Reorientation patterns in central-place foraging: internal clocks and klinokinesis

We study central-place foraging patterns of Aphaenogaster senilis ants at a population level by video framing individual ant trajectories in a circular arena with a nest connected to its centre. The ants naturally leave and enter the nest and forage generating non-trivial movement patterns around the nest. Our data analysis indicated that the trajectories observed can be classified into two strategies: the risk-averse strategy, which involves wandering around the nest without departing far from it and the risk-prone strategy, which involves long exploration paths with periodic returns to the central region, nearby the nest. We found that both risk-prone and risk-averse strategies exhibit qualitatively the same reorientation patterns, with the time between consecutive reorientations covering a wide range of scales, and fitting a stretched exponential function. Nevertheless, differences in the temporal scales and the time variability of such reorientation events differ, together with other aspects of motion, such as average speed and turns. Our results give experimental evidence that the internal mechanisms driving reorientations in ants tend to favour frequently long relocations, as theory predicts for efficient exploration in patchy landscapes, but ants engaged in central-place foraging can modulate such behaviour to control distances from the nest. Previous works on the species support the idea that risk-prone and risk-averse strategies may reflect actual differences between individuals age and experience; these factors (age and experience) should be then relevant in modulating the internal reorientation clocks. To support the validity of our findings, we develop a random-walk model combining stretched exponential reorientation clocks with klinokinesis that fits the time length and the travelled distance distributions of the observed trajectories.     

Supervised image segmentation using Q-Shift Dual-Tree Complex Wavelet Transform coefficients with a texton approach

In this study, we propose a simple and efficient texture-based algorithm for image segmentation. This method constitutes computing textons and bag of words (BOWs) learned by support vector machine (SVM) classifiers. Textons are composed of local magnitude coefficients that arise from the Q-Shift Dual-Tree Complex Wavelet Transform (DT-CWT) combined with color components. In keeping with the needs of our research context, which addresses land cover mapping from remote images, we use a few small texture patches at the training stage, where other supervised methods usually train fully representative textures. We accounted for the scale and rotation invariance issue of the textons, and three different invariance transforms were evaluated on DT-CWT-based features. The largest contribution of this study is the comparison of three classification schemes in the segmentation algorithm. Specifically, we designed a new scheme that was especially competitive and that uses several classifiers, with each classifier adapted to a specific size of analysis window in texton quantification and trained on a reduced data set by random selection. This configuration allows quick SVM convergence and an easy parallelization of the SVM-bank while maintaining a high segmentation accuracy. We compare classification results with textons made using the well-known maximum response filters bank and speed up robust features features as references. We show that DT-CWT textons provide better distinguishing features in the entire set of configurations tested. Benchmarks of our different method configurations were made over two substantial textured mosaic sets, each composed of 100 grey or color mosaics made up of Brodatz or VisTex textures. Lastly, when applied to remote sensing images, our method yields good region segmentation compared to the ENVI commercial software, which demonstrates that the method could be used to generate land cover maps and is suitable for various purposes in image segmentation.     

Is It Possible to Retain Customer Loyalty When a Service Has Failed?

This article aims to analyze the impact of recovery on loyalty in the context of a service chain characterized by the e‐quality–perceived value–loyalty relationship. A sample of 91 out of 1,201 respondents claimed to have had service problems with an online travel agency website. A causal model to test relationships was performed using structural equations modeling, and it was found that data fit with the proposed model. The really important point to retaining customers is quality. It is not so clear that the recovery effort might affect loyalty. Our findings underpin some previous stream of literature confirming that recovery does not always affect loyalty. Although the literature has not reached a consensus in this topic, we add new elements in this debate.     

High-dimensional MRI data analysis using a large-scale manifold learning approach

A novel manifold learning approach is presented to efficiently identify low-dimensional structures embedded in high-dimensional MRI data sets. These low-dimensional structures, known as manifolds, are used in this study for predicting brain tumor progression. The data sets consist of a series of high-dimensional MRI scans for four patients with tumor and progressed regions identified. We attempt to classify tumor, progressed and normal tissues in low-dimensional space. We also attempt to verify if a progression manifold exists—the bridge between tumor and normal manifolds. By identifying and mapping the bridge manifold back to MRI image space, this method has the potential to predict tumor progression. This could be greatly beneficial for patient management. Preliminary results have supported our hypothesis: normal and tumor manifolds are well separated in a low-dimensional space. Also, the progressed manifold is found to lie roughly between the normal and tumor manifolds.     

Locality-Sensitive Hashing for Chi2 distance

In the past 10 years, new powerful algorithms based on efficient data structures have been proposed to solve the problem of Nearest Neighbors search (or Approximate Nearest Neighbors search). If the Euclidean Locality Sensitive Hashing algorithm, which provides approximate nearest neighbors in a euclidean space with sublinear complexity, is probably the most popular, the euclidean metric does not always provide as accurate and as relevant results when considering similarity measure as the Earth-Mover Distance and 2 distances. In this paper, we present a new LSH scheme adapted to 2 distance for approximate nearest neighbors search in high-dimensional spaces. We define the specific hashing functions, we prove their local-sensitivity, and compare, through experiments, our method with the Euclidean Locality Sensitive Hashing algorithm in the context of image retrieval on real image databases. The results prove the relevance of such a new LSH scheme either providing far better accuracy in the context of image retrieval than euclidean scheme for an equivalent speed, or providing an equivalent accuracy but with a high gain in terms of processing speed.     

Project preparedness and the emergence of implementation problems in ERP projects

The problems that organizations face in implementing an enterprise-wide ERP project are linked to their level of understanding of what is involved in such an endeavor and how it influences their initial preparations. We sought to demonstrate empirically the causal relationship between the organization's preparedness and the emergence of implementation problems. We examined four case studies to extract insight into the criticality of certain factors and the type of problems created when no moderating measures were taken by project managers. Consequently, we developed a predictor-outcome model mapping a lack of preparedness with implementation problems.     

Torus cutter positioning in five-axis milling using balance of the transversal cutting force

This article introduces a new torus cutter positioning strategy for five-axis milling of free-form surfaces. This approach ensures elimination of local interference while also allowing better surface quality to be obtained than with positioning proposed by computer-aided manufacturing (CAM) software. In practice, the cutter axis is inclined to the rear in relation to the feed movement. A first inclination for the cutter axis is calculated to allow local interference to be eliminated. Then, an additional inclination is given to tool axis to achieve balancing of the transversal cutting force component perpendicular to the plane containing the tool axis and the feedrate vector. This particular machining situation considerably enhances the cutter's dynamic behaviour and gives better roughness values than those obtained with positioning by CAM software. A positioning method is adapted to the negative rearward inclination of the cutter axis, and it is then shown how transversal cutting force balancing is integrated in the form of an additional inclination. Finally, a comparison of the results obtained after milling with this new positioning and positioning calculated by a CAM program highlights the new method’s advantages.     

Measurements of Smoke Characteristics in HVAC Ducts

The characteristics of smoke traveling in an HVAC duct have been observed along with the response of selected duct smoke detectors. The simulated HVAC system consists of a 9 m long duct, 0.45 m in diameter. An exhaust fan is placed at one end of the duct and is capable of inducing airflow rates that range from 0 to 1.5 m ³/s. The flow is controlled by means of a manual damper. On the upstream end of the duct there is a square exhaust hood approximately 2.2 m at the bottom and 0.3 m at the top. The bottom of the hood is approximately 2.5 m above the floor a shroud extends down to approximately 1.5 m above the floor. The test section, placed immediately downstream of the hood, is 3.5 m long duct with a square cross section of 0.4 m on a side. The instrumentation includes oxygen, carbon monoxide and carbon dioxide gas analyzers and a load cell to determine the energy release rate of the fires tested. The smoke within the duct is characterized by means of a laser light sheet and charge coupled device (CCD) camera, two white light source and photocell ensembles, a Pitot tube and an array of eight thermocouples placed on the vertical plane of symmetry. A smoke detector was placed at the downstream end of the test section. Two types of detectors were tested, ionization and photoelectric, with a single sampling probe geometry. The fires tested cover a wide range of fuels (propane, heptane, toluene, toluene/heptane mixture, shredded paper, polyurethane foam, wood cribs) with the peak energy release rates up to 800 kW. The smoke detector performance, temperature, flow field, smoke particle size and particle distributions are dependent on the fire characteristics and airflow through the duct. The different measurements could be scaled by means of the fire size and airflow rate but left a strong dependency on the fuel and burning characteristics (i.e., smoldering, flaming). The optical density and mass optical density are analyzed as metrics for characterizing smoke and smoke detector response. Detailed comparisons between the different metrics used are presented throughout this work. Clear evidence of stratification and aging of the smoke along the duct are also presented. The limitations of the present configuration and the need for a larger scale study are also discussed.     

Four archetypes of process improvement: a Q-methodological study

This paper explores the process improvement approaches of organisations. It seeks to identify process redesign principles and the combinations of these principles that are used successfully in industry. We use Q-methodology to explore the viewpoints of a range of highly experienced process experts about the success of 16 improvement practices. Q-methodology enables the examination of the similarities and differences in the success of the improvement principles used by organisations in order to inform archetypes of process improvement. Overall, our findings suggest that process improvement is determined through the application of two foundational principles combined with one of four archetypes. ‘Remove non-value-adding tasks’ and ‘re-sequence tasks’ are described as foundational principles of process improvement, whilst outsourcing needs to be approached with caution. Furthermore, we articulate four distinctive archetypes comprising unique configurations of improvement principles that can be used to redesign operational processes. Based on this evidence we propose a typology of process improvement. This work suggests that rather than adopting generic improvement frameworks, managers should consult the typology to determine the archetype in closest proximity to their specific requirements. This study has several limitations including the small number of items populating the concourse and the fact that implementation problems are not taken into account.