Multispace & Multistructure. Neutrosophic Transdisciplinarity (100 Collected Papers of Sciences), Vol. IV

The fourth volume, in my book series of “Collected Papers”, includes 100 published and unpublished articles, notes, (preliminary) drafts containing just ideas to be further investigated, scientific souvenirs, scientific blogs, project proposals, small experiments, solved and unsolved problems and conjectures, updated or alternative versions of previous papers, short or long humanistic essays, letters to the editors - all collected in the previous three decades (1980-2010) – but most of them are from the last decade (2000-2010), some of them being lost and found, yet others are extended, diversified, improved versions. This is an eclectic tome of 800 pages with papers in various fields of sciences, alphabetically listed, such as: astronomy, biology, calculus, chemistry, computer programming codification, economics and business and politics, education and administration, game theory, geometry, graph theory, information fusion, neutrosophic logic and set, non-Euclidean geometry, number theory, paradoxes, philosophy of science, psychology, quantum physics, scientific research methods, and statistics. It was my preoccupation and collaboration as author, co-author, translator, or cotranslator, and editor with many scientists from around the world for long time. Many topics from this book are incipient and need to be expanded in future explorations

Investigating Neurocranial Sizes And Cephalic Indices Of Male Newborns In North of Tehran Using Millimetric Cephalometry

Background: Anthropometric studies have been introduced as the second priority of research in the field of medical sciences in Iran. Information obtained from such studies are used in industry and medical sciences, including the manufacturing of prostheses, gas masks, and surgical instruments. Considering the effect of genetic, age, sexual, climatic, and geographical factors and their high importance on anthropometric sizes, the aim of the present project is to investigate the neurocranial sizes of the cephalic indices of one-day-old male newborns in north of Tehran.Materials and Methods: This cross-sectional descriptive study was carried out on 100 neurocranial sizes of one-day-old male newborns in Shohada-e-Tajrish Hospital in Tehran using millimetric cephalometry. Measurements were performed according to the protocol and by a ruler, cephalometer, and tape meter (0.5mm precision rate). The data were collected and recorded in the relevant forms and then processed and analyzed by a biostatistics software. Results: The present research showed that the mean head length, head width, ear height, head circumference, brain volume, brain weight, cephalic index, and the brain index was 118.2 mm, 94.3 mm, 71.3 mm, 348.5 mm, 415.1 ml, 429.2 g, 78.1%, and 12.8%, respectively. Frequency of different head phenotypes has been reported according to the cephalic index as follows: round head (65%), long head (20%), broad head (12%), and super-broad head (3%).Conclusion: The results of the present study showed that the cephalic index of one-day-old male newborns in Tehran was lower than those born in Kermanshah, Sistan and Baluchestan, and Qazvin, which can be explained by the impact of gender, climate, and geographical area on neurocranial dimensions and sizes

Applications using estimates of forest parameters derived from satellite and forest inventory data

From the combination of optical satellite data, digital map data, and forest inventory plot data, continuous estimates have been made for several forest parameters (wood volume, age and biomass). Five different project areas within Sweden are presented which have utilized these estimates for a range of applications. The method for estimating the forest parameters was a ”k-Nearest Neighbor” algorithm, which used a weighted mean value of k spectrally similar reference plots. Reference data were obtained from the Swedish National Forest Inventory. The output was continuous estimates at the pixel level for each of the variables estimated. Validation results show that accuracy of the estimates for all parameters was low at the pixel level (e.g., for total wood volume RMSE ranged from 58-80%), with a tendency toward the mean, and an underestimation of higher values while overestimating lower values. However, when the accuracy of the estimates is assessed over larger areas, the errors are lower, with best results being 10% RMSE over a 100 ha aggregation, and 17% RMSE over a 19 ha aggregation. Applications presented in this paper include moose and bird habitat studies, county level planning activities, use as input information to prognostic programs, and computation of statistics on timber volume within drainage basins and smaller land holdings. This paper provides a background on the kNN method and gives examples of how end users are currently applying satellite-produced estimation data such as these

Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

European HYdropedological Data Inventory (EU-HYDI)

There is a common need for reliable hydropedological information in Europe. In the last decades research institutes, universities and government agencies have developed local, regional and national datasets containing soil physical, chemical, hydrological and taxonomic information often combined with land use and landform data. A hydrological database for western European soils was also created in the mid-1990s. However, a comprehensive European hydropedological database, with possible additional information on chemical parameters and land use is still missing. A comprehensive joint European hydropedological inventory can serve multiple purposes, including scientific research, modelling and application of models on different geographical scales. The objective of the joint effort of the participants is to establish the European Hydropedological Data Inventory (EU-HYDI). This database holds data from European soils focusing on soil physical, chemical and hydrological properties. It also contains information on geographical location, soil classification and land use/cover at the time of sampling. It was assembled with the aim of encompassing the soil variability in Europe. It contains data from 18 countries with contributions from 29 institutions. This report presents an overview of the database, details the individual contributed datasets and explains the quality assurance and harmonization process that lead to the final database

Investigation on the automatic geo-referencing of archaeological UAV photographs by correlation with pre-existing ortho-photos

We present a method for the automatic geo-referencing of archaeological photographs captured aboard unmanned aerial vehicles (UAVs), termed UPs. We do so by help of pre-existing ortho-photo maps (OPMs) and digital surface models (DSMs). Typically, these pre-existing data sets are based on data that were captured at a widely different point in time. This renders the detection (and hence the matching) of homologous feature points in the UPs and OPMs infeasible mainly due to temporal variations of vegetation and illumination. Facing this difficulty, we opt for the normalized cross correlation coefficient of perspectively transformed image patches as the measure of image similarity. Applying a threshold to this measure, we detect candidates for homologous image points, resulting in a distinctive, but computationally intensive method. In order to lower computation times, we reduce the dimensionality and extents of the search space by making use of a priori knowledge of the data sets. By assigning terrain heights interpolated in the DSM to the image points found in the OPM, we generate control points. We introduce respective observations into a bundle block, from which gross errors i.e. false matches are eliminated during its robust adjustment. A test of our approach on a UAV image data set demonstrates its potential and raises hope to successfully process large image archives

Using combined prediction models to quantify and visualize stormwater runoff in an urban watershed

Stormwater runoff can transport nutrients, sediments, chemicals, and pathogens to surface waterbodies. Managing runoff is crucial to preserving water quality in rapidly developing urban watersheds like those in Northwest Arkansas. A watershed containing the majority of the University of Arkansas campus was designated as the study area because stormwater from it drains into the West Fork of the White River, designated as an impaired waterbody due to siltation. The project objective was to develop methodology to test existing stormwater drainage infrastructure, identify potential areas of improvement, and estimate potentially contaminated runoff by combining two widely used prediction models. The U.S. Department of Agriculture’s Natural Resource Conservation Service’s curve number (CN) method was used to estimate runoff depths and volumes, while a flow-direction model was created that integrated topography, land use, and stormwater drainage infrastructure in a geographic information system. This study combined the CN and flow-direction models in a single geodatabase to develop flow direction/quantity models. Models were developed for 5-, 10-, 25-, 50-, and 100-year floods and varied by the antecedent moisture content. These models predicted flow directions within existing drainage infrastructure and runoff volumes for each flood, and served as a hypothetical flood analysis model. Results showed that between 24,000 m3 (5-year flood) and 60,000 m3 (100-year flood) of runoff would be transported to the West Fork of the White River. The methodology developed and results generated will help stormwater planners visualize localized runoff, and potentially adapt existing drainage networks to accommodate runoff, prevent flooding and erosion, and improve the quality of runoff entering nearby surface waterbodies

Short view of leukemia diagnosis and treatment in Iran

Background: Early diagnosis and treatment of leukemia patients remains a fundamental aim in clinical oncology, especially in developing country. Present study highlights the basic requirements of these patients in Iran. Better understanding of these issues may lead to improve the healthcare standards toward leukemia diagnosis and treatment. Methods: This descriptive study included 101 specialists in hematology-oncology and pathology serving in oncology centers. The participants were then asked to fill out a standard questionnaire on the issues around diagnosis and treatment of blood malignancies. Results: According to specialists, unfair distribution of facilities across the country, delayed diagnosis of disease, absence of psychological support for patients, and insufficient financial support were the main reasons of inappropriate diagnosis and treatment in leukemia patients. Conclusions: Our results show that making an amendment to health policies by preparing well-equipped medical centers in all provinces, improving the morale of patients through consultation during the process of treatment, and above all, subsiding leukemia patients' financial problems will promote the health standard regarding the leukemia diagnosis and treatment in Iran. © 2015, Tehran University of Medical Sciences (TUMS). All rights reserved

Efficient Estimation of Mutual Information for Strongly Dependent Variables

We demonstrate that a popular class of nonparametric mutual information (MI) estimators based on k-nearest-neighbor graphs requires number of samples that scales exponentially with the true MI. Consequently, accurate estimation of MI between two strongly dependent variables is possible only for prohibitively large sample size. This important yet overlooked shortcoming of the existing estimators is due to their implicit reliance on local uniformity of the underlying joint distribution. We introduce a new estimator that is robust to local non-uniformity, works well with limited data, and is able to capture relationship strengths over many orders of magnitude. We demonstrate the superior performance of the proposed estimator on both synthetic and real-world data.Comment: 13 pages, to appear in International Conference on Artificial Intelligence and Statistics (AISTATS) 201