Improved Light Extraction Efficiency in AlGaInP Light-Emitting Diodes by Applying a Periodic Texture on the Surface

In this letter, AlGaInP-GaP-based light-emitting diodes (LEDs) were fabricated with an Si substrate and an SiO₂-ITO-Ag omni-directional reflector using a metal-to-metal bonding technique. To enhance light extraction efficiency, a periodic texture was applied to the (Al_0.5Ga_0.5)_0.5In_0.5P surface layer of AlGaInP-Si LEDs by photolithography and a wet etching process. The exterior of the etched texture consists of a series of bowl-shaped recesses. With a 350-mA current injection, the typical output power of the AlGaInP-Si LEDs with and without the textured surface (LED-I and LED-II, respectively) were measured at approximately 118 and 81 mW, respectively, when the LED chips were bonded on the TO 46 without resin encapsulation. The enhancement of output power in LED-I can be attributed to a multitude of bowl-shaped notches on the surface, which resulted in a reduction of the reabsorption probability of the photons due to the fact that the photon path length in LED-I is shorter than in LED-II before the photons escape into the free space.     

Phosphor-Free GaN-Based Transverse Junction White-Light Light-Emitting Diodes With Regrown n-Type Regions

In this study, we demonstrate a GaN-based phosphor-free white-light light-emitting diode (LED), which is composed of GaN-based dual-wavelength (blue and yellow-green) multiple-quantum-wells (MQWs) and a transverse p-n junction. The device was realized by the regrowth of n-type GaN layers on the sidewall of p-type GaN and undoped MQWs. The problems related to the bias-dependent shape of the electroluminescence spectra that occur in traditional phosphor-free white-light LEDs (with vertical p-n junctions) are greatly minimized. The current-voltage performance of our device is comparable to that of the commercially available phosphor white-light LEDs. In addition, the dynamic measurement results indicate that we can attain a much higher modulation bandwidth (22 versus 3 MHz) with this device than with the currently available commercial ones.     

A Distributed Localization Scheme for Wireless Sensor Networks with Improved Grid-Scan and Vector-Based Refinement

Localization is a fundamental and essential issue for wireless sensor networks (WSNs). Existing localization algorithms can be categorized as either range-based or range-free schemes. Range-based schemes are not suitable for WSNs because of their irregularity of radio propagation and their cost of additional devices. In contrast, range-free schemes do not need to use received signal strength to estimate distances and only need simple and cheap hardware, and are thus more suitable for WSNs. However, existing range-free schemes are too costly and not accurate enough or are not scalable. To improve previous work, we present a fully distributed range-free localization scheme for WSNs. We assume that only a few sensor nodes, called anchors, know their locations, and the remaining (normal) nodes need to estimate their own locations by gathering nearby neighboring information. We propose an improved grid-scan algorithm to find the estimated locations of the normal nodes. Furthermore, we derive a vector-based refinement scheme to improve the accuracy of the estimated locations. Analysis, simulation, and experiment results show that our scheme outperforms the other range-free schemes even when the communication radius is irregular.     

Learning Association Rules and Tracking the Changing Concepts on Webpages: An Effective Pornographic Websites Filtering Approach

We applied the decision tree algorithm to learn association rules between webpage’s category (pornographic or normal) and the critical features. Based on these rules, we proposed an efficient method of filtering pornographic webpages with the following major advantages: 1) a weighted window-based technique was proposed to estimate for the condition of concept drift for the keywords found recently in pornographic webpages; 2) checking only contexts of webpages without scanning pictures; 3) an incremental learning mechanism was designed to incrementally update the pornographic keyword database.     

Integrating multivariate engineering process control and multivariate statistical process control

Multivariate engineering process control (MEPC) and multivariate statistical process control (MSPC) are two strategies for quality improvement that have developed independently. MEPC aims to minimize variability by adjusting process variables to keep the process output on target. On the other hand, MSPC aims to reduce variability by monitoring and eliminating assignable causes of variation. In this paper, the use of MEPC alone is compared to using the MEPC coupled with MSPC. We use simulations to evaluate the average run lengths (ARL) and the averages of the performance measure. The simulation results show that the use of both MEPC and MSPC can always outperform the use of either alone. To detect small sustained shifts of the mean vector, combing MEPC with a multivariate generally weighted moving average (MGWMA) chart (MEPC/MGWMA) is more sensitive than the MEPC/multivariate exponentially weighted moving average (MEWMA) chart and MEPC/Hotelling’s χ² chart. An example of the application, based on the proposed method, is also given.     

Generally weighted moving average control chart for monitoring process variability

Most applications of the EWMA control chart for monitoring processes depend on detecting shifts in the process mean. The problem of detecting an increase in process variability, which can also strongly affect the quality of products, is perhaps more important. When a process moves from the pilot phase to the production phase, the mean may not shift but the variation will probably increase because new sources of variation are introduced, including new people and materials. A simulation is performed to evaluate the ARL to false alarm and to monitor the change in the process variability of the EWMA control chart and the GWMA control chart. An extensive comparison reveals that the GWMA control chart is more sensitive than the EWMA control chart in monitoring the variance of a process. The results of this study can be applied to monitor the process variability in automated industries.     

Forecasting the volatility of a combined multi‐country stock index using GWMA algorithms

Globalization has increased the volatility of international financial transactions, particularly those related to international stock markets. An increase in the volatility of one country's stock market spreads throughout the globe, affecting other countries' stock markets. In particular, the Dow Jones Industrial Average plays an extremely important role in the international stock market. This paper uses the generally weighted moving average method and data from the Dow Jones Industrial Average, the National Association of Securities Dealers Automated Quotations, Japan's Nikkei 225, the Korea Composite Stock Price Index, and the Hong Kong Hang Seng Index to predict the performance of the Taiwan Capitalization Weighted Stock Index. This paper attempts to find the smallest prediction error using the optimal combination of generally weighted moving average model parameters and combinations of various international stock market data and compares the results to that found using the exponentially weighted moving average model to explore differences between the two types of forecasting models.     

Colossal Barocaloric Effect by Large Latent Heat Produced by First-Order Intersite-Charge-Transfer Transition

Materials which show novel thermal properties can be used to make highly efficient and environmentally friendly energy systems for thermal energy storage and refrigeration through caloric effects. An A-site-ordered quadruple perovskite-structure oxide, NdCu₃Fe₄O₁₂, is found to release significant latent heat, 25.5 kJ kg⁻¹ (157 J cc⁻¹), at the intersite-charge-transfer transition temperature near room temperature. The transition is first-order and accompanied by an unusual magnetic ordering and a large negative-thermal-expansion-like volume change, and thus, it causes a large entropy change (84.2 J K⁻¹ kg⁻¹). The observed entropy change is comparable to the largest changes reported in inorganic solid materials, and more importantly, it is utilized through a colossal barocaloric effect. The adiabatic temperature change by applying 5.1 kbar pressure is estimated to reach 13.7 K, which means efficient refrigeration can be realized through this effect.     

An agent‐based workflow scheduling mechanism with deadline constraint on hybrid cloud environment

Summary

With the advances of cloud computing, business and scientific‐oriented jobs with certain workflows are increasingly migrated to and run on a variety of cloud environments. These jobs are often with the property of deadline constraint and have to be completed within limited time. Therefore, to schedule a job with workflow (short for workflow) with deadline constraint is increasingly becoming a crucial research issue. In this paper, we, based on previous work, propose an agent‐based workflow scheduling mechanism to schedule workflows that are with deadline constraint into federated cloud environment.

Design and Methods

We add a workflow agent into the original framework to schedule the deadline‐constraint workflow. The workflow agent can smoothly schedule workflows to the cloud system according to their required resource and automatically monitor their execution. In order to accurately predict the execution time of each task to meet deadline constraint on certain VM with given resource, we inherit the use of rough set theory to estimate execution time of task in our previous work.

Result and Discussion

A heuristic algorithm that is embedded into the workflow agent is also proposed because the problem had been shown to be NP‐complete. The mechanism also adopts dynamic job dispatching method to reduce the usage of VM and to improve the resource utilization. We also conducted experiments to evaluate the efficiency and effectiveness.

Conclusion

The experimental results show that the prediction time is very close to the real execution time and can efficiently schedule multiple scientific workflows to meet the deadline constraints simultaneously.     

The associate impact of individual internal experiences and reference groups on buying behavior: A case study of animations,comics, and games consumers

Among the entertainment and media market, it can be observed that animations, comics, and video games (hereinafter abbreviated as ACG) have the highest output value and most market influence. Moreover, ACG also incorporates various industries and creates many derivative products. As the ACG industry emphasizes acousto-optics, imagery, and storylines, personal impressions derived from consumer experiences will influence consumer decisions. In addition, the ACG industry is mainly marketed towards younger age groups, with younger people being the main consumers; as such, these consumers’ decisions are more easily affected by peer behavior.This study aims to analyze the effects of internal cognitions and external influences on buying behavior of ACG consumers by applying the uncomplicated decision tree data mining algorithm. We analyze and develop the target attributes on measures of customer loyalty for ACG industry to set up the decision trees from the collected questionnaire data. The decision tree data mining method is applied to analyze the hidden association rules between the target attributes (i.e., consumer loyalty) and the critical influencing factors of consumer’s internal impressions and external influences for ACG consumers. The results and suggestions of this paper can be used as a reference for enterprises in the ACG industry to help with business policies concerning products’ extensional design, marketing, and CRM, and to further strengthen customer satisfaction and loyalty, thus increasing company profits.