BUSINESS PROCESS RE DESIGN IN EDUCATIONAL INSTITUTES IN MIDDLE EASTERN COUNTRIES: CASE STUDY

ABSTRACT Business process re-engineering (BPR) is the main way in which organization

Localizing Epileptic Foci Using Simultaneous EEG-fMRI Recording: Template Component Cross-Correlation

Conventional EEG-fMRI methods have been proven to be of limited use in the sense that they cannot reveal the information existing in between the spikes. To resolve this issue, the current study obtains the epileptic components time series detected on EEG and uses them to fit the Generalized Linear Model (GLM), as a substitution for classical regressors. This approach allows for a more precise localization, and equally importantly, the prediction of the future behavior of the epileptic generators. The proposed method approaches the localization process in the component domain, rather than the electrode domain (EEG), and localizes the generators through investigating the spatial correlation between the candidate components and the spike template, as well as the medical records of the patient. To evaluate the contribution of EEG-fMRI and concordance between fMRI and EEG, this method was applied on the data of 30 patients with refractory epilepsy. The results demonstrated the significant numbers of 29 and 24 for concordance and contribution, respectively, which mark improvement as compared to the existing literature. This study also shows that while conventional methods often fail to properly localize the epileptogenic zones in deep brain structures, the proposed method can be of particular use. For further evaluation, the concordance level between IED-related BOLD clusters and Seizure Onset Zone (SOZ) has been quantitatively investigated by measuring the distance between IED/SOZ locations and the BOLD clusters in all patients. The results showed the superiority of the proposed method in delineating the spike-generating network compared to conventional EEG-fMRI approaches. In all, the proposed method goes beyond the conventional methods by breaking the dependency on spikes and using the outside-the-scanner spike templates and the selected components, achieving an accuracy of 97%. Doing so, this method contributes to improving the yield of EEG-fMRI and creates a more realistic perception of the neural behavior of epileptic generators which is almost without precedent in the literature

Application of the modified Q-slope classification system for sedimentary rock slope stability assessment in Iran

Abstract(#br)The Q-slope system is an empirical method for discontinuous rock slope engineering classification and assessment. It has been introduced recently to provide an initial prediction of rock slope stability assessment by applying simple assumptions which tend to reflect different failure mechanisms. This study offers a correlation relationship between Q-slope and slope stability degree using case studies of sedimentary rock slopes from 10 regions of Iran. To this end, we have investigated 200 areas from these regions, gathered the necessary geotechnical data, have classified the slopes from a Q-slope perspective, and have estimated their stability relationships. Based on artificial intelligence techniques including k-nearest neighbours, support vector machine, Gaussian process, Decision tree, Random-forest, Multilayer perceptron, AdaBoost, Naive Bayes and Quadratic discriminant analysis, the relationships and classifications were implemented and revised in the Python high-level programming language. According to the results of the controlled learning models, the Q-slope equation for Iran has indicated that the stability-instability class distributions are limited to two linear states. These limits refer to the B-Line (lower limit) as

Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (MRI) have long been used as tools to examine brain activity. Since both methods are very sensitive to changes of synaptic activity, simultaneous recording of EEG and fMRI can provide both high temporal and spatial resolution. Therefore, the two modalities are now integrated into a hybrid tool, EEG-fMRI, which encapsulates the useful properties of the two. Among other benefits, EEG-fMRI can contribute to a better understanding of brain connectivity and networks. This review lays its focus on the methodologies applied in performing EEG-fMRI studies, namely techniques used for the recording of EEG inside the scanner, artifact removal, and statistical analysis of the fMRI signal. We will investigate simultaneous resting-state and task-based EEG-fMRI studies and discuss their clinical and technological perspectives. Moreover, it is established that the brain regions affected by a task-based neural activity might not be limited to the regions in which they have been initiated. Advanced methods can help reveal the regions responsible for or affected by a developed neural network. Therefore, we have also looked into studies related to characterization of structure and dynamics of brain networks. The reviewed literature suggests that EEG-fMRI can provide valuable complementary information about brain neural networks and functions

Developing a Block-chained knowledge management model (BCKMM): Beyond traditional knowledge management

Knowledge management (KM) is a known discipline that follows knowledge identification, knowledge organizing, knowledge storage, knowledge sharing and knowledge application towards value creation. Organisations try to apply and embed it into their primary activities and processes. Nevertheless, there are some challenges in KM execution including knowledge storage issue, knowledge sharing problems, and motivations especially copyright. Generated knowledge in the firms is increasing, which enforce organization to employ new strategies for knowledge storage. Also, knowledge sharing among the experts is challenging, and organizations need to ensure their experts about their rights when they share their invaluable knowledge. Above all, organizations need to provide security when experts document their knowledge and share it. The blockchain is a new decentralized technology that can potentially address the challenges above and improve knowledge management. It can overcome the knowledge storage issues when knowledge capacity increases in the organization. It can reinforce knowledge sharing. It can also address knowledge workers\u27 copyright and security, by tracing knowledge packages throughout the organization. This research, therefore, aims to develop a block chained knowledge management model (BCKMM). It will first choose a Knowledge Management (KM) model and will incorporate blockchain concept in the model. It then develops an advanced knowledge services framework for creation, application, and sharing of knowledge throughout the organization and especially between experts in a company by providing a decentralized framework based on blockchain concepts. This framework exploits blockchain concepts to achieve a distributed and flexible network to conquer the problems of traditional centralized knowledge management models through developing the primary process of knowledge management, based on blockchain concepts