Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Land resources assessment of El-Galaba basin, South Egypt for the potentiality of agriculture expansion using remote sensing and GIS techniques

The socio-economic development in Egypt is based on land resources. Recently, the Egyptian government is interested in developing low desert zone areas which are located between the recent Nile flood plain and the limestone plateau, from the east and west sides, and represent an important source of aggregate materials. Therefore, this study was carried out to investigate the potentiality of El-Galaba basin soils which are located in the western part of the Aswan Governorate and are characterized by Wadi El-Kubbaniya for the horizontal agricultural expansion and their optimum agricultural use. The investigated area was remotely sensed to identify the landscape and its land resources. Terrain units were identified using draped Landsat 8 satellite image over Digital Terrain Model (DTM) to express the landscape and the associated soil mapping units. Fifteen mapping units were identified and grouped. Land capability evaluation was performed using Cervatana capability model. The results of capability modeling revealed about 3.33% of land with good use capability, 76.06% land with moderate use capability, and 0.08% marginal or non-productive land. The main capability limitations were soil and erosion risks. The Almagra model was used to produce the optimum cropping pattern and limitations of soil units. Matching the crop requirements with soil characteristics, optimum cropping pattern was obtained for wheat, corn, melon, potatoes, sunflower, sugar beet, Alfalfa, peach, citrus, and olive. The results of the study revealed the potentiality of El-Galaba basin for agricultural uses

Sustainability indicators for agricultural land use based on GIS spatial modeling in North of Sinai-Egypt

Sustainable agriculture focuses on production that renews resources. Egypt has a lot of sustainability constraints such as salinity and alkalinity, lack of infrastructure and credit utilization. The current study focuses on assessment of sustainability factors for agricultural utilization through integrated biophysical, economic viability and social acceptability in the North Sinai area. Sustainable agricultural spatial model (SASM) was developed using Arc GIS 10 to identify and classify the area, according to sustainability degree of agricultural utilization, where the factors of productivity, security, protection, economic viability, and social acceptability in the different mapping units were assessed. The investigated area is classified into three different classes, I, II, are covered in about 7% of the total area where land management practices are marginally below the threshold for sustainability located in the northern part of the study area, where the sustainability values are ranging between 0.1 and 0.3. The areas characterized as class III do not meet sustainability requirements where the sustainable values <0.1. The current work shows how the decision-makers can increase the land sustainability classes I, II to 10% of the total area by controlling just two factors: social and economic factors

Application of near-infrared reflectance for quantitative assessment of soil properties

Beginning with a discussion of reflectance spectroscopy, this article attempts to provide a review on fundamental concepts of reflectance spectroscopic techniques. Their applications as well as exploring the role of Near-infrared reflectance spectroscopy that would be used for monitoring and mapping soil characteristics. This technique began to be used in the second half of the 20th century for industrial purposes. Moreover, this article explores the potentiality of predicting soil properties based on spectroscopic measurements .Quantitative prediction of soil properties such as; salinity, organic carbon, soil moisture and heavy metals can be conducted using various calibration models – such models were developed depending on the measured soil laboratory analyses data and soil reflectance spectra thereby resampled to satellite images - to predict soil properties. The most common used models are stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), multivariate adaptive regression splines (MARS), principal component regression (PCR) and artificial neural networks (ANN). Those methods are required to quickly and accurately measure soil characteristics at field to improve soil management and conservation at local and regional scales. Visable-Near Infra Red (VIS-NIR) has been recommended as a quick tool for mapping soil properties. Furthermore, VIS-NIR reflection spectroscopy reduces the cost and time, therefore has a wonderful ability and potential use as a rapid soil analysis for both precision soil management and assessing soil quality. Keywords: Near infrared spectroscopy, Soil salinity, Soil moisture, Soil organic carbon, Soil surface features and soil contaminatio

Environmental management of fuelwood resources in Wadi Allaqi (Egypt) : final report

Includes 3 inserted page

Vis‐nir spectroscopy and satellite landsat‐8 oli data to map soil nutrients in arid conditions: A case study of the northwest coast of egypt

The mapping of soil nutrients is a key issue for numerous applications and research fields ranging from global changes to environmental degradation, from sustainable soil management to the precision agriculture concept. The characterization, modeling and mapping of soil properties at diverse spatial and temporal scales are key factors required for different environments. This paper is focused on the use and comparison of soil chemical analyses, Visible near infrared and shortwave infrared VNIR‐SWIR spectroscopy, partial least‐squares regression (PLSR), Ordinary Kriging (OK), and Landsat‐8 operational land imager (OLI) images, to inexpensively analyze and predict the content of different soil nutrients (nitrogen (N), phosphorus (P), and potassium (K)), pH, and soil organic matter (SOM) in arid conditions. To achieve this aim, 100 surface samples of soil were gathered to a depth of 25 cm in the Wadi El‐Garawla area (the northwest coast of Egypt) using chemical analyses and reflectance spectroscopy in the wavelength range from 350 to 2500 nm. PLSR was used firstly to model the relationship between the averaged values from the ASD spectroradiometer and the available N, P, and K, pH and SOM contents in soils in order to map the predicted value using Ordinary Kriging (OK) and secondly to retrieve N, P, K, pH, and SOM values from OLI images. Thirty soil samples were selected to verify the validity of the results. The randomly selected samples included the spatial diversity and characteristics of the study area. The prediction of available of N, P, K pH and SOM in soils using VNIR‐SWIR spectroscopy showed high performance (where R2 was 0.89, 0.72, 0.91, 0.65, and 0.75, respectively) and quite satisfactory results from Landsat‐8 OLI images (correlation R2 values 0.71, 0.68, 0.55, 0.62 and 0.7, respectively). The results showed that about 84% of the soils of Wadi El‐Garawla are characterized by low‐to‐moderate fertility, while about 16% of the area is characterized by high soil fertility. © MDPI AG. All rights reserved

Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article)

Pre-harvest prediction of a crop yield may prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. Remote sensing has numerous returns in the area of crop monitoring and yield prediction which are closely related to differences in soil, climate, and any biophysical and biochemical changes. Different remote techniques could be used for crop monitoring and yield prediction including multi and hyper spectral data, radar and lidar imagery. This study reviews the potentialities, advantages and disadvantages of each technique and the applicability of these techniques under different agricultural conditions. It also shows the different methods in which these techniques could be used efficiently. In addition, the study expects future scenarios of remote sensing applications in vegetation monitoring and the ways to overcome any obstacles that may face this work. It was found that using satellite data with high spatial resolution are still the most powerful method to be used for crop monitoring and to monitor crop parameters. Assessment of crop spectroscopic parameters through field or laboratory devices could be used to identify and quantify many crop biochemical and biophysical parameters. They could be also used as early indicators of plant infections; however, these techniques are not efficient for crop monitoring over large areas

First demonstration of a 2µm few-mode TDFA for mode division multiplexing

We report the first demonstration of an inline few-mode thulium doped fiber amplifier (TDFA) operating at 2µm for mode division multiplexed transmission. Similar gain and noise figure performance for both the LP01 and LP11 modes are obtained in a cladding pumped 2-mode group TDFA. A maximum gain of 18.3dB was measured at 1970nm with a 3dB gain bandwidth of 75nm while the average noise figure was measured to be between 7 and 8dB for wavelengths longer than 1970nm