Scale Monitoring Research Articles

New Lepage-type control charts for joint monitoring of location and scale

This paper introduces two new Shewhart Lepage-type control charts for jointly monitoring the location and scale parameters of continuous processes. The first is based on the van der Waerden (VW) and Ansari-Bradley (AB) tests while the other is on the VW and Mood tests. Statistical performances, in terms of average run length (ARL), of these charts are evaluated numerically and compared with that of their two competitor charts in the existing literature under four distributional models for the process output, namely, the normal, lognormal, Laplace, and logistic. The numerical results show that irrespective of the process distribution, one of the proposed charts (based on VW and Mood tests) has the best performance among all the four charts in the detection of simultaneous shifts in both the parameters of all sizes as well as in the detection of shifts in the scale that have not been accompanied by shifts in the location (only-scale shifts) of all sizes. Moreover, the other proposed chart (based on VW and AB tests) performs better than one of the competitor charts in the existing literature for detecting simultaneous shifts as well as only-scale shifts of all sizes. In addition, it performs better than the other competitor chart for detecting the simultaneous shifts consisting of a large location shift and a small scale shift, however, performs worse than that for detecting the simultaneous shifts consisting of a small location shift and a scale shift of any size, as well as for detecting the only-scale shifts of all sizes. All the four charts perform almost similarly in the detection of only-location shifts of all sizes. Regression models are provided for the proposed charts, which facilitate their designs in practice. Finally, the practical implementation of the proposed charts is illustrated through a real-data example.

BioSense: An automated sensing node for organismal and environmental biology

Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.

Evaluation of reflective fiber-optic surface plasmon resonance sensor for monitoring scale deposition.

A reflective surface plasmon resonance (SPR) sensor was evaluated for real-time monitoring of scale deposition. The sensor consists of an optical fiber, only 5mm at the gold-coated tip of the sensing area. The effect of silica growth on the sensor response was evaluated using a Na2SiO3 solution. The sensitivity of the sensor to silica was 1.6 ± 0.3nm per one immersion in the solution of 1000mg/L (as SiO2) at 85°Cand subsequnt air drying, as indicated by theSPR peak shift. The amount of silica deposited on the gold surface was measured by the quartz crystal microbalancemethod, and the SPR sensitivity of 0.089nm/ng to silica mass was obtained. The detection limit (3σ) of the SPR sensor was 17ng, correspondingto a thickness of 2.5nm for amorphous silica. The SPR sensor wastested in geothermal brine sampled at the Sumikawa Geothermal Power Plant, where a clear SPR shift was observed, suggesting the effectiveness of the SPR sensor for scale monitoring.

Gradient Scale Monitoring for Federated Learning Systems

As the computational and communicational capabilities of edge and IoT devices grow, so do the opportunities for novel Machine Learning solutions. This leads to an increase in popularity of Federated Learning (FL), especially in cross-device settings. However, while there is a multitude of ongoing research works analyzing various aspects of the FL process, most of them do not focus on issues of operationalization and monitoring. For instance, there is a noticeable lack of research in the topic of effective problem diagnosis in FL systems. This work begins with a case study, in which we have intended to compare the performance of four selected approaches to the topology of FL systems. For this purpose, we have constructed and executed simulations of their training process in a controlled environment. We have analyzed the obtained results and encountered concerning periodic drops in the accuracy for some of the scenarios. We have performed a successful reexamination of the experiments, which led us to diagnose the problem as caused by exploding gradients. In view of those findings, we have formulated a potential new method for the continuous monitoring of the FL training process. The method would hinge on regular local computation of a handpicked metric - the gradient scale coefficient (GSC). We then extend our prior research to include a preliminary analysis of the effectiveness of GSC and average gradients per layer as potentially suitable for FL diagnostics metrics. In order to perform a more thorough examination of their usefulness in different FL scenarios, we simulate the occurrence of the exploding gradient problem, vanishing gradient problem and stable gradient serving as a baseline. We then evaluate the resulting visualizations based on their clarity and computational requirements. We introduce a gradient monitoring suite for the FL training process based on our results.

The Effect of Effleurage Massage on Reducing the Intensity of Pain in the First Stage of Active Labor

Childbirth is a process of expelling the results of fertilization through a flask from inside the uterus out of the uterus through the vagina spontaneously. Pain is an experience of feeling uncomfortable and unpleasant from a person's sensory and emotional side. The purpose of this study was to determine the effect of Mensage Effinage on reducing the intensity of labor pain in the first active phase. This study used a one-group pre-test post-test design experiment with a sample of 34 respondents. Data was collected using a Verbal Descriptor Scale (VDS) monitoring observation sheet. Data analysis using the normality test on the Wilcoxon Sign Ronk Anamis pararsenic test because the data is not normally distributed or has Wilcoxon status. The results of the study showed that the intensity of pain in the first stage of labor in the active phase can be reduced through effluent massage therapy. This is evidenced by the results of data analysis where p = 0.000 <0.05 so that the Ho result is rejected, H1 is accepted. Effluent Massage Therapy can be used as an alternative that needs to be applied to reduce pregnancy pain in mothers giving birth in the first phase of pregnancy. The conclusion is that massage effluent can reduce the intensity of pain in pregnant women in phase 1.

Fishing in the gene-pool: implementing trawl-associated eDNA metaprobe for large scale monitoring of fish assemblages

Marine biodiversity monitoring in the Mediterranean’s increasingly threatened ecosystems is crucial for effective ecosystem conservation and management. Here, we leveraged the Mediterranean International Trawl Survey program (MEDITS) to implement eDNA sampling through the recently tested ‘metaprobe’ procedure and characterize fish assemblages in three separate areas off the Italian coasts: Northern Adriatic Sea (NoAS), Ligurian and Northern Tyrrhenian Sea (LNTS), and Sardinian Sea (SaS). By combining the information from two homologous mitochondrial 12S metabarcodes––i.e., Elas02 and Tele02 targeting elasmobranchs and teleosts, respectively––we identified 108 species, over 60% of which overlapped with those caught by the trawl net. We produced an accurate reconstruction of fish community composition of the examined sites, reflecting differences in species assemblages linked with both geographic area and depth range. Metaprobe eDNA data consistently returned a biodiversity ‘bonus’ mostly consisting of pelagic taxa not captured through bottom trawl surveys, including rare and endangered taxa (e.g., elasmobranchs). Overall, the spatial characterisation of the assemblages across the surveyed areas was better delineated and more robust using eDNA metabarcoding than trawl data. Our results support the operationalisation of the metaprobe as a simple, inexpensive, versatile sampling tool, in association with pre-existing ship surveys, to overcome many of the limitations of marine data collection and strengthen marine management.

Large scale monitoring of a highway bridge with remote sensing and distributed fiber optic techniques during load tests

Many bridges in western countries are at the end of their lifetime and hence Structural Health Monitoring is vital to ensure their safe operation. Load test are one way to assess the utilization grade and to decide if actions have to be taken. We report on a comprehensive monitoring campaign of load tests of a large highway bridge on the Brenner highway. The Brenner highway is the main alpine truck crossing passages from Germany to Italy. Several different remote sensing techniques like static and dynamic laser scanning, robotic total station measurements and interferometric radar were used to determine displacements of the bridge deck and girders. Strain changes and bending were observed with distributed fiber optic sensing. Furthermore, the movements of the bridge bearings were determined with IoT tilt sensors. Data was acquired during static and dynamic load tests. We demonstrate that the utilization grade of the bridge can be assessed by an integrated analysis of the different sensor data in combination with a numerical model of the structure. As a consequence of the investigations it was decided by the highway operator to close one of the three lanes of the bridge.

Joint monitoring of location and scale for modern univariate processes

Autocorrelated sequences of individual observations arise in many modern-day statistical process monitoring (SPM) applications. Often times, interest involves jointly monitoring both process location and scale. To jointly monitor autocorrelated individuals data, it is common to first fit a time series model to the in-control process and subsequently use this model to de-correlate the observations so that traditional individuals and moving-range (I-MR) charts can be applied. If the time series model is correctly specified such that the resulting residuals are normal and independently distributed, then applying I-MR control charts to the residual process should work well. However, if the residual process deviates from normality and/or, due to time series model misspecification, contains levels of autocorrelation, the false alarm rate of such a strategy can dramatically rise. In this paper we propose improvements to a recently published distribution-free joint monitoring strategy that permits tighter control of the in-control average run length when model misspecification is a concern. We evaluate its average run length performance and conclude that the improved joint monitoring strategy proposed in this work is a very useful tool for today’s modern SPM practitioner. The proposed joint monitoring scheme is then applied to a real additive manufacturing process to illustrate its implementation in modern practice.

Validation of Acceleration Response Modelling for Modular High Rise Structures through Full Scale Monitoring

Validation of Acceleration Response Modelling for Modular High Rise Structures through Full Scale Monitoring

Experimental investigation on dynamic deformation characteristics of the overlying strata in backfilling strip mining at different time scales.

The dynamic subsidence disaster caused by underground mining of coal resources is a complex spatiotemporal process, which is a common disaster in mining areas. The backfilling strip mining technology is a green and sustainable coal mining method, which has been commonly used to reduce the subsidence disaster of the overlying strata and protect surface buildings. The transient deformation is the main reason of surface buildings damage; therefore, in this study, the similar material model was used to research dynamic deformation characteristics of the overlying strata in backfilling strip mining at different time scales, and the optical image method was employed to monitor and obtain the movement data of the overlying strata automatically. The data analysis shows that there is a time-scale effect in mining subsidence. The deformation of the overlying strata increases instantaneously at a certain time under the monitoring of small time scale, and this phenomenon gradually disappears as time scales increase. According to the subsidence velocity of small time scale, the subsidence state of the overlying strata can be further divided into the abrupt subsidence state and the gentle subsidence state. This is really significant for promoting the development of the backfilling strip mining technology and preventing the damage of surface buildings.

DeepSTARia: enabling autonomous, targeted observations of ocean life in the deep sea

The ocean remains one of the least explored places on our planet, containing myriad life that are either unknown to science or poorly understood. Given the technological challenges and limited resources available for exploring this vast space, more targeted approaches are required to scale spatiotemporal observations and monitoring of ocean life. The promise of autonomous underwater vehicles to fulfill these needs has largely been hindered by their inability to adapt their behavior in real-time based on what they are observing. To overcome this challenge, we developed Deep Search and Tracking Autonomously with Robotics (DeepSTARia), a class of tracking-by-detection algorithms that integrate machine learning models with imaging and vehicle controllers to enable autonomous underwater vehicles to make targeted visual observations of ocean life. We show that these algorithms enable new, scalable sampling strategies that build on traditional operational modes, permitting more detailed (e.g., sharper imagery, temporal resolution) autonomous observations of underwater concepts without supervision and robust long-duration object tracking to observe animal behavior. This integration is critical to scale undersea exploration and represents a significant advance toward more intelligent approaches to understanding the ocean and its inhabitants.

Application of Electrical Resistivity Tomography in Geotechnical and Geoenvironmental Engineering Aspect

Electrical resistivity tomography (ERT) has turned out to be one of the most applied and user-friendly geophysical methods in geotechnical and geoenvironmental research. ERT is an emerging technology that is becoming popular nowadays for investigating subsurface conditions. Multiple attributes of the technology using various electrode configurations significantly reduce measurement time and are suitable for applications even in hardly accessible mountain areas. It is a noninvasive test for subsurface characterization and a very sensitive method used to determine geophysical properties, i.e., structural integrity, water content, fluid composition, etc. This paper aimed to elucidate the ERT technique’s main features and applications in geotechnical and geoenvironmental engineering through four case studies. The first case study investigated the possible flow paths and areas of moisture accumulation after leachate recirculation in a bioreactor landfill. The second case study attempted to determine the moisture variation along highway pavement. The third case study explored the slope failure investigation by ERT. The fourth case study demonstrated the efficiency of the ERT method in the landfill evapotranspiration (ET) cover to investigate moisture variation on a broader scale and performance monitoring. In all of the four cases, ERT exhibited promising performance.

Basin scale monitoring of microplastics and phthalates in sediments from the Persian Gulf and the Gulf of Makran using GIS-based algorithms: Insights towards spatial variation and potential risk assessment

Information on sedimentary microplastics and phthalates has been restricted to the coastal regions of the Persian Gulf and the Gulf of Makran. Our basin-wide study monitored their levels, spatial behaviors, and potential risks using GIS-based techniques. Microplastics and phthalates ranged from 5 to 75 particles/kg d.w and 0.004–1.219 μg g−1 d.w, respectively. Microplastics were in the size category of 100 μm to 3 mm, and black microfibers (< 1 mm) and high-density polymers were dominant. The total number of microplastics was between 356.333 × 1012 and 469.075 × 1012 particles in the surface sediments of the studied regions (confidence interval = 99 %). Diethylhexyl phthalate (DEHP) and Di-isobutyl phthalate contributed 88 % of detected phthalates. Significant correlations among microplastic abundance, total phthalates, and DEHP were distinguished (p < 0.05). Overall, the findings reiterated the widespread presence of microplastics and a potential link between phthalates and microplastics. Semi-variogram, cluster Voronoi polygons, and Trend analysis identified spatial outliers and major deposition sites of microplastics and phthalates and consequently outlined the localities where upcoming studies should be concentrated. A hotspot of potential risks was marked using Fuzzy logic and GIS-based algorithms in the Sea of Makran, covering an area equal to 342. 99 km2.

Advanced EIS-Based Sensor for Online Corrosion and Scaling Monitoring in Pipelines of Geothermal Power Plants

Corrosion and scaling in metal pipelines are the major issues in the exploitation of geothermal sources. Geothermal fluids are complex mixtures consisting of dissolved gases and high-salinity solutions. This creates very aggressive environments primarily due to the high concentrations of carbon dioxide (CO2), hydrogen sulfide (H2S), chlorides, and other chemical species. Besides, the high temperature of the brines also increases corrosion rates, which can lead to failures related to stress and fatigue corrosion. On the other hand, reinjection of cooled brine exiting the heat exchanger favors the onset of scaling, since the chemicals dissolved in geothermal waters may tend to precipitate promoting inorganic depositions on the casing. Corrosion and scaling phenomena are difficult to detect visually or monitor continuously. Standard techniques based on pH, temperature pressure, electrical resistance measurements, chemistry composition, and physical properties are habitually applied as indirect methods for corrosion rate control. These methods, however, lack enough robustness for accurate and reliable measuring of the corrosion behavior of materials. To address this issue, a novel system has been proposed for the continuous monitoring of corrosion degradation caused by the effect of the geothermal brines. The present work aims to design, develop, and validate a dedicated electrochemical-based test system for online and onsite monitoring of the corrosion rate and scaling growth occurring on different materials exposed to real operating conditions. This system uses non-standard methods based on electrochemical impedance spectroscopy (EIS) to obtain quantitative data related to the material quality. It can be used to track the condition of the pipeline, reducing the operation and maintenance (O&amp;M) costs and shutdown times. By providing early corrosion rate data, this system allows the prediction of failures in critical units of the plant.

Remote monitoring of patients with rheumatoid arthritis in a low disease activity state: a mixed methods evaluation across six hospitals in London, UK.

This study evaluated the scale-up of a remote monitoring service, capturing monthly Rheumatoid Arthritis Impact of Disease scores and patient-generated text messages, for patients with rheumatoid arthritis (RA; in remission or with low disease activity) attending routine outpatient clinics across six hospitals. We explored patients and staff experiences and implementation outcomes. A pragmatic, mixed methods approach was used, with active patient involvement throughout. We undertook a rapid review, analysed service-level data, and conducted a patient survey and patient and staff interviews, informed by the Capability, Opportunity, Motivation, Behaviour (COM-B) and Exploration, Preparation, Implementation, Sustainment (EPIS) theoretical frameworks. The review included 37 articles, covering themes of patient and clinician acceptability, engagement, feasibility and clinical impact. Service-level data (n = 202) showed high levels of patient engagement with the service. The patient survey (n = 155) showed patients felt the service was easy to use, had confidence in it and felt it improved access to care. Patient interview (n = 22) findings mirrored those of the survey. Motivating factors included increased responsiveness and ease of contact with clinical teams. Views from staff interviews (n = 16) were more mixed. Some implementation barriers were specific to roll-out sites. Prioritization of staff needs was emphasized. Patients were positive about the service and engagement was high. Staff views and engagement were more mixed. Results suggest that equal levels of patient and staff engagement are required for sustainability. These findings further our understanding of the implementation challenges to scaling remote monitoring interventions for patients with rheumatoid arthritis in routine care settings.

A Tale of Two Sisters: The Southerner Pinna rudis Is Getting North after the Regional Extinction of the Congeneric P. nobilis (Mollusca: Bivalvia)

In the Mediterranean Sea, the bivalve genus Pinna is represented by two species: the endemic Pinna nobilis and the (sub)tropical Atlantic Pinna rudis. P. rudis is generally less common and mostly restricted to the warmer regions of the western Mediterranean. However, since a mass mortality event, caused by a pathogen infection, has brought P. nobilis to the brink of extinction, records of P. rudis have increased in several Mediterranean regions, where it had not been previously observed. This paper reports on the presence of several P. rudis individuals in the Ligurian Sea, the northernmost reach of this species in the western Mediterranean. P. rudis has become increasingly common between 2021 and 2023, with a total of 28 new records from seven localities along the Ligurian coast. The size of the individuals and their estimated growth rate (3.6 cm·a−1) indicated that a recruitment event most likely took place in summer 2020, when P. nobilis was no longer present in the area. Our observations suggest that the recruitment success of P. rudis increased following the decline of P. nobilis. However, considering the thermophilic nature of P. rudis, in all likelihood, the ongoing water warming is playing a crucial role in the successful establishment of this species in the Ligurian Sea. A full understanding of the recent range expansion of P. rudis in the Mediterranean is far from being achieved, and whether P. rudis will be able to fulfil the ecological role of P. nobilis is difficult to predict. Large scale monitoring remains the only effective way to know about the future of Pinnids in the Mediterranean Sea.

Salinization of groundwater in shale gas extraction area in the Sichuan Basin, China: Implications for water protection in shale regions with well-developed faults

The expanding growth of shale gas development has sparked global concern over water-related environmental issues. However, research on groundwater contamination in shale gas areas in China remains limited, impeding environmentally friendly industry practices. To address this gap, we investigated the Wufeng-Longmaxi shale region in the Sichuan Basin, encompassing both operational and prospective shale gas extraction sites, to assess the effects of shale gas operations on shallow groundwater quality. We found there was no significant correlation between groundwater quality and the minimum distance from the shale gas well pads, and some groundwater samples located far from shale gas well pads, rather than those close to pads, were salinized. These findings suggest minimal impacts from shale gas drilling and hydraulic fracturing. The salinized groundwater samples are characterized by high salinity levels and ion concentrations, and are located near fault zones. The primary source of shallow groundwater salinization was derived from the Triassic formation brines confirmed through the assessment of the sensitivity and conservative mixing models. Faults in the study area were identified as pathways for the upward migration of Triassic brines, evidenced by the proximity of salinized samples to fault zones. However, further investigation is required to ascertain whether shale gas extraction activities have induced the migration of formation brines. The occurrence and reactivation of faults, induced by microseismic activities, may pose an increased risk of groundwater contamination in tectonically complex fault zones during shale gas extraction. Therefore, it is imperative to enhance extraction strategies and technologies, particularly in shale regions with well-developed faults, such as optimizing well placement regulation, controlling hydraulic fracturing scale, and strengthening environmental monitoring. By shedding light on potential environmental ramifications of shale gas extraction, especially in fault-rich regions, our study informs water protection strategies and the sustainable advancement of the shale gas industry.

Tracking landscape scale vegetation change in the arid zone by integrating ground, drone and satellite data

AbstractA combined multiscale approach using ground, drone and satellite surveys can provide accurate landscape scale spatial mapping and monitoring. We used field observations with drone collected imagery covering 70 ha annually for a 5‐year period to estimate changes in living and dead vegetation of four widespread and abundant arid zone woody shrub species. Random forest classifiers delivered high accuracy (&gt; 95%) using object‐based detection methods, with fast repeatable and transferrable processing using Google Earth Engine. Our classifiers performed well in both dominant arid zone landscape types: dune and swale, and at extremes of dry and wet years with minimal alterations. This highlighted the flexibility of the approach, potentially delivering insights into changes in highly variable environments. We also linked this classified drone vegetation to available temporally and spatially explicit Landsat satellite imagery, training a new, more accurate fractional vegetation cover model, allowing for accurate tracking of vegetation responses at large scales in the arid zone. Our method promises considerable opportunity to track vegetation dynamics including responses to management interventions, at large geographic scales, extending inference well beyond ground surveys.

Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset

While remote sensing of grass senescence is addressed in the literature, knowledge of optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses is still limited. Notably, detection of senescent grass is important for understanding the available forage in rangeland environments and associated ecological implications. The free provision of remote sensing data from modern broadband multispectral sensors with improved spatial and spectral properties offers prospects for reliable and wall-to-wall monitoring of grassland senescence in rangeland ecosystems. The current study tested the potential of the modern multispectral remote sensing dataset (i.e., Sentinel 2 and Landsat) in mapping the senescent grass, and to identify the optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses. Locational information for both senescent and non-senescent grasses was acquired on the field and was used to train the classification process. A Random Forest classification approach was employed using the Landsat 8 and Sentinel 2 multispectral datasets to spectrally discern between senescent and non-senescent grasses. Our analysis yielded overall classification accuracies of 0.82 and 0.78 and kappa coefficients of 0.64 and 0.56 for Sentinel 2 and Landsat 8, respectively. Using the stepwise selection approach, the study further identified that the Red Edge Position (REP), and the visible green and red bands of the electromagnetic spectrum were the optimal waveband positions for separating between senescent and non-senescent grasses based on the broadband multispectral remote sensing. This study has demonstrated the value of the broadband multispectral remote sensing data in mapping autumn grassland senescence, and this lays a foundation for effective operational scale monitoring of foraging resources at the landscape scale, particularly during dry periods.

Spatial characterization of global heat waves using satellite-based land surface temperature

The warming world greatly suffers the increase of frequency, severity and duration of heat wave events, which would cause the significant societal and environmental damages and implications from local to global scales. In order to eliminate the limitations of site observations in accurate spatial extent identification and large scale monitoring, this study tried to investigate global heat waves in 2018 using spatial datasets of land surface temperature (LST) derived from AQUA TIR sensors. A 15-year daily maximum LST dataset was used to identify the hot days and heat wave events using the relative threshold method based on the probability density function analysis of LST sequences, then they were adopted in analyzing spatial patterns of duration, frequency and intensity of heat wave events using heat wave numbers (HWN), the frequency of the appeared heat wave days (HWF) and the amplitude of heat wave impacts (HWA) indices, respectively. Our results indicated that more heat wave events and longer heat wave duration happened in low and middle latitudes under the warmer land surface in 2018 than last decade, especially many heat wave events occurred in the environmentally fragile regions and the dense population area. Urban regions achieved the largest HWA by 45.5 ± 6.9℃. Both annual HWF and HWN highlighted seven hot spot regions influenced by heat waves, and most of them distributed around the latitude 30°N and the latitude 30°S. Extreme heat wave of hot spot regions in the Southern Hemisphere mainly happened in winter and spring in contrast to that in North Hemisphere appeared in summer and autumn. Dry climate could contribute to the occurrence of heat waves, and heat wave indices variations also implied the compound consequences between heat waves and drought. Our findings demonstrate that remote sensing datasets are capable of providing the continuous whole-Earth coverage of heat wave changes, and they would play more important roles in preventing or mitigating the impacts of extreme heat events on people and natural environment.