Does RFID improve firms’ financial performance? an empirical analysis

Radio frequency identification (RFID) is viewed as a technology that improves supply chain efficiency by enhancing inventory efficiency, optimizing logistics, and coordinating the flow of materials. Although RFID has gained great attention in many business applications, the financial gain that accrues over time from RFID adoption is not well understood. We examine the effects of RFID on firm profits while adjusting for self-selection of adoption choice. We find that firms self-select into a certain adoption mode on the basis of their organizational characteristics. Our results also show that RFID confers significant benefits for firms that have adopted RFID. Interestingly, improved inventory ratio and sales efficiency begin to play a greater role in shaping higher profitability over time for firms that have adopted RFID possibly due to time-consuming processes for them to reap the benefits from RFID. However, we find that the values of RFID that accrue to firms are not universal across firm. That is, our results suggest that RFID confers a significant value for certain firms while it does not for other firms with unobservable disadvantages. In sum, our study sheds new light on what drives firms to adopt RFID and on which firms achieve higher financial performance in a post-adoption period as a result of RFID adoption.     

Inexpensive high availability solutions for the SIP-based VoIP service

In the era of IP-based service, people expect a simple, cheap, and competent Voice over IP (VoIP) service as an alternative of the traditional voice over PSTN. The introduction of the SIP protocol realizes the expectation. Following the cost saving spirit of VoIP, we focus on studying inexpensive high availability solutions for the SIP-based VoIP Service. In this paper, Peer-to-Peer (P2P) based and DN-LB based schemes are mainly compared in the paper. A P2P-based scheme enables an inexpensive high availability solution to the VoIP service by the shared computation resources form P2P nodes. Such a P2P-based solution may be appropriate for an individual VoIP user. However, a caller may take a large volume of messages to find out a callee via the proxy nodes in the P2P network. This inherent property of a P2P network may induce the message overhead and long call setup delay. Based on above, another inexpensive scheme, which is a probing-based name resolution solution, is proposed to achieve high availability and load balancing for the VoIP service. We tag the probing mechanism onto the open source project Domain Name Relay Daemon (DNRD) to become a domain name resolution based load balancer (DN-LB). With DN-LB, all request messages from clients can be fairly distributed to all failure-proof proxy servers in the server farm without using any additional costly intermediate network device and changing the standard SIP architecture. Such a DN-LB based solution may be a good choice for a VoIP service provider.     

Intelligent planning and the design of a new risk-based,intelligent flight plan

A safe flight starts with effective performance of the pre-flight flight planning and briefing task. However, several problems related to the execution of this task can be identified. Potentially, the introduction of an improved flight plan provides an opportunity to improve the quality and availability of information provided to Flight Crew, thereby enhancing the quality of crew briefings. The proposed risk-based, intelligent flight plan is designed from the perspective of the current operational concept (e.g. fixed routes and ATC managerial role for separation), and associated airline Flight Planning and Dispatch functions. In this case, the focus is sharing information across specific airline stakeholders (e.g. Flight Operations Management and Safety functions) and Maintenance, to support a safe and efficient flight operation. Overall, the introduction of this new flight plan will result in the definition of new operational and organisational processes, along with a new way of performing the pre-flight, planning and briefing task. It is anticipated that this will impact positively on the operational and safety outcome of the flight.     

Two new control signal approaches for obtaining the MRAS-CDM and a real-time application

The coefficient diagram method (CDM) is one of the most effective control design methods. It creates control systems that are very stable and robust with responses without the overshoot and small settling time. Furthermore, all control parameters of the control systems are changed by varying some adjustment parameters in CDM depending on the demands. The model reference adaptive systems (MRAS) are the systems that follow and change the control parameters according to a given model reference system. There are several methods to combine the CDM with MRAS. One of these is to use the MRAS parameters as a gain of the CDM parameters. Another is to directly use the CDM parameters as the MRAS parameters. In the industrial applications, the system parameters can be changed frequently, but if the controller, by self-tuning, recalculates and develops its own parameters continuously, the system becomes more robust. Also, if the poles of the controlled systems approach the jw axis, the response of the closed-loop MRAS becomes more and more insufficient. In order to obtain better results, CDM is combined with a self-tuning model reference adaptive system. Systems controlled by a model reference adaptive controller give responses with small or without overshoot, have small settling times, and are more robust. Thus, in this paper, a hybrid combination of MRAS and CDM is developed and two different control structures of the control signal are investigated. The two methods are compared with MRAS and applied to real-time process control systems.     

Augmented AVI video file for road surveying

This paper presents a novel video augmentation approach: a video recording of a real entity augmented by structured data. This augmented video is a self-contained entity integrating values of perceptible and non-perceptible attributes of the same real entity. It simplifies data search, storage and management. Streaming, sharing, collaboration and synchronization are also simplified by the fact that the structured data (so-called augmented data) are integrated in the video file and therefore only one file needs to be manipulated. Our approach was applied to address the issues which road engineers experience while using the ROad Measurement and Data Acquisition System (ROMDAS). The ROMDAS system collects and analyses the road-condition state through video recordings and the data corresponding acquired by specific measuring devices. Currently, however, road engineers have to search the videos manually in order to find details of interest provided by the analysis of the data measured due to the separation of the video from the data. The ROMDAS system manages a large collection of road-condition data. It does not offer history management of video recordings of the same road captured at different time. We present this hybrid video augmentation system: the Augmented Video stream Framework (AVF). It allows creation, search, history management and playback of such augmented video files for effective road surveying based on the discrete data recorded in parallel to the video recordings. The AVF provides road engineers with effective and more comfortable perceptible and non-perceptible data search, storage and history management to be used for (collaborative) road inspection and maintenance.     

Performance evaluation of directory protocols on an optical broadcast-based distributed shared memory multiprocessor

Recent advances in the development of optical technologies suggest the possible emergence of broadcast-based optical interconnects within cache-coherent distributed shared memory (DSM) multiprocessor architectures. It is well known that the cache-coherence protocol is a critical issue in designing such architectures because it directly affects memory latencies. In this paper, we evaluate via simulation the performance of three directory-based cache-coherence protocols; strict request-response, intervention forwarding and reply forwarding on the Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus), which is a low-latency and high-bandwidth broadcast-based fiber-optic interconnection network supporting DSM. The simulated system contains 64 nodes, each of which has a processor, a cache controller, a directory controller and an output channel. Simulations have been conducted for each protocol to measure average processor utilization, average network latency and average number of packets transferred over the network for varying values of the important DSM parameters such as the ratio of the mean channel service time to mean thread run time (T/R), probability of a cache block being in modified state {P(M)}, the fraction of write misses {P(W)} and home node contention rate. The results reveal that for all cases, except for low values of P(M), intervention forwarding gives the worst performance (lowest processor utilization and highest latency). The performance of strict request-response and reply forwarding is comparable for several values of the DSM parameters and contention rate. For a contention rate of 0%, the increase of P(M) makes reply forwarding perform better than strict request-response. The performance of all protocols decreases with the increase of P(W) and contention rate. However, the performance of strict request-response is the least affected among other protocols due to the negative impact of the increase of P(W) and contention rate. Therefore, for the full contention case (i.e. contention rate of 100%); for low values of P(M), or for mid values of P(M) and high values of P(W), strict request-response performs better than reply forwarding. These results are significant in the sense that they provide an insight to multiprocessor architecture designers for comparing the performance of different directory-based cache-coherence protocols on a broadcast-based interconnection network for different values of the DSM parameters and varying rates of contention.     

Explanation and reliability of prediction models: the case of breast cancer recurrence

In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers’ predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation methodology for regression predictions, which helps the users to decide to what extent to trust a particular prediction. Both methods are successfully applied to a novel breast cancer recurrence prediction data set and the results are evaluated by expert oncologists.     

A Hybrid Intelligent Morphological Approach for Stock Market Forecasting

In this paper, a hybrid intelligent morphological approach is presented for stock market forecasting. It consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) and a Modified Genetic Algorithm (MGA), which searches for the minimum number of time lags for a correct time series representation, as well as by the initial weights, architecture and number of modules of the MMNN. Each element of the MGA population is trained via Back Propagation (BP) algorithm to further improve the parameters supplied by the MGA. Initially, the proposed method chooses the most tuned prediction model for time series representation, then it performs a behavioral statistical test in the attempt to adjust time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed method using four real world time series and five well-known performance measurements, demonstrating consistent better performance of this kind of morphological system.     

Models to predict the deformation modulus and the coefficient of subgrade reaction for earth filling structures

Determination of deformation modulus and coefficient of subgrade reaction of soils have major importance, whether the projects are in design, and construction or compaction assessment stage of earth filling structures. Plate load test is one of the frequently used method to directly determine the parameters but the method is both costly and time consuming. For this reason, this paper is concerned with the applications of artificial neural networks (ANN) and simple-multiple regression analysis to predict deformation modulus and coefficient of subgrade reaction of compacted soils from compaction parameters (such as maximum dry density (MDD) and optimum moisture content (OMC), field dry density (FDD), and field moisture content (FMC)). Regression analysis and artificial neural network estimation indicated that there are acceptable correlations between deformation modulus and coefficient of subgrade reaction and these parameters. Artificial neural networks model exhibits higher performance than traditional statistical model for predicting deformation modulus and coefficient of subgrade reaction.