The fastest way to obtain a large operating range is with a good design, with a correct blade loading distribution providing a negative slope of the pressure rise curve. The outcome of different optimizations is illustrated in Figure 9.1 and shows that maximizing the operating range may be at the expense of a lower efficiency.

So far we have only considered energy interactions between rays and surfaces. Two possible outcomes – absorption and reflection – have been identified, and corresponding models have been formulated and used to describe radiation heat transfer among surfaces. When a ray incident to a surface is neither absorbed nor reflected, it is said to be transmitted. Thermal radiation passing through lenses, filters, and the earth’s atmosphere are familiar examples of transmission. This chapter deals with emission, absorption, refraction, and scattering of thermal radiation within a medium through which it is propagating. Such media are said to be “participating.”

Normally, optimization seeks to minimize or maximize a deterministic single value. However, many situations deal with probability of events, which are the result of uncertainty or environmental vagaries. The objective is to minimize some statistic related to the process, procedure, or product. The statistic could be variance of a quality metric, quantity of off-spec material that results from manufacturing variability, an undesired event probability, system reliability, economic risk, etc. These objectives could be to minimize the 95% worst outcome, maximize the 99% minimum outcome, minimize variance, etc.

In routine applications, the legacy of solving the optimization and using the results will have given customers and implementers a good understanding of the context of the application and the appropriate choices for the models OF, DV, convergence criteria, thresholds, and constraints. However, for new applications, the implementer must design these procedures and must make the appropriate choices. The question is “How?” The answer is “trial and error.” Try something, and when you have the result, consider all aspects of the outcome. Then evaluate whether all aspects are satisfactory. If not satisfactory, improve the procedure.

Most engineering or science models provide a continuous-valued, deterministic response. These could either represent steady-state or transient phenomena. In contrast, models could predict a classification (nominal, class, text, or string variable) or a rank, but still a deterministic value. Alternately, Monte Carlo simulations predict a stochastic outcome, a range of possibilities, not a definitive value. There are diverse options to what you may be seeking to best fit, and you need to understand the application to choose the regression target.

Fulfilled people are rare in this world. Rather, people spend their lives dealing with stress, anxiety, sickness, and so on. From my pool of more than 350 subjects, I found people who had an SI near one were rare. In short, those who have the willingness to do something, or are highly motivated, and are active during their EKGs, generally have an SI close to one (1/f rhythm). The rate was less than half (Table 5-1, Figure 5-5). So many people exhibited a lower SI. I have recorded the heartbeats of people from Iran, Indonesia, Japan, America, Canada, Australia, Germany, Russia, and so forth. And SI more or less indicates a person's state of health. Specifically, unhappy states were detectable through a combination of interviews and mDFA. When interviewing people, I observe and make notes, then interpret the mDFA results later. But this is a hard task. I hope someone invents an mDFA device that would make it easier to manage daily life. The SI is a numerically expressed quantitative measure, the scaling exponent, which is the outcome of mDFA computation, which was first innovated by people such as Peng, Stanley, Goldberger, Ivanov, Glass, and others. 6.1 Discussion 6.2 Spatial and Temporal Dynamical Structure in Biology 6.3 Necessity of Physics and Mathematics in Biology

Is it worthwhile to know the future ? Perhaps it is, for knowing the future could help to avert a range of negative outcomes, from avoiding small embarrassing situations to preventing major catastrophic disasters. Unfortunately, knowing the future is a competency that escapes (most) humans. We cannot overcome the consequences of unknown potential problems simply because we cannot know the future.

Advocacy for educational Planning for the Future as an exercise in mining the best corporately-held mechanical engineering intelligence through a four-step process has been presented in previous chapters. This process can be enriched through the use of scenarios and thematic gap analyses. As a part of planning for the future of mechanical engineering, this chapter provides guidance in improvement work so that knowing what to think as best educational knowledge is connected in a congruent way to knowing what to do as best educational practice. The relationship between both knowledge and practice in mechanical engineering education is posited as a mental model for planning for the future. Further, in this chapter relevant target outcomes designed to measure student development related to the three areas of competency development, systems, sustainability, and ethics, are presented as rubrics. These target outcomes are derived from the stakeholder needs of employers, students, faculty and society, for early engineering success as described in Chapter Two.

In mechanical engineering education the practical application of theory proves invaluable in situations that require results. The foundation of a good education lies in the student’s ability to apply learning effectively, combining both content and competency mastery target outcomes to acquire relevant deliverables in the workplace.

In the previous chapter we outlined a radical shift in the way people's knowledge and experiences are put to use in the 21st century. This shift is a rational and logical outcome of the developments in certain areas of technology—particularly in DT—and their widespread application, effective use, and relentless deployment. Investors have also benefited from this wave and a new mode of investment, Global Capitalism, has evolved.

Our theme throughout this book can be summarized as “self-empowerment” for technical professionals through a set of Transformational Skills. These Transformational Skills are proposed to help them identify opportunities and develop and deploy them into New Solutions. In Chapter 5, we addressed the specific skills that professionals can acquire to identify opportunities by emphasizing a common language as well as scouring their field of activity for gaps in core capabilities and filling such gaps. We presented in Chapter 6 a set of enablers for professionals to enhance their effectiveness in developing New Solutions by aggregating resources and integrating knowledge from many sources. Armed with these skills, the professional becomes a system thinker and an integration engine, combining the essential resources from long reaches—his/her department, the company, suppliers, as well as end users. The entire world becomes the professional's working field. But all these skills and capabilities yield positive results only when the outcome is clear, tangible, and useful for the largest number of beneficiaries. The larger the user or beneficiary pool, the greater the reward. This is the transition point where the professional—a knowledge worker—transforms from an effective player in a limited playing field into an influential resource: a Global Intellect.

Information is intelligence that pertains to satisfying specific needs for learning and enlightenment. The specific dependability information needed depends on the relevancy of the information and the circumstances of use. Timing and availability of information are key factors of its usefulness, and sometimes they may impact decision outcomes due to inadequate information input or resulting from too much information, which can lead to unnecessary confusion. Dependability information is derived from data collected from various dependabilityrelated sources, especially on items used in the product or the system of interest. The classification of data plays an important role in managing a credible dependability database. Sound decision-making helps attain organizational goals and achieve dependability value.

Value is the relative worth of something desirable or significant. The worth of something may be expressed as monetary or material worth, or interpreted as its usefulness or importance to a process outcome. The economic value of an item reflects the value of its use or utilization in terms of goods and services. The exchange value of an item is determined by the market value and affected by supply and demand. In this respect, value is linked to price. In business, as Warren Buffett, the American investment entrepreneur, once said: “Price is what you pay. Value is what you get.”

The purpose of this research paper is to identify the potential success factors of ERP implementation in small and medium-sized enterprises (SMEs) in Malaysia and identify a priority order of those success factors. The approach of this investigation is empirical where data was collected in March 2011 in Malaysia using questionnaire from SMEs in Malaysia who have ERP experience in implementation. The research identifies success factors that are more important in implementing ERP in SMEs in Malaysia. This research extracted 22 success factors with higher degree of importance for implementation. The result shows that “top management awareness”, “financial budget during implementing”, “clear goals and objectives ERP strategy”, and also “implementation methodology” are the top five factors of importance. Although the study is limited to SMEs in Malaysia, and many of the small enterprises who either did not implement ERP or some form of that for being high cost or even are not informed of ERP exactly, the investigators attempt to achieve reasonable outcomes. Overall, the research identifies success factors of ERP implementation in SMEs in Malaysia.

The classical problem of a single consensus ranking determination for m rankings of n alternatives has a potential of wide applications in information technologies, and particularly in measurement and instrumentation. The Kemeny rule is one of deeply justified ways to solve the problem allowing to find such a linear order (Kemeny ranking) of alternatives that a distance (defined in terms of a number of pair-wise disagreements between rankings) from it to the initial rankings is minimal. But the approach can give considerably more than one optimal solutions what can reduce its applicability. On computational experiments outcomes, the paper demonstrates that a set of Kemeny rankings cardinality can be extremely large even in small size cases ( m = 4 , n = 15 … 20 ) and, consequently, special efforts to build an appropriate convoluting solution are needed.

According to the rapid pace of knowledge growth, universities try to improve their teaching method to enhance their student's knowledge level and apply some techniques to facilitate learning one of this new technique is e-portfolio. But Sometimes user's requirement as an important factor was ignored by designer and there is no motivation for students to use e-portfolio. The outcome of this study is a framework which e-portfolio expert could use as a guide line to design an e-portfolio toward user's requirement.

Blended learning based on lecture videos and face-to-face teaching provides good conditions for students as far as availability of teaching and participation in education are concerned. Use of lecture videos in higher education is already quite common. Lecture videos are well suited for increasing the flexibility in teaching as they provide a diversity of opportunities to participate in education regardless of time and place. This paper examines the motivation for the use of blended learning and looks at its effects on students' learning outcomes in master's studies in information technology. According to the results obtained, the main motivation for the use of videos is to compensate for occasional absences. The results indicate that the way students participate in lectures also has its effects on participation activity. Those students who combine both lecture videos and face-to-face education in their participation modes participate more actively in lectures and thus also are the most successful in completing the courses.

Data mining technology has been used widely for business, banking, and telecommunication purposes. Lately it is not uncommon this technology used for other fields. This paper outline the novel knowledge discovery process model known as hybrid model designed purposely for educational environment. This hybrid model consists of six steps; understanding of educational domain problem, understanding educational data, educational data pre-processing, educational data mining, evaluation of the discovered knowledge and use of the discovered knowledge. The outcome of this paper shows the new method of discovering new knowledge from educational data.

Online responses in multi-criteria decision making (MCDM) often contain inconsistencies caused by variations in the mood, attention, or mental state of the respondents. Such inconsistent responses in online questionnaires often lead to undesired outcomes. The initial research motivation was to resolve the shortcomings of conventional approaches in their diminishing efficiency when the number of criteria increases. In order to detect possible inconsistencies more efficiently, a qualitative triad comparison technique based on the rules derived from the properties of transitivity was proposed, in contrast to the consistency ratio computation technique used by the conventional quantitative approaches. The initial research attempt was on the experimentation of a more efficient comparison technique by comparing random responses three at a time, instead of computing the consistency ratio upon receiving all the responses. It was then found that the respondents tend to lose concentration when they are required to answer a large number of questions. In order to lighten the mental burden of the respondents, further research was then conducted to reorder the questions based on the optimal triads formation rules. Although the research outcomes appeared to have added a significant value to the conventional approaches, both the techniques were used only upon completion of the questionnaire. The research work was then directed to a possible detection at an earlier stage. It was then found that the detection could start as early as after receiving (n-1) responses, among a total of n(n-1)/2 responses. Subsequent to the (n-1) th response, detection would then be automatic, for each response. It is then argued that the proposed qualitative triad comparison approach for specially sequenced questions is necessary to automatically detect the logically inconsistent responses after receiving (n-1) responses, especially when more than half a dozen criteria are involved.

This study extracted 12 quality constructs from a questionnaire with 18 assessment indicators on company PDDRO (Plan, Design, Do, Review and Outcome) in the context of the TTQS (Taiwan TrainQuali Scorecard). The questionnaires were issued to TTQS consultants, corporate executives who had been trained in TTQS and colleague lecturers who had used Internet learning platforms in their teaching activities, in order to evaluate the level of importance of their expectations for the quality constructs of online learning platforms. The questionnaire contained paired questions (one positive question and one negative question) to divide the quality constructs into the quality attributes of the Kano Model. The purpose was to compare the variances in the subjects' feedback concerning these quality constructs in the context of customer attributes. Finally, this model integrated the Kano Model and TTQS indicators in the R&D of a new online learning platform.