Diagnostic Signals Research Articles

Application of Modern Machine Diagnostic Systems to Improve Safety in the Underground Mining Process

Abstract Currently used machine diagnostic systems are based on very modern solutions based on the acquisition and recording of their operating parameters in real time. Increasingly available and high-tech sensor systems mean that the number of recorded parameters is increasing and their quality is improving. These data are mainly used to assess the technical condition of machines and the processes they perform. In mining, these data can also be used to assess and, at a later stage, improve the safety of the underground mining process. Referring to this issue, the paper presents examples of the use of diagnostic systems for powered roof supports and longwall shearers to assess the safety status of the underground hard coal mining process. In the case of the wall support, the focus was on measuring the pressures in the stands of its individual sections. Temporary changes in the values of these pressures constitute a valuable source of information regarding the interaction of the support with the rock mass. In particular, this concerns the identification of the effects of the informational impact of the rock mass on the longwall excavation protected by the support. The research results presented in the paper, especially in the case of very dangerous dynamic impacts, indicate the possibility of both diagnosing the operating condition of the section and identifying symptoms of exposure to such events. This undoubtedly significantly expands the possibilities of using the measured pressures. Diagnostic signals from a longwall shearer are also widely used. The current intensities drawn by its motors while cutting the rock mass, as well as the advance speed and its position in the wall make it possible to analyze these parameters and their changes before, during and after the occurrence of various types of events. These data enable the assessment of the effects of the rock mass on its operational efficiency and safety status. It also enables the identification of symptoms that precede the occurrence of such events. The presented examples indicate the need for a broader and more holistic approach to the use of diagnostic parameters of mining machines. In particular, this concerns the study of the cooperation between the support and the rock mass and its influence on the efficiency and safety of the rock mass mining process. The subject matter addressed relates to very important and current issues, and the developed methodology and obtained results should be applied in practice as soon as possible.

Evaluation of CO2 Leakage Potential through Fault Instability in CO2 Geological Sequestration by Coupled THMC Modelling

A faulted saline aquifer system was simulated in a coupled thermal-hydraulic-mechanical-chemical (THMC) framework to examine the potential breaching of the caprock seal from long-term CO2 sequestration. The pH of the brines steadily dropped from 7.5 to 4.7 due to the continuous injection of scCO2 (supercritical CO2), which was caused by the rapid dissolution of calcite in the reservoir and associated fault zones, alongside alterations in the concentrations of primary and secondary minerals within the formation. The increase in pore pressure with the continuous injection of scCO2 triggered fault reactivation at 6y with the resultant leakage of CO2 along the fault. This builds CO2 saturation inside the fault at 7y to six-fold higher than pre-slip. Continuing shear reactivation and creation of reactive surface area following the initial CO2 leakage accelerates dissolution/precipitation reactions, in turn further increasing porosity and permeability of the reservoir and fault. In particular, the permeability and porosity in the fault zone were increased by only 2% and 6%, respectively – staunched by competitive feedbacks in dissolution countered by precipitation that are individually much larger. Comparison of mineral concentrations adjacent to the fault before-and-after instability revealed that the development of shear failure also promotes the transport of reactivated minerals into the fault zone. Among them, feldspar changes most significantly in later stages, dominated by dissolution with the volume fraction decreasing by 80% and increasing the aqueous concentration of K+ by approximately an order of magnitude. However, secondary minerals counter this dissolution through precipitation with the volume fraction of kaolinite increasing by an order of magnitude compared with the original fraction. Finally, the evolution of the fault sealing coefficient (FS) demonstrates that the porosity and permeability exert a pivotal influence in controlling the self-sealing behavior in the basal of the fault, while the upper fault layer exhibits self-enhancing response. A notable observation is that changes in mineral ion concentrations in fault zones could be applied as a significant diagnostic signal to monitor fault stability and the potential for progress of self-sealing behavior.

О возможности проведения контроля состояния асинхронных электродвигателей с частотно-регулируемым приводом методом спектрального анализа

One of the important tasks of current energy power industry is to increase the reliability of induction motors operation. A promising direction for monitoring their technical condition is application of methods of spectral analysis of signals. However, very little attention is paid to the diagnostic assessment of induction motors as a part of a variable frequency drive, as well as the use of frequency converters for diagnostic purposes. Therefore, the issues related to monitoring the condition of induction motors with variable frequency drives are relevant and require scientific research. The purpose of the study is experimental evaluation of the possibility to use the spectral analysis method to assess the technical condition of induction motors driven by frequency converters. The research has been carried out on AIR71A6 induction motor with a serviceable rotor winding and one broken rotor bar rotor winding. The internal magnetic field is considered as a diagnostic signal in steady-state and start-up mode. The condition has been evaluated based on the determination of harmonics in the amplitude spectrum from the dummy rotor winding. A Short Time Fourier Transform is used for signal processing. The spectra of the internal magnetic field have been obtained at start-up and in steady-state mode when the engine is fed by an autotransformer and frequency converter. A diagnostic sign of the presence of broken bars, that is an increase of harmonic amplitudes from the dummy rotor winding, manifested itself not only when the electric motor is fed by an autotransformer, but also frequency driven. Comparing the amplitudes of harmonics in the presence and absence of a frequency converter allows us to conclude that interference of the converter slightly affect the amplitudes of specific harmonics. Using the example of an internal magnetic field signal, it is shown that spectral analysis methods can be used to diagnose the induction motors condition with a variable speed drive.

Effects of Injected Current Streams on MHD Equilibrium Reconstruction of Local Helicity Injection Plasmas in a Spherical Tokamak

Open field line currents are intrinsic to DC helicity injection plasma startup and pose a challenge for inferring the plasma equilibrium with standard reconstruction analysis. Local helicity injection (LHI) is a type of DC helicity injection which uses small, modular current sources to drive force-free current along helical field lines to produce tokamak plasmas. MHD modeling and magnetic measurements during LHI indicate the injected current streams remain coherent as helical structures on the outboard edge of a core toroidal plasma that is tokamak-like in a toroidally averaged sense. To extract core plasma equilibrium properties, external magnetic diagnostics corrected for contributions from the injected current streams are fitted by a standard Grad-Shafranov equilibrium code. An iterative approach for estimating and subtracting the stream contributions from the diagnostic signals is described and applied to a model equilibrium database to reduce systematic errors introduced by the streams. Convergence is usually attained with 2 to 4 iterations, with derived equilibrium parameters matching the prescribed axisymmetric core values to within estimated experimental uncertainties. Accurate recovery of core parameters occurs when the ratio of the net toroidal windup current from the streams to the core plasma current is less than 0.2, which is typically satisfied in most experiments.

Detecting neoclassical tearing modes in high-temperature ITER plasma scenarios with the ITER prototype electron cyclotron emission diagnostic

Abstract Electron cyclotron emission (ECE) diagnostics for ITER serve two key purposes. The diagnostics will measure plasma electron temperature with high spatial and temporal resolution. Additionally, they will be used to detect neoclassical tearing modes (NTMs), a deleterious and nonlinearly unstable mode causing the growth of magnetic ‘seed’ islands. Interpreting ECE requires anticipation of physical limits including frequency cut-offs and harmonic overlap. In high temperature plasmas, the relativistic shift and broadening of the emission must also be considered to accurately reconstruct the electron temperature spatial profile. Accounting for these effects allows ECE diagnostics to be used for accurate measurement of the equilibrium electron temperature profile, as well as fluctuations about this equilibrium. One such fluctuation is caused by the fast radial transport of heat across rotating magnetic islands. ECE diagnostics can detect this change as an oscillation at the plasma rotation frequency to determine the existence and location of NTMs. This paper presents work on a synthetic diagnostic for ECE. The synthetic diagnostic tests simulated ECE signals, which are inferred from ITER scenarios perturbed by magnetic islands after accounting for all ECE physics. The synthetic diagnostic tests conventional ECE detection algorithms for NTMs in real-time on ITER-recommended hardware. Combined, these two areas of focus help determine design of the ECE system.

Radiofrequency Diagnostic of the Decaying Plasma in the “Gigantic” Coaxial Line at the Large Plasma Device

At the large-scale Krot facility (Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences), a new device was developed for laboratory simulations of the effects of the interaction of ultrawideband electromagnetic pulses with the partially ionized atmosphere and ionosphere: the “gigantic” coaxial line with a length of 10 m and diameter of 1.4 m that is filled by the decaying plasma of the inductive discharge. Two radiofrequency methods of wave diagnostics used in the device are described, the cutoff method and the wave interferometer, which can be used to determine the electron density of the plasma in the line in a wide range of values, Ne = 107–1011 cm–3. The measurement results are compared with the values obtained by the contact diagnostic, a probe with a microwave resonator. The interferometric method is implemented taking into account the nonuniform distribution of plasma density both along and across the transmission line, which, in the working range of pulsed and continuous diagnostic signals, is an oversized waveguide. The specific features of application and the limitations of the contact (probe) and contactless (wave) methods of diagnostics are discussed, taking into account the nonuniform plasma distribution in the coaxial line and the specific features of its construction.

Three-Component Synthesis of 1-Substituted 5-Aminotetrazoles Promoted by Bismuth Nitrate.

A nontoxic bismuth-promoted multicomponent synthesis of 5-aminotetrazoles and bistetrazoles is reported. The reaction of phenyl isothiocyanate, NaN3, and amine (primary aliphatic, aromatic, and aliphatic diamine) promoted by Bi(NO3)3·5H2O under microwave heating affords good yields, short reaction times, simple workup, and purification without column chromatography. A set of diagnostic 1H NMR signals was identified as a guide for quickly elucidating the exclusive (or main) regioisomer formed, with the stronger electron donor group located at heterocyclic nitrogen 1. This regioselectivity is strongly dependent on the electronic density of the amine. It is opposite to that obtained by several thiourea desulfurization methods promoted by thiophilic metals and metal-free protocols.

COMPUTER DIAGNOSTICS OF THE CONDITION OF SHIP ROLLING BEARINGS DURING THEIR OPERATION

The results of statistical processing of vibration signals obtained during diagnostics of ship power plants during the operation process are offered. A distinctive feature of monitoring the technical condition of elements of ship power plants is the uncertainty in the fixation and duration of extreme loads. The peculiarity of computer vibration diagnostics of rotary mechanisms is determined, which is that, in particular, physical measurements of vibration signals require their use in mathematical models of the physical state of the bearing. Calculations of statistical characteristics of vibration signals in the time domain, such as standard deviation (Std), asymmetry (Skewness), kurtosis (Kurtosis), full range of oscillations (Peak2Peak), root mean square value (RMS), cross factor (CrestFactor), form- factor (ShapeFactor), impulse factor (ImpulseFactor), marginal factor (MarginFactor), energy (Energy). The variety of diagnostic signals leads to the need to merge indicators of different dimensions without losing available information into a single generalizing indicator, for which the method of principal components is used in the work. The methodology of statistical processing and practical implementation of diagnostics of vibration signals during the analysis of damage evolution of rolling bearings of turbochargers of marine power plants are described. A new computer information parameter of vibration diagnostics based on the analysis of the first principal component in the time domain and spectral excess in the frequency domain has been identified and experimentally confirmed. The use of new diagnostic parameters makes it possible not only to judge the degree of damage to the rolling bearing, but also to observe the dynamics of the development of the defect and make a relative forecast of the working life of the rolling bearing. A real practical situation is considered - computer diagnostics of vibration signals of ship rolling bearings of a point defect of an external defect of a rolling bearing ring. Determining the applicability of the principal component method to other types of defects showed their potential for vibrodiagnosis of defects located on the inner ring of the bearing and when the ball falls out of the holder.

Application of vibration signals in railway track diagnostics using a mobile railway platform

The article presents a comprehensive method for using vibration signals to diagnose railway tracks. The primary objective is to gather detailed information on track conditions through a passive experiment. This involves using mobile diagnostic tools and techniques to assess railway infrastructure. The article elaborates on the range of diagnostic activities conducted in accordance with detailed railway regulations and highlights the benefits and capabilities of mobile diagnostics in railway transport. The research includes mobile field measurements across the general railway manager’s network, employing vibration signals to detect and evaluate track conditions. The methodology section provides a thorough description of the mobile measurement rail platform, detailing the equipment used, the routes taken for measurements, and the processes of data acquisition and processing. The data obtained from these measurements is crucial for understanding the actual technical condition of the railway tracks. The method of obtaining and processing data is explained in relation to the real technical condition of the railway track. This involves using transducers with specific parameters and parametrically defined signal recording, along with dedicated analysis techniques in post-processing. Vibration signals serve as the primary carrier of information in this diagnostic method. The article details the step-by-step procedures for collecting and analyzing these signals to provide accurate assessments of track conditions. Based on the results from the mobile measurement rail platform, the article characterizes various areas of diagnostics where vibration signals are particularly effective for technical evaluation. These areas include identifying track defects, monitoring track surface and railway crossing and assessing the overall structural health of the railway infrastructure. The use of vibration signals offers a non-invasive and efficient means of track diagnostics, providing real-time data for maintenance and repair decisions. In conclusion, the article underscores the significance of mobile diagnostics in enhancing the safety and reliability of railway transport. By leveraging vibration signals and advanced data processing techniques, this method provides a framework for continuous monitoring and assessment of railway track conditions, ultimately contributing to improved maintenance strategies and operational efficiency.

Gearbox fault identification using auto-encoder without training data from the damaged machine

Deep learning methods work well in machine diagnostics where operating conditions affect diagnostic signals. Classifiers are often used for fault identification, but these methods require training data sets measured for each fault. The solution to the lack of data is autoencoder-based network models, but these models can only detect, not identify faults.This article presents a new fault identification method based on auto-encoders (AE-based Fault Identification Technique AE-FIT) that does not require training data from the damaged machine. This method diagnoses pinion gearboxes operating under variable conditions (variable load, load-induced rotational speed, and temperature). The result of the technique is an interpretable diagnostic spectrum (AE-based Interpretable Order Spectrum AE-IOS).The method has been tested on two laboratory benches to detect misalignment, unbalance, and gearbox degradation. The damages introduced were used to validate a technique based on an auto-encoder trained only with data from undamaged machines.

Development of a Modular miRNA-Responsive Biosensor for Organ-Specific Evaluation of Liver Injury.

MicroRNAs (miRNAs) are increasingly being considered essential diagnostic biomarkers and therapeutic targets for multiple diseases. In recent years, researchers have emphasized the need to develop probes that can harness extracellular miRNAs as input signals for disease diagnostics. In this study, we introduce a novel miRNA-responsive biosensor (miR-RBS) designed to achieve highly sensitive and specific detection of miRNAs, with a particular focus on targeted organ-specific visualization. The miR-RBS employs a Y-structured triple-stranded DNA probe (Y-TSDP) that exhibits a fluorescence-quenched state under normal physiological conditions. The probe switches to an activated state with fluorescence signals in the presence of high miRNA concentrations, enabling rapid and accurate disease reporting. Moreover, the miR-RBS probe had a modular design, with a fluorescence-labeled strand equipped with a functional module that facilitates specific binding to organs that express high levels of the target receptors. This allowed the customization of miRNA detection and cell targeting using aptameric anchors. In a drug-induced liver injury model, the results demonstrate that the miR-RBS probe effectively visualized miR-122 levels, suggesting it has good potential for disease diagnosis and organ-specific imaging. Together, this innovative biosensor provides a versatile tool for the early detection and monitoring of diseases through miRNA-based biomarkers.

Multi-Chambered Core/Shell Supraparticles for Real-Time, Full-Time Diagnosis and Treatment Integration of Tumors.

To a certain extent, theranostic nanoplatforms promote tumor treatment efficiency. However, timely monitoring of the critical stages and signal sustainability of the entire process is challenging. In this study, multi-chambered core/shell magnetic nanoparticles (MC-MNPs) as drug and imaging agent multi-loaded nanocarriers with a synergistic release function are reported. Supraparticles with stable chambers are formed by the supercooling self-assembly of several core/shell magnetic nanoparticles composed of amphiphilic copolymers as the core and hydrophilic magnetic iron oxide nanoparticles as the shell. Desalinized doxorubicin and coumarin 6 are stored in different cavities of nanocarriers, and chitosan is used as an outer encapsulation layer. Based on their construction properties, MC-MNPs can exhibit gradient-degraded and steady-released controllability in the tumor environment. Furthermore, real-time accumulation situations and full-time diagnostic signals of nanocarriers are thoroughly demonstrated using fluorescence imaging and T2-weighted magnetic resonance imaging before and after magnetic hyperthermia in targeted tumors under an alternating magnetic field. Thus, MC-MNPs as theranostic nanocarriers exhibit great potential for the timely monitoring and full-time guidance of tumor treatment.

Жизненный цикл изделия: мониторинг процессов механической обработки и фильтрация виброакустических сигналов

Introduction. In modern manufacturing, the product life cycle comprises various stages, from conception to disposal. Among these stages, machining plays a significant role, as it directly influences the durability and functionality of the finished product. With increasing competition and the need to reduce production costs, optimizing machining processes has become a crucial task. Traditionally, conservative technological approaches have been used to ensure processing quality. However, this often leads to decreased productivity and higher costs. Modern monitoring and diagnostic techniques can significantly improve process control, particularly through tool condition monitoring. The subject. This paper discusses the stages of the product life cycle and emphasizes the importance of monitoring machining processes. It explores the potential of using vibroacoustic signals to continuously monitor equipment and product conditions. Special attention is paid to the use of vibroacoustic signals for diagnostics and quality control. Modern approaches to filtering these signals, including the use of the fast Fourier transform and various window functions, are analyzed in order to improve the accuracy of the analysis and identify potential defects. The purpose of this work is to develop an algorithm for an online monitoring system that will monitor the condition of cutting tools based on the creation of a digital shadow using a vibroacoustic complex. The main tasks to be solved are to establish the ranges of applicability of frequency response of acoustic signals and optimal window functions, as well as to establish the relationship between the degree of wear on the cutting tool and the results of vibration diagnostics and surface roughness. The methods and technologies for filtering vibroacoustic signals and their application in real–world production settings are discussed. Special attention is given to the role of digital twins in integrating monitoring and filtering data, allowing for the creation of a virtual model of a product to predict its behavior and optimize processes throughout the life cycle. A comparison of various monitoring methods and technologies is conducted, as well as an analysis of practical examples of digital twin implementation in production processes and its impact on improved control. Results and discussion are presented, identifying current research and practical advancements, while also proposing existing challenges and promising areas for future research in the fields of monitoring, signal filtering, and the use of digital twins in mechanical manufacturing.

Advances and Challenges in the Hunting Instability Diagnosis of High-Speed Trains.

With the continuous increase in train running speeds and the rapid complexity of operation environments, running stability of the high-speed train is facing significant challenges. A series of abnormal vibration issues, caused by hunting instability, have emerged, including bogie instability alarm, carbody swaying, and carbody shaking, posing a significant threat to the safe and stable operation of high-speed trains. Therefore, the monitoring and diagnosis of hunting instability have become important research topics in rail transit. This review follows the development of fault diagnosis for bogie hunting instability and carbody hunting instability. It first summarizes the existing evaluation standards and innovative diagnostic methods. Due to the current limitation of hunting instability evaluation standards, which can only detect large-amplitude hunting, this paper addresses the gap in evaluation criteria for early-stage, small amplitude hunting instability diagnosis. A thorough overview of the progress made by researches in this field of research is given, emphasizing three primary facets: diagnostic signal sources, diagnostic features, and diagnostic targets. Furthermore, given that existing methods only classify faults into small and large amplitudes, which does not meet the practical need for quickly and accurately identifying fault types and severity during operation, this review introduces existing works on the detailed assessment and fault tracing of hunting instability, as well as the mechanisms underlying its occurrence, with the aim of achieving a comprehensive diagnosis of hunting instability. Finally, the limitations of current methods and the future development trends in hunting instability diagnostics are discussed and summarized. This paper provides readers with a framework for the research process of hunting instability diagnosis, offering valuable references and innovative perspectives for their future research efforts.

Chromatographic purification technology optimisation of immunoglobulin G (IgG) from horse serum for animal chlamydia diagnostics

The study aimed to develop and evaluate an antibody quality improvement method to improve the accuracy and efficiency of chlamydia diagnosis in horses. The study was conducted in Kazakhstan and included 100 horses of the Kazakh breed infected with chlamydia, which was divided into two groups: experimental and control. In the experimental group, affinity chromatography was used to purify immunoglobulin G (IgG), which allowed for a 95% purity of the antibodies. The control group used a traditional diagnostic method without preliminary purification, which ensured IgG purity of only 60-65%. The data showed that the purified antibodies demonstrated improved diagnostic performance, including an increase in sensitivity of up to 92% and specificity of up to 95%. The purified antibodies provided effective binding to chlamydial antigens even at low concentrations (0.2cμg/ml), which is 2.5 times better than in the control group. The time to obtain a stable diagnostic signal was reduced by 33% and amounted to 20 minutes in the experimental group versus 30 minutes in the control group. The frequency of false-positive results in the experimental group was reduced to 5% and false-negative results to 4%, which significantly increases the overall reliability of diagnostics. The purified antibodies retained their activity for 12 months, demonstrating high stability and durability. These results highlighted the importance of using chromatographic purification to improve the quality of antibodies used for diagnostic purposes and offer a reliable approach for the accurate detection of chlamydia as well as other infectious diseases in animals. The introduction of such methods can significantly improve the efficiency of veterinary diagnostics and contribute to more timely and adequate treatment of animals

Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis

Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, due to their label-free, real-time, and high-sensitivity features. Their advantages in multiplex immunoassays of minimal liquid samples establish the leading position in various diagnostic studies. This review delineates the application principles of plasmonic sensing technologies, highlighting the importance of exosomes-based spectrum and image signals in disease diagnostics. It also introduces advancements in miniaturizing plasmonic biosensing platforms of exosomes, which can facilitate point-of-care testing for future healthcare. Nowadays, inspired by the surge of artificial intelligence (AI) for science and technology, more and more AI algorithms are being adopted to process the exosome spectrum and image data from plasmonic detection. Using representative algorithms of machine learning has become a mainstream trend in plasmonic biosensing research for exosome liquid biopsy. Typically, these algorithms process complex exosome datasets efficiently and establish powerful predictive models for precise diagnosis. This review further discusses critical strategies of AI algorithm selection in exosome-based diagnosis. Particularly, we categorize the AI algorithms into the interpretable and uninterpretable groups for exosome plasmonic detection applications. The interpretable AI enhances the transparency and reliability of diagnosis by elucidating the decision-making process, while the uninterpretable AI provides high diagnostic accuracy with robust data processing by a “black-box” working mode. We believe that AI will continue to promote significant progress of exosome plasmonic detection and mobile healthcare in the near future.

On the supramolecular interactions into a pH- and Metal-Actuated Molecular Shuttle: some insights from QTAIM modeling.

Supramolecular contacts responsible for chemical interaction of cucurbit[7]uril (CB[7]) macrocycle on a Tolyl-Viologen-Phenylene-Imidazole (T-VPI) molecular thread, at acid pH (T-VPI-H+) or after Ag+ cation addition (T-VPI-Ag+), are analytically addressed in a computational framework combining Quantum Theory of Atoms in Molecules (QTAIM) with Density Functional Theory (DFT). In this respect, the crystallographic structure (CCDC number 2217466) is taken as reference condition for addressing the nature of the chemical interactions driving the shuttling of the CB[7] between T and P stations recently observed in dilute water solutions. Beside the host(CB[7]) vs guest(T-VPI-H+ or T-VPI-Ag+) complexation, the coordination sphere of the Ag+ cation is also investigated by means of local electronic energy density - H(r) - descriptors. The derived non-covalent interaction patterns are found to support diagnostic 1H NMR signals used for detecting the mutual position of the CB[7] along the axle. This work highlights the potentialities of a QTAIM based approach in the characterization of supramolecular and metal-complexation effects in molecular aggregates such as not-interlocked synthetic molecular shuttles.

Lysosome-Targeted Polarity-Sensitive NIR Fluorescence Probe for Imaging Injured Lung and Liver in Diabetes.

Diabetes is a chronic disease marked by high blood glucose. With the progress of diabetes, complications gradually appear, and various organs may be affected. However, due to the lack of noninvasive in situ detection probes, the diagnosis of organ damage caused by diabetes is significantly delayed, which will cause many complications that cannot be treated in time. Here, we report a BODIPY-based fluorescent probe SNL, which can be used to detect lung and liver damage caused by diabetes. By introducing methylpiperazine and extending the conjugated system, SNL can locate lysosomes and exhibit absorption and emission both in the near-infrared (NIR) region. In addition, SNL is sensitive to polarity and can be used for sensitive detection of lysosomal polarity changes. Unexpectedly, SNL targets and images the lungs and liver of mice. Subsequently, hyperglycemia-stimulated cell models and diabetic mouse models were successfully established, and SNL was utilized to reveal that polarity can be used as a diagnostic signal of diabetic complications. Notably, SNL for the first time confirmed the lung injury and liver injury caused by diabetes using the fluorescent probes method, providing a new approach for the diagnosis of diabetes complications.

The effects of some common inorganic soil components on the pyrolytic analysis of plastics

Plastics and microplastics are major hazards for the environment and human health. Plastic materials in the sedimentary record are a marker for modern anthropogenic activity. One environmental reservoir of plastics and microplastics is soil. One method to detect the presence of plastics in soil, and to thereby recognise the presence of its hazards, is pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS). Yet soils are multicomponent mixtures and any analytical artefacts generated will reduce our ability to monitor the presence of plastics. We have studied the Py-GC-MS responses for the polymers polyethylene (PE), polypropylene (PP), polystyrene (PS) and poly(vinyl chloride) (PVC), in the presence of individual phyllosilicate (kaolinite, illite and montmorillonite) and iron oxyhydroxide/oxide (goethite and magnetite) minerals that may be present in soil. Our data show the distinctive polymer fingerprint becomes obscured when analysed with minerals. We have isolated the effects of some potential inorganic soil components so that responses from complex matrices can be deconvolved. The mineral-assisted aromatisation of compounds affects the diagnostic signals of the original polymer, particularly for those polymers (PE and PP) which have a large aliphatic hydrocarbon signature when analysed alone. Iron oxyhydroxide/oxide are shown to have a greater effect on the data than phyllosilicates. Our results will help guide future interpretations of Py-GC-MS data obtained when monitoring soils for plastic contamination. Py-GC-MS is most commonly applied as a qualitative method, yet detailed quantitation of the organic-inorganic reactions we have described, albeit analytically complex, would represent a useful future study.

Comparing different segments in shut-in pressure signals: new insights into frequency range and energy distribution

Water hammer diagnostics is an important fracturing diagnosis technique to evaluate fracture locations and other downhole events in fracturing. The evaluation results are obtained by analyzing shut-in water hammer pressure signal. The field-sampled water hammer signal is often disturbed by noise interference. Noise interference exists in various pumping stages during water hammer diagnostics, with significantly different frequency range and energy distribution. Clarifying the differences in frequency range and energy distribution between effective water hammer signals and noise is the basis of setting specific filtering parameters, including filtering frequency range and energy thresholds. Filtering specifically could separate the effective signal and noise, which is the key to ensuring the accuracy of water hammer diagnosis. As an emerging technique, there is a lack of research on the frequency range and energy distribution of effective signals in water hammer diagnostics. In this paper, the frequency range and energy distribution characteristics of field-sampled water hammer signals were clarified quantitatively and qualitatively for the first time by a newly proposed comprehensive water hammer segmentation-energy analysis method. The water hammer signals were preprocessed and divided into three segments, including pre-shut-in, water hammer oscillation, and leak-off segment. Then, the three segments were analyzed by energy analysis and correlation analysis. The results indicated that, one aspect, the frequency range of water hammer oscillation spans from 0 to 0.65 Hz, considered as effective water hammer signal. The pre-shut-in and leak-off segment ranges from 0 to 0.35 Hz and 0 to 0.2 Hz respectively. Meanwhile, odd harmonics were manifested in water hammer oscillation segment, with the harmonic frequencies ranging approximately from 0.07 to 0.75 Hz. Whereas integer harmonics were observed in pre-shut-in segment, ranging from 6 to 40 Hz. The other aspect, the energy distribution of water hammer signals was analyzed in different frequency ranges. In 0 − 1 Hz, an exponential decay was observed in all three segments. In 1 − 100 Hz, a periodical energy distribution was observed in pre-shut-in segment, an exponential decay was observed in water hammer oscillation, and an even energy distribution was observed in leak-off segment. In 100 − 500 Hz, an even energy distribution was observed in those three segments, yet the highest magnitude was noted in leak-off segment. In this study, the effective frequency range and energy distribution characteristics of the field-sampled water hammer signals in different segments were sufficiently elucidated quantitatively and qualitatively for the first time, laying the groundwork for optimizing the filtering parameters of the field filtering models and advancing the accuracy of identifying downhole event locations.