Multiple Dynamic Parameters Research Articles

Predicting and Measuring Fluid Responsiveness by Hemodynamic Indices versus Transthoracic Echocardiography in Clinically Shocked Patients

Abstract Background In the last decades dynamic measures had evolved as a better solution to predict fluid responsiveness in clinically shocked patients, out of multiple dynamic parameters we chose transthoracic echocardiographic based measurements of variations of aortic blood flow and inferior vena caval diameter. Aim of the Work to assess accuracy of transthoracic echo measured variation in aortic blood flow and inferior vena caval diameter with limited bolus crystalloid infusion in predicting fluid responsiveness in patients with clinical shock. Patients and Methods This study included 51 selected Patients with acute circulatory failure secondary to clinical shock admitted to ICU. All patients had undergone basic transthoracic echocardiographic (TTE) assessment of aortic blood flow (using left ventricular outflow tract velocity time integral as a surrogate) and inferior vena caval diameter changes with respiratory cycle, these measures were obtained at time of diagnosis of shock and repeated after infusion of 300 ml crystalloid fluid bolus (over fifteen minutes). Results Our results had revealed significant difference between responders and non-responders regarding aortic flow variation after fifteen minutes. Conclusion Based on the results obtained in this study, it was proved that transthoracic echocardiography can be used as an accurate method to predict fluid responsiveness in shocked patient with fluid challenge.

Audiovisualization of real-time neuroimaging data.

Advancements in brain imaging techniques have significantly expanded the size and complexity of real-time neuroimaging and behavioral data. However, identifying patterns, trends and synchronies within these datasets presents a significant computational challenge. Here, we demonstrate an approach that can translate time-varying neuroimaging data into unique audiovisualizations consisting of audible representations of dynamic data merged with simplified, color-coded movies of spatial components and behavioral recordings. Multiple variables can be encoded as different musical instruments, letting the observer differentiate and track multiple dynamic parameters in parallel. This representation enables intuitive assimilation of these datasets for behavioral correlates and spatiotemporal features such as patterns, rhythms and motifs that could be difficult to detect through conventional data interrogation methods. These audiovisual representations provide a novel perception of the organization and patterns of real-time activity in the brain, and offer an intuitive and compelling method for complex data visualization for a wider range of applications.

Research on Automatic Generation Method of Urban Spatial Form Based on Parameter Translation——Taking the Planning of Suijiang New Town in Guangning County as an example

With the development and innovation of computer technology such as artificial intelligence, traditional urban design has an opportunity to reform and innovate. This paper proposes a set of automatic urban spatial form generation design proceses based on the parameter translation, taking the design of the Suijiang New Town Project in Guangning County, Guangdong province as an example. For different parameter types, three design methods, which are morphological control of key factors, rule formulation of multi-agent, and iterative optimization of design mode are summarized and compared. The three design methods are applied in process of "data investigation, parameter translation, stepwise generation, and iterative optimization" based on the single static parameter, multiple static parameters, and multiple dynamic parameters respectively, and established a generation system for different design objects and design stage. Research provides a solution for the practical application of parametric design and lays a foundation for the future development of the parametric urban design.

Research on Automatic Generation Method of Urban Spatial Form Based on Parameter Translation——Taking the Planning of Suijiang New Town in Guangning County as an example

With the development and innovation of computer technology such as artificial intelligence, traditional urban design has an opportunity to reform and innovate. This paper proposes a set of automatic urban spatial form generation design proceses based on the parameter translation, taking the design of the Suijiang New Town Project in Guangning County, Guangdong province as an example. For different parameter types, three design methods, which are morphological control of key factors, rule formulation of multi-agent, and iterative optimization of design mode are summarized and compared. The three design methods are applied in process of "data investigation, parameter translation, stepwise generation, and iterative optimization" based on the single static parameter, multiple static parameters, and multiple dynamic parameters respectively, and established a generation system for different design objects and design stage. Research provides a solution for the practical application of parametric design and lays a foundation for the future development of the parametric urban design.

A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles

The performance of plug-in hybrid electric vehicles (PHEVs) depends on the energy management strategy (EMS). An optimal EMS can maximize energy utilization by coordinating the battery energy and fuel consumption. However, the real-time EMS remains a challenge due to the multiple dynamic parameters. To this end, this paper proposed a real-time EMS based on the adaptive regulation of multiple parameters including the driving cycle, driving distance and battery state of charge (SOC). First, the rule-based EMS (RB-EMS) is designed, in which the operative threshold of the charge depleting mode (CD) and charge sustaining (CS) mode are initially determined through engineering experience considering the engine and battery SOC characteristics. Meanwhile, equivalent consumption minimization strategy (ECMS) is utilized to replace the traditional rules in the CD-CS region, which benefits to find the real-time optimal solution in a wider range and simultaneously addresses the fixed torque distribution between the engine and motor in the RB strategy. After then, to improve the adaptability of EMS in different driving cycles, the threshold in the RB strategy and equivalent factor (s(t)) in the ECMS strategy with different driving distances and initial SOC values are optimized with a global genetic algorithm (GA). Besides, a driving cycle recognition algorithm (DCR) based on fuzzy logic is online executed with GPS and onboard sensors, with which the optimal engine and motor torque distribution under the current conditions are adaptively determined according to the identified driving cycles and the above-mentioned offline results. Finally, the proposed adaptive hybrid energy management (HEM) approach was validated by the numerical simulation and hard-in-loop (HIL) experiments. Results show that the proposed HEM strategy can adaptively change the dynamic parameters to achieve the optimized CD stage in real-time; meanwhile, it can achieve a suboptimal result compared with DP which has reduced the fuel consumption by 14.82% for high power demand level.

Resource allocation for dynamic parameter estimation in parallel channels

Any sensing task is constrained by the resources that the sensor has at its disposal. For example, a radar system can only allocate a certain amount of transmit energy per unit time to track all necessary target parameters. When multiple dynamic parameters must be tracked over time, a common approach is to schedule updates at regular intervals, subject to all of the tasks assigned to the radar timeline. However, with the advent of much more agile sensors, such as all-digital apertures, it is possible to conceive of increasingly adaptive and dynamic resource allocation strategies that will yield better performance in target-dense, resource-constrained scenarios. In this study, the authors investigate adaptive resource allocation in a canonical scenario by assuming a parallel, linear channel model, and derive various allocation strategies and compare their short- and long-term performance. The authors also investigate effectiveness as a function of varying circumstances, such as varying signal-to-noise ratio and strength of the target dynamics, and relate the results to adaptive allocation strategies for phased arrays and all-digital apertures.

Role of Software Agent in Big Data Analytics

The concept of software agent and big data plays a crucial responsibility in several fields. With new multiple dynamic parameters of software agent with the concept of big data analytic is growing rapidly in few years. The emergent figure of applications helping millions of users and dealing with terabytes of data require to a faster processing prototype. The concept of software agent in Object-Oriented techniques describes an advance and stimulating way of analyzing, developing, and modelling advanced and useful data analysis. This paper intends to identify and describing how such a concept of software agent might be useful to big data in the appearance of an adaptive software agent System based analytics and upcoming rising challenges. Proposed critique focuses at inquire about new attributes and recitation that how such a technology be able to useful to big data in the appearance of an adaptive software-agent system as long as vibrant analytics capabilities. This ongoing review of research has capable outcomes but will be require for discussing and validating.

Timing is everything: Anticipatory stress dynamics among cortisol and blood pressure reactivity and recovery in healthy adults

Psychological states of anticipation modulate biological stress responsivity. While researchers generally investigate how subjective distress corresponds to the magnitude of stress reactivity, physiological recovery after acute stressors must also be considered when investigating disease vulnerabilities. This study assessed whether anticipatory stress would correspond to stress reactivity and recovery of salivary cortisol and blood pressure levels in response to a well-validated psychosocial stressor. Thirty participants (63% female; mean ± SEM age 45.4 ± 2.12 years) were exposed to the Trier Social Stress Test (TSST) consisting of a public speech and mental arithmetic. Ten salivary cortisol samples and systolic and diastolic blood pressure recordings were collected at time points spanning 50 min before and up to 50 min after stress exposure. These data were transformed into parameters representing stress reactivity (area under the curve) and stress recovery (percent change). The Primary Appraisal Secondary Appraisal scale assessed anticipatory stress before exposure to the TSST. Our results revealed that increased anticipatory stress predicted increased stress reactivity for cortisol (p = 0.009) but not blood pressure. For stress recovery, increased anticipatory stress predicted greater decrements of cortisol concentration (p = 0.015) and blood pressure (p = 0.039), even when controlling for total systemic “output” by incorporating baseline activity. This efficient shutdown of stress responses would have otherwise been ignored by solely investigating reactive increases. These findings underscore the importance of measuring multiple dynamic parameters such as recovery when investigating physiological stress response patterns as a function of psychosocial factors.

Determining Protease Activity In Vivo by Fluorescence Cross-Correlation Analysis

To date, most biochemical approaches to unravel protein function have focused on purified proteins in vitro. Whereas they analyze enzyme performance under assay conditions, they do not necessarily tell us what is relevant within a living cell. Ideally, cellular functions should be examined in situ. In particular, association/dissociation reactions are ubiquitous, but so far there is no standard technique permitting online analysis of these processes in vivo. Featuring single-molecule sensitivity combined with intrinsic averaging, fluorescence correlation spectroscopy is a minimally invasive technique ideally suited to monitor proteins. Moreover, endogenous fluorescence-based assays can be established by genetically encoding fusions of autofluorescent proteins and cellular proteins, thus avoiding the disadvantages of in vitro protein labeling and subsequent delivery to cells. Here, we present an in vivo protease assay as a model system: Green and red autofluorescent proteins were connected by Caspase-3- sensitive and insensitive protein linkers to create double-labeled protease substrates. Then, dual-color fluorescence cross-correlation spectroscopy was employed to study the protease reaction in situ. Allowing assessment of multiple dynamic parameters simultaneously, this method provided internal calibration and improved experimental resolution for quantifying protein stability. This approach, which is easily extended to reversible protein-protein interactions, seems very promising for elucidating intracellular protein functions.