An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Optimizing the AI Development Process by Providing the Best Support Environment

The purpose of this study is to investigate the development process for Artificial inelegance (AI) and machine learning (ML) applications in order to provide the best support environment. The main stages of ML are problem understanding, data management, model building, model deployment and maintenance. This project focuses on investigating the data management stage of ML development and its obstacles as it is the most important stage of machine learning development because the accuracy of the end model is relying on the kind of data fed into the model. The biggest obstacle found on this stage was the lack of sufficient data for model learning, especially in the fields where data is confidential. This project aimed to build and develop a framework for researchers and developers that can help solve the lack of sufficient data during data management stage. The framework utilizes several data augmentation techniques that can be used to generate new data from the original dataset which can improve the overall performance of the ML applications by increasing the quantity and quality of available data to feed the model with the best possible data. The framework was built using python language to perform data augmentation using deep learning advancements

The Effects of a Guided Imagery Intervention on the Working Memory of Primary Aged Students

Many practitioners view working memory as the temporary capacity to store and manipulate information. Current findings suggest a developmental trajectory of working memory and other executive functions. Limited research has been effective in improving working memory using short term methods; however, recent findings suggest guided imagery and mindfulness meditation improves working memory in children. This study examined whether or not a 30 day guided imagery intervention affected the working memory of students in the primary grades of an elementary school. Participants from a sample of convenience were randomly assigned to a guided imagery intervention (n = 12) or to a waitlist control group (n = 12) and received the intervention following the 30 day implementation. Pretest and post test data determined no interaction between the groups and pretest and posttest measures following interaction. Qualitative data from teacher reports note growth in the ability to complete tasks independently and following multistep directions. The study supports the feasibility of using a time limited guided imagery intervention with younger students during the school day to foster classroom climate and student mood. Study design elements hampered determining the impact of the guided imagery intervention on working memory and executive functioning. Additional studies may demonstrate these effects

Emerging approaches for data-driven innovation in Europe: Sandbox experiments on the governance of data and technology

Europe’s digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces

Modern Climatology - Full Text

Climatology, the study of climate, is no longer regarded as a single discipline that treats climate as something that fluctuates only within the unchanging boundaries described by historical statistics. The field has recognized that climate is something that changes continually under the influence of physical and biological forces and so, cannot be understood in isolation but rather, is one that includes diverse scientific disciplines that play their role in understanding a highly complex coupled “whole system” that is the Earth’s climate. The modern era of climatology is echoed in this book. On the one hand it offers a broad synoptic perspective but also considers the regional standpoint as it is this that affects what people need from climatology, albeit water resource managers or engineers etc. Aspects on the topic of climate change – what is often considered a contradiction in terms – is also addressed. It is all too evident these days that what recent work in climatology has revealed carries profound implications for economic and social policy; it is with these in mind that the final chapters consider acumens as to the application of what has been learned to date. This book is divided into four sections that cover sub-disciplines in climatology. The first section contains four chapters that pertain to synoptic climatology, i.e., the study of weather disturbances including hurricanes, monsoon depressions, synoptic waves, and severe thunderstorms; these weather systems directly impact humanity. The second section on regional climatology has four chapters that describe the climate features within physiographically defined areas. The third section is on climate change which involves both past (paleoclimate) and future climate: The first two chapters cover certain facets of paleoclimate while the third is centered towards the signals (observed or otherwise) of climate change. The fourth and final section broaches the sub-discipline that is often referred to as applied climatology; this represents the important goal of all studies in climatology–one that affects modes of living. Here, three chapters are devoted towards the application of climatological research that might have useful application for operational purposes in industrial, manufacturing, agricultural, technological and environmental affairs. Please click here to explore the components of this work.https://digitalcommons.usu.edu/modern_climatology/1014/thumbnail.jp

Non-destructive identification of defects and classification of Hass avocado fruits with the use of a hyperspectral image

Received: January 15th, 2022 ; Accepted: April 19th, 2022 ; Published: May 2nd, 2022 ; Correspondence: [email protected] analysis and instrumental analytical methods are used in determining the maturity and quality monitoring of avocado fruits, which are labor-intensive and do not allow the determination of fruit quality in real time. The use of hyperspectral imaging (HSI) methods in the range of 400–1,000 nm and of the multivariate analysis was demonstrated for a non-destructive grading of Hass avocado fruits into quality classes according to the number of hidden defects. Using the sensory analysis, avocado fruits were separated into quality classes according to the number of defects after being stored for 10 days. Development of a classification model included several steps: image recording and analysis using the ANOVA and PCA method, image segmentation (selection of ROI), pre-processing (SNV-correction, centering), selection of a multivariate classification method (PLS-DA, SIMCA) and a spectral range, model verification. The analysis of hyperspectral images of avocado fruits has detected spectral regions with the maximal variance responsible for the change of the content of pigments and moisture within the avocado fruit exocarp. Comparison of PLS-DA and SIMCA models on the basis of best accuracy and test-validation results was carried out. Comparison of models showed SIMCA model as the most efficient model for fruit classification into quality classes depending on the number of hidden defects. The implementation of the developed approach as a digital avocado fruit sorting system at different stages of the product life cycle is proposed

Emerging approaches for data-driven innovation in Europe

Europe's digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces

The Shock of Attention

Narrative Medicine is a clinical practice, a scholarly field, and a site of intensive research worldwide. This essay describes the inauguration of the field of narrative medicine in 2000 at Columbia University in New York, NY, USA and the principles and practices that have devolved from the initiating work. Clinical implications of narrative concepts of health care as learned from actual medical practice are described. The three movements of narrative medicine—attention, representation, and affiliation—are explored as means of engaging participants in creative acts of discovery and relation. Examples are provided of narratively-informed teaching and health care practice. Conceptual frameworks from aesthetic theory, phenomenology, literary and narrative theories, and cognitive sciences are advanced to portray the integrated study of individuals-in-the-world made possible by contemporary narrative medicine thought.  Emerging concepts of enchantment, embodiment, and enactivism suggest future directions for the field.

iTReX: Interactive exploration of mono- and combination therapy dose response profiling data

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application’s ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at (https://itrex.kitz-heidelberg.de).Peer reviewe