Detection Of Disturbances Research Articles

Advanced Bayesian Method for Timely Small-Scale Forest Loss Detection in the Brazilian Amazon and Cerrado with Sentinel-1 Time-Series

Abstract. The world’s forests are undergoing significant changes due to loss and degradation, emphasizing the need for Near Real-Time (NRT) monitoring to prevent further damage. Traditional monitoring methods using optical imagery are hindered by cloud coverage, while newer Synthetic Aperture Radar (SAR) systems, although operational in all weather conditions, face challenges such as sensitivity to soil moisture and the need for spatial filtering to reduce speckle effects. These limitations affect the detection of small-scale forest loss, especially in seasonally variable regions like dry forests and savannas. This paper presents a SAR-based forest disturbance detection method using Bayesian inference. Unlike traditional methods, this approach maintains the native resolution of the data by avoiding spatial filtering. Forest disturbance is modelled as a change-point detection problem within a non-filtered Sentinel-1 time series, where each new observation updates the probability of forest loss by leveraging prior information and a data model. This sequential adaptation ensures robustness against variations and trends, making it effective in monitoring disturbances across diverse forest types, including areas affected by seasonality. The proposed method was tested against other NRT monitoring systems for the year 2020, using small validation polygons (under 1 hectare) in the Brazilian Amazon and Cerrado savanna. Results demonstrate significant improvements in detecting small-scale disturbances and drastically reduced false alarm rates in both biomes. Notably, in the seasonality-sensitive Cerrado, our solution completely outperforms the leading and only existing optical technology.

Integrating UAV LiDAR and multispectral data to assess forest status and map disturbance severity in a West African forest patch

Unmanned aerial vehicle (UAV) technologies have emerged as promising tools to improve forest ecosystem assessments. These technologies offer high-resolution data that can significantly enhance evaluations of forest structure, condition, and disturbance severity. UAV sensors such as LiDAR and multispectral provide complementary information about forest attributes, capturing structural and spectral details, yet their integration for comprehensive forest assessment remains understudied. In this paper, we explored the potential of combining UAV LiDAR and multispectral data to assess the disturbance severity of a West African forest patch (Benin). We developed an integrated disturbance index (IDI) that fuses structural properties from LiDAR data and spectral characteristics from multispectral vegetation indices through principal component analysis (PCA). This allowed us to delineate low (> 0.65), medium (0.35–0.65), and high (< 0.35) forest disturbance levels. We applied the IDI to the 560-ha Ewe-Adakplame relict forest in Benin, West Africa, and achieved 95 % overall accuracy in disturbance detection, outperforming both LiDAR-only (80 %) and multispectral-only (75 %) approaches. The IDI revealed that 23 % of the forest area has experienced low disturbance, while 28 % and 49 % face medium and high disturbance levels, respectively. These findings highlight that more than three-quarters of this relict forest is under considerable stress, underscoring the urgent need for tailored conservation strategies to strengthen forest resilience. This method's ability to differentiate disturbance levels can inform resource allocation, prioritize conservation efforts, and guide the development of site-specific management plans. The integration of UAV LiDAR and multispectral data demonstrated here has potential for application across diverse tropical forest patches, providing an effective means to monitor forest health, assess disturbance severity, and support data-driven decision-making in forest conservation and sustainable management.

An advanced quantum support vector machine for power quality disturbance detection and identification

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O(N2log(N))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$O(N^{2} \\log (N))$\\end{document}, superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.

Advanced power quality monitoring with dilated convolutional neural networks for smart nanogrids

This study presents the development of an advanced power quality monitoring (PQM) system using dilated convolutional deep neural networks (DCDNN) for smart nanogrids. Traditional convolutional neural networks (CNNs) face challenges in efficiently handling high-dimensional data and identifying nuanced features due to their limited receptive field. To overcome these limitations, our DCDNN model employs a novel use of dilated convolutions to expand the receptive field without increasing network complexity, enabling the capture of long-range dependencies essential for accurate classification of power quality disturbances (PQDs). In addition, the integration of multi-channel time-series data processing, including voltage, current, and frequency measurements, is unique to our approach. The model processes multi-channel time-series data, including voltage, current, and frequency measurements, achieving an average accuracy of 98.9% across 29 PQD classes and 100% accuracy in 6 main classes, with a reduced detection time of 47 µs per segment. This approach not only improves real-time disturbance detection but also enhances the reliability and efficiency of electrical systems. The results demonstrate significant improvements over existing methods, underscoring the potential of DCDNNs in advancing PQM practices. Future work will focus on validating the model in real-world conditions and optimizing its computational efficiency.

Power Quality Disturbance Monitoring in PV Integrated Power System with Mode Decomposition and Ensemble Extreme Learning Machine

Power quality disturbance (PQD) monitoring has become an important issue in modern power system due to integration of several renewable energy sources such as photovoltaic (PV), wind energy system (WES), Fuel cells etc. This research presents a mode decomposed based ensemble extreme learning machine (EELM) to recognise and classify the PQD events with higher accuracy in terms of rapid learning speed and smaller computational burden in a complete PV based power system, The PQD signals are decomposed using a variational mode decomposition (VMD) to obtain effective band limited intrinsic mode functions (BIIMF) which leads to compute robust features and improves classification accuracy. For Power Quality Disturbances detection and classification, an ensemble extreme learning machine is suggested since an ensemble outperforms any single contributing model in terms of performance and prediction. The proposed VMD-EELM approach is validated in a modified IEEE 13 Bus system integrating PV with eleven types of PQDs. The proposed research having 100% classification accuracy for no noise and 99.88%, 99.94% 99.94% for 20dB, 30dB and 40dB noises respectively. It is being demonstrated that the suggested method can reliably identify and track PQD occurrences both with and without noise.

Optimizing vision screening and referral of students: A pre-post analysis of a comprehensive multifaceted intervention for school nurses in Qatar

Background: Vision acuity screening and referrals are pivotal in the early detection and management of visual disturbances among school children. This quality improvement project aimed to evaluate the adherence of school nurses in governmental schools in Qatar to established vision acuity screening and referral standards and to enhance this adherence through a targeted intervention. Methods: Utilizing a before-and-after study design, this clinical audit focused on students in grades 1, 3, 5, 7, and 9 during the academic years 2021–2022 (baseline) and 2022–2023 (post-intervention). The methodology encompassed a baseline assessment, root cause analysis, intervention implementation, and a post-intervention assessment. Data extraction was performed from Qatar’s electronic health records (EHRs) system. Results: In the baseline assessment, 75.5% (95%CI 73.4–81.6) of 48,089 eligible students were screened, with 9.9% displaying abnormal visual acuity. Only 38.6% of those with abnormal visual acuity were referred. Post-intervention, screening compliance increased to 91.5% (95%CI 91.2–93.9, p &lt; 0.001) among 48,946 students. Of those screened, 10.1% showed abnormal visual acuity, with referrals surging to 99.6% (p &lt; 0.001). The appropriateness of referrals also improved significantly post-intervention (p &lt; 0.001). Conclusion: The intervention yielded a statistically significant improvement in the adherence of school nurses to vision acuity screening and referral standards in Qatar. Application to practice: The study highlights the importance of targeted interventions in improving school nurses’ adherence to vision screening and referral protocols, emphasizing the need for regular training, policy support, and the use of EHRs to enhance the early detection and management of visual disturbances among students, contributing to better health and educational outcomes.

A composite power quality disturbance detection method based on extremum extension optimized SVMD and Teager Energy Operator

A composite power quality disturbance detection method based on extremum extension optimized SVMD and Teager Energy Operator

An improved assessment of forest disturbance using a novel approach of combining a Gaussian mixture model with an EM algorithm

Forests have experienced unprecedented decline and mortality beyond their historical range in past decades, which is attributed to disturbances like drought, fire, insects and disease. Traditional disturbance detection methods that typically employ a time series perspective to identify discrete disturbance events within continuous tree growth signals and were mainly designed for gap-scale often fail to identify disturbances across populations. To more accurately identify growth suppression and release clusters based on the perspective of forest population dynamics, here we applied a novel method of detecting forest disturbances using a Gaussian mixture model with an expectation maximisation algorithm (DGE), to fit annual distributions of growth indicators, i.e., tree-ring index. We further compared our novel approach of DGE with five traditional methods based on two sets of real tree-ring data and simulated tree-ring data. The results show an improvement of accuracy (35.5 %–48.1 %), sensitivity (36.6 %–58.1 %), precision (21.2 %–51.5 %) and specificity (11.1 %–20.6 %) for the average of two real radial growth datasets and an improvement of accuracy (7.6 %–13.9 %), sensitivity (70 %), precision (100 %) and specificity (9.1 %–9.7 %) for the simulation radial growth dataset, indicating that our DGE approach performs much better than the traditional methods. This study contributes to an improved strategy and understanding for sustainable forest management in the context of climate warming, demonstrating that our DGE approach can be better applied to global forest disturbance detection under global change.

An Improved LandTrendr Algorithm for Forest Disturbance Detection Using Optimized Temporal Trajectories of the Spectrum: A Case Study in Yunnan Province, China

Forest disturbance mapping plays an important role in furthering our understanding of forest dynamics. The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used in forest disturbance mapping. However, it neglects the quality of the temporal trajectory and its change trends for forest disturbance mapping. Therefore, the aim of this paper is to improve LandTrendr (iLandTrendr) for forest disturbance mapping by optimizing its temporal trajectories and the post-processing of detection results. Specifically, the temporal trajectory of complex forest disturbance types was optimized using the Savitzky–Golay (SG) filter with constraints. That is, the smooth value generated from the SG filter for the disturbance year was replaced by the satellite observations when the nonlinear abrupt signals were included in the multi-temporal data. The forest disturbance detected by LandTrendr was further modified using the consistency of spectral variation trends. A case study using iLandTrendr to detect forest disturbance in Yunnan Province was conducted. Compared to the LandTrendr method, which has an overall accuracy (OA) of 35.88%, iLandTrendr generated forest disturbance mapping with an OA of 89.32%, which was significantly higher. The total mapped area of disturbance was 1,985,820.9 km2, accounting for 49.69% of the total area. The disturbances were predominately caused by natural factors, such as wildfires, pests and diseases, and forest degradation, accounting for 85.31% of the total disturbed area. iLandTrendr can quickly and accurately detect the occurrence year of complex forest disturbance types and can be extended for the forest disturbance mapping of a large area.

Bark beetle detection method using electronic nose sensors. A possible improvement of early forest disturbance detection?

Bark beetle detection method using electronic nose sensors. A possible improvement of early forest disturbance detection?

Development of a Maintenance System for Low Voltage Electricity Distribution Networks

This research focuses on important efforts to ensure the availability and reliability of electricity supply through the design and implementation of effective systems for monitoring and maintaining low voltage electricity distribution networks. The methods used in this research include analysis of maintenance needs, development of software for network monitoring, and integration of sensor technology for early detection of disturbances. The developed system aims to increase maintenance efficiency by providing real-time information about network conditions, enabling preventive action before disruptions occur. In addition, this system is also designed to reduce downtime and maintenance costs, as well as increase the reliability of electricity supply for customers. The research results show that implementing advanced sensor technology and monitoring software can significantly improve response to disruptions and minimize the impact on customer service. Thus, this system not only increases operational efficiency but also provides long-term benefits in maintaining the stability and reliability of electricity supply.

Measurement channels’ information relationships in body surface potential mapping

Body Surface Potential Mappings (BSPM) measurements allow heart rate monitoring. Based on measurements obtained from BSPM, defects and heart rhythm disorders can be detected. Due to the multitude of data obtained from BSPM measurements, the crucial aspect in the potential test signals analysis on the human body is the automation of the disturbance detection process. This automation process involves the use of advanced algorithms and machine learning techniques to analyze the recorded data and identify patterns indicative of heart rhythm disorders. In the article, the information dependencies in the measurement channels were analyzed and presented. PCA analysis was used to determine informational relationships between the channels. The study was carried out on the data from simulations made for the BSPM measuring vest on a simulation system created for this purpose and based on the ECG signal generator. As a result of the conducted research indicated informational relationships between the channels for signals with disturbances: Atrial fibrillation, Brachycardia, Normal signal, PVC, Tachycardia, Ventricular Fibrillation, and Ventricular Tachycardia.

Retraction Note: Detection and classification of power quality disturbances or events by adaptive NFS classifier

Retraction Note: Detection and classification of power quality disturbances or events by adaptive NFS classifier

Forest disturbance regimes and trends in continental Spain (1985–2023) using dense landsat time series

Forest disturbance regimes across biomes are being altered by interactive effects of global change. Establishing baselines for assessing change requires detailed quantitative data on past disturbance events, but such data are scarce and difficult to obtain over large spatial and temporal scales. The integration of remote sensing with dense time series analysis and cloud computing platforms is enhancing the ability to monitor historical disturbances, and especially non-stand replacing events along climatic gradients. Since the integration of such tools is still scarce in Mediterranean regions, here, we combine dense Landsat time series and the Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) method to monitor forest disturbance in continental Spain from 1985 to 2023. We adapted the CCDC-SMA method for improved disturbance detection creating new spectral libraries representative of the study region, and quantified the year, month, severity, return interval, and type of disturbance (stand replacing, non-stand replacing) at a 30 m resolution. In addition, we characterised forest disturbance regimes and trends (patch size and severity, and frequency of events) of events larger than 0.5 ha at the national scale by biome (Mediterranean and temperate) and forest type (broadleaf, needleleaf and mixed). We quantified more than 2.9 million patches of disturbed forest, covering 4.6 Mha over the region and period studied. Forest disturbances were on average larger but less severe in the Mediterranean than in the temperate biome, and significantly larger and more severe in needleleaf than in mixed and broadleaf forests. Since the late 1980s, forest disturbances have decreased in size and severity while increasing in frequency across all biomes and forest types. These results have important implications as they confirm that disturbance regimes in continental Spain are changing and should therefore be considered in forest strategic planning for policy development and implementation.

Biomarkers of sleep-wake disturbance as predictors of cognitive decline and accelerated disease progression.

In older adults, where sleep disturbances and cognitive impairment are common, mounting evidence suggests a potential connection between sleep and cognitive function, highlighting the significance of utilizing sleep as a biomarker for early detection of cognitive impairment to improve clinical outcomes in a noninvasive, cost-effective manner. This review describes the relationship between sleep and cognitive function in older adults, encompassing both subjective and objective measures of sleep quality, duration, architecture, and sleep-disordered breathing. The authors consider the directionality of the associations observed in prospective and cross-sectional studies, exploring whether sleep disturbances precede cognitive decline or vice versa. Furthermore, they discuss the potential bidirectional relationships between sleep and Alzheimer's disease (AD) risks in older adults while also examining the neurodegenerative pathways of this relationship. Routine sleep monitoring in primary care settings has the potential to bolster early detection and treatment of sleep disturbance, and by extension, reduce the risk of dementia. Improving sleep assessment tools, such as wearables, provide scalable alternatives to traditional methods like polysomnography, potentially enabling widespread monitoring of sleep characteristics. Standardized measurement and inclusive participant recruitment are needed to enhance generalizability, while longitudinal studies are essential to understand the interaction between sleep and AD pathology.

A acute pontine infarction with one-and-a-half syndrome as the manifestation: A case report.

Sudden ocular dyskinesia is usually associated with ophthalmic diseases and rarely with cerebrovascular diseases. This is a rare case of a patient with a sudden onset of ocular dyskinesia due to occlusion of the anterior inferior cerebellar artery and the spiral modiolar artery. This article describes eye movement disorders associated with cerebrovascular disease, aiming to improve our understanding of cerebrovascular diseases and improve the ability of early diagnosis and differential diagnosis. A 52-year-old man presented with acute pontine cerebral infarction 2 days before presentation. The main symptoms were the inability to adduct and abduct the left eyeball, the ability to abduct but not adduct the right eyeball, and horizontal nystagmus during abduction. Cranial computed tomography in our emergency department suggested cerebral infarction, and magnetic resonance imaging examination after admission confirmed the diagnosis of acute pontine cerebral infarction. This patient was ultimately diagnosed with acute pontine cerebral infarction. He received aspirin, clopidogrel, and butylphthalide, as well as acupuncture and Chinese herbal medicine. After 10 days of treatment, the patient's paralysis of the eye muscles improved significantly. Eye movement disorders are sometimes an early warning sign of impending vertebrobasilar ischemic stroke. Patients with acute ischemic stroke who have early detection of oculomotor disturbances should be promptly imaged, as missed diagnosis may lead to serious consequences or even death. It provided us with a new diagnostic idea.

Application of Electrical Protection on Subsea Electrically Trace Heated Pipe-in-Pipe Line

The deployment of Electrically Trace Heated Pipe-in-Pipe (ETH-PiP) brings the challenges of reliable protection selection and setting. This paper presents a study of relays and protection function optimization for ETH-PiP systems. The study is based on the developed simulation model of a typical ETH-PiP system. The paper presents simulation studies of physical phenomena occurring during electrical disturbances in the ETH-PiP system and offers detailed analyses applicable to system protection, including detection of disturbances, location, and elimination.

61. Detection of unrecorded environmental disturbances using feed intake data on growing pigs

61. Detection of unrecorded environmental disturbances using feed intake data on growing pigs

Extending Canadian forest disturbance history maps prior to 1985

AbstractAn accurate depiction of wildfire, harvesting, and insect outbreak disturbances is essential for sustainable ecosystem management of forests in Canada. Even though the advent of temporally consistent 30‐m resolution Landsat data has enabled the detailed mapping of forest disturbances in Canada from 1985 onward, the disturbance record prior to 1985 remains sparse. This study aimed to extend the existing pre‐1985 disturbance history record by mapping wildfire, harvest, and insect outbreaks in Canadian forests between 1965 and 1984. Our geospatial data processing methodology relied on multilayer perceptrons (MLP) trained on spectral recovery signatures to map and age these disturbances. Our model detected approximately 4.8, 7.3, and 3.8 million ha of burnt, harvested, and insect‐ravaged forest areas, respectively, that were absent from national and provincial disturbance databases and forest inventories. Results were validated using both internal and external validation datasets. Our disturbance detection methodology was highly effective, with an internal validation kappa score of 0.91 and an external score of 0.81. The fire and harvest age disturbance MLPs, whose predictions can also be used as a proxy of forest stand age, performed adequately on the internal (fire R2 = 0.675; root mean squared error [RMSE] = 4.42; harvest R2 = 0.723; RMSE = 3.17) and external validation datasets (fire R2 = 0.242; RMSE = 4.69; harvest R2 = 0.257; RMSE = 5.46), outperforming existing forest age disturbance products. Finally, we relied on several open data products, such as provincial forest inventories, to correct our disturbance type and year prediction whenever these more reliable, but incomplete, data sources were available. Specific years were not assigned to insect outbreaks due to the lack of dependable training and validation data. We also illustrate how extending the existing forest disturbance record by 20 years may provide a more in‐depth understanding of landscape‐disturbance dynamics with a case study of the 2023 Canadian wildfire season.

Wavelet transform-based composite disturbance detection method for power quality in distribution networks

Wavelet transform-based composite disturbance detection method for power quality in distribution networks