[en] A system e-learning of radiation protection lesson as a teaching aids has built, for lectures activities and teaching aids in deepening the course materials of radiation protection in STTN. This system contains learning materials of radiation protection lesson, they are : Basic radiation physics, Dosimetry, basic radiation protection, radiation measuring equipment, effects of radiation, radioactive waste management, transport of radioactive substances, etc. In building of this system, Moodle platform is used with the support from some softwares, they are : Apache web server, MySql, PHP in local host computer that use XAMPP 1.8.1. The one who has rights to access this system is an admin who has obligation to manage the system and to edit, add, and remove the materials and users that consist of teachers, students, and operators who have access to use that system, as a learning aids as well as teaching aids. The materials of E-learning displayed in the forms : lesson materials, animations, pictures, and simulations. The results of this system show that E-learning able to works well as a teaching aids. With this teaching aids, we hope it will increase the quality of learning and teaching process in STTN and also it will increase the accreditation of STTN. (author)

[en] In this paper, an approach is proposed for controlling the uncertain Lorenz system. Based on an identification technique, a controller is designed that guarantees the regulation of all states in the presence of system uncertainty. Since in some applications the challenging problem of output tracking is desired, we have proposed several effective set-point tracking control techniques. The control schemes that are based on the feedback linearization method, can stabilize the internal dynamics of the system. Simulation results have illustrated the effectiveness of the proposed schemes

No abstract available

No abstract available

[en] This paper deals with an online identification of the Generalized Maxwell Slip (GMS) friction model for both presliding and sliding regime at the same time. This identification is based on robust adaptive observer without friction force measurement. To apply the observer, a new approach of calculating the filtered friction force from the measurable signals is introduced. Moreover, two approximations are proposed to get the friction model linear over the unknown parameters and an approach of suitable filtering is introduced to guarantee the continuity of the model. Simulation results are presented to prove the efficiency of the approach of identification.

[en] Disasters continue to have a dramatic impact on lives, livelihoods and environments communities depend on. In response to these losses, the global community has developed various theories, assessment methodologies and policies aimed at reducing global losses. A contemporary outcome of these interventions is to build the disaster resilience. However, despite the disaster resilience-building endeavours espoused by policies, theories and methodologies, very little progress is being made in reducing disaster losses. This paper argues that a possible reason behind the limitations of current resilience-building policies and methodologies could be that most of these policies are based a mechanistic scientific paradigm that places an emphasis on system components that are perceived to build resilience and not the function of systems as a whole. This often leads to resilience-building initiatives that are based on a ‘one-size-fits-all’ approach. This paper argues for the use of a complex adaptive systems approach to building resilience. This approach argues that contextual factors within different social systems will have a nonlinear affect on disaster resilience-building efforts. Therefore, it is crucial to move away from ‘one-size-fits-all’ approaches to more flexible approaches to building resilience. These hypotheses are tested by means of a correlation statistical analysis of agricultural communities in Southern Africa. Results of this analysis indicate that unique resilience profiles are evident in almost all of the communities studied. This indicates that resilience is not the same for everybody, and that resilience-building endeavours should be flexible enough to be adapted for different contexts.

[en] The maximal capacity per number of couplings is calculated for Ashkin-Teller type neural networks using a replica-symmetric Gardner approach. The results are compared with numerical simulations. The best value obtained at κ=0 is α_c=2.26±0.01

[en] Complex adaptive systems (CAS) are ubiquitous in nature. They are basic in social sciences. An overview of CAS is given with emphasize on the occurrence of bad side effects to seemingly 'wise' decisions. Hence application to terrorism is given. Some conclusions on how to deal with this phenomena are proposed

[en] The selection of a suitable job applicant from the pool of thousands applications is often daunting job for an employer. The categorization of job applications submitted in form of Resumes against available vacancy(s) takes significant time and efforts of an employer. Thus, Resume Classification System (RCS) using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process. Moreover, the automation of this process can significantly expedite and transparent the applicants’ screening process with mere human involvement. This experimental study presents an automated NLP and ML-based RCS that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of RCS and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP and ML techniques for RCS, the extracted features were evaluated on nine ML classification models namely Support Vector Machine - SVM (Linear, SGD, SVC and NuSVC), Naïve Bayes (Bernoulli, Multinomial and Gaussian), K-Nearest Neighbor (KNN), and Logistic Regression (LR). The Term-Frequency-Inverse-Document-Frequency (TF-IDF) feature representation scheme was proved suitable for RCS. The developed models were evaluated using the Confusion Matrix, F-Score, Recall, Precision, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume classification task, the SVM class of Machine Learning classifiers performed better on the study dataset of over nine hundred sixty plus parsed resumes with more than 96% accuracy. The promising results suggest that NLP and ML techniques employed in this study could be used for developing an efficient RCS. (author)

[en] Active and adaptive systems consist of various components with different functionalities. As the complexity of the systems increases, reliability and robustness studies become a more complicated task. Sensitivity analysis helps system designers to understand interactions between the system components and to identify the important parameters with significant overall influences on the system performance. To analyze the complex interactions of the components and parameters of an active system with respect to system performance, a framework structure with active vibration damping in a lab scale test rig was set up. With this test rig an experimental sensitivity analysis was performed to investigate the influences of the system components and parameters on the vibration reduction. A higher robustness of the active system was achieved by using adaptive control. (paper)