Utilizing Distributed Temperature and Pressure Data To Evaluate The Production Distribution in Multilateral Wells

One of the issues with multilateral wells is determining the contribution of each lateral to the total production that is measured at the surface. Also, if water is detected at the surface or if the multilateral well performance declines, then it is difficult to identify which lateral or laterals are causing the production decline. One way to estimate the contribution from each lateral is to run production Logging Tools (PLT). Unfortunately, PLT jobs are expensive, time-consuming, labor-intensive and involve operational risks. An alternative way to measure the production from each lateral is to use Distributed Temperature Sensing (DTS) technology. Recent advances in DTS technology enable measuring the temperature profile in horizontal wells with high precision and resolution. The changes in the temperature profile are successfully used to calculate the production profile in horizontal wells. In this research, we develop a computer program that uses a multilateral well model to calculate the pressure and temperature profile in the motherbore. The results help understand the temperature and pressure behaviors in multilateral wells that are crucial in designing and optimizing DTS installations. Also, this model can be coupled with an inversion model that can use the measured temperature and pressure profile to calculate the production from each lateral. Our model shows that changing the permeability or the water cut produced from one lateral results in a clear signature in the motherbore temperature profile that can be measured with DTS technology. However, varying the length of one of the lateral did not seem to impact the temperature profile in the motherbore. For future work, this research recommends developing a numerical reservoir model that would enable studying the effect of lateral inference and reservoir heterogeneity. Also recommended is developing an inversion model that can be used to validate our model using field data

Ultra-broadband and polarization-insensitive metasurface absorber with behavior prediction using machine learning

The solar spectrum energy absorption is very important for designing any solar absorber. The need for absorbing visible, infrared, and ultraviolet regions is increasing as most of the absorbers absorb visible regions. We propose a metasurface solar absorber based on Ge2Sb2Te5 (GST) substrate which increases the absorption in visible, infrared and ultraviolet regions. GST is a phase-changing material having two different phases amorphous (aGST) and crystalline (cGST). The absorber is also analyzed using machine learning algorithm to predict the absorption values for different wavelengths. The solar absorber is showing an ultra-broadband response covering a 0.2–1.5 µm wavelength. The absorption analysis for ultra-violet, visible, and near-infrared regions for aGST and cGST is presented. The absorption of aGST design is better compared to cGST design. Furthermore, the design is showing polarization insensitiveness. Experiments are performed to check the K-Nearest Neighbors (KNN)-Regressor model’s prediction efficiency for predicting missing/intermediate wavelengths values of absorption. Different values of K and test scenarios; C-30, C-50 are used to evaluate regressor models using adjusted R2 Score as an evaluation metric. It is detected from the experimental results that, high prediction proficiency (more than 0.9 adjusted R2score) can be accomplished using a lower value of K in KNN-Regressor model. The design results are optimized for geometrical parameters like substrate thickness, metasurface thickness, and ground plane thickness. The proposed metasurface solar absorber is absorbing ultraviolet, visible, and near-infrared regions which will be used in solar thermal energy applications

Recent advances in biosensors for detection of COVID-19 and other viruses

This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers

Recent advances in biosensors for detection of COVID-19 and other viruses

This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers

Univalence criteria for linear fractional differential operators associated with a generalized Bessel function

In this paper our aim is to establish some generalizations upon the sufficient conditions for linear fractional differential operators involving the normalized forms of the generalized Bessel functions of the first kind to be univalent in the open unit disk as investigated recently by [{sc E. Deniz, H. Orhan, H.M. Srivastava}, {it Some sufficient conditions for univalence of certain families of integral operators involving generalized Bessel functions}, Taiwanese J. Math. {bf 15} (2011), No. 2, 883-917] and [{sc \u27A. Baricz, B. Frasin}, {it Univalence of integral operators involving Bessel functions}, Appl. Math. Letters {bf 23} (2010), No. 4, 371--376]. Our method uses certain Luke\u27s bounding inequalities for hypergeometric functions ${}_{p+1}F_p$ and ${}_pF_p$

Prevalence of overweight and obesity based on the body mass index; a cross-sectional study in Alkharj, Saudi Arabia

Background: Obesity and overweight are accompanied with several different chronic diseases. Overweight and obesity can be measured by using body mass index (BMI) and is also used widely as an index of relative adiposity among any population. The aim of the study is to evaluate the prevalence of overweight and obesity among general population in Al-Kharj, Saudi Arabia.Methods: Cross-sectional analysis was undertaken from a representative sample (N = 1019) of the Al Kharj population. Anthropometric measurements including the waist circumference (in centimeters), height (in meters), and weight (in kilograms) of the subjects were undertaken by means of standard apparatus. SPSS 24.0 was utilized for statistical analysis of the data.Results: Majority of respondents in this study were overweight and obese (54.3%) compared with 45.7% being non-obese. A linear positive association of increasing BMI with older age groups was present in males and females. Men had larger waist circumference, weight and height measures as compared with their female counterparts. Regression analysis showed increasing age, being married and high serum cholesterol to be the significant predictors of overweight and obesity while gender, education level, job status, and having diabetes were not.Conclusions: The obesity-overweight prevalence in the Saudi population is high mainly across both genders. However, the associated factors are potentially preventable and modifiable. The regional barriers to lifestyle modifications and interventions to encourage active lifestyles, especially among adolescents to limit the occurrence of obesity and ultimately promote health and wellbeing, are warranted. Furthermore, prospective studies are needed in future to confirm the aetiological nature of such associations

Prevalence of prediabetes, diabetes and Its predictors among females in alkharj, saudi arabia: A cross-sectional study.

BACKGROUND/OBJECTIVE: The prevalence of prediabetes and diabetes is reaching epidemic proportions across the globe. Therefore, this study aims to determine the prevalence of prediabetes and diabetes, together with its accompanying risk factors, among young females. METHODS: An exploratory cross-sectional survey was conducted with 638 Saudi females in Alkharj, Saudi Arabia. Statistical analysis was carried out using STATA version 14. Odds ratios for the risk of diabetes and associated factors were calculated using log-binomial and multinomial logistic regression. Standardized prevalence and strata-specific prevalence of diabetes and prediabetes for different risk factors were also calculated. FINDINGS: The study revealed that nondiabetics and prediabetics were more prevalent between the ages of 18 and 24 years, while diabetic patients were consistently between 25 to 44 years of age. The average value for HbA1c in females was 5.44. Regression analysis shows that being older, married, obese, a smoker or less educated significantly increases the risk for both prediabetes and diabetes. Mutivariable analysis revealed obesity had a significant association with both prediabetes and diabetes. Prediabetics were 2.35 times more likely to be obese as compared to nondiabetics, with 95% CI (1.38-3.99). Similarly, diabetics were 6.67 times more likely to be obese compared to nondiabetics 95% CI (1.68-26.42). CONCLUSION: Our study shows the prevalence of diabetes and prediabetes among females from Al Kharj was 3.8% and 18.8%, respectively. The diabetic and prediabetic female participants had higher mean BMI and waist circumference, were older in age, were married, and smoked as compared to nondiabetics. In the context of the findings of our study, and keeping in view the the burden of this disease globally and in our population, it has now become extremely important to understand these factors and encourage health-promoting behaviors to construct effective interventions

Prevalence of prediabetes, diabetes, and Its associated risk factors among males in Saudi Arabia: A population-based survey

Objectives: The study aims at determining the prevalence of prediabetes and diabetes and at ascertaining some concomitant risk factorsamong males in Saudi Arabia.Methods: A population-based cross-sectional study including 381 Saudi adult males from different institutions was recruited. Odds ratios for diabetes risk and risk factors were calculated using log-binomial and multinomial logistic regression, using STATA version 12.Results: The participants included 381 diabetic males with a median age of 45 years, average body mass index of 25 ± 40 kg/m2, whereas waist circumferences ranged from 66 to 180 cm in the male study population. In addition, 27.82% had normal BMI, 32.28% were overweight, and 36.22% were obese. Around 36% had higher waist circumference, that is, \u3e102 cm. Age, BMI, marital status, and educational attainment were statistically significant predictors for prediabetes and diabetes.Conclusion: This study found that the prevalence of diabetes and prediabetes was 9.2% and 27.6%, respectively, for male Al-Kharj study population. The factors that increase the risk of diabetes and prediabetes include older age, obesity and overweight, being married, smoker, and having a civilian job and less education. All these factors were found statistically significant except smoking status and job type. In order to evaluate the causal relationship of these factors, prospective studies are required in future

Agricultural Academy

Abstract ShalabY, M. Y., K. h. al-Zahrani, M. b. baig and g. S. Straquadine, 2012. realizing sustainable agriculture through rural extension and environmental friendly farming technologies: basic ingredients. Bulg. J. Agric. Sci., With an only 3% percent farming area, egypt is still an agricultural country. its development primarily depends upon agricultural resources. agriculture contributes approximately 14% of the gdP and absorbs about 31% of workforce. about 53% population lives in rural areas where directly or indirectly their livelihood depends upon agricultural sector. despite its positive and significant contributions to food security/supply, economy, employment, export earnings, ecological balance, agriculture faces many threats and challenges which, in turn, result unsustainable crop productions. the prominent challenges faced include land and water issues; high degree of land fragmentations; old cultivation techniques, low yields with old traditional varieties, lack of information on marketing; post-harvest losses; degradation of natural resources and environmental issues;, inadequate support services; framework and institutional constraints; and lack of agricultural development policies etc. in the present scenario, it seems imperative for agriculture sector to adopt new environmental friendly farming systems primarily based on the principles of sustainable agriculture. On the other hand, the role of rural extension has also been changed due to the low contributions made by old primitive cultivation techniques, the promising emerging new farming technologies, and the declining socio-economic conditions of rural etc. this article examines the changing scenarios, possibility of employing environmental friendly farming practices and elevating the working capabilities of the extension workers through well-planned capacity building programs. an effort has been made to identify and enlist the basic ingredients essential for the sustainable farming and efficient rural extension

threats and challenges to sustainable agriculture and rural development in egypt: implications for agricultural extension.

ABSTRACT Egypt is an agricultural based country. Its development primarily depends upon rural resources. Agriculture contributes approximately 14% of the GDP and absorbs about 31% of workforce. About 53% population lives in rural areas where directly or indirectly their livelihood depends upon agricultural sector. Despite its positive and significant contributions to food security/supply, economy, employment, export earnings, ecological balance, yet the agriculture of the country faces many threats and challenges which, in turn, impacts rural development initiatives. The prominent challenges include land and water issues; old cultivation techniques; lack of information on marketing; poverty; degradation of natural resources and environmental issues; population growth; inadequate support services; framework and institutional constraints; and lack of agricultural and rural development policies. In this article, an effort has been made to identify the constraints faced by the agricultural sector, discuss the available farm management options, and to outline the vibrant strategy backed by an efficient and effective Extension to realize sustainable yields and rural development in the country