Attribute-based Analysis Research Articles

Bakestones in Northern Norway: An Archaeological Witness to Medieval Foodways and Maritime Trade

BAKESTONES ARE A 11TH-CENTURY EARLY MEDIEVAL INNOVATION associated with flatbread production that reflect a distinctive Norwegian food culture spanning some 600 years. Large scale manufacture of chlorite schist bakestones at Ølve-Hatlestrand, western Norway, was linked to further distribution in Norway from the port of Bergen. Bakestones were an important item of maritime trade transported to northern Norway in exchange for stockfish (dried cod) and are most commonly found in coastal fishing villages and settlement mound sites. An attribute-based analysis of bakestones from northern Norway in the Arctic University Museum of Norway archaeological collection, the first comprehensive study outside of medieval towns, explores the role of bakestones for Norse/Norwegian maritime settlement and as a valued trade commodity, as demonstrated by reuse of bakestone fragments. In addition to documenting spatial and temporal bakestone distribution, geochemical (XRF) characterisation and a preliminary analysis of organic residues were also undertaken. Although bakestones are primarily associated with Norwegian settlement, their role in contexts with a combination of indigenous Sámi and Norse/Norwegian elements is also evaluated.

High Hemoglobin and High ESA Responsiveness Are Good Prognostic Factors in CKD Patients: An Attribute-Based Analysis Using a Cross-Classification Approach in the BRIGHTEN Study

High Hemoglobin and High ESA Responsiveness Are Good Prognostic Factors in CKD Patients: An Attribute-Based Analysis Using a Cross-Classification Approach in the BRIGHTEN Study

Attribute-based analysis of SDGs penetration level among Shinkin Bank staff

Attribute-based analysis of SDGs penetration level among Shinkin Bank staff

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice.

Arrhythmias are common, affecting millions of patients worldwide. Current treatment strategies are associated with significant side effects and remain ineffective in many patients. To improve patient care, novel and innovative therapeutic concepts causally targeting arrhythmia mechanisms are needed. To study the complex pathophysiology of arrhythmias, suitable animal models are necessary, and mice have been proven to be ideal model species to evaluate the genetic impact on arrhythmias, to investigate fundamental molecular and cellular mechanisms, and to identify potential therapeutic targets. Implantable telemetry devices are among the most powerful tools available to study electrophysiology in mice, allowing continuous ECG recording over a period of several months in freely moving, awake mice. However, due to the huge number of data points (>1 million QRS complexes per day), analysis of telemetry data remains challenging. This article describes a step-by-step approach to analyze ECGs and to detect arrhythmias in long-term telemetry recordings using the software, Ponemah, with its analysis modules, ECG Pro and Data Insights, developed by Data Sciences International (DSI). To analyze basic ECG parameters, such as heart rate, P wave duration, PR interval, QRS interval, or QT duration, an automated attribute analysis was performed using Ponemah to identify P, Q, and T waves within individually adjusted windows around detected R waves. Results were then manually reviewed, allowing adjustment of individual annotations. The output from the attribute-based analysis and the pattern recognition analysis was then used by the Data Insights module to detect arrhythmias. This module allows an automatic screening for individually defined arrhythmias within the recording, followed by a manual review of suspected arrhythmia episodes. The article briefly discusses challenges in recording and detecting ECG signals, suggests strategies to improve data quality, and provides representative recordings of arrhythmias detected in mice using the approach described above.

Proposal for Employing User-Generated Content as a Data Source for Measuring Tourism Destination Image

The user-generated content (UGC) published on the internet offers great advantages as a source of data. This study proposes a way to make better use of its potential to measure tourism destination image, based on the recommendations of Echtner and Ritchie. These authors indicate that in order to obtain a comprehensive picture of tourism destinations, it is necessary to carry out a holistic and attribute-based analysis using unstructured and structured data, respectively. Since UGC provides both types of data, here we propose its use to study and compare the images of five tourist cities. Our results demonstrate that UGC is a valid source for the study of tourism destination image, confirming the need to adopt a holistic and attribute-based approach to this concept. They also show that not all attributes influence the overall impression of a destination.

Revisiting Video Saliency Prediction in the Deep Learning Era.

Predicting where people look in static scenes, a.k.a visual saliency, has received significant research interest recently. However, relatively less effort has been spent in understanding and modeling visual attention over dynamic scenes. This work makes three contributions to video saliency research. First, we introduce a new benchmark, called DHF1K (Dynamic Human Fixation 1K), for predicting fixations during dynamic scene free-viewing, which is a long-time need in this field. DHF1K consists of 1K high-quality elaborately-selected video sequences annotated by 17 observers using an eye tracker device. The videos span a wide range of scenes, motions, object types and backgrounds. Second, we propose a novel video saliency model, called ACLNet (Attentive CNN-LSTM Network), that augments the CNN-LSTM architecture with a supervised attention mechanism to enable fast end-to-end saliency learning. The attention mechanism explicitly encodes static saliency information, thus allowing LSTM to focus on learning a more flexible temporal saliency representation across successive frames. Such a design fully leverages existing large-scale static fixation datasets, avoids overfitting, and significantly improves training efficiency and testing performance. Third, we perform an extensive evaluation of the state-of-the-art saliency models on three datasets : DHF1K, Hollywood-2, and UCF sports. An attribute-based analysis of previous saliency models and cross-dataset generalization are also presented. Experimental results over more than 1.2K testing videos containing 400K frames demonstrate that ACLNet outperforms other contenders and has a fast processing speed (40 fps using a single GPU). Our code and all the results are available at https://github.com/wenguanwang/DHF1K.

Consumer attitudes towards nanotechnology in food products: an attribute-based analysis

Purpose Nanotechnology is a technology that holds much promise for food production. It is however not clear to what extent consumers will accept different types of nanotechnologies in food products. The paper researches consumer attitudes towards differing applications of food nanotechnologies. Design/methodology/approach Using an attribute-based approach, we compare a product with three different nanotechnology application types (during production, implemented in packaging, and as additive to the food) with the same product without any nanotechnology attributes. Findings Adding a nanotechnology attribute is shown to contribute to the overall product attitude, without substantially affecting the contribution of the other product attributes to overall product attitude. There are no indications that nanotechnology provides a non-compensatory barrier against acceptance. Risk perception increased for applications proximate to consumers while benefits remained similar across conditions. Practical implications Low distance applications (e.g., nano-additives that enter the body) are perceived as riskier, but not necessarily more beneficial, implying they will be rejected more readily by consumers. Food producers should primarily focus on reducing consumers’ risk perceptions of nanotechnologies and providing (and communicating clearly) the end-user benefits obtained from the technology. Originality/value This study shows that introducing nanotechnology into a product has an almost uniquely additive contribution to attitude. The study aimed at relevant nanotechnology applications for food products, as opposed to general attitudes towards technologies. The study also confirms in an experimental setting earlier exploratory findings that food applications closer to the end user are less acceptable and provides a case for nanotechnology food innovations.

Attribute-based analysis of time-lapse ground-penetrating radar data

Analysis of time-lapse ground-penetrating radar (GPR) data can provide information regarding subsurface hydrological processes, such as preferential flow. However, the analysis of time-lapse data is often limited by data quality; for example, for noisy input data, the interpretation of difference images is often difficult. Motivated by modern image-processing tools, we have developed two robust GPR attributes, which allow us to distinguish amplitude (contrast similarity) and time-shift (structural similarity) variations related to differences between individual time-lapse GPR data sets. We tested and evaluated our attributes using synthetic data of different complexity. Afterward, we applied them to a field data example, in which subsurface flow was induced by an artificial rainfall event. For all examples, we identified our structural similarity attribute to be a robust measure for highlighting time-lapse changes also in data with low signal-to-noise ratios. We determined that our new attribute-based workflow is a promising tool to analyze time-lapse GPR data, especially for imaging subsurface hydrological processes.

Subsurface Utility Extraction and Characterization: Combining GPR Symmetry and Polarization Attributes

Polarization of the electromagnetic wavefield has significant implications for the acquisition and interpretation of ground-penetrating radar (GPR) data. Based on the geometrical and physical properties of the subsurface scatterer and the physical properties of its surrounding material, strong polarization phenomena might occur. Here, we develop an attribute-based analysis approach to extract and characterize buried utility pipes using two broadside antenna configurations. First, we enhance and extract the utilities by making use of their distinct symmetric nature through the application of a symmetry-enhancing image-processing algorithm known as phase symmetry. Second, we assess the polarization characteristics by calculating two attributes (polarization angle and linearity) using principal component analysis. Combination of attributes derived from these steps into a novel depolarization attribute allows one to efficiently detect and distinguish different utilities present within 3-D GPR data. The performance of our analysis approach is illustrated using synthetic examples and evaluated using field examples (including a dual-configuration 3-D data set) collected across a field site, where detailed ground-truth information is available. Our results demonstrate that the proposed approach allows for a more detailed extraction and combination of utility relevant information compared to approaches relying on single-component data and, thus, eases the interpretation of multicomponent GPR data sets.

Attribute-based analysis of hunters’ lease preferences

Understanding of hunter preferences for hunting lease attributes is important to landowners because such knowledge provides key information for managing and marketing fee-hunting in order to maximize revenues. Premised on this insight, we used attribute based modeling to investigate how hunter preferences for potential leases were influenced by lease and hunter-specific attributes. A mail survey of Mississippi licensed hunters provided the necessary data. Estimation results based on McFadden conditional logit regression suggested that lease attributes including game diversity, lease location relative to hunter residence, lease size, lease duration and lease rate influenced willingness to pay for additional units of lease attributes. Depending on the specific levels of these attributes, WTP could vary as much as $5.70 per acre. Of the hunter-specific attributes, age and income significantly influenced hunter decision to buy a lease or opt for status quo. Results of this study should assist landowners in increasing financial returns from fee-hunting endeavors through appropriate changes to their hunting access policies and wildlife management activities in response to hunter preferences regarding lease attributes.