Flow In Real Time Research Articles

Development of an Automated Management System for Agricultural Technologies in Horticulture

The implementation of intelligent technologies in industrial horticulture is possible with the help of an automated system for managing production processes. (Research purpose) To develop and substantiate the parameters of an automated management system for agricultural technologies in horticulture with the ability to conduct land inspections using a mobile application. (Materials and methods) ADO.NET driver Npqsql was used for work with the database. Dapper was used as Object Relational Mapping. The web application used the Model View Controller design pattern, and Bootstrap as the css framework. Data visualization from the database was carried out using cloud technology, placing the site using a set of Internet Information Services. Jquery (a set of JavaScript functions) served as the main framework for working with the client-side of the program code. The authors also used the PostgreSql database management system. The mobile application was created in the Android studio integrated environment. (Results and discussion) The authors developed an automated system for managing agricultural technologies. They formed the structure of the hardware and software base. They created the system ability to operate in a dialogue mode with the user through forms, based on the algorithm for choosing the optimal options for technological processes in the horticultural products production. A mobile application was implemented to conduct digital land inspections. They determined the procedure for conducting land inspections by agronomists using a mobile application. (Conclusions) The authors developed a system for the automated technologies formation and management in horticulture, which provided operational processing of information flows in real time, reflecting the characteristics of the plants’ growth and state in critical phases of development. They provided modern recording devices and a mobile application operation. They showed that the system automatically optimized machine technologies for the cultivation of horticultural crops according to biological (realization of the potential biological productivity of crops) and economic (increasing the efficiency of using production resources) criteria.

An Approach to Build e-Health IoT Reactive Multi-Services Based on Technologies around Cloud Computing for Elderly Care in Smart City Homes

Although there are e-health systems for the care of elderly people, the reactive characteristics to enhance scalability and extensibility, and the use of this type of system in smart cities, have been little explored. To date, some studies have presented healthcare systems for specific purposes without an explicit approach for the development of health services. Moreover, software engineering is hindered by agile management challenges regarding development and deployment processes of new applications. This paper presents an approach to develop health Internet of Things (IoT) reactive applications that can be widely used in smart cities for the care of elderly individuals. The proposed approach is based on the Rozanski and Woods’s iterative architectural design process, the use of architectural patterns, and the Reactive Manifesto Principles. Furthermore, domain-driven design and the characteristics of the emerging fast data architecture are used to adapt the functionalities of services around the IoT, big data, and cloud computing paradigms. In addition, development and deployment processes are proposed as a set of tasks through DevOps techniques. The approach validation was carried out through the implementation of several e-health services, and various workload experiments were performed to measure scalability and performance in certain parts of the architecture. The system obtained is flexible, scalable, and capable of handling the data flow in near real time. Such features are useful for users who work collaboratively in the care of elderly people. With the accomplishment of these results, one can envision using this approach for building other e-health services.

Short-term Passenger Flow Forecast of Urban Subway Transportation Based on Deep Learning Methods

With the acceleration of urban construction, many urban rail transits has entered the stage of network development. Facing the rail passenger flow with larger flow and more complex characteristics, how to forecast the rail passenger flow in real time and accurately has become an important topic in the field of transportation planning. Rail passenger flow is closely related to the travel patterns of urban residents, and is affected by factors such as weather conditions and station development Based on the heterogeneous data such as rail passenger flow data, POI data, meteorological data, air quality data and road network distribution data, this article analyzes the spatial and temporal distribution characteristics of rail passenger flow, conducts functional clustering analysis of stations, and constructs hybrid neural network using deep learning method to realize high-precision prediction of short-term passenger flow at rail stations.

Augmented Reality Fluorescence Imaging in Cerebrovascular Surgery: A Single-Center Experience with Thirty-Nine Cases

Several intraoperative imaging methods exist in cerebrovascular surgery to visualize and analyze the vascular anatomy flow. A new method based on multispectral fluorescence (MFL) imaging of indocyanine green (ICG) video angiography (VA) allows real-time, augmented reality (AR) visualization of blood flow superimposed on white-light microscopic images. We describe our single-center experience using MFL AR in cerebrovascular surgery. Case descriptions are provided of cerebrovascular surgery with intraoperative use of MFL AR images performed at our institution from June 2018 to April 2020. MFL superimposes the blood flow in real time on white-light microscopic images. We used MFL AR imaging as well as standard ICG-VA visualization and intraoperative digital subtraction angiography (DSA) as a control. A total of 39 cases (33 aneurysm clippings, 4 arteriovenous malformations, and 2 external carotid-internal carotid bypass surgeries), were performed using MFL technology-based AR visualization of ICG. MFL AR imaging and DSA showed a high correlation concerning aneurysm occlusion and vessel patency. In arteriovenous malformation resection surgery, MFL AR imaging facilitated early identification of the feeding arteries and draining veins. Because of increased sensitivity of MFL AR, a reduced dose of ICG could be used, allowing repeated intraoperative imaging. There were no postoperative complications, side effects, or technical problems related to the use of MFL AR imaging. MFL AR is an easy-to-use adjunct in cerebrovascular surgery and shows a high correlation with intraoperative DSA. No interruption of the surgery is necessary because MFL AR images of the blood flow are superimposed in real time on white-light microscopic images.

Practical applications of ultrasound in dermatology

Ultrasound has evolved in dermatology from an experimental phase to a daily practice imaging technique. Its several advantages include its safety, good balance between penetration and resolution, high definition, and the detection of blood flow in real time. Its applications are growing and include the support of the diagnosis and extent in all axes, including depth, vascularity patterns, staging, and follow up of multiple cutaneous diseases—benign cutaneous tumors, vascular anomalies, nail lesions, skin cancer, inflammatory cutaneous diseases, and aesthetics complications.

Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

A scalable distributed online algorithm for optimal power flow in distribution system

In this paper, a scalable distributed online algorithm is proposed for solving optimal power flow (OPF) in real time. The operation of distribution systems can be continuously driven towards given OPF targets only depending on local and some aggregated information of each distribution area. Particularly, the proposed algorithm is applicable to the distribution system where each area operates in arbitrary topologies. The algorithm uses the primal-dual gradient method to update DER set-points recurrently based on the sensitivities of the OPF targets; moreover, we decompose the sensitivities into several separate items and give the method to calculate these separate items in a distributed way. The decomposition and distributed calculation methods are all deduced under the general power flow model that completely specifies the physics of electrical networks with arbitrary topologies. The effectiveness of the algorithm is verified using a modified 502-node distribution system. Compared with the online algorithms requiring to process some operation details in a centralized way, the proposed algorithm can achieve almost the same convergence properties with reducing the calculation time sharply.

Shallow Sand Equations: Real-Time Height Field Simulation of Dry Granular Flows.

Granular media is the second-most-manipulated substance on Earth, second only to water. However, simulation of granular media is still challenging due to the complexity of granular materials and the large number of discrete solid particles. As we know, dry granular materials could form a hybrid state between a fluid and a solid, therefore we propose a two-layer model and divide the simulation domain into a dilute layer, where granules can move freely as a fluid, and a dense layer, where granules act more like a solid. Motivated by the shallow water equations, we derive a set of shallow sand equations for modeling dry granular flows by depth-integrating three-dimensional governing equations along its vertical direction. Unlike previous methods for simulating a 2D granular media, our model does not restrict the depth of the granular media to be shallow anymore. To allow efficient fluid-solid interactions, we also present a ray casting algorithm for one-way solid-fluid coupling. Finally, we introduce a particle-tracking method to improve the visual representation. Our method can be efficiently implemented based on a height field and is fully compatible with modern GPUs, therefore allows us to simulate large-scale dry granular flows in real time.

A Real-Time Blood Flow Measurement Device for Patients with Peripheral Artery Disease

PurposeTo evaluate the feasibility of a new optical device that measures peripheral blood flow as a diagnostic and monitoring tool for patients with peripheral artery disease (PAD). Materials and MethodsIn this prospective study, 167 limbs of 90 patients (mean age, 76 y; 53% men) with suspected PAD were evaluated with the FlowMet device, which uses a new type of dynamic light-scattering technology to assess blood flow in real time. Measurements of magnitude and phasicity of blood flow were combined into a single-value flow–waveform score and compared vs ankle–brachial index (ABI), toe–brachial index (TBI), and clinical presentation of patients per Rutherford category (RC). Receiver operating characteristic curves were constructed to predict RC. Area under the curve (AUC), sensitivity, and specificity were compared among flow–waveform score, ABI, and TBI. ResultsQualitatively, the FlowMet waveforms were analogous to Doppler velocity measurements, and degradation of waveform phasicity and amplitude were observed with increasing PAD severity. Quantitatively, the flow, waveform, and composite flow–waveform scores decreased significantly with decreasing TBI. In predicting RC ≥ 4, the flow–waveform score (AUC = 0.83) showed a linear decrease with worsening patient symptoms and power comparable to that of TBI (AUC = 0.82) and better than that of ABI (AUC = 0.71). Optimal sensitivity and specificity pairs were found to be 56%/83%, 72%/81%, and 89%/74% for ABI, TBI, and flow–waveform score, respectively. ConclusionsThe technology tested in this pilot study showed a high predictive value for diagnosis of critical limb ischemia. The device showed promise as a diagnostic tool capable of providing clinical feedback in real time.

A New Integrated Modeling Approach with Case Studies for Gas Transmission of Container Closure Headspace.

This article presents a new theoretical integrated modeling approach for calculating container closure integrity (CCI) that concurrently accounts for both diffusion and mass/volumetric flow in real time; practical case studies are also presented. For pharmaceutical, biological, cell, and gene therapies, container closure systems (CCSs) must ensure drug sterility and stability by safeguarding against microbial contamination and gaseous ingress (e.g., oxygen, carbon dioxide, moisture) according to product requirements. In addition to the testing approach for evaluating CCI performance, a modeling approach can be an important part of a CCI control strategy. Modeling is a powerful tool that provides information in situations where testing is not feasible, technically impossible, too time-consuming, or too expensive. Previously published models have lacked a systematic approach or the versatility needed to coherently and concurrently integrate both diffusion and effusion to solve problems arising in field applications. The new integrated modeling approach described in this article applies a robust numerical method to real-world applications. The model is based on the law of conservation and continuity for molecular flow, Fick's law of diffusion, and the Darcy-Weisbach theory of frictional mass/volumetric flow. This new integrated modeling approach handles time-dependent diffusion and effusion by combining diffusion and mass/volumetric flow seamlessly in real time. For a CCS under vacuum filled with nitrogen, this new modeling approach is able to reveal that oxygen ingress into the CCS through a leak path will enter in two phases, starting with effusion and continuously followed by diffusion in a seamless transition. Our integrated modeling approach is able to calculate and capture the exact timing of the phase transition point, providing unique understanding of complicated CCS problems. Using the finite difference method, all modeling results are numerically solved from the governing equations along with initial and boundary conditions for each individual case. The modeling results were precise and consistent with previously published testing results. This new integrated modeling approach displayed its capability and versatility to handle complicated leakage scenarios in practical applications. As a part of CCI control strategy, the modeling approach is a powerful tool for evaluating leaks, gauging their leak sizes, determining whether the CCS conforms to product requirements, and making informed decisions accordingly. Although additional studies are to be carried out to fully develop the potential of this model, the applications hold great promise and in addition provide insight into CCI and may also provide a solid foundation for CCI testing method development and validation for CCI performance.

Robotic left colectomy with double indocyanine green guidance and intracorporeal anastomoses.

Radical surgery is the mainstay of treatment of colon cancer. Lymphatic drainage of splenic flexure colon cancer is variable, and the exact site of lymphatic dissection is uncertain. Hence, a true consensus of what kind of colectomy should be performed for tumours of the splenic flexure is lacking. Segmental left colectomy (splenic flexure colectomy) (extended), left colectomy as well as subtotal colectomy (extended right colectomy) all have their proponents. Robotic colectomy addresses the limitations of straight laparoscopic colon resections. We report our technique of single-docking totally robotic left hemicolectomy for splenic flexure adenocarcinoma using Da Vinci Xi® Surgical System (Intuitive Surgical, USA) with indocyanine green near-infrared fluorescence for the assessment of both the lymph nodes and intestinal blood flow in real time.

RETRACTED: Visualization system of characteristic town building protection based on FPGA and GIS system

Abstract The ongoing development for modern cities and quality of life, coupled with computing resources, has led to increased smart city services availability. Smart cities are committed to providing residents with a better experience while reducing resource demand and pollution. As such, considered an essential factor for sustainable growth. Of the many problems that have emerged, one of the world's most critical urban issues has been traffic, which has resulted in a massive waste of time and energy and increased pollution. One of the first steps for optimized traffic in the city is to get accurate information to understand the city's traffic flow in real time. It can be achieved by applying automated video analysis, distributed throughout the city, and through a camera group's video stream. The effect of the substance on the surface, discoloration, loss of material, and air pollution can be related to contamination of structural defects and views. Whatever discoloration and air pollution defects in the structure buildings do not matter, have many coasts involved. Both peripheral and central do the processing of the image sequence. Believe that this is due to usability. Too many of the advantages of focusing on maintenance and cost in a large city are very promising strategies for effective traffic management. However, since the computational cost is a vast, high-performance computing method that is excellent in energy efficiency, it has been required. The high-performance algorithm to effectively implement real-time traffic flow information at low energy and power costs can process camera image sequence traffic.

Uyarlanabilir, Dağıtılmış ve Akıllı Trafik Işık Sistemi

In this study, it is aimed to create a low cost adaptive traffic light system. For this purpose, a probabilistic, discrete-time traffic simulator has been developed that can be used to model existing traffic networks. A unique adaptive traffic light system, which achieves higher service quality compared to a fixed time adjusted traffic light system, was developed and tested on the simulator. It has been shown that the developed system can manage the traffic flow in real time and is not affected by the information loss caused by the oversaturation of the modeled roads.

Photostimulated Spiropyran for Instantaneous Visualization of Thermal Field Distribution and Flow Pattern.

The study of thermocapillary convection has attracted the attention of researchers due to the importance in both fundamental and industrial aspects. To trace the state of flows in real time during thermocapillary convection, the development of imaging methods and tools is essential. Here we use a benzothiazole unit-bearing spiropyran (BS1-SP) as a photostimulated indicator to visualize the details of instantaneous temperature distribution and flow pattern on the surface of volatile solvent simultaneously with a high spatial and temporal resolution during convection. This work provides insights into dynamic self-organization and thermo-hydrodynamics taking place in evaporating systems, and a useful tool to study these behaviors.

A less invasive system for the direct measurement of ventilation in fish

Most previous systems for quantifying ventilatory flow in fish involve prior anesthesia and difficult surgery to sew or glue membranes to the animal, which are undoubtedly stressful. By modification of the original “van Dam box” design and incorporation of an electromagnetic blood flow probe, we have developed a less invasive system that avoids these problems and provides breath-to-breath measurements of ventilatory flow in real time. The fish can be quickly moved in and out of the apparatus, facilitating repeated measurements on the same animal after different treatments. We have used the system to document the hyperventilatory and hypoventilatory responses to environmental hypoxia and hyperoxia, respectively, in both ∼400-g trout (Oncorhynchus mykiss) and 10-g goldfish (Carassius auratus); the method is easily adaptable to fish of other sizes. Separate experiments on trout have demonstrated that responses to these treatments in buccal pressure amplitude, breathing frequency, and ventilation index are not altered by the attachments used in the apparatus. This less invasive methodology may prove more acceptable to animal ethics committees.

Irregular Respiratory Motion Compensation for Liver Contrast-Enhanced Ultrasound via Transport-Based Motion Estimation.

Contrast-enhanced ultrasound (CEUS) imaging has been widely applied for the detection and characterization of focal liver lesions (FLLs), for its ability to visualize the blood flow in real time. However, cyclic liver motion poses a great challenge to the recovery of perfusion curves as well as quantitative kinetic parameters estimation. Recently, a few gating methods have been proposed to eliminate unexpected intensity fluctuations by the breathing phase estimation, with the assumption that each breathing phase corresponds to a specific lesion position strictly. While practical liver motion tends to be irregular due to changes in the patient's underlying physiologic status, that is, the same phase might not correspond to the same position. To tackle the challenge of motion irregularity, we present a novel motion estimation-based respiratory compensation method, named RCME, which first estimates salient motion information through the framework of optimal transport (OT) by jointly modeling pixel intensity as well as their locations and then employs sparse subspace clustering (SSC) to identify the subset of frames acquired at the same position. Our proposed method is evaluated on 15 clinical CEUS sequences in both qualitative and quantitative manners. Experimental results demonstrate good performance on irregular liver motion compensation.

Intraoperative Imaging of Cortical Blood Flow by Camera-Based Photoplethysmography at Green Light

Intraoperative evaluation of blood perfusion in the brain cortex is an important but hitherto unresolved problem. Our aim was to demonstrate the feasibility of cerebral microcirculation assessment during open brain surgery by using camera-based photoplethysmography (cbPPG) synchronized with an electrocardiograph. Cortical blood flow was monitored in five patients with different diagnoses. Two cases (tumor resection and extra-intracranial bypass grafting) are presented in detail. Blood-flow parameters were visualized after processing cortex images recorded under green-light illumination before and after surgical intervention. In all cases, blood flow was successfully visualized in >95% of open brain. Distributions of blood pulsation amplitude, a parameter related to cortical blood perfusion; pulse arrival time; and blood-pressure-pulse shape were calculated with high spatial resolution (in every pixel). Changes in cerebral blood supply caused by surgical intervention were clearly revealed. We have shown that the temporal spread of pulse arrival time and the spatiotemporal variability of pulse shape are very sensitive markers of brain circulatory disturbances. The green-light cbPPG system offers a new approach to objective assessment of blood-flow changes in the brain during surgical intervention. The proposed system allows for contactless monitoring of cortex blood flow in real time with high resolution, thus providing useful information for surgery optimization and minimization of brain tissue damage.

Design of multi-parameter auto-detecting instrument system for hydrological hole pumping test based on single-chip microcomputer

In order to meet the requirements of modern hydrogeo-logic investigation, one multi-parameter auto-detecting instrument system for hydrological hole pumping test based on single-chip microcomputer is designed. The pumping test system contains one lower computer module and one upper computer module. The lower computer module uses the STM32 single chip microcomputer as the central module of the controller, supplemented by the sensor module, the power module, the communication module and the display module to realize the automatic detection of the multi-parameters of the hydrological hole pumping test It can display the information of pumping speed, water level, water temperature and water flow in real time and realize the function of pumping timing switch. The upper computer module allows users to check hydrological hole pumping test information and control the whole system operation. The information transmission between the lower computer module and the upper computer module is completed by Narrow Band Internet of Things (NB-Iot). By analyzing the data of pumping test system after compensation, the max relative error of liquid level is all less than 1%. All these data confirm that the pumping test system proposed in this paper has certain application value.

Microgrid Energy Management Systems Design by Computational Intelligence Techniques

With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart homes and hybrid electric vehicles, the design of a suitable Energy Management System (EMS) able to schedule the local energy flows in real time has a key role for the development of Renewable Energy Sources (RESs) and for reducing pollutant emissions. In the literature, most EMSs proposed are based on the implementation of energy systems prediction which enable to run a specific optimization algorithm. Such strategy, known as Rolling Time Horizon (RTH), demonstrated very effective when the supporting prediction system performs well. However, it is featured by high operational times. In this work, different lightweight EMS models synthesized through machine learning algorithms have been compared considering six different simulation scenarios. Results shows that an RTH-based EMS owns the best overall performances. However, in some case studies, also other EMSs show competitive results, especially those based on Adaptive Neuro Fuzzy Inference Systems (ANFIS) trained by clustering, which in one case outperform RTH EMSs, and in other 3 cases (out of 6) yields performances close to RTH EMSs within 5%. A second contribution concerns the RTH EMS implementation on a small micro-controller, highlighting the high computational effort which can range in the order of minutes. Conversely, the ANFIS EMS shows always almost negligible computational costs (less than one second) and therefore can be used in realistic scenarios on cheap devices at run time. The paper also proposed a novel graphic tool to better represent, observe and analyze microgrid energy flows in each time slot or along the overall considered dataset.

Damage Localization in a Building Structure during Seismic Excitation

Aging of buildings during their service life has attracted the attention of researchers on structural health monitoring (SHM). This paper is related with detecting damage in building structures at the earliest possible stage during seismic activity to facilitate decision-making on evacuation before physical inspection is possible. For this, a simple method for damage assessment is introduced to identify the damage story of multistory buildings from acceleration measurements under a wave propagation approach. In this work, damage is assumed as reduction in shear wave velocities and changes in damping ratios that are directly related with stiffness loss. Most damage detection methods are off-line processes; this is not the case with this method. First, a real-time identification system is introduced to estimate the current parameters to be compared with nominal values to detect any changes in the characteristics that may indicate damage in the building. In addition, this identification system is robust to constant disturbances and measurement noise. The time needed to complete parameter identification is shorter compared to the typically wave method, and the damage assessment can keep up with the data flow in real time. Finally, using a robust threshold, postprocess of the compared signal is performed to find the location of the possible damage. The performance of the proposed method is demonstrated through experiments on a reduced-scale five-story building, showing the ability of the proposed method to improve early stage structural health monitoring.