Development of a pit filling algorithm for LiDAR canopy height models

LiDAR canopy height models (CHMs) can exhibit unnatural looking holes or pits, i.e., pixels with a much lower digital number than their immediate neighbors. These artifacts may be caused by a combination of factors, from data acquisition to post-processing, that not only result in a noisy appearance to the CHM but may also limit semi-automated tree-crown delineation and lead to errors in biomass estimates. We present a highly effective semi-automated pit filling algorithm that interactively detects data pits based on a simple user-defined threshold, and then fills them with a value derived from their neighborhood. We briefly describe this algorithm and its graphical user interface, and show its result in a LiDAR CHM populated with data pits. This method can be rapidly applied to any CHM with minimal user interaction. Visualization confirms that our method effectively and quickly removes data pits.     

Estimating residual variance in random forest regression

Random forest, a data-mining technique which uses multiple classification or regression trees, is a popular algorithm used for prediction. Inference and goodness-of-fit assessment, however, may require an estimator of variability; in many applications the residual variance is of primary interest. This paper proposes two estimators of residual variance for random forest regression that take advantage of byproducts of the algorithm. The first estimator is based on the residual sum of squares from a random forest fit and uses a bootstrap bias correction. The second estimator is a difference-based estimator that uses proximity measures as weights. The estimators are evaluated through Monte Carlo simulations. Applications of the methods to the problem of assessing the relative variability of males and females on cognitive and achievement tests are discussed, and the methods are applied to estimate the residual variance in test scores for male and female students on the mathematics portion of the 2007 Arizona Instrument to Measure Standards.     

F-MPJ: scalable Java message-passing communications on parallel systems

This paper presents F-MPJ (Fast MPJ), a scalable and efficient Message-Passing in Java (MPJ) communication middleware for parallel computing. The increasing interest in Java as the programming language of the multi-core era demands scalable performance on hybrid architectures (with both shared and distributed memory spaces). However, current Java communication middleware lacks efficient communication support. F-MPJ boosts this situation by: (1) providing efficient non-blocking communication, which allows communication overlapping and thus scalable performance; (2) taking advantage of shared memory systems and high-performance networks through the use of our high-performance Java sockets implementation (named JFS, Java Fast Sockets); (3) avoiding the use of communication buffers; and (4) optimizing MPJ collective primitives. Thus, F-MPJ significantly improves the scalability of current MPJ implementations. A performance evaluation on an InfiniBand multi-core cluster has shown that F-MPJ communication primitives outperform representative MPJ libraries up to 60 times. Furthermore, the use of F-MPJ in communication-intensive MPJ codes has increased their performance up to seven times.     

Selecting Significant Effects in Factorial Designs Taking Type II Errors into Account

When analysing the effects of a factorial design, it is customary to take into account the probability of making a Type I error (the probability of considering an effect significant when it is non‐significant), but not to consider the probability of making a Type II error (the probability of considering an effect as non‐significant when it is significant). Making a Type II error, however, may lead to incorrect decisions regarding the values that the factors should take or how subsequent experiments should be conducted. In this paper, we introduce the concept of minimum effect size of interest and present a visualization method for selecting the critical value of the effects, the threshold value above which an effect should be considered significant, which takes into account the probability of Type I and Type II errors. Copyright © 2006 John Wiley & Sons, Ltd.     

Inverse Analysis of Impact Test Data: Experimental Study on Polymeric Materials Displaying Brittle Behaviour

This paper deals with the influence of the testing equipment on impact load measurements. A previously developed method of analysis and processing of the experimental data based on a refined analogical model of the impact event and inverse problem techniques is used. This method makes it possible to obtain the mechanical response of the material, notwithstanding the disturbance of the dynamic effects associated to the test. Results from tests carried out both on falling weight and swing pendulum instrumented testing machines are compared. It is shown that this method can give an accurate estimation of the actual bending force in impact testing independent of the testing equipment.     

Mutuality and Reciprocity in the Psychological Contracts of Employees and Employers.

The authors assessed the joint perceptions of the employee and his or her employer to examine mutuality and reciprocity in the employment relationship. Paired psychological contract reports were obtained from 80 employee-employer dyads in 16 university-based research centers. On the basis of in-depth study of the research setting, research directors were identified as primary agents for the university (employer) in shaping the terms of employment of staff scientists (employees). By assessing the extent of consistency between employee and employer beliefs regarding their exchange agreement, the present study mapped the variation and consequences of mutuality and reciprocity in psychological contracts. Results indicate that both mutuality and reciprocity are positively related to archival indicators of research productivity and career advancement, in addition to self-reported measures of Met Expectations and intention to continue working with the employer. Implications for psychological contract theory are presented. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Utility of Nasal Challenges to Phenotype Asthma Patients

Asthma is a heterogeneous disease in terms of both phenotype and response to therapy. Therefore, there is a great need for clinically applicable tools allowing for improved patient classification, and selection for specific management approaches. Some interventions are highly helpful in selected patients (e.g., allergen immunotherapy or aspirin desensitization), but they are costly and/or difficult to implement. Currently available biomarkers measurable in peripheral blood or exhaled air display many limitations for asthma phenotyping and cannot identify properly the specific triggers of the disease (e.g., aeroallergens or NSAID). The united airway concept illustrates the relevant epidemiological and pathophysiological links between the upper and lower airways. This concept has been largely applied to patient management and treatment, but its diagnostic implications have been less often explored. Of note, a recent document by the European Academy of Allergy and Clinical Immunology proposes the use of nasal allergen challenge to confirm the diagnosis of allergic asthma. Similarly, the nasal challenge with lysine acetylsalicylate (L-ASA) can be used to identify aspirin-sensitive asthma patients. In this review, we will summarize the main features of allergic asthma and aspirin-exacerbated respiratory disease and will discuss the methodology of nasal allergen and L-ASA challenges with a focus on their capacity to phenotype the inflammatory disease affecting both the upper and lower airways.     

Collective intelligence as mechanism of medical diagnosis: The iPixel approach

Collective intelligence (CI) is an active field of research, which capitalizes the knowledge of human collectives in order to create, to innovate and to invent. There are two important mechanisms to implement CI: recommender and reputation systems. Recommender systems are used to provide filtered information from a large amount of elements. The recommendations are intended to provide interesting elements to users. Recommendation systems can be developed using different techniques and algorithms where the selection of these techniques depends on the area in which they will be applied. This work presents iPixel Recommender Engine, which is focused on the medical field. iPixel Recommendation Engine supports the process of differential diagnosis by recommending mammographic evaluations. Each mammogram is collectively tagged by the users’ community with a semantic sense; this feature allows iPixel acquires collective knowledge. iPixel can associate more than one feature with each mammogram. This work also presents a qualitative evaluation, where the basic features that a recommendation system should have in the medical field were obtained. Finally, a comparison was carried out with other similar recommender systems in order to know the Pixel advantages.     

Basic actions to improve energy efficiency in commercial buildings in operation

Numerous studies have focused on improving energy efficiency in commercial buildings. Engineers and researchers have developed complex methods to improve energy efficiency, but buildings are often managed by non-specialised technicians who need understandable and cost-effective actions to implement in their buildings. This paper presents basic actions on which to base improvements in energy efficiency in commercial buildings in operation. Furthermore, obtained results and details of the implementation of these techniques in various buildings in the Universitat Politècnica de València are presented.