Occurrences of traffic congestions within the urban traffic network are increasing in a rapid rate due to the rising traffic demands of the outnumbered vehicles on road. The effectiveness of management from traffic signal timing planner is the key solution to solve the traffic congestions, but unfortunately the current traffic light signal system is not fully optimized based on the dynamic traffic conditions on the road. Adaptable traffic signal timing plan system with ability to learn from their past experiences is needed to overcome the dynamic changes of the urban traffic network. The ability of Qlearning to prospect gains from future actions and obtain rewards from its past experiences allows Q-learning to improve its decisions for the best possible actions. A good valuable performance has been shown by the proposed learning algorithm that able to improve the traffic signal timing plan for the dynamic traffic flows within a traffic network.

Economic use of early stage prototyping is of paramount importance to companies engaged in the development of innovative products, services and systems because it directly impacts their bottom-line [1, 2]. There is likewise a need to understand the dimensions and lenses that make up an economic profile of prototypes. Yet, there is no reliable understanding of how resources expended and views of dimensionality across prototyping translate into value [3, 4]. To help practitioners, designers, and researchers leverage prototyping most economically, we seek to understand the tradeoff between design information gained and the resource expended into prototyping to gain that information [5]. We investigate this topic by conducting an inductive study on industry projects across disciplines and knowledge domains, while collecting and analyzing empirical data on their physical prototyping process [3]. Our research explores ways of quantifying prototyping value and reinforcing the asymptotic relationship between value and fidelity [6]. Most intriguingly, it reveals insightful heuristics that practitioners can exploit to generate high value from low and high fidelity prototypes alike.

Economic use of early-stage prototyping is of paramount importance to companies engaged in the development of innovative products, services, and systems because it directly impacts their bottom line. There is likewise a need to understand the dimensions, and lenses that make up an economic profile of prototypes. Yet, there is little reliable understanding of how resources expended and views of dimensionality across prototyping translate into value. To help practitioners, designers, and researchers leverage prototyping most economically, we seek to understand the tradeoff between design information gained through prototyping and the resources expended prototyping. We investigate this topic by conducting an inductive study on industry projects across disciplines and knowledge domains while collecting and analyzing empirical data on their prototype creation and test processes. Our research explores ways of quantifying prototyping value and reinforcing the asymptotic relationship between value and fidelity. Most intriguingly, the research reveals insightful heuristics that practitioners can exploit to generate high value from low and high fidelity prototypes alike.

Process control and optimization is important in ensuring a process in sustaining profitable income while maintaining the required quality. The fermentation process has attracted attention from food processing, pharmaceutical energy, and waste treatment industries due to its lower impact on the environment and lower operating cost than the conventional chemical process. However, its control and monitoring are relatively complex as living cell activity is not easily comprehended and highly nonlinear. Various control techniques applied in the fermentation process are reviewed, including a summary of the control objective, manipulated and controlled variables chosen, and their strengths and limitations in determining the optimal fermentation process.

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