Multiple reaction monitoring for the detection of disease-related synaptic proteins

Synaptic dysfunction occurs early in Alzheimer＇s disease （AD） and is acknowledged as a primary pathologic target for treatment. Synaptic degeneration is the pathological feature most strongly correlated with loss of cognitive function ante mortern （Terry et al., 1991）. Synapses are heavily damaged in hippocampal and neocortical regions of AD brain, whereas motor and occipital cortices are relatively spared （Honer et al., 1992）. Despite extensive work, the molecular mechanisms underlying synaptic degeneration are largely unknown.

Detection Situation and Improvement of Lightning Monitoring Network in Guizhou Province

Based on lightning location data of lightning monitoring network in Guizhou Province in recent eight years,the effective detection radius of a station and the effective detection range of lightning monitoring network in Guizhou Province were analyzed. The results show that the effective detection radius of a lightning monitoring sub-station in Guizhou Province is 160 km; some counties in the southwest,northwest and northeast of Guizhou were not detected. To improve the detector efficiency of lightning monitoring network in Guizhou Province,it is suggested that nine sub-stations should be built in Weining,Shuicheng,Qinglong,Pingtang,Rongjiang,Yuping,Songtao,Tongren and Renhuai,so that the effective detection efficiency will reach more than 95%.

Monitoring and detection of steel bridge diseases:A review

Fatigue,corrosion,and bolt loosening are the main causes of structural performance degradation and collapse in steel bridges.Accurate monitoring of steel bridge diseases is a basic premise for ensuring high-quality operation and maintenance of steel bridges.In this regard,a summary and analysis were conducted on the classification of steel bridge diseases,monitoring and detection methods,application statuses,and major difficulties.The main causes,research status,and development trends of steel bridge diseases are discussed.The results showed that,for fatigue crack problems,fatigue crack initiation has a small scale,high difficulty in monitoring and detection,few methods,and low accuracy.As the cracks grow,the difficulty of monitoring and detection decreases,the number of methods increases,and the accuracy improves.Fatigue crack monitoring and detection are affected by the environmental and vehicular loads.Superficial corrosion features are evident in steel bridges,and corrosion identification methods and technologies are rapidly developing.Monitoring and detecting corrosion in concealed areas is difficult and requires further improvements in monitoring and detection technologies and their accuracy.Monitoring and detection methods and supporting equipment for bolt loosening in steel bridges are rapidly developing.The development of intelligent monitoring and detection technologies and supporting equipment is an important research topic that urgently needs to be addressed for the full-lifecycle operation and maintenance of steel bridges and the sustainable development of bridge engineering.Developing new intelligent sensing components based on high-performance materials and sensing element design theory to improve the monitoring and detection perception ability is an important development direction for steel bridge monitoring and detection.Research on intelligent monitoring and detection technologies,standardized indicators,and related topics based on intelligent operations and maintenance provide great support for the development of steel-bridge disease monitoring and detection.

A Real-Time System for Monitoring Distant Dynamic Displacement of Structures

Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-time coordinate of an object in a certain coordinate system can be obtained, and further dynamic displacement data and curve of the object can also be achieved. That is, automatic gathering and real-time processing of data can be carried out by this system simultaneously. For this system, first, an untouched monitoring technique is adopted, which can monitor or detect objects several to hundreds of meters apart; second, it has flexible installation condition and good monitoring precision of sub-millimeter degree; third, it is fit for dynamic, quasi-dynamic and static monitoring of large engineering structures. Through several tests and applications in large bridges, good reliability and dominance of the system is proved.

GNSS receiver autonomous integrity monitoring(RAIM)algorithm based on robust estimation

Integrity is significant for safety-of-life applications. Receiver autonomous integrity monitoring（RAIM） has been developed to provide integrity service for civil aviation. At first,the conventional RAIM algorithm is only suitable for single fault detection, single GNSS constellation. However, multiple satellite failure should be considered when more than one satellite navigation system are adopted. To detect and exclude multi-fault, most current algorithms perform an iteration procedure considering all possible fault model which lead to heavy computation burden. An alternative RAIM is presented in this paper based on multiple satellite constellations（for example, GPS and Bei Dou（BDS） etc.） and robust estimation for multi-fault detection and exclusion, which can not only detect multi-failures,but also control the influences of near failure observation. Besides, the RAIM algorithm based on robust estimation is more efficient than the current RAIM algorithm for multiple constellation and multiple faults. Finally, the algorithm is tested by GPS/Bei Dou data.

A NEW METHOD OF FREQUENCY-HOPPING(FH) SIGNAL DETECTION

A new Frequency-Hopping(FH) signal detection method is proposed.Different from pre-vious methods which need to monitor the total band,it can monitor part of the band and decrease the range of the bandwidth.According to this method,a new detection model is set and the computation formulas of the detection probability and false-alarm probability are given.The parameters of a VHF radio are used to prove the validity of the method.Simulation results show that this method can de-crease the range of the bandwidth and detect the FH signal with some penalty on the SNR and signal loss.

Monitoring of winter wheat distribution and phenological phases based on MODIS time-series： A case study in the Yellow River Delta, China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

Field testing innovative differential geospatial and photogrammetric monitoring technologiesin mountainous terrain near Ashcroft,British Columbia,Canada

This paper presents a novel approach to continuously monitor very slow-moving translational landslides in mountainous terrain using conventional and experimental differential global navigation satellite system(d-GNSS)technologies.A key research question addressed is whether displacement trends captured by a radio-frequency“mobile”d-GNSS network compare with the spatial and temporal patterns in activity indicated by satellite interferometric synthetic aperture radar(InSAR)and unmanned aerial vehicle(UAV)photogrammetry.Field testing undertaken at Ripley Landslide,near Ashcroft in south-central British Columbia,Canada,demonstrates the applicability of new geospatial technologies to monitoring ground control points(GCPs)and railway infrastructure on a landslide with small and slow annual displacements(<10 cm/yr).Each technique records increased landslide activity and ground displacement in late winter and early spring.During this interval,river and groundwater levels are at their lowest levels,while ground saturation rapidly increases in response to the thawing of surficial earth materials,and the infiltration of snowmelt and runoff occurs by way of deep-penetrating tension cracks at the head scarp and across the main slide body.Research over the last decade provides vital information for government agencies,national railway companies,and other stakeholders to understand geohazard risk,predict landslide movement,improve the safety,security,and resilience of Canada’s transportation infrastructure;and reduce risks to the economy,environment,natural resources,and public safety.

Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model

Railroad condition monitoring is paramount due to frequent passage through densely populated regions.This significance arises from the potential consequences of accidents such as train derailments,hazardous materials leaks,or collisions which may have far-reaching impacts on communities and the surrounding areas.As a solution to this issue,the use of distributed acoustic sensing(DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures.Nevertheless,analyzing DAS data to assess railroad health or detect potential damage is a challenging task.Due to the large amount of data generated by DAS,as well as the unstructured patterns and substantial noise present,traditional analysis methods are ineffective in interpreting this data.This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs,augmented by sliding window techniques(CNN-LSTM-SW),to advance the state-of-the-art in the railroad condition monitoring system.As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks.Extracting insights from the data of High tonnage load(HTL)-a 4.16 km fiber optic and DAS setup,we were able to distinguish train position,normal condition,and abnormal conditions along the railroad.Notably,our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup.Moreover,in terms of pinpointing the train's position,the CNN-LSTM architecture showcased an impressive 97%detection rate.Applying a sliding window,the CNN-LSTM labeled data,the remaining 3%of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition.Altogether,these proposed models exhibit promising potential for accurately identifying various railroad conditions,including anomalies and discrepancies that warrant thorough exploration.

A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring

The aim of non-intrusive appliance load monitoring （NIALM） is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to 0N-0FF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness- of-fit （GOF） methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-0FF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K- means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K- means clustering. The results of the algorithm implemen- tation were discussed and ideas on future work were also proposed.

Theoretical investigation on self-recovery regulation method for diesel engine misalignment through co-simulation

Misalignment is one of the most common faults for the diesel engine.In order to eliminate the misalignment fault of the diesel engine in the process of operation,a targeting self-recovery regulation system is constructed by using a movable base and displacement sensors.Misalignment is monitored and detected in real time,the value of misalignment is calculated rapidly and accurately,andintelligent decision is made.Then,the base is moved reversely with a definite target to drive the shaft to translate or rotate,so that the shafts can be recovered to alignment online.A co-simulation model for the self-recovery system is established which consists of a dynamic model of the crankshaft system and control model.The self-recovery regulation process of misalignment is simulated.The simulation results show that the system can accurately calculate the misalignment values,with an error of less than 5%,and can automatically eliminate the misalignment fault of the diesel engine online.The research results provide theoretical support for the self-recovery regulation of misalignment fault,and due to the universality of structure and principle,the self-recovery system is not only suitable for diesel engine,but also for other rotating machineries.

Visualization analysis of rainfall-induced landslides hazards based on remote sensing and geographic information system-an overview

In recent years,RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research.In order to systematically understand their application situation,this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods.Then,this article focuses on sub domain analysis from four aspects:landslide detection and monitoring,prediction models,sensitivity mapping,and risk assessment.The study found that the number of literature in thisfield has steadily increased and is expected to continue to rise.This literature review has attracted widespread attention from the academic community,but it challenging to meet research needs.Frequent and effective cooperationis between countries,institutions,and authors is very beneficial for promoting progress in thisfield.The future development direction is a new intelligent hybrid model that integrates multiple research methods.This study can provide researchers in thisfield with the core research force,hot topic evolution,and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations’sustainable development goals.

Reliability and safety analysis of redundant vehicle management computer system

Redundant techniques are widely adopted in vehicle management computer （VMC） to ensure that VMC has high reliability and safety. At the same time, it makes VMC have special characteristics, e.g., failure correlation, event simultaneity, and failure self-recovery. Accordingly, the reliability and safety analysis to redundant VMC system （RVMCS） becomes more difficult. Aimed at the difficulties in RVMCS reliability modeling, this paper adopts generalized stochastic Petri nets to establish the reliability and safety models of RVMCS. Then this paper analyzes RVMCS oper- ating states and potential threats to flight control system. It is verified by simulation that the reli- ability of VMC is not the product of hardware reliability and software reliability, and the interactions between hardware and software faults can reduce the real reliability of VMC obviously. Furthermore, the failure undetected states and false alarming states inevitably exist in RVMCS due to the influences of limited fault monitoring coverage and false alarming probability of fault mon- itoring devices （FMD）. RVMCS operating in some failure undetected states will produce fatal threats to the safety of flight control system. RVMCS operating in some false alarming states will reduce utility of RVMCS obviously. The results abstracted in this paper can guide reliable VMC and efficient FMD designs. The methods adopted in this paper can also be used to analyze other intelligent systems＇ reliability.