Advanced Monitoring Techniques Research Articles

Automated Surface Crack Identification of Reinforced Concrete Members Using an Improved YOLOv4-Tiny-Based Crack Detection Model

In recent times, the deployment of advanced structural health monitoring techniques has increased due to the aging infrastructural elements. This paper employed an enhanced You Only Look Once (YOLO) v4-tiny algorithm, based on the Crack Detection Model (CDM), to accurately identify and classify crack types in reinforced concrete (RC) members. YOLOv4-tiny is faster and more efficient than its predecessors, offering real-time detection with reduced computational complexity. Despite its smaller size, it maintains competitive accuracy, making it ideal for applications requiring high-speed processing on resource-limited devices. First, an extensive experimental program was conducted by testing full-scale RC members under different shear span (a) to depth ratios to achieve flexural and shear dominant failure modes. The digital images captured from the failure of RC beams were analyzed using the CDM of the YOLOv4-tiny algorithm. Results reveal the accurate identification of cracks formed along the depth of the beam at different stages of loading. Moreover, the confidence score attained for all the test samples was more than 95%, which indicates the accuracy of the developed model in capturing the types of cracks in the RC beam. The outcomes of the proposed work encourage the use of a developed CDM algorithm in real-time crack detection analysis of critical infrastructural elements.

A Comprehensive Proteogenomic and Spatial Analysis of Innate and Acquired Resistance of Metastatic Melanoma to Immune Checkpoint Blockade Therapies.

This study paves the way for the creation of innovative therapeutic strategies, aimed at subverting resistance to immune checkpoint blockade (ICB) therapies in metastatic melanoma patients. By unraveling the specific molecular mechanisms underlying resistance, scientists can design effective alternative treatments that target pathways such as pathways associated with cell cycle dysregulation and c-MYC signaling. Furthermore, through the application of advanced immune monitoring techniques such as NanoString Digital Spatial Profiling (DSP) and Cyclic Immunofluorescence (CyCIF), this study has significantly enriched our understanding of the tumor microenvironment. This enhanced characterization facilitates the discovery of potential biomarkers that may forecast a patient's response to ICB treatment. Ultimately, these advancements could potentially refine patient outcomes and foster the development of more personalized cancer treatments in the future.

Open-Path Cavity Ring-Down Spectroscopy for Simultaneous Detection of Hydrogen Chloride and Particles in Cleanroom Environment.

The present study addresses advanced monitoring techniques for particles and airborne molecular contaminants (AMCs) in cleanroom environments, which are crucial for ensuring the integrity of semiconductor manufacturing processes. We focus on quantifying particle levels and a representative AMC, hydrogen chloride (HCl), having known detrimental effects on equipment longevity, product yield, and human health. We have developed a compact laser sensor based on open-path cavity ring-down spectroscopy (CRDS) using a 1742 nm near-infrared diode laser source. The sensor enables the high-sensitivity detection of HCl through absorption by the 2-0 vibrational band with an Allan deviation of 0.15 parts per billion (ppb) over 15 min. For quantifying particle number concentrations, we examine various detection methods based on statistical analyses of Mie scattering-induced ring-down time fluctuations. We find that the ring-down distributions' 3rd and 4th standard moments allow particle detection at densities as low as ~105 m-3 (diameter > 1 μm). These findings provide a basis for the future development of compact cleanroom monitoring instrumentation for wafer-level monitoring for both AMC and particles, including mobile platforms.

Anesthesia for Minimal Invasive Cardiac Surgery: The Bonn Heart Center Protocol.

The development and adoption of minimally invasive techniques has revolutionized various surgical disciplines and has also been introduced into cardiac surgery, offering patients less invasive options with reduced trauma and faster recovery time compared to traditional open-heart procedures with sternotomy. This article provides a comprehensive overview of the anesthesiologic management for minimally invasive cardiac surgery (MICS), focusing on preoperative assessment, intraoperative anesthesia techniques, and postoperative care protocols. Anesthesia induction and airway management strategies are tailored to each patient's needs, with meticulous attention to maintaining hemodynamic stability and ensuring adequate ventilation. Intraoperative monitoring, including transesophageal echocardiography (TEE), processed EEG monitoring, and near-infrared spectroscopy (NIRS), facilitates real-time assessment of cardiac and cerebral perfusion, as well as function, optimizing patient safety and improving outcomes. The peripheral cannulation techniques for cardiopulmonary bypass (CPB) initiation are described, highlighting the importance of cannula placement to minimize tissue as well as vessel trauma and optimize perfusion. This article also discusses specific MICS procedures, detailing anesthetic considerations and surgical techniques. The perioperative care of patients undergoing MICS requires a multidisciplinary approach including surgeons, perfusionists, and anesthesiologists adhering to standardized treatment protocols and pathways. By leveraging advanced monitoring techniques and tailored anesthetic protocols, clinicians can optimize patient outcomes and promote early extubation and enhanced recovery.

Assessment of Species Composition, Diversity and Carbon Stock in a Community Managed Forest of Udaypur District of Nepal

Species composition, diversity, and carbon sock of forests are all critical factors that affect the ability of forests to provide various important ecosystem services. However, there is a notable dearth of research regarding these factors in the community forests of Udaypur district. Therefore, this research was undertaken to assess species composition, and biodiversity and quantify the carbon stock potential of the Sringar community forest (CF) of the Udaypur district of Eastern Nepal. A total of 57 circular plots of 500m2 were inventoried using a systematic random sampling method with 0.5 % sampling intensity. In the CF, 17 tree species from 15 genera and 11 families were identified. The dominant tree species, Shorea robusta observed with a maximum importance value index (IVI) (176.15). According to our study, the total biomass and carbon stock in Sringar CF were 276.98 ton ha-1 and 138.18 ton ha-1, respectively. Accurate estimation of soil carbon stocks is crucial for long-term forest management and climate change mitigation, and the integration of advanced monitoring techniques and predictive models to enhance accuracy and account for future climate projections is needed.

Exploring technological and operational challenges in high-pressure: High-temperature drilling techniques

This comprehensive review explores the complex landscape of HPHT drilling, examining the underlying principles, innovative technologies, and operational strategies used to overcome challenges such as wellbore stability, fluid management, equipment reliability, and downhole conditions. By understanding and addressing these challenges, the oil and gas industry can advance HPHT drilling capabilities and unlock new sources of energy to meet growing global demand. High-pressure, high-temperature (HPHT) drilling techniques have become indispensable for the oil and gas industry to tap into deep hydrocarbon reservoirs located in challenging environments. Despite their significance, HPHT drilling operations pose a myriad of technological and operational challenges that demand meticulous attention to safety, efficiency, and success. This comprehensive review embarks on an exploration of the intricate landscape of HPHT drilling, delving into the fundamental principles, innovative technologies, and operational strategies pivotal in surmounting obstacles such as wellbore stability, fluid management, equipment reliability, and downhole conditions. The review begins by delineating the distinctive characteristics of HPHT environments, highlighting the formidable challenges inherent in drilling operations conducted under extreme pressures and temperatures. Subsequently, it elucidates the critical importance of maintaining wellbore stability amidst such conditions, elucidating the risks of collapse and blowouts and offering insights into mitigation strategies ranging from meticulous drilling fluid selection to advanced wellbore monitoring techniques. Fluid management emerges as a focal point, as the review navigates through the complexities of selecting, formulating, and managing drilling fluids tailored for HPHT environments. Innovative technologies such as nanotechnology applications and synthetic-based mud systems are explored for their efficacy in addressing fluid-related challenges and enhancing drilling performance. The reliability and integrity of equipment constitute another critical aspect examined in the review, with emphasis on mechanical challenges, material selection, and proactive maintenance strategies essential for safeguarding equipment performance and longevity in HPHT operations. Furthermore, the review presents case studies and best practices gleaned from successful HPHT drilling projects, offering valuable insights and lessons learned to inform future endeavors.

Damage identification in sandwich structures using Convolutional Neural Networks

The increasing complexity of structures and materials, coupled with ever more stringent demands for safety and cost reduction in maintenance operations, has driven the need to develop advanced techniques for structural integrity monitoring, known as Structural Health Monitoring (SHM). In this context, this study investigates the use of image processing techniques of mode shapes in a composite sandwich panel, employing various Convolutional Neural Network (CNN) models, with the purpose of identifying damage. The main objective is to classify the type of damage, whether it is in the core, at the interface, or in the laminate, followed by the precise location of the flaw and determination of damage dimensions in terms of length and width. To achieve these goals, the finite element method was used to create a representative database, and subsequently, an efficient data management system and model implementation were established to optimize computational costs. The results obtained show that, in the classification task, a high accuracy in damage type identification (99%) was achieved, even when images had high levels of noise. Regarding the regression task for damage localization, satisfactory results were obtained, while the dimensioning of damage length proved acceptable, albeit with some limitations, and inclination identification presented challenges. This study represents a significant advancement in the field of SHM and explores both the advantages and limitations of using Convolutional Neural Networks for this purpose. The results obtained provide valuable insights for the practical application of these techniques in the detection and assessment of damage in sandwich structures, thus contributing to enhancing the safety and efficiency of maintaining such structures.

Monitoring the bogie lateral dynamics of a high-speed train through wireless sensor nodes

The condition monitoring of railway vehicles and of the infrastructure (track and overhead equipment) through the use of vehicle-based sensors is becoming a major trend for the railway industry and, to this aim, new rolling stock generations are often natively fitted with numerous sensors. However, older vehicles are not equipped with sensors and monitoring systems so that, considering the long service life of the rolling stock, advanced monitoring and condition-based maintenance techniques can only be applied to a relatively minor portion of the in-service railway fleets. This paper presents results from a research programme aimed at designing and testing an innovative monitoring system based on wireless sensor nodes, suitable for the retrofitting of older rolling stock generations, not natively equipped with advanced sensing and monitoring capabilities. The prototype monitoring system was used to detect the incipient bogie instability, which can be related to degradation in some suspension components or to increase of the equivalent conicity at wheel/rail contact. Different vehicle conditions and the influence of the track were considered in the study. Experimental tests have shown that is possible to identify not only how the vehicle condition is evolving in time but also the effect that maintenance operations like wheel reprofiling may have on the bogie lateral dynamics.

Efficient bridge steel bearing health monitoring using laser displacement sensors and wireless accelerometers

Steel bearings have been commonly used to counteract induced loading from thermal and traffic conditions in numerous bridges. However, their effectiveness has been compromised due to aging and maintenance limitations, potentially impacting the overall bridge system performance. Existing monitoring techniques for detecting malfunctioning steel bearings lack automation and precision, making them inadequate for long-term and real-time bridge dynamics assessment. This study proposes a response-based approach to identify bearing malfunction by analyzing the traffic-induced response in the bearing vicinity. To implement this approach, laser displacement sensors and wireless acceleration sensors were employed to monitor both malfunctioning and well-functioning steel bridge bearings. Significant differences in bearing performance were observed through response analysis and comparison. Laser sensor measurements revealed larger vertical deflections in the girder at malfunctioned bearing under traffic loading. Moreover, the investigation of the acceleration response in the bearing locality indicated that bearing malfunction could alter the vibrational characteristics of the vicinity, significantly affecting Cross Power Spectral Density (CPSD) and cross-correlation. To quantitatively evaluate the performance of steel bearings, a Condition Score (CS) was introduced. The CS exhibited a strong correlation with bearing damage, providing valuable insights for maintenance and decision-making processes in bridge asset management. This study offers a comprehensive and automated method for identifying steel bridge bearing malfunction by utilizing advanced monitoring techniques and introducing the CS for assessment. The results obtained from this approach can enhance bridge maintenance strategies and contribute to effective bridge asset management.

Detection of epileptiform discharges by patient state index and density spectral array during anesthetic emergence after resection of hemispheric brain tumor. Case report

Background: Processed electroencephalogram (EEGp) plays a crucial role in anesthesia as a quantitative and automated method for assessing anesthetic depth. It utilizes mathematical algorithms to furnish objective measures, offering an intuitive interface for anesthesiologists lacking specialized neurophysiology training. Among the array of visual graphical analyses, the Density Spectral Array (DSA) stands out as particularly promising. DSA furnishes a comprehensive and visual depiction of the patient's brain activity, aiding anesthesiologists in maintaining optimal anesthetic depth during surgical procedures Conclusion: This report underscores the potential of analyzing Case report: A 51-year-old male with a medical history of neurofibromatosis type 2 and a frontal hemispheric brain tumor underwent resection in the operating room under total intravenous anesthesia. Anesthetic depth was monitored utilizing SedLine® Patient Status Index (PSi). The resection proceeded uneventfully; however, upon extubation, EEG processing revealed epileptiform discharges concurrent with tonic-clonic movement of the right hand electroencephalogram waveforms alongside the latest SedLine-derived Density Spectral Array to confirm the presence of partial epileptic seizures during the immediate post-extubation and postoperative phases following brain tumor resection. Employing advanced monitoring techniques like Density Spectral Array aids in early detection of neurological complications, facilitating timely intervention and potentially enhancing patient outcomes during the critical postoperative period.

Impact of pH on the Catalytic Activity of Metal Nanoparticles in Organic Reactions in Ghana

Purpose: The aim of the study was to assess the impact of pH on the catalytic activity of metal nanoparticles in organic reactions in Ghana. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The pH of the reaction medium can significantly influence the surface charge, morphology, and composition of metal nanoparticles, consequently affecting their catalytic performance. At specific pH levels, the protonation or deprotonation of functional groups on the nanoparticle surface can occur, altering the electronic properties and reactivity of the catalyst. Additionally, pH can influence the stability of metal nanoparticles and their interaction with reactants, intermediates, and products. Studies have shown that optimizing pH conditions can enhance catalytic activity, selectivity, and recyclability of metal nanoparticles in various organic transformations, including hydrogenation, oxidation, and coupling reactions. Understanding the pH dependence of metal nanoparticle catalysis is crucial for the rational design and optimization of efficient catalytic systems for organic synthesis. Implications to Theory, Practice and Policy: Surface chemistry theory, electrochemical theory and colloid chemistry theory may be used to anchor future studies on assessing the impact of pH on the catalytic activity of metal nanoparticles in organic reactions in Ghana. Implementation of advanced experimental techniques for real-time monitoring of pH effects on nanoparticle catalysis. Techniques such as in situ spectroscopy and surface characterization methods can provide valuable insights into dynamic changes in nanoparticle structure and reactivity during catalytic reactions. Integration of pH considerations into regulatory frameworks and guidelines for sustainable catalysis.

Pre-collapse spaceborne deformation monitoring of the Kakhovka dam, Ukraine, from 2017 to 2023

The Kakhovka Dam on the Dnieper River in Kherson Oblast, Ukraine, was completed in 1956 as the final dam in the Dnieper reservoir cascade. On the morning of June 6th, 2023, a substantial portion of the dam suffered a collapse while under Russian control. This incident was documented through satellite optical and radar images, providing valuable evidence of the dam’s condition. Here we present the results of multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) monitoring of the Kakhovka dam. The dam is vital for water management and hydroelectric power generation. Utilizing multi-temporal InSAR (MT-InSAR) data, we assessed the dam deformations prior to the collapse. Our findings indicate movements of the south side, facing the Dniprovska Gulf, compatible with several possible damage mechanisms. This study highlights the significance of employing spaceborne advanced monitoring techniques to detect signs of distress and ensure the stability of critical infrastructure.

Facial appearance associates with longitudinal multi-organ failure: an ICU cohort study

BackgroundFacial appearance, whether consciously or subconsciously assessed, may affect clinical assessment and treatment strategies in the Intensive Care Unit (ICU). Nevertheless, the association between objective clinical measurement of facial appearance and multi-organ failure is currently unknown. The objective of this study was to examine whether facial appearance at admission is associated with longitudinal evaluation of multi-organ failure.MethodsThis was a sub-study of the Simple Intensive Care Studies-II, a prospective observational cohort study. All adult patients acutely admitted to the ICU between March 26, 2019, and July 10, 2019, were included. Facial appearance was assessed within three hours of ICU admission using predefined pictograms. The SOFA score was serially measured each day for the first seven days after ICU admission. The association between the extent of eye-opening and facial skin colour with longitudinal Sequential Organ Failure Assessment (SOFA) scores was investigated using generalized estimation equations.ResultsSOFA scores were measured in 228 patients. Facial appearance scored by the extent of eye-opening was associated with a higher SOFA score at admission and follow-up (unadjusted 0.7 points per step (95%CI 0.5 to 0.9)). There was no association between facial skin colour and a worse SOFA score over time. However, patients with half-open or closed eyes along with flushed skin had a lower SOFA score than patients with a pale or normal facial skin colour (P-interaction < 0.1).ConclusionsThe scoring of patients’ facial cues, primarily the extent of eye-opening and facial colour, provided valuable insights into the disease state and progression of the disease of critically ill patients. The utilization of advanced monitoring techniques that incorporate facial appearance holds promise for enhancing future intensive care support.

Evolution of distal limb perfusion management in adult peripheral venoarterial extracorporeal membrane oxygenation with femoral artery cannulation.

Limb ischaemia is a clinically relevant complication of venoarterial extracorporeal membrane oxygenation (VA ECMO) with femoral artery cannulation. No selective distal perfusion or other advanced techniques were used in the past to maintain adequate distal limb perfusion. A more recent trend is the shift from the reactive or emergency management to the pro-active or prophylactic placement of a distal perfusion cannula to avoid or reduce limb ischaemia-related complications. Multiple alternative cannulation techniques to the distal perfusion cannula have been developed to maintain distal limb perfusion, including end-to-side grafting, external or endovascular femoro-femoral bypass, retrograde limb perfusion (e.g., via the posterior tibial, dorsalis pedis or anterior tibial artery), and, more recently, use of a bidirectional cannula. Venous congestion has also been recognized as a potential contributing factor to limb ischaemia development and specific techniques have been described with facilitated venous drainage or bilateral cannulation being the most recent, to reduce or avoid venous stasis as a contributor to impaired limb perfusion. Advances in monitoring techniques, such as near-infrared spectroscopy and duplex ultrasound analysis, have been applied to improve decision-making regarding both the monitoring and management of limb ischaemia. This narrative review describes the evolution of techniques used for distal limb perfusion during peripheral VA ECMO.

Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System

Recently, the implementation of software/hardware systems based on advanced artificial intelligence techniques for continuous monitoring of the electrical parameters of intelligent networks aimed at managing and controlling energy consumption has been of great interest. The contribution of this paper, starting from a recently studied DC-MG, fits into this context by proposing an intuitionistic fuzzy Takagi–Sugeno approach optimized for the energy management of isolated direct current microgrid systems consisting of a photovoltaic and a wind source. Furthermore, a lead-acid battery guarantees the stability of the DC bus while a hydrogen cell ensures the reliability of the system by avoiding blackout conditions and increasing interaction with the loads. The fuzzy rule bank, initially built using the expert’s knowledge, is optimized with the aforementioned procedure, maximizing external energy and minimizing consumption. The complete scheme, modeled using MatLab/Simulink, highlighted performance comparable to fuzzy Takagi–Sugeno systems optimized using a hybrid approach based on particle swarm optimization (to structure the antecedents of the rules) and minimum batch squares (to optimize the output).

Remote sensing based forest cover classification using machine learning

Pakistan falls significantly below the recommended forest coverage level of 20 to 30 percent of total area, with less than 6 percent of its land under forest cover. This deficiency is primarily attributed to illicit deforestation for wood and charcoal, coupled with a failure to embrace advanced techniques for forest estimation, monitoring, and supervision. Remote sensing techniques leveraging Sentinel-2 satellite images were employed. Both single-layer stacked images and temporal layer stacked images from various dates were utilized for forest classification. The application of an artificial neural network (ANN) supervised classification algorithm yielded notable results. Using a single-layer stacked image from Sentinel-2, an impressive 91.37% training overall accuracy and 0.865 kappa coefficient were achieved, along with 93.77% testing overall accuracy and a 0.902 kappa coefficient. Furthermore, the temporal layer stacked image approach demonstrated even better results. This method yielded 98.07% overall training accuracy, 97.75% overall testing accuracy, and kappa coefficients of 0.970 and 0.965, respectively. The random forest (RF) algorithm, when applied, achieved 99.12% overall training accuracy, 92.90% testing accuracy, and kappa coefficients of 0.986 and 0.882. Notably, with the temporal layer stacked image of the Sentinel-2 satellite, the RF algorithm reached exceptional performance with 99.79% training accuracy, 96.98% validation accuracy, and kappa coefficients of 0.996 and 0.954. In terms of forest cover estimation, the ANN algorithm identified 31.07% total forest coverage in the District Abbottabad region. In comparison, the RF algorithm recorded a slightly higher 31.17% of the total forested area. This research highlights the potential of advanced remote sensing techniques and machine learning algorithms in improving forest cover assessment and monitoring strategies.

Quantitative Analysis of Hydraulic Fracturing Test Site 2 Completion Designs Using Crosswell Strain Measurement

Summary The implementation of effective completion design configurations during hydraulic stimulation is critical for the economic development of unconventional reservoirs. Low-frequency distributed acoustic sensing (LF-DAS)-based crosswell strain measurement is an advanced monitoring technique used to diagnose completion design efficiency but has been primarily restricted to qualitative analysis. In this study, we apply our novel Green-function based inversion algorithm to calculate fracture geometry (i.e., width) using the Department of Energy sponsored Hydraulic Fracturing Test Site 2 (HFTS-2) data set. The adopted algorithm relies on a 3D displacement discontinuity method to construct geomechanical models inverting linear elastic strain to hydraulic fracture widths. We use the inversion algorithm to calculate dynamic fracture widths using LF-DAS data recorded at two horizontal monitoring wells with permanent optical fiber installations. The inverted fracture widths at the monitoring wells from more than 100 hydraulic fracturing stages are used to diagnose the efficiency of eight unique completion designs implemented across three fracturing wells. We develop several metrics to evaluate completion design efficiency including the evenness of fracture widths at the monitoring wells, fracture density (i.e., number of fracture hits per foot), and fracture-width-density (i.e., fracture width/stage length). We observe a significant impact on completion efficiency with varying degrees of limited entry, tapered configurations, and stage length designs. Results indicate improved hydraulic stimulation is achieved with the implementation of limited-entry designs for extended stage lengths (ESLs), but no observable trend for normal stage lengths (NSLs). Tapered configurations significantly improve efficiency for ESLs but indicate little impact on normal-length designs. Reducing the space between perforation clusters (PCs) is determined to negatively impact design performance. Additionally, our quantitative analysis describes the impact of the nearby depletion zone on completion design efficiency. The methodology developed in this study provides operators with another level of quantitative information to optimize hydraulic fracturing treatments and reduce costs associated with the development of unconventional wells.

Structural health assessment through continuous vibration-based monitoring of a stainless-steel lattened in-operation railway bridge

Damage detection in railway bridges is challenging due to changing environmental and operational conditions. Advanced monitoring techniques, such as operational modal analysis (OMA), use vibration to assess the integrity of structures during their operational life. Cov-SSI, a time-based OMA algorithm, is widely used for the automatic identification of system dynamics. Static modelling is essential to separate variability from benign effects and ensure accurate monitoring. Monitoring the health of the structure through control charts highlights damages that influence the dynamic response. This study focuses on the continuous monitoring of railway bridge networks using advanced methods such as MOVA and MOSS. In particular, this work is part of a research project dedicated to the continuous monitoring of a real bridge in operation: the Quisi Viaduct in Alicante (Spain). The viaduct is a stainless steel single-span bolted railway bridge, built at the beginning of the 20th century. The viaduct is equipped with a set of high-sensitivity accelerometers and a dense network of deformation sensors that store a compendium of data of approximately 3 years. This contribution discusses the quality of the data sets and the monitoring strategy, with a focus on the effectiveness of using statistical process control charts to assess the structural health of railway bridges.

Entendendo o risco de descoloração como consequência de transientes hidráulicos

Abstract This paper presents an analysis of the challenges associated with discoloration risk management and the impact of hydraulic transients on water quality within distribution systems. Focusing on the Metropolitan Region of Curitiba, Paraná, Brazil, the study employs a comprehensive methodology that combines the Characteristic Method for simulating hydraulic transients and real-time monitoring of water quality parameters, such as turbidity and chlorine levels. A framework for managing discoloration risk is proposed, highlighting strategies for identifying potential causes, assessing risk severity, developing prevention and mitigation strategies, and implementing continuous improvement processes. The experimental section details the impact of hydraulic transient events on water quality, with significant findings indicating a direct correlation between flow dynamics and water quality degradation, particularly in terms of increased turbidity and decreased chlorine levels during pump deactivation. The study contributes valuable insights into effective water distribution system management, emphasizing the importance of advanced monitoring and risk management techniques to maintain high-quality water standards.

Precision Mapping of Coastal Wetlands: An Integrated Remote Sensing Approach Using Unoccupied Aerial Systems Light Detection and Ranging and Multispectral Data

Coastal wetlands, crucial for global biodiversity and climate adaptation, provide essential ecosystem services such as carbon storage and flood protection. These vital areas are increasingly threatened by both natural and human-induced changes, prompting the need for advanced monitoring techniques. This study employs unmanned aerial systems (UASs) equipped with light detection and ranging (LiDAR) and multispectral sensors to survey diverse wetland types across 8 sites in North Carolina. Utilizing high-resolution elevation data and detailed vegetation analysis, coupled with sophisticated machine learning algorithms, we achieved differentiated and highly precise classifications of wetland types. Classification accuracies varied by type, with estuarine intertidal emergent wetlands showing the highest classification accuracies due to less complex vegetation structure and clearer spectral signatures, especially when collections account for tidal influence. In contrast, palustrine forested and scrub–shrub wetlands presented lower accuracies, often due to the denser, mixed, and more complex vegetation structure and variable inundation levels, which complicate spectral differentiation and ground returns from LiDAR sensors. Overall, our integrated UAS-derived LiDAR and multispectral approach not only enhances the accuracy of wetland mapping but also offers a scalable, efficient, and cost-effective method that substantially advances conservation efforts and informs policy-making for coastal resilience. By demonstrating the usefulness of small-scale aerial data collection in ecological mapping, this study highlights the transformative potential of merging advanced technologies in environmental monitoring, underscoring their critical role in sustaining natural habitats and aiding in climate change mitigation strategies.