Monitoring Data Research Articles

Understanding Ionospheric and Geomagnetic Effects on Residual Biases in Radio Occultation Data for Stratospheric Climate Monitoring

AbstractHigher‐order residual ionospheric errors in Global Navigation Satellite System (GNSS) radio occultation (RO) data can induce a systematic residual ionospheric bias (RIB) in RO bending angles, which can impact stratospheric climate monitoring. The main RIB causes are the ray path splitting of dual‐frequency GNSS signals, the electron density distribution along the ray path and at the RO receiver location, and the geomagnetic field. In this study, we investigate the ionospheric and geomagnetic effects on RO‐retrieved stratospheric bending angle and temperature profiles by inspecting the kappa and bi‐local RIB correction methods, the current state‐of‐the‐art methods, using multiple RO satellite mission data in different ionospheric and geomagnetic conditions. We find that, globally, the layer mean/median RIBs of the kappa and bi‐local correction methods exhibit similar behaviors for different RO missions: the estimated bending angle RIBs reach about −0.025/−0.01 and −0.024/−0.008 μrad in the upper (40–45 km) and lower (30–35 km) stratosphere, respectively, while the temperature RIBs reach about −1.0/−0.3 and −0.2/−0.1 K in these layers. However, in the equatorial day time region, the RIB statistics of the mission results diverge. Both the kappa and bi‐local RIBs increase in magnitude with increasing ionization and geomagnetic field strength but show no increase with an increasing degree of ionospheric asymmetry. Overall, the more refined bi‐local method has higher capacity than the comparatively simple kappa method to represent the variability of the ionospheric and geomagnetic conditions that affect RO events. This is important for regional‐scale studies, where the geomagnetic term can be of key relevance.

Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.

Soft ground micro TBM jack speed and torque prediction using machine learning models through operator data and micro TBM-log data synchronization

Tunnel Boring Machines (TBMs) are pivotal in underground projects like subways, highways, and water supply tunnels. Predicting and monitoring jack speed and torque is crucial for optimizing TBM excavation efficiency. Conventionally, skilled operators manually adjust numerous tunnelling parameters to regulate the machine's progress. In contrast, machine learning (ML) algorithms offer a promising avenue where computers learn from operator actions to establish parameter relationships autonomously. This study introduces an innovative approach to enhancing operator monitoring and TBM data comprehension. A robust correlation between TBM operator behaviour and TBM logged data is established by leveraging an Optuna-assisted ML methodology—the research light on the intricate dynamics influencing TBM advance rate parameters. Operational data is collected from micro slurry tunnel boring machine (MSTBM) umbrella support excavations. The proposed framework harnesses Optuna, an advanced hyperparameter optimization platform, to dynamically refine jack speed and torque settings. Through meticulous analysis of the interplay between TBM operator decisions and real-time logged data, the AI model discerns patterns, empowering informed decision-making. Using Optuna, a range of models, including random forest (RF), K-nearest neighbours (kNN), decision tree (DT), XGBoost, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were automatically compared and tuned. The best model's (RF) performance is evaluated through a correlation coefficient (R2) of 96%, mean squared error (MSE) of 119.7, and mean absolute error (MAE) of 4.42 for jack speed decision making while 83% of R2, MSE of 0.62, and MAE of 0.42 for the torque decision making. This intelligent model can assist the TBM operator in making decisions about TBM control.

Microalgal assemblages response to water quality remediation in coastal waters of Perth, Australia

Nutrient reduction is an essential environmental policy for water quality remediation, but climate change can offset the ecological benefits of nutrient reduction and lead to the difficulty of environmental evaluation. Here, based on the records of three lipid microalgal biomarkers and stable isotopes of carbon and nitrogen in two sediment cores from the embayment of Perth, Australia, we reconstructed the microalgal biomasses (diatoms, dinoflagellates and coccolithophores) over the past century and evaluated the ecological effects of nutrient reduction on them, using Change Point Modeling (CPM) and redundancy analysis (RDA). The CPM result showed that total microalgal biomarkers increased by 25% and 51% in deep and shallow areas, respectively, due to nutrient enrichment caused by industrial wastewater in the 1950s and the causeway construction in the 1970s, and dinoflagellates were beneficiaries of eutrophication. The nutrient reduction policy since the 1980s had not decreased total microalgal biomass, and diatoms were beneficiaries of this period. RDA based on time series of sediment cores and water monitoring data revealed that the increase of sea-surface temperature and the decrease of rainfall since the 1980s may be important factors sustaining the high total microalgal biomass and increasing the degree of diatom dominance. The result also indicated that the variations of microalgal assemblages may better explain the effect of nutrient reduction rather than total microalgal biomass.

Analysis of the Surrounding Rock Full-Displacement Variation in Large-Span Mudstone Highway Tunnels

Analysis of the Surrounding Rock Full-Displacement Variation in Large-Span Mudstone Highway Tunnels

Thermal parameter identification of concrete dams based on hybrid particle swarm optimization using distributed optical fiber monitoring data

The temperature of concrete arch dam during construction is affected by factors such as ambient temperature, cooling through water, and surface insulation. There is often a significant discrepancy between the actual thermal parameters and the values measured in indoor experimental tests. In this study, intelligent identification of thermal parameters is performed based on temperature monitoring data and an intelligence optimization algorithm so that the thermal characteristics of concrete in a real pouring environment can be obtained in real time. A hybrid algorithm of particle swarm optimization and grey wolf optimizer (HPSOGWO) is proposed. The performance of the hybrid particle swarm algorithm is verified to have some advantages over the other seven algorithms by twelve different test functions. Considering the influences of environmental temperature changes and multistage cooling water, HPSOGWO is utilized for inverse analysis of the thermal parameters of concretes with the same strength grade and concretes with different strength grades. The analysis results show that the concrete temperature values calculated based on the inversion of the HPSOGWO algorithm are in good agreement with the measured values, and the HPSOGWO algorithm has good adaptability in the inversion of thermal parameters of arch dams. The inversion results are of essential to clarify the relationship between temperature changes and thermal parameters.

Channel-attention-based LSTM network for modeling temperature-induced responses of cable-stayed bridges

Temperature has a significant impact on cable-stayed bridges, yielding structural responses comparable to those from vehicular loads, winds, etc. However, advanced numerical techniques for evaluating long-term temperature-induced responses (TIRs) of cable-stayed bridges are complicated and computationally inefficient. Therefore, this study leverages recent advances in deep learning and develops a channel-attention-based bidirectional long short-term memory network (CABLe) to directly get the complex mapping between structural temperatures and TIRs from the monitoring data. The key concept behind is the proposed channel attention mechanism (CAM), where its attention weights are calculated using a cosine similarity between latent sequential features to find the most informative contents of the signal. A comparison study is conducted with the bidirectional long short-term memory (BiLSTM) to show the benefits of the proposed CAM. The proposed method successfully predicts TIRs of a cable-stayed bridge using the imbalanced data. Results indicate that the CABLe outperforms the BiLSTM network and shows a high prediction accuracy with unseen temperature data.

Real-World Continuous Glucose Monitoring Data from a Population with Type 1 Diabetes in South Korea: Nationwide Single-System Analysis.

Background: We used continuous glucose monitoring (CGM) data to investigate glycemic outcomes in a real-world population with type 1 diabetes (T1D) from South Korea, where the widespread use of CGM and the nationwide education program began almost simultaneously. Methods: Data from Dexcom G6 users with T1D in South Korea were collected between January 2019 and January 2023. Users were included if they provided at least 90 days of glucose data and used CGM at least 70% of the days in the investigational period. The relationship between CGM utilization and glycemic metrics, including the percentage of time in range (TIR), time below range (TBR), and time above range (TAR), was assessed. The study was approved by the Institutional Review Board of Samsung Medical Center (SMC 2023-05-030). Results: A total of 2288 users were included. Mean age was 41.5 years (57% female), with average uploads of 428 days. Mean TIR was 62.4% ± 18.5%, mean TBR <70 mg/dL was 2.6% ± 2.8%, mean TAR >180 mg/dL was 35.0% ± 19.3%, mean glucose was 168.1 ± 35.8 mg/dL, mean glucose management indicator was 7.2% ± 0.9%, and mean coefficient of variation was 36.7% ± 6.0%. Users with higher CGM utilization had higher TIR (67.8% vs. 52.7%), and lower TBR <70 mg/dL (2.3% vs. 4.7%) and TAR >180 mg/dL (30.0% vs. 42.6%) than those with low CGM utilization (P < 0.001 for all). Users whose data were shared with others had higher TIR than those who did not (63.3% vs. 60.8%, P = 0.001). Conclusions: In this South Korean population, higher CGM utilization was associated with a favorably higher mean TIR, which was close to the internationally recommended target. Using its remote data-sharing feature showed beneficial impact on TIR.

Adaptive Vibration Monitoring of Railway Track Structures Using the UWFBG by the Identification of Train-Load Patterns

Due to the capability of multiplexing thousands of sensors on a single optical cable, ultra-weak fiber Bragg grating (UWFBG) vibration sensing technology has been utilized in monitoring the vibration response of large-scale infrastructures, particularly urban railway tracks, and the volume of the collected monitoring data can be huge with the great number of sensors. Even though the train-induced vibration responses of urban railway tracks constitute the most informative and crucial component, they comprised less than 7% of the total operational period. This is mainly attributed to the temporal sparsity of commuting trains. Consequently, the majority of the stored data consisted of low-informative environmental noise and interference excitation data, leading to an inefficient structural health monitoring (SHM) system. To address this issue, this paper introduced an adaptive monitoring strategy for railway track structures, which is capable of identifying train-load patterns by leveraging deep learning techniques. Inspired by image semantic segmentation, a U-net model with one-dimensional convolution layers (U-net-1D) was developed for the pointwise classification of vibration monitoring data. The proposed model was trained and validated using a dataset obtained from an actual urban railway track in China. Results indicated that the proposed method outperforms the traditional dual-threshold method, achieving an Intersection over Union (IoU) of 94.27% on the segmentation task of the test dataset.

Recent Development in Intelligent Compaction for Asphalt Pavement Construction: Leveraging Smart Sensors and Machine Learning.

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC's full potential.

Analysis of spatial characteristics and influencing factors of the flow network of highly educated talents from national and local perspective

Based on dynamic monitoring data on China’s population, by using complex networks, spatial analysis and mathematical measurement, this study reveals the spatial characteristics and influencing factors of the network of flows of highly educated talents in the Yangtze River Delta region from the national and local perspectives. In the two perspectives, the network has strong isomorphism and certain differences. The in-flow of highly educated talents from cities with high administrative levels and more developed economies to Shanghai constitutes the core of the entire network. From a national perspective, highly educated talents tend to converge to the Yangtze River Delta region. From a local perspective, it was found that these talents cluster towards a limited number of cities in the region. From both perspectives, the flow network has developed into a “core-periphery” progressive hierarchical structure, with Shanghai becoming the sole core city. There is little difference in the influencing factors of talent mobility from both macro and meso perspectives. Highly educated talents would frequently flow between cities with strong economic development levels, and cities with high education level, scientific and technological level, complete infrastructure, and good aesthetics. However, geographical distance still plays a hindering role in the flow of highly educated talents, and factors such as cultural identity, institutional, and social modality differences among regions also have a certain effect on the flow of these talents.

Analysis of the effectiveness of hygiene measures and COVID-19 vaccination at a tertiary-care university hospital during the first two years of the SARS-CoV-2 pandemic

ObjectiveAssessment of the effectiveness of protective measures at a tertiary-care hospital during the SARS-CoV-2 infection waves to provide advice for future pandemics. DesignRetrospective cohort study among hospital staff using in-house surveillance data. SettingUniversity Hospital Erlangen (UKER), a tertiary-care provider in Bavaria, Germany. MethodsWe outline the preventive measures introduced at UKER and retrospectively assess their effectiveness using anonymized monitoring data that were collected during the SARS-CoV-2 pandemic from February 2020 to the end of January 2022. Analysed data includes the incidence of SARS-CoV-2 infections among employees, the frequency of high-risk contacts with infected patients or staff members and breakthrough infections considering the context of exposure. ResultsThe cumulative incidence of SARS-CoV-2 infections among UKER employees was higher before, but lower after the vaccination campaign when compared to the general population. Healthcare workers (HCW), notably physicians and nurses, were especially at risk of infection compared to other UKER employees with less direct patient contact (OR 1.36 [95% CI 1.18-1.57 p < 0.001]). Breakthrough infections mostly occurred after exposure during private life, i.e. in situations without protective equipment. The frequency of high-risk contacts during direct patient care remained stable after SARS-CoV-2 vaccination. Prior to vaccination, 5.2% of HCW with direct patient care tested positive for SARS-CoV-2 within 14 days. After vaccination until the onset of the Omicron wave, conversion rate dropped to 0%. ConclusionsThis study provides real-world data on the effectiveness of vaccination, contact tracing, personal protective equipment and general hygiene measures during the SARS-CoV-2 pandemic. Based on our findings, we recommend a protective approach combining all these preventive measures.

A field study of a novel permeable-reactive-biobarrier to remediate chlorinated hydrocarbons contaminated groundwater

Chlorinated hydrocarbons (CHs) pose significant health risks due to their suspected carcinogenicity, necessitating urgent remediation efforts. While the combination of zero-valent iron (Fe0) and microbial action shows promise in mitigating CH contamination, field studies on this approach are scarce. We devised a novel three-layer permeable reactive barrier (PRB) material incorporating Fe0 and coconut shell biochar, effectively implemented at a typical CH-contaminated site. Field monitoring data revealed conducive conditions for reductive dechlorination of CHs, characterized by low oxygen levels and a relatively neutral pH in the groundwater. The engineered PRB material consistently released organic carbon and iron, fostering the proliferation of CH-dechlorinating bacteria. Over a 250-day operational period, the pilot-scale PRB demonstrated remarkable efficacy in CH removal, achieving removal efficiencies ranging from 21.9% to 99.6% for various CH compounds. Initially, CHs were predominantly eliminated through adsorption and iron-mediated reductive dechlorination. However, microbial reductive dechlorination emerged as the predominant mechanism for sustained and long-term CHs removal. These findings underscore the economic viability and effectiveness of our approach in treating CH-contaminated groundwater, offering promising prospects for broader application in environmental remediation efforts.

Study on load transfer mechanisms and impact on adjacent railway subgrade within loess regions

Clarifying load transfer mechanism and the influence of the widening of a newly-built railway is the premise for the construction of adjacent project in loess region. This paper uses monitoring datas obtained from three sections in different stages to analyze the distribution laws of load exert on piles and soil between piles, investigates the variation laws of filling height on the earth pressure at different excavation steps and elucidates the influence of the filling height of newly built subgrade on the existing subgrade. In addition, a fitting formula y = a(1-e-bx) + cx is proposed to describe the relationship between the ratio of the additional displacement to the filling height, which is applicable for similar projects.

Development of a smart lumbar posture correction vest

Lack of proper lumbar posture is responsible for the occurrence of neck and shoulder pain, low back pain, and muscle and joint injuries in the abdominal region in more than 75% of individuals. This scenario is influenced by the excessive use of computers in the workplace, prolonged periods of incorrect sitting posture, inadequate posture during daily activities, and repetitive and localized movements, among other factors. Consequently, numerous studies are being carried out with the aim of reducing or eliminating the frequency of the problem. The most common solution proposed by researchers is the use of lumbar posture correction waistcoats. This article has automated a posture correction waistcoat that protects the wearer but also monitors their posture. When incorrect posture is identified, the waistcoat generates a warning signal. To do this, gyro sensors, accelerometer sensors, and vibration motors were installed and supervised by an Arduino microcontroller. A communication system based on Bluetooth technology was used, integrated into an Android app that interacts with the user, displaying monitoring data and providing graphical representations of inappropriate postures. In addition, a health and well-being module was implemented, which suggests a physical exercise routine to prevent back pain. In addition, a therapeutic massage module was included for relaxation and an interface for storing music that can be played while wearing the waistcoat. The results demonstrated that the implemented system generated a smart vest capable of monitoring and alerting the user when an incorrect posture was identified. Furthermore, the results indicated that the health and well-being module provided a positive user experience.

Identification of Trends in Dam Monitoring Data Series Based on Machine Learning and Individual Conditional Expectation Curves

Dams are complex systems that involve both the structure itself and its foundation. Rheological phenomena, expansive reactions, or alterations in the geotechnical parameters of the foundation, among others, result in non-reversible and cumulative modifications in the dam response, leading to trends in the monitoring data series. The accurate identification and definition of these trends to study their evolution are key aspects of dam safety. This manuscript proposes a methodology to identify trends in dam behavioural data series by identifying the influence of the time variable on the predictions provided by the ML models. Initially, ICE curves and SHAP values are employed to extract temporal dependence, and the ICE curves are found to be more precise and efficient in terms of computational cost. The temporal dependencies found are adjusted using a GWO algorithm to different function characteristics of irreversible processes in dams. The function that provides the best fit is selected as the most plausible. The results obtained allow us to conclude that the proposed methodology is capable of obtaining estimates of the most common trends that affect movements in concrete dams with greater precision than the statistical models most commonly used to predict the behaviour of these types of variables. These results are promising for its general application to other types of dam monitoring data series, given the versatility demonstrated for the unsupervised identification of temporal dependencies.

Investigating Landsat data for continuous monitoring of ground electrical conductivity: implication for propagation at medium frequency in Ondo West

Ground electrical conductivity (GEC) has many applications, which makes it worthy of continuous research. Apart from the configurations of the radio transmitter and receiver, it is a major determinant of the electric field intensity of radio waves in the Medium Frequency (MF) band. It is usually measured in such a way that both the researcher and the measuring instrument are in direct contact with the ground, and measurements are made at some predetermined constant intervals to ensure good spatial coverage and even spatial distribution. The direct method becomes cumbersome when the field of study is large because the GEC over the licensed coverage area must be known such that electric field intensity can be predicted. At the planning stage, this ensures all parts of the licensed region are reached by useful signals that can suppress interference at all times and seasons. The study is motivated by the possibility of using Landsat to estimate GEC. This study utilizes Landsat images to first estimate the normalized difference salinity index and then, with the aid of map algebra, generate another raster 2 whose pixels' values are the GEC. The study covers a landmass of 967 km in the Ondo West Local Government Area (OWLGA). During analysis, the GEC was divided into five classes. The results reveal that the ranges of very low, low, moderate, high, and very high GEC are 0.124 to 0.437, 0.438 to 0.937, 0.938 to 1.635, 1.636 to 2.524, and 2.525 to 3.600 mS/m, which have mean values of 0.346 ± 0.109, 0.754 ± 0.103, 1.071 ± 0.089, 1.351 ± 2 0.067, and 1.564 ± 0.106 mS/m, and are dispersed over 176.45, 202.24, 214.14, 374.17, and 228.56 km , respectively. The values of GEC, their respective coverage, and spatial distribution recorded in this study are strong enough to sustain the propagation of useful electric field intensity over the entire landmass of Ondo West Local Government if a medium wave is established. The study recommends that the change in GEC with time in this field be studied.

Awareness of enterprise finance support programmes: the role of networks, gender and ethnicity

PurposeThis paper investigates how gender, ethnicity, and network membership interact to influence how small and medium-sized enterprise (SME) owner-managers become aware of finance support programmes developed by government policy and/or support schemes advanced by the banking industry.Design/methodology/approachDrawing on expectation states theory (EST), we develop eight sets of hypotheses and employ the UK SME Finance Monitor data to test them using bivariate probit regression analysis.FindingsIn general, network membership increases awareness, but more so for government programmes. We also find no differences between female and male owner-managers when in networks. However, we identify in-network and out-network differences by ethnicity, with minority females seemingly better off than minority males.Practical implicationsBusiness networks are better for disseminating government programmes than industry-led programmes. For native White women, network membership can enhance policy awareness advantage further, whilst for minorities, networks significantly offset the big policy awareness deficits minorities inherently face. However, policy and practice need to address intersectional inequalities that remain in access to networks themselves, information access within networks, and the significant out-network deficits in awareness of support programmes afflicting minorities.Originality/valueThis study provides one of the first large-scale empirical examinations of intersectional mechanisms in awareness of government and industry-led enterprise programmes. Our novel and nuanced findings advance our understanding of the ways in which gender and ethnicity interact with network dynamics in entrepreneurship.

Sanitary and biological condition of open sources is a determining factor in the reconstruction of elements of water supply systems

The results of the analysis of monitoring data of the Kuibyshev reservoir, as a source of domestic and drinking water supply, for the period 2000-2018 are presented. It has been established that in summer, during the period of massive development of cyanobacteria, biological pollution of the reservoir is observed, the quality of water deteriorates in terms of smell, color, pH, and dissolved oxygen content. During the period of deterioration in the quality of water in the water supply source in large Volga cities, problems arise in purifying natural water to meet the requirements of the drinking water supply of the population. To solve the problem, recommendations have been developed to improve the technological scheme for purifying Volga water from organic and toxic substances.

Warming drives dissolved organic carbon export from pristine alpine soils

Despite decades of research, the influence of climate on the export of dissolved organic carbon (DOC) from soil remains poorly constrained, adding uncertainty to global carbon models. The limited temporal range of contemporary monitoring data, ongoing climate reorganisation and confounding anthropogenic activities muddy the waters further. Here, we reconstruct DOC leaching over the last ~14,000 years using alpine environmental archives (two speleothems and one lake sediment core) across 4° of latitude from Te Waipounamu/South Island of Aotearoa New Zealand. We selected broadly comparable palaeoenvironmental archives in mountainous catchments, free of anthropogenically-induced landscape changes prior to ~1200 C.E. We show that warmer temperatures resulted in increased allochthonous DOC export through the Holocene, most notably during the Holocene Climatic Optimum (HCO), which was some 1.5–2.5 °C warmer than the late pre-industrial period—then decreased during the cooler mid-Holocene. We propose that temperature exerted the key control on the observed doubling to tripling of soil DOC export during the HCO, presumably via temperature-mediated changes in vegetative soil C inputs and microbial degradation rates. Future warming may accelerate DOC export from mountainous catchments, with implications for the global carbon cycle and water quality.