دانلود گزارش کارورزی و کار آموزی

چکیده : 

A method for precisely synchronizing an external serial data stream to the pulse-per-second (PPS) output

signal from global navigation satellite-based systems (GNSS) receiver is described in this paper. A signal

timing device was designed that used a digital signal processor (DSP) with serial inputs and input captures

to generate time-stamps based on an internal timer. All temporal measurements were made

directly in hardware, which eliminated any concerns of software latency. The resolution of the signal timing

device was 17.1 ls. Testing showed that time measurements made on the signal timing device were

consistent with measurements made using a digital oscilloscope. The standard error between the signal

timing device and digital oscilloscope was 47.2 ls which translated to less than a millimeter of horizontal

position error for agricultural operations at speeds less than 10 m/s. The signal timing device was shown

to be useful for synchronizing GNSS receivers with ground based position measurement systems used to

assess dynamic accuracy in agricultural applications by mitigating error due to latency.

چکیده : 

Homogeneity plays an important role in ornamental plant and flower production. As assessing the quality

of seedlings is an effective way of predicting plant growth performance, a vision system capable of performing

this task is desirable. Yet, the optical sorting of agricultural products must find ways to incorporate

knowledge from human experts into the computational solution. Our aim is evaluating feature

selection techniques with respect to the performance of vision-based inspection and classification of

pot plant seedlings. A large feature set was initially obtained from seedlings images and several subsets

were generated with various features selection techniques. The performance of each subset was compared

to some of the most popular classifiers in the literature: Naive Bayes, k-Nearest Neighbors, Logistic

Regression, C4.5, Random Forest, Multilayer Perceptron as well as Partial Least Squares and Support Vector

Machine Discriminant Analysis. The best classifier and subset configuration is presented; our results

show that feature selection was indeed advantageous, generating accuracy gains of up to 7.4%.

چکیده : 

Fusarium wilt in cucumbers, caused by the pathogenic fungus Fusarium oxysporum f. sp. cucumerinum,is a serious and destructive disease worldwide. An effort was made to explore the role of Trichodermaharzianum T-E5 in reducing the incidence of Fusarium wilt. Three treatments (Control, T1, and T2) wereestablished in the greenhouse experiment. The effects of T-E5 on the composition of root exudates andfungal community in the cucumber rhizosphere were measured. Compared with the control, the applica-tion of a bio-organic fertilizer (BIO) enriched with T-E5 was found to decrease the incidence of Fusariumwilt notably and promote the growth of cucumber plants. Based on real-time PCR, the population of F.oxysporum in the control without T-E5 increased from 103to 104ITS copies g−1soil, whereas the popu-lation decreased from 103to 102ITS copies g−1soil in the T1 and T2 treatment groups when T-E5 wasincluded. Significant difference in fungal community was also found among the treatment groups. HPLCanalysis showed that the detected levels of phenolic compounds in control were significantly higher thanthe levels in the samples subjected to T1 and T2 treatments. The root exudates from the control groupsignificantly increased the numbers of germinating spores of the pathogen compared with those fromthe samples treated with T1 and T2. In conclusion, the modification of root exudates and the fungal com-munity by the application of BIO might account for the effective suppression of Fusarium wilt disease incucumbers.

چکیده : 

Climate change effects on Fusarium oxysporum f.sp. lactucae (FOL) on lettuce plants grown under simu-lated climate change were studied. An artificial growing substrate was infested with FOL to reach a finalconcentration of 1 × 104CFU g−1of substrate. A non infested tank was used as control.Subsequently, 32 pots (2 l each) were prepared from the infested tank and other 32 pots were preparedfrom the non infested tank (control). Lettuce plants were then transplanted into the pots and grownin phytotrons under four simulated environmental conditions: (1) 800 ppm CO2, 22–26◦C; (2) 800 ppmCO2, 18–22◦C; (3) 400 ppm CO2, 22–26◦C and (4) 400 ppm CO2, 18–22◦C. Substrate samples were col-lected from each phytotron at 7, 14, 21 and 28 days after transplanting. Plate counts, enzymatic assays,and polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) analyses were per-formed to evaluate the effect of climate change on the microbial population. The abundance of Fusariumspp. and the severity of Fusarium wilt of lettuce varied significantly as a consequence of increased tem-perature (22–26◦C). Increased CO2levels showed no effect on the severity of Fusarium wilt of lettuceand on the abundance of Fusarium spp. On the other hand, the total bacterial abundance was reduced atelevated CO2concentration (800 ppm). PCR-DGGE fingerprints of the ascomycete community obtainedfrom DNA directly extracted from infested substrate samples did not change as a consequence of elevatedtemperature and CO2.Enzymatic activities were not affected by the elevated CO2level. Our study indicates that the CO2concentration used in our experiment had no detectable impact on Fusarium wilt of lettuce. Only tem-perature influenced all observed parameters, but did not affect the fungal species diversity. Other factors,such as nutrient limitation and the effect of plant species needs further study.© 2013 Elsevier B

چکیده : 

This paper is a survey of existing estimation methods for pharmacokinetic/pharmacodynamic (PK/PD)

models based on stochastic differential equations (SDEs). Most parametric estimation methods proposed

for SDEs require high frequency data and are often poorly suited for PK/PD data which are usually sparse.

Moreover, PK/PD experiments generally include not a single individual but a group of subjects, leading to a

population estimation approach. This review concentrates on estimation methods which have been applied

to PK/PD data, for SDEs observed with and without measurement noise, with a standard or a population

approach. Besides, the adopted methodologies highly differ depending on the existence or not of an explicit

transition density of the SDE solution.

چکیده : 

Synchronous behavior, as a form of social interaction, has been widely reported for odontocete cetaceansobserved at the sea urface. However, few studies have quantified synchronous behavior underwater.Using data from an animal-borne data recorder and camera, we described how a pair of deep-diving odon-tocetes, long-finned pilot whales, coordinated diving behavior. Diving data during overlapping periodsof 3.7 h were obtained from two whales within a stable trio. The tagged whales made highly synchronousmovements, and their dive durations differed only slightly (3 ± 3 s). The pair of whales maintained aconstant and narrow vertical separation (ca. 3 m) throughout synchronous dives. The overall flukingrate for the same travel speed during synchronous dives was virtually the same as that during asyn-chronous dives, suggesting that synchronous behavior did not affect locomotion effort. In addition, apossible affiliative behavior was recorded by the animal-borne camera: another individual appeared in8% of the frames, both with and without body contact to the tagged whale. The primary type of bodycontact was flipper-to-body. Our study, the first on underwater synchronous behavior and body contactof pilot whales, highlights the utility of using animal-borne devices for enabling new insights into socialinteractions.

چکیده : 

tA procedure to study human operant conditioning is described using a timeout from a high-force require-ment as reinforcer when a high force response was required. Experiment 1 reported evidence that atimeout from a high-force requirement acted as a reinforcer and a second experiment demonstratedsensitivity to delay to escape from the force requirement as a parameter of choice in a self-controlparadigm. The results of the two experiments indicate a functional similarity between unconditionedreinforcers (e.g., food) used in nonhuman subjects and the present reinforcer, demonstrating that thepresent procedure is well-suited to study operant conditioning in humans.

چکیده : 

In the behavioral, social, psychological, and the medical sciences, the most widely used

models in assessing latent variables are the structural equation models (SEMs). However,

most of the existing statistical methods for analyzing SEMs have been developed for

normally distributed data. Transformation SEMs are useful tools for tackling the nonnormality

of multidimensional data and simultaneously revealing the interrelationships

among latent variables. The main objective of this paper is to develop a Bayesian

diagnostic procedure for transformation SEMs. The first- and second-order local inference

measures are established with the objective functions that are defined based on the

logarithm of Bayes Factor. Markov chain Monte Carlo (MCMC) methods with the Bayesian

P-splines approach are developed to compute the local influence measures and to

estimate nonparametric transformations, latent variables, and unknown parameters.

Compared with conventional maximum likelihood-based diagnostic procedures, the

Bayesian diagnostic approach could detect outliers and/or influential points in the observed

data, as well as conduct model comparison and sensitivity analysis through various

perturbations of the data, sampling distributions, or prior distributions of the model

parameters. Simulation studies reveal the empirical performance of the proposed Bayesian

diagnostic procedure. An actual data set that is extracted from a study based on the Hong

Kong Diabetes Registry is used to illustrate the application of our methodology

چکیده : 

P-Splines are commonly used for derivative estimation where a non-linear relationship

exists between the response and explanatory variables. However, questions about the error

of these estimates have arisen. Incorporating an extra penalty term in a P-Spline model

is proposed as an improvement when derivative estimation is of primary concern. This

additive penalty approach to derivative estimation is shown to improve on the P-Spline

estimates based on the results of a simulation study to compare the performance when

estimating the first and second derivatives of six simulated functions. A method for

generating variability bands for P-Spline derivative estimates with and without an additive

penalty is given. The proposed additive penalty variability bands are shown to behave

better than their single penalty counterpart. Motivating examples in environmental and

sports science are used to demonstrate the need for accurate derivative estimates and the

benefit of using an additional penalty term to this end.