Dynamic Data Acquisition Research Articles

Research on Evaluation System of Medical Imaging Equipment Dynamic Acquisition Technology Based on Multiple Criteria Decision Analysis

To construct an evaluation model system for dynamic data acquisition methods of medical imaging equipment in hospitals. The analytic hierarchy process (AHP) of the multiple criteria decision analysis method is used to study the model. Through multi-criteria decision analysis, the evaluation index system of dynamic data acquisition technology of medical imaging equipment is constructed from aspects such as the reliability and traceability of data acquisition, the interactivity of the acquisition mode, and the extensibility of application analysis. A hierarchical evaluation model of dynamic acquisition technology for medical imaging equipment is constructed, which includes 5 primary indexes, 14 secondary indexes, and 38 tertiary indexes. We have constructed a qualitative and quantitative evaluation index for the dynamic acquisition technology of medical imaging equipment. The weight of the quantitative index "reliability" is 30%, and the weights of the qualitative indexes "security", "traceability", "interactivity", and "scalability" are 25%, 20%, 15%, and 10% respectively. It can be used to study the methods and technical characteristics of three kinds of dynamic data acquisition of medical equipment and form a technical evaluation scheme for dynamic data acquisition.

A dynamic approach for MR T2-weighted pelvic imaging

T2-weighted 2D fast spin echo sequence serves as the standard sequence in
clinical pelvic MR imaging protocols. However, motion artifacts and blurring caused
by peristalsis present significant challenges. Patient preparation such as administering
antiperistaltic agents is often required before examination to reduce artifacts, which
discomfort the patients. This work introduce a novel dynamic approach for T2
weighted pelvic imaging to address peristalsis-induced motion issue without any patient
preparation.
Approach: A rapid dynamic data acquisition strategy with complementary sampling
trajectory is designed to enable highly undersampled motion-resistant data sampling,
and an unrolling method based on deep equilibrium model is leveraged to reconstruct
images from the dynamic sampled k-space data. Moreover, the fix-point convergence of
the equilibrium model ensures the stability of the reconstruction. The high acceleration
factor in each temporal phase, which is much higher than that in traditional static
imaging, has the potential to effectively freeze pelvic motion, thereby transforming
the imaging problem from conventional motion prevention or removal to motion
reconstruction.
Main results: Experiments on both retrospective and prospective data have
demonstrated the superior performance of the proposed dynamic approach in reducing
motion artifacts and accurately depicting structural details compared to standard static
imaging.
Significance: The proposed dynamic approach effectively captures motion states
through dynamic data acquisition and deep learning-based reconstruction, addressing
motion-related challenges in pelvic imaging.

Wireless Temperature Calibration for Medical Centrifuge

Based on the complexity and metrological requirements of temperature calibration within the rotor of a medical low-temperature centrifuge, a wireless temperature calibration device for medical centrifuges has been designed and developed. The temperature probe is integrated into the centrifuge tube, and the entire device can be placed inside the rotor of the centrifuge, allowing for the measurement and analysis of the actual temperature of the test solution inside the centrifuge tube under dynamic rotation conditions. Furthermore, the force situation during dynamic calibration is analyzed and performance tested. The calibration device can consistently and dependably achieve dynamic temperature data acquisition under high-speed conditions of medical low-temperature centrifuges, facilitating dynamic temperature calibration within the centrifuge rotor and offering support for centrifuge temperature control processes.

Dual-Modal Optical Imaging of Tissue Perfusion in Response to Cooling Stimulation Facilitates Early Detection of Pressure Ulcer.

Pressure ulcers present a significant human and economic challenge, lacking a reliable method for early detection. To address this, we developed a system capable of early detection by using cooling stimulation and dynamic data acquisition techniques to monitor blood perfusion and skin temperature. The system consists of laser speckle perfusion imaging and thermal imaging. And we performed simulations to demonstrate that the system is capable of detect tissue damage across multiple layers, from superficial to deep. Testing on a rabbit ear model demonstrated that this approach, which combines dynamic perfusion and temperature parameters, effectively distinguishes early pressure ulcer areas from normal skin with a significant p value of 0.0015. This distinction was more precise compared to methods relying solely on static parameters or one parameter. Our study thereby offers a promising advancement in the proactive management and prevention of pressure ulcers.

Dynamometer card generation for pumping units based on CNN and electrical parameters

In the actual production process of oil fields, the real-time and accurate acquisition of dynamic recorder data from the pumping unit is of great significance for the diagnosis of well failures. The traditional method of obtaining the card of the dynamometer usually includes installing a load sensor on the auxiliary head of the pumping unit. However, due to the harsh environment of the oil field production site, these load sensors often suffer from damage, distortion, and aging, resulting in large measurement errors and low reliability. This paper proposes a mixed model of pumping based on motor electrical parameter data and CNN convolutional neural network, which has good consistency with actual data in terms of predictive performance. Thus, the highlights of this paper can be summed up in two points: (1) Based on the mathematical model of the AC motor, the speed of the motor and the torque output of the motor are accurately estimated. (2) The convolutional neural network is introduced to compensate for the errors caused by the defects of the pumping unit mechanism model.

Dynamic neuroreceptor positron emission tomography in non-anesthetized rats using point source based motion correction: A feasibility study with [11C]ABP688

To prevent motion artifacts in small animal positron emission tomography (PET), animals are routinely scanned under anesthesia or physical restraint. Both may potentially alter metabolism and neurochemistry. This study investigates the feasibility of fully awake acquisition and subsequent absolute quantification of dynamic brain PET data via pharmacokinetic modelling in moving rats using the glutamate 5 receptor radioligand [11C]ABP688 and point source based motion correction. Five male rats underwent three dynamic [11C]ABP688 PET scans: two test-retest awake PET scans and one scan under anesthesia for comparison. Specific radioligand binding was determined via the simplified reference tissue model (reference: cerebellum) and outcome parameters BPND and R1 were evaluated in terms of stability and reproducibility. Test-retest measurements in awake animals gave reliable results with high correlations of BPND (y = 1.08 × −0.2, r = 0.99, p < 0.01) and an acceptable variability (mean over all investigated regions 15.7 ± 2.4%). Regional [11C]ABP688 BPNDs under awake and anesthetized conditions were comparable although in awake scans, absolute radioactive peak uptakes were lower and relative blood flow in terms of R1 was higher. Awake small animal PET with absolute quantification of neuroreceptor availability is technically feasible and reproducible thereby providing a suitable alternative whenever effects of anesthesia are undesirable, e.g. in sleep research.

Mass spectrometry based proteomics: Changing the impact of protein analysis in forensic science

AbstractMass spectrometry‐based proteomics, born from the invention of soft ionization, has revolutionized protein analysis. In‐depth characterization of complex and variable mixtures of proteins, such as that found at crime‐scenes, is now possible. This capability continues to expand with advancements in mass spectral instrumentation and computational power, allowing for the development of dynamic data acquisition approaches and sophisticated bioinformatics tools for data interpretation. Proteins are the major component of all biological material, as a result they have been of interest to forensic practitioners for centuries. While DNA is the gold standard for identification, proteins are able to provide contextual information about biological material not available through genomic approaches alone. Mass spectrometry‐based proteomics has expanded the breadth and ease of forensic protein analysis with several emerging applications that promise to become common place as investigative tools, particularly in forensic serology and toxicology. This includes the targeted analysis of protein biomarkers for the classification of biological tissue and fluids, the detection of protein toxins such as ricin and identification of peptide hormones such as insulin. The ability of protein analysis to leverage information from the genome has been demonstrated to achieve individual identification from biological material, an emerging technology that can be used in the absence of extractable DNA. In recent years, there has been considerable scrutiny of forensic techniques that lack objectivity and a statistical basis, an alternative molecular approach for forensic analysis that utilizes the high specificity of mass spectrometry, is a welcome addition with many potential future applications.This article is categorized under: Forensic Chemistry and Trace Evidence &gt; Trace Evidence

Research on impact resistance of new suspended elastic safety net system

In order to reduce the direct injuries caused by falling from height, we propose a new type of suspended elastic safety net system suitable for pylon assembly construction. Analyze the correlation between damping and toughness of the protection system, establish the falling object motion control method, and construct the mechanism model of the protection system accordingly. Full-scale impact tests are carried out using test pylons on transmission line projects. Test data is collected using high-speed cameras and dynamic data acquisition equipment to analyze the key impact dynamic response characteristics of the test model. The experimental results show that the system has three working stages. In the first stage, the elastic safety net is transformed from an initial equilibrium state, where it is subjected only to its gravity, to a taut state. In the second stage, as the elongation of the system continues to increase in the direction of impact, the internal forces transmitted by the net to the elastic rod and the supporting rope gradually increase until the energy dissipating devices connected to the elastic rod are activated. In the third stage, additional elongation of the system is provided by activating the support springs in the bearing supports. Based on the failure modes observed in the tests, the key factors leading to the breakdown of the system are investigated. A new numerical simulation method is developed which is not only capable of predicting the load-bearing capacity of an elastic safety net system under the impact of a falling object, but also accurately simulates the main operating characteristics, such as buckling of the elastic rods, activation of the energy-absorbing devices, and deformation of the net ropes. The numerical simulation model was evaluated using the experimental results, and the numerical response of the system was in good agreement with the experimental data.

A Geospatial Decision Support System for Supporting the Assessment of Land Degradation in Europe

Nowadays, Land Degradation Neutrality (LDN) is on the political agenda as one of the main objectives in order to respond to the increasing degradation processes affecting soils and territories. Nevertheless, proper implementation of environmental policies is very difficult due to a lack of the operational, reliable and easily usable tools necessary to support political decisions when identifying problems, defining the causes of degradation and helping to find possible solutions. It is within this framework that this paper attempts to demonstrate a new valuable web-based operational LDN tool as a component of an already running Spatial Decision Support System (S-DSS) developed on a Geospatial Cyberinfrastructure (GCI). The tool could be offered to EU administrative units (e.g., municipalities) so that they may better evaluate the state and the impact of land degradation in their territories. The S-DSS supports the acquisition, management and processing of both static and dynamic data, together with data visualization and on-the-fly computing, in order to perform modelling, all of which is potentially accessible via the Web. The land degradation data utilized to develop the LDN tool refer to the SDG 15.3.1 indicator and were obtained from a platform named Trends.Earth, designed to monitor land change by using earth observations, and post-processed to correct some of the major artefacts relating to urban areas. The tool is designed to support land planning and management by producing data, statistics, reports and maps for any EU area of interest. The tool will be demonstrated through a short selection of practical case studies, where data, tables and stats are provided to challenge land degradation at different spatial extents. Currently, there are WEBGIS systems to visualize land degradation maps but—to our knowledge—this is the first S-DSS tool enabling customized LDN reporting at any NUTS (nomenclature of territorial units for statistics) level for the entire EU territory.

Effects of dynamic [18F]NaF PET scan duration on kinetic uptake parameters in the knee.

Accurately estimating bone perfusion and metabolism using [18F]NaF kinetics from shorter scan times could help address concerns related to patient comfort, motion, and throughput for PET scans. We examined the impact of changing the PET scan duration on the accuracy of [18F]NaF kinetic parameters in the knee. Both knees of twenty participants with and without osteoarthritis were scanned using a hybrid PET-MRI system (53 ± 13 years, BMI 25.9 ± 4.2 kg/m2, 13 female). Seventeen participants were scanned for 54 ± 2 min, and an additional three participants were scanned for 75 min. Patlak K i and Hawkins kinetic parameters (K i, K 1, extraction fraction) were assessed using 50- or 75-minutes of scan data as well as for scan durations that were retrospectively shortened. The error of the kinetic uptake parameters was calculated in bone regions throughout the knee. The mean error of Patlak K i, Hawkins K i, K 1, and extraction fraction was less than 10% for scan durations exceeding 30 min and decreased with increasing scan duration. The length of dynamic data acquisition can be reduced to as short as 30 min while retaining accuracy within the limits of reproducibility of Hawkins kinetic uptake parameters.

A convolutional neural network-based system to estimate the arterial plasma radioactivity curve in 18F-FDG dynamic brain PET study.

Quantitative images of metabolic activity can be derived through dynamic PET. However, the conventional approach necessitates invasive blood sampling to acquire the input function, thus limiting its noninvasive nature. The aim of this study was to devise a system based on convolutional neural network (CNN) capable of estimating the time-radioactivity curve of arterial plasma and accurately quantify the cerebral metabolic rate of glucose (CMRGlc) directly from PET data, thereby eliminating the requirement for invasive sampling. This retrospective investigation analyzed 29 patients with neurological disorders who underwent comprehensive whole-body 18F-FDG-PET/CT examinations. Each patient received an intravenous infusion of 185 MBq of 18F-FDG, followed by dynamic PET data acquisition and arterial blood sampling. A CNN architecture was developed to accurately estimate the time-radioactivity curve of arterial plasma. The CNN estimated the time-radioactivity curve using the leave-one-out technique. In all cases, there was at least one frame with a prediction error within 10% in at least one frame. Furthermore, the correlation coefficient between CMRGlc obtained from the sampled blood and CNN yielded a highly significant value of 0.99. The time-radioactivity curve of arterial plasma and CMRGlc was determined from 18F-FDG dynamic brain PET data using a CNN. The utilization of CNN has facilitated noninvasive measurements of input functions from dynamic PET data. This method can be applied to various forms of quantitative analysis of dynamic medical image data.

Experimental Study on Axial Stress and Hammer Impacting Energy of Offshore Standard Penetration Test

Standard penetration test (SPT) has been widely used in offshore exploration because of its unique advantages. Unlike onshore exploration, offshore construction areas are characterized by high waves and water depths ranging from several meters to tens of meters. As a result, the reliability of offshore SPT is significantly reduced compared with onshore SPT. Currently, the probe rod length correction of SPT is not involved in geotechnical engineering investigation codes and related research, which greatly limits the application of this method in offshore exploration. Therefore, a series of SPTs were carried out in offshore environments with different water depths, with a maximum rod length of 65 m. The acceleration and axial stress at each test point of the rod were monitored by the dynamic signal data acquisition system, and the hammer impacting energy at each test point was obtained by Force–Velocity (F-V) method. The test results show that the correction of the rod length of the offshore SPT is different from that of the traditional SPT, and it needs to be further corrected for the water depth. In this paper, a modified method of rod length for offshore SPT is proposed, which can provide reference for the application of offshore SPT.

Investigation on the flow-induced vibration of tandem cylinders with varying diameters at subcritical Reynolds numbers

The flow interference among multiple cylinders is highly complex, and investigating it holds substantial engineering relevance. This paper considering three different arrangements at subcritical Reynolds numbers, a series of experiments were conducted to study the flow-induced vibrations (FIV) of rigid cylinders in tandem with variable diameters. Both cylinders are free to respond in both in-line (IL) and cross-flow (CF) directions. Utilizing a dynamic data acquisition system to obtain micro-strain time history information. The reference cylinder diameter (D) of 30 mm was employed as the standard for the spacing ratio (S/D). Both cylinders were exposed to uniform water flow at varying S/D: 3.0, 4.0, 6.0, and 8.0. A parametric investigation was conducted to analyze the effects of the relative spacing of the cylinders on the vibration response of the system. The results indicate that vibration intensity escalates as flow velocity increases. For the same cylinders, unsteady wave-induced vibrations (WIV) behind the upstream cylinder hinder the appearance of the lower branch of the amplitude response. Furthermore, the S/D substantially influences the cylinder vibration response. In the arrangement where the small cylinder is located upstream, the downstream cylinder undergoes WIV under smaller S/D, and the shielding effect is inconspicuous. In contrast, when the larger cylinder is located upstream, the shielding effect intensifies, leading to synchronous vibrations of the upstream and downstream cylinders. The S/D appears to exert minimal influence on the vibration under these conditions. In summary, the interference between cylinders in tandem configurations significantly impacts the vibration characteristics of the cylinders.

Quantitative assessment of translocator protein (TSPO) in the non-human primate brain and clinical translation of [18F]LW223 as a TSPO-targeted PET radioligand

ObjectivesTranslocator protein 18 kDa (TSPO) positron emission tomography (PET) can be harnessed for the non-invasive detection of macrophage-driven inflammation. [18F]LW223, a newly reported TSPO PET tracer which was insensitive to rs6971 polymorphism, showed favorable performance characteristics in a recent imaging study involving a rat myocardial infarction model. To enable quantitative neuroimaging with [18F]LW223, we conducted kinetic analysis in the non-human primate (NHP) brain. Further, we sought to assess the utility of [18F]LW223-based TSPO imaging in a first-in-human study. MethodsRadiosynthesis of [18F]LW223 was accomplished on an automated module, whereas molar activities, stability in formulation, lipophilicity and unbound free fraction (fu) of the probe were measured. Brain penetration and target specificity of [18F]LW223 in NHPs were corroborated by PET-MR imaging under baseline and pre-blocking conditions using the validated TSPO inhibitor, (R)-PK11195, at doses ranging from 5 to 10 mg/kg. Kinetic modeling was performed using one-tissue compartment model (1TCM), two-tissue compartment model (2TCM) and Logan graphical analyses, using dynamic PET data acquisition, arterial blood collection and metabolic stability testing. Clinical PET scans were performed in two healthy volunteers (HVs). Regional brain standard uptake value ratio (SUVr) was assessed for different time intervals. Results[18F]LW223 was synthesized in non-decay corrected radiochemical yields (n.d.c. RCYs) of 33.3 ± 6.5% with molar activities ranging from 1.8 ± 0.7 Ci/µmol (n = 11). [18F]LW223 was stable in formulation for up to 4 h and LogD7.4 of 2.31 ± 0.13 (n = 6) and fu of 5.80 ± 1.42% (n = 6) were determined. [18F]LW223 exhibited good brain penetration in NHPs, with a peak SUV value of ca. 1.79 in the whole brain. Pre-treatment with (R)-PK11195 substantially accelerated the washout and attenuated the area under the time-activity curve, indicating in vivo specificity of [18F]LW223 towards TSPO. Kinetic modeling demonstrated that 2TCM was the most suitable model for [18F]LW223-based neuroimaging. Global transfer rate constants (K1) and total volumes of distribution (VT) were found to be 0.10 ± 0.01 mL/cm3/min and 2.30 ± 0.17 mL/cm3, respectively. Dynamic PET data analyses across distinct time windows revealed that the VT values were relatively stable after 60 min post-injection. In a preliminary clinical study with two healthy volunteers, [18F]LW223 exhibited good brain uptake and considerable tracer retention across all analyzed brain regions. Of note, an excellent correlation between SUVr with VT was obtained when assessing the time interval from 20 to 40 min post tracer injection (SUVr(20–40 min), R2 = 0.94, p < 0.0001), suggesting this time window may be suitable to estimate specific binding to TSPO in human brain. ConclusionOur findings indicate that [18F]LW223 is suitable for quantitative TSPO-targeted PET imaging in higher species. Employing state-of-the-art kinetic modeling, we found that [18F]LW223 was effective in mapping TSPO throughout the NHP brain, with best model fits obtained from 2TCM and Logan graphical analyses. Overall, our results indicate that [18F]LW223 exhibits favorable tracer performance characteristics in higher species, and this novel imaging tool may hold promise to provide effective neuroinflammation imaging in patients with neurological disease.

Dynamic detection of three-dimensional crop phenotypes based on a consumer-grade RGB-D camera.

Nondestructive detection of crop phenotypic traits in the field is very important for crop breeding. Ground-based mobile platforms equipped with sensors can efficiently and accurately obtain crop phenotypic traits. In this study, we propose a dynamic 3D data acquisition method in the field suitable for various crops by using a consumer-grade RGB-D camera installed on a ground-based movable platform, which can collect RGB images as well as depth images of crop canopy sequences dynamically. A scale-invariant feature transform (SIFT) operator was used to detect adjacent date frames acquired by the RGB-D camera to calculate the point cloud alignment coarse matching matrix and the displacement distance of adjacent images. The data frames used for point cloud matching were selected according to the calculated displacement distance. Then, the colored ICP (iterative closest point) algorithm was used to determine the fine matching matrix and generate point clouds of the crop row. The clustering method was applied to segment the point cloud of each plant from the crop row point cloud, and 3D phenotypic traits, including plant height, leaf area and projected area of individual plants, were measured. We compared the effects of LIDAR and image-based 3D reconstruction methods, and experiments were carried out on corn, tobacco, cottons and Bletilla striata in the seedling stage. The results show that the measurements of the plant height (R²= 0.9~0.96, RSME = 0.015~0.023 m), leaf area (R²= 0.8~0.86, RSME = 0.0011~0.0041 m 2 ) and projected area (R² = 0.96~0.99) have strong correlations with the manual measurement results. Additionally, 3D reconstruction results with different moving speeds and times throughout the day and in different scenes were also verified. The results show that the method can be applied to dynamic detection with a moving speed up to 0.6m/s and can achieve acceptable detection results in the daytime, as well as at night. Thus, the proposed method can improve the efficiency of individual crop 3D point cloud data extraction with acceptable accuracy, which is a feasible solution for crop seedling 3D phenotyping outdoors.

Failure Analysis of Gantry Crane Slewing Bearing Based on Gear Position Accuracy Error

In large ports, shipyards, and other places of handling operations, gantry cranes are widely used. As a typical slewing crane, its slewing-bearing large gear ring is prone to tooth breakage problems due to the existence of long-term complex alternating load. This paper presents a new fault analysis method based on the gear position accuracy error. Firstly, the relative displacement relationship between the pinion and large gear ring and the large gear ring gear teeth stress nature were analyzed through the establishment of the rotary table structure, slewing bearing, and cylinder structure assembly finite element model. In addition, a dynamic data acquisition instrument and resistance strain gauge data were separately applied to analyze the pinion and large gear ring displacement along with cylinder structure stress for the test position accuracy error. The final results show that under the frontal load condition, the horizontal displacement deviation of the pinion gear and the large gear ring is approximately 2.9 mm, which leads to an increase of 23% in the stress value of the upper tooth root at the engagement of the large gear ring, causing extrusion and accelerating the fatigue and even fracture of the gear teeth.

Influence of Contoured Insoles with Different Materials on Kinematics and Kinetics Changes in Diabetic Elderly during Gait.

Background: Alterations in the lower limb kinematics and kinetics of diabetic patients have been reported in previous studies. Inappropriate choices of orthopedic insole materials, however, fail to prevent diabetic foot ulcers and modify abnormal gait. The aim of this study was to quantitatively compare the effects of contoured insoles with different materials on the kinematics of and kinetics changes in the diabetic elderly during gait. Methods: There were 21 diabetic patients who participated in this study. Three-dimensional (3D) experimental contoured insoles constructed of soft (i.e., Nora Lunalastik EVA and PORON® Medical 4708) and rigid (i.e., Nora Lunalight A fresh and Pe-Lite) materials with Langer Biomechanics longitudinal PPT® arch pads were adopted. An eight-camera motion capture system (VICON), two force plates, and an insole measurement system—Pedar® with 99 sensors—were utilized to obtain the kinematics and kinetics data. The plug-in lower body gait model landmarks were used for dynamic data acquisition during gait. The corresponding data from five gait cycles were selected and calculated. Results: The range of motions (ROMs) of the ankle joint (p = 0.001) and knee joint (p = 0.044) were significantly influenced when the contoured insoles were worn in comparison to the barefoot condition. The joint moments of the lower limbs with maximum ankle plantarflexion during the loading response and maximum knee and hip flexions were significantly influenced by the use of contoured insoles with different materials in the diabetic elderly. The peak plantar pressure (PPP) of the forefoot (p < 0.001), midfoot (p = 0.009), and rearfoot (p < 0.001) was significantly offloaded by the contoured insoles during the stance phase, whilst the PPP of the rearfoot (p < 0.001) was significantly offloaded during the swing phase. Conclusions: The contoured insoles, especially those constructed with soft materials, significantly offloaded the PPP during gait—hence accommodating certain abnormal gait patterns more effectively compared to going barefoot.

Radiosynthesis and Evaluation of a C-11 Radiotracer for Transient Receptor Potential Canonical 5 in the Brain.

TRPC5 belongs to the mammalian superfamily of transient receptor potential (TRP) Ca2+-permeable cationic channels and it has been implicated in various CNS disorders. As part of our ongoing interest in the development of a PET radiotracer for imaging TRPC5, herein, we explored the radiosynthesis, and in vitro and in vivo evaluation of a new C-11 radiotracer [11C]HC070 in rodents and nonhuman primates. [11C]HC070 was radiolabeled utilizing the corresponding precursor and [11C]CH3I via N-methylation protocol. Ex vivo biodistribution study of [11C]HC070 was performed in Sprague-Dawley rats. In vitro autoradiography study was conducted for the rat brain sections to characterize the radiotracer distribution in the brain regionals. MicroPET brain imaging studies of [11C]HC070 were done for 129S1/SvImJ wild-type mice and 129S1/SvImJ TRPC5 knockout mice for 0-60-min dynamic data acquisition after intravenous administration of the radiotracer. Dynamic PET scans (0-120min) for the brain of cynomolgus male macaques were performed after the radiotracer injection. [11C]HC070 was efficiently prepared with good radiochemical yield (45 ± 5%, n = 15), high chemical and radiochemical purity (> 99%), and high molar activity (320.6 ± 7.4GBq/μmol, 8.6 ± 0.2Ci/μmol) at the end of bombardment (EOB). Radiotracer [11C]HC070 has good solubility in the aqueous dose solution. The ex vivo biodistribution study showed that [11C]HC070 had a quick rat brain clearance. Autoradiography demonstrated that [11C]HC070 specifically binds to TRPC5-enriched regions in rat brain. MicroPET study showed the peak brain uptake (SUV value) was 0.63 in 129S1/SvImJ TRPC5 knockout mice compared to 1.13 in 129S1/SvImJ wild-type mice. PET study showed that [11C]HC070 has good brain uptake with maximum SUV of ~ 2.2 in the macaque brain, followed by rapid clearance. Our data showed that [11C]HC070 is a TRPC5-specific radiotracer with high brain uptake and good brain washout pharmacokinetics in both rodents and nonhuman primates. The radiotracer is worth further investigating of its suitability to be a PET radiotracer for imaging TRPC5 in animals and human subjects in vivo.

“Fed-DRL”: A Timeliness Optimization Method for Dynamic Data Acquisition System Based on Mobile Edge Computing

Age of Information (AoI) is a metric to describe the timeliness of a system proposed in recent years. It measures the freshness of the latest received data from the perspective of the target node in the system. This work studies a kind of dynamic data acquisition system for urban security that can update and control the situation of urban environmental security by collecting environmental data. The collected data packets need to be uploaded to the cloud center in time for data update, which has high requirements on the timeliness of the system and freshness of data. However, due to the limited computing capacity of mobile terminals and the pressure of bandwidth for data transmission, problems such as high data execution delay and transmission interruption are caused. Emerging mobile edge computing (MEC), a new model of computing that extends cloud computing capabilities to the edge network, promises to solve these problems. This work focuses on the timeliness of the system, as measured by the average AoI across all mobile terminals. First, a timeliness optimization model is defined, and a multi-agent deep reinforcement learning (DRL) algorithm combined with an attention mechanism is proposed to carry out computing offloading and resource allocation through the continuous interaction between agent and environment; then, in order to improve algorithm performance and data security, the federated learning mode is proposed to train agents; finally, the proposed algorithm is compared with other main baseline algorithms based on deep reinforcement learning. The simulation results show that the proposed algorithm not only outperforms other algorithms in optimizing system timeliness, but also improves the stability of training.

A Real-Time Recognition System of Driving Propensity Based on AutoNavi Navigation Data.

Driving propensity is the driver’s attitude towards the actual traffic situation and the corresponding decision-making or behavior during the driving process. It is of great significance to improve the accuracy of safety early warning and reduce traffic accidents. In this paper, a real-time identification system of driving propensity based on AutoNavi navigation data is proposed. The main work includes: (1) A dynamic data acquisition method of AutoNavi navigation is proposed to obtain the time, speed and acceleration of the driver during the navigation process. (2) The dynamic data collection method of AutoNavi navigation is analyzed and verified through the dynamic data obtained in the real vehicle experiment. The principal component analysis method is used to process the experimental data to extract the driving propensity characteristics variables. (3) The fruit fly optimization algorithm combined with GRNN (generalized neural network) and the feature variable set are used to build a FOA-GRNN-based model. The results show that the overall accuracy of the model can reach 94.17%. (4) A driving propensity identification system is constructed. The system has been verified through real vehicle test experiments. This paper provides a novel and convenient method for building personalized intelligent driver assistance systems in practical applications.