Monitoring Method Research Articles

The Diversity of MOF Structures and Their Impact on Photoelectrochemical Sensors for Monitoring Environmental Pollution

The problem of environmental pollution is one of the most important in the modern world. Pollution causes an increase in human diseases, the extinction of many species of plants and animals, global warming, and many weather anomalies. One of the great challenges for scientists is the development of methods for monitoring and removing the emerging pollutants. This review focuses on Metal–Organic Frameworks (MOFs) and their use as working material to construct different types of sensors for application in environmental pollution monitoring. In particular, the detection of heavy metals (mercury, lead, and arsenic) and organic compounds (drugs, biomolecules, and pesticides) are considered. The collected data show that photoelectrochemical (PEC) sensors based on MOFs are the most fascinating materials due to various combinations (e.g., surface modification) and operational possibilities. PEC sensors achieve enormous sensitivity, which increases even to the pico level, making it the best tool in sensing applications. This review also highlights the main sensor challenges. Most of them are concerned with the possibility of reusing the sensor, its regeneration, and safe disposal. In addition, more attention should be paid to the sensor manufacturing process, which often uses toxic compounds, and research to eliminate them in favor of non-toxic compounds.

The Diversity of MOF Structures and Their Impact on Photoelectrochemical Sensors for Monitoring Environmental Pollution

The problem of environmental pollution is one of the most important in the modern world. Pollution causes an increase in human diseases, the extinction of many species of plants and animals, global warming, and many weather anomalies. One of the great challenges for scientists is the development of methods for monitoring and removing the emerging pollutants. This review focuses on Metal–Organic Frameworks (MOFs) and their use as working material to construct different types of sensors for application in environmental pollution monitoring. In particular, the detection of heavy metals (mercury, lead, and arsenic) and organic compounds (drugs, biomolecules, and pesticides) are considered. The collected data show that photoelectrochemical (PEC) sensors based on MOFs are the most fascinating materials due to various combinations (e.g., surface modification) and operational possibilities. PEC sensors achieve enormous sensitivity, which increases even to the pico level, making it the best tool in sensing applications. This review also highlights the main sensor challenges. Most of them are concerned with the possibility of reusing the sensor, its regeneration, and safe disposal. In addition, more attention should be paid to the sensor manufacturing process, which often uses toxic compounds, and research to eliminate them in favor of non-toxic compounds.

Wind turbine gearbox condition monitoring using AI-enabled virtual indicators*

Abstract At present, the condition monitoring of wind turbine gearbox mostly involves selecting monitoring indicators in advance and establishing normal behavior models based on deep learning. However, single physical monitoring indicator cannot fully characterize the operating status of the wind turbine gearbox. To address the above issues, a wind turbine gearbox condition monitoring method using convolutional neural network (CNN)-long and short term memory network (LSTM)-autoencoder (AE) enabled virtual indicators is proposed in this paper. This method first establishes a cleaning principle to preprocess the data of supervisory control and data acquisition (SCADA) system and screen out effective SCADA data further. A CNN-LSTM-AE monitoring model was trained using SCADA data during normal operating process, various characteristic parameters of the gearbox are integrated to construct the AI-enabled virtual indicators, and meanwhile warning thresholds was determined based on the probability density distribution of virtual indicators to analyze the operating status of the gearbox. Finally, the proposed method was validated using real SCADA data from two wind turbines in a cooperated wind farm with my group. Compared with a single physical indicator, the virtual indicator enables to detect gearbox early faults 5 d in advance, indicating that the proposed method can effectively alert wind turbine gearbox failures.

Devices and Methods for Dosimetry of Personalized Photodynamic Therapy of Tumors: A Review on Recent Trends.

Significance: Despite the widespread use of photodynamic therapy in clinical practice, there is a lack of personalized methods for assessing the sufficiency of photodynamic exposure on tumors, depending on tissue parameters that change during light irradiation. This can lead to different treatment results. Aim: The objective of this article was to conduct a comprehensive review of devices and methods employed for the implicit dosimetric monitoring of personalized photodynamic therapy for tumors. Methods: The review included 88 peer-reviewed research articles published between January 2010 and April 2024 that employed implicit monitoring methods, such as fluorescence imaging and diffuse reflectance spectroscopy. Additionally, it encompassed computer modeling methods that are most often and successfully used in preclinical and clinical practice to predict treatment outcomes. The Internet search engine Google Scholar and the Scopus database were used to search the literature for relevant articles. Results: The review analyzed and compared the results of 88 peer-reviewed research articles presenting various methods of implicit dosimetry during photodynamic therapy. The most prominent wavelengths for PDT are in the visible and near-infrared spectral range such as 405, 630, 660, and 690 nm. Conclusions: The problem of developing an accurate, reliable, and easily implemented dosimetry method for photodynamic therapy remains a current problem, since determining the effective light dose for a specific tumor is a decisive factor in achieving a positive treatment outcome.

Integrated Neural Network Approach for Enhanced Vital Signal Analysis Using CW Radar

This study introduces a novel approach for analyzing vital signals using continuous-wave (CW) radar, employing an integrated neural network model to overcome the limitations associated with traditional step-by-step signal processing methods. Conventional methods for vital signal monitoring, such as electrocardiograms (ECGs) and sphygmomanometers, require direct contact and impose constraints on specific scenarios. Conversely, our study primarily focused on non-contact measurement techniques, particularly those using CW radar, which is known for its simplicity but faces challenges such as noise interference and complex signal processing. To address these issues, we propose a temporal convolutional network (TCN)-based framework that seamlessly integrates noise removal, demodulation, and fast Fourier transform (FFT) processes into a single neural network. This integration minimizes cumulative errors and processing time, which are common drawbacks of conventional methods. The TCN was trained using a dataset comprising preprocessed in-phase and quadrature (I/Q) signals from the CW radar and corresponding heart rates measured via ECG. The performance of the proposed method was evaluated based on the L1 loss and accuracy against the moving average of the estimated heart rates. The results indicate that the proposed approach has the potential for efficient and accurate non-contact vital signal analysis, opening new avenues in health monitoring and medical research. Additionally, the integration of CW radar and neural networks in our framework offers a robust and scalable solution, enhancing the practicality of non-contact health monitoring systems in diverse environments. This technology can be leveraged in healthcare robots to provide continuous and unobtrusive monitoring of patients’ vital signs, enabling timely interventions and improving overall patient care.

Reinforcement Learning-Based Adaptive Classification for Medication State Monitoring in Parkinson's Disease.

Parkinson's Disease (PD) patients frequently transition between the 'ON' state, where medication is effective, and the 'OFF' state, affecting their quality of life. Monitoring these transitions is vital for personalized therapy. We introduced a framework based on Reinforcement Learning (RL) to detect transitions between medication states by learning from continuous movement data. Unlike traditional approaches that typically identify each state based on static data patterns, our approach focuses on understanding the dynamic patterns of change throughout the transitions, providing a more generalizable medication state monitoring method. We integrated a deep Long Short-Term Memory (LSTM) neural network and three one-class unsupervised classifiers to implement an RL-based adaptive classifier. We tested on two PD datasets: Dataset PD1 with 12 subjects (14-minute average recording) and Dataset PD2 with seven subjects (120-minute average recording). Data from wrist and ankle wearables captured transitions during 2 to 4-hour daily activities. The algorithm demonstrated its effectiveness in detecting medication states, achieving an average weighted F1-score of 82.94% when trained and tested on Dataset PD1. It performed well when trained on Dataset PD1 and tested on Dataset PD2, with a weighted F1-score of 76.67%. It surpassed other models, was resilient to severe PD symptoms, and performed well with imbalanced data. Notably, prior work has not addressed the generalizability from one dataset to another, essential for real-world applications with varied sensors. Our innovative framework revolutionizes PD monitoring, setting the stage for advanced therapeutic methods and greatly enhancing the life quality of PD patients.

An improved cohesive hierarchical clustering for indoor air quality monitoring in smart gymnasium with healthy sport areas

With the rise of healthy lifestyles, sports arenas have become important exercise venues for people. However, indoor air quality issues are becoming increasingly prominent, posing a potential threat to the health of athletes. Traditional indoor air quality monitoring systems often suffer from problems such as unreasonable layout of monitoring points and inaccurate data processing, making it difficult to effectively cope with complex indoor environments. This article proposes an intelligent monitoring system for indoor air quality in sports venues based on an improved cohesive hierarchical clustering algorithm to address these issues. The system optimizes the layout of monitoring points, combines real-time data collection on the Android platform, and accurately processes non smooth data using an improved cohesive hierarchical clustering algorithm based on BiLSTM. The experimental results show that the algorithm not only improves the accuracy of monitoring point selection, but also optimizes the number of monitoring points and effectively supplements missing data. In addition, the algorithm significantly reduces computational complexity and improves computational efficiency while ensuring clustering quality. Application testing shows that the indoor air environment monitoring system constructed in this article can obtain and analyze data in real time, effectively control the concentration of key pollutants such as PM2.5 and carbon dioxide, and create a healthier sports environment for sports venues. This study provides new ideas and methods for indoor air quality monitoring, which has important practical application value.

Bridging the Gap: commodifying infrastructure spatial dynamics with crowdsourced smartphone data

Structural information deficits about aging bridges have led to several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of widespread deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive and has produced structural information, i.e., modal frequencies, in crowdsensing applications. Even so, current methods cannot extract spatial vibration characteristics with uncontrolled datasets that are needed for damage identification. Here we present an extensive real-world study with crowdsourced smartphone-vehicle trips within motor vehicles in which we estimate absolute value mode shapes and simulate damage detection capabilities. Our method analyzes over 800 trips across four road bridges with main spans ranging from 30 to 1300 m in length, representing about one-quarter of bridges in the United States. We demonstrate a bridge health monitoring platform compatible with ride-sourcing data streams that check conditions daily. The result has the potential to commodify data-driven structural assessments globally.

Bee monitoring by community scientists: comparing a collections-based program with iNaturalist.

Bee monitoring, or widespread efforts to document bee community biodiversity, can involve data collection using lethal (specimen collections) or non-lethal methods (observations, photographs). Additionally, data can be collected by professional scientists or by volunteer participants from the general public. Collection-based methods presumably produce more reliable data with fewer biases against certain taxa, while photography-based approaches, such as data collected from public natural history platforms like iNaturalist, can involve more people and cover a broader geographic area. Few efforts have been made to quantify the pros and cons of these different approaches. We established a community science monitoring program to assess bee biodiversity across the state of Pennsylvania (USA) using specimen collections with nets, blue vane traps, and bowl traps. We recruited 26 participants, mostly Master Gardeners, from across the state to sample bees after receiving extensive training on bee monitoring topics and methods. The specimens they collected were identified to species, stored in museum collections, and the data added to public databases. Then, we compared the results from our collections to research-grade observations from iNaturalist during the same time period (2021 and 2022). At state and county levels, we found collections data documented over twice as much biodiversity and novel baseline natural history data (state and county records) than data from iNaturalist. iNaturalist data showed strong biases toward large-bodied and non-native species. This study demonstrates the value of highly trained community scientists for collections-based research that aims to document patterns of bee biodiversity over space and time.

Monitoring the world-oldest zoo-housed male giant panda (Ailuropoda melanoleuca): A case-study on pathway utilization.

Habitat use monitoring is necessary for a comprehensive understanding of the biological and psychological requirements of animals under human care, facilitating improved exhibit designs and promoting animal welfare. Current studies focused on the space use of the exhibit while limited studies examined the pathway use of the zoo-housed animals. This study aimed to investigate the habitat use of An An, the world-oldest male giant panda (Ailuropoda melanoleuca) at Ocean Park Hong Kong using a pathway utilization monitoring method. The result showed uneven utilization of pathways favoring the upper area near his den over the lower area which required longer walking in the hilly exhibit. Moreover, the comparisons of walking directions showed a preference for gentle slopes during uphill movement and a favor for steep slopes during downhill movement. Our study also compared the walking distance between uphill and downhill movements and the results showed that An An walked longer distances heading upward than moving downward due to his choice of pathways. Our results are in line with the findings in the wild population, showing a preference for gentle slopes, especially in uphill movement. Our study demonstrated the value of the pathway monitoring technique. Due to its easy and time-effective use, this technique can be incorporated into the care teams' operation, providing valuable information on daily activity (e.g. accessing the walking ability of the aged animals by investigating the pathway use and walking distance) and habitat use. Consequently, the pathway monitoring technique can help improve exhibit designs promoting welfare.

Implementation of an intelligent process monitoring system for screw presses using the CRISP-DM standard

AbstractIncreasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.

Brainwaves in the Cloud: Cognitive Workload Monitoring Using Deep Gated Neural Network and Industrial Internet of Things

Monitoring and classifying cognitive workload in real time is vital for optimizing human–machine interactions and enhancing performance while ensuring safety, particularly in industrial scenarios. Considering this significance, the authors aim to formulate a cognitive workload monitoring system (CWMS) by leveraging the deep gated neural network (DGNN), a hybrid model integrating bi-directional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) networks. In our experimental setup, each of the four virtual users is equipped with a Raspberry Pi Zero W module to ensure efficient data transmission, thereby enhancing the reliability and efficacy of the monitoring process. This seamless monitoring framework utilizes the constrained application protocol (CoAP) and the Things Board platform to evaluate cognitive workload in real time. The most popular EEG benchmark dataset, the STEW is utilized for workload classification in this study. We employ the short-time Fourier transformation (STFT) to extract frequency bands corresponding to users in both high and low cognitive workload modes. The proposed DGNN models achieve a perfect accuracy of 99.45%, outperforming every previous state-of-the-art model. We meticulously monitored critical parameters, including latency, classification processing time, and cognitive workload levels. This research demonstrates the importance of continuous monitoring for increasing productivity and safety in industries by introducing a novel method of real-time cognitive workload monitoring. The implementation codes for each experiment are documented and made available for reproducibility.

Continuous Drive Friction Welded Al/Cu Joints Produced Using Short Welding Time, Elevated Rotational Speed, and High Welding Pressures.

The present study aimed to enhance the efficiency and efficacy of the Al/Cu joint production process implemented by the company VEMID Ltd., Jagodina, Serbia, by attaining sound joints within a very short welding time. For this purpose, the present study aimed at investigating the accuracy and the quality of the continuous drive friction welding (CDFW) process, as well as the optimum combination of CDFW parameters with highest joint efficiency in terms of investigated properties. The accuracy was estimated through an analysis of temperature-time curves recorded during CDFW using an infrared camera. The quality was evaluated through an investigation of the properties of Al/Cu joints produced using different friction (66.7, 88.9, and 133.3 MPa) and forging (88.9, 222.2, and 355.6 MPa) pressures and a constant total welding time (4 s) and rotational speed (2100 rpm). Thermal imaging with an infrared camera demonstrated that the actual total welding time was 15% longer compared to the nominal value. This was attributed to the slow pressure response of the pneumatic brake system. The relative changes in the maximum surface temperature (TMS) during the CDFW process corresponded to changes in welding pressures, indicating the potential of the thermal imaging method for monitoring and assessing this process. A preliminary investigation demonstrated that Al/Cu joints produced using welding pressures less than 88.9 MPa often displayed the presence of non-joined micro-regions at the Al/Cu interface and a significant thickness of interfacial Al2Cu (up to 1 µm). However, when friction pressure was set at 66.7 MPa, an increase in the forging pressure to 222.2 MPa eliminated the presence of non-joined micro-regions and reduced the thickness of Al2Cu to 0.5 µm on the average level. These Al/Cu joints achieved the highest joint efficiencies in terms of strength (100%) and ductility (61%). They exhibited an electrical conductivity higher than 92% of the theoretical value. A further increase in any welding pressure produced similar or deteriorated properties, accompanied by an increase in the consumption of raw materials and energy. Such turn of events was counterproductive to the original goal of increasing the efficiency and efficacy of the CDFW process.

Synthesis, Characterization, and Potential Usefulness in Liver Function Assessment of Novel Bile Acid Derivatives with Near-Infrared Fluorescence (NIRBAD).

Conventional serum markers often fail to accurately detect cholestasis accompanying many liver diseases. Although elevation in serum bile acid (BA) levels sensitively reflects impaired hepatobiliary function, other factors altering BA pool size and enterohepatic circulation can affect these levels. To develop fluorescent probes for extracorporeal noninvasive hepatobiliary function assessment by real-time monitoring methods, 1,3-dipolar cycloaddition reactions were used to conjugate near-infrared (NIR) fluorochromes with azide-functionalized BA derivatives (BAD). The resulting compounds (NIRBADs) were chromatographically (FC and PTLC) purified (>95%) and characterized by fluorimetry, 1H NMR, and HRMS using ESI ionization coupled to quadrupole TOF mass analysis. Transport studies using CHO cells stably expressing the BA carrier NTCP were performed by flow cytometry. Extracorporeal fluorescence was detected in anesthetized rats by high-resolution imaging analysis. Three NIRBADs were synthesized by conjugating alkynocyanine 718 with cholic acid (CA) at the COOH group via an ester (NIRBAD-1) or amide (NIRBAD-3) spacer, or at the 3α-position by a triazole link (NIRBAD-2). NIRBADs were efficiently taken up by cells expressing NTCP, which was inhibited by taurocholic acid (TCA). Following i.v. administration of NIRBAD-3 to rats, liver uptake and consequent release of NIR fluorescence could be extracorporeally monitored. This transient organ-specific handling contrasted with the absence of release to the intestine of alkynocyanine 718 and the lack of hepatotropism observed with other probes, such as indocyanine green. NIRBAD-3 administration did not alter serum biomarkers of hepatic and renal toxicity. NIRBADs can serve as probes to evaluate hepatobiliary function by noninvasive extracorporeal methods.

Research on Attitude Analysis of Hydraulic Support Based on Column Length

The existing hydraulic support attitude monitoring and control methods integrate multiple sensing technologies, but it is difficult to apply and promote due to unclear monitoring mechanisms, complicated types, and a large number of sensors. Aiming at the problem of attitude monitoring and control of hydraulic support, firstly, the kinematics mathematical model of hydraulic support is established by the key marker point method and the two-dimensional bar system model, and the forward and inverse solution algorithm of hydraulic support attitude based on the double-drive length of front and rear columns is proposed. Secondly, the rigid body dynamics simulation model of hydraulic support is constructed to verify the theoretical analytical model and prove the correctness of the algorithm. The rigid body simulation model can be used as the attitude monitoring system of hydraulic support. Thirdly, considering the influence of material elastic deformation, a rigid-flexible coupling dynamic simulation model of hydraulic support is established to explore the influence of elastic deformation of components on the attitude of hydraulic support and its causes. On this basis, the rigid-flexible coupling dynamic simulation model of the hydraulic support with clearance is established by replacing the revolute pair in the simulation model with the solid axis pin connection unit. The influence of the axis pin connection clearance on the attitude of the hydraulic support and its causes are explored, and the actual attitude and change characteristics of the hydraulic support under simulated real conditions are obtained. Finally, considering the force compression of the hydraulic cylinder of the column, the rigid-flexible and machine-liquid coupling dynamic simulation model of the hydraulic support with clearance is constructed.

Integrating Sentinel 2 Imagery with High-Resolution Elevation Data for Automated Inundation Monitoring in Vegetated Floodplain Wetlands

Monitoring inundation in flow-dependent floodplain wetlands is important for understanding the outcomes of environmental water deliveries that aim to inundate different floodplain wetland vegetation types. The most effective way to monitor inundation across large landscapes is with remote sensing. Spectral water indices are often used to detect water in the landscape, but there are challenges in using them to map inundation within the complex vegetated floodplain wetlands. The current method used for monitoring inundation in the large floodplain wetlands that are targets for environmental water delivery in the New South Wales portion of the Murray–Darling Basin (MDB) in eastern Australia considers the complex mixing of water with vegetation and soil, but it is a time-consuming process focused on individual wetlands. In this study, we developed the automated inundation monitoring (AIM) method to enable an efficient process to map inundation in floodplain wetlands with a focus on the lower Lachlan floodplain utilising 25 Sentinel-2 image dates spanning from 2019 to 2023. A local adaptive thresholding (ATH) approach of a suite of spectral indices combined with best available DEM and a cropping layer were integrated into the AIM method. The resulting AIM maps were validated against high-resolution drone images, and vertical and oblique aerial images. Although instances of omission and commission errors were identified in dense vegetation and narrow creek lines, the AIM method showcased high mapping accuracy with overall accuracy of 0.8 measured. The AIM method could be adapted to other MDB wetlands that would further support the inundation monitoring across the basin.

Microclimatic drivers of winter bat activity in coast redwood forests

Abstract Bats are among the least well-known mammals, particularly in terms of their behavior and activity patterns during the winter. Here, we use passive acoustic monitoring to overcome some of the challenges inherent in surveying cryptic forest bats during the wet season to quantify overwintering behavior for 11 species in California coast redwood forests under varying microclimates. Because different species are active at different forest heights, we also examined the effect of acoustic detector placement (treetop or ground level). Generalized linear mixed models were used to relate acoustic detection probability for 8 species to daytime and nighttime temperature, relative humidity, water vapor pressure, and detector placement. The results indicate that daytime maximum temperature best explained variation in nightly probability of detection, and temperature threshold at which bats were predicted to be detected varied considerably across species. By using more precise species detection methods, we were able to resolve significant differences in activity patterns between Myotis yumanensis and M. californicus, 2 species with similar acoustic signatures that are often lumped together. Myotis californicus was predicted to have a 50% probability of detection at maximum daytime temperature as low as 12.5 °C, whereas M. yumanensis was not predicted to have 50% detection probability until maximum daytime temperature was at least 22 °C, suggesting that M. californicus spends less time in torpor. Also, monitoring at the top of the canopy revealed 4 migratory species to be present in the ecosystem on significantly more monitoring nights than could be observed using conventional ground-based monitoring methods. Improving winter bat survey methods provides evidence that diverse bat species are more active in redwood forests during the winter than previously documented. This finding suggests that coastal forests could provide important winter bat habitat for both resident and migratory species.

Smart grid power quality monitoring and regulation based on photoelectric sensors and optical system signal processing

With the rapid development of smart grids, power quality monitoring and regulation have become an important task to ensure the stable operation of the power grid. Traditional power quality monitoring methods have problems such as low monitoring accuracy and poor real-time performance. Therefore, this article proposes a smart grid power quality monitoring and adjustment method based on photoelectric sensors and optical system signal processing to improve monitoring accuracy and real-time performance. This article uses photoelectric sensors to monitor the power quality in the power grid in real-time, and transmits the monitoring data to the optical system. Signal processing of monitoring data through optical systems to extract key parameters related to power quality. Finally, by adjusting the relevant equipment of the smart grid, the regulation and optimization of power quality can be achieved. The experimental results show that the smart grid power quality monitoring and adjustment method based on photoelectric sensors and optical system signal processing can effectively improve monitoring accuracy and real-time performance. Through real-time monitoring and regulation of power quality, the stability and reliability of the power grid have been significantly improved.

Geochemistry and microbiology of boreal alluvial soil under salinisation

Soil salinisation in taiga landscapes is in most cases caused by anthropogenic activities. The study was carried out in Perm region (Russia) on the territory of the Verkhnekamskoe Potash Deposit. The inflow of sodium chloride drainage water into ground and surface waters during the production of potash fertilisers contributed to the technogenic salinisation of river valleys in the taiga zone. The purpose of the research was to investigate the microbial composition and chemical properties of alluvial soils in the area affected by NaCl waters in the Lyonva River valley. The chemical properties of soils were determined standard methods such as potentiometric method, titration, and spectrophotometry. The microbial community was determined by 16 s rRNA gene metagenomic analysis. The soil's mineralogical composition was determined using a binocular microscope and diffractometer for XRD. The morphology and microstructure of the samples has been studied using an analytical scanning electron microscope. In Solonchaks, an interdependence of salinity and bacterial species composition was discovered, along with the bacteria's geochemical processes. The topsoil contains a considerable amount of toxic salts, ranging from 5.9 to 17%. The ratio of exchangeable cations in the soil absorption complex changes when exchangeable calcium is replaced by sodium. Salinisation caused the neutralisation of acidic alluvial soils. Bacteria originating from marine and highly mineralised environments predominate in the soil. The soils are dominated by bacteria originating from marine and highly mineralised environments, such as Proteobacteria, Shewanella (75–79%), Thiomicrospira (26%), Desulfuromonas, Marinomonas and Idiomarina (9–10%), Alicyclobacillus (4%). The correlation revealed the connection of some taxa with ions of aqueous extract, as well as with exchangeable sodium, mobile iron and total sulphur. Some bacteria promote azonal geochemical processes within alluvial forest soil, such as the reduction of iron and manganese, the production of sulphides, and the oxidation of sulphur, hydrogen, and iron. Sulphide accumulation resulting in the formation of a hydrotroilite horizon (FeS × nH2O) and iron-bearing formations were found on the soil surface. Studying the properties and degree of soil disturbance makes it possible to identify the contribution of enterprises to environmental pollution, as well as to apply new methods for monitoring, purifying, and storing potassium waste.

The impact of left atrial morphology on thrombosis risk in atrial fibrillation

Atrial fibrillation (AF) is an arrhythmia disease that can disrupt the movement of the left atrium (LA), which will facilitate thromboembolism in LA and further exacerbate the stroke risk. The left atrial appendage (LAA) is a vital site for thrombus formation. The morphology of the LAA is believed to have a profound effect on thrombosis. However, due to the limitation of observation and monitoring methods, the mechanism of this effect is still unrevealed. Virchow's triad is a theory of thrombogenic mechanisms, stating that endothelial injury, blood hypercoagulability, and blood stasis are the three main factors involved in thrombus formation. In this study, we conducted personalized simulation analyses for 12 AF patients and discussed simulation results from the perspective of Virchow's triad. The simulation results indicate the effectiveness of Virchow's triad in elucidating the thrombosis mechanism within the LAA. The LAA is more prone to thrombosis than the other region of LA, because of the lower blood flow velocity and significantly higher endothelial cell activation potential values there, which indicates a higher risk of blood stasis and endothelial injury, respectively. Furthermore, a comprehensive score is proposed to assess the performance of Virchow's triad in the LAA. The morphology of LA has a significant influence on the performance of the three factors in Virchow's triad. Hence, for a quickly assessment of LAA thrombosis risk in AF patients through the lens of Virchow's triad, we need a more refined shape scoring system that can better reflect patient-specific morphological features of the entire LA.