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Bayesian model averaging (BMA) provides a coherent and systematic mechanism for accounting for model uncertainty. It can be regarded as an direct application of Bayesian inference to the problem of model selection, combined estimation and prediction. BMA produces a straightforward model choice criterion and less risky predictions. However, the application of BMA is not always straightforward, leading to diverse assumptions and situational choices on its different aspects. Despite the widespread application of BMA in the literature, there were not many accounts of these differences and trends besides a few landmark revisions in the late 1990s and early 2000s, therefore not accounting for advancements made in the last decades. In this work, we present an account of these developments through a careful content analysis of 820 articles in BMA published between 1996 and 2016. We also develop a conceptual classification scheme to better describe this vast literature, understand its trends and future directions and provide guidance for the researcher interested in both the application and development of the methodology. The results of the classification scheme and content review are then used to discuss the present and future of the BMA literature.

The Scientific Committee confirms that the Threshold of Toxicological Concern (TTC) is a pragmatic screening and prioritisation tool for use in food safety assessment. This Guidance provides clear step‐by‐step instructions for use of the TTC approach. The inclusion and exclusion criteria are defined and the use of the TTC decision tree is explained. The approach can be used when the chemical structure of the substance is known, there are limited chemical‐specific toxicity data and the exposure can be estimated. The TTC approach should not be used for substances for which EU food/feed legislation requires the submission of toxicity data or when sufficient data are available for a risk assessment or if the substance under consideration falls into one of the exclusion categories. For substances that have the potential to be DNA‐reactive mutagens and/or carcinogens based on the weight of evidence, the relevant TTC value is 0.0025 μg/kg body weight (bw) per day. For organophosphates or carbamates, the relevant TTC value is 0.3 μg/kg bw per day. All other substances are grouped according to the Cramer classification. The TTC values for Cramer Classes I, II and III are 30 μg/kg bw per day, 9 μg/kg bw per day and 1.5 μg/kg bw per day, respectively. For substances with exposures below the TTC values, the probability that they would cause adverse health effects is low. If the estimated exposure to a substance is higher than the relevant TTC value, a non‐TTC approach is required to reach a conclusion on potential adverse health effects. This publication is linked to the following EFSA Supporting Publications article: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2019.EN-1661/full

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

Dictionary learning has become a research hotspot. How to construct a robust dictionary is a key issue. In face recognition problem, differences in expressions, postures, and lighting conditions are key factors that affect the accuracy. Therefore, images of the same face can be very different in different situations. In real-world scenario, the samples of each face are very limited, which make it hard for the network to generalize well. Therefore, To solve the problem mentioned above, this paper proposes a method to construct a robust dictionary. In the method, virtual samples are generated to appropriately reflect the diversity of the face images, and based on this, two dictionaries are constructed and a corresponding fusion classification scheme is designed. The main advantages of this method are as follows: firstly, the simultaneous use of virtual samples and original samples can better reflect the facial appearance of each morphology, and the dictionaries obtained help to improve the robustness of the dictionary learning algorithm. Secondly, the proposed fusion classification scheme can give full play to the performance of the double dictionary learning algorithm. The results of out experiments show that the proposed algorithm is superior to some existing algorithms.

Australia is in the midst of an extinction crisis, having already lost 10% of terrestrial mammal fauna since European settlement and with hundreds of other species at high risk of extinction. The decline of the nation's biota is a result of an array of threatening processes; however, a comprehensive taxon‐specific understanding of threats and their relative impacts remains undocumented nationally. Using expert consultation, we compile the first complete, validated, and consistent taxon‐specific threat and impact dataset for all nationally listed threatened taxa in Australia. We confined our analysis to 1,795 terrestrial and aquatic taxa listed as threatened (Vulnerable, Endangered, or Critically Endangered) under Australian Commonwealth law. We engaged taxonomic experts to generate taxon‐specific threat and threat impact information to consistently apply the IUCN Threat Classification Scheme and Threat Impact Scoring System, as well as eight broad‐level threats and 51 subcategory threats, for all 1,795 threatened terrestrial and aquatic threatened taxa. This compilation produced 4,877 unique taxon–threat–impact combinations with the most frequently listed threats being Habitat loss, fragmentation, and degradation (n = 1,210 taxa), and Invasive species and disease (n = 966 taxa). Yet when only high‐impact threats or medium‐impact threats are considered, Invasive species and disease become the most prevalent threats. This dataset provides critical information for conservation action planning, national legislation and policy, and prioritizing investments in threatened species management and recovery. Australia is in the midst of an extinction crisis as a result of an array of threatening processes; however, a comprehensive taxon‐specific understanding of threats and their relative impacts remains undocumented nationally. Using expert consultation, we compile the first complete, validated, and consistent taxon‐specific threat and impact dataset for all 1,796 nationally listed threatened taxa in Australia. This compilation produced 4,877 unique taxon–threat combinations with the most frequently listed threats being Habitat loss, fragmentation, and degradation (n = 1,210 taxa), and Invasive species and disease (n = 966 taxa).

This study aims to develop different-classification-scheme-based building-seismic-resilience (BSR)-mapping models using random forest (RF) and a support vector machine (SVM). Based on a field survey of earthquake-damaged buildings in Shuanghe Town, the epicenter of the Changning M 5.8 earthquake that occurred on 17 June 2019, we selected 19 influencing factors for BSR assessment to establish a database. Based on three classification schemes for the description of BSR, we developed six machine learning assessment models for BSR mapping using RF and an SVM after optimizing the hyper-parameters. The validation indicators of model performance include precision, recall, accuracy, and F1-score as determined from the test sub-dataset. The results indicate that the RF- and SVM-based BSR models achieved prediction accuracies of approximately 0.64–0.94 for different classification schemes applied to the test sub-dataset. Additionally, the precision, recall, and F1-score indicators showed satisfactory values with respect to the BSR levels with relatively large sample sizes. The RF-based models had a lower tendency for overfitting compared to the SVM-based models. The performance of the BSR models was influenced by the quantity of total datasets, the classification schemes, and imbalanced data. Overall, the RF- and SVM-based BSR models can improve the evaluation efficiency of earthquake-damaged buildings in mountainous areas.

PurposeTo explore a new classification scheme for acute ocular burns.MethodsMedical records of 345 patients (450 eyes) with acute ocular burns treated at Shandong Eye Institute between January 2013 and January 2018 with a 12-month minimum follow-up were retrospectively reviewed. A total of 8 parameters in the acute phase were evaluated and graded on a scale from 0 to 3 according to their severity.ResultsThe key factors affecting the prognosis of acute ocular burns were conjunctival involvement (386 eyes, 85.8%), corneal epithelial defect (349 eyes, 77.6%), and limbal ischemia (244 eyes, 54.2%). Visual acuity in 181/450 eyes (40.2%) was worse than 6/60. The injury severity of the cornea, limbus, bulbar conjunctiva, eyelid, and fornix and intraocular signs in the acute phase was significantly correlated with the logarithm of the minimum angle of resolution (logMAR) visual acuity (correlation coefficient [R] 0.481–0.933, P < 0.0001) and corneal opacification, neovascularization, and symblepharon scores in the stable phase (R 0.513–0.855, P < 0.0001). The mean total score for the 8 parameters in the acute phase was 5.34 ± 4.04 (range 0–14); higher scores indicated worse visual acuity (R = 0.899, P < 0.0001). The total score for acute-phase parameters was significantly correlated with that for the stable-phase parameters (R = 0.872, P < 0.0001).ConclusionsThe severity of acute-phase parameters is significantly correlated with the final visual outcome and prognosis. The new grading scheme can help clinicians more accurately analyze the degree of ocular burns, determine a reasonable treatment protocol, and rationally evaluate the prognosis.

Seasonally dynamic plant communities present challenges for remote mapping, but estimating phenology can help identify periods of peak spectral distinction. While phenology is widely used in environmental and agricultural mapping, its broader ecological applications remain underexplored. Using a temperate wetland complex as a case study, we leveraged NDVI time series from Sentinel imagery to refine a wetland classification scheme by identifying periods of maximum plant community distinction. We estimated plant phenology with ground-reference points and mapped the study area using Random Forest (RF) with both Sentinel and PlanetScope imagery. Most plant communities showed distinct phenological variations between April–June (growing season) and September–October (transitional season). Merging phenologically similar communities improved classification accuracy, with April and September imagery yielding better results than the peak summer months. Combining both seasons achieved the highest classification accuracy (~77%), with key RF predictors including digital elevation, and near-infrared and tasseled cap indices. Despite its higher spatial resolution, PlanetScope underperformed compared to Sentinel, as spectral similarities between plant communities limited classification accuracy. While Sentinel provides valuable data, higher spectral resolution is needed for distinguishing similar plant communities. Integrating phenology into mapping frameworks can improve the detection of rare and ephemeral vegetation, aiding conservation efforts.

Despite a considerable expansion in the present therapeutic repertoire for other malignancy managements, mortality from head and neck cancer (HNC) has not significantly improved in recent decades. Moreover, the second primary cancer (SPC) diagnoses increased in patients with HNC, but studies providing evidence to support SPCs prediction in HNC are lacking. Several base classifiers are integrated forming an ensemble meta-classifier using a stacked ensemble method to predict SPCs and find out relevant risk features in patients with HNC. The balanced accuracy and area under the curve (AUC) are over 0.761 and 0.847, with an approximately 2% and 3% increase, respectively, compared to the best individual base classifier. Our study found the top six ensemble risk features, such as body mass index, primary site of HNC, clinical nodal (N) status, primary site surgical margins, sex, and pathologic nodal (N) status. This will help clinicians screen HNC survivors before SPCs occur.

Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.