Method for rapid warning and activity concentration estimates in online waterγ-spectrometry systems

Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.

Extraordinary tunable dynamic range of electrochemical aptasensor for accurate detection of ochratoxin A in food samples

We report the design of a sensitive,electrochemical aptasensor for detection of ochratoxin A(OTA)with an extraordinary tunable dynamic sensing range.This electrochemical aptasensor is constructed based on the target induced aptamer-folding detection mechanism and the recognition between OTA and its aptamers results in the conformational change of the aptamer probe and thus signal changes for measurement.The dynamic sensing range of the electrochemical aptasensor is successfully tuned by introduction of free assistant aptamer probes in the sensing system.Our electrochemical aptasensor shows an extraordinary dynamic sensing range of 11-order magnitude of OTA concentration from 10^−8 to 10^2 ng/g.Of great significance,the signal response in all OTA concentration ranges is at the same current scale,demonstrating that our sensing protocol in this research could be applied for accurate detections of OTA in a broad range without using any complicated treatment of signal amplification.Finally,OTA spiked red wine and maize samples in different dynamic sensing ranges are determined with the electrochemical aptasensor under optimized sensing conditions.This tuning strategy of dynamic sensing range may offer a promising platform for electrochemical aptasensor optimizations in practical applications.

Wide dynamic detection range of methane gas based on enhanced cavity absorption spectroscopy

Integrated cavity output spectroscopy(ICOS) is an effective technique in trace gase detection.The strong absorption due to the long optical path of this method makes it challenging in the application scenes that have large gas concentration fluctuation,especially when the gas concentration is high.In this paper,we demonstrate an extension of the dynamic range of ICOS by using a detuned laser combined with an off-axis integrating cavity.With this,we improve the upper limit of the dynamic detection range from 0.1%(1000 ppm) to 20% of the gas concentration.This method provides a way of using ICOS in the applications with unpredictable gas concentrations such as gas leak detection,ocean acidification,carbon sequestration,etc.

Minimal Context-Switching Data Race Detection with Dataflow Tracking

Data race is one of the most important concurrent anomalies in multi-threaded programs.Emerging con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race detector.However,this constraint-based approach has serious limitations on helping programmers analyze and understand data races.First,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the program.Second,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving process.This ad hoc recommendation imposes too many context switches,which complicates the data race analysis.To address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context switches.Specifically,we reduce the false positives by ana-lyzing and tracking the dataflow in the program.By this means,DFTracker thus reduces the unnecessary analysis of false race schedules.We further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data race.Our experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race detec-tor.Therefore,DFTracker can be used as an effective tool to understand the data race for programmers.

YOLO-MFD:Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

Remote sensing imagery,due to its high altitude,presents inherent challenges characterized by multiple scales,limited target areas,and intricate backgrounds.These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery.Additionally,these complexities contribute to inaccuracies in target localization and hinder precise target categorization.This paper addresses these challenges by proposing a solution:The YOLO-MFD model(YOLO-MFD:Remote Sensing Image Object Detection withMulti-scale Fusion Dynamic Head).Before presenting our method,we delve into the prevalent issues faced in remote sensing imagery analysis.Specifically,we emphasize the struggles of existing object recognition algorithms in comprehensively capturing critical image features amidst varying scales and complex backgrounds.To resolve these issues,we introduce a novel approach.First,we propose the implementation of a lightweight multi-scale module called CEF.This module significantly improves the model’s ability to comprehensively capture important image features by merging multi-scale feature information.It effectively addresses the issues of missed detection and mistaken alarms that are common in remote sensing imagery.Second,an additional layer of small target detection heads is added,and a residual link is established with the higher-level feature extraction module in the backbone section.This allows the model to incorporate shallower information,significantly improving the accuracy of target localization in remotely sensed images.Finally,a dynamic head attentionmechanism is introduced.This allows themodel to exhibit greater flexibility and accuracy in recognizing shapes and targets of different sizes.Consequently,the precision of object detection is significantly improved.The trial results show that the YOLO-MFD model shows improvements of 6.3%,3.5%,and 2.5%over the original YOLOv8 model in Precision,map@0.5 and map@0.5:0.95,separately.These results illustrate the clear advantages of the method.

Measuring ground deformations caused by 2015 Mw7.8 Nepal earthquake using high-rate GPS data

The April 25, 2015 Mw7.8 Nepal earthquake was successfully recorded by Crustal Movement Observation Network of China （CMONOC） and Nepal Geodetic Array （NGA）. We processed the high-rate GPS data （1 Hz and 5 Hz） by using relative kinematic positioning and derived dynamic ground motions caused by this large earthquake. The dynamic displacements time series clearly indicated the displacement amplitude of each station was related to the rupture directivity. The stations which located in the di- rection of rupture propagation had larger displacement amplitudes than others. Also dynamic ground displacement exceeding 5 cm was detected by the GPS station that was 2000 km away from the epicenter. Permanent coseismic displacements were resolved from the near-field high-rate GPS stations with wavelet decomposition-reconstruction method and P-wave arrivals were also detected with S transform method. The results of this study can be used for earthquake rupture process and Earthquake Early Warning studies.

Application of Dynamic Linear Detecting Method in Data Processing of the Portable Blood Sugar Analyzer

In this paper,a dynamic linear detecting method,that the non-linear coefficient NL% was led and the non-linearity of data were estimated continuously and dynamically and determined when NL% exceeded reference value (5%),was used for data processing and could solve the problem caused by the phenomenon of substrate depleting occurred following the redox reaction in portable blood sugar analyzer.By contrast to the conventional end-point method,the dynamic linear detecting method is based on multipoint data collecting.Experiments of measuring the calibration glucose solution with 8 various concentrations from 50 mg/dl to 400 mg/dl were carried out with the analyzer developed by our group.The linear regression curve,whose correlation for the data was 0.9995 and the residual was 2.8080,were obtained.The obtained correlation,residual, and the computation workload are all fit for the portable blood sugar analyzer.

Optimal Reordering Trace Files for Improving Software Testing Suitcase

An invariant can be described as an essential relationship between program variables.The invariants are very useful in software checking and verification.The tools that are used to detect invariants are invariant detectors.There are two types of invariant detectors:dynamic invariant detectors and static invariant detectors.Daikon software is an available computer program that implements a special case of a dynamic invariant detection algorithm.Daikon proposes a dynamic invariant detection algorithm based on several runs of the tested program;then,it gathers the values of its variables,and finally,it detects relationships between the variables based on a simple statistical analysis.This method has some drawbacks.One of its biggest drawbacks is its overwhelming time order.It is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file.A mechanism is proposed in order to reduce differences in adjacent trace files.It is done by applying some special techniques of mutation/crossover in genetic algorithm(GA).An experiment is run to assess the benefits of this approach.Experimental findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

Use of fluorescent europium chelates as labels for detection of microcystin-LR in Taihu Lake, China

A method using the time-resolved fluorescence technology to establish a highly sensitive microcystin-LR （MC-LR） indirect com- petitive immunoassay was proposed in this work. This method was used to monitor the MC-LR level in source water and treated drinking water from Taihu Lake. Algae in the water samples were removed by eentrifugation, and the MC-LR level was quantified using this method. Testing results showed that the sensitivity of this method was 0.01 μg/L, and the dynamic measuring range was from 0.05 to 2 μg/L. The av- erage recovery was 115%, and the variation （CV） within and between different batches were 7.3% and 9.7%, respectively. Testing results also indicated that this time-resolved fluoroimmunoassay was sensitive and accurate in measuring MC-LR level, especially for quantitative analy- sis MC-LR level in bulk water.

A dynamic detection method to improve SLAM performance

Simultaneous localization and mapping(SLAM) technology is a research hotspot in the field of intelligent mobile robot, and many researchers have developed many classic systems in the past few decades. However, most of the existing SLAM methods assume that the environment of the robot is static, which results in the performance of the system being greatly reduced in the dynamic environment. To solve this problem, a new dynamic object detection method based on point cloud motion analysis is proposed and incorporated into ORB-SLAM2. First, the method is regarded as a preprocessing stage, detecting moving objects in the scene, and then removing the moving objects to enhance the performance of the SLAM system. Experiments performed on a public RGB-D dataset show that the motion cancellation method proposed in this paper can effectively improve the performance of ORB-SLAM2 in a highly dynamic environment.

Unmanned vehicle dynamic obstacle detection,tracking and recognition method based on laser sensor

Purpose-In response to these shortcomings,this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approach-The existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate.The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle,and cannot meet the requirements of real traffic scene applications.Findings-First,based on the geometric features of dynamic obstacles,the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking;second,the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle,and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition.Finally,the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/value-The paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.The accuracy and effectiveness of the proposed method are verified by real vehicle tests.

Graphene aerogel-based vibration sensor with high sensitivity and wide frequency response range

Compared with piezoresistive sensors,pressure sensors based on the contact resistance effect are proven to have higher sensitivity and the ability to detect ultra-low pressure,thus attracting extensive research interest in wearable devices and artificial intelligence systems.However,most studies focus on static or low-frequency pressure detection,and there are few reports on high-frequency dynamic pressure detection.Limited by the viscoelasticity of polymers(necessary materials for traditional vibration sensors),the development of vibration sensors with high frequency response remains a great challenge.Here,we report a graphene aerogel-based vibration sensor with higher sensitivity and wider frequency response range(2 Hz–10 kHz)than both conventional piezoresistive and similar sensors.By modulating the microscopic morphology and mechanical properties,the super-elastic graphene aerogels suitable for vibration sensing have been prepared successfully.Meanwhile,the mechanism of the effect of density on the vibration sensor’s sensitivity is studied in detail.On this basis,the sensitivity,signal fidelity and signal-to-noise ratio of the sensor are further improved by optimizing the structure configuration.The developed sensor exhibits remarkable repeatability,excellent stability,high resolution(0.0039 g)and good linearity(non-linearity error<0.8%)without hysteresis.As demos,the sensor can not only monitor low-frequency physiological signals and motion of the human body,but also respond to the high-frequency vibrations of rotating machines.In addition,the sensor can also detect static pressure.We expect the vibration sensor to meet a wider range of functional needs in wearable devices,smart robots,and industrial equipment.

mNGS-based dynamic pathogen monitoring for accurate diagnosis and treatment of severe pneumonia caused by fungal infections

Metagenomic next-generation sequencing(mNGs)has been widely applied to identify pathogens associated with infectious diseases.However,limited studies have explored the use of mNGs-based dynamic pathogen monitoring in intensive care unit patients with severe pneumonia.Here,we present a clinical case of an 86-year-old male patient with severe pneumonia caused by a fungal infection.During the clinical treatment,four mNGS analyses were performed within two consecutive weeks.Various respiratory fungal pathogens,including Candida orthopsilosis,Candida albicans,and Aspergillus fumigatus were detected by mNGS of bronchoalveolar lavage fluid(BALF).Based on conventional pathogen identification and clinical symptoms,the patient was diagnosed with severe pneumonia caused by a fungal infection.The abundance of fungal species decreased gradually in response to antifungal and empirical therapies,and the fungal infections were effectively con-trolled.In summary,our results demonstrated that mNGS could effectively identify pathogens in patients with severe pneumonia.Additionally,dynamic pathogen monitoring based on mNGS could assist in the precise diag-nosis of complex infections and may facilitate rapid induction of the most appropriate therapy.

Simulating orthokinetic heterocoagulation and cluster growth in destabilizing suspensions

Using direct numerical simulation, we investigate the coagulation behavior of non-Brownian colloidal particles as exemplified by Al2O3 particles. This yields the so-called capture efficiency, for which we give an analytical expression, as well as other time-dependent variables such as the cluster growth rate. Instead of neglecting or strongly approximating the hydrodynamic interactions between particles, we include hydrodynamic and non-hydrodynamic interactions in a Stokesian dynamics approach and a comprehensive modeling of the interparticle forces. The resulting parallelized simulation framework enables us to investigate the dynamics of polydisperse particle systems composed of several hundred particles at the same high level of modeling we used for a close investigation of the coagulation behavior of two unequal particles in shear flow. Appropriate cluster detection yields all the information about large destabilizing systems, which is needed for models used in flow-sheet simulations. After non-dimensionalization, the results can be generalized and applied to other systems tending to secondary coagulation