, HRTEM, and SAED studies of a CaO:MgAl2O4 nanocomposite would reveal important details about its morphology, crystallography, and nanostructure. Photocatalytic activity was quantified by studying the degradation of Acid Red-88 (AR-88) dye in a deionized solution, achieving a 70% dye degradation under UV irradiation in 120 min. Plant growth examinations were carried out using dye degraded water to test its suitability for agriculture. The electrochemical energy storage and sensing applications of the prepared nanocomposite were examined using CaO:MgAl2O4 modified carbon paste electrode through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). In conclusion, the synthesized CaO:MgAl2O4 nanocomposite demonstrated promising morphological and structural characteristics, efficient photocatalytic activity, and potential applications in electrochemical energy storage, highlighting its versatility for various technological and environmental applications. [ABSTRACT FROM AUTHOR]

Dormancy has been suggested to be used for metal oxide gas sensors to specifically refer to their response decrease or disappearance after long periods of inactivity at room temperature. In this paper, 1 wt% Pt–SnO 2 composite nanoceramics with especially strong responses to hydrogen–containing atmospheres at room temperature, e.g. a response of 11170 to 1 % H 2 –20 % O 2 –N 2 , were successfully prepared through pressing and sintering. A clear dormancy has been observed for them as their responses decreased seriously with time when they were stored in ambient air at room temperature. Commercial ceramic heating plates (Metal Ceramics Heater, MCH) of 10 mm × 10 mm were attached to bars of 1 wt% Pt–SnO 2 composite nanoceramics. Dormant bars after 12 months of storage were completely regenerated through a heat–treatment of 200 °C for 10 min by attached MCH ceramic heating plates. According to X-ray photoelectron spectroscopy analyses, the formation of hydroxyl groups on SnO 2 in Pt–SnO 2 composite nanoceramics and deposition of impurity gases like H 2 S were proposed responsible for the dormancy, and their removal through mild heat–treatments result in the regeneration. It is suggested that heaters like commercial MCH ceramic heating plates should be included in future room–temperature gas sensors to provide a practicable regeneration from dormancy when needed. [ABSTRACT FROM AUTHOR]

The global population is rapidly increasing, so there is a critical requirement to satisfy the food production demand. Conventional methods of agriculture are inadequate to meet building demand which leads to declining farming sector and adaptable to other industries. Most of the farming activities are highly dependent on the labor which leads to increase in cost and time of operation. The rapid growth of mechanization for all farm activities cannot completely reduce the human involvement. As a result, agricultural automation is critically important. In terms of automation, this study emphasizes the crucial role of UAVs in precision and smart agriculture. The adoption of drones for various farm operations has the possibility to minimize labor requirements as well as operational time. This review provides overview of conceptual design, command flow operation, Micro-controller boards, remote-control systems and attachments like sensors, cameras, motors in UAVs for the purpose of automation in farm activities. The Internet of Things (IoT) employed in UAVs with image processing and machine learning algorithms provides accurate and precision results in farm activities. Furthermore, this study discusses future advancements, limitations and challenges for farmers in adapting to UAVs. Highlights: The recent literature from 2014 to 2024 on UAVs in agriculture were examined by PRISMA Methodology The overview of IOT system (sensors, cameras, micro-controllers and connectivity gateways) in UAVs were provided. The applications of UAVs in smart farming were compared and summarized by emphatical data and case studies. The limitations and future views of UAVs in farming are provided. [ABSTRACT FROM AUTHOR]

Modifying the coordination or local environments of single‐, di‐, tri‐, and multi‐metal atom (SMA/DMA/TMA/MMA)‐based materials is one of the best strategies for increasing the catalytic activities, selectivity, and long‐term durability of these materials. Advanced sheet materials supported by metal atom‐based materials have become a critical topic in the fields of renewable energy conversion systems, storage devices, sensors, and biomedicine owing to the maximum atom utilization efficiency, precisely located metal centers, specific electron configurations, unique reactivity, and precise chemical tunability. Several sheet materials offer excellent support for metal atom‐based materials and are attractive for applications in energy, sensors, and medical research, such as in oxygen reduction, oxygen production, hydrogen generation, fuel production, selective chemical detection, and enzymatic reactions. The strong metal–metal and metal–carbon with metal–heteroatom (i.e., N, S, P, B, and O) bonds stabilize and optimize the electronic structures of the metal atoms due to strong interfacial interactions, yielding excellent catalytic activities. These materials provide excellent models for understanding the fundamental problems with multistep chemical reactions. This review summarizes the substrate structure‐activity relationship of metal atom‐based materials with different active sites based on experimental and theoretical data. Additionally, the new synthesis procedures, physicochemical characterizations, and energy and biomedical applications are discussed. Finally, the remaining challenges in developing efficient SMA/DMA/TMA/MMA‐based materials are presented. [ABSTRACT FROM AUTHOR]

Musculoskeletal disorders are common among older adults, affecting mobility and quality of life. Effective rehabilitation is essential, but the implementation of programs faces challenges. Traditional methods often necessitate in-person assessments, which can be difficult for older adults with mobility limitations. Telerehabilitation offers a solution, bringing therapy closer to patients. However, the accurate remote monitoring of health and performance remains a challenge. This study addresses this gap by developing and validating the System for Tracking and Evaluating Performance (STEP). STEP is a hardware-software system that automates physical performance tests, eliminating the need for constant expert supervision. The system focuses on three standard tests: the Six-Minute Walking Test (6MWT), the Ten-Meter Walking Test (10MWT), and the 30-s Sit-to-Stand Test (30STS). Validation compared results from the STEP app with in-person assessments by physicians for patients undergoing rehabilitation after knee or hip arthroplasty. The study found strong positive correlations between the app's results and the physicians' assessments for all tests. These findings demonstrate the STEP system's potential as a reliable tool for remote physical performance assessment. Further research is needed to explore its integration into clinical practice and cost-effectiveness in reducing the need for operator assistance in monitoring patients with physical limitations. [ABSTRACT FROM AUTHOR]

Throughout history, the implementation of structural health monitoring systems has played a crucial role in evaluating the responses of dams to environmental and human-induced threats. By continuously monitoring structural integrity and analyzing dynamic characteristics, these systems offer a robust alternative to traditional visual inspection methods, ensuring the long-term safety of dams. This paper delves into the intricate process of operational modal analysis applied to dams, encompassing data collection, preprocessing, and the utilization of diverse modal identification techniques across both time and frequency domains. Moreover, it explores innovative approaches aimed at overcoming challenges encountered in previous methodologies. Also, the evolution of automated modal identification techniques and their application in dams are investigated. It explores the advancements in this field and their implications for enhancing the efficiency and accuracy of modal analysis processes. Furthermore, this paper evaluates the effectiveness of damage detection methods in dams based on operational modal identification. [ABSTRACT FROM AUTHOR]