The Kaolinite development process to improve the characteristics. The Kaolinite mixed ratios are 10% 20% 30% and 40% and the result indicated that the experiment mortar-mix ratio at 0:8:2.75:0.2:0.05:0.04:0.5 of cement powder sand Kaolinite limestone superplasticised and clean water compared to No MK control sample covered ASTM standard in tensile strength more than 100 ksc compressive strength of MK20% with superplasticised at 434 ksc. The water absorption values under the standard passed

This research is aimed at studying the development of strong and high strength fiberboard from coconut fiber and corn plant. This is the second consecutive year by using natural latex. To enhance thermal insulation performance. By using the cement ratio: fine sand: coconut pulp: corn fiber: water: surfactant: surfactant is 1: 0.2: 0.0125: 0.0375: 0.3: 0.03: 0.04 by weight Then add natural rubber latex at cement ratio: 5: 1 ratio of natural rubber latex: 1: 0.05 1: 0.10 1: 0.15 1: 0.20 and 1: 0.25 by weight tested in accordance with TIS 878- 2537 on cement slabs. : High density the test results show that the ratio of 1: 0.05 is the fiber cement ratio. The most appropriate the amount of natural latex said. Can make fiber Cement sheets. Best compression and bending strength. And has passed the standard TIS 878-2537 cement fiberboard developed this can help reduce the amount of natural latex is very high. And have a good thermal insulation properties

This paper presents a low cost wireless sensor network system which is developed to be an alerting system for relatives or caregivers when the elder is immobile for a long period. The alerting signal is sent via the Internet link and a mobile phone. Remote transducers capture input signal from the mobility subject. If the target subject has immobilized behavior for long period which is analyzed abnormal condition the system will send the alerting messages to a selected mobile phone or Internet link. After detection of motionless elder from the set–point periods the system immediately distributes messages via SMS on the mobile network and a server to warn a relative or a caregiver with the image option to send. The sensor modules have ease-ofinstallation with coverage area nearly 3 square meters 80 % of detection level suitable for a small room and a bathroom

This paper presents a dynamic associative network model for e-commerce site selection system based on the psycholinguistic theories of human memory; Spreading Activation Network (SAN). The system is designed to give personalized suggestions based on the user’s current personal preferences other user’s common preferences web-link structure and activation rules. This work employs a SAN as a technique to provide the evaluation and selection mechanism that provides multiple parallel processes for perception by representing dynamic associations among web-links user activities and the relevance subjects of the websites. The system attempts to evaluate a number of sites in an unpredictable complex dynamic environment. Spreading activation explains the predictive top-down effect of knowledge. These processes select the group of the most suitable websites (context) in response to the current conditions (e-commerce activities) while the system continues working towards the user objective goal.

Presenting information to an e-learning environment is a challenge mostly because of the hypertext/hypermedia nature and the richness of the context and information provides. This paper proposes a dynamic semantic model for e-learning system based on the psycholinguistic theories of human memory Spreading Activation Network (SAN). This work employs a SAN as a technique to provide the interfaces action selection mechanism in an uncertain environment. The paper combines the SAN with the temporal logic to provide an e-learning system that a learning activity level evolves according to their expected contextual relevance. The system differs from the other e-learning by representing dynamic associations between learning activities and the relevance subjects. This system equipped with an Event-Triggered learning interface (context) adaptation component. This component provides multiple parallel processes for perception. These processes provide context screen selection and learning task operation based upon the user current situation. The SAN attempts to achieve a number of goals in an unpredictable complex dynamic environment. Spreading activation explains the predictive top-down effect of knowledge. It supports general heuristics which may be used as the first step of more elaborated methods. This model is suited to deal with the interaction between semantic and episodic memories as well as many other practical issues regarding e-learning including the retroactive effect of semantics over perception. The system uses the SAN to activate the most suitable interface screen (context) in response to the current conditions (learning activities) while the system continues working towards the learning objective goal. The paper presents our efforts to realize such e-learning system. The proposed paradigm has been implemented to develop a prototypical system and the experiments also illustrate the robustness of such an e-learning framework.