Method for monitoring and forecasting landslide phenomenon based on machine learning.

A landslide involves the downward movement of a mass of rock, debris, earth, or soil. Landslides happen when gravitational forces and other types of shear stresses on a slope surpass the shear strength of the materials. Additionally, landslides can be triggered by processes that weaken the shear strength of the slope's material. Shear strength primarily depends on two factors such as frictional strength, which is the resistance to movement between the interacting particles of the slope material, and cohesive strength, which is the bonding between those particles. A landslide is a terrible natural disaster that causes much damage to both human life and the economy. It often occurs in steep mountainous areas or hilly regions, ranging in scale from medium to large. It progresses slowly (20-50 mm/year), but when it occurs, it can move at a speed of 3 m/s. Therefore, early detection or prevention of this disaster is an essential and significant task. This paper developed a method to collect and analyze data, with the purpose of determining the possibility of landslide occurrences to reduce its potential losses.•The proposed method is convenient for users to grasp information of landslide phenomenon.•A machine learning model is applied to forecast landslide phenomenon.•Internet of things (IoT) system is utilized to manage and send a warning text to individual email address and mobile devices.

3D spheroid HepaRG and fluorescent biphasic tracer for CYP3A4-mediated antibiotic interaction monitoring in sepsis.

Cytochrome P450 3A4 (CYP3A4) is a crucial enzyme in the metabolism of xenobiotics, particularly in drug metabolism interactions (DDIs), making it a significant factor in clinical drug use. However, current assay techniques are both laborious and costly, making it difficult to construct a high-throughput monitoring method that can be used in conjunction with the clinic. This poses certain safety hazards for drug combination. Therefore, it is crucial to develop a synchronized monitoring method for the inhibition and induction of CYP3A4. In this study, we utilized 3D culture technology to develop a HepaRG cells spheroid model. The CYP450 and transporter expression, the albumin secretion, and urea synthesis capacity characteristics were analyzed. The NEN probe was utilized as a tracer molecule for CYP3A4. The fluorescence intensity of metabolites was characterized by laser confocal technique to determine the inhibition and expression of CYP3A4 in the HepaRG cell spheroid model by the antibiotics for sepsis. The results indicate that the HepaRG sphere model successfully possessed the physiological phenotype of the liver, which could be used for drug interaction monitoring. Through positive drug testing, NEN probe was able to achieve bidirectional characterization of CYP3A4 induction and inhibition. The monitoring method described in this paper was successfully applied to drug interaction monitoring of commonly used antibiotics in sepsis patients, which is a convenient and rapid monitoring method. The proposed method offers a new strategy for monitoring CYP3A4-mediated drug-drug interactions with a high-throughput assay, which will help to improve the safety of clinical drug combination.

Study on the Recognition of Coal Miners' Unsafe Behavior and Status in the Hoist Cage Based on Machine Vision.

The hoist cage is used to lift miners in a coal mine's auxiliary shaft. Monitoring miners' unsafe behaviors and their status in the hoist cage is crucial to production safety in coal mines. In this study, a visual detection model is proposed to estimate the number and categories of miners, and to identify whether the miners are wearing helmets and whether they have fallen in the hoist cage. A dataset with eight categories of miners' statuses in hoist cages was developed for training and validating the model. Using the dataset, the classical models were trained for comparison, from which the YOLOv5s model was selected to be the basic model. Due to small-sized targets, poor lighting conditions, and coal dust and shelter, the detection accuracy of the Yolov5s model was only 89.2%. To obtain better detection accuracy, k-means++ clustering algorithm, a BiFPN-based feature fusion network, the convolutional block attention module (CBAM), and a CIoU loss function were proposed to improve the YOLOv5s model, and an attentional multi-scale cascaded feature fusion-based YOLOv5s model (AMCFF-YOLOv5s) was subsequently developed. The training results on the self-built dataset indicate that its detection accuracy increased to 97.6%. Moreover, the AMCFF-YOLOv5s model was proven to be robust to noise and light.

Research on a real-time dynamic monitoring method for silent aspiration after stroke based on semisupervised deep learning: A protocol study.

Objective: This study aims to establish a real-time dynamic monitoring system for silent aspiration (SA) to provide evidence for the early diagnosis of and precise intervention for SA after stroke. Methods: Multisource signals, including sound, nasal airflow, electromyographic, pressure and acceleration signals, will be obtained by multisource sensors during swallowing events. The extracted signals will be labeled according to videofluoroscopic swallowing studies (VFSSs) and input into a special dataset. Then, a real-time dynamic monitoring model for SA will be built and trained based on semisupervised deep learning. Model optimization will be performed based on the mapping relationship between multisource signals and insula-centered cerebral cortex-brainstem functional connectivity through resting-state functional magnetic resonance imaging. Finally, a real-time dynamic monitoring system for SA will be established, of which the sensitivity and specificity will be improved by clinical application. Results: Multisource signals will be stably extracted by multisource sensors. Data from a total of 3200 swallows will be obtained from patients with SA, including 1200 labeled swallows from the nonaspiration category from VFSSs and 2000 unlabeled swallows. A significant difference in the multisource signals is expected to be found between the SA and nonaspiration groups. The features of labeled and pseudolabeled multisource signals will be extracted through semisupervised deep learning to establish a dynamic monitoring model for SA. Moreover, strong correlations are expected to be found between the Granger causality analysis (GCA) value (from the left middle frontal gyrus to the right anterior insula) and the laryngeal rise time (LRT). Finally, a dynamic monitoring system will be established based on the former model, by which SA can be identified precisely. Conclusion: The study will establish a real-time dynamic monitoring system for SA with high sensitivity, specificity, accuracy and F1 score.

Monitoring parameter change for bivariate time series models of counts.

In this study, we consider an online monitoring procedure to detect a parameter change for bivariate time series of counts, following bivariate integer-valued generalized autoregressive heteroscedastic (BIGARCH) and autoregressive (BINAR) models. To handle this problem, we employ the cumulative sum (CUSUM) process constructed from the (standardized) residuals obtained from those models. To attain control limits, we develop limit theorems for the proposed monitoring process. A simulation study and real data analysis are conducted to affirm the validity of the proposed method.

Predicting highly dynamic traffic noise using rotating mobile monitoring and machine learning method.

Traffic noise, characterized by its highly fluctuating nature, is the second biggest environmental problem in the world. Highly dynamic noise maps are indispensable for managing traffic noise pollution, but two key difficulties exist in generating these maps: the lack of large amounts of fine-scale noise monitoring data and the ability to predict noise levels in the absence of noise monitoring data. This study proposed a new noise monitoring method, the Rotating Mobile Monitoring method, that combines the advantages of stationary and mobile monitoring methods and expands the spatial extent and temporal resolution of noise data. A monitoring campaign was conducted in the Haidian District of Beijing, covering 54.79 km of roads and a total area of 22.15 km2, and gathered 18,213 A-weighted equivalent noise (LAeq) measurements at 1-s intervals from 152 stationary sampling sites. Additionally, street view images, meteorological data and built environment data were collected from all roads and stationary sites. Using computer vision and GIS analysis tools, 49 predictor variables were measured in four categories, including microscopic traffic composition, street form, land use and meteorology. Six machine learning models and linear regression models were trained to predict LAeq, with random forest performing the best (R2 = 0.72, RMSE = 3.28 dB), followed by K-nearest neighbors regression (R2 = 0.66, RMSE = 3.43 dB). The optimal random forest model identified distance to the major road, tree view index, and the maximum field of view index of cars in the last 3 s as the top three contributors. Finally, the model was applied to generate a 9-day traffic noise map of the study area at both the point and street levels. The study is easily replicable and can be extended to a larger spatial scale to obtain highly dynamic noise maps.

Short communication: A simple and accurate method of measuring the zeta-potential of microfluidic channels.

We describe an improved method for determining the electroosmotic mobility and zeta potential of surfaces based on a current-monitoring method. This technique eliminates the requirement for measurements of channel dimensions and sample conductivities, leading to a simple high precision measurement. The zeta potential of PDMS is measured for native surfaces and surfaces treated with a nonionic surfactant in low-conductivity electrolytes.

Analysis and measurement method for carbon-14 in biological samples / 中国辐射卫生

Objective To optimize the measurement and analysis method for carbon-14 in environmental biological samples, and to provide technical support for the formulation of standard methods for carbon-14 measurement in environmental biological samples. Methods Through the optimization research on the carbon dioxide absorption method, the moisture content and carbon content of biological samples were measured with the moisture meter and the element analyzer according to the simplified personnel operation and the optimized process steps of the method, and intra- and inter-laboratory validation of the method was carried out. Results Under typical conditions, the lower limit of detection of the method reached 3 Bq/kg, and there were a relative standard deviation within laboratories of less than 17% and a relative standard deviation between laboratories of less than 14%, with a relative error of less than 19%. Most of the sample pretreatment was directly completed by the instrument and equipment, which improved the precision and accuracy of the measurement of moisture content and carbon content in samples, and reduced the influence of experiment personnel’s operation differences on the test results. Conclusion The lower limit of detection, precision, and accuracy of the optimized method meet the relevant requirements for the determination of carbon-14 in biological samples.

Electroosmotic Flow Hysteresis for Fluids with Dissimilar pH and Ionic Species.

Electroosmotic flow (EOF) involving displacement of multiple fluids is employed in micro-/nanofluidic applications. There are existing investigations on EOF hysteresis, i.e., flow direction-dependent behavior. However, none so far have studied the solution pair system of dissimilar ionic species with substantial pH difference. They exhibit complicated hysteretic phenomena. In this study, we investigate the EOF of sodium bicarbonate (NaHCO3, alkaline) and sodium chloride (NaCl, slightly acidic) solution pair via current monitoring technique. A developed slip velocity model with a modified wall condition is implemented with finite element simulations. Quantitative agreements between experimental and simulation results are obtained. Concentration evolutions of NaHCO3-NaCl follow the dissimilar anion species system. When NaCl displaces NaHCO3, EOF reduces due to the displacement of NaHCO3 with high pH (high absolute zeta potential). Consequently, NaCl is not fully displaced into the microchannel. When NaHCO3 displaces NaCl, NaHCO3 cannot displace into the microchannel as NaCl with low pH (low absolute zeta potential) produces slow EOF. These behaviors are independent of the applied electric field. However, complete displacement tends to be achieved by lowering the NaCl concentration, i.e., increasing its zeta potential. In contrast, the NaHCO3 concentration has little impact on the displacement process. These findings enhance the understanding of EOF involving solutions with dissimilar pH and ion species.

Time of Detection of Prions in the Brain by Nanoscale Liquid Chromatography Coupled to Tandem Mass Spectrometry Is Comparable to Animal Bioassay.

Prions cause transmissible and inevitably fatal neurological diseases in agriculturally important animals, including bovine spongiform encephalopathy in domestic cattle, scrapie in sheep and goats, and chronic wasting disease in cervids. Because animals are largely asymptomatic throughout the course of the disease, early detection of prion disease is important. Hamsters were peripherally (ip) inoculated with hamster-adapted (Sc237) prions. By week 13 of a 14-week disease course, clinical signs appeared. A multiple-reaction-monitoring-based method was used to quantitate the amount of proteinase-K-digested prions (PrP 27-30) and the extent of methionine 213 oxidation present in the brains of infected hamsters. Detectable amounts of PrP 27-30 were present in all animals after 4 weeks. The extent of methionine 213 oxidation decreased over time. When we compared our quantitation results to those from other researchers using bioassay, we observed that consistent detection of PrP 27-30 by mass spectrometry occurs at a time when prions are reliably detected by bioassay.

Novel method for evaluating the health condition of mice in space through a video downlink.

Clarification of the criteria for managing animal health is essential to increase the reliability of experiments and ensure transparency in animal welfare. For experiments performed in space, there is no consensus on how to care for animals owing to technical issues, launch mass limitation, and human resources. Some biological processes in mammals, such as musculoskeletal or immune processes, are altered in the space environment, and mice in space can be used to simulate morbid states, such as senescence acceleration. Thus, there is a need to establish a novel evaluation method and evaluation criteria to monitor animal health. Here, we report a novel method to evaluate the health of mice in space through a video downlink in a series of space experiments using the Multiple Artificial-gravity Research System (MARS). This method was found to be more useful in evaluating animal health in space than observations and body weight changes of the same live mice following their return to Earth. We also developed criteria to evaluate health status via a video downlink. These criteria, with "Fur condition" and "Respiratory" as key items, provided information on the daily changes in the health status of mice and helped to identify malfunctions at an early stage. Our method and criteria led to the success of our missions, and they will help establish appropriate rules for space experiments in the future.

Testicular quantitative ultrasound: A noninvasive monitoring method for evaluating spermatogenic function in busulfan-induced testicular injury mouse models.

Busulfan-induced testicular injury mouse models are commonly used for experiments on spermatogonial stem cell transplantation, treatments for azoospermia due to spermatogenic failure and preserving male fertility after chemotherapy. Here, we investigated the value of testicular quantitative ultrasound for evaluating spermatogenic function in this model. In this study, testicular ultrasound was performed on mice from day 0 to 126 after busulfan treatment (n = 48), and quantitative data, including the testicular volume, mean pixel intensity and pixel uniformity, were analysed. The results revealed that from day 0 to 36, the testicular volume was positively associated with the testicle-to-body weight ratio (r = .92). On day 63, the pixel uniformity, which remained stable from day 0 to 36, declined significantly compared with that on day 36 (p < .01). On day 126, when the whole progression of spermatogenesis could be observed in most tubules, the mean pixel intensity also returned to normal (p > .05). In conclusion, testicular quantitative ultrasound could be used as a noninvasive and accurate monitoring method for evaluating spermatogenic function in busulfan-induced testicular injury mouse models.

[Monitoring Method of Total Particulate Matter in Ultra-low-emission and High-humidity Exhaust Gas from Stationary Sources and an Actual Test in a Gas Power Plant].

With the implementation of ultra-low-emission transformation in coal-fired power plants and other related industries in China, the concentrations of filterable particulate matter (FPM) and gaseous pollutants in exhaust gas from stationary sources have reduced significantly, while the emission of condensable particulate matter (CPM) remains a concern. In this study, the monitoring methods of FPM and CPM at a relatively low FPM concentration in exhaust gas from stationary sources in China and abroad were comprehensively analyzed. On the basis of existing research and experimental exploration, the monitoring methods of FPM and CPM were further studied. A direct condensation sampling and monitoring method for total particulate matter (TPM) in ultra-low-emission and high-humidity exhaust gas from stationary sources, which is suitable for the actual situation in China, was developed and established before being used to measure TPM in exhaust gas from a gas power plant in Beijing. The results showed that the emission concentration of TPM in the exhaust gas from the gas power plant was between 1.98 mg·m-3 and 3.77 mg·m-3 (average of 2.81 mg·m-3), whereas the average emission concentration of FPM was only 0.10 mg·m-3. The emission type of particulate matter in exhaust gas from the gas power plant was mainly CPM, which accounted for 93.8% to 99.2% of the TPM (average of 97.0%). The proportion of FPM to TPM ranged from 0.7% to 6.2% (average of 3.0%). The emission concentration of filterable CPM was slightly higher than that of FPM.

Real-Time Monitoring Method for Layered Compaction Quality of Loess Subgrade Based on Hydraulic Compactor Reinforcement.

Hydraulic compactor is an efficient reinforcement machine for loess subgrade. However, it is difficult to control the layered compaction quality of the subgrade. This research presents a real-time layered compactness monitoring method for hydraulic compactor reinforcement of subgrade in loess areas. The hydraulic force coefficient is first introduced, and the dynamic response model of the hydraulic rammer and soil is established. The relationship between the acceleration of the hydraulic rammer and the compactness of subgrade is then obtained based on the collision theory in elastic half space. A full-scale test using a hydraulic compactor to reinforce loess subgrade was also carried out. Results show that the hydraulic compactor obtains the effective influence depth for the reinforcement of loess subgrade. Within the effective reinforcement depth, the relationship between the peak acceleration of the rammer and the layered compactness of subgrade can be well fitted by a quadratic function model. The layered compactness of the subgrade and the working state of the hydraulic compactor can then be remotely monitored at a mobile terminal in real time. Furthermore, the monitoring technology was applied to Huangling-Yan'an Expressway in China, significantly improving the accuracy and efficiency of real-time monitoring of the layered compactness of subgrade in the loess area.

Expansion of Human iPSC-Derived Ureteric Bud Organoids with Repeated Branching Potential.

Ureteric bud (UB) is the embryonic kidney progenitor tissue that gives rise to the collecting duct and lower urinary tract. UB-like structures generated from human pluripotent stem cells by previously reported methods show limited developmental ability and limited branching. Here we report a method to generate UB organoids that possess epithelial polarity and tubular lumen and repeat branching morphogenesis. We also succeed in monitoring UB tip cells by utilizing the ability of tip cells to uptake very-low-density lipoprotein, cryopreserving UB progenitor cells, and expanding UB tip cells that can reconstitute the organoids and differentiate into collecting duct progenitors. Moreover, we successfully reproduce some phenotypes of multicystic dysplastic kidney (MCDK) using the UB organoids. These methods will help elucidate the developmental mechanisms of UB branching and develop a selective differentiation method for collecting duct cells, contributing to the creation of disease models for congenital renal abnormalities.

A sampling and estimation method for monitoring poultry red mite (Dermanyssus gallinae) infestation on caged-layer poultry farms.

BACKGROUND: The poultry red mite, Dermanyssus gallinae, is a serious problem in the laying hen industry worldwide. Currently, the foremost control method for D. gallinae is the implementation of integrated pest management, the effective application of which necessitates a precise monitoring method. OBJECTIVES: The aim of the study was to propose an accurate monitoring method with a reliable protocol for caged-layer poultry farms, and to suggest an objective classification for assessing D. gallinae infestation on caged-layer poultry farms according to the number of mites collected using the developed monitoring method. METHODS: We compared the numbers of mites collected from corrugated cardboard traps, regarding with length of sampling periods, sampling sites on cage, and sampling positions in farm buildings. The study also compared the mean numbers of mites collected by the developed method with the infestation levels using by the conventional monitoring methods in 37 caged-layer farm buildings. RESULTS: The statistical validation provided the suitable monitoring method that the traps were installed for 2 days on feed boxes at 27 sampling points which included three vertical levels across nine equally divided zones of farms. Using this monitoring method, the D. gallinae infestation level can be assessed objectively on caged-layer poultry farms. Moreover, the method is more sensitive than the conventional method in detecting very small populations of mites. CONCLUSIONS: This method can be used to identify the initial stages of D. gallinae infestation in the caged-layer poultry farms, and therefore, will contribute to establishment of effective control strategies for this mite.

Intraoperative core temperature monitoring: accuracy and precision of zero-heat flux heated controlled servo sensor compared with esophageal temperature during major surgery; the ESOSPOT study.

Monitoring of intraoperative core temperature is strongly recommended to reduce the risk of perioperative thermic imbalance and related complications. The zero-heat-flux sensor (3M Bair Hugger Temperature monitoring system, ZHF), measures core temperature in a non-invasive manner. This study was aimed at comparing accuracy and precision of the ZHF sensor compared to the esophageal thermometer. Patients scheduled for major elective abdominal or urologic surgery were considered eligible for enrollment. Core body temperature was measured using both an esophageal probe (TESO) and a ZHF sensor (TZHF) every 15 min from induction until the end of general anaesthesia. A Bland-Altman plot for repeated measures was performed. The proportion of measurements within ± 0.5 °C was estimated; from a clinical point of view, a proportion greater than 90% was considered sufficiently accurate. Lin's concordance correlation coefficient (CCC) for repeated measures were calculated. To evaluate association between the two methods, a generalized estimating equation (GEE) simple linear regression model, was elaborated. A GEE multiple regression model was also performed in order to adjust the estimate of the association between measurements from surgical and patient's features. Ninety-nine patients were enrolled. Bland-Altman plot bias was 0.005 °C with upper and lower limits of agreement for repeated measures of 0.50 °C and - 0.49 °C. The percentage of measurements within 0.5 °C of the reference value was 97.98% (95% confidence interval 92.89-99.75%), indicating a clinically sufficient agreement between the two methods. This was also confirmed by a CCC for repeated measures of 0.89 (95% CI 0.80 to 0.94). The GEE simple regression model (slope value of 0.77) was not significantly influenced by any patient or surgical variables. According to GEE multiple regression model results, the explored patient- and surgery-related variables did not influence the association between methods. ZHF sensor has shown a clinically acceptable accuracy and precision for body core temperature monitoring during elective major surgery. CLINICAL TRIALS: Clinical trial number: NCT03820232.

Effect of surface conduction-induced electromigration on current monitoring method for electroosmotic flow measurement.

Current monitoring method for measurement of EOF in microchannels involves measurement of time-varying current while an electrolyte displaces another electrolyte having different conductivity due to EOF. The basic premise of the current monitoring method is that an axial gradient in conductivity of a binary electrolyte in a microchannel advects only due to EOF. In the current work, using theory and experiments, we show that this assumption is not valid for low concentration electrolytes and narrow microchannels wherein surface conduction is comparable with bulk conduction. We show that in presence of surface conduction, a gradient in conductivity of binary electrolyte not only advects with EOF but also undergoes electromigration. This electromigration phenomenon is nonlinear and is characterized by propagation of shock and rarefaction waves in ion concentrations. Consequently, in presence of surface conduction, the current-time relationships for forward and reverse displacement in the current monitoring method are asymmetric and the displacement time is also direction dependent. To quantify the effect of surface conduction, we present analytical expressions for current-time relationship in the regime when surface conduction is comparable to bulk conduction. We validate these relations with experimental data by performing a series of current monitoring experiments in a glass microfluidic chip at low electrolyte concentrations. The experimentally validated analytical expressions for current-time relationships presented in this work can be used to correctly estimate EOF using the current monitoring method when surface conduction is not negligible.

[Research progress on the monitoring methods of atmospheric nitrogen deposition]. / å¤§æ°”æ°®æ²‰é™ç›‘æµ‹æ–¹æ³•ç ”ç©¶è¿›å±•.

As an important component of global nitrogen (N) biogeochemical cycle, atmospheric N deposition refers to the removal process of reactive N, including oxidized N (NOy) and reduced N (NHx), from the atmosphere to earth surface through dry and wet deposition. Nitrogen deposition can exert important impacts on the structure and functioning of both terrestrial and aquatic ecosystems. Increasing N deposition poses a potential threat to natural ecosystems and human health. It is a challenge to accurately monitor the composition and flux of dry and wet N deposition in different ecosystems, using a unified technology. Here, we reviewed the research progress on monitoring methods of dry and wet N deposition in China and aboard, including micrometeorology, inferential method, model estimation, surrogate surface, precipitation collection, and ion exchange resin methods. We further discussed the advantages and disadvantages of each method in terms of its applications at regional, national, and global scales. This review would provide a methodological support to establish national monitoring network for atmospheric N deposition.

Establishment of a multiplex RT-PCR assay for identification of atmospheric virus contamination in pig farms.

Spread of pathogens in pig farms not only causes transfection of diseases to other pigs or even farmers working in the farms, but also induces pollution to the living atmospheric environment of the residents around the farm. Therefore, it is necessary to establish a rapid and simple monitoring method. In this study, full genome sequences of common viruses were analyzed in pig farms, in combination with the design of primers, optimization of the reaction parameters, so as to establish a multiplex RT-PCR assay for the identification of classical swine fever virus (CSFV), Japanese encephalitis virus (JEV), porcine reproductive and respiratory syndrome virus (PRRSV), porcine circovirus Type 2 (PCV-2), porcine pseudorabies virus (PRV) and porcine parvovirus virus (PPV), which are common in pig farms. This method has a minimal detectable concentration of 10-3 ng/µL, which is highly specific. Furthermore, multiplex RT-PCR was applied to examine air samples from 4 pig farms located in different cities of China. The results were in line with those obtained by single PCR. Therefore, this study can be expected to provide essential technique support for the early warning mechanism as well as disease prevention and control system against the major viruses.