Smartphone-based personalized blood glucose prediction

AbstractEffective blood glucose control is essential for patients with diabetes. However, individual patients may not be able to monitor their blood glucose level regularly because of all manner of real-life interference. In this paper, we propose a personalized diabetes prediction mechanism that leverages smartphone-collected patient data and population data to drive personalized prediction. Unlike existing predictive models, this model utilizes pooled population data and captures patient similarities, and eventually produces a personalized blood glucose prediction for an individual. We have implemented the proposed model as a mobile application and have performed extensive experiments to evaluate its performance. The experimental results demonstrate that the proposed prediction mechanism can improve the prediction accuracy and remedy the problem of sparse data in the existing approaches

Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems

Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system

Giants on an Island: Threats and Conservation Challenges of Elephants Due to Herbivorous Diets

Elephants are highly generalized herbivores with a wide dietary range encompassing natural vegetation and cultivated crops. Their foraging strategies vary across different temporal and spatial contexts, as well as among distinct social groups. A significant number of elephants in Asia and Africa reside beyond the boundaries of national parks, nature reserves, and protected areas. Consequently, many elephants face elevated risks of mortality or injury while seeking essential nutrients. This chapter provides an overview of the critical role played by dry-zone forests as habitats for elephants. Furthermore, it explores how human-dominated landscapes influence elephant feeding behaviors and foraging strategies, emphasizing the need to enhance our current understanding of these behaviors and their implications for the future

Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems

Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system

The effects of war on children, an ecological integration

grantor: University of TorontoThe purpose of this study was to extend Elbedour, Bensel & Bastien's (1993) model of development for children of war to an actual population. This research aimed at rectifying the lack of comparative analysis within war literature, constructing upon it through inclusion of the variable culture. The frequently overlooked Sri Lankan population was examined, investigating children exposed to various war traumas, including war orphans and refugees, children orphaned for reasons other than war and a comparison group. Measures of cognitive maturity, interviews and observations were used to understand the children's ecological environments and their role in mitigating effects of war. Results revealed that children who appeared most adjusted, performing more successfully on cognitive tests resided in ecologically stable environments, characterized by healthy, interactive relationships across all subsystems. This was in contrast to children who appeared maladjusted and unable to complete the cognitive tests, whose ecological environments reflected social insolation and impoverishment across all subsystems.M.A

Design and Deployment of Resource-Aware Distributed Multi-Agent Algorithms

Distributed Unmanned Aerial Systems (UAS) are limited in computational resources, communication resources, and energy resources, which in turn drastically reduce their utility in multi-UAS applications. Orthodox countermeasures which include adding additional computational devices, advanced communication devices, or heavier batteries with more power, inversely correlate to the performance of the UAS. The reason being the added weight and the increased power requirements offset the additional resources the countermeasures provide. Hence, the feasible solution is to intelligently utilize the limited resources available. We present the resource-aware development of distributed multi-UAS control algorithms as the pathway toward intelligent resource utilization. This dissertation first introduces co-regulation techniques to dynamically allocate resources in distributed multi-agent systems controlled by consensus algorithms. Our need-based resource allocation shows significant savings in resources and a shorter time to convergence of the consensus algorithm whilst providing the user the option to adjust the controller gains for the user\u27s desired level of performance. We prove that our co-regulation techniques are robust to delays in communication. Our second contribution is a novel algorithm that combines consensus algorithms with active learning to drastically reduce the resource and time costs of re-training the convolutional neural network. Our final contribution is a series of resource-aware design decisions on the successful implementation of a hierarchical reinforcement learning-based linear quadratic integral (HRL-LQI) controller on a swarm of four UAS systems

Blood Glucose Prediction Models for Personalized Diabetes Management

Effective blood glucose (BG) control is essential for patients with diabetes. This calls for an immediate need to closely keep track of patients' BG level all the time. However, sometimes individual patients may not be able to monitor their BG level regularly due to all kinds of real-life interference. To address this issue, in this paper we propose machine-learning based prediction models that can automatically predict patients BG level based on their historical data and known current status. We take two approaches, one for predicting BG level only using individual's data and second is to use a population data. Our experimental results illustrate the effectiveness of the proposed model

Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems

Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system