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2022 ◽  
Vol 75 ◽  
pp. 102477
Author(s):  
A.S.I. Vilaça ◽  
L. Simão ◽  
O.R.K. Montedo ◽  
A.P. Novaes de Oliveira ◽  
F. Raupp-Pereira

Lupus ◽  
2021 ◽  
pp. 096120332110625
Author(s):  
Kimberley Yuen ◽  
Dorcas Beaton ◽  
Kathleen Bingham ◽  
Patricia Katz ◽  
Jiandong Su ◽  
...  

Objective We previously demonstrated the utility of the Automated Neuropsychological Assessment Metrics (ANAM) for screening cognitive impairment (CI) in patients with systemic lupus erythematosus (SLE) and developed composite indices for interpreting ANAM results. Our objectives here were to provide further support for the ANAM’s concurrent criterion validity against the American College of Rheumatology neuropsychological battery (ACR-NB), identify the most discriminatory subtests and scores of the ANAM for predicting CI, and provide a new approach to interpret ANAM results using Classification and Regression Tree (CART) analysis. Methods 300 adult SLE patients completed an adapted ACR-NB and ANAM on the same day. As per objectives, six models were built using combinations of ANAM subtests and scores and submitted to CART analysis. Area under the curve (AUC) was calculated to evaluate the ANAM’s criterion validity compared to the adapted ACR-NB; the most discriminatory ANAM subtests and scores in each model were selected, and performance of models with the highest AUCs were compared to our previous composite indices; decision trees were generated for models with the highest AUCs. Results Two models had excellent AUCs of 86 and 89%. Eight most discriminatory ANAM subtests and scores were identified. Both models demonstrated higher AUCs against our previous composite indices. An adapted decision tree was created to simplify the interpretation of ANAM results. Conclusion We provide further validity evidence for the ANAM as a valid CI screening tool in SLE. The decision tree improves interpretation of ANAM results, enhancing clinical utility.


Author(s):  
M. A. Günen

Abstract. Technical and physical limitations often do not allow images to be acquired with high spatial and spectral resolution. Pansharpened images obtained by fusing high spatial resolution panchromatic images and multi-spectral images are widely used in GIS applications. In this study, it is aimed to increase the spatial resolution of the RASAT and Landsat-8 multispectral satellite images with synthetic Sentinel-2 panchromatic data. Six different pansharpening methods were used to test the success of the synthetic panchromatic data generation method using dataset with two different land use/land cover properties. Seven full reference image quality assessment metrics and two referenceless image quality assessment metrics were used to perform quantitative comparison.


2021 ◽  
Vol 17 (6) ◽  
pp. 2583-2605
Author(s):  
Sooin Yun ◽  
Jason E. Smerdon ◽  
Bo Li ◽  
Xianyang Zhang

Abstract. Spatiotemporal paleoclimate reconstructions that seek to estimate climate conditions over the last several millennia are derived from multiple climate proxy records (e.g., tree rings, ice cores, corals, and cave formations) that are heterogeneously distributed across land and marine environments. Assessing the skill of the methods used for these reconstructions is critical as a means of understanding the spatiotemporal uncertainties in the derived reconstruction products. Traditional statistical measures of skill have been applied in past applications, but they often lack formal null hypotheses that incorporate the spatiotemporal characteristics of the fields and allow for formal significance testing. More recent attempts have developed assessment metrics to evaluate the difference of the characteristics between two spatiotemporal fields. We apply these assessment metrics to results from synthetic reconstruction experiments based on multiple climate model simulations to assess the skill of four reconstruction methods. We further interpret the comparisons using analysis of empirical orthogonal functions (EOFs) that represent the noise-filtered climate field. We demonstrate that the underlying features of a targeted temperature field that can affect the performance of CFRs include the following: (i) the characteristics of the eigenvalue spectrum, namely the amount of variance captured in the leading EOFs; (ii) the temporal stability of the leading EOFs; (iii) the representation of the climate over the sampling network with respect to the global climate; and (iv) the strength of spatial covariance, i.e., the dominance of teleconnections, in the targeted temperature field. The features of climate models and reconstruction methods identified in this paper demonstrate more detailed assessments of reconstruction methods and point to important areas of testing and improving real-world reconstruction methods.


Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1739
Author(s):  
Carl Nordman ◽  
Don Faber-Langendoen ◽  
Joanne Baggs

Open woodlands dominated by shortleaf pine (Pinus echinata Mill.) and oak are historically an important component of the landscape across the southeastern United States. These ecosystems support numerous wildlife species, many of which have declined in recent years as the amount and condition of their habitat have declined. Land managers and private landowners need guidance on how to efficiently and accurately quantify the condition and wildlife habitat value of the pine stands that they manage. Here we provide a set of rapid assessment metrics, based on NatureServe’s ecological integrity assessment (EIA) method, to (a) identify exemplary tracts that provide the best habitat for key wildlife species, and (b) monitor restoration efforts to assess progress toward the improved quality of existing tracts. To ensure an ecologically appropriate scaling of metrics, we distinguished six types of shortleaf pine–oak woodland: A.—Interior Highlands shortleaf pine–oak (including A.1—shortleaf pine–oak forest and woodlands; A.2—shortleaf pine–bluestem woodlands); B—montane longleaf pine–shortleaf pine woodlands; C—southern Appalachian pine–oak woodlands; D—West Gulf coastal plain shortleaf pine–oak woodlands; and E—southeast coastal plain and Piedmont shortleaf pine–oak woodlands. We relied on a narrative conceptual model and peer review-based indicator selection to identify a core set of 15 stand-level metrics (two were optional). Individual assessment points (thresholds) and ratings (Excellent, Good, Fair, and Poor) were developed that were sensitive to the distinct attributes of each of the five shortleaf pine–oak and Appalachian pine–oak types. Values for the metrics can all be collected using rapid field methods, such as using basal area prisms and ocular (visual) estimates of cover. Protocols for the consistent application of these EIA methods are provided. A case study is presented from the Cherokee National Forest in Tennessee. These methods provide improved and rapid EIA metrics for all shortleaf pine–oak ecosystems in the southeastern US to help guide conservation-minded landowners in assessing the biodiversity and priority wildlife values of shortleaf pine–oak and southern Appalachian pine–oak ecosystems.


Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1708
Author(s):  
Bashir B. Tiamiyu ◽  
Boniface K. Ngarega ◽  
Xu Zhang ◽  
Huajie Zhang ◽  
Tianhui Kuang ◽  
...  

Understanding how species have adapted and responded to past climate provides insights into the present geographical distribution and may improve predictions of how biotic communities will respond to future climate change. Therefore, estimating the distribution and potentially suitable habitats is essential for conserving sensitive species such as Garuga forrestii W.W.Sm., a tree species endemic to China. The potential climatic zones of G. forrestii were modelled in MaxEnt software using 24 geographic points and nine environmental variables for the current and future (2050 and 2070) conditions under two climate representative concentration pathways (RCP4.5 and RCP8.5) scenarios. The resulting ecological niche models (ENMs) demonstrated adequate internal assessment metrics, with all AUC and TSS values being >0.79 and a pROC of >1.534. Our results also showed that the distribution of G. forrestii was primarily influenced by temperature seasonality (% contribution = 12%), elevation (% contribution = 27.5%), and precipitation of the wettest month (% contribution = 35.6%). Our findings also indicated that G. forrestii might occupy an area of 309,516.2 km2 in southwestern China. We note that the species has a potential distribution in three provinces, including Yunnan, Sichuan, and Guangxi. A significant decline in species range is observed under the future worst case of high-emissions scenario (RCP8.5), with about 19.5% and 20% in 2050 and 2070, respectively. Similarly, higher elevations shift northward to southern parts of Sichuan province in 2050 and 2070. Thus, this study helps highlight the vulnerability of the species, response to future climate and provides an insight to assess habitat suitability for conservation management.


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8020
Author(s):  
Ahmad Kamal Mohd Nor ◽  
Srinivasa Rao Pedapati ◽  
Masdi Muhammad ◽  
Víctor Leiva

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models’ adoption in the industry.


2021 ◽  
Author(s):  
Stéphan Vincent-Lancrin

As the global economy and workforce are constantly being diversified with a greater emphasis on technology, 21st Century citizens are required to acquire basic digital literacy competencies. In this brief, we examine the concept of literacy and digital literacy. Then, we review the latest digital literacy studies in the United Nations Educational, Scientific and Cultural Organization (UNESCO), the European Commission, the United Kingdom, and the United States. Lastly, we provide suggestions by comparing digital literacy studies, including ICT studies, in South Korea with international literacy assessment metrics. This brief aims to contribute to developing digital literacy measurements applicable to ICT in education internationally and mitigate the digital divide.


Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 464
Author(s):  
Stefanos Balaskas ◽  
Maria Rigou

This article investigates the effect of personality traits on the attitude of web users towards online advertising. Utilizing and analyzing personality traits along with possible correlations between these traits and their influence on consumers’ buying behavior is crucial not only in research studies; this also holds for commercial implementations, as it allows businesses to set up and run sophisticated and strategic campaign designs in the field of digital marketing. This article starts with a literature review on advertisement recall and personality traits, which is followed by the procedure and processes undertaken to conduct the experiment, construct the online store, and design and place the advertisements. Collected data from the personality questionnaire and the two experiment questionnaires (pre and post-test) are presented using descriptive statistics, and data collected from the eye-tracking are analyzed using visual behavior assessment metrics. The results show that personality traits and especially honesty–humility can prove to be a predictive force for some important aspects of banner advertisement recognizability.


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