The reasonant influence of supply chain management practices on organizational performance

A single supply chain management (SCM) practice will have a certain impact on organizational performance(OP). However, since it is placed in a system that many other practices are conducted simultaneously, the practice itself will interact with other ones and have a greater impact on OP. This mechanism is named the "resonant" influence. The technique of Structural equation modelling (SEM) was used to test the above mechanism with data collected from Vietnamese garment enterprises. The tcst results showed that the model without mutual interaction among SCM practices could explain 42.8%, 26.3% and 34% variance of operational performance, customer satisfaction and financial performance. While the one containing this interaction is capable to explain 69.5%, 33.1% and 57.3%, respectively.

Assessing student individual performance within PBL teams: findings from the implementation of a new mechanism

This paper reports on the experience of the implementation of a new mechanism to assess individual student contribution within project work, where students work in teams to solve a large-scale open-ended interdisciplinary project. The study takes place at the University of Minho, with first year engineering students, enrolled in the Industrial Management and Engineering (Integrated Masters) degree. The aim of this paper is to describe the main principles and procedures underlying the assessment mechanism created and also provide some feedback from its first implementation, based on the students, lecturers and tutors perceptions. For data collection, a survey was sent to all course lecturers and tutors involved in the assessment process. Students also contributed with suggestions, both on a workshop held at the end of the project and also by answering a survey on the overall satisfaction with PBL experience. Findings show a positive level of acceptance of the new mechanism by the students and also by the lecturers and tutors. The study identified the need to clarify the criteria used by the lecturers and the exact role of the tutor, as well as the need for further improvement of its features and procedures. Some recommendations are also issued regarding technical aspects related to some of the steps of the procedures, as well as the need for greater support on the adjustment and final setting of the individual grades.

A geographic opportunistic forwarding strategy for vehicular named data networking

Studies in Computational Intelligence, 616

A hybrid MAC Scheme to improve the transmission performance in body sensor networks

Wireless body sensor networks (WBSNs) constitute a key technology for closing the loop between patients and healthcare providers, as WBSNs provide sensing ability, as well as mobility and portability, essential characteristics for wide acceptance of wireless healthcare technology. However, one important and difficult aspect of WBSNs is to provide data transmissions with quality of service, among other factors due to the antennas being small size and placed close to the body. Such transmissions cannot be fully provided without the assumption of a MAC protocol that solves the problems of the medium sharing. A vast number of MAC protocols conceived for wireless networks are based on random or scheduled schemes. This paper studies firstly the suitability of two MAC protocols, one using CSMA and the other TDMA, to transmit directly to the base station the signals collected continuously from multiple sensor nodes placed on the human body. Tests in a real scenario show that the beaconed TDMA MAC protocol presents an average packet loss ratio lower than CSMA. However, the average packet loss ratio is above 1.0 %. To improve this performance, which is of vital importance in areas such as e-health and ambient assisted living, a hybrid TDMA/CSMA scheme is proposed and tested in a real scenario with two WBSNs and four sensor nodes per WBSN. An average packet loss ratio lower than 0.2 % was obtained with the hybrid scheme. To achieve this significant improvement, the hybrid scheme uses a lightweight algorithm to control dynamically the start of the superframes. Scalability and traffic rate variation tests show that this strategy allows approximately ten WBSNs operating simultaneously without significant performance degradation.

Performance of the ATLAS muon trigger in pp collisions at s√=8 TeV

The performance of the ATLAS muon trigger system has been evaluated with proton--proton collision data collected in 2012 at the Large Hadron Collider at a centre-of-mass energy of 8 TeV. The performance was primarily evaluated using events containing a pair of muons from the decay of Z bosons. The efficiency is measured for the single-muon trigger for a kinematic region of the transverse momentum pT between 25 and 100 GeV, with a statistical uncertainty of less than 0.01% and a systematic uncertainty of 0.6%. The performance is also compared in detail to the predictions from simulation. The efficiency was measured over a wide pT range (a few GeV to several hundred GeV) by using muons from J/ψ mesons,W bosons, and top and antitop quarks. It showed highly uniform and stable performance.

Extreme value Birnbaum-Saunders regression models applied to environmental data

Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

Using balanced scorecards to evaluate the data warehouse system utility

Dissertação de mestrado em Systems Engineering

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Assessment of the mechanical performance of recycled unbound materials for pavement subbase layers

The cyclic load triaxial test is a laboratory test that allows studying the mechanical behaviour of unbound granular materials used in base/subbase layers of road pavements. The resilient modulus and permanent strains are required as inputs in structural pavement design. This paper presents some results obtained for recycled materials (crushed concrete aggregate and blended crushed waste aggregate), with a view to promoting their use in pavement structures. Results relating to a reference material (limestone) are also presented, for comparison. All the test results discussed in this paper were obtained in variable cyclic radial pressure (VCP) tests. The tests performed (VCP) aim to study the influence of water content on the resilient modulus of recycled materials, as well as on the resistance to permanent deformation. Using the experimental data as a basis, further modelling work was carried out to establish the stresses developing in base/capping layers in typical Belgian road pavements. These numerical results allow to propose some simplifications of the stress paths applied in the testing procedures and to establish a new test protocol that also considers compaction during construction works. The results of this research work provide an excellent set of findings for the mechanical characterization of unbound base materials through the cyclic triaxial test, and contribute to a better understanding and correct application of recycled materials under geotechnical engineering background

Estimativa dos parâmetros de compactação de solos usando técnicas de data mining

Dissertação de mestrado integrado em Engenharia Civil

Avaliação da performance clínica de lentes de contacto descartáveis diárias: um estudo piloto

Dissertação de mestrado em Optometria Avançada

Towards a pervasive data mining engine: architecture overview

Current data mining engines are difficult to use, requiring optimizations by data mining experts in order to provide optimal results. To solve this problem a new concept was devised, by maintaining the functionality of current data mining tools and adding pervasive characteristics such as invisibility and ubiquity which focus on their users, providing better ease of use and usefulness, by providing autonomous and intelligent data mining processes. This article introduces an architecture to implement a data mining engine, composed by four major components: database; Middleware (control); Middleware (processing); and interface. These components are interlinked but provide independent scaling, allowing for a system that adapts to the user’s needs. A prototype has been developed in order to test the architecture. The results are very promising and showed their functionality and the need for further improvements.