Evaluation of a University-Community Partnership to Provide Home-Based, Mental Health Services for Children from Families Living in Poverty

A university-community partnership is described that resulted in the development of community-based mental health services for young children from families living in poverty. The purpose of this pilot project was to implement an evidence-based treatment program in the homes of an at-risk population of children with significant emotional and behavior problems that were further complicated by developmental delays. Outcomes for 237 children who participated in the clinic’s treatment program over a 2 year period are presented. Comparisons are included between treatment completers and non-completers and the issues of subject attrition, potential subject selection bias, and the generalizability of the results are addressed. The need for more professionals who are trained to address mental health issues in very young children who live in very challenging conditions are discussed

Multi-agent pathfinding for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs), commonly known as drones, have become more and more prevalent in recent years. In particular, governmental organizations and companies around the world are starting to research how UAVs can be used to perform tasks such as package deliver, disaster investigation and surveillance of key assets such as pipelines, railroads and bridges. NASA is currently in the early stages of developing an air traffic control system specifically designed to manage UAV operations in low-altitude airspace. Companies such as Amazon and Rakuten are testing large-scale drone deliver services in the USA and Japan. To perform these tasks, safe and conflict-free routes for concurrently operating UAVs must be found. This can be done using multi-agent pathfinding (mapf) algorithms, although the correct choice of algorithms is not clear. This is because many state of the art mapf algorithms have only been tested in 2D space in maps with many obstacles, while UAVs operate in 3D space in open maps with few obstacles. In addition, when an unexpected event occurs in the airspace and UAVs are forced to deviate from their original routes while inflight, new conflict-free routes must be found. Planning for these unexpected events is commonly known as contingency planning. With manned aircraft, contingency plans can be created in advance or on a case-by-case basis while inflight. The scale at which UAVs operate, combined with the fact that unexpected events may occur anywhere at any time make both advanced planning and planning on a case-by-case basis impossible. Thus, a new approach is needed. Online multi-agent pathfinding (online mapf) looks to be a promising solution. Online mapf utilizes traditional mapf algorithms to perform path planning in real-time. That is, new routes for UAVs are found while inflight. The primary contribution of this thesis is to present one possible approach to UAV contingency planning using online multi-agent pathfinding algorithms, which can be used as a baseline for future research and development. It also provides an in-depth overview and analysis of offline mapf algorithms with the goal of determining which ones are likely to perform best when applied to UAVs. Finally, to further this same goal, a few different mapf algorithms are experimentally tested and analyzed

Multi-robot preemptive task scheduling with fault recovery: a novel approach to automatic logistics of smart factories

This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as Multi-Robot Preemptive Task Scheduling with Fault Recovery (MRPF). It considers the interaction between running processes and their tasks for management at each new event, prioritizing the more relevant tasks without idleness and latency. The benefit of this approach is the optimization of production in smart factories, where autonomous robots are being employed to improve efficiency and increase flexibility. The evaluation of MRPF is performed through experimentation in small-scale warehouse logistics, referred to as Augmented Reality to Enhanced Experimentation in Smart Warehouses (ARENA). An analysis of priority scheduling, task preemption, and fault recovery is presented to show the benefits of the proposed approach.This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 and in part by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).info:eu-repo/semantics/publishedVersio

Bin Assignment and Decentralized Path Planning for Multi-Robot Parcel Sorting

At modern warehouses, mobile robots transport packages and drop them into collection bins/chutes based on shipping destinations grouped by, e.g., the ZIP code. System throughput, measured as the number of packages sorted per unit of time, determines the efficiency of the warehouse. This research develops a scalable, high-throughput multi-robot parcel sorting solution, decomposing the task into two related processes, bin assignment and offline/online multi-robot path planning, and optimizing both. Bin assignment matches collection bins with package types to minimize traveling costs. Subsequently, robots are assigned to pick up and drop packages into assigned bins. Multiple highly effective bin assignment algorithms are proposed that can work with an arbitrary planning algorithm. We propose a decentralized path planning routine using only local information to route the robots over a carefully constructed directed road network for multi-robot path planning. Our decentralized planner, provably probabilistically deadlock-free, consistently delivers near-optimal results on par with some top-performing centralized planners while significantly reducing computation times by orders of magnitude. Extensive simulations show that our overall framework delivers promising performances

An approach to reconcile the agile and CMMI contexts in product line development

Software product line approaches produce reusable platforms and architectures for products set developed by specific companies. These approaches are strategic in nature requiring coordination, discipline, commonality and communication. The Capability Maturity Model (CMM) contains important guidelines for process improvement, and specifies "what" we must have into account to achieve the disciplined processes (among others things). On the other hand, the agile context is playing an increasingly important role in current software engineering practices, specifying "how" the software practices must be addressed to obtain agile processes. In this paper, we carry out a preliminary analysis for reconciling agility and maturity models in software product line domain, taking advantage of both.Postprint (published version

Boost the Impact of Continuous Formal Verification in Industry

Software model checking has experienced significant progress in the last two decades, however, one of its major bottlenecks for practical applications remains its scalability and adaptability. Here, we describe an approach to integrate software model checking techniques into the DevOps culture by exploiting practices such as continuous integration and regression tests. In particular, our proposed approach looks at the modifications to the software system since its last verification, and submits them to a continuous formal verification process, guided by a set of regression test cases. Our vision is to focus on the developer in order to integrate formal verification techniques into the developer workflow by using their main software development methodologies and tools.Comment: 7 page

A concrete product derivation in software product line engineering: a practical approach

Software Product Lines enable the development of a perfect family of products by reusing shared assets in a systematic manner. Product derivation is a critical activity in software product line engineering and one of the most pressing issues that a software product line must address. This work introduces an approach for automating the derivation of a product from a software product line. The software product line is part of a product family that evolved from a non-structured approach to managing variability. The automated derivation approach relies on product configurations and the refactoring of feature models. The approach was deployed and evaluated in the automotive domain using a real-world software product line. The outcome demonstrates that the approach generates a product in an automated and successful manner.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

The evolution of Europe’s financial stability agenda

PhD Thesi