Journal of Integrated Design and Process Science

In the paper, hierarchical combinatorial synthesis of Web-hosting system is described. The composition problem is examined as analysis and selection of system component alternatives (e.g., hardware alternatives, software alternatives, communication alternatives). The modular design process is based on Hierarchical Morphological Multicriteria Design (HMMD) approach: (i) design of a tree-like model for the system, (ii) generation of design alternatives for each leaf node of the system model, (iii) generation of criteria and their scales for each node of the system model, (iv) assessment of the design alternatives upon the corresponding criteria, (v) multicriteria analysis of the alternatives for system components/parts to get ordinal estimates of quality for each design alternative, (vi) composing the selected alternatives into a resultant combination or a composite design alternative for the higher hierarchical level of the system model (while taking into account ordinal quality of the alternatives above and ordinal quality of compatibility between the alternatives). The multicriteria analysis of the design alternatives and their composition is based on a hierarchical Bottom-Up framework. A two-criteria combinatorial problem (morphological clique problem) is used for the composition and Pareto-effective solutions are searched for. An example of Web-hosting system consists of the following basic system parts: (a) facilities (servers, telecommunication facilities), (b) Internet access, (c) software part (Web server, Web technology). The set of basic criteria involves the following: price, reliability, scalability, stability of work, maintenance cost, etc. Numerical examples illustrate the system modeling and design processes.

In the best-effort IP network, quality of service (QoS) cannot be guaranteed, and thus packets could possibly be delayed or lost. Packet delay/loss will inevitably degrade the perceptual quality of real-time multimedia-over-IP service, such as Voice-over-IP (VoIP), Video-on-Demand (VoD), etc. In general, packet loss/delay exhibits temporal dependence. In order to efficiently conduct error recovery/concealment and improve the perceptual quality of the transmitted multimedia contents, packet loss/delay has to be precisely modeled. Different mathematical models, such as Bernoulli Model, Gilbert Model, Extended Gilbert Model, have been proposed to model network trace. However, none of them is able to precisely model the networked multimedia trace like the General Markov Model (GMM), which has rarely been applied in practice due to massive computational resource requirements. With the developments of modern computing hardware and parallel processing algorithms, GMM is becoming computationally feasible. In this paper, in order to obtain network traces for real-time VoD transmission, different connections have been setup to simulate a real VoD system. Data packets have been transmitted between the server and clients under RTP/UDP/IP protocol stack. Different models have been applied to analyze and model the obtained video transmission network traces. Specifically, a 6-state GMM has been applied to analyze the network trace, and the parameterized model has been obtained for further error recover/concealment. Compared to the other models, GMM offers the best modeling precision, in terms of loss-run distribution (LSD) and Forward Error Correction (FEC) performance prediction. The parameterized GMM is very useful to model and analyze network traces and further improve the QoS in multimedia-over-IP based on the modeling and analysis.

System reliability is important for systems engineers, since it is directly related to a company's reputation, customer satisfaction, and system design costs. Improving system reliability has been an important task for the system engineers and a number of studies have been published to discuss methods for improving system reliability. For this purpose sensitivity analysis and fault diagnosis has been used in various studies, where identification of significant and problematic components plays an important role. Both sensitivity analysis and fault diagnosis require understanding the system structure and component relationships; and Bayesian networks (BN) have been shown to be an effective tool for modeling the systems and quantifying the component interactions. In this study, we use BN for sensitivity analysis and fault diagnosis to improve system reliability. We focus on the complex systems, where the number of components and component interactions can be very large. In this study, we first discuss sensitivity analysis in complex systems using BN, which can be used for identification of significant system components. Sensitivity analysis using BN is concerned with the question of how sensitive system reliability is to possible changes in the nodes in BN. In this paper we demonstrate that BN can be efficiently and effectively used for sensitivity analysis in complex system reliability. This study is the first that considers component reliabilities and uses BN for sensitivity analysis in complex systems. In this paper as a part of our method for sensitivity analysis, an efficient algorithm (SA) is introduced to perform sensitivity analysis in complex systems. Our SA algorithm is based on a graph traversal algorithm that can be effectively used in BN. The SA algorithm traverses the BN through the connected nodes and evaluates the reliabilities to perform sensitivity analysis. Our method helps the systems engineers understand the cause and effect relationships between system components and their reliability and discover the key components that have significant effects on system reliability. Once the key components are identified, system structure can be revised to improve the overall system reliability. Next we discuss fault diagnosis in complex systems and show how fault diagnosis can be used to improve complex system reliability. Due to component aging and environmental factors, the system components in real-life complex systems may fail or not function as expected. Such failures may cause unprecedented changes in the system reliability values and affect the reliability of not only the failed component, but also the overall system. One important issue in complex systems is that, the system engineers must process large amounts of information before making operational decisions. Since BN combine expert knowledge of the system with probabilistic theory for construction of effective diagnosis methodologies, they have been applied to fault diagnosis in various studies. In this paper, we present a new method for fault diagnosis in complex systems. Our method uses the complex system reliability to detect the faulty components. We continuously monitor the overall system reliability value, and our fault diagnosis mechanism is only triggered when significant changes to system reliability are detected. As part of our method, an efficient search algorithm is designed specifically for BN. This algorithm is empowered with popular heuristics. In this paper, we discuss how our method can be efficiently applied to complex systems since our search algorithm needs to check only a small portion of the system's components before detecting the failed one. We believe that our method provides system engineers with invaluable information to diagnose the faulty component and improve reliability in complex systems.

This paper delineates a new conceptualization of the integration of technology, psychology, medicine, and science, with an emphasis on quantum physics. The focus is on developing a new paradigm to comprehend the ultimate reality and its relation to the human journey in time, space, and beyond. As we learn more through scientific and technological advancements, we recognize that the human being has been ignoring his higher self. An increased understanding of the human brain, body, mind, and spirit is leading to the recognition of the extraordinary, yet often concealed, abilities we all possess. In addition, our increased understanding of the existing realities in parallel dimensions utilizing the superstring theory, is leading to a broader perception of the whole reality and its effect on the human development and condition. The relationship between electromagnetic frequencies and energies and human physiology and psychology requires further study to produce a more definitive understanding of the human makeup. As we increase our understanding and measuring of electromagnetic frequencies and energy in human beings, we will be better able to manipulate this energy. Through this integration of the sciences we will finally develop a more precise map for human composition, development, health, and recovery from diseases.