Monitoring Applications Research Articles

Rapid green analytical methodology for simultaneous monitoring of nitrosamines and semi-volatile organic compounds in water and human urine samples.

Nitrosamines and semi-volatile organic compounds (SVOCs) are carcinogenic contaminants in water and biological matrices. Conventional analytical methods often struggle to detect trace concentrations due to poor extraction efficacies. This study presents a novel, low-cost, in-syringe-assisted fast extraction cum cleanup technique coupled with GC-FID for monitoring four nitrosamines and two SVOCs in drinking water and human urine samples to measure the contamination and exposure levels. This extraction protocol combines a novel green in-syringe liquid-liquid extraction step using dimethyl carbonate as the green extraction solvent, coupled with a semi-automated solid-phase extraction cleanup process. Then, the final extractant is analyzed using gas chromatography-flame ionization detection (GC-FID) for monitoring. The method demonstrated excellent linearity (R2 > 0.998) between 1.5 and 500ngmL⁻1 for all six target compounds. Detection limits ranged from 1.0 to 2.0ngmL⁻1. Extraction recoveries were between 87 and 105% for both urine samples and water samples. Intra-day and inter-day precision were below 9% RSD. The blue applicability grade index evaluation scored 70.0, indicating good practical applicability. The developed analytical protocol offers a sensitive, accurate, low-cost, rapid, and environmentally friendly method for simultaneously quantifying multiple nitrosamines and SVOCs in environmental and human samples. Its performance characteristics and sustainability metrics suggest the potential for broad application in monitoring and exposure studies.

Survey on machine vision-based intelligent water quality monitoring techniques in water treatment plant: Fish activity behavior recognition-based schemes and applications

Abstract Water is a vital resource essential to the survival and development of all creatures. With the rapid growth of industry and agriculture, people face a severe threat of ecological destruction and environmental pollution while living earthly lives. Water pollution, in particular, harms people’s health the most. As a result, water supply security has become a top priority. As a critical point in water supply safety, monitoring water quality effectively and forecasting sudden water contamination on time has become a research hotspot worldwide. With the rapid development and wide applications of artificial intelligence and computer vision technologies, biological activity identification-based intelligent water quality monitoring methods have drawn widespread attention. They were taking fish activities as the water-quality indicator has gained extensive attention by introducing advanced computer vision and artificial intelligence technologies with low cost and ease of carrying. This article comprehensively reviews recent progress in the research and applications of machine vision-based intelligent water quality monitoring and early warning techniques based on fish activity behavior recognition. In detail, it addresses water quality-oriented fish detection and tracking, activity recognition, and abnormal behavior recognition-based intelligent water quality monitoring. It analyzes and compares the performance and their favorite application conditions. Finally, it summarizes and discusses the difficulties and hotspots of water quality monitoring based on the fish’s abnormal behavior recognition and their future development trends.

An Innovative Deep Neural Network Model for Precise Calorie Burn Prediction from Physical Activity Data

Accurate prediction of calories burned during physical activities is crucial for various applications in health monitoring, fitness tracking, and personalized nutrition. Traditional methods often lack the precision needed for individualized estimates, which has increased interest in advanced machine learning approaches. This research introduces a deep learning model designed to predict calories burned with enhanced accuracy by capturing complex, non-linear relationships in the data. The model employs a multilayer perceptron neural network, Leaky ReLU activations, dropout regularization, and the Adam optimizer to improve generalizability and prevent overfitting. The evaluation of training and validation loss over epochs demonstrated the model's robustness and capacity to generalize effectively to novel data. The model's performance was evaluated using various metrics, achieving superior results with a remarkable Mean Absolute Error (MAE) of 0.27% and an accuracy of 99.73%, outperforming other models discussed in the literature. These findings indicate that deep learning offers significant potential for improving calorie prediction models, providing more reliable fitness and health management tools.

Autonomous Sensing Architected Materials

AbstractIntegrating autonomous sensing materials into future applications necessitates developing advanced multiscale multiphysics predictive models. This study introduces an experimentally informed predictive framework for autonomous sensing architected materials, combining theoretical and computational methodologies. By incorporating stress‐dependent electrical resistivity through anisotropic piezoresistive constitutive effects, alongside considering material, geometric, and contact nonlinearities, the proposed multiscale model captures the architecture‐dependent piezoresistive responses of lattice composites produced via additive manufacturing of polyetherimide (PEI)/carbon nanotube (CNT) nanoengineered feedstock. The PEI/CNT composite exhibits exceptional strength (105 MPa), stiffness (3368 MPa), and strain sensitivity (gauge factor ≈13), translating into remarkable piezoresistive characteristics for the PEI/CNT lattice composites, surpassing existing works (gauge factor ≈3 to 11). This multiscale finite element model accurately predicts both macroscopic piezoresistive responses and the influence of architectural and topological variations on electric current paths, validated via infrared thermography analysis. Additionally, an Ashby chart for the gauge factor of PEI/CNT lattice composites suggests their prediction through a scaling law similar to mechanical properties, underscoring the tunable strain and damage sensitivity of these materials. The combined experimental, theoretical, and numerical findings offer critical insights into optimizing piezoresistive composites through architected design, with profound implications for smart orthopedics, structural health monitoring, sensors, batteries, and other multifunctional applications.

Stretchable Sweat Lactate Sensor with Dual-Signal Read-Outs.

Innovations in wearable sweat sensors hold great promise to provide deeper insights into molecular level health information non-invasively. Lactate, a key metabolite present in sweat, holds immense significance in assessing physiological conditions and performance in sports physiology and health sensing. This paper presents the development and characterization of stretchable electrodes with ultrahigh active surface area of 648 % for lactate sensing. The as-printed stretchable electrodes were functionalized with an electron transfer layer comprising Toluidine Blue O and multi-walled carbon nanotubes (MWCNTs), and an enzymatic layer consisting of lactate dehydrogenase with β-Nicotinamide adenine dinucleotide as the cofactor for lactate selectivity. This sensor achieves a dual-signal read-out in which both electrochemical and fluorescence signals were obtained during lactate detection, demonstrating promising sensor performance in terms of sensitivity and reliability. We demonstrate the robustness of the dual-signal sensor under simulated conditions of physical deformation and shifted excitation. Under these compromised conditions, the performance of the stretchable electrodes remained largely unaffected, showcasing their potential for robust and adaptable sensing platforms in wearable health monitoring applications.

Smart Carbon Fiber-Reinforced Polymer Composites for Damage Sensing and On-Line Structural Health Monitoring Applications.

High electrical conductivity, along with high piezoresistive sensitivity and stretchability, are crucial for designing and developing nanocomposite strain sensors for damage sensing and on-line structural health monitoring of smart carbon fiber-reinforced polymer (CFRP) composites. In this study, the influence of the geometric features and loadings of carbon-based nanomaterials, including reduced graphene oxide (rGO) or carbon nanofibers (CNFs), on the tunable strain-sensing capabilities of epoxy-based nanocomposites was investigated. This work revealed distinct strain-sensing behavior and sensitivities (gauge factor, GF) depending on both factors. The highest GF values were attained with 0.13 wt.% of rGO at various strains. The stability and reproducibility of the most promising self-sensing nanocomposites were also evaluated through ten stretching/relaxing cycles, and a distinct behavior was observed. While the deformation of the conductive network formed by rGO proved to be predominantly elastic and reversible, nanocomposite sensors containing 0.714 wt.% of CNFs showed that new conductive pathways were established between neighboring CNFs. Based on the best results, formulations were selected for the manufacturing of pre-impregnated materials and related smart CFRP composites. Digital image correlation was synchronized with electrical resistance variation to study the strain-sensing capabilities of modified CFRP composites (at 90° orientation). Promising results were achieved through the incorporation of CNFs since they are able to form new conductive pathways and penetrate between micrometer-sized fibers.

Hierarchical Porous Biowaste‐Based Dual Humidity/Pressure Sensor for Robotic Tactile Sensing, Sustainable Health, and Environmental Monitoring

A crucial tradeoff between material efficacy and environmental impact is often encountered in the development of high‐performance sensors. The use of rare‐earth elements or intricate fabrication techniques is sometimes needed for conventional sensing materials, posing concerns regarding sustainability. Exploring the potential of tomato peel (TP) as a dual‐purpose sensing dielectric layer for pressure and humidity monitoring is a paradigm shift, capitalizing on its porous structure and hygroscopic nature. TP‐based humidity sensor (TP‐HS) exhibits impressive results, with a wide humidity sensing range (5%–95%), fast response/recovery time (6.5/9 s), a high sensitivity (12 500 pF %RH−1), and a high stability (30 days). Additionally, TP‐based pressure sensor (TP‐PS) also shows excellent performance in accurately sensing pressure changes in a wide range (0–196 kPa). TP‐HS can easily distinguish between breathing rates (normal, fast, and slow) and moisture content present in different moisturizers (aloe vera and sanitizer) along with its successful use for proximity sensing. Alternatively, TP‐PS demonstrates weight measurement (490 and 980 N), grip recognition (measuring the pressure exerted by each finger), and gesture detection (by monitoring multiple bending angles 0°, 30°, 50°, and 80°). A wearable, biocompatible dual sensor based on a promising sustainable material for environmental, robotic, and health monitoring applications is successfully demonstrated.

Implementasi Smart Cow Farming Technology untuk Monitoring Pertumbuhan Sapi dan Peningkatan Skala Usaha pada Kelompok Peternak Sapi DiBa Farm Kabupaten Lampung Selatan

The DiBa Farm Livestock Group faces several urgent issues that need to be addressed not all cows can achieve the minimum weight gain target of 1.5 kg per day. Monitoring of cow growth productivity is done manually; (3) the calculation of the cost of goods sold is also still done manually. The partner's marketing of livestock products is limited to regular customers within the South Lampung area only. Based on these prioritized issues, the proposed solutions and methods are implementing a wheelbarrow tool with a digital scale for transporting cow feed using IoT technology. Implementing a cow growth monitoring application using RFID. Implementing a website-based application to automatically determine the Cost of Goods Sold for cows. Implementing a digital marketing application for cow sales that can be accessed via a website. Providing training and assistance related to digital marketing strategies. Based on the evaluation results, it was found that the implementation of Smart Cow Farming Technology 100% improved the partners' knowledge of its usage. The average cow growth increased monthly, from 45 kg to 50 kg. Additionally, there was a 25% increase in profits due to the implementation of the cost of goods sold calculation application and the online cow sales application. The evaluation results from the digital marketing training activities also showed that 85% of the partners' knowledge and understanding improved in terms of using digital marketing.

Theoretical designing of Ni-doped PtSTe monolayer as a promising gas sensor upon thermal runaway gases in the Li-ion battery

This study proposes Ni-doped Janus PtSTe (Ni-PtSTe) as a novel sensing material for detecting three critical gases (CO, CO2, and C2H4), with potential applications in gas sensors and Li-ion battery monitoring. The Ni-doping favors the Te surface of the PtSTe monolayer, with a formation energy of -1.19 eV. The adsorption capacity follows the order: CO > C2H4 > CO2, with chemisorption in CO and C2H4 systems and physisorption in CO2 system. The analysis of electronic properties in gas adsorbed Ni-PtSTe systems indicate a reduced bandgap in CO and C2H4 systems, resulting in the sensing response of 99.25% and 97.71%, respectively. The work function increases significantly in the presence of CO and C2H4, suggesting its suitability as a work function-based sensor. These results highlight the promising sensing capabilities of Ni-PtSTe monolayer, expanding the horizon for PtSTe-based materials and inspiring cutting-edge research.

Efficient physics-informed transfer learning to quantify biochemical traits of winter wheat from UAV multispectral imagery

Accurate and efficient estimation of biochemical traits, including leaf index area (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC), is crucial for crop growth monitoring in agricultural management. Recent advancements in unmanned aerial vehicle (UAV) multispectral remote sensing have enabled fast and cost-effective measurements of these traits. However, traditional statistical regression models trained on specific datasets lack scalability and transferability across practical field conditions without retraining. This study proposed an efficient physics-informed transfer learning model (PITL) for winter wheat biochemical traits estimation from UAV multispectral data. The PITL integrates the strengths of physical radiative transfer simulations and deep neural network architectures through transfer learning to improve the estimation of biochemical traits from UAV multispectral data. The PITL was tested with convolutional neural network (CNN), deep neural network (DNN), and long short-term memory (LSTM) architectures. Results indicated that PITLDNN had better accuracy than PITLCNN and PITLLSTM models in predicting LAI (R2=0.94, RMSE = 0.32 m2/m2), LCC (R2=0.81, RMSE = 5.20 μg/cm2) and CCC (R2=0.928, RMSE = 0.2 g/m2). Moreover, PITLDNN demonstrated higher capability in computational efficiency, making it suitable for processing large volumes of UAV multispectral data in crop growth monitoring applications. Furthermore, PITL's integration of radiative transfer knowledge with labeled field data yielded higher predictive accuracy compared to physically-based inversion model, pure data-driven deep neural network approaches, and hybrid models. This study highlighted the performance of PITLDNN in accurately and efficiently quantifing biochemical traits from UAV multispectral data, thereby providing timely and accurate information for guiding crop growth monitoring applications.

A Statistical Approach for Functional Reach-to-Grasp Segmentation Using a Single Inertial Measurement Unit.

The aim of this contribution is to present a segmentation method for the identification of voluntary movements from inertial data acquired through a single inertial measurement unit placed on the subject's wrist. Inertial data were recorded from 25 healthy subjects while performing 75 consecutive reach-to-grasp movements. The approach herein presented, called DynAMoS, is based on an adaptive thresholding step on the angular velocity norm, followed by a statistics-based post-processing on the movement duration distribution. Post-processing aims at reducing the number of erroneous transitions in the movement segmentation. We assessed the segmentation quality of this method using a stereophotogrammetric system as the gold standard. Two popular methods already presented in the literature were compared to DynAMoS in terms of the number of movements identified, onset and offset mean absolute errors, and movement duration. Moreover, we analyzed the sub-phase durations of the drinking movement to further characterize the task. The results show that the proposed method performs significantly better than the two state-of-the-art approaches (i.e., percentage of erroneous movements = 3%; onset and offset mean absolute error < 0.08 s), suggesting that DynAMoS could make more effective home monitoring applications for assessing the motion improvements of patients following domicile rehabilitation protocols.

An ingenious chemiluminescence sensing strategy for recalcitrant triphenyl phosphate based on oxidant-free UV-activated MIL-100(Fe) gel system

BackgroundOrganophosphate flame retardants (OPFRs) are notorious emerging contaminants threatening the environment and human health. Triphenyl phosphate (TPHP), which has an extremely serious biotoxicity, is a typical harmful OPFR. Due to its wide use, TPHP has been discovered in various environmental mediums. Moreover, it is pretty recalcitrant to the removal process, resulting in the need for a technique to understand it better. Hence, accurate and quick discrimination of TPHP in the environment is critical to further evaluate its potential effect on ecosystems and human health. ResultsAn ingenious oxidant-free chemiluminescence (CL) sensor based on the oxidant-free UV/MIL-100(Fe) gel system was established for TPHP detection. The oxidation of luminol in the UV-activated MIL-100(Fe) gel has resulted in remarkable CL emission, which is contributed by reactive oxygen species (ROS) generated by it. Notably, the CL intensity was inhibited significantly after introducing TPHP. An investigation into the mechanism underlying the effect of CL suppression demonstrated that TPHP competed with luminol to consume ROS from UV-activated MIL-100(Fe) gel, contributing to CL inhibition. The subsequent sensing performance experiments demonstrated the advantages of environmentally friendly, economic efficiency, user-friendly operation, rapid determination, potential for compact size, high selectivity, and sensitivity. Additionally, these investigations confirmed the low limit of detection (210 ng L−1) and wide linear range (10–1000 μg L−1). SignificanceIn this paper, a green, economical, and oxidant-free CL sensing strategy for TPHP has been established. It has the advantage of being rapid, having the potential for compact size, high selectivity, and sensitivity. This ingenious method has promising applications in real-time and online environmental monitoring, and it paves the way for the rapid and environmentally friendly identification of emerging contaminants that are structurally stable and recalcitrant to remove.

Technological advances in high mountains: Development of an augmented reality and IoT Application for snow cover monitoring on Huascaran, Peru

Climate change has had a negative impact on Andean glaciers, especially on the snow-capped Huascaran in Peru, which has experienced a loss of 42 % of its glacier mass. Lack of access to accurate snowpack data complicates understanding and informed decision-making in mountainous environments. To address this problem, an application has been developed based on an AR-IoT architecture that integrates augmented reality (AR) and the Internet of Things (IoT) for the periodic monitoring of snow cover on the Huascaran snow-capped mountain. This application uses data collected from meteorological repositories and simulates IoT interactions, while a mobile app offers an interactive and understandable visualization of the data. The Scrum methodology was applied iteratively, from the creation of the 3D model to the connection with the Ubidots platform to store and share the data. The analysis of 58 readings, obtained between April and May 2023, showed correlations between variables such as snow depth, temperature, and wind speed, which served to validate the operation of the AR-IoT technology implemented. These variables offer an initial understanding of atmospheric conditions in high mountains. In addition, the technology developed has the potential to expand to include more relevant data in future analyses. The tool demonstrates the effectiveness of combining these technologies for monitoring high mountain snow cover and making informed decisions about water resources management.

Enhancing structural damage evaluation of PC girder bridges through digital twinning Bayesian model updating

Developing a unified analytical framework to assess the structural performance of deteriorated prestressed concrete (PC) girder bridges is important for their maintenance. This study aims to construct a physics-based digital twin framework for PC girder bridges incorporating Bayesian model selection and updating, a high-fidelity three-dimensional (3D) finite element (FE) model, and a method for simulating prestressed tendon damage. In this study, three PC girder specimens were designed and tested to evaluate the effects of tendon rupture near the anchorage region on structural performance. Bayesian model selection was used to determine the most plausible model class based on the estimated model evidence. The most probable values (MPVs) of the model parameters, derived from the estimated posterior probability density functions (PDFs), were then used to calibrate the FE model, which reflects a healthy state of the PC girder bridge. This updated FE model serves as the baseline for conducting what-if scenario simulations. Subsequently, nonlinear segments of the constitutive model, obtained from coupon tests, and a simulation method for tendon rupture were incorporated into the updated FE model. Nonlinear static simulation results obtained through the updated model were found to be consistent with both the observed crack pattern and the load–deflection curve. The reliability of the digital twinning FE model for assessing tendon damage and for predicting the residual load-bearing capacity was verified through comparison with measured data. Therefore, the digital twinning Bayesian model updating approach shows potential applications in continuous structural health monitoring (SHM) of PC bridges.

Implementation of Teacher Attendance Monitoring Application at SMK Ibnu Sina Batam

This study aims to analyze and design a web-based teacher attendance monitoring application at SMK Ibnu Sina Batam. We expect this application to enhance the efficiency and effectiveness of teacher attendance management through the use of digital technology. The research methods used include literature studies, interviews, and direct observation of the teacher attendance management process. We design the system using the Unified Modeling Language (UML), implement it using the CodeIgniter framework, and use MySQL as its database. We conduct testing using the Black Box method to verify that all functions operate as anticipated. The results show that this application can monitor teacher attendance in real-time and improve discipline. By cutting down on manual checks and enhancing transparency and accountability in school management, the implementation of this application significantly enhances the quality of education at SMK Ibnu Sina Batam.

Class incremental learning with analytic learning for hyperspectral image classification

Hyperspectral image classification (HSIC) is crucial for applications in agriculture and environmental monitoring. As ground objects evolve and remote sensing technology progresses, there is a growing need for HSIC models that can adapt to new data classes without requiring to be retrained from scratch. Throughout the continual learning procedure, the model is expected to not only effectively extract spatial–spectral features from the hyperspectral image but also alleviate the issue of catastrophic forgetting, i.e., the model forgets the learned classes’ knowledge when accessing novel classes during the training process. In this paper, for the HSIC task, we propose a class incremental learning method (HSI-CIL) that is based on analytic learning, a technique that converts network training into linear problems. Specifically, The HSI-CIL model consists of a lightweight feature extractor, a Feature Processing Module (FPM) and an Analytic Linear Classifier (ALC). This model does not need data storage for old and new classes and has only one epoch in the incremental learning stage, so it has lower consumption of resources and training time than several attempts have been proposed for addressing catastrophic forgetting. We perform abundant experiments with the proposed HSI-CIL on three publicly available hyperspectral datasets including Indian Pines, Pavia University, and Salinas. The experiments demonstrate that our HSI-CIL exceeds the state-of-the-art class incremental learning (CIL) techniques applied in HSIC with a certain gap.

Emerging Low Detection Limit of Optically Activated Gas Sensors Based on 2D and Hybrid Nanostructures.

Gas sensing is essential for detecting and measuring gas concentrations across various environments, with applications in environmental monitoring, industrial safety, and healthcare. The integration of two-dimensional (2D) materials, organic materials, and metal oxides has significantly advanced gas sensor technology, enhancing its sensitivity, selectivity, and response times at room temperature. This review examines the progress in optically activated gas sensors, with emphasis on 2D materials, metal oxides, and organic materials, due to limited studies on their use in optically activated gas sensors, in contrast to other traditional gas-sensing technologies. We detail the unique properties of these materials and their impact on improving the figures of merit (FoMs) of gas sensors. Transition metal dichalcogenides (TMDCs), with their high surface-to-volume ratio and tunable band gap, show exceptional performance in gas detection, especially when activated by UV light. Graphene-based sensors also demonstrate high sensitivity and low detection limits, making them suitable for various applications. Although organic materials and hybrid structures, such as metal-organic frameworks (MoFs) and conducting polymers, face challenges related to stability and sensitivity at room temperature, they hold potential for future advancements. Optically activated gas sensors incorporating metal oxides benefit from photoactive nanomaterials and UV irradiation, further enhancing their performance. This review highlights the potential of the advanced materials in developing the next generation of gas sensors, addressing current research gaps and paving the way for future innovations.

Retraction Note: Application of IoT real-time monitoring based on optical sensors in residents’ sports and health service system

Retraction Note: Application of IoT real-time monitoring based on optical sensors in residents’ sports and health service system

Functionalized Face Masks as Smart Wearable Sensors for Multiple Sensing.

Wearable sensors provide continuous physiological information and measure deviations from healthy baselines, resulting in the potential to personalize health management and diagnosis of diseases. With the emergence of the COVID-19 pandemic, functionalized face masks as smart wearable sensors for multimodal and/or multiplexed measurement of physical parameters and biochemical markers have become the general population for physiological health management and environmental pollution monitoring. This Review examines recent advances in applications of smart face masks based on implantation of digital technologies and electronics and focuses on respiratory monitoring applications with the advantages of autonomous flow driving, enrichment enhancement, real-time monitoring, diversified sensing, and easily accessible. In particular, the detailed introduction of diverse respiratory signals including physical, inhalational, and exhalant signals and corresponding associations of health management and environmental pollution is presented. In the end, we also provide a personal perspective on future research directions and the remaining challenges in the commercialization of smart functionalized face masks for multiple sensing.

Enhanced seagull optimization for enhanced accuracy in CUDA-accelerated Levenberg–Marquardt backpropagation neural networks for earthquake forecasting

Hyperparameter tuning is crucial for enhancing the accuracy and reliability of artificial neural networks (ANNs). This study presents an optimization of the Levenberg–Marquardt backpropagation neural network (LM-BPNN) by integrating an improved seagull optimization algorithm (ISOA). The proposed ISOA-LM-BPNN model is designed to forecast earthquakes in the Caribbean region. The study further explores the impact of data and model parallelism, revealing that hybrid parallelism effectively mitigates the limitations of both. This leads to substantial gains in throughput and overall performance. To address computational demands, this model leverages the compute unified device architecture (CUDA) framework, enabling hybrid parallelism on graphics processing units (GPUs). This approach significantly enhances the model’s computational speed. The experimental results demonstrate that the ISOA-LM-BPNN model achieves a 20% improvement in accuracy compared to four baseline algorithms across three diverse datasets. The integration of ISOA with LM-BPNN refines the neural network’s hyperparameters, leading to more precise earthquake predictions. Additionally, the model’s computational efficiency is evidenced by a 56% speed increase when utilizing a single GPU, and an even greater acceleration with dual GPUs connected via NVLink compared to traditional CPU-based computations. The findings underscore the potential of ISOA-LM-BPNN as a robust tool for earthquake forecasting, combining high accuracy with enhanced computational speed, making it suitable for real-time applications in seismic monitoring and early warning systems.