Distributed control for cooperative Parabolic systems with conjugation conditions

In this paper, we consider cooperative Parabolic systems defined on bounded, continuous and strictly Lipschitz domain of n R with conjugation conditions. We study the optimal control for these systems with Dirichlet conditions. Also, we establish the problem with Neumann conditions .The control in our problems is of distributed type

La planification controversée du Grand Caire avant/après 2011

La révolution de 2011 a suscité chez les Égyptiens un sentiment nouveau de patriotisme. Parallèlement, la société civile se montre de plus en plus décidée à reprendre son avenir en main. Quelques ONG commencent ainsi à prendre part au débat sur le développement urbain du Caire et de l’Égypte. Des nouvelles stratégies ont été formulées ou reconsidérées : « Development Corridors », « Egypt 712 », « Egypt Vision : 2030 ». Simultanément, les responsables politiques de l’aménagement du territoire réalisent l’inapplicabilité des plans stratégiques directeurs jusqu’à présent conçus pour Le Caire. En réaction au surinvestissement de la capitale par l’ancien régime, Le Caire n’est aujourd’hui plus le point focal du nouveau plan stratégique directeur « Egypt 2052 » conçu par le GOPP, organe chargé de la planification du territoire égyptien. Presque du jour au lendemain, le GOPP s’est convaincu que s’éloigner du Caire serait la solution à tous les problèmes d’aménagement que rencontre l’Égypte. Très loin du « Grand Caire 2050 » (précédent grand plan stratégique directeur) et de tous les remèdes proposés afin de résoudre les problèmes du Grand Caire, nous sommes aujourd’hui face à un document de planification dans lequel la capitale égyptienne est rarement mentionnée. Cette recherche met, tout d’abord, en lumière les paradoxes et les enjeux des nouvelles stratégies proposées, soit par la société civile ou par le GOPP, avant de s’interroger sur leur efficacité, et leur validité dans le contexte égyptien.The revolution of 2011 aroused among the Egyptians a new sense of patriotism. Meanwhile, the civil society shows more determined to take charge of its future. Some NGOs begin to take part in the debate on “Cairo and Egypt’s urban development”. New strategies have been formulated or reconsidered: "Development Corridors", "Egypt 712", "Egypt Vision: 2030". Simultaneously, decision makers of strategic planning realize the inapplicability of strategic plans so far designed for Cairo. In response to overinvestment in the capital by the former regime, Cairo today is no longer the focal point of the new strategic plan "Egypt 2052", proposed by the GOPP (responsible organization for planning the Egyptian territory). Almost suddenly, the GOPP is convinced that moving away from Cairo would solve all development problems facing Egypt. Very far from the "Cairo 2050" (previous general strategic plan) and all proposed to solve the problems of Greater Cairo remedies, we are now faced with a strategic planning document in which the Egyptian capital is rarely mentioned.This research highlights, first, the paradoxes and challenges of new strategies proposed by either the civil society or the GOPP before questioning their effectiveness and validity in the Egyptian context

A Predictive Model for Student Performance in Classrooms using Student Interactions with an eTextbook

With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a huge amount of data has been collected related to students’ learning. With the careful analysis of this data, educators can gain useful insights into their students’ performance and their behavior in learning a particular topic. This paper proposes a new model for predicting student performance based on an analysis of how students interact with an interactive online eTextbook. By being able to predict students’ performance early in the course, educators can easily identify students at risk and provide a suitable intervention. We considered two main issues: the prediction of good/bad performance and the prediction of the final exam grade. To build the proposed model, we evaluated the most popular classification and regression algorithms. Random Forest Regression and Multiple Linear Regression have been applied in Regression. While Logistic Regression, decision tree, Random Forest Classifier, K Nearest Neighbors, and Support Vector Machine have been applied in classification. Based on the findings of the experiments, the algorithm with the best result overall in classification was Random Forest Classifier with an accuracy equal to 91.7%, while in the regression it was Random Forest Regression with an R2 equal to 0.977

Identifying Difficult exercises in an eTextbook Using Item Response Theory and Logged Data Analysis

The growing dependence on eTextbooks and Massive Open Online Courses (MOOCs) has led to an increase in the amount of students' learning data. By carefully analyzing this data, educators can identify difficult exercises, and evaluate the quality of the exercises when teaching a particular topic. In this study, an analysis of log data from the semester usage of the OpenDSA eTextbook was offered to identify the most difficult data structure course exercises and to evaluate the quality of the course exercises. Our study is based on analyzing students' responses to the course exercises. We applied item response theory (IRT) analysis and a latent trait mode (LTM) to identify the most difficult exercises .To evaluate the quality of the course exercises we applied IRT theory. Our findings showed that the exercises that related to algorithm analysis topics represented the most difficult exercises, and there existing six exercises were classified as poor exercises which could be improved or need some attention.Comment: 6 pages,5 figure

Pattern & presentation of colorectal cancer in central Sudan, a retrospective descriptive study, 2010-2012.

Aims & Objective: To determine the age and gender distribution and clinical presentation of patients together with histological types of colorectal cancer cases presented to Ibn Sina specialized hospital. Patients and Methods: This retrospective study was conducted in Ibn Sina Hospital (Sudan). Seventy three (73) patients of colorectal cancer who presented in the period from January 2010 to December 2012 were included. Data were collected from their hospital records and analyzed using SPSS computer program 17. Results: More than 17 % of the study populations was below the age of 40 years, and 43.84% was below 50 years. The male to female ratio was 1:1.02. Rectal bleeding is the commonest presenting symptom and well differentiated adenocarcinoma is the dominating tumor grade. 8.3 % of patients presented with liver metastasis. Conclusion: Colorectal cancer in this study was found more in young age groups with a peak frequency at the fifth and sixth decades

Effects of image pansharpening on soil total nitrogen prediction models in South India

Image fusion is in its infancy in the application of Digital Soil Mapping, and the incorporation of the image pansharpened spectral indices into the soil prediction models has seldom been analyzed. This research performed image pansharpening of Landsat 8, WorldView-2, and Pleiades-1A in a smallholder village called Masuti in South India using three pansharpening techniques: Brovey, Gram-Schmit (GS), and Intensity-Hue-Saturation (IHS) methods. The research analyzed the relationships between multispectral (MS) and pansharpened (PAN) spectral indices and soil total nitrogen (TN), developed the soil TN prediction models using Random Forest methods, and explored the effects of different PAN spectral indices on soil TN prediction models. The results showed the spectral behavior of PAN spectral indices and MS spectral indices were similar. The results also demonstrated that soil TN models based on MS/PAN spectral indices have slightly higher model performance and more detailed characterization of TN spatial pattern compared with soil TN models based on MS spectral indices. Soil TN models based on the GS PAN and MS spectral indices attained slightly higher prediction accuracy compared with those based on other PAN and MS spectral indices. This research advocates the promotion of image pansharpening techniques in digital soil mapping and soil nutrient management research

Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields

Soil prediction models based on spectral indices from some multispectral images are too coarse to characterize spatial pattern of soil properties in small and heterogeneous agricultural lands. Image pan-sharpening has seldom been utilized in Digital Soil Mapping research before. This research aimed to analyze the effects of pan-sharpened (PAN) remote sensing spectral indices on soil prediction models in smallholder farm settings. This research fused the panchromatic band and multispectral (MS) bands of WorldView-2, GeoEye-1, and Landsat 8 images in a village in Southern India by Brovey, Gram-Schmidt and Intensity-Hue-Saturation methods. Random Forest was utilized to develop soil total nitrogen (TN) and soil exchangeable potassium (Kex) prediction models by incorporating multiple spectral indices from the PAN and MS images. Overall, our results showed that PAN remote sensing spectral indices have similar spectral characteristics with soil TN and Kex as MS remote sensing spectral indices. There is no soil prediction model incorporating the specific type of pan-sharpened spectral indices always had the strongest prediction capability of soil TN and Kex. The incorporation of pan-sharpened remote sensing spectral data not only increased the spatial resolution of the soil prediction maps, but also enhanced the prediction accuracy of soil prediction models.Small farms with limited footprint, fragmented ownership and diverse crop cycle should benefit greatly from the pan-sharpened high spatial resolution imagery for soil property mapping. Our results show that multiple high and medium resolution images can be used to map soil properties suggesting the possibility of an improvement in the maps’ update frequency. Additionally, the results should benefit the large agricultural community through the reduction of routine soil sampling cost and improved prediction accuracy

Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings

Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workers are faced with choosing the appropriate remote sensing data. The objective of this research is to analyze the spatial resolution effects of different remote sensing images on soil prediction models in two smallholder farms in Southern India called Kothapally (Telangana State), and Masuti (Karnataka State), and provide empirical guidelines to choose the appropriate remote sensing images in DSM. Bayesian kriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (Kex) in the topsoil (0e15 cm) at different spatial resolutions by incorporating spectral indices from Landsat 8 (30 m), RapidEye (5 m), and WorldView-2/GeoEye-1/Pleiades-1A images (2 m). Some spectral indices such as band reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multiple images showed relatively strong correlations with soil Kex in two study areas. The research also suggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-based soil prediction models would not necessarily have higher prediction performance than coarse spatial resolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settings need select the appropriate spectral indices and consider different factors such as the spatial resolution, band width, spectral resolution, temporal frequency, cost, and processing time of different remote sensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted to smallholder farm settings all over the world and help smallholder farmers implement sustainable and field-specific soil nutrient management scheme

Editorial: Methods and application in cardiovascular and smooth muscle pharmacology: 2021

Despite significant advances in basic, translational, and clinical research tackling heart disease, cardiovascular pathologies remain among the leading causes of mortality and morbidity worldwide, being responsible for one-third of global deaths as estimated by the WHO (Organization, 2021). The complexity of risk factors and pathways underlying the development of cardiovascular disorders (CVDs) limits the efficacy of a given therapeutic intervention and necessitates combined pharmacological approaches, as well as lifestyle modification to provide a reasonable health impact (Arnett et al., 2019). Be that as it may, there remains a considerable room for scientific inquiry in pursuit of novel and more refined avenues to prevent, diagnose, mitigate, and reverse different forms of cardiovascular ailment, as well as optimize patient management. Indeed, such a need for research in this field was even further emphasized as the world faced heightened health challenges during the COVID-19 pandemic with cardiovascular complications being among the most serious consequences of SARS-CoV-2 infection (Wehbe et al., 2020)

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (Kex) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale