Measurement of Competitiveness Degree in Tunisian Deposit Banks: An Application of the Panzar and Rosse Model

This paper explores the use of the Panzar-Rosse statistic as a basis for empirical assessment of competitive conditions among Tunisian deposit banks. The elaborated model has been tested with an interest revenues equation and a total revenues equation. Proceeding by means of an Ordinary Least Square analysis, the H-statistics is respectively estimated at 0.87 and 0.91. The computations undertaken using bank fixed effects and bank random effects General Least Square methods yield similar results. With reference to the reviewed literature, we are inclined to believe that Tunisian banks implement neither a joint monopoly nor a collusive competition context, and that they evolve within an oligopolistic competition context in a contestable market. Thus, it confirms the presence of a competitive environment.Tunisia, Banking competition, Contestability, Panzar-Rosse statistic, Panel data.

TEACHING READING COMPREHENSION BY EXPERIENCE-TEXT-RELATIONSHIP (ETR) STRATEGY TO THE EIGHT GRADE STUDENTS OF SMP SANDIKA PALEMBANG

The objective of the study is to find out whether or not there is a significant different on students’ reading comprehension who were taught using Experience- Text- Relationship (ETR) Strategy and who were taught using method usually used by teacher to the eighth grade students of SMP SANDIKA Palembang. The population of the study was the Eighth Grade Students of SMP SANDIKA Palembang in the academic year of 2013/2014. The sample of the study was taken from convenience sampling. The writer was selected the class VIII 2 as the control group and VIII 1 as the experimental group. It consisted of 83 students, 40 students for control group and 43 students for experimental group. In this study, the writer used quasi experimental design. The instrument used in collecting the data was written test. The test was administered twice, as the pre-test and post-test for both control and experimental group. The results of the test were analyzed by using t-test. The result showed that teaching reading using ETR had a significant difference on the students’ comprehension. It can be seen from the result of pretest to post-test of each group. The achievement of experimental group was higher than the achievement of control group. Based on the independent t-test analysis from students’ pretest to posttest score in experimental group taught using Experience – Text - Relationship (ETR) strategy, it was found that t-output was lower than p-value or t-obtained was higher than t-table. From the table analysis, p-output was 0.000 and the t-obtained 7.312. The result was consulted to t-table at the significance level 0.05 in two-tailed testing with the degree freedom (1.989). since the p-output was lower than 0.05 level than t-value was higher than critical value of t-table, so the Ho (the null hypotheses) was rejected and the Ha (the alternative hypotheses)  was accepted. It means that there was a significant difference on students’ reading comprehension taught using Experience- Text- Relationship (ETR) strategy of SMP SANDIKA Palembang

Implementation of evidence-based teaching and psychological evaluation of primary school children in a high HIV endemic setting: the case of Botswana

The three chapters in the present PhD thesis are based on 1 manuscript under review; 1 published paper; and another manuscript in preparation. The first is an overview of the existing literature on HEU and HIV-affected children (children living in an HIV-positive environment) and their psychological well-being in association with school performance. The second paper is a pilot case study exploring the use of Frequency Building, Precision Teaching, and positive reinforcement in a special school in Gaborone, Botswana, with children with neurodevelopmental disabilities. The last study is a case-control study on HEU and HUU children. The aim was to investigate if improving the reading performance (applying Frequency Building and Precision Teaching) of HEU and HUU students increased their self-esteem, happiness, and hope. Ethical approval was obtained from the Ethics Commission of the Department of Developmental and Social Psychology of the Sapienza University of Rome, from the Botswana Ministry of Education, and the Botswana Ministry of Health and Wellness

Advanced random forest approaches for outlier detection

Outlier Detection (OD) is a Pattern Recognition task which consists of finding those patterns in a set of data which are likely to have been generated by a different mechanism than the one underlying the rest of the data. The importance of OD is visible in everyday life. Indeed, fast, and accurate detection of outliers is crucial: for example, in the electrocardiogram of a patient, an abnormality in the heart rhythm can cause severe health problems. Due to the high number of fields in which OD is needed, several approaches have been designed. Among them, Random Forest-based techniques have raised great interest in the research community: a Random Forest (RF) is an ensemble of Decision Trees where each tree is diverse and independent. They are characterized by a high degree of flexibility, robustness, and high generalization capabilities. Even though originally designed for classification and regression, in the latest years, due to their success, there has been an increased development of RF-based approaches for other learning tasks, including OD. The forerunner of several RF methods for OD is Isolation Forest (iForest), a technique which main principle is isolation, i.e. the separation of each object from the rest of the data. Since outliers are different from the rest of the data and thus easier to separate, we can easily identify them as those objects isolated after few splits in the tree. iForests have been employed in a great variety of application fields, showing excellent performances. This thesis is inserted into the above scenario: even if some extensions of basic RF-based approaches for OD have been proposed, their potentialities have not been fully exploited and there is large room for improvements. In this thesis, we introduce some advanced RF-based techniques for OD, investigating both methodological issues and alternative uses of these flexible approaches. In detail, we moved along four research directions. The starting point of the first one is the absence of RF methods for OD able to work with non-vectorial data: here we propose ProxIForest, an approach which works with all types of data for which a distance measure can be defined, thus including non-vectorial data as well. Indeed, for the latter, many powerful distances have been proposed. The second direction focuses on how to measure the outlierness degree of an object in an RF, i.e. the anomaly score, since most extensions of iForest concern only the tree building procedure. In detail, we propose two novel classes of methods: the first class exploits the information contained within a tree. The second one focuses on the ensemble aspect of RFs: the aggregation of the anomaly scores extracted from each tree is crucial to correctly identify outliers. As to the third research direction we took a different perspective exploiting the fact that each tree in a forest is a space partitioner encoding relations, i.e. distances, between objects. Whereas this aspect has been widely researched in the clustering field, it has never been investigated for OD: we extract from an iForest a distance measure and input it to an outlier detector. As last research direction, we designed a new variant of iForest to characterize multiple sclerosis given a brain connectivity network: we cast the problem as an OD task, by making an analogy between disconnected brain regions, the hallmark of the disease, and outliers. All proposals have been thoroughly empirically validated on either classical or ad hoc datasets: we performed several analyses, including comparisons to state-of-the-art approaches and statistical tests. This thesis proves the suitability of RF-based approaches for OD from different perspectives: not only they can be successfully used for the task, but we can also use them to extract distances or features. Further, by contributing to this field, this thesis proves that there are still many aspects requiring further investigation

Peningkatan Hasil Belajar Sosiologi Menggunakan Model Pembelajaran Make a Match

This study aims to obtain information regarding the impact of learning sociology using “a make-a-match” in the X grades at SMA Negeri 1 Atambua. This study is an action research study (PTK) that is being conducted in order to improve student learning. The subject of this study is 36 students of X grades majoring in Social Science 4 at SMA Negeri 1 Atambua in the academic year 2022/2023.  This study is being conducted in two stages, where each rule will be carried out twice, and the final rule will be an evaluation or final test. The findings showed that applying the “make a match” learning model in sociology subjects improved student learning outcomes. It can be shown that the overall score of student learning outcomes at the Pre-cycle meeting is 2,422 with an average of 67.27, whereas the total value of student learning outcomes at Cycle I is 2,612 with an average of 72.55. Meanwhile, the number of results in cycle II is 3,102, with an average of 86 16. This result met the researcher's Success Criteria, 80% higher than the Minimum Completeness Criteria, 73

ASBESTO SIGUE MATANDO

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