Narrow Results

Let K be any unital commutative ℚ-algebra and z = (z₁, z₂, …, z_n) commutative or noncommutative variables. Let t be a formal central parameter and K[[t]]〈〈z〉〉 the formal power series algebra of z over K[[t]]. In [29], for each automorphism F_t(z) = z - H_t(z) of K[[t]]〈〈z〉〉 with H_t=0(z) = 0 and o(H(z)) ≥ 1, a (noncommutative symmetric) system [28] Ω_Ft has been constructed. Consequently, we get a Hopf algebra homomorphism from the Hopf algebra [9] of NCSFs (noncommutative symmetric functions). In this paper, we first give a list for the identities between any two sequences of differential operators in the system Ω_Ft by using some identities of NCSFs derived in [9] and the homomorphism . Secondly, we apply these identities to derive some formulas in terms of differential operator in the system Ω_Ft for the Taylor series expansions of u(F_t) and ; the D-Log and the formal flow of F_t and inversion formulas for the inverse map of F_t. Finally, we discuss a connection of the well-known Jacobian conjecture with NCSFs.

Artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

The causes of complex manufacturing systems' failures are preliminarily analyzed. The techniques of creating highly reliable manufacturing systems are then discussed. It has been shown that modern safety and quality analysis conducted from early design phase to operation and maintenance will enhance the reliability of complex manufacturing systems. In addition, optimization between human operators and machines reduces confusion and increases the reliability of such systems. Employing teamwork in all aspects of the organizational systems will enhance the productivity of complex systems. Moreover, there should also exist a unique and systemic mechanism which integrates the performance of the organizational systems with those of technical (engineering) and nontechnical (people) subsystems.

This paper draws on work previously published on the difference between knowledge and information by the author (2014) as well as research into management and systems design, to propose a model for what a knowledge management system (KMS) might be comprised of. It proposes an architecture comprising specific elements that must be in place for a system to be considered a “KMS”. These elements are further broken down into a range of components within those elements that may exist to contribute to the functioning of a KMS.

In this paper, a class of efficient iterative methods with increasing order of convergence for solving systems of nonlinear equations is developed and analyzed. The methodology uses well-known third-order Potra–Pták iteration in the first step and Newton-like iterations in the subsequent steps. Novelty of the methods is the increase in convergence order by an amount three per step at the cost of only one additional function evaluation. In addition, the algorithm uses a single inverse operator in each iteration, which makes it computationally more efficient and attractive. Local convergence is studied in the more general setting of a Banach space under suitable assumptions. Theoretical results of convergence and computational efficiency are verified through numerical experimentation. Comparison of numerical results indicates that the developed algorithms outperform the other similar algorithms available in the literature, particularly when applied to solve the large systems of equations. The basins of attraction of some of the existing methods along with the proposed method are given to exhibit their performance.

This study focuses on high-tech startups in the Norwegian context and investigates the use of data and/or big data to support validation at the early stage of development. Early-stage startups often fail due to lack of validation and incur loss to all the stakeholders involved in the process. Early validation through a lean or agile approach can help these young companies to manoeuvre through a turbulent external environment. The results from the study show that big data and/or data can act as a support at this stage but is not necessarily the sole solution to the problem. There are various barriers that need to be addressed for successful data-based validation. Based on multi-case study research method, this study proposes an early validation user guide (EVU) to overcome these barriers and make data adoption easier. The EVU can provide startups support to show how and when to use data and/or big data depending on the market context. The study thus contributes to the current body of knowledge of innovation and entrepreneurship concerning validation for early stage startups and can also guide practitioners in validating their startup ideas.

This paper examines the evolution of the cellular mobile communications system which is treated as an example of a Complex Product System (CoPS). It distinguishes between architectural, component and systemic innovations in order to isolate different phases of innovation in the birth, growth and transformation of the cellular systems and other CoPS. An architectural phase in the birth and early development of a CoPS is powerfully influenced by regulators, system suppliers, standard-making bodies and large users. In this phase, emphasis is placed on developing and testing new system architectures prior to commercialisation. In a new product generation phase, the rate of component and systemic innovation increases and successive new product generations are introduced without fundamentally changing the established architectural design. Industries supplying CoPS are often relatively stable because suppliers have developed core capabilities to accommodate the periodic introduction of new product generations.

Development of nanosystems require integration of nanostructures into a microscale object that can interact with its environment and collectively provide a unique function. MEMS provides an ideal platform for such integration since it forms the link between the nano and macroscales while offering electronic, acoustic, thermal, optical, magnetic, mechanical, and fluidic interfaces with its environment. MEMS can also be used to develop instruments that can probe and manipulate nanostructures. This paper describes a few examples of both roles that MEMS can play.

Successes of information and cognitive science brought a growing understanding that mind is based on intelligent cognitive processes, which are not limited by language and logic only. A nice overview can be found in the excellent work of Jeff Hawkins "On Intelligence." This view is that thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized and programmed. Semiotic approach allows for the universal representation of such diagrams. In our approach, logic is just a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. However, most of the efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. This extension of fuzzy logic is inspired by processes in the brain-mind. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the "bottom" to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the "top" of mental hierarchy. Knowledge instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. In this paper we are trying to formalize cognitive processes of the human mind using approaches above, and provide interfaces that could allow for their practical realization in software and hardware. Future research directions are discussed.

Systems engineering is a critical core competency for successful NASA missions. This monograph summarizes the collective wisdom of some of NASA's best technical minds on the subject. So here the word "we" represents all contributors to this effort: Michael Bay, Bill Gerstenmaier, Mike Griffin, Jack Knight, Wiley Larson, Ken Ledbetter, Gentry Lee, Michael Menzel, Brian Muirhead, John Muratore, Bob Ryan, Mike Ryschkewitsch, Dawn Schaible, Chris Scolese, and Chris Williams. Among them, they have more than 390 years — almost four centuries — of experience in aerospace and systems engineering.

Burnout phenomenon emerges from a constellation of factors which cannot be described in terms of cause-effect relationships. This study investigated levels of burnout in nurses working in Critical Care Units with a systemic approach, giving evidence of relation between nurses staff burnout and psychosocial workplace factors. The purpose of this study was to examine the relationship between job burnout in emergency and intensive care nurse with specific areas of work life in their organizations, using Maslach and Leiter work life model [23].

A cross-sectional survey was designed using the Italian version of the "Organizational Checkup System" in a sample of 180 Italian nurses.

Results showed that high burnout levels were strongly related to high demands, low control, low fairness, lack of social support, and individual disagreement on values in the workplace. High professional efficacy levels were instead correlated to professional reward and leadership involvement.

The article concludes by suggesting the possible areas for intervention in order to prevent job burnout and building job engagement.

This chapter provides guidance for solving practical, high-level management and policy challenges in sustainability and disaster resilience. These two fields must be considered together so that they do not work at cross-purposes. Sustainability is framed in a positive and useful way that transcends shallow and self-serving treatments that are all too common. Although sustainability is a multi-faceted problem, climate change is the focus because it is globally important and because it is particularly troublesome due to its global and long-term scale. The discussion on sustainability highlights the challenge of extreme uncertainty. Knowledge of system complexity is necessary for understanding and contending with extreme uncertainty. Thus, this chapter summarizes some fundamental knowledge and draws from it recommendations for decision-making. An example illustrates the suggested approach and provides additional insight. Complex systems are hard to understand and no course of action is guaranteed to be successful. However, without systems thinking, failure is almost assured. The recommendations in this chapter, although not infallible, will help find effective ways to intervene in societal systems to meet stated objectives while avoiding unintended consequences.