4. Mathematical model

In order to fully present the hydrodynamics in the dam reservoir, volume of fluid (VOF) model should be developed. Owing to large size and complexity of the computational grid, as well as limited computer capacity, such an approach would be very difficult to apply for a big water bodies. In the literature, VOF technique was adopted only for the CFD modeling of reservoir in a downscale model (1:50) [22].

Sulejow reservoir due to the location, shape of the bowl, and uncovered, flat shores is particularly exposed to the effect of wind. Direction and energy of wind determine the waving movement and mixing of the water, so the impact of the factor, on flow distribution in the CFD simulations, should be taken into consideration.

In the model under wind conditions, the speed, at which the plate was moving on the water surface (which reflects the speed and direction of the wind in the area of the Sulejow reservoir), was determined. For this purpose, independent, 2-D, two-phase problem was resolved. Data concerning wind parameters were provided by the Institute of Meteorology and Water Management in Warsaw and come from the meteorological station (Sulejow-Kopalnia) located near the reservoir. Average speed and direction of wind were selected as 3 m/s and southeast, respectively, based on the daily data from year 2007.

#### 4.1. Simplified VOF CFD model

The 2-D model of a straight channel with a length of 80 m and a width of 6 m (3 m layer of water, 3 m layer of air) was elaborated. Figure 10 shows the hexahedral, structured mesh which has been generated for the simplified geometry. The grid contains 159,242 elements Three-Dimensional CFD Simulations of Hydrodynamics for the Lowland Dam Reservoir http://dx.doi.org/10.5772/intechopen.80377 49

Figure 10. Fragment of the structured, hexahedral mesh generated with Gambit program.

(159,116 hexahedra, 126 wedges) and 321,872 nodes, respectively. A boundary layer was generated consisting of 10 rows.

Wind accelerates surface fluid particles by imparting momentum to the fluid, through surface stresses. In the analysis, the water flows through the air momentum. In the computational domain, the water phase has two outlets that allow for fluid reversing. Analysis of flow in 2-D model was intended to determine which boundary condition best describes the situation that prevails over the water surface by the effect of wind.

#### 4.2. Boundary conditions

The quality of the numerical grid was determined by the shape and the size of the computing field and the total number of elements used in the generated numerical grid and through the position of the first node relative to the plane of the wall. To assess the quality of the numerical

, skewness, and aspect ratio for the analyzed numerical grid.

Parameter Values for numerical grids in this work The limit values in the numerical simulations

—determining the quality of the mesh in the boundary layer

In order to fully present the hydrodynamics in the dam reservoir, volume of fluid (VOF) model should be developed. Owing to large size and complexity of the computational grid, as well as limited computer capacity, such an approach would be very difficult to apply for a big water bodies. In the literature, VOF technique was adopted only for the CFD modeling of reservoir in

Sulejow reservoir due to the location, shape of the bowl, and uncovered, flat shores is particularly exposed to the effect of wind. Direction and energy of wind determine the waving movement and mixing of the water, so the impact of the factor, on flow distribution in the

In the model under wind conditions, the speed, at which the plate was moving on the water surface (which reflects the speed and direction of the wind in the area of the Sulejow reservoir), was determined. For this purpose, independent, 2-D, two-phase problem was resolved. Data concerning wind parameters were provided by the Institute of Meteorology and Water Management in Warsaw and come from the meteorological station (Sulejow-Kopalnia) located near the reservoir. Average speed and direction of wind were selected as 3 m/s and southeast,

The 2-D model of a straight channel with a length of 80 m and a width of 6 m (3 m layer of water, 3 m layer of air) was elaborated. Figure 10 shows the hexahedral, structured mesh which has been generated for the simplified geometry. The grid contains 159,242 elements

, skewness, and aspect ratio for the numerical grid generated in this

grid elements, three parameters were used (Gambit User Guide):

y+ 1.8 ≤2 Skewness 0.8 0–1 Aspect ratio 1.7 ≥1

• "Skewness"—determining the quality of the individual grid elements

• "Aspect ratio"—defining the degree of deformation of the mesh elements

• Parameter y+

48 Dam Engineering

Ranges of parameters: y+

Table 1. Values of parameter: y+

work are given in Table 1.

4. Mathematical model

a downscale model (1:50) [22].

4.1. Simplified VOF CFD model

CFD simulations, should be taken into consideration.

respectively, based on the daily data from year 2007.

For two-dimensional CFD model, the following boundary conditions were imposed:.

Inlet boundary conditions applied for the analyzed domain were two tributaries (Pilica and Luciaza rivers) and outlet (dam) as given in Figure 11.

Simulated inflow boundaries were specified with mass flow rates, normal to the boundary. Velocities at each inlet were calculated from the inlet area measurements of stream flow for the Pilica and Luciaza rivers, made by the Regional Board and Water Management in Warsaw in 2007. The monthly values of mass flow rates in the Pilica and Luciaza rivers are presented in Table 2.

The definition of the inlet requires the values of the velocity vectors and turbulence properties. For the air inlet, the simulations were first conducted at a speed of 3 m/s. Velocity profile obtained at the outlet of the computational domain was loaded as an input file to receive the velocity profile at the inlet. This approach allows to obtain a fully developed velocity profile for a small domain.

Implementation of volume of fluid model requires an additional boundary condition to be specified, namely, the turbulence intensity at the inlet and turbulent viscosity ratio. Introduction of disturbance into the flow reflects the real features of the flow pattern.

Another parameter, the turbulent viscosity ratio (the ratio of turbulent to laminar (molecular) viscosity), was used as given in Eq. (2). The default value of 10% was applied in the simulations:

Three-Dimensional CFD Simulations of Hydrodynamics for the Lowland Dam Reservoir

β ¼ vt

The air outlet of pressure type was defined in the model. Pressure outlet boundary conditions require specification of a static pressure at the outflow. Convergence difficulties were minimized by specified values for the backflow quantities (backflow turbulence intensity and

Table 3 summarizes the solution conditions and methods used in the modeling process.

Based on the analysis of two-dimensional two-phase model, an appropriate input data for the wall boundary condition, corresponding to real wind conditions at the surface, could be determined. Figure 12 depicts the velocity profile of water at the outlet of the computational domain. As a result, the replacement velocity of wind, which corresponds to the real, average value in the area of the Sulejow reservoir, was obtained. The mean value of 0.147 m/s was used

Due to the fact that the Sulejow reservoir is a shallow, polymictic lake, the wind will be important for the distribution and mixing of the water masses of two tributaries. The results

Space Two-dimensional

Gradient Least squares cell based Pressure Body force weighted

Momentum Second-order upwind

Turbulence energy kinetic Second-order upwind Specific dissipation rate Second-order upwind

Time Steady

Pressure–velocity coupling scheme Coupled

Volume fraction Compressive

viscosity ratio). A no-slip boundary condition was applied on the wall.

in the simulations of flow hydrodynamics in the modeled reservoir.

4.5. Results of CFD calculations at wind conditions

Table 3. Solution conditions and methods for volume of fluid model.

shown in Figure 13 confirm this assumption.

4.3. Solution methods

4.4. Results of VOF model

Model volume of fluid

Discretization method

v (2)

http://dx.doi.org/10.5772/intechopen.80377

51

Figure 11. Inlet region in the analyzed domain.

Table 2. The monthly values of mass flow rate in the Pilica and Luciaza rivers in 2007.

The turbulence intensity, I [%], is defined as the ratio of the root-mean-square of the velocity fluctuations u` to the mean flow velocity Uavg. The turbulence intensity at the core of a fully developed duct flow can be estimated from Eq. (1):

$$I = \frac{\mu}{\mathcal{U}\_{avg}} = 0,16\Re^{\ddagger} \tag{1}$$

Kennedy [15] carried out sensitivity analysis on the turbulence intensity in the simple, channel geometry and found that value 4% was able to match the conditions well. The author concluded that this parameter had a noticeable effect on the solution. Following the above findings, turbulence intensity level of 4% was specified at the inlet.

Another parameter, the turbulent viscosity ratio (the ratio of turbulent to laminar (molecular) viscosity), was used as given in Eq. (2). The default value of 10% was applied in the simulations:

$$
\beta = \frac{v\_t}{v} \tag{2}
$$

The air outlet of pressure type was defined in the model. Pressure outlet boundary conditions require specification of a static pressure at the outflow. Convergence difficulties were minimized by specified values for the backflow quantities (backflow turbulence intensity and viscosity ratio). A no-slip boundary condition was applied on the wall.

#### 4.3. Solution methods

Table 3 summarizes the solution conditions and methods used in the modeling process.

#### 4.4. Results of VOF model

The turbulence intensity, I [%], is defined as the ratio of the root-mean-square of the velocity fluctuations u` to the mean flow velocity Uavg. The turbulence intensity at the core of a fully

Kennedy [15] carried out sensitivity analysis on the turbulence intensity in the simple, channel geometry and found that value 4% was able to match the conditions well. The author concluded that this parameter had a noticeable effect on the solution. Following the above find-

¼ 0, 16R�1

8 (1)

/s) Luciaza river (mass flow rate m3

/s)

I ¼ u Uavg

developed duct flow can be estimated from Eq. (1):

Figure 11. Inlet region in the analyzed domain.

50 Dam Engineering

Month Pilica river (mass flow rate m3

January 19.86 2.80 February 46.31 5.50 March 22.33 4.92 April 23.04 2.19 May 17.36 1.39 June 15.65 1.64 July 16.45 1.91 August 12.83 1.39 September 13.21 1.59 October 16.37 1.69 November 25.50 2.21 December 25.65 2.55

Table 2. The monthly values of mass flow rate in the Pilica and Luciaza rivers in 2007.

ings, turbulence intensity level of 4% was specified at the inlet.

Based on the analysis of two-dimensional two-phase model, an appropriate input data for the wall boundary condition, corresponding to real wind conditions at the surface, could be determined. Figure 12 depicts the velocity profile of water at the outlet of the computational domain. As a result, the replacement velocity of wind, which corresponds to the real, average value in the area of the Sulejow reservoir, was obtained. The mean value of 0.147 m/s was used in the simulations of flow hydrodynamics in the modeled reservoir.

#### 4.5. Results of CFD calculations at wind conditions

Due to the fact that the Sulejow reservoir is a shallow, polymictic lake, the wind will be important for the distribution and mixing of the water masses of two tributaries. The results shown in Figure 13 confirm this assumption.

Table 3. Solution conditions and methods for volume of fluid model.

Figure 12. The velocity profile at the interface of water-air.

Figure 13. Comparison of velocity field (m/s) in the Sulejow reservoir in October at wind and no-wind conditions.

The findings suggest that when steady flow pattern develops in the basin, large regions of recirculation are formed below the outlet of the reservoir. Figure 14 shows contours of velocity field in the Sulejow reservoir in (a) March (b) July, and (c) December.

Figure 14. Contours of velocity field (m/s) in the Sulejow reservoir in (a) march (b) July, and (c) December.

Three-Dimensional CFD Simulations of Hydrodynamics for the Lowland Dam Reservoir

http://dx.doi.org/10.5772/intechopen.80377

53

Three-Dimensional CFD Simulations of Hydrodynamics for the Lowland Dam Reservoir http://dx.doi.org/10.5772/intechopen.80377 53

Figure 14. Contours of velocity field (m/s) in the Sulejow reservoir in (a) march (b) July, and (c) December.

The findings suggest that when steady flow pattern develops in the basin, large regions of recirculation are formed below the outlet of the reservoir. Figure 14 shows contours of velocity

Figure 13. Comparison of velocity field (m/s) in the Sulejow reservoir in October at wind and no-wind conditions.

field in the Sulejow reservoir in (a) March (b) July, and (c) December.

Figure 12. The velocity profile at the interface of water-air.

52 Dam Engineering


*4.2. Baseline Method:* EvoColor

No standard design method exists to address the class of missions we consider in this study. Little related work exists—see Section 2—and refers only to mission-specific methods that follow the neuro-evolutionary approach. Indeed, as no extensive comparison has ever been performed between neuro-evolutionary methods across multiple missions, a state of the art in neuro-evolutionary robotics has not been identified, yet. Together with the results obtained with TuttiFrutti, in the following we will present also those obtained by EvoColor—a method based on neuro-evolution for the automatic design of swarms of e-pucks that can display and perceive colors. The results we will present should not therefore be considered as a comparison between TuttiFrutti and the state of the art in neuro-evolutionary robotics. In this context, our results should rather be considered as a comparison between TuttiFrutti and a reasonable instance of the neuro-evolutionary approach.

EvoColor is an adaptation of EvoStick [65]—a standard neuro-evolutionary method previously used as a yardstick in studies on the automatic design of robot swarms [8,10,15,53]. To the best of our knowledge, EvoStick is the only neuro-evolutionary method that has been tested via simulations and robot experiments on multiple missions without undergoing any per-mission modification. EvoColor produces control software for swarms of e-pucks that operate with RM 3—see Section 3.1. The control software has the form of a fully connected feed-forward artificial neural network with 41 input nodes (*in*), 8 output nodes (*out*) and no hidden layers. In this topology, the input nodes and output nodes are directly connected by synaptic connections (*conn*) with weights (*ω*) in a range of [−5, 5]. The activation of each output node is determined by the weighted sum of all inputs nodes filtered through a standard logistic function. EvoColor selects appropriate synaptic weights using an evolutionary process based on elitism and mutation. Just as in TuttiFrutti, the evolutionary process is conducted through simulations performed in ARGoS3, version beta 48, together with the argos3-epuck library. The evolution ends when an *a priori* defined budget of simulations is exhausted. Table 3 summarizes the topology of the neural network, the novelties with respect to EvoStick and the parameters used in the evolutionary process.

**Table 3.** Network topology and parameters of the evolutionary process in EvoColor. Novelties with respect to EvoStick are highlighted in gray. They concern the capability of displaying and perceiving colors. The neural network operates according to RM 3, see Table 1.

\* The number of generations is computed according to the budget of simulations. \*\* The population obtained in the last generation is post-evaluated to select the best individual.

The readings of the proximity (*prox*) and ground (*gnd*) sensors are passed directly to the network. Information about the number of neighboring peers (*n*) is provided through the function *z* (*n*) ∈ [0, 1], with *z* (*n*) = 1 − 2 1+*e*(*n*) . The vector *Vn* and each vector in *Vc*∈{*R*,*G*,*B*,*C*,*M*,*Y*} are translated into scalar projections onto four unit vectors that point at 45°, 135°, 225°, and 315° with respect to the front of the robot. Then, each projection is passed to the network through an independent input node. The last input of the network comes from a bias node. Four output nodes encode tuples (*v* ) of negative and positive components of the velocity of the wheels. Each tuple is obtained from two independent output nodes and is defined as *v* = ([−12, 0], [0, 12]). The velocity of a wheel (*v*) is computed as the sum of the two elements in a tuple (*v* ). Similarly, the color displayed by the RGB LEDs of the robot is selected by comparing the value of the output nodes that correspond to colors in the set {∅, *C*, *M*,*Y*}. The color displayed corresponds to the maximum value found across the four colors.

EvoColor differs from EvoStick in two aspects: the reference model and how the output of the neural network is mapped to the velocity of the robots. First, EvoColor is based on RM 3 and EvoStick on RM 1.1. In accordance to RM 3, EvoColor does not integrate the capability of the e-pucks for sensing the intensity of ambiance light—originally integrated in EvoStick. The second difference between EvoColor and EvoStick is how the output of the neural network is mapped to the velocity of the e-pucks. In EvoColor, we introduce a velocity mapping based on tuples to facilitate the evolution of standstill behaviors—as we expect robots need them to perform STOP and AGGREGATION.

In EvoStick, the control software maps directly a single output node of the neural network into velocity commands (*v* = [−12, 12]) for each wheel (*vk*∈{*l*,*r*})—a robot can stand still only if the velocity of the two wheels is set exactly to 0. A standstill behavior is then difficult to achieve since only one pair of values in the output nodes maps exactly to *vl* = 0 and *vr* = 0; Moreover, the output nodes can not maintain a steady value because they are subject to the injection of sensory noise. In EvoColor, the control software maps the sum of elements of a tuple (*v* ) into velocity commands for each wheel *vk*∈{*l*,*r*}—each tuple is defined by two output nodes and provides a negative and a

positive component to compute the velocity. We expect that this mapping facilitates the evolution of standstill behaviors: first, robots can stand still if the elements of each tuple (*v* ) are any pair of values of equal magnitude—steady values are not required provided that the output nodes that encode the same tuple vary proportionally; second, the sum of the positive and negative components can cancel out the sensory noise injected in the output nodes that encode a tuple—given a proper tuning of the synaptic weights. If one compares EvoColor with EvoStick, the first has more freedom to tune neural networks that lead to standstill behaviors.


**4.1 Iris melanoma**

## *4.1.1 Epidemiology*

Iris melanoma constitutes about 4% of uveal melanomas [14]. The mean age at presentation is 40–47 years. It is very rarely seen in the pediatric age group. Males and females are equally affected. It is most commonly seen in Caucasians (97.8%) [15].


**NEW APPROACHES TO THE STUDY OF MARINE MAMMALS**

Edited by **Aldemaro Romero**  and **Edward O. Keith**

http://dx.doi.org/10.5772/2731 Edited by Aldemaro Romero and Edward O. Keith

#### **Contributors**

Aldemaro Romero, Marnel Mouton, Alfred Botha, Letizia Marsili, Silvia Maltese, Daniele Coppola, Laura Carletti, Ilaria Caliani, Matteo Giannetti, Tommaso Campani, Matteo Baini, Silvia Casini, Cristina Panti, Christina Fossi, Kazue Ohishi, Tadashi Maruyama, Rintaro Suzuki, Juan Arbiza, Miguel Castro-Ramos, Andrea Blanc, Alberto Ponce De Leon, Helena Katz, Mario Clara, Edward O. Keith, Frank Gobas, Juan Jose Alava, John Pierce Wise, Catherine Wise, John Wise, Sandra Wise, Cristina Brito

#### **© The Editor(s) and the Author(s) 2012**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2012 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

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New Approaches to the Study of Marine Mammals Edited by Aldemaro Romero and Edward O. Keith p. cm. ISBN 978-953-51-0844-3 eBook (PDF) ISBN 978-953-51-7039-6


**5.6. Develop alternative livelihood projects and enforcement of wildlife laws.**

This chapter presses the need for alternative livelihood projects like bee keeping and the cultivation of snails to be promoted in forest-dependent communities that live around council forest landscapes in Cameroon as a strategy of reducing illegal hunting. As Lindsey et al. [22] explain, for illicit hunter to be curbed, livelihood interventions that generate alternative protein supply must be combined with proper enforcement of the relevant legal regulatory framework that prevails in that jurisdiction.


**4. Anthropogenic factors**

There are several anthropogenic factors that play a significant role in increasing the impact of challenges on water resources, and these factors occur in Lebanon and remained untreated. However, the majority of anthropogenic challenges on water resources are represented mainly by the increased population and the related increase in water demands. This also results in many aspects of water deterioration (quantity and quality) and water loss due to the unwise use of these resources. Nevertheless, the anthropogenic factors that influence water resources can be resolved if consumers regulate their use of water and this in turn needs awareness and economic controls on water use.

#### **4.1 Increased population**

Normally, population size increases, but it would be wisdom to adapt the water use with the existing population size. In other words, the ratio of increase in population should be accompanied with more management approaches to have steady state of supply/demand. However, this is not the case in Lebanon where the increased population adds more stress on water supply.

**97**

**Figure 6.**

*Striking Challenges on Water Resources of Lebanon DOI: http://dx.doi.org/10.5772/intechopen.81878*

**4.2 Unwise use of water resources**

bodies and in groundwater reservoirs.

**4.3 Lack of governmental controls**

whole country.

m3

density.

The population growth rate in Lebanon is estimated at 1.1% [19]. This means that there is about 1.1% annual increase in demand for water and this also will be joined by additional human requirements. Of course, this will result in more water

/year for the

/year/capita. **Figure6**

demand for different purposes, which is estimated at 12.8 million m3

/year/capita [20], this estimate will increase by about 2.5 m3

has become minimal and therefore, the river is in jeopardy.

*Population density and water demand in Lebanon between 2003 and 2017.*

According to the estimated water demand in Lebanon, which is about 220

reveals an example of the increased water demand integrated with the population

Due to the lack of awareness for wise-use of water resources in Lebanon, the existed water supply deficit often results negative behaviours of consumers. This is well pronounced in the agricultural sector (which accounts for about 70% of water use) where farmers often follow chaotic irrigation approaches. Therefore, uncontrolled digging for boreholes followed with extensive pumping has become a common observation. Pumping of water directly form rivers and springs is also widespread, and this has reflected in the loss of volume of water in surface water

The unwise use of water resources in Lebanon accompanied with the lack of governmental controls have reflected in poor water quality and quantity, and thus several examples on water deterioration have been witnessed. The Litani River is one example where the water quality has been totally deteriorated and water volume

There is still a debate on the management status of water sector in Lebanon, notably that the country has witnessed several geo-political conflicts that have resulted in negative impact on water resources and the related sectors (e.g., agriculture, energy, and food). These conflicts have created distortion in most of the gauge stations (hydrological and meteorological), break down in the institutional framework, and ignorance of legislations to mitigate the encroachments on water resources, plus the lack of adaptation instruments to face the physical and man-made *Striking Challenges on Water Resources of Lebanon DOI: http://dx.doi.org/10.5772/intechopen.81878*

*Hydrology - The Science of Water*

**4. Anthropogenic factors**

*Transboundary rivers of Lebanon.*

**Figure 5.**

and economic controls on water use.

increased population adds more stress on water supply.

**4.1 Increased population**

There are several anthropogenic factors that play a significant role in increasing the impact of challenges on water resources, and these factors occur in Lebanon and remained untreated. However, the majority of anthropogenic challenges on water resources are represented mainly by the increased population and the related increase in water demands. This also results in many aspects of water deterioration (quantity and quality) and water loss due to the unwise use of these resources. Nevertheless, the anthropogenic factors that influence water resources can be resolved if consumers regulate their use of water and this in turn needs awareness

Normally, population size increases, but it would be wisdom to adapt the water use with the existing population size. In other words, the ratio of increase in population should be accompanied with more management approaches to have steady state of supply/demand. However, this is not the case in Lebanon where the

**96**

The population growth rate in Lebanon is estimated at 1.1% [19]. This means that there is about 1.1% annual increase in demand for water and this also will be joined by additional human requirements. Of course, this will result in more water demand for different purposes, which is estimated at 12.8 million m3 /year for the whole country.

According to the estimated water demand in Lebanon, which is about 220 m3 /year/capita [20], this estimate will increase by about 2.5 m3 /year/capita. **Figure6** reveals an example of the increased water demand integrated with the population density.

#### **4.2 Unwise use of water resources**

Due to the lack of awareness for wise-use of water resources in Lebanon, the existed water supply deficit often results negative behaviours of consumers. This is well pronounced in the agricultural sector (which accounts for about 70% of water use) where farmers often follow chaotic irrigation approaches. Therefore, uncontrolled digging for boreholes followed with extensive pumping has become a common observation. Pumping of water directly form rivers and springs is also widespread, and this has reflected in the loss of volume of water in surface water bodies and in groundwater reservoirs.

The unwise use of water resources in Lebanon accompanied with the lack of governmental controls have reflected in poor water quality and quantity, and thus several examples on water deterioration have been witnessed. The Litani River is one example where the water quality has been totally deteriorated and water volume has become minimal and therefore, the river is in jeopardy.

#### **4.3 Lack of governmental controls**

There is still a debate on the management status of water sector in Lebanon, notably that the country has witnessed several geo-political conflicts that have resulted in negative impact on water resources and the related sectors (e.g., agriculture, energy, and food). These conflicts have created distortion in most of the gauge stations (hydrological and meteorological), break down in the institutional framework, and ignorance of legislations to mitigate the encroachments on water resources, plus the lack of adaptation instruments to face the physical and man-made

changes, and certainly change in climate and new water demands. The unfavorable situation has made it difficult to implement any economic policy in order to regulate water consumption and collect financial resource to develop the water sector.


*2.4. Skin Substitute Irradiation*

Exposition of skin substitutes to ionizing radiation was performed at the initial step of epidermis regeneration by keratinocyte precursor cells. Accordingly, samples were irradiated 6 h after keratinocyte seeding onto dermal reconstructs, a time sufficient for their recovery from trypsinization and attachment to their new environment. A 137Cs source was used (γ rays, IBL637 irradiator, Cis-Bio international, Saclay, France). Low dose irradiations (50 mGy) were performed at the dose rate of 50 mGy/min, and irradiations at higher dose (2 Gy) at the dose rate of 850 mGy/min. Control samples were shamirradiated (same processing except γ ray delivery). The control, 50 mGy and 2 Gy cohorts comprise respectively 14, 14 and 13 skin substitutes.


*1.5. Professionals*

This area of the model outlines the 'Ideal Optometrist'. In June 2015, the General Optical Council prepared a report on the public's perception of the optical profession, based on a survey of 2250 adults from across the UK. The sample was designed to reflect a cross-section of the population across various ages and socio-economic grades. Findings suggested that overall confidence in and satisfaction with opticians (both Optometrists and Dispensing Opticians) is high.

In 2018, Irving, et al. [9] suggested that patient's knowledge of eye care had notable gaps with the potential for these gaps to increase risk of vision loss due to late detection of ocular conditions. In recent years, Optometrists have been able to access further additional professional qualifications. In 2007, the Department of Health (UK) granted permission for Optometrists to train as Independent Prescribers. This qualification allows Optometrists to manage a wider range of patients than with the entry-level qualification providing patients with more accessible eye care. In addition to independent prescribing, in the past decade, a range of Professional Certificates have been launched, allowing Optometrists to upskill in a number of areas including glaucoma management, pediatric eye care and low vision. The number of Optometrists currently undertaking these qualifications is increasing and in recent years, employment vacancies have been advertised specifically requiring these additional qualifications as part of the 'essential criteria'.

These additional qualifications allow Optometrists to maintain and update their skills and knowledge in alignment with emerging evidence, allowing for career progression. In the past two decades, there has been a shift towards the majority of Optometrists working for large multiple franchises rather than independent practices. These additional qualifications allow Optometrists to enhance their employability and transferability.

Across the globe, the term 'Optometrist' does not refer to a consistent standard for the profession. In many countries, including the United States and Canada, newly qualified Optometrists hold an OD (Doctorate of Optometry) and have a wider scope of practice than in the UK, o ffering a greater range of therapeutic services to patients and are regarded as highly qualified professionals.

In other countries in Europe, Optometrists have a more limited role, closer aligned with Dispensing Opticians where medical eye related issues are always referred to a hospital Ophthalmologist.

It may be argued that the drive towards higher qualifications is partly motivated by the increasingly litigious nature of health care or the age of self-diagnosis via the internet. Where patients are considered as consumers, Optometrists may feel obliged to maintain their knowledge and skills to a higher level and therefore undertake courses to protect themselves.

However, whatever the motivation, there does seem to be an appetite for further qualifications in Optometry and this increased level of skills and knowledge will benefit both patients and practitioners [10].

#### *1.6. Undergraduate Optometry Students' Perspective of the Place Model*

Due to the limited published data on professional perspectives in Optometry, we conducted a scoping exercise to determine how the Place Model may be applied to the profession.

## **2. Materials and Methods**

The Place Model [1] Survey was originally designed to determine the views of teachers and teacher educators of the Place Model and Freidson's three logics [11]. For the purposes of this study, the Place Model Survey has been adapted to determine the views of undergraduate Optometry students (Appendix A).

Data collection took place during the 2018–2019 academic year and the study was approved by Research Governance at Ulster University which adheres to the tenets of the Declaration of Helsinki.

Participants were undergraduate Optometry students at Ulster University and prior to participation, were provided with an information sheet outlining the study. All undergraduate Optometry students were invited to participate in the study. Consent was assumed when the subject choose to complete the survey as indicated on the survey itself (Appendix A).

Participants were provided with an oral description of the Place Model and were then asked to complete the survey. This comprised; initial demographic questions, Place Model questions where the subject was asked to locate themselves (with an X mark) and their perception of the status and professional learning journey which is typical of optometrists within their own (United Kingdom based) education system (with a 0 mark). In addition, they were asked for a written explanation of these two locations [11].

Following survey completion, statistical analyses were performed using IBM Statistical Package for the Social Sciences (SPSS) (version 25).