Model Internalisasi Nilai-nilai Multikultural dalam Pembelajaran PAI di SMA Negeri 1 Nganjuk

This study uses a qualitative approach with a type of case study that focuses on cultivating multicultural values ​​that are contained in the learning process inside and outside the class of SMA Negeri 1 Nganjuk. The purpose of this study is to reveal and develop a model of internalization of multicultural values ​​that grows and develops in the research locus. Sources of data related to this study were obtained using in-depth interview techniques, participatory observation and documentation which were processed using the interactive model of Miles and Huberman. From this focus, a multicultural value internalization model was produced through the management of systematic and coordinated learning as well as habituation and training in developing student character.

Functional requirements of intentional control over the integrated cortico-thalamo-cortical and basal ganglia systems using neural computations

Large scale brain models encompassing cortico-cortical, thalamo-cortical and basal ganglia processing are fundamental to understand the brain as an integrated system in healthy and disease conditions but are complex to analyze and interpret. Neuronal processes are typically segmented by region and modality in order to explain an experimental observation at a given scale, but integrative frameworks linking scales and modalities are scarce. Here, we present a set of functional requirements used to evaluate the recently developed large-scale brain model against a learning task involving coordinated learning between cortical and sub-cortical systems. The original Information Based Exchange Brain model (IBEx) is decomposed into functionally relevant subsystems, and each subsystem is analyzed and tuned independently and with regard to its relevant functional requirements. Intermediate conclusions are made for each subsystems according to the constraints imposed by these requirements. Subsystems are then re-introduced into the global framework. The relationship between the global framework and phenotypes associated with Huntington’s disease is then discussed and the framework considered in the context of other state-of-the-art integrative brain models.

INTELLECTUAL HUMILITY AS STRENGHT IN THE PROCESSES OF THE FORMATION AND DEVELOPMENT OF KNOWLEDGE

The aim of the research: To describe the intellectual humility of a teacher as strength in the processes of forming and developing knowledge. Intellectual virtues presuppose the objective of deep, factual perception with a possibility to make mistakes. A lot of pedagogues lack the activities important for virtues without dependence on mood or similar supporting means (the total level of virtue). A teacher who distinguishes with intellectual humility, who belongs to the virtues of the first range (the initiation of learning process and its guiding towards the right direction) and has connection with other intellectual virtues, aims for coordinated learning relationship where the sense of equality is present, as well as broaden perspective, distinguishes with flexibility while re-considering convictions, fear which is felt by an intellectually humble person when he/she misses information or something is not understood. An intellectually humble teacher, developing his/her knowledge, distinguishes with the constant pursuit for development, ability to self-evaluate strengths and weaknesses, seeing relationship with others as perspective for development The definition of intellectual humility is usually related with the recognition that your convictions and opinions can be wrong. This virtue is concerned with the strengths of a teacher. Being an intellectually humble person opens possibilities to the independence of a learner while forming or developing knowledge and understanding. In this process the potential of colleagues is recognized, strong and weak points are firmly pointed out, intellectual arrogance is avoided. This presupposes more possibilities for the change of learning both to a learner and the one dependant on him/her.

A Simulation Model for Interaction between Cyclic Variation of Brain Tissue Temperature and Intracranial Pressure

The brain responds with high sensitivity in case of cerebral damage. Brain temperature (BT), cerebral blood flow (CBF), cerebral blood volume (CBV) and intracranial pressure (ICP) are essential parameters for brain revival in case of cerebral damage. For this reason, the coordinated learning of BT, CBF and ICP is required for improving the remedial impacts. Thus, in this exploration, a simulation model has been developed for association between brain tissue temperature (BTT), CBF, CBV and ICP to improve the apprehensions of the ICP. It includes the cardiac output, partial pressures of oxygen and carbon dioxide in cerebral artery and vein, temperature variations of brain tissue, cerebral metabolic process and pressure-volume relationship for cranial cavity. The model simulates the interaction between arterial blood pressure, BTT, produced amount of CO2 from brain tissue, changes in CBV and changes in ICP. The results show that the ICP and CBV will increase with an increase in brain tissue temperature. This model elaborates the physiology of BTT and ICP with less complexity.

Coordinated Learning by Model Difference Identification in Multiagent Systems with Sparse Interactions

Multiagent Reinforcement Learning (MARL) is a promising technique for agents learning effective coordinated policy in Multiagent Systems (MASs). In many MASs, interactions between agents are usually sparse, and then a lot of MARL methods were devised for them. These methods divide learning process into independent learning and joint learning in coordinated states to improve traditional joint state-action space learning. However, most of those methods identify coordinated states based on assumptions about domain structure (e.g., dependencies) or agent (e.g., prior individual optimal policy and agent homogeneity). Moreover, situations that current methods cannot deal with still exist. In this paper, a modified approach is proposed to learn where and how to coordinate agents’ behaviors in more general MASs with sparse interactions. Our approach introduces sample grouping and a more accurate metric of model difference degree to identify which states of other agents should be considered in coordinated states, without strong additional assumptions. Experimental results show that the proposed approach outperforms its competitors by improving the average agent reward per step and works well in some broader scenarios.