Riemannian classification of single-trial surface EEG and sources during checkerboard and navigational images in humans

Objective Different visual stimuli are classically used for triggering visual evoked potentials comprising well-defined components linked to the content of the displayed image. These evoked components result from the average of ongoing EEG signals in which additive and oscillatory mechanisms contribute to the component morphology. The evoked related potentials often resulted from a mixed situation (power variation and phase-locking) making basic and clinical interpretations difficult. Besides, the grand average methodology produced artificial constructs that do not reflect individual peculiarities. This motivated new approaches based on single-trial analysis as recently used in the brain-computer interface field. Approach We hypothesize that EEG signals may include specific information about the visual features of the displayed image and that such distinctive traits can be identified by state-of-the-art classification algorithms based on Riemannian geometry. The same classification algorithms are also applied to the dipole sources estimated by sLORETA. Main results and significance We show that our classification pipeline can effectively discriminate between the display of different visual items (Checkerboard versus 3D navigational image) in single EEG trials throughout multiple subjects. The present methodology reaches a single-trial classification accuracy of about 84% and 93% for inter-subject and intra-subject classification respectively using surface EEG. Interestingly, we note that the classification algorithms trained on sLORETA sources estimation fail to generalize among multiple subjects (63%), which may be due to either the average head model used by sLORETA or the subsequent spatial filtering failing to extract discriminative information, but reach an intra-subject classification accuracy of 82%.

Effects of Climate on Exterior Wood Coating Performance: A Comparison of Three Industrial Coatings in a Warm-Summer Mediterranean and a Semi-Arid Climate in Oregon, USA

Wood exposed in exterior applications degrades and changes color due to weathering and fungal growth. Wood coatings can reduce the effects of weathering by reducing the damaging effects of ultraviolet light, reducing water absorption, and slowing fungal growth on the surface. Coating performance depends on the blend of resins, oils, and pigments and varies considerably among different wood species and conditions. Specific information describing expected service for different wood species and exposure conditions is not commonly available; certain combinations may work well in one climate or on one timber species, but underperform elsewhere. This study compared the performance of three industrial wood coatings on two wood species for two temperate climates under natural weathering conditions. Most of the coatings/species combinations lost their protective properties within 12 to 15 months; however, fungal growth was more prevalent at the wetter site than at the drier site for several combinations. Film-forming coatings often peeled and cracked, while penetrating coatings weathered and changed color relatively uniformly during the study. While no coating was completely effective, the results illustrate the benefits of using coatings that promote the development of natural, uniform-patinaed wood surfaces. The findings also guide coating maintenance programs for mass timber structures exposed to natural weathering conditions.

How do Hispanics/Latinos Perceive and Value the Return of Research Results?

Introduction: Interest in the return of research results has been increasing; however, little is known about how Hispanics/Latinos perceive and value receiving results. This study examined differences among Hispanics/Latinos by education and income in the experience and expectations about the return of research results, perceived value of specific types of information, and the least and most valuable specific information. Method: Retrospective observational design using a cross-sectional national survey sample of Hispanics/Latinos (n = 327). Results: Higher educational attainment was positively associated with the expectation to receive research results, likelihood to participate in research if given study findings, and likelihood to trust researchers if given results. Higher income was positively associated with the perceived value of getting results. Respondents with higher education specifically perceived greater value in information about how lifestyle and genetics affect their risk of disease, how genetics affect how they respond to medications, their ancestry, available clinical trials near them, and how to connect with other study participants. Respondents with higher income perceived greater value in information about how genetics affect their risk of disease and how they respond to medications. Conclusion: The findings offer important insights for planning research initiatives and for developing culturally targeted educational materials for Hispanics/Latinos.

Multi-cancer classification; an analysis of neural network complexity

Background: Cancer identification is generally framed as binary classification, normally discrimination of a control group from a single cancer group. However, such models lack any cancer-specific information, as they are only trained on one cancer type. The models fail to account for competing cancer risks. For example, an ostensibly healthy individual may have any number of different cancer types, and a tumor may originate from one of several primary sites. Pan-cancer evaluation requires a model trained on multiple cancer types, and controls, simultaneously, so that a physician can be directed to the correct area of the body for further testing. Methods: We introduce novel neural network models to address multi-cancer classification problems across several data types commonly applied in cancer prediction, including circulating miRNA expression, protein, and mRNA. In particular, we present an analysis of neural network depth and complexity, and investigate how this relates to classification performance. Comparisons of our models with state-of-the-art neural networks from the literature are also presented. Results: Our analysis evidences that shallow, feed-forward neural net architectures offer greater performance when compared to more complex deep feed-forward, Convolutional Neural Network (CNN), and Graph CNN (GCNN) architectures considered in the literature. Conclusion: The results show that multiple cancers and controls can be classified accurately using the proposed models, across a range of expression technologies in cancer prediction. Impact: This study addresses the important problem of pan-cancer classification, which is often overlooked in the literature. The promising results highlight the urgency for further research.

A simple method for unsupervised anomaly detection: An application to Web time series data

We propose a simple anomaly detection method that is applicable to unlabeled time series data and is sufficiently tractable, even for non-technical entities, by using the density ratio estimation based on the state space model. Our detection rule is based on the ratio of log-likelihoods estimated by the dynamic linear model, i.e. the ratio of log-likelihood in our model to that in an over-dispersed model that we will call the NULL model. Using the Yahoo S5 data set and the Numenta Anomaly Benchmark data set, publicly available and commonly used benchmark data sets, we find that our method achieves better or comparable performance compared to the existing methods. The result implies that it is essential in time series anomaly detection to incorporate the specific information on time series data into the model. In addition, we apply the proposed method to unlabeled Web time series data, specifically, daily page view and average session duration data on an electronic commerce site that deals in insurance goods to show the applicability of our method to unlabeled real-world data. We find that the increase in page view caused by e-mail newsletter deliveries is less likely to contribute to completing an insurance contract. The result also suggests the importance of the simultaneous monitoring of more than one time series.

ENHANCING READING COMPETENCE THROUGH THE BRAILLE SYSTEM FOR VISUALLY IMPAIRED PEOPLE: A PRELIMINARY STUDY

This paper addresses the issue of the development of the English language reading skills and subskills using the Braille System as the fundamental tool for visually impaired people. The purpose of this study was to determine the relationship between the use of the Braille System and the reading skill competence. This research was applied to 21 pupils with different blindness conditions and from 10 to 32 years old who belong to the Special Education School “Julius Dophner” in the city of Ambato, Ecuador. This preliminary study was carried out using a quasi-experimental design, where pre-tests and post-tests were applied during a three-week period. Pupils took active part in the reading of the different primers at the beginning and after the treatment process. A t-test was used to examine the hypothesis. The results revealed that the improvement in the English language reading for specific information subskill through the Braille system was meaningful. Additional testing should be done to validate the scores obtained by visually impaired students with the use of this tool. Pupils showed a positive and very enthusiastic attitude about the learning process of the English language through this tool with which they are familiar.

Impacts of Slow-Release Nitrogen Fertilizer Rates on the Morpho-Physiological Traits, Yield, and Nitrogen Use Efficiency of Rice under Different Water Regimes

The efficient use of water and fertilizer is vital for optimizing plant growth and yield in rice production. To achieve sustainable rice production and resource management, the ways in which applied water and nitrogen affect the root and shoot morpho-physiology, as well as yield, must be understood. In this study, a pot experiment was conducted to investigate the effects of slow-release nitrogen fertilizer (sulfur-coated urea) application at three levels (light nitrogen (NL), medium nitrogen (NM), and heavy nitrogen (NH)) on the growth, yield, and nitrogen use efficiency (NUE) of rice grown under three water regimes (wetting and soil saturation (WSS), wetting and moderate drying (WMD) and wetting and severe drying (WSD)). The results revealed that differences in water regimes and fertilizer rates led to significant differences in the roots, shoots, yield, and NUE of rice. Increasing the N dosage by 5% enhanced the root and biomass production by 16% in comparison with that of the other groups. The NH×WSS treatment produced the greatest root length, weight, density, active absorption, and oxidation. However, the integration of WSS × NL generated the maximum value of nitrogen apparent recovery efficiency (63.1% to 67.6%) and the greatest value of nitrogen partial factor productivity (39.9 g g−1 to 41.13 g g−1). Transmission electron microscopy (TEM) images showed that plants grown under high and medium nitrogen fertilizer rates with WSS had improved leaf mesophyll structure with normal starch grains, clear cell walls, and well-developed chloroplasts with tidy and well-arranged thylakoids. These results show that TEM images are useful for characterizing the nitrogen and water status of leaves in the sub-micrometer range and providing specific information regarding the leaf microstructure. The findings of this study suggest that the application of NH×WSS can produce improvements in growth traits and increase rice yield; however, the NL×WSS treatment led to greater NUE, and the authors recommend its usage in rice agriculture.

Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry

Purpose of Review Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.

The Formal Modeling Of Engineering Design Information By Means Of An Axiomatic System

<div>There is mounting evidence in the current literature which suggests that our collective understanding of engineering design is insufficient to support the continued growth of the engineering endeavor. Design theory is the emergent research field that addresses this problem by seeking to improve our understanding of, and thus our ability to, design. The goal of this author's work is to demonstrate that formal techniques of logic can improve our understanding of design. Specifically, a formal system called the Hybrid Model (HM) is presented; this system is a set-theoretic description of engineering design information that is valid independent of (a) the processes that generate or manipulate the information and (b) the role of the human designer. Because of this, HM is universally applicable to the representation of design-specific information throughout all aspects of the engineering enterprise. The fundamental unit in HM is a design entity, which is defined as a unit of information relevant to a design task. The axioms of HM define the structure of design entities and the explicit means by which they may be rationally organized. HM provides (a) a basis for building taxonomies of design entities, (b) a generalized approach for making statements about design entities independent of how the entities are generated or used, and (c) a formal syntactic notation for the standardization of design entity specification. Furthermore, HM is used as the foundation of DESIGNER, an extension to the Scheme programming language, providing a prototype-based object-oriented system for the static modeling of design information. Objects in the DESIGNER language satisfy the axioms of HM while providing convenient programming mechanisms to increase usability and efficiency. Several design-specific examples demonstrate the applicability of DESIGNER, and thus of HM as well, to the accurate representation of design information. </div>

Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis

Personal computer-based data collection and analysis systems may now be more resilient due to the recent advances in digital signal processing technology. The signal processing approach known as Speaker Recognition, uses the specific information contained in voice waves to automatically identify the speaker. For a single source, this study examines systems that can recognize a wide range of emotional states in speech. Since it offers insight into human brain states, it's a hot issue in the development during the interface between human and computer arrangement for speech processing. Mostly, it is necessary to recognize the emotional state of people in the arrangement. This research analyses an effort to discern various emotional stages such as anger, joy, neutral, fear and sadness by classification methods. The acoustic feature, a measure of unpredictability, is used in conjunction with a non-linear signal quantification approach to identify emotions. The unpredictability of all the emotional signals is included in a feature vector constructed from the calculated entropy measurements. In the next step, the acoustic features through speech signal are used for the training in the proposed neural network that are given to linear discriminator analysis approach for further greater classification with acoustic feature extraction. Besides, this research article compares the proposed work with various modern classifiers such as K- nearest neighbor, support vector machine and linear discriminator approach. Moreover, this proposed algorithm is based on acoustic features in Linear Discriminant Analysis (LDA) with acoustic feature extraction machine algorithm. The great advantage of this proposed algorithm is that it separates negative and positive features of emotions and provides good results during classification. According to the results from efficient cross-validation in the proposed framework, accessible sample of dataset of Emotional Speech, a single-source LDA classifier can recognize emotions in speech signals with above 90 percent of accuracy for various emotional stages.