Long-term Monitoring Research Articles

Intelligent full-stage stable fault diagnosis method for subsea production system

Subsea production system is a large slowly changing system. Through fault diagnosis, the state of it can be got in time, and this can ensure the smooth production of oil and gas. When the training data is insufficient, the performance of data driven methods is poor. Influenced by slow changes, the performance of model-based fault diagnosis methods may fluctuate. For long-term stable monitoring of subsea production system, an intelligent full-stage stable fault diagnosis method for subsea production system is proposed. A fault diagnosis framework with both digital and model drives is proposed. Model-based diagnostic model is used to achieve accurate diagnosis under insufficient data. Data driven model is trained continuously to achieve stable diagnosis. A dynamic digital twin model is proposed to accelerate the process of training. A case from the South China Sea is used to study the performance of this method. The results show that this method is stable over a long period.

A Statistical Analysis of Crab Pulsar Giant Pulse Rates

A small number of pulsars are known to emit giant pulses (GPs), single pulses much brighter than average. Among these is PSR J0534+2200, also known as the Crab pulsar, a young pulsar with high GP rates. Long-term monitoring of the Crab pulsar presents an excellent opportunity to perform statistical studies of its GPs and the processes affecting them, potentially providing insight into the behavior of other neutron stars that emit bright single pulses. Here, we present an analysis of a set of 24,985 Crab GPs obtained from 88 hr of daily observations at a center frequency of 1.55 GHz by the 20 m telescope at the Green Bank Observatory, spread over 461 days. We study the effects of refractive scintillation at higher frequencies than previous studies and compare methods of correcting for this effect. We also search for deterministic patterns seen in other single-pulse sources, possible periodicities seen in several rotating radio transients and fast radio bursts, and clustering of GPs like that seen in the repeating fast radio burst FRB 121102.

OxcarNet: sinc convolutional network with temporal and channel attention for prediction of oxcarbazepine monotherapy responses in patients with newly diagnosed epilepsy

Objective.Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the importance of precise prediction of treatment outcomes with the initial AED regimen in patients with epilepsy.Approach. We introduce OxcarNet, an end-to-end neural network framework developed to predict treatment outcomes in patients undergoing oxcarbazepine monotherapy. The proposed predictive model adopts a Sinc Module in its initial layers for adaptive identification of discriminative frequency bands. The derived feature maps are then processed through a Spatial Module, which characterizes the scalp distribution patterns of the electroencephalography (EEG) signals. Subsequently, these features are fed into an attention-enhanced Temporal Module to capture temporal dynamics and discrepancies. A channel module with an attention mechanism is employed to reveal inter-channel dependencies within the output of the Temporal Module, ultimately achieving response prediction. OxcarNet was rigorously evaluated using a proprietary dataset of retrospectively collected EEG data from newly diagnosed epilepsy patients at Nanjing Drum Tower Hospital. This dataset included patients who underwent long-term EEG monitoring in a clinical inpatient setting.Main results.OxcarNet demonstrated exceptional accuracy in predicting treatment outcomes for patients undergoing Oxcarbazepine monotherapy. In the ten-fold cross-validation, the model achieved an accuracy of 97.27%, and in the validation involving unseen patient data, it maintained an accuracy of 89.17%, outperforming six conventional machine learning methods and three generic neural decoding networks. These findings underscore the model's effectiveness in accurately predicting the treatment responses in patients with newly diagnosed epilepsy. The analysis of features extracted by the Sinc filters revealed a predominant concentration of predictive frequencies in the high-frequency range of the gamma band.Significance. The findings of our study offer substantial support and new insights into tailoring early AED selection, enhancing the prediction accuracy for the responses of AEDs.

Design and application of a prefabricated structure for large-span arch open-cut highway tunnels

To enhance construction efficiency, improve lining quality, and reduce project costs, this study proposes a prefabricated arch structure scheme for large-span open-cut highway tunnels, specifically targeting the ongoing construction of an eight-lane open-cut tunnel on Chongqing’s Xinsen Avenue. By establishing a finite element “load-structure” integrated calculation model and adopting an iterative analysis method, the impact of joint stiffness variations on internal force distribution was determined. Combined with long-term field monitoring and measurements, a thorough analysis was conducted on the performance of the prefabricated arch structure during different construction stages. The results indicate that after assembly into a ring, the structure undergoes a self-adaptive adjustment phase, during which the joints experience significant stress and deformation. Following top-layer backfilling and water level restoration, the axial force on the joints increases, while stress and deformation decrease, exerting a positive effect on the joint bearing capacity. From the construction period to post-opening operations, the stress variation experienced by the joints remains within a controllable range, demonstrating substantial safety margins. This fully validates the safety and reliability of the prefabricated arch structure scheme.

ASO Visual Abstract: Current State of Evidence Based Long-Term Monitoring Protocols for Breast Plastic Surgery Patients

ASO Visual Abstract: Current State of Evidence Based Long-Term Monitoring Protocols for Breast Plastic Surgery Patients

A Vision and Proof of Concept for New Approach to Monitoring for Safer Future Smart Transportation Systems.

Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring.

SzCORE: Seizure Community Open-Source Research Evaluation framework for the validation of electroencephalography-based automated seizure detection algorithms.

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the EEG 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine-learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open-Source Research Evaluation) framework and benchmark are made publicly available along with an open-source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.

Long-term Safety Monitoring and Efficacy Status of Infliximab and its Biosimilars in Psoriasis Management

Abstract: Infliximab (INF), a murine human monoclonal antibody, is a substantially more successful biologic than topical drugs for treating mild to severe psoriasis because it clears the skin rapidly due to its fast onset of action. Loss of responsiveness over time and some adverse effects, especially the experience of infusion reactions, are the major causes of non-compliance with INF medication. Therefore, evaluation of the long-term reliability of anti-tumor necrosis factor (TNF) medications is necessary for the assessment of the risks associated with long-term anti-TNF therapy. For psoriasis, there are registered safety statistics; however, these individuals might not receive the same level of care as those in a randomized study. Few assessments of the safety of anti- TNF medications across indications, including their biosimilars, are present, but it’s still unknown how anti-infliximab antibodies arise and produce harmful effects. INF biosimilars, when subjected to human studies to reduce cost and improve access, provide therapeutic benefits with associated adverse events, showing variations in incidence depending upon varying patient populations and no new safety indications. During therapy, certain individuals develop antibodies against INF, which are believed to be linked to a loss of response (LOR). Additional research aimed at identifying individuals who are susceptible to treatment resistance is likely to assist doctors in accurately selecting the appropriate candidates for anti-TNF-α therapy and enhancing the long-term effectiveness of the treatment. From clinical studies, we expect to learn about how to utilize INF or its biosimilars more effectively in the management of psoriasis. Therefore, the paper focuses on the efficacy and safety monitoring of INF and developed biological therapies.

A Sensor Probe with Active and Passive Humidity Management for In Situ Soil CO2 Monitoring.

Soil CO2 concentration and flux measurements are important in diverse fields, including geoscience, climate science, soil ecology, and agriculture. However, practitioners in these fields face difficulties with existing soil CO2 gas probes, which have had problems with high costs and frequent failures when deployed. Confronted with a recent research project's need for long-term in-soil CO2 monitoring at a large number of sites in harsh environmental conditions, we developed our own CO2 logging system to reduce expense and avoid the expected failures of commercial instruments. Our newly developed soil probes overcome the central challenge of soil gas probes-surviving continuous exposure to soil moisture while remaining open to soil gases-via three approaches: a 3D printed housing (economical for small-scale production) following design principles that correct the usual water permeability flaw of 3D printed materials; passive moisture protection via a hydrophobic, CO2-permeable PTFE membrane; and active moisture protection via a low-power micro-dehumidifier. Our CO2 instrumentation performed well and yielded a high-quality dataset that includes signals related to a prescribed fire as well as seasonal and diel cycles. We expect our technology to support underground CO2 monitoring in fields where it is already practiced and stimulate its expansion into diverse new fields.

Influence of increasing noise at the offshore wind farm area on fish vocalization phenology: A long-term marine acoustical monitoring off the foremost offshore wind farm in Taiwan

The rapid increase of offshore projects at Taiwan Strait in recent decade has been debated for elevated noise levels. However, there are no studies on long-term assessment of noise levels and impact of noise on marine organisms. The passive acoustic monitoring was conducted at the foremost wind farm area in Taiwan to assess the sound levels and the impact of noise on fish vocalization behavior. Predominately, in the soundscape around the Taiwan Strait, two chorusing types (Type 1 and Type 2) from the Sciaenid family of fishes exist. Ambient sound levels significantly increased from 2014 to 2019, while the chorusing Types 1 and 2 were observed in a lower percentage of the recordings. Additionally, chorusing peak intensity and duration significantly reduced over the years for both choruses. This is the first field-based evidence to demonstrate the consequences of increasing anthropogenic noise having the potential to alter the vocalization behavior of the fish.

In-season monitoring of harvest and effort from a large-scale subsistence salmon fishery in western Alaska

In-season management of salmon harvest requires real-time data. Specifically, following a brief period of open fishing, knowledge of harvest outcomes is useful when deciding the nature of subsequent fishing periods. This in-season management strategy is relatively new to the lower Kuskokwim River of western Alaska, where depressed salmon runs have caused restrictions to the subsistence fishery. We have developed an in-season monitoring program to rapidly inform managers about fishery outcomes from short-duration (6-24 hours) fishery openings. Completed trip information and one or more aerial surveys are combined to estimate daily effort and harvest from drift gillnet fishers spanning 11 communities and ~130 river kilometers. We present a re-analysis of the 40 monitored openers in June-July 2016-2023, and validate harvest estimates of Chinook, chum, and sockeye salmon by comparing them to post-season estimates derived from an independent long-term monitoring program. Our results indicate that the program has produced estimates of sufficient quality to inform in-season managers, although it will likely need alterations to be successful in years with less restricted fishing.

Experimental and theoretical research on deformation monitoring of distributed piezoelectric inclinometer tube

To improve horizontal displacement monitoring in geotechnical engineering, researchers have focused on developing distributed, automated, and cost-effective inclinometers capable of long-term monitoring. However, existing technologies cannot fully satisfy these requirements. This study proposes a novel piezoelectric inclinometer tube that incorporates a sensor-enabled piezoelectric geocable (SPGC), which was mounted along the surfaces of three tubes made of different materials. The distributed strain along the inclinometer tube was measured using the impedance–strain effect of the SPGC, and a deflection curve was obtained. The results of the failure test showed that the piezoelectric inclinometer tube remained within its measurement range before breaking. Furthermore, the placement of the SPGC on the outer surface of the tube demonstrated superior monitoring effectiveness under bending deformation, providing qualitative insights into the deformation and fracture behavior of the tube. Loading and unloading tests demonstrated excellent reproducibility of strain in the piezoelectric inclinometer tube. Among the tested materials, the inclinometer tube made of acrylonitrile butadiene styrene exhibited acceptable elasticity, large bending displacement, and a high strain transfer rate, making it ideal for monitoring significant deformations in rock and soil. A linear calibration equation was established for the normalized impedance change–strain relationship of the piezoelectric inclinometer tube through calibration tests. Furthermore, a theoretical model was derived to convert the normalized impedance change to the deflection of the piezoelectric inclinometer tube using the finite difference method. Theoretical and experimental values were significantly consistent, with strain and deflection measurement accuracies of 10 με and 0.1 mm, respectively, and an average calculation error of less than 4%. These results are expected to improve the distributed monitoring of horizontal displacement in rock and soil.

Uncrewed surface vehicles (USVs) as platforms for fisheries and plankton acoustics

Abstract Uncrewed surface vehicles (USVs) equipped with echosounders have the potential to replace or enhance acoustic observations from conventional research vessels (RVs), increase spatial and temporal coverage, and reduce cost and carbon emission. We discuss the objectives, system requirements, infrastructure, and regulations for using USVs with echosounders to conduct ecological experiments, acoustic-trawl surveys, and long-term monitoring. We present four example applications of USVs with lengths <8 m, and highlight some advantages and disadvantages relative to RV-based data acquisitions. Sail-driven USVs operate continuously for months and are more mature than motorized USVs, but they are slower. To maintain the pace of an RV, multiple sail-powered USVs sample in coordination. In comparison, motorized USVs can travel as fast as RVs and therefore may facilitate a combined survey, interleaving USV and RV transects, with RV-based biological sampling. Important considerations for all USVs include platform design, noise and transducer motion mitigation, communications and operations infrastructure, onboard data processing, biological sampling approach, and legal requirements. This technology is evolving and applied in multiple disciplines, but further development and institutional commitment are needed to allow USVs equipped with echosounders to become ubiquitous and useful components of a worldwide network of autonomous ocean observation platforms.

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances.

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

Global Perspectives on Mycotoxin Reference Materials (Part I): Insights from Multi-Supplier Comparison Study Including Aflatoxin B1, Deoxynivalenol and Zearalenone.

We conducted a comprehensive examination of liquid mycotoxin reference standards. A total of 30 different standards were tested, each containing 10 samples of three distinct substances: Aflatoxin B1, Deoxynivalenol, and Zearalenone. The standards were sourced from 10 different global market leading manufacturers. To facilitate comparison, all the standard sets were adjusted to the same concentration level. The standards were analyzed using the techniques LC-MS/MS, HPLC-DAD, and LC-HRMS to assess their quality attributes. Regarding the validation of the reference values, it was observed that 30% of the suppliers provided reference standards that were either below the lower acceptance limit or above the higher acceptance limit, confirmed by both the LC-MS/MS and HPLC-DAD methods. Furthermore, a total of 12 impurities were found in the DON standards, 10 in the AFB1 standards, and 8 in the ZON standards, distributed across all the suppliers. Therefore, this study suggests relevant adjustments to the ISO 17034 standard, proposing that the purity of a raw material should be uniformly based on q-NMR analysis, as most manufacturers state the purity of their certificates is determined using HPLC-UV or LC-MS/MS. Liquid standards with a shelf life of ≤1 year should not exceed an uncertainty of 3%. Standards that have a longer shelf life should not have more than 5% uncertainty. This study also emphasizes the importance of stability. The standards should undergo continuous long-term monitoring; otherwise, products may exhibit a target value of only 80%, as seen in one instance. It is also recommended to include proof of HPLC and LC-MS/MS analyses on the certificate of each released batch of a final product.

Long-Term Monitoring and Management of Genetically Modified Canola in Natural Environments: A 15-Year Study

The unintentional release of living modified organisms (LMOs) into natural environments poses potential ecological risks, particularly in terms of gene flow and biodiversity loss. Since 2009, the Ministry of Environment (MOE) in South Korea has conducted an extensive monitoring project to detect and manage LMOs, with a primary focus on LM canola. This study evaluates the outcomes of the LMO monitoring project over the past 15 years (2009–2023), analyzing the distribution, persistence, and management of LM canola across various environments. Our findings reveal that LM canola predominantly proliferates along roadsides, with occasional occurrences at festival and planting sites. Out of 10,571 monitored sites, 4326 suspicious samples were collected, with a significant increase observed in 2017, underscoring the ongoing risk of accidental releases. This study highlights the critical role of specific environments in the spread of LM canola, and assesses the effectiveness of post-management strategies in controlling these populations. The National Institute of Ecology (NIE) has developed and implemented advanced monitoring protocols and post-management systems tailored to the characteristics of the monitoring sites and the nature of the LMOs. These efforts have been effective in controlling the spread of LM canola, thereby helping to preserve the biodiversity of South Korea’s natural environments. In conclusion, the proactive and adaptive strategies employed by the NIE are essential for mitigating the ecological risks associated with LMOs. Our study emphasizes the importance of on-going vigilance and the continuous refinement of monitoring and management practices to safeguard biodiversity and ecosystems.

A probabilistic hazard assessment for cyanobacterial toxins accounting for regional geography and water body trophic status

Under climate change scenarios freshwater eutrophication is expected to increase, and with it the occurrence of cyanobacterial toxin-producing harmful algal blooms. In the current study, microcystin toxin occurrence data from literature sources and a long-term provincial monitoring program were used to conduct a probabilistic hazard assessment for Alberta, Canada. The large temporal and spatial range of data makes Alberta a model system for identifying regional geography and water body trophic status factors driving toxin concentrations. Environmental exposure distributions of microcystin concentrations were plotted and used to identify the likelihood of a given sample exceeding water guideline values as a function of regional geography, total phosphorus and chlorophyll-a concentration. This process identified regions with intensive cultivation and those most prone to water deficits associated with climate change to be most associated with exceedances of regulatory guideline values. Elevated phosphorus and chlorophyll-a concentrations were also drivers of toxin occurrence. This assessment can be used to identify water bodies of greatest risk to human and animal populations from cyanotoxins and thereby inform regulators as to most effective monitoring strategies.

Genetic rescue often leads to higher fitness as a result of increased heterozygosity across animal taxa.

Biodiversity loss has reached critical levels partly due to anthropogenic habitat loss and degradation. These landscape changes are damaging as they can fragment species distributions into small, isolated populations, resulting in limited gene flow, population declines and reduced adaptive potential. Genetic rescue, the translocation of individuals to increase genetic diversity and ultimately fitness, has produced promising results for fragmented populations but remains underutilized due to a lack of long-term data and monitoring. To promote a better understanding of genetic rescue and its potential risks and benefits over the short-term, we reviewed and analysed published genetic rescue attempts to identify whether genetic diversity increases following translocation, and if this change is associated with increased fitness. Our review identified 19 studies that provided genetic and fitness data from before and after the translocation; the majority of these were on mammals, and included experimental, natural and conservation-motivated translocations. Using a Bayesian meta-analytical approach, we found that on average, genetic diversity and fitness increased in populations post translocations, although there were some exceptions to this trend. Overall, genetic diversity was a positive predictor of population fitness, and in some cases this relationship extended three generations post-rescue. These data suggest a single translocation can have lasting fitness benefits, and support translocation as another tool to facilitate conservation success. Given the limited number of studies with long-term data, we echo the need for genetic monitoring of populations post-translocation to understand whether genetic rescue can also limit the loss of adaptive potential in the long-term.

Assessment of land cover degradation due to mining activities using remote sensing and digital photogrammetry

Appropriate environment management requires an understanding of how mining activity alters environmental characteristics and how those changes affect an area. Therefore, to reduce the adverse effects of mining activity on the land, it becomes crucial to have relevant information about responses to environmental degradation. This study aims to assess the impact of semi-mechanised and artisanal mining activities on the land cover using remote sensing data and photogrammetric analysis, in the Mbale locality, Northern Cameroon. For this purpose, the maximum likelihood classification algorithm of the supervised classification method combined with field surveys was used to map environmental changes, based on Sentinel-2 images of 2019, 2021, and 2023. Normalized Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Brithness index (BI), and Soil crust index (SCI), were calculated to assess changes in vegetation, bare soil, water body, and exploited area. The orthophoto obtained from photogrammetric processing was performed to outline river network change through visual interpretation techniques and to calculate the volume of pits created by mining. The result of classified images indicated that vegetation cover decreased by 11.74% over the studied years. However, bare soil and exploited areas increased by 9.2% and 5.4% respectively. The calculated spectral indices show that between 2019 and 2023 the locality of Mbale considerably lost its vegetation cover, in favor of bare soil. The color of the soil and the granulometric size of the topsoil have also changed. The photogrammetry analysis highlighted the deviation of the main river and estimated the volume of pits created by mining activity to 22188.7 m3. The mining activities caused a loss of the vegetation cover, generated big pits, and multiple deviations of the Lom River from its natural course, which have a substantial negative influence on the ecosystem. Such data can be used for long-term environmental management, reclamation and rehabilitation monitoring, and mining area restoration.