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This research constructs an experimental system for upper limb stroke ill persons. It can apply to any dwelling for patient practices. The idea of stroke rehabilitation system is applied International Classification of Functioning, Disability and Health structure and includes Barthel scale for ill person's health evaluation. These systems are designed by IOT and Android APP system to control the rehabilitation system. The findings demonstrate that it can work effectively for the patients at home. This study can collect patients' data from every day rehabilitation and send these data to medical cloud. These data can be analyzed statistically and converted to Microsoft Excel files to drop bar charts for visually display. The system reports can upload to medical institution cloud computing systems for doctors' reference. This study demonstrates the implementation of results and provides program codes for readers' reference.

Personal financial planning involves managing all the money activities during a planner's lifetime. Traditional personal financial planning procedures begin with the planner's financial status, goals, and expectations for the future before future cash flows of different time periods under various scenarios can be determined. If the planning results fail to meet the planner's expectation, the planner adjusts tunable parameters repeatedly until an acceptable financial arrangement can be obtained. Such a ''trial-and-error approach'' or ''what-if analysis'' does not promise to achieve the optimal plan while numerous outcomes burden the planner. Multiple objectives with different goals of different importance levels might be involved in this decision-making problem. Since the objectives tend to conflict with each other, this study proposes to solve the problem based on a decision model that incorporates a fuzzy multiple objective programming method to achieve better solutions than using ''trial-and-error''.

Corporate competence sets can be expanded through capital investment and be dynamically changed overtime, which can explain the phenomenon of "taking loss at the ordering time and making profit at the time of delivery". Such phenomenon has existed in practice for a long time, but there are no mathematical model that can explain it adequately. This paper utilizes multiple criteria and multiple constraint levels linear programming (MC2LP) model and its extended techniques to explore the linear programming models with changeable parameters. The parameters include: unit profit, available resources, and input-output coefficients of production function. With those parameters changed with capital investment and/or time, we study how to find dynamic best solutions to make "taking loss at the ordering time and making profit at the time of delivery" feasible. For more general cases we also sketch a generalized mathematical programming model with changeable parameters and control variables.

There are many parameters in challenging decision problems, including the alternatives, the criteria, resources, the perception of decision problems, decision makers and their psychological states, information inputs from the environment, and self-suggestion, etc. At any moment of time, some of these parameters can catch our attention, called alerted parameters; some cannot, called unalerted parameters. Some parameters are visible, some are invisible. In addition, the parameters themselves can vary over certain ranges or domains. All of these make challenging decision problems very complex. We call this kind of problems as decision problems with changeable spaces (parameters). We may focus on certain parameters with certain assumed values to find an "optimal" solution, which may lead to solve wrong problem with bad solution. Quite often, our focus may be just a small part of what we know, or just a part of what we are most familiar with. We may often neglect what we are not familiar with, and pay no attention to what we do not know. As a consequence, we may see just a small part of the problem domain (including all parameters and their possible variations over time). The portion (of the problem domain) that we cannot see is our decision blind. Suppose our alerted domain (those parameters and their variations that are currently under our consideration) to be fixed in only a small part of the problem domains. Then very likely we could end up with a serious mistake. This situation is known as decision trap. In this article, we will introduce a systematic scheme, based on habitual domain theory, to help us reduce decision blinds and avoid decision traps so that we could make decision with good quality. Then we will also introduce the concept of competence set analysis to help us cope with challenging decision problems. This including: (i) how to effectively expand our competence (resources, skill, know-how, information, ideas, effort, etc.) as to solve a given problem effectively; and (ii) given a set of competence, how to maximize its value by solving a set of value added problems. Furthermore, we will introduce innovation dynamics which describe the dynamics of how to solve a set of problems with our existent or acquired competence (to relieve the pains or frustrations of "certain customers or decision makers" at certain situations) as to create value, and how to distribute this created value so that we can continuously expand out competence set to solve more challenging problems and create more value.

In this article, we show that, in high-tech industries, there are significant differences in the Habitual Domains (HD) of technologists/researchers (T/R) and managers/leaders (M/L). The differences are measured specifically in the following: characteristics, attitudes toward career and life, perception of business problems, business competences and resources. We then describe how a T/R can effectively transform himself/herself into a successful M/L, by transforming his/her HD closer to that of a successful M/L.

About ten years ago, nobody could image individual investor can complete overall investment procedure with abundant information resources and on-line transaction indoor at unbelievable low cost just after 10 years later. Thanking for the low-price computer, convenient internet with wide bandwidth, and various analysis tools for asset valuation with open source code, the dream of indoor financial investment come true. In this paper, we describe how an indoor investor can establish his analysis and trading platform by the software and data, historical and real-time, provided on internet at low cost and how the information resource can be activated by end user computing capability. In addition, we discuss the opportunities for indoor investors outperform the market.

There exists a behavior mechanism which continuously allocates our attention to various events. Broadly classified, there are seven goals in our life: survival and security, perpetuation of the species, feelings of self-importance, social approval, sensuous gratification, cognitive consistency and curiosity, and self-actualization. For each goal, there is an ideal value or equilibrium point to pursue and/or maintain. If there is a significant discrepancy of current status from the ideal or equilibrium point, a charge (mental pressure) will be produced. At any moment, the totality of all charges created by all goals from all events is called “the charge structure” at that moment. Our mind will try to allocate the attention and resources to reduce the charge to a minimum level. The goals that catch our attention are awakened; otherwise unawakened. The priority for the goals to get our attention follows a dynamic scheme of multiple goals optimization. In this paper, we shall describe and illustrate the dynamic multiple goals optimization, a basic framework of behavior mechanism. Applications to vast decision making problems, especially the challenging ones, will also be mentioned. The mechanism will open up our minds as to make decisions more effectively.

This paper applies the neural network method to establish an index arbitrage model and compares the arbitrage performances to that from traditional cost of carry arbitrage model. From the empirical results of the Nikkei 225 stock index market, following conclusions can be stated: (1) The basis will get enlarged for a time period, more profitability may be obtained from the trend. (2) If the neural network is applied within the index arbitrage model, twofold of return would be obtained than traditional arbitrage model can do. (3) If the T_basis has volatile trend, the neural network arbitrage model will ignore the peak. Although arbitrageur would lose the chance to get profit, they may reduce the market impact risk.