Design of Distributed Reconfigurable Robotics Systems with ReconROS

Robotics applications process large amounts of data in real time and require compute platforms that provide high performance and energy efficiency. FPGAs are well suited for many of these applications, but there is a reluctance in the robotics community to use hardware acceleration due to increased design complexity and a lack of consistent programming models across the software/hardware boundary. In this article, we present ReconROS , a framework that integrates the widely used robot operating system (ROS) with ReconOS, which features multithreaded programming of hardware and software threads for reconfigurable computers. This unique combination gives ROS 2 developers the flexibility to transparently accelerate parts of their robotics applications in hardware. We elaborate on the architecture and the design flow for ReconROS and report on a set of experiments that underline the feasibility and flexibility of our approach.

Handling Iterations in Distributed Dataflow Systems

Over the past decade, distributed dataflow systems (DDS) have become a standard technology. In these systems, users write programs in restricted dataflow programming models, such as MapReduce, which enable them to scale out program execution to a shared-nothing cluster of machines. Yet, there is no established consensus that prescribes how to extend these programming models to support iterative algorithms. In this survey, we review the research literature and identify how DDS handle control flow, such as iteration, from both the programming model and execution level perspectives. This survey will be of interest for both users and designers of DDS.

Solving Patient Allocation Problem during an Epidemic Dengue Fever Outbreak by Mathematical Modelling

Dengue fever is a mosquito-borne disease that has rapidly spread throughout the last few decades. Most preventive mechanisms to deal with the disease focus on the eradication of the vector mosquito and vaccination campaigns. However, appropriate mechanisms of response are indispensable to face the consequent events when an outbreak takes place. This study applied single and multiple objective linear programming models to optimize the allocation of patients and additional resources during an epidemic dengue fever outbreak, minimizing the summation of the distance travelled by all patients. An empirical study was set in Ciudad del Este, Paraguay. Data provided by a privately run health insurance cooperative was used to verify the applicability of the models in this study. The results can be used by analysts and decision makers to solve patient allocation problems for providing essential medical care during an epidemic dengue fever outbreak.

Human Resources and their Tendency to Information Security Crimes Based on Holland Theory

Security systems are often the target of cyber-criminals and professional hackers, but often they fail in hiding all traces of the attack, thereby leaving critical evidence that could lead to identifying and arresting the criminal. However, hacking skills vary from one hacker to another depending on the hacker's personal traits, behavior, and intellectual tendencies. The aim of this study is to develop a proposed descriptive model of the behavioral patterns and motives of hackers based on programmable psychological theories, modeled using object-oriented programming models. The study proposes a descriptive model of an inverse algorithm that simulates Holland's Theory of Behavioral Patterns. Findings show that this descriptive model is applicable to be produced as a code map for the human resources of an investigative nature.

PROFIT DISTRIBUTION IN IPD PROJECTS BASED ON WEIGHT FUZZY COOPERATIVE GAMES

Integrated Project Delivery (IPD) is regarded as an effective project delivery method that can deal with the challenge of the rapid development of the architecture, engineering, and construction (AEC) industry. In the IPD team, the alliance profit is not distributed fairly and effectively due to uncertainty, preventing the achievement of the IPD project goals. This study focuses on optimizing the profit distribution among stakeholders in IPD projects and uses quadratic programming models to solve fuzzy cooperative games in the IPD. A payoff function is used in the fuzzy alliance to determine the characteristics of the interval-valued fuzzy numbers, and different weights of the alliance and the efficiency of the player’s participation in the IPD are considered in the profit distribution. A case study is conducted, and the results of the proposed method are compared with those of crisp cooperative games. The results show that the fuzzy cooperative game increases the profit of participants in IPD projects. It is more practical to use weight fuzzy cooperative games than crisp games to express imputation. Moreover, the quadratic programming models and methods result in a fair and efficient profit distribution scheme in IPD projects.

Effective Optimisation of the Patient Circuits of an Oncology Day Hospital: Mathematical Programming Models and Case Study

In this paper, we first use the information we have on the patients of an oncology day hospital to distribute the treatment schedules they have in each of the visits to this centre. To do this, we propose a deterministic mathematical programming model in such a way that we minimise the duration of the waiting room stays of the total set of patients and taking into account the restrictions of the circuit. Secondly, we will look for a solution to the same problem under a stochastic approach. This model will explicitly consider the existing uncertainty in terms of the different times involved in the circuit, and this model also allows the reorganisation of the schedules of medical appointments with oncologists. The models are complemented by a tool that solves the problem of assigning nurses to patients. The work is motivated by the particular characteristics of a real hospital and the models are used and compared with data from this case.

Improving the Resilience of Port–Hinterland Container Logistics Transportation Systems: A Bi-Level Programming Approach

Port–hinterland container logistics transportation systems (PHCLTSs) are significant to economic and social development. However, various kinds of unconventional emergency events (UEEs), such as natural or human-caused disasters, threaten PHCLTSs. This study aims to measure and improve the resilience of PHCLTSs. Bi-level programming models with two different lower level models are established to help PHCLTSs recover their capacity efficiently in the face of UEEs. In the upper level model, the government makes immediate recovery decisions about a damaged PHCLTS with the goal of improving the resilience of the PHCLTS. In the lower level models, truck carriers make decisions about transportation routes and freight volume in the recovered PHCLTS. They cooperate fully to pursue the maximization of total profit and are coordinated by a central authority, or they make their own decisions to pursue maximization of their own profit noncooperatively. An algorithm combining particle swarm optimization (PSO) and traditional optimization algorithms is proposed to solve the bi-level programming models. The numerical experimental results show the validity of the proposed models.