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Register a new userGroundwater Level Measurement
طرق قياس منسوب الماء الجوفي
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Authors
بلال علوان( طالب )
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Bilal Elwan
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يعتبر تحديد منسوب المياه الجوفية من الأعمال المهمة للدراسات الجيوتكنيكية وخصوصاً إذا ما كان منسوب المياه في نطاق تنفيذ الأساسات حيث إن معظم المشاكل الفنية التي لها علاقة بالتربة تكون بسبب المياه الجوفية ،توجد عدة أجهزة لقياس مستوى الماء الجوفي وذلك على نحو مباشر مثل سابرة قياس مستوى الماء، جهاز أنبوب الهواء المانومتري، كما تتضمن التقنيات المتطورة مجوعة من المسجلات البيانية الرقمية ذات القدرة التخزينية العالية للبيانات ولديها الامكانية لإجراء معالجات سريعة.
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References used
A Field Manual for Groundwater-level Monitoring at the Texas Water Development Board, Janie Hopkins, P.G. & Bryan Anderson.
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A model has been developed for optimal reservoir operation system of Alfatha dam upstream Samerra
Barrage which is considered as a strategic node in the Tigris river system in Iraq. This model is based on
combining an optimization dynamic programmi
ng model with a flood routing simulation model within an
optimal control framework. The predictive reservoir operation method provides optimal time operation of
flood control system with incorporation of current and predicted flood wave. The model utilizes a
dynamic programming with successive algorithm, interacting with hydrologic routing method.
From the developed optimization model connected with the hydrologic routing model analyses of a
number of predicted flood waves of Tigris river released from Mosul dam, Greater Zab and Dokan dam
on the Lesser Zab have been accomplished. It was concluded that the optimal maximum operation water
level in the reservoir of proposed Alfatha dam realized that all the hydraulic topographic and design
constraints were 164 m.a.s.l. for the two depended predicted operation scenarios of the reservoir. While a
maximum water level of 165 m.a.s.l. is the optimal operation level for the worst operation scenario of the
reservoir. This operation scenario is recommended in the future hydraulic design of the Alfatha dam.
Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training data sets. The majority of such measurement data is embedded in scientific literature and the ability to automatically extract these
data is essential to support the development of reliable property prediction methods. In this work, we describe a methodology for developing an automatic property extraction framework using material solubility as the target property. We create a training and evaluation data set containing tags for solubility-related entities using a combination of regular expressions and manual tagging. We then compare five entity recognition models leveraging both token-level and span-level architectures on the task of classifying solute names, solubility values, and solubility units. Additionally, we explore a novel pretraining approach that leverages automated chemical name and quantity extraction tools to generate large datasets that do not rely on intensive manual tagging. Finally, we perform an analysis to identify the causes of classification errors.
With substantial development in the field of surveying instruments, and the
emergence of various and different generations of Theodolites which are used to measure
horizontal directions and angles, it is urgent to develop the methods of assessing a
nd
determining the accuracy of measurements using these instruments. This is achieved by
taking into consideration all the internal and external factors affecting the measurement
system. The research team studied the famous error equation (Colcord equation) of
horizontal directions and tested it using a several equipments of theodolites and targets on
a 250 m long, 5-sections base-line. The team concluded that there was a need to develop
Colcord equation by taking into consideration the factors involved mainly by Colcord
equation (positioning error, initial sitting error, reading error) in addition to the type of
target and the distance of the line of-sight. The proposed equation was tested in practice,
which proved that expected errors of measurements were close to their actual values. This
could be adopted to estimate the root mean square error (r. m. s. e.) of measurements of
horizontal directions.
Document alignment techniques based on multilingual sentence representations have recently shown state of the art results. However, these techniques rely on unsupervised distance measurement techniques, which cannot be fined-tuned to the task at hand
. In this paper, instead of these unsupervised distance measurement techniques, we employ Metric Learning to derive task-specific distance measurements. These measurements are supervised, meaning that the distance measurement metric is trained using a parallel dataset. Using a dataset belonging to English, Sinhala, and Tamil, which belong to three different language families, we show that these task-specific supervised distance learning metrics outperform their unsupervised counterparts, for document alignment.
This research has tried to study side of the overlap between the
financial accounting and the tax accounting, which could cause effects on
the results of tax by the measurement accounting acts, this research has
studied the impact of the earnin
gs management in companies on the
results of tax of these companies, and the impact of the income
smoothing, as a special mechanism of the earnings management, on the
results of tax, the income smoothing has been studied in this research as
the management when smoothes the income it doesn't recognize incomes
in the accounting periods itself where there are these profits, but it acts on
distribution the income among the years to ease fluctuations in earnings
among accounting periods, and thus it avoids large payable taxes in the
progressive tax which is compatible with high profits, so that it works to
keep profits within the limits of acceptable deductions, but in this
research and in the case of the non-progressive Syrian corporation tax
which removes the last effect of income smoothing on the Syrian
corporation tax, but can be done to take advantage of the process of tax
planning to schedule the taxes that are paid among years, therefore this
research has tested the relationship between income smoothing and
taxable income, and has tested whether there are tax motivations behind
the income smoothing, that has been made through testing the
relationship between income smoothing with both of "the differences
between taxable income and book income" and "the variation in taxable
income".
The assumed relationships has been tested through testing the
hypotheses by using the appropriate statistical methods to the data which
are obtained from a sample of companies listed in the Damascus
Securities during 2006 to 2012.
I found that there is a strong relationship between earnings
management and taxable income, but theses earnings are not managed
because of tax motivations, and there is a weak relationship between
income smoothing and taxable income
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