Design of Signal Lamp Filament Monitoring Alarm Instrument

To improve the reliability of the light emitting diode(LED)signal lamp filament current monitoring alarm instrument for metro systems,a new type of hot standby online monitoring apparatus was developed which is based on synchronous transmission data(STD)bus technology.In this system,a double hot standby mode can be achieved by adopting bus arbitration.In addition,to detect the effective value of alternating current which is from 0 to 200 mA in the signal lamp lighting circuit,a precision rectifier signal conditioning circuit and an isolated acquisition circuit were designed.This new type of alarm instrument has high detection accuracy and could meet the functional requirements for metro signal systems after comparing it with some industry products that were applied on the spot.

Advances in Wireless,Batteryless,Implantable Electronics for Real‑Time,Continuous Physiological Monitoring

This review summarizes recent progress in developing wireless,batteryless,fully implantable biomedical devices for real-time continuous physiological signal monitoring,focusing on advancing human health care.Design considerations,such as biological constraints,energy sourcing,and wireless communication,are discussed in achieving the desired performance of the devices and enhanced interface with human tissues.In addition,we review the recent achievements in materials used for developing implantable systems,emphasizing their importance in achieving multi-functionalities,biocompatibility,and hemocompatibility.The wireless,batteryless devices offer minimally invasive device insertion to the body,enabling portable health monitoring and advanced disease diagnosis.Lastly,we summarize the most recent practical applications of advanced implantable devices for human health care,highlighting their potential for immediate commercialization and clinical uses.

Long-term hydrochemical monitoring and geothermometry:understanding groundwater salinization and thermal fluid contamination in Mila’s basin,Northeastern Algeria

The regular hydrochemical monitoring of groundwater in the Mila basin over an extended period has provided valuable insights into the origin of dissolved salts and the hydrogeochemical processes controlling water salinization.The data reveals that the shallow Karst aquifer shows an increase in TDS of 162 mg L^(-1) while the ther-mal carbonate aquifer that is also used for drinking water supply exhibits an increase of 178 mg L^(-1).Additionally,significant temperature variations are recorded at the sur-face in the shallow aquifers and the waters are carbo-gaseous.Analysis of dissolved major and minor elements has identified several processes influencing the chemical composition namely:dissolution of evaporitic minerals,reduction of sulphates,congruent and incongruent car-bonates’dissolution,dedolomitization and silicates’weathering.The hydrogeochemical and geothermometric results show a mixing of saline thermal water with recharge water of meteoric origin.Two main geothermalfields have been identified,a partially evolved water reservoir and a water reservoir whosefluid interacts with sulphuric acid(H_(2)S)of magmatic origin.These hot waters that are char-acterized by a strong hydrothermal alteration do ascend through faults and fractures and contribute to the contamination of shallower aquifers.Understanding the geothermometry and the hydrogeochemistry of waters is crucial for managing and protecting the quality of groundwater resources in the Mila basin,in order to ensure sustainable water supply for the region.A conceptual model for groundwater circulation and mineralization acquisition has been established to further enhance under-standing in this regard.

Comparison of CWSI and T_(s)-T_(a)-VIs in moisture monitoring of dryland crops(sorghum and maize)based on UAV remote sensing

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.Utilizing unmanned aerial vehicle(UAV)remote sensing,we explored the applicability of an empirical crop water stress index(CWSI)based on canopy temperature and three-dimensional drought indices(TDDI)constructed from surface temperature(T_(s)),air temperature(T_(a))and five vegetation indices(VIs)for monitoring the moisture status of dryland crops.Three machine learning algorithms(random forest regression(RFR),support vector regression,and partial least squares regression)were used to compare the performance of the drought indices for vegetation moisture content(VMC)estimation in sorghum and maize.The main results of the study were as follows:(1)Comparative analysis of the drought indices revealed that T_(s)-T_(a)-normalized difference vegetation index(TDDIn)and T_(s)-T_(a)-enhanced vegetation index(TDDIe)were more strongly correlated with VMC compared with the other indices.The indices exhibited varying sensitivities to VMC under different irrigation regimes;the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment(r=-0.93).(2)Regarding spatial and temporal characteristics,the TDDIn,TDDIe and CWSI indices showed minimal differences Over the experimental period,with coefficients of variation were 0.25,0.18 and 0.24,respectively.All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops,but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event.(3)For prediction of the moisture content of single crops,RFR models based on TDDIn and TDDIe estimated VMC most accurately(R^(2)>0.7),and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples,with R^(2)and RMSE of 0.62 and 14.26%,respectively.Thus,TDDI proved more effective than the CWSI in estimating crop water content.

Early proactive monitoring of DNA-thioguanine in patients with Crohn’s disease predicts thiopurine-induced late leucopenia in NUDT15/TPMT normal metabolizers

BACKGROUND Thiopurine-induced leucopenia significantly hinders the wide application of thiopurines.Dose optimization guided by nudix hydrolase 15(NUDT15)has significantly reduced the early leucopenia rate,but there are no definitive biomarkers for late risk leucopenia prediction.AIM To determine the predictive value of early monitoring of DNA-thioguanine(DNATG)or 6-thioguanine nucleotides(6TGN)for late leucopenia under a NUDT15-guided thiopurine dosing strategy in patients with Crohn’s disease(CD).METHODS Blood samples were collected within two months after thiopurine initiation for detection of metabolite concentrations.Late leucopenia was defined as a leukocyte count<3.5×10^(9)/L over two months.RESULTS Of 148 patients studied,late leucopenia was observed in 15.6%(17/109)of NUDT15/thiopurine methyltransferase(TPMT)normal and 64.1%(25/39)of intermediate metabolizers.In patients suffering late leucopenia,early DNATG levels were significantly higher than in those who did not develop late leucopenia(P=4.9×10^(-13)).The DNATG threshold of 319.43 fmol/μg DNA could predict late leucopenia in the entire sample with an area under the curve(AUC)of 0.855(sensitivity 83%,specificity 81%),and in NUDT15/TPMT normal metabolizers,the predictive performance of a threshold of 315.72 fmol/μg DNA was much more remarkable with an AUC of 0.902(sensitivity 88%,specificity 85%).6TGN had a relatively poor correlation with late leucopenia whether in the entire sample(P=0.021)or NUDT15/TPMT normal or intermediate metabolizers(P=0.018,P=0.55,respectively).CONCLUSION Proactive therapeutic drug monitoring of DNATG could be an effective strategy to prevent late leucopenia in both NUDT15/TPMT normal and intermediate metabolizers with CD,especially the former.

Gut microbiota dysbiosis contributes toα-synuclein-related pathology associated with C/EBPβ/AEP signaling activation in a mouse model of Parkinson’s disease

Parkinson’s disease is a neurodegenerative disease characterized by motor and gastrointestinal dysfunction.Gastrointestinal dysfunction can precede the onset of motor symptoms by several years.Gut microbiota dysbiosis is involved in the pathogenesis of Parkinson’s disease,whether it plays a causal role in motor dysfunction,and the mechanism underlying this potential effect,remain unknown.CCAAT/enhancer binding proteinβ/asparagine endopeptidase(C/EBPβ/AEP)signaling,activated by bacterial endotoxin,can promoteα-synuclein transcription,thereby contributing to Parkinson’s disease pathology.In this study,we aimed to investigate the role of the gut microbiota in C/EBPβ/AEP signaling,α-synuclein-related pathology,and motor symptoms using a rotenone-induced mouse model of Parkinson’s disease combined with antibiotic-induced microbiome depletion and fecal microbiota transplantation.We found that rotenone administration resulted in gut microbiota dysbiosis and perturbation of the intestinal barrier,as well as activation of the C/EBP/AEP pathway,α-synuclein aggregation,and tyrosine hydroxylase-positive neuron loss in the substantia nigra in mice with motor deficits.However,treatment with rotenone did not have any of these adverse effects in mice whose gut microbiota was depleted by pretreatment with antibiotics.Importantly,we found that transplanting gut microbiota derived from mice treated with rotenone induced motor deficits,intestinal inflammation,and endotoxemia.Transplantation of fecal microbiota from healthy control mice alleviated rotenone-induced motor deficits,intestinal inflammation,endotoxemia,and intestinal barrier impairment.These results highlight the vital role that gut microbiota dysbiosis plays in inducing motor deficits,C/EBPβ/AEP signaling activation,andα-synuclein-related pathology in a rotenone-induced mouse model of Parkinson’s disease.Additionally,our findings suggest that supplementing with healthy microbiota may be a safe and effective treatment that could help ameliorate the progression of motor deficits in patients with Parkinson’s disease.

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials,the past two decades have witnessed the rapid advances of soft electronics.The soft electronics have huge potential in the physical sign monitoring and health care.One of the important advantages of soft electronics is forming good interface with skin,which can increase the user scale and improve the signal quality.Therefore,it is easy to build the specific dataset,which is important to improve the performance of machine learning algorithm.At the same time,with the assistance of machine learning algorithm,the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis.The soft electronics and machining learning algorithms complement each other very well.It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future.Therefore,in this review,we will give a careful introduction about the new soft material,physiological signal detected by soft devices,and the soft devices assisted by machine learning algorithm.Some soft materials will be discussed such as two-dimensional material,carbon nanotube,nanowire,nanomesh,and hydrogel.Then,soft sensors will be discussed according to the physiological signal types(pulse,respiration,human motion,intraocular pressure,phonation,etc.).After that,the soft electronics assisted by various algorithms will be reviewed,including some classical algorithms and powerful neural network algorithms.Especially,the soft device assisted by neural network will be introduced carefully.Finally,the outlook,challenge,and conclusion of soft system powered by machine learning algorithm will be discussed.

Netrin-1 signaling pathway mechanisms in neurodegenerative diseases

Netrin-1 and its receptors play crucial roles in inducing axonal growth and neuronal migration during neuronal development.Their profound impacts then extend into adulthood to encompass the maintenance of neuronal survival and synaptic function.Increasing amounts of evidence highlight several key points:(1)Diminished Netrin-1 levels exacerbate pathological progression in animal models of Alzheimer’s disease and Parkinson’s disease,and potentially,similar alterations occur in humans.(2)Genetic mutations of Netrin-1 receptors increase an individuals’susceptibility to neurodegenerative disorders.(3)Therapeutic approaches targeting Netrin-1 and its receptors offer the benefits of enhancing memory and motor function.(4)Netrin-1 and its receptors show genetic and epigenetic alterations in a variety of cancers.These findings provide compelling evidence that Netrin-1 and its receptors are crucial targets in neurodegenerative diseases.Through a comprehensive review of Netrin-1 signaling pathways,our objective is to uncover potential therapeutic avenues for neurodegenerative disorders.

Monitoring and Recognition of Debris Flow Infrasonic Signals

Low frequency infrasonic waves are emitted during the formation and movement of debris flows, which are detectable in a radius of several kilometers, thereby to serve as the precondition for their remote monitoring.However, false message often arises from the simple mechanics of alarms under the ambient noise interference.To improve the accuracy of infrasound monitoring for early-warning against debris flows, it is necessary to analyze the monitor information to identify in them the infrasonic signals characteristic of debris flows.Therefore, a large amount of debris flow infrasound and ambient noises have been collected from different sources for analysis to sum up their frequency spectra, sound pressures, waveforms, time duration and other correlated characteristics so as to specify the key characteristic parameters for different sound sources in completing the development of the recognition system of debris flow infrasonic signals for identifying their possible existence in the monitor signals.The recognition performance of the system has been verified by simulating tests and long-term in-situ monitoring of debris flows in Jiangjia Gully,Dongchuan, China to be of high accuracy and applicability.The recognition system can provide the local government and residents with accurate precautionary information about debris flows in preparation for disaster mitigation and minimizing the loss of life and property.

Variational Mode Decomposition for Rotating Machinery Condition Monitoring Using Vibration Signals

The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.

Breathable Electronic Skins for Daily Physiological Signal Monitoring

With the aging of society and the increase in people’s concern for personal health,long-term physiological signal monitoring in daily life is in demand.In recent years,electronic skin(e-skin)for daily health monitoring applications has achieved rapid development due to its advantages in high-quality physiological signals monitoring and suitability for system integrations.Among them,the breathable e-skin has developed rapidly in recent years because it adapts to the long-term and high-comfort wear requirements of monitoring physiological signals in daily life.In this review,the recent achievements of breathable e-skins for daily physiological monitoring are systematically introduced and discussed.By dividing them into breathable e-skin electrodes,breathable e-skin sensors,and breathable e-skin systems,we sort out their design ideas,manufacturing processes,performances,and applications and show their advantages in long-term physiological signal monitoring in daily life.In addition,the development directions and challenges of the breathable e-skin are discussed and prospected.

A Virtual TDOA Localization Scheme of Chinese DTMB Signal in Radio Monitoring Networks

As the radio communications technology widely used,wireless location technology plays a more important role in maintaining the order of the air waves.However concretely effective symbol calibration method with regard to Chinese DTMB signal of different frame mode is quite under research due to the multiple structure of DTMB signal.In this paper,we propose a Time Difference of Arrival(TDOA)-based passive location scheme using least square principle.Utilizing the large number of anchor nodes in wireless monitoring network,a novel algorithm is formulated to solve the None-LineOf-Sight problem.The derived Cramer Rao Lower Bound of the localization method guides to the accuracy of the position outcome with regards to the calibration precision.In contrast with traditional multi-terminal location schemes,our location scheme can reduce calculation complexity and location costs abruptly.A twostep NLOS identification algorithm is proposed.Computer simulation is employed to verify the well performance of the calibration method of3-4 dB superiority than normal method and also the whole localization scheme for less than 50 meters through channel of SNR lower than dB.Simulation also shows that our algorithm can effectively identify NLOS path and improve positioning accuracy.

Development of a wide-range and fast-response digitizing pulse signal acquisition and processing system for neutron flux monitoring on EAST

The neutron count rate fluctuation reaches six orders of magnitude between the ohmic plasma scenario and high power of auxiliary heating on an experimental advanced superconducting tokamak(EAST).The measurement result of neutron flux monitoring(NFM)is a significant feedback parameter related to the acquisition of radiation protection-related information and rapid fluctuations in neutron emission induced by plasma magnetohydrodynamic activity.Therefore,a wide range and high time resolution are required for the NFM system on EAST.To satisfy these requirements,a digital pulse signal acquisition and processing system with a wide dynamic range and fast response time was developed.The present study was conducted using a field-programmable gate array(FPGA)and peripheral component interconnect extension for instrument express(PXIe)platform.The digital dual measurement modes,which are composed of the pulse-counting mode and AC coupled square integral's Campbelling mode,were designed to expand the measurement range of the signal acquisition and processing system.The time resolution of the signal acquisition and processing system was improved from 10 to 1 ms owing to utilizing highspeed analog-to-digital converters(ADCs),a high-speed PXIe communication with a direct memory access(DMA)mode,and online data preprocessing technology of FPGA.The signal acquisition and processing system was tested experimentally in the EAST radiation field.The test results showed that the time resolution of NFM was improved to 1 ms,and the dynamic range of the neutron counts rate was expanded to more than 10^(6) counts per second.The Campbelling mode was calibrated using a multipoint average linear fitting method;subsequently,the fitting coefficient reached 0.9911.Therefore,the newly developed pulse signal acquisition and processing system ensures that the NFM system meets the requirements of high-parameter experiments conducted on EAST more effectively.

Adaptive Signal Enhancement Unit for EEG Analysis in Remote Patient Care Monitoring Systems

In this paper we propose an efcient process of physiological artifact elimination methodology from brain waves(BW),which are also commonly known as electroencephalogram(EEG)signal.In a clinical environment during the acquisition of BW several artifacts contaminates the actual BW component.This leads to inaccurate and ambiguous diagnosis.As the statistical nature of the EEG signal is more non-stationery,adaptive ltering is the more promising method for the process of artifact elimination.In clinical conditions,the conventional adaptive techniques require many numbers of computational operations and leads to data samples overlapping and instability of the algorithm used.This causes delay in diagnosis and decision making.To overcome this problem in our work we propose to set a threshold value to diminish the problem of round off error.The resultant adaptive algorithm based on this strategy is Non-linear Least mean square(NL2MS)algorithm.Again,to improve this algorithm in terms of ltering capability we perform data normalization,using this algorithm several hybrid versions are developed to improve ltering and reduce computational operations.Using the method,a new signal enhancement unit(SEU)is realized and performance of various hybrid versions of algorithms examined using real EEG signals recorded from the subject.The ability of the proposed schemes is measured in terms of convergence,enhancement and multiplications required.Among various SEUs,the MCN2L 2MS algorithm achieves 14.6734,12.8732,10.9257,15.7790 dB during the artifact removal of RA,EMG,CSA and EBA components with only two multiplications.Hence,this algorithm seems to be better candidate for artifact elimination.

Vibration Signals and Condition Monitoring for Wind Turbines

Rolling element bearings are critical parts of modern wind turbines as they carry the loads of the turning structure and the wind force. The stochastic nature of the wind loads makes it difficult to estimate the useful operational life of the bearings. Condition monitoring of these bearings in a real time environment could be very helpful in estimating their performance and in scheduling maintenance actions when a condition-based maintenance strategy is followed. This procedure can be successfully implemented by using vibration analysis in the time domain or in the frequency domain, giving useful results about the current condition of bearings and the location of potential faults. Permanently located transducers on proper positions on the bearings’ housings can be used in order to collect, process and evaluate real time measurements and provide information about the bearing’s performance. In this work, a test rig is utilized in order to evaluate the performance of rolling bearings. The results of the experimentation are satisfactory and the progress of fatigue failures can be predicted through vibration analysis techniques showing that implementation in real scale may be useful.

Sensorless Monitoring of a Motor-Drive Machanical System Based on Adaptive Signal Decomposition

A method for estimating current harmonics of an induction motor is introduced which is used for sensorless monitoring of a mechanical system driven by the motor. The method is based on an adaptive signal representation and is proposed to extract weak harmonics from a noisy current signal, especially in the presence of additive interference caused by transient modulation waves. As an application, a rotor unbalance experiment of rotating machinery driven by an induction motor is carried out, The result shows that the eccentricity harmonic magnitude of a current signal obtained by the method represents the rotor unbalance conditions sensitively. Vibration analysis is used to validate the proposed method.

Quality monitoring and biases estimation of BOC navigation signals

Many safety-critical applications that utilize the global navigation satellite system (GNSS) demand highly accurate positioning information, as well as highly integrity and reliability. Due to GNSS signals are easily distorted by the interferences or disturbances, the signal quality monitoring (SQM) is necessary to detect the presence of dangerous signal distortions. In this paper, we developed an SQM software for binary offset carrier (BOC) modulated navigation signals. Firstly, the models of BOC signal with ideal and distortion are presented respectively. Then the architecture of SQM software is proposed. Moreover, the effect of the white gaussian noise (WGN) and the front-end filter on the correlation peak of the receiver is analyzed. Finally, the biases induced by the signal distortion are evaluated. The experiments simulate the relationships between the code phase shift and the normalized correlation value in the case of the signal digital distortion and the analog distortion. The simulation results demonstrate that the proposed SQM method can effectively monitor the signal distortion and accurately estimate the correlation peak deviation caused by the distortion.

Signal averaging for noise reduction in anesthesia monitoring and control with communication channels

This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.

Embedded Remote Monitoring System of Agricultural Environment Based on DSP

Agricultural environmental remote monitoring,data collection and network transmission are the development directions of modern agriculture.The embedded video remote monitoring system is designed with DSP processor DM642,which can collect the video signal of agricultural environment and biological information,as well as complete the extraction of video signal and network transmission.This system can be applied to the agro-ecological and environmental resources monitoring,agricultural disaster monitoring and warning and other digital agricultures.

Research on Monitoring and Early-warning Standards of Ancient and Famous Trees Protection in the Humble Administrator's Garden

Based on the current situation and symptoms of the trees' growth in the Humble Administrator's Garden,this paper put forward corresponding monitoring and early-warning standards and technical measures of the ancient and famous trees protection in the Humble Administrator's Garden specifically.The aim of doing this is to establish a scientific basis for the protection of the ancient and famous trees in the Humble Administrator's Garden by setting up systematic fundamental data,dynamic protection standard grades and technique measures of protecting the trees.The main symptom of trees in the Humble Administrator's Garden is the erosion and decay of the tree trunks.Fifteen tree trunks need technical protection,which holds 65.22% of the total sum of trees in the Humble Administrator's Garden.Therefore,much more emphasis should be paid in strengthening technical protection procedures of monitoring and early warning of the tree trunks in the future protection of the ancient and famous trees in the garden.Besides,the rejuvenation technique of rooting zone and rooting system,tree pruning technique as well as tree supporting measures according to the specific condition and symptom of the trees should be concerned with in order to protect the ancient and famous trees in the Humble Administrator's Garden in a more scientific and effective way.