Eterično ulje u perzijskoj kadulji, Salvia rhytidea Benth.

Chemical composition of volatile compounds from Salvia rhytidea Benth. was analyzed, for the first time, by gas chromatography/mass spectrometry. The volatiles were isolated from dried aerial parts of the plant by hydro-distillation. A total yield of 2.0 mg of essential oil per g of plant dry mass was obtained and sixty compounds were identified, representing 98.2% of total volatiles. The essential oil was characterized by a high content of hydrocarbon and oxygenated monoterpenes. The main constituents were p-cymene-8-ol (11.9%), spathulenol (7.3%), pulegone (6.4%), sabinene (5.8%), terpinen-4-ol (5.5%) and alpha-copaene (5.3%).Po prvi put je ispitivan kemijski sastav hlapljivih komponenata iz biljke Salvia rhytidea Benth. plinskom kromatografijom/masenom spektrometrijom. Hlapljivi sastojci su izolirani iz osušenih vršnih dijelova biljke destilacijom vodenom parom. Dobiveno je 2,0 mg eteričnog ulja po gramu suhe biljke, a identificirano je 60 spojeva (98,2% od ukupnih hlapljivih komponenata). Eterično ulje sadrži visoki udio ugljikovodičnih i oksigeniranih monoterpena. Glavni sastojci su p-cimen-8-ol (11,9%), spatulenol (7,3%), pulegon (6,4%), sabinen (5,8%), terpinen-4-ol (5,5%) i alpha-kopaen (5,3%)

Eterično ulje u perzijskoj kadulji, Salvia rhytidea Benth.

Chemical composition of volatile compounds from Salvia rhytidea Benth. was analyzed, for the first time, by gas chromatography/mass spectrometry. The volatiles were isolated from dried aerial parts of the plant by hydro-distillation. A total yield of 2.0 mg of essential oil per g of plant dry mass was obtained and sixty compounds were identified, representing 98.2% of total volatiles. The essential oil was characterized by a high content of hydrocarbon and oxygenated monoterpenes. The main constituents were p-cymene-8-ol (11.9%), spathulenol (7.3%), pulegone (6.4%), sabinene (5.8%), terpinen-4-ol (5.5%) and alpha-copaene (5.3%).Po prvi put je ispitivan kemijski sastav hlapljivih komponenata iz biljke Salvia rhytidea Benth. plinskom kromatografijom/masenom spektrometrijom. Hlapljivi sastojci su izolirani iz osušenih vršnih dijelova biljke destilacijom vodenom parom. Dobiveno je 2,0 mg eteričnog ulja po gramu suhe biljke, a identificirano je 60 spojeva (98,2% od ukupnih hlapljivih komponenata). Eterično ulje sadrži visoki udio ugljikovodičnih i oksigeniranih monoterpena. Glavni sastojci su p-cimen-8-ol (11,9%), spatulenol (7,3%), pulegon (6,4%), sabinen (5,8%), terpinen-4-ol (5,5%) i alpha-kopaen (5,3%)

Model-based Control of Upper Extremity Human-Robot Rehabilitation Systems

Stroke rehabilitation technologies have focused on reducing treatment cost while improving effectiveness. Rehabilitation robots are generally developed for home and clinical usage to: 1) deliver repetitive and stimulating practice to post-stroke patients, 2) minimize therapist interventions, and 3) increase the number of patients per therapist, thereby decreasing the associated cost. The control of rehabilitation robots is often limited to black- or gray-box approaches; thus, safety issues regarding the human-robot interaction are not easily considered. Furthermore, despite numerous studies of control strategies for rehabilitation, there are very few rehabilitation robots in which the tasks are implemented using optimal control theory. Optimal controllers using physics-based models have the potential to overcome these issues. This thesis presents advanced impedance- and model-based controllers for an end-effector-based upper extremity stroke rehabilitation robot. The final goal is to implement a biomechanically-plausible real-time nonlinear model predictive control for the studied rehabilitation system. The real-time term indicates that the controller computations finish within the sampling frequency time. This control structure, along with advanced impedance-based controllers, can be applied to any human-environment interactions. This makes them promising tools for different types of assistive devices, exoskeletons, active prostheses and orthoses, and exercise equipment. In this thesis, a high-fidelity biomechatronic model of the human-robot interaction is developed. The rehabilitation robot is a 2 degree-of-freedom parallelogram linkage with joint friction and backlash, and nonlinear dynamics. The mechatronic model of the robot with relatively accurate identified dynamic parameters is used in the human-robot interaction plant. Different musculoskeletal upper extremity, biomechanic, models are used to model human body motions while interacting with the rehabilitation robot model. Human-robot interaction models are recruited for model-in-loop simulations, thereby tuning the developed controllers in a structured resolution. The interaction models are optimized for real-time simulations. Thus, they are also used within the model-based control structures to provide biofeedback during a rehabilitation therapy. In robotic rehabilitation, because of physical interaction of the patient with a mechanical device, safety is a fundamental element in the design of a controller. Thus, impedance-based assistance is commonly used for robotic rehabilitation. One of our objectives is to achieve a reliable and real-time implementable controller. In our definition, a reliable controller is capable of handling variable exercises and admittance interactions. The controller should reduce therapist intervention and improve the quality of the rehabilitation. Hence, we develop advanced impedance-based assistance controllers for the rehabilitation robot. Overall, two types of impedance-based (i.e., hybrid force-impedance and optimal impedance) controllers are developed and tuned using model-in-loop simulations. Their performances are assessed using simulations and/or experiments. Furthermore, their drawbacks are discussed and possible methods for their improvements are proposed. In contrast to black/gray-box controllers, a physics-based model can leverage the inherent dynamics of the system and facilitate implementation of special control techniques, which can optimize a specific performance criterion while meeting stringent system constraints. Thus, we present model-based controllers for the upper extremity rehabilitation robot using our developed musculoskeletal models. Two types of model-based controllers (i.e., nonlinear model predictive control using external 3-dimensional musculoskeletal model or internal 2-dimensional musculoskeletal model) are proposed. Their performances are evaluated in simulations and/or experiments. The biomechanically-plausible nonlinear model predictive control using internal 2-dimensional musculoskeletal model predicts muscular activities of the human subject and provides optimal assistance in real-time experiments, thereby conforming to our final goal for this project

The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies

Retention of dye tracer in side basins exchanging with subcritical and supercritical flows

River engineeringTransport and fate of pollutants in river

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.The authors would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canada Research Chairs program for financial support of this research

EXPLANATORY ANALYSES OF WORK TRIP GENERATION USING MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR)

In transportation planning, forecasts have commonly followed the sequential four-step model in which, trip generation (production and attraction) plays a critical role. Among the methods applied to model trip generation, regression with Gaussian distribution of errors are recognized as the most prevailing techniques to describe the relationships between production/attraction and explanatory variables by estimating the global, fixed coefficients. Considering that, trip generation is almost impressed by spatial factors which vary over the study area; the main objective of this research is to apply Mixed Geographically Weighted Regression (MGWR) on 253 traffic analysis zones (TAZs) in Mashhad, Iran, by applying travel demand data and relating factors in 2018 to investigate the spatial non-stationarity which are not revealed when global specifications are applied. The influence of certain explanatory variables on response variables may be global, whereas others are local, accordingly, MGWR performs better compared with geographically weighted regression. The results of Moran’s I as spatial autocorrelation index performing on residuals of global, mixed models proved the reliability of the proposed model over the traditional one. The spatial model indicated an improvement in model performance using goodness-of-fit criteria with the coefficient of determination varying from 0.84–0.95 compared with 0.76 and 0.6 in the conventional model. The results of such analysis can provide descriptive and predictive tools at the planning-level for decision-makers

A comparative study of preliminary dosimetry for human based on distribution data in rats with 111In, 90Y, 153Sm, and 177Lu labeled rituximab

Radio immunotherapy is one of the most important and effective therapies for B-cell non Hoddgkin’s lymphoma treatment. Today, anti CD-20 antibodies labeled with beta emitter radionuclides are used in radio immunotherapy. Various radionuclides for labeling anti CD-20 antibodies have been studied and developed for the treatment and diagnosis of malignancies. This paper describes the preparation, bio-distribution and absorbed dose rate of 111In, 90Y, 177Lu, and 153Sm labeled anti CD-20 antibodies (rituximab) in human organs, after injection to rats. The macro cyclic bifunctional chelating agent, N-succinimidyl-1, 4, 7, 10-tetraazacyclododecane-1, 4, 7, 10-tetraacetic acid (DOTA-NHS) for conjugation to antibody, was used to prepare DOTA-rituximab. The conjugates were purified by molecular filtration, the average number of DOTA conjugated per mAb was calculated and total concentration was determined by spectrophotometric method. Radio-labeling was performed at 40 °C for 24 hours. After the quality control studies, the final radioactive solution was injected intravenously into rats through their tail vein. The tissue uptakes of each injection were measured. Then we calculated S values for 177Lu and 153Sm by using specific absorbed fractions and data used in the manner of radio-labeled analysis and dosimetry for humans. The absorbed dose rate of each organ was calculated in the specific time by medical internal radiation dose method with linear approximation in the activity measurements

On the Relationship Between Muscle Synergies and Redundant Degrees of Freedom in Musculoskeletal Systems

It has been suggested that the human nervous system controls motions in the task (or operational) space. However, little attention has been given to the separation of the control of the task-related and task-irrelevant degrees of freedom.Aim: We investigate how muscle synergies may be used to separately control the task-related and redundant degrees of freedom in a computational model.Approach: We generalize an existing motor control model, and assume that the task and redundant spaces have orthogonal basis vectors. This assumption originates from observations that the human nervous system tightly controls the task-related variables, and leaves the rest uncontrolled. In other words, controlling the variables in one space does not affect the other space; thus, the actuations must be orthogonal in the two spaces. We implemented this assumption in the model by selecting muscle synergies that produce force vectors with orthogonal directions in the task and redundant spaces.Findings: Our experimental results show that the orthogonality assumption performs well in reconstructing the muscle activities from the measured kinematics/dynamics in the task and redundant spaces. Specifically, we found that approximately 70% of the variation in the measured muscle activity can be captured with the orthogonality assumption, while allowing efficient separation of the control in the two spaces.Implications: The developed motor control model is a viable tool in real-time simulations of musculoskeletal systems, as well as model-based control of bio-mechatronic systems, where a computationally efficient representation of the human motion controller is needed