204,573 research outputs found
The relationship between self-efficacy beliefs and social-emotional competence in at-risk girls
No full textLittle is known about the relationship between self-efficacy beliefs and social-emotional competence in ethnic minority middle school girls. These children face a number of challenges related to their minority status, peer relationships, school transition, and entry into adolescence. School psychologists have attempted to increase the chances of success among this population by trying to build their resilience. Unfortunately, there is little prior research on the relationship between protective factors such as self-efficacy, optimism, social skills, and pro-social classroom behaviors for this unique population. A goal of this study was to generate data that would appropriately inform social and emotional interventions. This study examined the relationship between self-efficacy beliefs and social-emotional competence in 16 at-risk 7th and 8th grade students over the course of one school year. A cross-lagged panel design determined the trajectory of change among self-efficacy beliefs and social-emotional competence variables over time. Crosstab and chi-square analyses examined relationships among variables on an individual level. The strongest relationships were found among the same variables over time, indicating that interventions should focus on a single skill set of concern for the greatest improvement in that skill set over time. Some data suggest a relationship between optimism and social-emotional competence, which would indicate that optimism interventions may be helpful in improving social-emotional competence for this population. Optimism may be necessary but not sufficient for improvement in social-emotional competence. Future research may benefit from examining these relationships across a longer period of time and examining how different cultural variables may impact our understanding of the relationship between self-efficacy beliefs and social-emotional competence.Psy. D.Includes bibliographical referencesby Heather M. Hame
Abbas Ghasemi Hamed "De l'obligation d'information dans le contrat. Etude comparée du droit français et droit islamique imamite" , sous la direction de M. le Professeur Francis Kernaleguen, 11 septembre 1998
No full textAbbas Ghasemi Hamed "De l'obligation d'information dans le contrat. Etude comparée du droit français et droit islamique imamite" , sous la direction de M. le Professeur Francis Kernaleguen, 11 septembre 1998. In: Revue juridique de l'Ouest, 1998-3. p. 413
Automatic detection of quality soil spectra in an online vis-NIR soil sensor
No full textThe quality of online visible and near infrared (vis-NIR) soil spectra can be deteriorated by interferences of ambient light, and debris e.g., stones, roots or plant residues among others, which considerably reduces the accuracy of the predictions. Filtering of very noisy and non-soil spectra from good-quality soil spectra needs to be performed prior the modelling. Nevertheless, manual filtering of the large amount of vis-NIR online measurement is a laborious and time-consuming task. This study was conducted to develop an automatic filtering system of very noisy and non-soil spectra. Soil and non-soil spectra obtained during online vis-NIR measurements in four commercial fields in Flanders, Belgium were used to build two databases. The main difference in the databases is that flat spectra, mostly found in wet soil conditions, were considered as non-soil spectra in the first group and as soil spectra in the second group. Similarity algorithms [i.e., Pearson correlation, Spearman correlation, Euclidian distance, cosine distance and principal component analysis (PCA)] and machine learning algorithms (i.e., linear discriminant analysis, support vector machine and K-nearest neighbors) for spectra filtering using the two databases were evaluated and compared. Results suggest that the similarity algorithms were not successful to classify spectra into soil and non-soil classes for both groups, since the best prediction accuracy in cross validation achieved by the cosine distance algorithm was 76.11%. However, the machine learning algorithms provided high classification accuracies for both databases. For the former database, the best classification result of 98.5% in cross-validation and 98.6% in independent validation was obtained by using the k-nearest neighbor algorithm. While for the latter database, the best result was achieved by the support vector machine algorithm with a gaussian kernel obtaining 81.4% in cross-validation and 82.03% in independent validation. The best performing model was used to build a cleaning function to automatically pre-process and classify spectra into soil or non-soil classes. This automatic spectrum filtering system enables time saving and ensures only high-quality spectra are used for accurate online prediction of soil properties, necessary for sensor-based variable rate applications
Dr. Duane M. Jackson, Morehouse College, July 2011
No full textThis video is a conversation with Dr. Duane M. Jackson. Dr. Jackson talks about his paper, "Recall and the Serial Position Effect: The Role of Primacy and Recency on Accounting Students' Performance." Jackie Daniel, AUC Woodruff Library, is the interviewer
Abbas Ghasemi Hamed "De l'obligation d'information dans le contrat. Etude comparée du droit français et droit islamique imamite" , sous la direction de M. le Professeur Francis Kernaleguen, 11 septembre 1998
No full textAbbas Ghasemi Hamed "De l'obligation d'information dans le contrat. Etude comparée du droit français et droit islamique imamite" , sous la direction de M. le Professeur Francis Kernaleguen, 11 septembre 1998. In: Revue juridique de l'Ouest, 1998-3. p. 413
Monthly density of black flies collected in Abu-Hamed and Galabat in 2007–2008 and 2009–2010.
No full textMonthly density of black flies collected in Abu-Hamed and Galabat in 2007–2008 and 2009–2010.</p
GC-MS Analysis of Aroma of Medemia argun (Mama-n-Khanen or Mama-n-Xanin), an Ancient Egyptian Fruit Palm
No full textThe fruits of the edible and medicinal Egyptian palm, Medemia argon, were collected from Aswan in Egypt and the essential oil (EO) from fruits and headspace (HS) of the seeds and fleshy mesocarps were analyzed by GC and GC-MS. Results obtained by GC-MS analysis indicated a high variability in the oil and in the headspace from seeds and mesocarps. Sesquiterpene derivatives were the main group of volatiles in the EO from fruits and in the HS from seeds (45.0 and 64.0%, respectively), while oxygenated hydrocarbon derivatives were the main constituents in the HS obtained from fleshy mesocarps (96.5%). The different chemical composition of the headspace obtained from the seeds and mesocarps of M argon can be correlated with the different roles that the different constituents play in the prevention of dehydration of the fruits in the desert region from where the plant was collected
Global dynamics of imperfect axially forced microbeams
No full textAbstract not availableMergen H. Ghayesh, Hamed Farokh
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