Monitoring Applications Research Articles

The Evolution and Impact of GIS in Environmental Science

This article examines the transformative role of Geographic Information Systems (GIS) in environmental science, focusing on its applications in environmental monitoring, climate change research, and disaster management. It highlights how GIS integration with advanced technologies like remote sensing, machine learning, and big data analytics has enhanced our understanding and management of complex environmental systems. The article discusses specific case studies, including global forest cover analysis, coral reef monitoring, climate change modeling, and disaster response efforts. It also explores the future prospects of GIS, including its potential integration with artificial intelligence and improved satellite technology. The article underscores GIS's critical role in providing accurate, timely, and actionable environmental insights, enabling more effective decision-making and problem-solving in addressing global environmental challenges.

Trends of Remote Sensing applications for Amazon monitoring

Trends of Remote Sensing applications for Amazon monitoring

Revolutionizing agri-food technology: Development and validation of the Portable Intelligent Oil Recognition System (PIORS)

The increasing demand for high-quality vegetable oils has amplified the need for efficient, and accurate, oil recognition systems. This paper introduces the Portable Intelligent Oil Recognition System (PIORS), a pioneering advancement in the field. PIORS stands as the first-ever portable, AI-powered device tailored for rapid and accurate oil type identification, capable of delivering results within a mere five seconds. Using advanced machine learning, PIORS can distinguish between various oil types such as sesame, black seed, flaxseed, and almond oils, ensuring the quality and safety of the final product. The device's innovative design integrates an advanced sensor array with seamless Bluetooth connectivity, offering real-time data synchronization with mobile applications. Several Machine learning models, including Support Vector Machines (SVM), AdaBoost, Random Forest, K-Nearest Neighbors (K-NN), Gradient-Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) were implemented and thoroughly validated to obtain correct categorization. The results show that the XGBoost instantaneous classification algorithm continuously beat the competition, obtaining an astounding success rate of 97 percent in predicting and differentiating between the four oil classes. PIORS not only sets a new standard for speed and efficiency in oil quality assessment but also paves the way for groundbreaking applications in food safety, environmental monitoring, and quality control processes. This paper details the development, implementation, and validation of PIORS, showcasing its potential to revolutionize the agri-food industry with its cutting-edge, AI-driven approach to oil recognition.

Simultaneous measurement of temperature and refractive index based on fiber optic Fabry–Pérot cavity in batteries

Abstract A Fabry-Pérot (FP) sensor for simultaneous measurement of refractive index and temperature of an all-fiber synchronous liquid based on the Vernier effect is proposed, special application for internal monitoring of lithium-ion battery electrolytes. The theoretical model of high sensitivity cascade Fabry-Pérot interferometer (PFPI) is established, the sensitivity amplification mechanism of PFPI is analyzed in detail, the perfect matching scheme for measuring the refractive index of the electrolyte inside the battery is designed according to the matching conditions of the optical path, and the corresponding sensors for simultaneous measurement of the temperature and the refractive index are fabricated. The experimental results show that the sensor has a maximum refractive index sensitivity of 15391.5033 nm/RIU, a good linear fit, and can effectively monitor the refractive index change of the electrolyte by 10-4 orders of magnitude during normal charging and discharging of lithium-ion batteries. Compared to traditional epoxy adhesive bonded or laser processed FP sensors, this all-fibre optic structure not only improves temperature and corrosion resistance, but also significantly reduces temperature cross-sensitivity. Its small size and excellent resistance to high temperature and corrosion ensure the sensor's excellent response and broad application prospects in monitoring the refractive index of the electrolyte in 18650 lithium-ion batteries, which provides effective technical support for monitoring the health status of batteries and detecting early safety issues.

Utilizing the Achievements of Data Acquiring Solution and Cloud Service Platform for China-Africa Cooperation on Satellite Remote Sensing Application

Abstract. This study examines the collaborative approach in China-Africa satellite remote sensing initiatives based on solutions of satellite data acquiring, sharing and application experiences. It specifically addresses the data coverage requirements and current coverage scenarios of China's natural resources land satellites in Africa. By establishing the China-Africa Cooperation Centers on Satellite Remote Sensing Application, adopting a multi-level distributed technology center model, and implementing cross-continental remote sensing cloud services through an internet-based cloud platform, this initiative aims to enhance the direct utilization of satellite data in Africa. Furthermore, strategic satellite observation window designs tailored to seasonal changes have increased the frequency of valid coverage over the entire African continent to at least once a year. Such advancements pave the way for real-time monitoring applications, particularly in crucial areas such as sustainable development projects and disaster response efforts. Therefore, the study also provides some useful case studies that are anchored on the Sustainable Development Goals.

Vehicle flow indication and identification using FBG sensors

Abstract This paper proposes a vehicle flow monitoring system utilizing fiber Bragg grating (FBG) sensor technology. The system comprises a prototype road model along with a car with varying loads. The FBG sensor is embedded in the road surface for sensing the strain induced by the vehicle movement. The laboratory experiments were conducted using an embankment model equipped with FBG sensor to identify vehicle movement under static and movable loads. Under static condition, different loads were applied on the car and the corresponding wavelength shifts have been measured using FBG-interrogator. Under dynamic condition, the vehicle was moved with certain speed and the corresponding wavelength shifts were recorded over time. The sensitivity of FBG for vehicle load sensing has been obtained as 0.3 pm/N. The proposed vehicle flow sensor system offers good accuracy and sensitivity and has potential applications for real-time vehicle flow monitoring. The proposed sensor system can be used to analyze road surface irregularities, crucial for assessing heavy-vehicle fatigue.

Bioinspired Ultra‐Stretchable and Highly Sensitive Structural Color Electronic Skins

AbstractOrganisms possess remarkably adaptive ability to complex environments. For example, chameleons can alter their skin color to adapt to varying environments, which has inspired significant advances in bioinspired soft electronic skins (E‐skins), and their wide applications in wearable sensors, intelligent robots, and health monitoring. However, current bioinspired E‐skins face challenges in ultra‐stretchability, high sensitivity, and long‐term stability owing to the intrinsic limitations associated with their mismatched interface between soft matrix and hard conductive fillers, hindering their practical applications. Here, it is reported that bioinspired structural color electronic skins (SC E‐skins) that consist of liquid metal particles (LMPs), periodical ordered colloidal crystal arrays, and ultra‐stretchable hydrogel, imparting synergistic and durable electrical–optical sensing capabilities. Such SC E‐skins demonstrate outstanding performances including superior flexibility (elongation at break > 1100%), high sensitivity (gauge factor = 3.26), fast synergetic electric–optical response time (≈100 ms), outstanding durability (over 1500 cycles), and high accuracy (R2 > 99.5%). These bioinspired SC E‐skins with excellent capability of converting mechanical signals into synergetic electrical–optical outputs hold great promise for smart wearable devices, affording a new horizon in developing advanced health monitoring technologies.

LINE-1 cfDNA Methylation as an Emerging Biomarker in Solid Cancers

Epigenetic dysregulation is a hallmark of many human malignancies, with DNA methylation being a primary mechanism influencing gene expression and maintaining genomic stability. Genome-wide hypomethylation, characteristic of many cancers, is partly attributed to the demethylation of repetitive elements, including LINE-1, a prevalent non-LTR retrotransposon. The methylation status of LINE-1 is closely associated with overall genomic methylation levels in tumors. cfDNA comprises extracellular DNA fragments found in bodily fluids such as plasma, serum, and urine, offering a dynamic snapshot of the genetic and epigenetic landscape of tumors. This real-time sampling provides a minimally invasive avenue for cancer diagnostics, prognostics, and monitoring. The methylation status of LINE-1 in cfDNA has emerged as a promising biomarker, with several studies highlighting its potential in diagnosing and predicting outcomes in cancer patients. Recent research also suggests that cfDNA-based LINE-1 methylation analysis could serve as a valuable tool in evaluating the efficacy of cancer therapies, including immunotherapy. The growing clinical significance of cfDNA calls for a closer examination of its components, particularly repetitive elements like LINE-1. Despite their importance, the role of LINE-1 elements in cfDNA has not been thoroughly gauged. We aim to address this gap by reviewing the current literature on LINE-1 cfDNA assays, focusing on their potential applications in diagnostics and disease monitoring.

Frequency Selective Surfaces: Design, Analysis, and Applications

This paper aims to provide a general review of the fundamental ideas, varieties, methods, and experimental research of the most advanced frequency selective surfaces available today. Frequency-selective surfaces are periodic structures engineered to work as spatial filters in interaction with electromagnetic (EM) waves with different frequencies, polarization, and incident angles in a desired and controlled way. They are usually made of periodic elements with dimensions less than the operational wavelength. The primary issue examined is the need for more efficient, compact, and adaptable electromagnetic filtering solutions. The research method involved a comprehensive review of recent advancements in FSS design, focusing on structural diversity, miniaturization, multiband operations, and the integration of active components for tunability and reconfigurability. Key findings include the development of highly selective miniaturized FSSs, innovative applications on flexible and textile substrates, and the exploration of FSSs for liquid and strain sensing. The conclusions emphasize the significant potential of FSS technology to enhance wireless communication, environmental monitoring, and defense applications. This study provides valuable insights into the design and application of FSSs, aiming to guide future research and development in this dynamic field.

Structural Damage Detection Using PZT Transmission Line Circuit Model

Arrangements of piezoelectric transducers, such as PZT (lead zirconate titanate), have been widely used in numerous structural health monitoring (SHM) applications. Usually, when two or more PZT transducers are placed close together, significant interference, namely crosstalk, appears. Such an effect is usually neglected in most SHM applications. However, it can potentially be used as a sensitive parameter to identify structural faults. Accordingly, this work proposes using the crosstalk effect in an arrangement of PZT transducers modeled as a multiconductor transmission line to detect structural damage. This effect is exploited by computing an impedance matrix representing a host structure with PZTs attached to it. The proposed method was assessed in an aluminum beam structure with two PZTs attached to it using finite element modeling in OnScale® software to simulate both healthy and damaged conditions. Similarly, experimental tests were also carried out. The results, when compared to those obtained using a traditional electromechanical impedance (EMI) method, prove that the new approach significantly improved the sensitivity of EMI-based technique in SHM applications.

Flexible Thermoelectric BiSbTe/Carbon Paper/BiSbTe Sandwiches for Bimode Temperature‐Pressure Sensors

AbstractBimode temperature‐pressure sensors hold significant promise in personal health monitoring, wearables and robotic signal detection. Traditional bimode sensors typically combine two independent sensors, leading to fabrication complexity. This study develops a bimode temperature‐pressure sensor by using a facile electrodeposition method to create sandwiched BiSbTe/Carbon Paper/BiSbTe thin films and stacking them to a vertical structure. It demonstrates high sensitivity for temperature sensing, capable of detecting temperature difference as low as 1 K, and a rapid response time of 0.92 s due to a vertical structure. Utilizing its thermoelectric mechanism, the sensor achieves self‐powered sensing for finger touch and respiration states. Furthermore, its island‐like contact surface ensures high sensitivity with an extremely fast response time of 0.17 s, by rapidly changing contact resistance under pressure, allowing it to detect various human behaviors, including body movements and micro‐expressions. Beyond its sensing capabilities, the film excels in flexibility, electromagnetic interference shielding, and stability, presenting significant potential for integration into self‐powered electronic skin systems for health monitoring, wearables, artificial intelligence, and other electronic skin applications.

Self-supervised Learning Approach for Anomaly Detection in Rotating Machinery

Early fault detection in rotating machinery needs careful expert analysis of vibration data for monitoring a component state. Online methods that automatically set a threshold and raise an alarm when the vibration signature is anomalous are meant to efficiently manage key assets in a preventive maintenance plan. In recent years a focus has raised on data driven methods in parallel with the increasing attention towards machine learning and, particularly, deep learning models. In this regard, for rotating equipment components, an important aspect relates to labelled data scarcity for supervised training. On the other hand, the advent of the Internet of Things allows to gather data from multiple assets with relevant information on the asset state itself. Self-supervised learning methods in deep learning application are currently tackling this problem. Investigating Self-learning approaches to integrate domain knowledge and learn relevant features from unlabeled data is therefore important for condition monitoring applications. In this paper a methodology is proposed based on cycle consistency representation learning for training an embedder network on univariate unlabeled data. In order to learn a distance metric in the embedding space the original data are transformed to generate sequences of augmented inputs to enforce learnable pattern similarity in the augmented pairs. A differentiable cycle-consistency loss is chosen to maximize the numbers of augmented pairs in the learned embedding space that have minimum features distance. The pretext task in the described self-supervised setting aims to train a feature extractor for discriminating dissimilar samples in the embedding space by a distance metric and to provide a useful representation for down-stream tasks. The paper analyzes the performance of the approach for anomaly detection in rotating machinery. The methodology is tested on vibration data provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, considering different accelerated life test campaigns. The data were collected to monitor the fault development in bearings and the model shows how the learned embedding space discriminates effectively anomalous samples from normal ones in the degradation stages of the bearings.

DeepLPos: a comprehensive hybrid deep learning model for lying position recognition using a tactile sensor array system

Abstract Unpredictable limb movements or turning motions can significantly disrupt the accurate extraction of physiological signals, such as respiratory and heart rates. In clinical environments, reliable detection of lying positions is crucial for continuous patient monitoring, particularly during sleep. In this paper, a smart sleeping position recognition system is proposed, which employs a tactile pressure sensor array based on the unique structure of ‘the electrostatic double-layer capacitors’. The sensor array, comprising 64 rows and 32 columns (2048 nodes), captures four types of healthy lying positions using an 8-bit AD module. Despite challenges arising from limited experimental samples for accurate training, we propose DeepLPos, a hybrid deep learning approach combining generative adversarial networks and the you only look once network. To tackle the differentiation challenge between supine and prone positions, we introduce an SPD Conv attention module to enhance the resolution of detailed descriptions in pressure images. The model is further pruned to optimize both structure and parameters, enabling efficient real-time detection. Evaluated on the SLP dataset, the proposed system achieves an accuracy of 97.5% with a real-time processing speed of 0.069 s per frame, demonstrating its potential for practical, high-precision measurement and monitoring applications in healthcare.

Low-power and cost-effective readout circuit design for compact semiconductor gas sensor systems

This study introduces a novel readout circuit architecture that enhances semiconductor gas sensor systems by reducing power consumption, enabling miniaturization, and improving economic viability. Validated at the PCB level, the design shows strong commercial potential by addressing power efficiency and signal accuracy challenges. The technology is adaptable for applications in environmental monitoring, industrial safety, and medical diagnostics, where efficient and reliable gas sensing is essential.

Analysis of Candidate miRNAs' Expression in Pancreatic Cancer.

Pancreatic cancer (PC) is one of the most aggressive types of cancer. Despite advances in molecular diagnostics, PC diagnosis relies on imaging technologies and morphological assessment of fine needle aspirates (FNAs). MicroRNA (miRNA) involvement in PC pathogenesis and potential diagnostics application have been suggested, albeit current supporting evidence is lacking. Here, we investigated the association of selected miRNAs with PC incidence and clinical characteristics. Fold expression of miR-216a-3p, -217-5p, -221-3p, -222-3p, and miR-196a-5p was assessed in 73 PC FNA cell-block sections and 6 healthy pancreas tissues using Taqman advanced miRNA assays. Potential miRNA targets were ascertained using immunocytochemistry. miR-196a-5p was upregulated in PC compared to healthy pancreatic tissue (β = -0.05, 95% CI: -0.065 - (-0.035); p < 0.001). miR-221-3p and miR-222-3p fold expression were strongly correlated (r = 0.897, p < 0.001), whereas miR-196a-5p fold expression correlated with that of miR-221-3p (r = 0.688, p < 0.001) and miR-222-3p (r = 0.489, p < 0.001). Moreover, miR-196a-5p fold expression positively correlated with tumor stage (r = 0.32, p = 0.034). miR-217-5p fold expression inversely correlated with KRAS expression (r = -0.69, p = 0.0027). Our study shows miR-196a-5p has reasonable specificity to PC and thus may have diagnostic and prognostic potential in PC as proposed in the literature. Moreover, KRAS and NFKBIA may be potential targets for miR-217-5p and miR-196a-5p, respectively. Thus, these miRNAs may be involved in tumor progression and may have valuable applications in novel therapeutics or treatment monitoring.

Scintillation properties of flexible scintillator composed on polydimethylsiloxane integrated with Lu2GdAl3Ga2O12:Pr2O3 phosphors

This study explored the luminescence properties and sensitivity of a flexible scintillator composed on polydimethylsiloxane integrated with Lu2GdAl3Ga2O12 doped with 1 mol% Pr2O3 phosphors. The flexible scintillator was fabricated using a molding technique. The luminescence properties were investigated under photo, proton, and X-ray excitation at room temperature. The scintillator exhibited sharp and narrow emission peaks attributed to f-f transitions, and a decay time of 14 μs. It demonstrated a sensitivity of 0.638 μC/R cm3 under an X-ray dose of 1.841 R. This study demonstrates a high performance flexible scintillator suitable for advanced radiation detection applications in medical imaging and safety monitoring.

High-Sensitivity and Damage Redundant Detection Capable Gel-State [BMIM][BF4] Electronic Skin for Aerospace Applications

Electronic skin (e-skin) holds significant potential for applications in robotics, Internet-of-things, and health monitoring. However, conventional e-skins often exhibit decreased sensitivity and delayed response in ultra-low temperature environments due to the freezing of conductive materials. Moreover, an increased brittleness can cause substrate damage, limiting their application in cryogenic conditions. To address existing challenges, an ionic liquid [BMIM][BF4] is cross-linked with N,N'-Bis(2-hydroxyethyl)oxamide to form an ionic gel for this study. The gel exhibits excellent electrical performance at −71 °C, significantly expanding the operating temperature range of e-skins. Additionally, gelation decreases resistive drift and leakage inherent to ionic liquids. The prepared sensing units demonstrate high sensitivity to pressure loading across an ultra-wide temperature range of −50–50 °C, with linear sensing within the range of 48–32000 Pa and a rapid response time of 0.05 s. Integration of the units into large-scaled e-skin enables precise recognition of static and dynamic pressure loads in ultra-low temperature environments (−50 °C). Furthermore, a space glove model assembled using the sensing units achieves accurate recognition of hand gestures in extreme conditions. The sensing units retain over 60 % of their pressure response even when damaged, and demonstrate resilience to environmental factors including low temperatures and vacuum conditions. Along with exceptional performance and environmental resilience under harsh conditions, the gel-state [BMIM][BF4] e-skin shows great potential in deep space exploration, polar expeditions, and other challenging environments.

Fabrication of an electrochemical sensor based on Zinc Vanadate anchored with multi-walled carbon nanotubes modified glassy carbon electrode for the determination of Carbendazim

The detection of Carbendazim (CBZ) insecticides has prompted concerns regarding their potential impacts on both human health and the environment. To address this issue, a novel nanocomposite comprising Zinc Vanadate (ZVO) was prepared using a common hydrothermal technique and incorporated with multi-walled carbon nanotube (MWCNT) to form ZVO/MWCNT nanocomposite. Thus, a glassy carbon electrode (GCE) has been modified using this ZVO/MWCNT nanocomposite, which offering a simple and affordable platform for the electrochemcial detection of CBZ. The structure and composition of the nanomaterial were confirmed through various microscopic and spectroscopic techniques, while its electrochemical characteristics were investigated using electrochemical impedance spectroscopy (EIS), differential pulse voltammetry (DPV), and cyclic voltammetry (CV) techniques. The GCE/ZVO/MWCNT modified electrode exhibited a broad linear response ranging from 0.05 to 110μM, with a low detection limit of 0.005μM and high sensitivity (2.93 μA μM−1 cm−2), with recoveries ranging from 98% to 99.93%, respectively. Moreover, GCE/ZVO/MWCNT electrode demonstrated excellent stability, repeatability, and practical utility for CBZ detection. Based on electrochemical studies that indicate the formation of oxidized products consistent with a 2-electron transfer process. Therefore, this innovative GCE/ZVO/MWCNT modified electrode offers promising prospects for sensitive and selective CBZ determination, with potential applications in environmental monitoring and health protection.

Leather-Based Shoe Soles for Real-Time Gait Recognition and Automatic Remote Assistance Using Machine Learning.

Real-time monitoring of gait characteristics is crucial for applications in health monitoring, patient rehabilitation feedback, and telemedicine. However, the effective and stable acquisition and automatic analysis of gait information remain significant challenges. In this study, we present a flexible sensor based on a carbon nanotube/graphene composite conductive leather (CGL), which uses collagen fiber with a three-dimensional network structure as the flexible substrate. The CGL-based sensor demonstrates a high dynamic range, with notable pressure responses ranging from 0.6 to 14.5 kPa and high sensitivity (S = 0.2465 kPa-1). We further developed a device incorporating the CGL-based sensor to collect foot characteristic signals from human motion and designed smart sports shoes to facilitate effective human-computer interaction. Machine learning was employed to collect and process gait characteristic information in various states, including standing, sitting, walking, and falling. For real-time monitoring of falls, we optimized the K-Nearest Time Series Classifier (KNTC) algorithm, achieving an accuracy of 0.99 and a prediction time of only 13 ms, which highlights the system's excellent intelligent response capabilities. The system maintained a gait recognition accuracy of 90% across diverse populations, with low false-positive (3.3%) and false-negative (3.3%) rates. This work demonstrates stable gait recognition capabilities and provides valuable methods and insights for plantar behavior monitoring and data analysis, contributing to the development of advanced real-time gait monitoring systems.

Detection of E. coli using bacteriophage T7 and analysis of excitation‑emission matrix fluorescence spectroscopy

Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and E. coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier and Random Forest, demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 hours. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodologies, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.