Use of remote sensing data in assessment land cover changes, land use patterns and land capabilities in AL-Qassim region, Saudi Arabia

© 2017 by International Journal of Ecology & Development. The Qassim region of central Saudi Arabia is one of the most important agricultural regions in the country especially for date cultivation. In the present study, Land sat TM and ETM+ data for the period 1999-2013 are used to study the land use, land cover changes in the area. Satellite images from path/row 168/042 constitute the study area. Three major land use/land cover classes are considered: hilly areas (364,407 ha), vegetated land (1,776,698 ha), and sand dunes (1,523,669 ha). The vegetated land constitutes the class 1, which is comprised of the wades mainly devoted used for for date production. Sand dunes are designated the class 11 and covered a large portion of the study area whereas the Hilly areas are unproductive and constitute as class 111.The vegetative land are surrounded by sand dune which is the most fragile system of the area and leads to damage some productive lands in the area. It is necessitates to study the area for suitable land management practices and for possible approach to stop the sand drifting or sand encroachment in the area. The land use capabilities classification of the study area includes three main classes: LUC I, LUCII, and LUC III. Slopes ranging between 0°and 20°correspond to areas that areflat, gently undulating, undulating, rolling, strongly rolling, moderately steep and steep, respectively. The slope categories dictate the usage patterns of the lands in the study area, which range from suitable to unsuitable to productive lands

Risk assessment through evaluation of potentially toxic metals in the surface soils of the Qassim area, Central Saudi Arabia

© Società Geologica Italiana, Roma 2016. Metal pollution is an increasing environmental problem worldwide, especially in regions undergoing rapid development. The present work highlights the extent of metal pollution in the central part of Saudi Arabia, which is currently experiencing significant agricultural development. The study determined concentrations of Hg, Cd, Zn, As, Mo, Cu, Pb and Cr in surface soils, assessing the level of pollution and potential ecological risks using soil quality guidelines, the geo-accumulation index (Igeo), the Hakanson potential ecological risk index (RI) and standard statistical analysis methods. Overall, the mean potential ecological risk values of metals in the surveyed soils display the following decreasing trend: H

Examining the factors influencing the mobile learning usage during COVID-19 pandemic : an integrated SEM-ANN method

The way in which the emotion of fear affects the technology adoption of students and teachers amid the COVID-19 pandemic is examined in this study. Mobile Learning (ML) has been used in the study as an educational social platform at both public and private higher-education institutes. The key hypotheses of this study are based on how COVID-19 has influenced the incorporation of mobile learning (ML) as the pandemic brings about an increase in different kinds of fear. The major kinds of fear that students and teachers/instructors are facing at this time include: fear because of complete lockdown, fear of experiencing education collapse and fear of having to give up social relationships. The proposed model was evaluated by developing a questionnaire survey which was distributed among 280 students at Zayed University, on the Abu Dhabi Campus, in the United Arab Emirates (UAE) with the purpose of collecting data from them. This study uses a new hybrid analysis approach that combines SEM and deep learning-based artificial neural networks (ANN). The importance-performance map analysis is also used in this study to determine the significance and performance of every factor. Both ANN and IPMA research showed that Attitude (ATD) are the most important predictor of intention to use mobile learning. According to the empirical findings, perceived ease of use, perceived usefulness, satisfaction, attitude, perceived behavioral control, and subjective norm played a strongly significant role justified the continuous Mobile Learning usage. It was found that perceived fear and expectation confirmation were significant factors in predicting intention to use mobile learning. Our study showed that the use of mobile learning (ML) in the field of education, amid the coronavirus pandemic, offered a potential outcome for teaching and learning; however, this impact may be reduced by the fear of losing friends, a stressful family environment and fear of future results in school. Therefore, during the pandemic, it is important to examine students appropriately so as to enable them to handle the situation emotionally. The proposed model has theoretically given enough details as to what influences the intention to use ML from the viewpoint of internet service variables on an individual basis. In practice, the findings would allow higher education decision formers and experts to decide which factors should be prioritized over others and plan their policies appropriately. This study examines the competence of the deep ANN model in deciding non-linear relationships among the variables in the theoretical model, methodologically

Delineation of Copper Mineralization Zones at Wadi Ham, Northern Oman Mountains, United Arab Emirates Using Multispectral Landsat 8 (OLI) Data

© Copyright © 2020 Howari, Ghrefat, Nazzal, Galmed, Abdelghany, Fowler, Sharma, AlAydaroos and Xavier. Copper deposits in the ultramafic rocks of the Semail ophiolite massifs is found to be enormous in the region of northern Oman Mountains, United Arab Emirates. For this study, samples of copper were gathered from 14 different sites in the investigation area and were analyzed in the laboratory using the X-ray diffraction, GER 3700 spectroradiometer, and Inductively Coupled Plasma-Mass Spectrometer. Detection and mapping of copper-bearing mineralized zones were carried out using different image processing approaches of minimum noise fraction, principal component analysis, decorrelation stretch, and band ratio which were applied on Landsat 8 (OLI) data. The spectra of malachite and azurite samples were characterized by broad absorption features in the visible and near infrared region (0.6–1.0 µm). The results obtained from the principal component analysis, minimum noise fraction, band ratio, decorrelation stretch, spectral reflectance analyses, and mineralogical and chemical analyses were found to be similar. Thus, it can be concluded that multispectral Landsat 8 data are useful in the detection iron ore deposits in arid and semi-arid regions

Assessment of Heavy Metal Contamination in the Soils of the Gulf of Aqaba (Northwestern Saudi Arabia): Integration of Geochemical, Remote Sensing, GIS, and Statistical Data

Ghrefat, H.; Zaman, H.; Batayneh, A.; El Waheidi, M.M.; Qaysi, S.; Al-Taani, A.; Jallouli, C., and Badhris, O., 2021. Assessment of heavy metal contamination in the soils of the Gulf of Aqaba (Northwestern Saudi Arabia): Integration of geochemical, remote sensing, GIS, and statistical data. Journal of Coastal Research, 37(4), 864872. Coconut Creek (Florida), ISSN 0749-0208. Rock and soil sample geochemical analysis was conducted to investigate the extent and causes of soil contamination in the Gulf of Aqaba region in NW Saudi Arabia. The inductively coupled plasma mass spectrometry was used to determine the concentrations of Pb, Zn, Cu, Co, Cr, Mn, Fe, Hg, Mo, and Cd in 23 soil samples and 25 samples from granitic and Cenozoic marine sedimentary formations. The geochemical results have been integrated with remote sensing, GIS, and statistical analysis to assess the severity of soil pollution in the area. The concentrations of heavy metals (ppm) in the collected soil samples were as follows: Fe (2259.70), Mn (101.85), Zn (20.15), Pb (10.74), Cr (8.67), Cu (6.10), Co (1.35), Mo (0.69), Hg (0.30), and Cd (0.17). A significant variation in the mean metal concentrations was observed for the rock samples. The correlation analysis results showed that different degrees of positive and negative relationships exist among different metals in the area. Two factors (PC1 and PC2) were identified using the principal component analysis (PCA) and were responsible for about 60% of the total variance in the data. The studied metals were separated and classified into two factors based on their geochemical features and source. In contrast, the hierarchical cluster analysis grouped the identified metals into different groups based on the similarity of their characteristics. The principal component (PC2) applied to the Sentinel-2A image classified the land cover in the area into three classes: vegetation, barren rocks, and urban area. The enrichment factor shows a relatively higher percentage of enriched Mo; however, the indices of geo-accumulation and potential ecological risk generally reveal no substantial metallic contamination in the study area. The main sources of soil contamination with metals are rock-weathering processes and various agricultural works that are widely practiced in the area

Targeting the affective brain-a randomized controlled trial of real-time fMRI neurofeedback in patients with depression.

open access articleFunctional magnetic resonance imaging neurofeedback (fMRI-NF) training of areas involved in emotion processing can reduce depressive symptoms by over 40% on the Hamilton Depression Rating Scale (HDRS). However, it remains unclear if this efficacy is specific to feedback from emotion-regulating regions. We tested in a single-blind, randomized, controlled trial if upregulation of emotion areas (NFE) yields superior efficacy compared to upregulation of a control region activated by visual scenes (NFS). Forty-three moderately to severely depressed medicated patients were randomly assigned to five sessions augmentation treatment of either NFE or NFS training. At primary outcome (week 12) no significant group mean HDRS difference was found (B = −0.415 [95% CI −4.847 to 4.016], p = 0.848) for the 32 completers (16 per group). However, across groups depressive symptoms decreased by 43%, and 38% of patients remitted. These improvements lasted until follow-up (week 18). Both groups upregulated target regions to a similar extent. Further, clinical improvement was correlated with an increase in self-efficacy scores. However, the interpretation of clinical improvements remains limited due to lack of a sham-control group. We thus surveyed effects reported for accepted augmentation therapies in depression. Data indicated that our findings exceed expected regression to the mean and placebo effects that have been reported for drug trials and other sham-controlled high-technology interventions. Taken together, we suggest that the experience of successful self-regulation during fMRI-NF training may be therapeutic. We conclude that if fMRI-NF is effective for depression, self-regulation training of higher visual areas may provide an effective alternative

DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies

Multivariate statistical analysis of urban soil contamination by heavy metals at selected industrial locations in the Greater Toronto area, Canada

A good understanding of urban soil contamination with metals and the location of pollution sources due to industrialization and urbanization is important for addressing many environmental problems. The results are reported here of an analysis of the metals content in urban soils samples next toindustrial locations in the Greater Toronto Area (GTA) in Ontario, Canada. Theanalyzed metals are Cr, Mn, Fe, Ni, Cu, Zn, and Pb. Multivariate geostatistcalanalysis (correlation matrix, cluster analysis, principal component analysis) is used to estimate soil chemical content variability. The correlation matrix exhibits a positive correlation with Mn, Fe, Cu, Zn, Cd, and Pb. The principal component analysis (PCA) displays two components. The first component explains the major part of the total variance and is loaded heavily with Cr, Mn, Fe, Zn,and Pb, and the sources are industrial activities and traffic flows. The second component is loaded with Ni, and Cd, and the sources could be lithology andtraffic flow. The results of the cluster analysis demonstrate three major clusters: 1) Mn-Zn, 2) Pb-Cd-Cu and Cr, 3) Fe-Ni. The geo-accumulation index and the pollution load index are determined and show the main I geovalues to be in the range of 0-1.67; the values indicate that the soil samples studied for industrial locations in the GTA are slightly to moderately contaminated with Cr, Fe, Cu, Zn, and Cd, and moderately contaminated with Pb,while Ni, and Mn fall in class "0". Regarding the pollution load ingindex (PLI), the lowest values are observed at stations 6, 7, 9, 10, 11, 12,25, 27 and 28, while the highest values are recorded for stations 1, 5, 6, 13,14, 16, 17, 18, 20, 22 and 24, and very high PLI readings are seen for stations 5, 13, 16, 17, 18, 22 and 24. These data confirm that the type of industries, especially metallurgical and chemical related ones, in the study area, in addition to high traffic flows, are the main sources for soil pollution in the GTA

A priori collaboration in population imaging: The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement consortium

AbstractIntroductionVirchow-Robin spaces (VRS), or perivascular spaces, are compartments of interstitial fluid enclosing cerebral blood vessels and are potential imaging markers of various underlying brain pathologies. Despite a growing interest in the study of enlarged VRS, the heterogeneity in rating and quantification methods combined with small sample sizes have so far hampered advancement in the field.MethodsThe Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement (UNIVRSE) consortium was established with primary aims to harmonize rating and analysis (www.uconsortium.org). The UNIVRSE consortium brings together 13 (sub)cohorts from five countries, totaling 16,000 subjects and over 25,000 scans. Eight different magnetic resonance imaging protocols were used in the consortium.ResultsVRS rating was harmonized using a validated protocol that was developed by the two founding members, with high reliability independent of scanner type, rater experience, or concomitant brain pathology. Initial analyses revealed risk factors for enlarged VRS including increased age, sex, high blood pressure, brain infarcts, and white matter lesions, but this varied by brain region.DiscussionEarly collaborative efforts between cohort studies with respect to data harmonization and joint analyses can advance the field of population (neuro)imaging. The UNIVRSE consortium will focus efforts on other potential correlates of enlarged VRS, including genetics, cognition, stroke, and dementia