Effects of individuals' locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies

High-tech companies encounter intense competition in today's global economy. With rapid changes in working environments, high-tech employees must learn quickly and effectively to solve difficult problems and increase their productivity. Many large high-tech companies have recently implemented electronic learning (e-learning) for employee training. However, e-learning systems are expensive and often underutilised. Therefore, understanding the factors associated with acceptance to e-learning are of priority concern. By integrating locus of control, computer self-efficacy and technology acceptance model (TAM) into one model, this study examines the feasibility of the extended TAM to explain employee acceptance of e-learning systems. Data were collected from 223 employees at five high-tech companies located in the Hsinchu Science Park, Taiwan. Analytical results indicate that locus of control had significant direct effects on perceived usefulness and perceived ease of use. Computer self-efficacy had significant direct effects on perceived ease of use and behavioural intention to use. Overall, analytical results provide strong support for using the extended TAM to explain user acceptance of e-learning systems. The research and practical implications of findings are discussed.     

Association-Based Active Access Control models with balanced scalability and flexibility

In existing Active Access Control (AAC) models, the scalability and flexibility of security policy specification should be well balanced, especially: (1) authorizations to plenty of tasks should be simplified; (2) team workflows should be enabled; (3) fine-grained constraints should be enforced. To address this issue, a family of Association-Based Active Access Control (ABAAC) models is proposed. In the minimal model ABAAC₀, users are assigned to roles while permissions are assigned to task-role associations. In a workflow case, to execute such an association some users assigned to its component role will be allocated. The association's assigned permissions can be performed by them during the task is running in the case. In ABAAC₁, a generalized association is employed to extract common authorizations from multiple associations. In ABAAC₂, a fine-grained separation of duty (SoD) is enforced among associations. In the maximal model ABAAC₃, all these features are integrated, and similar constraints can be specified more concisely. Using a software workflow, case validation is performed. Comparison with a representative association based AAC model and the most scalable AAC model so far indicates that: (1) enough scalability is achieved; (2) without decomposition of a task, different permissions can be authorized to multiple roles in it; (3) separation of more fine-grained duties than roles and tasks can be enforced.     

Explicit solution of relative entropy weighted control

We consider the minimization over probability measures of the expected value of a random variable, regularized by relative entropy with respect to a given probability distribution. In the general setting we provide a complete characterization of the situations in which a finite optimal value exists and the situations in which a minimizing probability distribution exists. Specializing to the case where the underlying probability distribution is Wiener measure, we characterize finite relative entropy changes of measure in terms of square integrability of the corresponding change of drift. For the optimal change of measure for the relative entropy weighted optimization, an expression involving the Malliavin derivative of the cost random variable is derived. The theory is illustrated by its application to several examples, including the case where the cost variable is the maximum of a standard Brownian motion over a finite time horizon. For this example we obtain an exact optimal drift, as well as an approximation of the optimal drift through a Monte-Carlo algorithm.     

Data-driven asymptotic stabilization for discrete-time nonlinear systems

In this paper, we propose a data-driven feedback controller design method based on Lyapunov approach, which can guarantee the asymptotic stability of the closed-loop and enlarge the estimate of domain of attraction (DOA) for the closed-loop. First, sufficient conditions for a feedback controller asymptotically stabilizing the discrete-time nonlinear plant are proposed. That is, if a feedback controller belongs to an open set consisting of pairs of control input and state, whose elements can make the difference of a control Lyapunov function (CLF) to be negative-definite, then the controller asymptotically stabilizes the plant. Then, for a given CLF candidate, an algorithm, to estimate the open set only using data, is proposed. With the estimate, it is checked whether the candidate is or is not a CLF. If it is, a feedback controller is designed just using data, which satisfies sufficient conditions mentioned above. Finally, the estimate of DOA for closed-loop is enlarged by finding an appropriate CLF from a CLF candidate set based on data. Because the controller is designed directly from data, complexity in building the model and modeling error are avoided.     

On control of discrete-time state-dependent jump linear systems with probabilistic constraints: A receding horizon approach

In this article, we consider a receding horizon control of discrete-time state-dependent jump linear systems, a particular kind of stochastic switching systems, subject to possibly unbounded random disturbances and probabilistic state constraints. Due to the nature of the dynamical system and the constraints, we consider a one-step receding horizon. Using inverse cumulative distribution function, we convert the probabilistic state constraints to deterministic constraints, and obtain a tractable deterministic receding horizon control problem. We consider the receding horizon control law to have a linear state-feedback and an admissible offset term. We ensure mean square boundedness of the state variable via solving linear matrix inequalities off-line, and solve the receding horizon control problem on-line with control offset terms. We illustrate the overall approach applied on a macroeconomic system.     

DCS控制系统在粗苯蒸馏工艺中的应用

针对传统粗苯蒸馏工艺人工调节频繁,调节不及时、不准确,产品质量不稳定,劳动负荷大等问题,通过引入DCS控制系统,建立计算机自动调节模型,调整函数的微分和积分变量实现脱苯塔塔顶回流量、进再生塔的蒸汽量和管式加热炉富油出口温度的自动控制,从而实现粗苯生产工艺的稳定、高产、优质和低耗。     

直播稻田化学除草试验

直播稻田用不同药剂在播种后各一次化除或播种后两次化除都具有很好的除草效果。播后前期是化学除草的关键时期播后前期每６６７米＾２用１０％苄嘧黄隆１５克＋６０％丁划胺６０毫升，１０％苄嘧黄隆１５克＋５０％杀草丹１５０毫升，１７．２％新幼禾葆２４０克或１５．８％幼禾葆２００克防铲均较好。     

焦炉集气管压力专家协调控制

针对压力对象相互耦合的实际特点，提出了基于专家系统的集成管压力协调控制方法，使系统在PID控制的基础上，按实际运行状况智能选择控制策略。     

工程造价管理改革刍议

工程造价管理改革就是改革现行的定额、费率和价格，定额从指令性变为参考性，费率自主确定，价格市场化；推行监理制，采用FIDIC的合同管理方法和工程量表的做法。     

离子膜碱浓缩装置的比较

从工艺流程、设备材质、操作、汽耗等方面对双效逆流降膜浓缩装置和双效逆流板式蒸发器浓缩装置进行了比较,结果表明两种浓缩装置各有特点。