Smart Cupboard for Assessing Memory in Home Environment

Sensor systems for the Internet of Things (IoT) make it possible to continuously monitor people, gathering information without any extra effort from them. Thus, the IoT can be very helpful in the context of early disease detection, which can improve peoples'' quality of life by applying the right treatment and measures at an early stage. This paper presents a new use of IoT sensor systemswe present a novel three-door smart cupboard that can measure the memory of a user, aiming at detecting potential memory losses. The smart cupboard has three sensors connected to a Raspberry Pi, whose aim is to detect which doors are opened. Inside of the Raspberry Pi, a Python script detects the openings of the doors, and classifies the events between attempts of finding something without success and the events of actually finding it, in order to measure the user''s memory concerning the objects'' locations (among the three compartments of the smart cupboard). The smart cupboard was assessed with 23 different users in a controlled environment. This smart cupboard was powered by an external battery. The memory assessments of the smart cupboard were compared with a validated test of memory assessment about face-name associations and a self-reported test about self-perceived memory. We found a significant correlation between the smart cupboard results and both memory measurement methods. Thus, we conclude that the proposed novel smart cupboard successfully measured memory

Resource allocation model for a computer system

This article describes a simulation for resource allocation. As a particular case, it describes how would be the assignment in an ad hoc network. We present the results of a simulation allocating resources from one system (communications network) to another system (agent collections). In the description of the model first shows the behavior of the allocation of resources towards the nodes depending on the usefulness of the network and the satisfaction of the agents. Based on the mathematical model of the tragedy of the commons, the behavior of the system is shown. The usefulness and satisfaction of the system are measured. Resource allocation is modeled as an interaction. The computational model Interaction Nets is used as the basis of the simulation

Plant Products Affect Growth and Digestive Efficiency of Cultured Florida Pompano (Trachinotus carolinus) Fed Compounded Diets

Costs of compounded diets containing fish meal as a primary protein source can be expected to rise as fish meal prices increase in response to static supply and growing demand. Alternatives to fish meal are needed to reduce production costs in many aquaculture enterprises. Some plant proteins are potential replacements for fish meal because of their amino acid composition, lower cost and wide availability. In this study, we measured utilization of soybean meal (SBM) and soy protein concentrate (SPC) by Florida pompano fed compounded diets, to determine the efficacy of these products as fish meal replacements. We also calculated apparent digestibility coefficients (ADCs) for canola meal (CM), corn gluten meal (CGM), and distillers dried grains with solubles (DDGS), following typical methods for digestibility trials. Juvenile Florida pompano were fed fish-meal-free diets containing graded levels of SBM and SPC, and weight gain was compared to a control diet that contained SBM, SPC, and fish meal. Fish fed diets that contained 25–30 percent SBM in combination with 43–39 percent SPC had weight gain equivalent to fish fed the control diet with fish meal, while weight gain of fish fed other soy combinations was significantly less than that of the control group. Apparent crude protein digestibility of CGM was significantly higher than that of DDGS but not significantly different from CM. Apparent energy digestibility of DDGS was significantly lower than CGM but significantly higher than CM. Findings suggested that composition of the reference diet used in a digestibility trial affects the values of calculated ADCs, in addition to the chemical and physical attributes of the test ingredient