The papers in this special section focus on data analytics for public health care. Growing and intensive attention for the use of data analytics in healthcare due to its proven benefits to gain insights on the macro and micro depths to support decisionmaking at the patient, clinical, bench side and business levels. Health data analytics contributed to the improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment, narrow the gap between bedside and bench side and provide more informed decision-making. However, the recent pandemic of COVID-19 draw the world attention to need to extend the power of data analytics beyond the legacy data channels including Electronic Health Records (EHRs) and Patient Portals. Data analytics can assist population health management in improving patient outcomes, enhancing care management, and address social determinants of health. As the population health analytics continues to grow in the healthcare space, healthcare systems must be able to gather data from multiple sources, apply variety of data analytics (e.g., Descriptive Analytics, Predictive Analytics Prescriptive Analytics), and manage the provide insights for the population.

As blockchain technology is becoming a driving force in the global economy, it is also gaining critical acclaim in the e-commerce industry. Both the blockchain and e-commerce are inseparable as they involve transactions. Blockchain protect transactions and e-commerce activities rely on them. Blockchain technology enables a decentralized marketplace to support important business activities like secure payments, managing the supply chain and reducing the fraud to mention few. In this special issue editorial we are introducing 11 research articles in this hot area of research that were selected by our reviewers from over than 250 submissions. As blockchain technology is becoming a driving force in the global economy, it is also gaining critical acclaim in the e-commerce industry. Both the blockchain and e-commerce are inseparable as they involve transactions. Blockchain protect transactions and e-commerce activities rely on them. Blockchain technology enables a decentralized marketplace to support important business activities like secure payments, managing the supply chain and reducing the fraud to mention few. In this special issue editorial we are introducing 11 research articles in this hot area of research that were selected by our reviewers from over than 250 submissions.

The use of remote labs in undergraduate courses has been reported in literature several times since the mid 90's. Nevertheless, very few articles present results about the correspondent learning gains obtained by students, and in what conditions those systems can be more efficient, thus suggesting a lack of data concerning their pedagogical effectiveness. This paper addresses such a gap by presenting some initial findings concerning the use of a remote lab (VISIR), in a large undergraduate course on Physics, with over 550 students enrolled.

ISyRAmI (Intelligent Systems Research for Ambient Intelligence) proposed by IST is an Artificial Intelligence oriented methodology and architecture for the development of Ambient Intelligence (AmI) systems. The ISyRAmI architecture considers the following four modules: Data/Information/Knowledge acquisition; Data/Information/Knowledge storage, conversion, and handling; Intelligent Reasoning; and Decision Support/Intelligent Actuation. Also, Dr. Hoon Ko had presented about ISyRAmI SF, which was involved security model to ISyRAmI in ICWMC2009, ISyRAmI is consists of four modules, that is context allocator, context analyzer, context collector and context detector. Because there are various and many contexts in Ambient Intelligence Environment, contexts are needed to classify according to a characteristic and a purpose. Therefore, we studied contexts classification that will be generated from ISyRAmI.

This paper presents a methodology to address reactive power compensation using Evolutionary Particle Swarm Optimization (EPSO) technique programmed in the MATLAB environment. The main objective is to find the best operation point minimizing power losses with reactive power compensation, subjected to all operational constraints, namely full AC power flow equations, active and reactive power generation constraints. The methodology has been tested with the IEEE 14 bus test system demonstrating the ability and effectiveness of the proposed approach to handle the reactive power compensation problem.

With the increasing importance of e-commerce across the Internet, the need for software agents to support customers and suppliers in buying and selling goods/services is growing rapidly. It is becoming increasingly evident that in a few years the Internet will host a large number of interacting software agents. Most of them will be economically motivated, and will negotiate a variety of goods and services. It is therefore important to consider the economic incentives and behaviors of economic software agents, and to use all available means to anticipate their collective interactions. This paper addresses this concern by presenting a multiagent market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, and considering risk preferences. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions. The results of the negotiations between agents are analyzed by data mining algorithms to extract rules that give agents feedback to improve their strategies

We forecast a future in which the global economy and the Internet will host a large number of interacting software agents. Most of them will be economically motivated, and will negotiate a variety of goods and services. It is therefore important to consider the economic incentives and behaviours of economic software agents, and to use all available means to anticipate their collective interactions. This paper addresses this concern by presenting a multi-agent market simulator designed for analysing agent market strategies based on a complete understanding of buyer and seller behaviours, preference models and pricing algorithms. The results of the negotiations between agents will be analysed by data mining tools in order to extract rules that will give the agents feedback to improve their strategies.

With the increasing importance of electronic commerce across the Internet it is becoming increasingly evident that in a few years the Internet will host large numbers of interacting software agents. A vast number of them will be economically motivated, and will exchange a variety of goods and services. It is therefore important to consider the economic incentives and behaviours of economic software agents, and to use every available means to anticipate their collective interactions. We address this concern by presenting a market simulator designed for analysing agent market strategies based on a complete understanding of buyer and seller behaviours, preference model and pricing algorithms.

This paper starts by presenting a scheduling framework, based on genetic algorithms for the resolution of real world scheduling problems, which considers job release times, job due dates and different assembly levels. This framework is based on a decomposition of the job-shop scheduling problem into a series of deterministic single machine scheduling problem. A coordination mechanism is proposed.

Presents some important issues that must drive the knowledge representation in the area of intelligent tutoring systems (ITSs). A framework for representation of the domain knowledge used by an ITS in control centre operator training is presented. The requirements that such a structure must obey and how it is manipulated by the ITS are discussed. This paper also discusses the problems that have conditioned the success of this kind of system in the industrial environment, namely the learning material preparation process. A set of tools was developed in order to automate this process as much as possible and thus minimise this obstacle.

The use of information technologies within distribution utilities is increasing. However, a problem is emerging: traditional technologies cannot cope with large distribution automation systems based on heterogeneous sub-systems. To overcome this problem, we identify the need for systems integration, high-level co-operation protocols and powerful and flexible, yet robust, models of interaction. We describe a system architecture for distribution automation composed of object-oriented agents developed in different environments, interconnected using CORBA (Common Object Request Broker Architecture).

Knowledge management is one of the most important goals of any organization. Therefore, several automatic tools are used for that purpose, e.g. Knowledge Based Systems (KBS), Experts Systems, Data Mining Applications and Computer Aided Decision Systems. The validation and verification (V&V) process is fundamental in order to ensure the quality of used knowledge. The usage of automatic verification tools can be a reliable, inexpensive and reusable way to overcome the constant growth of the Knowledge Bases, the shortening of development times and the costs of Validation, specially field tests. This paper addresses the verification of Knowledge Based Systems, focussing on VERITAS, a verification tool initially developed to verify a KBS used to assist operators of Portuguese Transmission Control Centers in incident analysis and power restoration VERITAS performs knowledge base structural analysis allowing the detection of knowledge anomalies.

This paper deals with a new architecture and negotiation protocol for the dynamic scheduling of manufacturing systems. The architecture is based on the paradigm of distributed intelligence and multi-agent systems. In what concerns the architecture, this paper has two original contributions: the existence of agents representing tasks together with agents representing resources and the important role of the resource manager agent. A new negotiation protocol for manufacturing systems is also presented in this paper. The purpose of this protocol is to dynamically assign operations to the resources of the manufacturing system in order to accomplish the proposed tasks. This protocol involves a renegotiation phase whenever exceptions appear.>

This paper deals with task and execution planning for manufacturing systems. We describe a system named TPMS (task planning for manufacturing systems). The final goal of this system is to automatically generate programs or sets of commands for robots, AGV's, numerical control machines and other components of the system without the need of spending much time programming the flexible manufacturing system (FMS) components. The sequence of operations to be handled by the manufacturing system depends on the constraints for the task. Three types of constraints are defined and explained: processing constraints, feasibility constraints and geometric constraints. The symbolic plan for the task is represented in the form of a precedence graph. High level operations can be converted into programs or sets of instructions to control the manufacturing system's equipment. The main contributions of TPMS are the following. To put together symbolic planning and execution planning; it is being developed to work for complete manufacturing systems (and not for controlling only one robot); different industrial operations (assembly, welding, drilling, ...) can be considered at the same time.>