Farm forests, biomass poverty and chronic seasonal hunger.

2021 ◽  
Author(s):  
Nathan Morrow ◽  
Nancy B. Mock ◽  
Andrea Gatto ◽  
Andrea Colantoni ◽  
Luca Salvati
Keyword(s):  
Food Insecurity ◽  
Clear Trend ◽  
Data Set ◽  
Mixed Effect ◽  
Quantitative Evidence ◽  
Conditional Effects ◽  
Effect Regression ◽  
Crop Residue Burning ◽  
Land Holdings ◽  
First Time

Abstract Seasonal hunger persists as the most common food insecurity experience for millions of small dryland farmers. This study tests the inter-relationships among food insecurity, farm forests and biomass poverty using a longitudinal data set from the Amahara region of Ethiopia. These data form part of the Ethiopia Socio-economic Survey data that collected panel data over three survey rounds from 640 households. The unique population representative data set includes for the first time includes socio-economic, wellbeing micro-landuse measures including farm forests. Hierarchical mixed effect regression models assessed the relationship between biomass poverty and food insecurity as well as the conditional effects of biomass poverty among the poorest farmers and women headed households. During the three waves over a six-year study period, farmers reported increased stress from smaller land holdings, higher prices and climate related shocks. During the same period, a clear trend towards spontaneous dispersed afforestation is observed both by researchers and space-based satellite remote sensing. Dedicating approximately 10% of farm area to forest reduces Months of Food Insecurity by half. Greatest reductions in food insecurity from farm forests are reported by female headed, ultra-poor, and crop residue-burning households. Biomass poverty may therefore be a primary constraint to resilient food security on these farms. This investigation provides novel representative quantitative evidence of induced intensification with important implications of for nature-based solutions for healthy and resilient people and planet.

scholarly journals Potential of community-based risk estimates for improving hospital performance measures and discharge planning

2021 ◽  
Vol 10 (2) ◽  
pp. e001230
Author(s):  
Michael Reid ◽  
George Kephart ◽  
Pantelis Andreou ◽  
Alysia Robinson
Keyword(s):  
Discharge Planning ◽  
Care Transitions ◽  
Hospital Setting ◽  
Hospital Performance ◽  
Case Mix ◽  
Readmission Rates ◽  
Mixed Effect ◽  
Adjusted Risk ◽  
Community Risk ◽  
Effect Regression

BackgroundRisk-adjusted rates of hospital readmission are a common indicator of hospital performance. There are concerns that current risk-adjustment methods do not account for the many factors outside the hospital setting that can affect readmission rates. Not accounting for these external factors could result in hospitals being unfairly penalized when they discharge patients to communities that are less able to support care transitions and disease management. While incorporating adjustments for the myriad of social and economic factors outside of the hospital setting could improve the accuracy of readmission rates as a performance measure, doing so has limited feasibility due to the number of potential variables and the paucity of data to measure them. This paper assesses a practical approach to addressing this problem: using mixed-effect regression models to estimate case-mix adjusted risk of readmission by community of patients’ residence (community risk of readmission) as a complementary performance indicator to hospital readmission rates.MethodsUsing hospital discharge data and mixed-effect regression models with a random intercept for community, we assess if case-mix adjusted community risk of readmission can be useful as a quality indicator for community-based care. Our outcome of interest was an unplanned repeat hospitalisation. Our primary exposure was community of residence.ResultsCommunity of residence is associated with case-mix adjusted risk of unplanned repeat hospitalisation. Community risk of readmission can be estimated and mapped as indicators of the ability of communities to support both care transitions and long-term disease management.ConclusionContextualising readmission rates through a community lens has the potential to help hospitals and policymakers improve discharge planning, reduce penalties to hospitals, and most importantly, provide higher quality care to the people that they serve.

Intuitionistic fuzzy two-factor variance analysis of movie ticket sales

2021 ◽  
pp. 1-11
Author(s):  
Velichka Traneva ◽  
Stoyan Tranev
Keyword(s):  
Comparative Analysis ◽  
Real Data ◽  
Intuitionistic Fuzzy Sets ◽  
Real Numbers ◽  
Data Set ◽  
Important Method ◽  
Ticket Sales ◽  
Intuitionistic Fuzzy ◽  
The One ◽  
First Time

Analysis of variance (ANOVA) is an important method in data analysis, which was developed by Fisher. There are situations when there is impreciseness in data In order to analyze such data, the aim of this paper is to introduce for the first time an intuitionistic fuzzy two-factor ANOVA (2-D IFANOVA) without replication as an extension of the classical ANOVA and the one-way IFANOVA for a case where the data are intuitionistic fuzzy rather than real numbers. The proposed approach employs the apparatus of intuitionistic fuzzy sets (IFSs) and index matrices (IMs). The paper also analyzes a unique set of data on daily ticket sales for a year in a multiplex of Cinema City Bulgaria, part of Cineworld PLC Group, applying the two-factor ANOVA and the proposed 2-D IFANOVA to study the influence of “ season ” and “ ticket price ” factors. A comparative analysis of the results, obtained after the application of ANOVA and 2-D IFANOVA over the real data set, is also presented.

A comparison of methodological approaches to the subfamilial classification of the Naididae (Oligochaeta)

1987 ◽  
Vol 65 (3) ◽  
pp. 691-707 ◽  
Author(s):  
A. F. L. Nemec ◽  
R. O. Brinkhurst
Keyword(s):  
Data Matrix ◽  
Jaccard Coefficient ◽  
Data Set ◽  
Parsimony Method ◽  
Fit Statistics ◽  
Reconstruction Methods ◽  
Linkage Methods ◽  
The Family ◽  
First Time

A data matrix of 23 generic or subgeneric taxa versus 24 characters and a shorter matrix of 15 characters were analyzed by means of ordination, cluster analyses, parsimony, and compatibility methods (the last two of which are phylogenetic tree reconstruction methods) and the results were compared inter alia and with traditional methods. Various measures of fit for evaluating the parsimony methods were employed. There were few compatible characters in the data set, and much homoplasy, but most analyses separated a group based on Stylaria from the rest of the family, which could then be separated into four groups, recognized here for the first time as tribes (Naidini, Derini, Pristinini, and Chaetogastrini). There was less consistency of results within these groups. Modern methods produced results that do not conflict with traditional groupings. The Jaccard coefficient minimizes the significance of symplesiomorphy and complete linkage avoids chaining effects and corresponds to actual similarities, unlike single or average linkage methods, respectively. Ordination complements cluster analysis. The Wagner parsimony method was superior to the less flexible Camin–Sokal approach and produced better measure of fit statistics. All of the aforementioned methods contain areas susceptible to subjective decisions but, nevertheless, they lead to a complete disclosure of both the methods used and the assumptions made, and facilitate objective hypothesis testing rather than the presentation of conflicting phylogenies based on the different, undisclosed premises of manual approaches.

scholarly journals Prediction of IL4 Inducing Peptides

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Sandeep Kumar Dhanda ◽  
Sudheer Gupta ◽  
Pooja Vir ◽  
G. P. S. Raghava
Keyword(s):  
Mhc Class Ii ◽  
Critical Role ◽  
Interleukin 4 ◽  
Th2 Response ◽  
Data Set ◽  
T Helper 2 ◽  
Cell Responses ◽  
Antibody Class ◽  
First Time ◽  
Motif Information

The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response.

Decision fusion-based approach for content-based image classification

2017 ◽  
Vol 10 (3) ◽  
pp. 310-331 ◽  
Author(s):  
Sudeep Thepade ◽  
Rik Das ◽  
Saurav Ghosh
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Image Recognition ◽  
Extraction Techniques ◽  
Data Set ◽  
Content Type ◽  
Knowledge Fusion ◽  
Knn Classifier ◽  
Query Classification ◽  
First Time

Purpose Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques. Design/methodology/approach Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. Findings The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose. Originality/value To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

The NOVAC database of volcanic SO2 emissions

2021 ◽  
Author(s):  
Bo Galle ◽  
Keyword(s):  
Sulphur Dioxide ◽  
Meteorological Conditions ◽  
So2 Emissions ◽  
Volcanic Gas ◽  
Data Set ◽  
Gas Emissions ◽  
First Time ◽  
Global Data ◽  
Web Repository ◽  
The Web

<p>We present a detailed global data-set of volcanic sulphur dioxide (SO2) emissions during the period 2005-2017. Measurements were obtained by scanning-DOAS instruments of the NOVAC network at 32 volcanoes, and processed using a standardized procedure. We reveal the daily statistics of volcanic gas emissions under a variety of volcanological and meteorological conditions. Data from several volcanoes are presented for the first time. Our results  are compared with yearly averages derived from measurements from space by the Aura/OMI instrument and with historical inventories of GEIA. This comparison shows some interesting differences which reasons are briefly discussed. Data is openly available through the web repository at https://novac.chalmers.se/.</p>

scholarly journals Orbital and atmospheric characterization of the planet within the gap of the PDS 70 transition disk

2018 ◽  
Vol 617 ◽  
pp. L2 ◽  
Author(s):  
A. Müller ◽  
M. Keppler ◽  
Th. Henning ◽  
M. Samland ◽  
G. Chauvin ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Near Infrared ◽  
Direct Detection ◽  
Spectral Energy Distribution ◽  
Spectral Energy ◽  
Infrared Range ◽  
Data Set ◽  
Cloud Properties ◽  
Spectrophotometric Data ◽  
First Time

Context. The observation of planets in their formation stage is a crucial but very challenging step in understanding when, how, and where planets form. PDS 70 is a young pre-main sequence star surrounded by a transition disk, in the gap of which a planetary-mass companion has recently been discovered. This discovery represents the first robust direct detection of such a young planet, possibly still at the stage of formation. Aims. We aim to characterize the orbital and atmospheric properties of PDS 70 b, which was first identified on May 2015 in the course of the SHINE survey with SPHERE, the extreme adaptive-optics instrument at the VLT. Methods. We obtained new deep SPHERE/IRDIS imaging and SPHERE/IFS spectroscopic observations of PDS 70 b. The astrometric baseline now covers 6 yr, which allowed us to perform an orbital analysis. For the first time, we present spectrophotometry of the young planet which covers almost the entire near-infrared range (0.96–3.8 μm). We use different atmospheric models covering a large parameter space in temperature, log g, chemical composition, and cloud properties to characterize the properties of the atmosphere of PDS 70 b. Results. PDS 70 b is most likely orbiting the star on a circular and disk coplanar orbit at ~22 au inside the gap of the disk. We find a range of models that can describe the spectrophotometric data reasonably well in the temperature range 1000–1600 K and log g no larger than 3.5 dex. The planet radius covers a relatively large range between 1.4 and 3.7 RJ with the larger radii being higher than expected from planet evolution models for the age of the planet of 5.4 Myr. Conclusions. This study provides a comprehensive data set on the orbital motion of PDS 70 b, indicating a circular orbit and a motion coplanar with the disk. The first detailed spectral energy distribution of PDS 70 b indicates a temperature typical of young giant planets. The detailed atmospheric analysis indicates that a circumplanetary disk may contribute to the total planetflux.

scholarly journals Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI

2016 ◽  
Vol 16 (1) ◽  
pp. 47-69 ◽  
Author(s):  
R. Alfaro-Contreras ◽  
J. Zhang ◽  
J. R. Campbell ◽  
J. S. Reid
Keyword(s):  
Interannual Variability ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
First Time ◽  
Aerosol Index

Abstract. Seven and a half years (June 2006 to November 2013) of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol and cloud layer products are compared with collocated Ozone Monitoring Instrument (OMI) aerosol index (AI) data and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products in order to investigate variability in estimates of biannual and monthly above-cloud aerosol (ACA) events globally. The active- (CALIOP) and passive-based (OMI-MODIS) techniques have their advantages and caveats for ACA detection, and thus both are used to derive a thorough and robust comparison of daytime cloudy-sky ACA distribution and climatology. For the first time, baseline above-cloud aerosol optical depth (ACAOD) and AI thresholds are derived and examined (AI  =  1.0, ACAOD  =  0.015) for each sensor. Both OMI-MODIS and CALIOP-based daytime spatial distributions of ACA events show similar patterns during both study periods (December–May) and (June–November). Divergence exists in some regions, however, such as Southeast Asia during June through November, where daytime cloudy-sky ACA frequencies of up to 10 % are found from CALIOP yet are non-existent from the OMI-based method. Conversely, annual cloudy-sky ACA frequencies of 20–30 % are reported over northern Africa from the OMI-based method yet are largely undetected by the CALIOP-based method. Using a collocated OMI-MODIS-CALIOP data set, our study suggests that the cloudy-sky ACA frequency differences between the OMI-MODIS- and CALIOP-based methods are mostly due to differences in cloud detection capability between MODIS and CALIOP as well as QA flags used. An increasing interannual variability of  ∼  0.3–0.4 % per year (since 2009) in global monthly cloudy-sky ACA daytime frequency of occurrence is found using the OMI-MODIS-based method. Yet, CALIOP-based global daytime ACA frequencies exhibit a near-zero interannual variability. Further analysis suggests that the OMI-derived interannual variability in cloudy-sky ACA frequency may be affected by OMI row anomalies in later years. A few regions are found to have increasing slopes in interannual variability in cloudy-sky ACA frequency, including the Middle East and India. Regions with slightly negative slopes of the interannual variability in cloudy-sky ACA frequencies are found over South America and China, while remaining regions in the study show nearly zero change in ACA frequencies over time. The interannual variability in ACA frequency is not, however, statistically significant on both global and regional scales, given the relatively limited sample sizes. A longer data record of ACA events is needed in order to establish significant trends of ACA frequency regionally and globally.

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