Oil palm counting and age estimation from WorldView-3 imagery and LiDAR data using an integrated OBIA height model and regression analysis

Copyright © 2018 Hossein Mojaddadi Rizeei et al. The current study proposes a new method for oil palm age estimation and counting from Worldview-3 satellite image and light detection and range (LiDAR) airborne imagery. A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting. The sensitivity analysis was conducted on four SVM kernel types with associated segmentation parameters to obtain the optimal crown coverage delineation. Extracting tree's crown was integrated with height model and multiregression methods to accurately estimate the age of trees. The multiregression model with multikernel sizes was examined to achieve the most optimized model for age estimation. Applied models were trained and examined over five different oil palm plantations. The results of oil palm counting had an overall accuracy of 98.80%, while the overall accuracy of age estimation showed 84.91%, over all blocks. The relationship between tree's height and age was significant which supports the polynomial regression function (PRF) model with a 3 × 3 kernel size for under 10-12-year-old oil palm trees, while exponential regression function (ERF) is more fitted for older trees (i.e., 22 years old). Overall, recent remote sensing dataset and machine learning techniques are useful in monitoring and detecting oil palm plantation to maximize productivity

Plastic Deformation in Laser-Induced Shock Compression of Monocrystalline Copper

Copper monocrystals were subjected to shock compression at pressures of 10–60 GPa by a short (3 ns initial) duration laser pulse. Transmission electron microscopy revealed features consistent with previous observations of shock-compressed copper, albeit at pulse durations in the µs regime. The results suggest that the defect structure is generated at the shock front. A mechanism for dislocation generation is presented, providing a realistic prediction of dislocation density as a function of pressure. The threshold stress for deformation twinning in shock compression is calculated from the constitutive equations for slip, twinning, and the Swegle-Grady relationship

Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia

© 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. It is known, that the polluted air influences straightforwardly on human wellbeing. Along these lines, the air quality checking surveys the nature of air and recognize defiled territories. Geographic information systems (GIS) provides appropriate tools for the purpose of creating models and describing spatial relationships. This study aims to develop an AQI prediction algorithm based on some meteorological parameters collected using an inverse distance weighted geostatistical technique analysis results, from measurements of three meteorological stations adjacent to the study area Kuala Lumpur of the period June to August 2018. A GIS spatial statistical analysis approach was used. An ordinary least squares (OLS) process was adopted for the 3 months data separately and three models have been obtained. An accuracy value of model performance has been computed were set as (97, 99, and 97%) respectively, specified thru the analysis. So as to test the model, validation applied again using predicted AQI and compared them with observed AQI data, the accuracy was set as (96, 99, and 93%), respectively. The result indicated a very good fit of the OLS model to the observed points, verified that the consequences of these analyses are able to monitor and predict AQI with high accuracy

A geospatial solution using a TOPSIS approach for prioritizing urban projects in Libya

© 2018 Proceedings - 39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018 The world population is growing rapidly; consequently, urbanization has been in an increasing trend in many developing cities around the globe. This rapid growth in population and urbanization have also led to infrastructural development such as transportation systems, sewer, power utilities and many others. One major problem with rapid urbanization in developing/third-world countries is that developments in mega cities are hindered by ineffective planning before construction projects are initiated and mostly developments are random. Libya faces similar problems associated with rapid urbanization. To resolve this, an automating process via effective decision making tools is needed for development in Libyan cities. This study develops a geospatial solution based on GIS and TOPSIS for automating the process of selecting a city or a group of cities for development in Libya. To achieve this goal, fifteen GIS factors were prepared from various data sources including Landsat, MODIS, and ASTER. These factors are categorized into six groups of topography, land use and infrastructure, vegetation, demography, climate, and air quality. The suitability map produced based on the proposed methodology showed that the northern part of the study area, especially the areas surrounding Benghazi city and northern parts of Al Marj and Al Jabal al Akhdar cities, are most suitable. Support Vector Machine (SVM) model accurately classified 1178 samples which is equal to 78.5% of the total samples. The results produced Kappa statistic of 0.67 and average success rate of 0.861. Validation results revealed that the average prediction rate is 0.719. Based on the closeness coefficient statistics, Benghazi, Al Jabal al Akhdar, Al Marj, Darnah, Al Hizam Al Akhdar, and Al Qubbah cities are ranked in that order of suitability. The outputs of this study provide solution to subjective decision making in prioritizing cities for development

Experimental study of three-nucleon dynamics in proton-deuteron breakup reaction

Proton–deuteron breakup reaction can serve as a tool to test stateof- the-art descriptions of nuclear interactions. At intermediate energies, below the threshold for pion production, comparison of the data with exact theoretical calculations is possible and subtle effects of the dynamics beyond the pairwise nucleon–nucleon interaction, namely the three-nucleon force (3NF), are significant. Beside 3NF, Coulomb interaction or relativistic effects are also important to precisely describe the differential cross section of the breakup reaction. The data analysis and preliminary results of the measurement of proton-induced deuteron breakup at the Cyclotron Center Bronowice, Institute of Nuclear Physics, Polish Academy of Sciences in Kraków are presented

Deuteron-deuteron collision at 160 MeV

The experiment was carried out using BINA detector at KVI in Groningen. For the first time an extensive data analysis of the data collected in back part of the detector is presented, where a clusterization method is utilized for angular and energy information. We also present differential cross-sections for the (dd$\rightarrow$dpn) breakup reaction within \textit{dp} quasi-free scattering limit and their comparison with first calculations based on Single Scattering Approximation (SSA) approach.Comment: 6 pages, 4 figures, presented at Jagiellonian Symposium 2015 in Krakow, PhD wor

Transient x-ray diffraction used to diagnose shock compressed Si crystals on the Nova laser

Transient x-ray diffraction is used to record time-resolved information about the shock compression of materials. This technique has been applied on Nova shock experiments driven using a hohlraum x-ray drive. Data were recorded from the shock release at the free surface of a Si crystal, as well as from Si at an embedded ablator/Si interface. Modeling has been done to simulate the diffraction data incorporating the strained crystal rocking curves and Bragg diffraction efficiencies. Examples of the data and post-processed simulations are presented

Oral Candidiasis amongst cancer patients at Qods Hospital

Background: Within the past two decades, Candida species have emerged as major human pathogens and are currently the fourth most common cause of nosocomial infection. Propose of this study was to determine the occurrence of oral Candidiasis among cancer patients at Qods hospitals in Sanandaj.Materials and Methods: Sixty cancer patients were examined for oral candidiasis. For all patients, the clinical diagnosis had to be confirmed microbiologically by the presence of yeasts and / or hyphae or pseudohyphae on potassium hydroxide–treated smears of oral swabs. Oral samples were obtained and cultured on Sabouraud's dextrose agar and CHROMagar.Results: 25 out of the 60 patients (41.7%) were males and 35 (58.3%) were females ranging in age from 15 to 79 years. Gastrointestinal cancer and Breast cancer were the most frequent cancer in the studied group, accounting for 65 % and 18.4 % respectively. The mean weight of the patients was 52.67 Kg (range, 38– 80 Kg). Similarly, the mean of hospital stay was 3.58 days (range; 1-9 days). From these patients, 19 Candida spp were isolated; C. albicans alone outnumbered other species and accounted for 73.68% episodes of trash. For C. albicans isolates, the MIC values ranges from 1 to 9 Z g / ml μg / ml for polyenes and from 0.03 to 16 Z g / ml for the azole antifungals. All the Candida albicans had closely related MFCs values.Conclusion: In conclusions, the finding of our study strongly suggest that oral candidiasis is a frequent complication among cancer patients, being C. albicans the main etiological agent.Keywords: Cancer, Oral candidiasis, Candida albicans, Antifungal agentsdoi: 10.4314/ajcem.v12i3.

Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms

© 2019 SPIE. Landslides are type of natural geohazard interfering with many economical and social activities and causing serious damages on human life. It is ranked as a great disaster, threatening life, property and environment. Therefore, early prediction of landslide prone areas is vital. Variety of causative factors such as glaciers melting, excessive raining, mining, volcanic activities, active faults, earthquake, logging, erosion, urbanization, construction, and other human activities can trigger landslide occurrence. Then, identification of factors that directly influences the slide events is highly in demand. Some topographical, geological, and hydrological datasets (e.g., slope, aspect, geology, terrain roughness, vegetation index, distance to stream, distance to road, distance to fault, land use, precipitation, profile curvature, plan curvature) are considered to be effective conditioning factors. However, the importance of each factor differs from one study to another. This study investigates the effectiveness of four sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into four groups G1, G2, G3, and G4. Three machine learning algorithms namely, Random Forest (RF), Naive Bayes (NB), and Boosted Logistic Regression (LogitBoost) were constructed based on each dataset in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 227 landslide inventory datasets of the study area were used where 70% was used for training and 30% for testing. To this end, in the present research, the two main objectives were: 1) Investigation on effectiveness of 14 landslides conditioning factors (altitude, slope, aspect, total curvature, profile curvature, plan curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), distance to fault, distance to road, distance to stream, land use, and geology) by analyzing and determining the most important factors using variance-inflated factor (VIF), Pearson's correlation and Chi-square techniques. Consequently, 4 categories of datasets were defined; first dataset included all 14 conditioning factors, second dataset included Digital Elevation Models (DEM) derivatives (morphometrice factors), third dataset was only based on 5 factors namely lithology, land use, distance to stream, distance to road, and distance to fault, and last dataset was included 8 factors selected using factor analysis and optimization. 2) Evaluate the sensitivity of each modeling technique (NB, RF and LogitBoost) to different conditioning factors using the area under curve (AUC). Eventually, RF technique using optimized variables (G4) performed well with AUC of 0.940 followed by LogitBoost (0.898) and NB (0.864)