Maxime Gonthier
email: maxime.gonthier@ens-lyon.fr

Exploiting data locality to maximize the performance of data-sharing tasksets

Keywords: Memory-aware scheduling, Eviction policy, Tasks sharing data, Runtime systems Compas'

The use of accelerators such as GPUs has become mainstream to achieve high performance on modern computing systems. GPUs come with their own (limited) memory and are connected to the main memory of the machine through a bus (with limited bandwidth). When a computation is started on a GPU, the corresponding data needs to be transferred to the GPU before the computation starts. Such data movements may become a bottleneck for performance, especially when several GPUs have to share the communication bus. Task-based runtime schedulers have emerged as a convenient and efficient way to use such heterogeneous platforms. With such systems, the scheduler has the ability to choose which task to allocate to which GPU and to reorder tasks so as to minimize data movements. We focus on this problem of partitioning and ordering tasks that share some of their input data. We present a novel dynamic strategy based on data selection, to efficiently allocate tasks to GPUs, and a custom eviction policy. We compare them to existing strategies using standard scheduling techniques in runtime systems. All strategies have been implemented on top of the StarPU runtime, and we show that our dynamic strategy achieves better performance when scheduling tasks on multiple GPUs with limited memory.

Introduction

High-performance computing applications, such as simulation for aeronautics, or seismology, keep demanding increasingly intense computation power on ever-growing sets of data. GPUs have risen as an efficient and quick way to run those simulations. They have their own dedicated high-bandwidth memory, which requires transferring data between CPUs and GPUs. To tackle this concern, it has become very common to use the task-based programming paradigm, i.e. to express the application computation as a Directed Acyclic Graph (DAG), and let a dynamic runtime system such as OmpSs [START_REF] Bueno | Productive programming of GPU clusters with OmpSs[END_REF], PaRSEC [START_REF] Bosilca | PaR-SEC: A programming paradigm exploiting heterogeneity for enhancing scalability[END_REF], or StarPU [START_REF] Augonnet | StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures[END_REF] manage the execution of the task graph with a scheduler. The memory embedded in GPUs is relatively limited, and the bus that connects them to the main memory has a very limited bandwidth. The runtime scheduler thus not only has to care for task acceleration, it also has to take into account the movement of data within the system. This means that it must carefully decide the task mapping on GPUs according to data locality, as well as the ordering of the tasks itself, to privilege the temporal locality of data, thus favoring data reuse. It is also essential to trigger data transfers ahead of task execution (data prefetches) so that they can be overlapped. Last but not least, when the embedded memory of the GPU is full, the runtime has to carefully decide which data should be evicted from it, to make room for further data. We focus in this paper on the problem of partitioning and scheduling a set of tasks on multiple GPUs with limited memory, where tasks share some of their input data, as well as managing data movements (loads and evictions). More precisely, we want to determine the order in which tasks must be processed on each GPU to optimize for data reuse as well as maximize overlap between computations and data movements. Our objective is twofold: (i) we partition the work on each GPU to reach a good load balance, and (ii) we want to minimize the total amount of data transferred to the GPUs for the processing of all tasks with a constraint on the memory size. Solving the bi-objective optimization problem for the currently available tasks can lead to a large reduction in data transfers and hence a performance increase, which is ultimately our goal. In this paper, we make the following contributions:

• We propose a new heuristic based on performing as much computations as possible with data at hand, as well as an eviction policy focused on finding the least-used data in the future (Section 3.2). • We implement it into the StarPU runtime and first study the performance obtained on an independant set of task: the 2D-blocked matrix multiplication • We extend our scheduler to deal with dependencies and use the Cholesky decomposition to evaluate it. Overall, our evaluation shows that our heuristic surpasses previous strategies, in particular in the most constrained situations.

Problem modeling

We consider the problem of scheduling tasks on K GPUs, denoted by GPU 1 , . . . , GPU K . As proposed in previous work [START_REF] Kaya | Heuristics for scheduling file-sharing tasks on heterogeneous systems with distributed repositories[END_REF], tasks sharing their input data can be modeled as a bipartite graph G = (T ∪ D, E). The vertices of this graph are on one side the tasks T = {T 1 , . . . , T m } and on the other side the data D = {D 1 , . . . , D n }. An edge connects a task T i and a data D j if task T i requires D j as input data. For the sake of simplicity, we denote by D(T i ) = {D j s.t. (T i , D j ) ∈ E} the set of input data for task T i . Each data D j takes in memory a size w j . Each of the K GPUs is equipped with a memory of limited size M. Initially, all input data are stored in the main memory of the machine. During the processing of a task T i on GPU k , all its inputs D(T i ) must be in the memory of GPU k . Note that we do not consider the data output of tasks. Each of the m tasks must be processed on some of the K GPUs. Our goal is to determine both how to partition the task set to the GPUs and in which order to process them on each GPU. As explained above, our objective is also to come up with a schedule with few data movements, as they can largely impact the overall processing time. Hence we also need to detail when each data must be loaded or evicted from the memory of each GPUs. We study both the scheduling of an independant set of task or one with dependencies.

Algorithms

Dynamic scheduler of STARPU: DMDAR

"Deque Model Data Aware" (see Algorithm 1 in the complementary material) is a dynamic scheduling heuristic designed to schedule tasks on heterogeneous processing units in the StarPU runtime [START_REF] Augonnet | Data-Aware Task Scheduling on Multi-Accelerator based Platforms[END_REF]. It takes data transfer time into account and schedules tasks where their completion time Figure 1: Small example with a 2D grid dependencies. The graph of input data dependencies is shown on the left, together with the task partition among GPUs. A possible schedule is described on the right. Each GPU memory is limited to M = 2 and each data has the same size:

1. GPU 1 processes tasks T 1 , T 2 , T 5 , T4 (in this order), and data D 1 has to be loaded twice. GPU 2 processes tasks T 3 , T 6 , T 9 , T 8 , T 7 in this order to avoid multiple loads of the same data. The total amount of data movement is 11.

times (data transfer included) is expected to be minimal. It computes the expected completion time C k (T i ) of the first task T i in the queue on each GPU k , based on a prediction of the time for transferring the data to the GPU (or communication time) comm and of the task computation time comp:

C k (T i ) = j∈D(T i ) D k / ∈InMem(k) comm k (D j ) + comp k (T i ) (1)
The data transfer time is counted only if the data is not already in the memory of GPU k . Then, the task is allocated on the GPU where its completion time is minimal. Here, we consider the DMDAR variant, which includes an additional Ready strategy: on each GPU, tasks are reordered at runtime in order to favor tasks with the most input data already loaded into memory.

Data-Aware Reactive Task Scheduling

We propose here a novel dynamic strategy, called DARTS (for Data-Aware Reactive Task Scheduling), adapted from previous algorithms specifically designed for linear algebra operations, such as outer products and matrix products [START_REF] Beaumont | Analysis of dynamic scheduling strategies for matrix multiplication on heterogeneous platforms[END_REF]. The paradigm is to to perform as many tasks as possible with the data at hand. The main idea of our new algorithm, detailed in Algorithm 2, is to first consider data movement before task allocation. Whenever some GPU k requests some new task, we first look in the set dataNotInMem k (which initially contains all data of available task) for the data D that, if moved into the memory of GPU k , would maximize the number of new "free" tasks, i.e., tasks that can be allocated and processed on GPU k without any additional data movement. Once such a data is found, all these tasks are allocated on GPU k . More precisely, they are put in a local data-structure (plannedTasks k ) from which tasks are popped one after the other upon request. The process is started again when plannedTasks k is empty. It may happen that we do not find any data that enables some free task. It occurs for example at the very beginning of the computation when all tasks depend on two or more data: at least two data must be loaded in order to produce some free task. In this case, some random unprocessed task is allocated to GPU k . On the contrary, when there exists several candidate data which may produce the maximum number of "free" tasks, we select a data among the candidates that is useful for the highest number of tasks and has the highest priority. We also designed a custom eviction policy: since we plan ahead which tasks will be allocated to a GPU (through the use of plannedTasks), we can take this information into account when we have to remove some data from the memory. This strategy is named Least Used in the Future (LUF) and is detailed in Algorithm 3. We consider all data currently in memory and check if they are used as input for a future task. There are two types of such future tasks: tasks in plannedTasks k that have been reserved for a later allocation on the GPU as mentioned above, but also tasks in taskBuffer k , which are the tasks that have been popped from plannedTasks k for execution on GPU k , and thus whose GPU placement cannot be changed any more (the required data for these tasks may already have been prefetched). We first try to evict a data which is not useful for any task in taskBuffer k , and which is an input of few tasks in plannedTasks k . If this is not possible, we apply Belady's rule [START_REF] Belady | A study of replacement algorithms for a virtual-storage computer[END_REF] on tasks already allocated: we select the input data whose next usage in taskBuffer k is the furthest in the future, which is known to minimize data movement.

Experimental results

In the following experiments we measure the obtained performance as the throughput of elementary computational operations performed per time unit (in GFlop/s) as well as the total volume of data transferred between CPU and GPUs (which we try to minimize). Our goal is to show that through a reduction in data transfers we achieve a better throughput.

Settings

All strategies mentioned above have been implemented in the StarPU runtime system [START_REF] Augonnet | StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures[END_REF]. We performed real experiments on tesla V100 GPUs. We have limited the GPUs memory (500 MB for the outer product and 2000 MB for Cholesky) in order to better distinguish the performance of different strategies even on small datasets. Our main application scenario are tasks from a 2D matrix multiplication with tiles of size 960 × 960: the matrix product C = A × B is decomposed into tasks corresponding to the multiplication of one block-row of A with one block-column of B. Input data is thus the rows of A and columns of B. Our second application is the Cholesky factorization with tiles of size 1920 × 1920 in single precision. We use the two scheduling heuristics presented above, together with the baseline EAGER scheduler that lets GPUs pick up tasks on demand from a shared queue that contains the tasks in the natural order. All the schedulers use the LRU eviction policy except for DARTS+LUF. When measuring GFlop/s, the cost of computing the schedule is considered. Each result is the average of the performance obtained over 10 iterations. For most of the results, the deviance is less than 2%, thus, we do not show error bars in the following graphs.

Result on the Matrix Multiplication General overview

On Figure 2a the dotted horizontal black lines represents the maximum performance that the GPU can achieve when processing elementary matrix products (without I/Os) and is thus our asymptotic goal. On Figure 2a and 2b the red dotted vertical line denotes the situation when the GPUs cumulated memory can fit exactly only one of the two input matrices, and the orange line denotes the situation when it can accommodate both input matrices. Figure 2b shows the amount of data transfers. There, the black dotted curve represents the maximum number of transfers that can be done during the minimum time for computation (given by the bound on the throughput), thus the hard limitation induced by the PCI bus bandwidth: a strategy exceeding this amount necessarily requires more time for the data transfers than the optimal

Compas'2023 : Parallélisme / Architecture/ Système LISTIC/USMB -Annecy France, 4-7 juillet 2023 q q q q q q q q q q q q q q q A and B fits in cumulated memory B fits in cumulated memory Working set (MB) GFlop/s q q q q DARTS+LUF DMDAR DARTS EAGER (a) GFlop/s. q q q q q q q q q q q q q q q A and B fits in cumulated memory B fits in cumulated memory Working set (MB)

Data transfers (MB)

q q q q q DARTS+LUF DMDAR DARTS EAGER

(b) Amount of data transfers.

EAGER's results

The EAGER heuristic switches to a pathological behavior at the red line. We can see the throughput plummeting (Figure 2a) . It is accompanied with a rise in data transfers after the red line as you can see on Figure 2b. EAGER tends to process tasks along the rows of C. This allows to reuse the same block-row of matrix A for tasks that compute tiles of the same row of C, but requires reloading the whole matrix B for each new block-row of A when the memory is constrained, which is a well-known pathological case of the LRU eviction policy. This explains why EAGER's performance drops at the red dotted line.

DMDAR's results

DMDAR does not suffer from the pathological case affecting EAGER because its Ready strategy allows it to rather process tasks that need the block-column of B already in memory instead of reloading the whole B matrix. DMDAR's data transfers however start to rise for the last two working set sizes as we can see on Figure 2b. That corresponds to the performance drop of the last two points of Figure 2a. This is due to a conflict between data prefetching and eviction. Indeed, once the GPU is filled with data, it is not clear for DMDAR whether some data should be evicted in order to perform more prefetches. It will thus rather stop prefetching data as long as all the data currently in the GPU will be useful for the subsequent tasks to be executed there. Also, when some data is actually evicted from a GPU, DMDAR does not reconsider the task mapping according to the new set of data loaded on the GPU. Thus, it cannot make a balance between prefetching and eviction.

DARTS' results

On Figure 2a, DARTS+LUF achieves near perfect performance and has a 9.4% improvement over DMDAR. Indeed, loading a single data that enables multiple tasks minimizes data transfers and increases overlap between transfers and computations. However, after the orangedotted line, when the memory is constrained, DARTS without the custom LUF eviction policy has to load and evict data following LRU's policy. So the tasks in taskBuffer that are supposed to be ready for computation, have to load more data. Indeed, the previous tasks caused evictions, and the data evicted might be needed by the tasks in taskBuffer. This causes a domino effect where each new task requires a new data load. For DARTS+LUF, when an eviction is needed, it avoids as much as possible evicting a data that is used by the few tasks already planned for computation. This allows us to avoid the pathological case of DARTS, and to achieve a better balance between prefetching and eviction since DARTS+LUF maintains in plannedTasks and taskBuffer an accurate overview of the tasks to be computed, even when eviction removes some data from a GPU.

To deal with multiple GPUs, DARTS assigns to each GPU its own set of data dataNotInMem k to pick from. However all GPUs share the same set of tasks. Once a task is allocated to a GPU, it is removed from the common set of available tasks. Thus our scheduler will naturally assign to the other GPUs data from a line or row that has not been used for tasks yet. This will evenly distribute tasks among GPUs and mostly separate data usage between GPUs.

Result on the Cholesky factorization

To tackle the issue of a taskset with dependencies, DARTS was first adapted to deal with a dynamic submission of task. dataNotInMem k is now a list that is updated upon the arrival of new unprocessed data. Moreover, when choosing the optimal data to load D opt , DARTS now looks at the maximum priority (computed from the bottom-level [START_REF] Casanova | Parallel Algorithms[END_REF] i.e. the time to completion if the platform is constrained) of the tasks associated with the candidate data. This priority is used as a tiebreak after the number of "free" task that D opt enables.

Figure 3 shows the results on the Cholesky decomposition with 2 GPUs. Here, the green vertical line marks the working set size where all of the input data fit into memory. In this experiment, we compare our solution DARTS to DPLASMA, an implementation of dense linear algebra applications using the state of the art PaRSEC runtime [START_REF] Bosilca | Flexible development of dense linear algebra algorithms on massively parallel architectures with dplasma[END_REF].

Apart from DARTS, we observe that all the schedulers suffer from the memory constraint, associated with a rise in data transfers on Figure 3b. By looking at the priorities and the amount of work associated with a data , DARTS is able to have a healthy balance between critical path progression and locality. If only a few tasks are ready, the priority will guide the choice of D opt toward task on the critical path. On the contrary, if a lot of tasks are ready, one can more easily find locality and thus a large number of tasks will be computed with the same data. Moreover, DARTS requires significantly less data transfers than it's competitor as we can see on Figure 3b. This reduction is associated with better performance.

Conclusion

Limiting data movements is crucial for performance in modern computing platforms, and especially for machines equipped with several GPUs sharing the same communication bus to the main memory. We proposed a new strategy, named DARTS, which achieves very good performance when the memory is a scarce resource through a reduction in data transfers and a better overlap of communications and computations. DARTS fills each GPU with as many task as possible when they are idling. When you have a large number of GPUs, letting a GPU idling could be a good solution as it enables other GPUs to maximize their data locality. Tackling this issue could allow us to scale DARTS on larger machines. In efforts to make DARTS more generic, we would like to adapt it to heterogeneous platforms by using both CPUs and GPUs. Lastly, we would like to experiment using out-of-core applications, since DARTS is not specific to GPUs and could be applied to any problem where tasks are sharing input data.

Compas'2023 : Parallélisme / Architecture/ Système LISTIC/USMB -Annecy France, 4-7 juillet 2023 q q q q q q q q q q q q All data fit GFlop/s q q q q DARTS+LUF DMDAR PaRSEC EAGER (a) GFlop/s. q q q q q q q q q q q q All data fit T i ← pop(T)

4:

For each GPU k , compute C k (T i ) using Eq. 1 Select V the data whose next use in taskBuffer k is the furthest in the future 9: end if 10: Remove tasks depending on V from plannedTasks k 11: Evict V from memory, push it to dataNotInMem k

Compas' 2023 :

 2023 Parallélisme / Architecture/ Système LISTIC/USMB -Annecy France, 4-7 juillet 2023

GFlop

P

Figure 2 :

 2 Figure 2: Performance on the 2D matrix multiplication with 2 real Tesla V100 GPUs.

Compas' 2023 :

 2023 Parallélisme / Architecture/ Système LISTIC/USMB -Annecy France, 4-7 juillet 2023

G

  Amount of data transfers.

Figure 3 :Algorithm 1 1 :

 311 Figure 3: Performance on the Cholesky decomposition with 2 Tesla V100 GPUs.

5 : 7 : 8 : 2 :else 4 : 5 : 7 : 3

 57824573 Select k such that C k (T i ) is minimal 6: Allocate T i on GPU k for each D i ∈ D(T i ) do Request data prefetch for D j on GPU k 9: Add D i to InMem(k) : Parallélisme / Architecture/ Système LISTIC/USMB -Annecy France, 4-7 juillet 2023 Algorithm 2 DARTS on GPU k When GPU k requests a new task 1: if plannedTasks k = ∅ then Return pop(plannedTasks k ) 3: for each data D ∈ dataNotInMem k do Compute n(D), the number of tasks that depend only on D and some data already loaded Let n max be the maximum of n(D) for D ∈ dataNotInMem k 8: if n max > 0 then 9: Candidates ← set of data D with n(D) = n max 10: Select D opt ∈ Candidates such that the number of unprocessed tasks depending on D opt is maximum (break ties randomly) 11: plannedTasks k ← set of unprocessed tasks depending only on D opt and on other data already in memory 12: T ← pop(plannedTasks k ), remove D opt from dataNotInMem k unprocessed task T , remove the inputs of T from dataNotInMem k Eviction procedure LUF for DARTS on GPU k 1: for each data D in the memory of GPU k do 2: n b (D) ← number of tasks using D in taskBuffer k 3: n p (D) ← number of tasks using D in plannedTasks k 4: end for 5: if the minimum value of n b (D) on any data D is 0 then 6:Select V such that n b (V) = 0 and n p (V) is minimum 7: else 8:


Mohammad Amin Bozorgmanesh
email: mohammadamin.bozorgmanesh@gmail.com

Thermal Effects of Electromagnetic Waves on the Human Body

Keywords: The thermal effect, Electromagnetic waves, Cell phone, Health of the human body

The advancement of technology, the development of telecommunication industries, the construction of new electrical devices, and their use in life have affected human health in various ways. One of these means of communication, which is an inseparable part of human daily life, is the mobile phone. The effects of electromagnetic waves caused by this communication device are divided into two categories: thermal effect and non-thermal effect. The results of research and laboratory tests show that the heat waves of mobile phones have negative effects on the vital organs of the body. Our goal in this article is to investigate the thermal effects of mobile phone electromagnetic waves on the vital organs of the human body.

INTRODUCTION

With the increasing use of electrical devices, humans are under the influence of low-frequency electromagnetic fields, some of which have a therapeutic aspect [START_REF] Narita | Induction of apoptotic cell death in human leukemic cell line, HL-60, by extremely low frequency electric magnetic fields: analysis of the possible mechanisms in vitro[END_REF][START_REF] Mairs | Microsatellite analysis for determination of the mutagenicity of extremely low-frequency electromagnetic fields and ionising radiation in vitro[END_REF]. Therefore, following scientific and technological advances [START_REF] Mobasseri | Impact of driving style on fuel consumption[END_REF][START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF][START_REF] Mobasseri | Intelligent Circuit Application for Detecting the amount of Air in Automobile Tires[END_REF][START_REF] Mobasseri | Intelligent Circuit Application for Monitoring the Employees Attendance[END_REF], the construction of new devices, and the presence of humans exposed to these waves, much research was conducted on the effect of these waves on biological systems [START_REF] Winker | Chromosomal damage in human diploid fibroblasts by intermittent exposure to extremely low-frequency electromagnetic fields[END_REF][START_REF] Moulder | Power-frequency fields and cancer[END_REF]. Some devices that emit electromagnetic waves that humans are exposed to are mobile phones, wireless phones, televisions, computers, microwaves, telecommunication towers, high pressure, etc. [START_REF] Palicot | On the use of cognitive radio for decreasing the electromagnetic radiations[END_REF]. Scientists' research shows that long-term exposure to these waves causes some diseases, such as tumors, cancer, infertility, and cell and DNA destruction, some of which will be mentioned in the following parts of the article. These radiations are divided by parameters such as signal changes, radiation intensity, and the amount of radiation, and the highest amount of radiated waves are related to mobile phones [START_REF] Hutter | Subjective symptoms, sleeping problems, and cognitive performance in subjects living near mobile phone base stations[END_REF]. Germany is one of the countries under the radiation of GSM telephone masts with high-frequency sources in residential areas [START_REF] Haumann | HF-Radiation levels of GSM cellular phone towers in residential areas. 2nd International Workshop on Biological effects of EMFS[END_REF]. Cell phones have a frequency of 900 to 1800 MHz with a pulse of 218 Hz. This frequency range is used in most European and Asian countries, including Iran [START_REF] Sicard | Introduction to GSM. 5th[END_REF]. Of course, it should be noted that cell phone waves are safe because they are non-ionizing [START_REF] Kundi | Mobile telephones and cancer-a review of epidemiological evidence[END_REF][START_REF] Khurana | Cell phone and DNA story overlooked studies[END_REF]. Excessive use of this means of communication is such that many people are exposed to radiation from the age of three until the end of their lives [START_REF] Calvente | Exposure to electromagnetic fields (non-ionizing radiation) and its relationship with childhood leukemia: a systematic review[END_REF].

Literature Review

Sami Gabrie et al. conducted several experiments on the effect of dielectric properties on different body tissues in the frequency range of 10 to 20 Hz [START_REF] Gabriel | The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz[END_REF][START_REF] Gabriel | The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues[END_REF]. Stephen B. Bauman et al. in 1997 investigated the effect of changing frequency and temperature on the electrical conductivity of the human brain [START_REF] Baumann | The electrical conductivity of human cerebrospinal fluid at body temperature[END_REF]. Schmid et al. conducted experiments on the dielectric properties of the human brain [START_REF] Schmid | Dielectric properties of the human brain measured less than 10 hours post mortem[END_REF]. T. V. Yioultsis researched the effects of electromagnetic wave radiation and temperature analysis on the body caused by various antennas [START_REF] Yioultsis | A comparative study of the biological effects of various mobile phone and wireless LAN antennas[END_REF]. Ismail et al. modeled the propagation of electromagnetic waves caused by mobile phones in the human head with the FDTD numerical method in the two-dimensional environment [START_REF] Binti Ismail | Modeling of electromagnetic wave penetration in a human head due to emissions from cellular phone[END_REF]. Mohammad Rashed Iqbal Faruque compared some types of antennas, including Helical, Monopole, Pifa, and Patch, at different frequencies with the help of the FDTD numerical method [START_REF] Faruque | SAR analysis in human head tissues for different types of antennas[END_REF]. Boyan Tomovs obtained the traces of electromagnetic fields in two dimensions and by FDTD numerical method on the human head in four different modes [START_REF] Tomovski | Effects of electromagnetic field over a human body, sar simulation with and without nanotextile in the frequency range 0.9-1.8 ghz[END_REF].

In general, numerical methods are used to determine the distribution of the field inside and around the human body in the presence of electromagnetic sources, and various papers have been published in this field [START_REF] Richmond | Health impacts of radio frequency from smart meters[END_REF][START_REF] Rom | Environmental and occupational medicine[END_REF].

The effect of electromagnetic waves on the human body

This investigation was a survey research based on the functional goal and the methodology. The time domain was 2015-2014. Electromagnetic waves carry energy, and the waves absorbed by the human body can increase the temperature of body tissues [START_REF] Organization | WHO's Agenda for EMF Research[END_REF][START_REF] Vecchia | Exposure to high frequency electromagnetic fields, biological effects and health consequences (100 kHz-300 GHz)[END_REF]. Electromagnetic fields increase the concentration of free radicals in the chemical environment, and many medical studies have shown the existence of uncontrollable free radicals that directly cause problems [START_REF] Khurana | Cell phones and brain tumors: a review including the long-term epidemiologic data[END_REF][START_REF] Cardis | The INTERPHONE study: design, epidemiological methods, and description of the study population[END_REF].

Thermal effects of electromagnetic waves:

a) Damage to the eye: The cornea of the eye does not have a temperature regulation mechanism and is sensitive to heat loss. The cornea can absorb microwave waves and seriously damage it [START_REF] Xu | Exposure to 1800 MHz radiofrequency radiation induces oxidative damage to mitochondrial DNA in primary cultured neurons[END_REF][START_REF] Lai | Magnetic-field-induced DNA strand breaks in brain cells of the rat[END_REF]. b) Damage to the skin: When the number of microwave waves does not exceed 10 GHz, due to their low penetrating power, they remain on the surface of the body and are absorbed by the skin; Therefore, there is a possibility of burns on the surface of the skin [START_REF] Xu | Exposure to 1800 MHz radiofrequency radiation induces oxidative damage to mitochondrial DNA in primary cultured neurons[END_REF][START_REF] Santini | Study of the health of people living in the vicinity of mobile phone base stations: I. Influences of distance and sex[END_REF]. c) Damage to the ear: The effect of mobile phones on the human ear has been confirmed in the long term in the form of feeling extra sounds. Generally, there are agreements on the fact that radio frequency energy causes the expansion of brain tissues, which is caused by small but rapid temperature changes in the tissues. This causes pressure waves to be sent through the skull to the inner ear; Where the sound sensitive receivers are located. In this way, the thermal effect of the mobile phone on the human ear in the long term appears as the feeling of sounds. d) Decreased fertility in men: Testicles are one of the organs sensitive to heat and harmful waves. Absorption of energy caused by radio waves in the testicles can cause temporary sterility and in some cases, permanent sterility [START_REF] Anvari | Effects of Electromagnetic Waves on Humans[END_REF]. e) Increase in body temperature: An increase in body temperature is one of the thermal effects of electromagnetic waves [START_REF] Santini | Study of the health of people living in the vicinity of mobile phone base stations: I. Influences of distance and sex[END_REF]. f) Damage to the brain: The effects of exposure of the brain to mobile phone waves cause the skin on the upper level of the skull to be affected by these waves [START_REF] Dehghan | Thermal and non-thermal effects of electromagnetic waves on human body parts[END_REF]. The part of the head near which the mobile phone is placed is more affected than other areas.

3-2 Non-thermal effects of electromagnetic waves:

When an increase in temperature does not accompany the absorption of electromagnetic waves, we call it the non-thermal effect of electromagnetic waves. The most significant effects of mobile phones on the cells and tissues of living organisms are caused by their non-thermal effects, which depend on the user's distance from the generating source [START_REF] Ghaffari Turan | Effects of electromagnetic waves in the frequency range of 900 to 1800 MHz on humans[END_REF]. a) Destructive neurodegenerative diseases: These diseases, such as Alzheimer's and Parkinson's, are among the complications caused by these waves [START_REF] Palicot | On the use of cognitive radio for decreasing the electromagnetic radiations[END_REF][START_REF] Krajewski | Numerical assessment of electromagnetic exposure during live-line works on high-voltage objects[END_REF]. In Figure 1, the electromagnetic radiation near the human head caused by a mobile phone is shown: b) Other disorders: Headache, dizziness, convulsions, changes in heart rate rhythm, anxiety during sleeping hours, loss of concentration and memory, migraine, distraction, and increased blood pressure are among the non-thermal side effects of radio frequencies [START_REF] Kula | Effects of electromagnetic field on free-radical processes in steelworkers. Part I: Magnetic field influence on the antioxidant activity in red blood cells and plasma[END_REF].

4-Ways to prevent and reduce risks from electromagnetic waves:

A) Reducing the duration of the conversation: Studies show that if you talk on a cell phone for more than two minutes, the brain is affected by cell phone waves.

b) Starting to talk after establishing communication:

According to studies and research, it is better to put the mobile phone to the ear after the telephone connection is established; Because, at the initial moment of communication, the amount of waves emitted is very high.

c) Using hands-free: One of the best-suggested ways is to use a wired "hands-free" or "headset" instead of directly using a mobile phone. This will reduce the possible damage to these three vital organs due to the increased distance of mobile phone waves from the ear, brain, and skull.

d) Use of antioxidants: using a cell phone is inevitable; One of the proposed options to eliminate and reduce the effects of electromagnetic waves on the human body is using antioxidants. Considering that the best way to against electromagnetic waves is prevention, in Table 1, a practical proposal is included in this field.

Table 1: Providing a proposal to prevent the absorption of electromagnetic waves [START_REF] Sarkar S | AND ELECTROMAGNETIC RADIATIONS IN MRI ON FOLLICULOGENESIS PROCESS IN ADULT RATS[END_REF]

Clinical application New findings

Mobile phones should not be used as much as possible, and the central stations transmitting the waves should not be close to residential areas, hospitals, and schools.

Cell phones should not be viewed as a safe device. Long-term use of it can eventually lead to cancer.

5-Conclusion:

According to the conducted research, in using mobile phones, we should use our utmost care and shorten the conversations as much as possible. The electromagnetic field disrupts the nervous and cellular systems and causes diseases such as cancer, tumor, infertility, and changes in heart rate and blood pressure. In order to protect against the possible effects of these waves, a series of protection methods are suggested, including shortening the duration of phone calls, keeping the phone away from the body, using mobile phone anti-wave covers, using hands-free, be considered. Therefore, investigating the effects of electromagnetic waves on the human body is essential.

Funding information:

The author received no financial support for this research, authorship, and/or publication.

Declaration of interests:

The author declares that there is no conflict of interest in this work.

Ethical approval: This article does not contain any studies with human participants or animals performed by the author.

REFERENCES:

Figure 1 :

 1 Figure 1: Radiation of electromagnetic waves near the human head[START_REF] Vecchia | Exposure to high frequency electromagnetic fields, biological effects and health consequences (100 kHz-300 GHz)[END_REF][START_REF] Khurana | Cell phones and brain tumors: a review including the long-term epidemiologic data[END_REF]


Ashraf Ahmed
email: ashraf.ahmed@univnantes.fr

Abdul-Hamid Soubra
email: abed.soubra@univ-nantes.fr

M Asce

Subset Simulation and its Application to a Spatially Random Soil

The failure probability of a footing resting on a spatially random soil is currently computed using Monte Carlo simulation (MCS) methodology. This approach is well known to be very time-consuming especially for computing small failure probabilities. One alternative to MCS is the subset simulation approach. This approach is used in this paper to perform a probabilistic analysis at the serviceability limit state of a strip footing resting on a soil with a spatially varying Young modulus. The random field was discretized using Karhunen-Loeve expansion. The probabilistic numerical results have shown that the failure probability calculated by subset simulation is very close to that computed by MCS methodology but with a significant reduction in the number of simulations.

INTRODUCTION

The failure probability of geotechnical problems modeled by random fields is generally calculated using Monte Carlo simulation (MCS) methodology. This method is very expensive for the computation of a small failure probability. [START_REF] Au | Estimation of small failure probabilities in high dimentions by subset simulation[END_REF] proposed an efficient approach (called subset simulation) to calculate the small failure probabilities. Except [START_REF] Au | Implementing advanced Monte Carlo simulation under spreadsheet environment[END_REF] and [START_REF] Santoso | Modified Metropolis-Hasting algorithm with reduced chain-correlation for efficient subset simulation[END_REF] who applied this approach to the random field problems, the subset simulation method was mainly applied in literature to problems where the uncertainties of the different parameters were modeled by random variables. These authors have considered only one-dimensional random field problems. It should be noted that [START_REF] Au | Implementing advanced Monte Carlo simulation under spreadsheet environment[END_REF] have discretized the random field into a finite number of random variables equal to the number of elements of the deterministic model. Thus the random dimension depends on the number of elements of the deterministic model. In this paper, the subset simulation method was proposed to calculate the failure probability in case of a twodimensional random field discretized by K-L expansion. As will be explained hereafter, the random dimension in the present work does not depend on the number of elements of the deterministic model but on the size M of the K-L expansion. The proposed procedure was applied to perform a probabilistic analysis at the serviceability limit state (SLS) of a shallow strip footing subjected to a central vertical load (P V ). The uncertain random field considered in the analysis is the soil Young modulus (E). The footing vertical displacement was used to represent the system response. The deterministic model used to compute the system response is based on numerical simulations using the commercial software FLAC 3D .

KARHNUEN-LOEVE EXPANSION

Consider a random process H(X, θ) where X denotes the spatial coordinates and θ indicates the random nature of the corresponding quantity. If μ is the mean of the process, then the process can be expanded as follows [START_REF] Spanos | Stochastic Finite Element Expansion for Random Media[END_REF]:

) ( ) ( ) , H( 1 θ ξ φ λ μ θ i i i i X ∑ ∞ = + = Χ (1)
where λ i and i φ are the eigenvalues and eigenfunctions of the covariance function C(X 1 , X 2 ) and ξ(θ) is a vector of standard uncorrelated random variables. The series expansion in Equation ( 1) is referred to as the K-L expansion. For practical implementation, the approximate random process is defined by truncating the series in Equation ( 1) to a finite number of terms M. The choice of the number of terms M depends on the desired accuracy of the problem being treated.

SUBSET SIMULATION

Subset simulation was proposed by [START_REF] Au | Estimation of small failure probabilities in high dimentions by subset simulation[END_REF] to compute the small failure probabilities. The basic idea of subset simulation can be described as follows: Consider a failure region F defined by the condition G<0 where G is the performance function and let (s 1 , …, s k , ..., s Nt ) be N t samples located in the space of the uncertain variables. It is possible to divide the failure region F into a number of nested failure regions F 1 , …, F j , ..., F m of increasing size where

F F F F m j = ⊃ ⊃ ⊃ ⊃
... ... 1 (Fig. 1).

Figure 1. Nested Failure domain

An intermediate failure region F j can be defined by G j <C j where C j >0. Thus, there is a decreasing sequence of positive numbers C 1 , …, C j , ..., C m corresponding respectively to F 1 , …, F j ,…, F m where C 1 >…>C j >...> C m =0. The N t samples (s 1 , …, s k , ..., s Nt ) will be divided into groups where each group contains a given number N s of samples (s 1 , …, s k , ..., s Ns ). The failure probability corresponding to an intermediate failure region F j is calculated as follows:

∑ = = Ns k k F j s I F P j 1 ) ( ) (
(2)

1 = j F I if j F s ∈ and 0 = j F I
otherwise. The conditional failure probability P(F)=P(F m ) can be calculated from the previous nested sequence of conditional failure regions as follows:

P(F)=P(F m )=P(F m |F m-1 )xP(F m-1 |F m-2 )xP(F m-2 |F m-3 )x ... xP(F 2 |F 1 )xP(F 1 ) (3)

IMPLEMENTATION OF SUBSET SIMULATION ALGORITHM IN THE CASE OF A SPATIALLY VARYING SOIL PROPERTY

As mentioned previously, this paper aims at employing the subset simulation methodology for the computation of the failure probability in the case of a spatially varying soil property. In order to achieve this objective, K-L expansion described before is used to discretize the random field. Therefore, the random field is transformed into a finite number of random variables

M i i ..., , 1 } { = ξ
. Due to this transformation, the use of subset simulation becomes an easy task. The algorithm of subset simulation in case of a spatially varying soil property can thus be described by the following steps: 1-Choose the number M of terms of K-L expansion. This number must be sufficient to rigorously represent the target random field. 2-Generate a vector {ξ 1 , …, ξ k , ..., ξ M } by direct Monte Carlo simulation with the target probability density function P t . Notice that P t is normal in our case. 3-Use the K-L expansion to obtain the first realisation of the random field. Then, use the deterministic model to calculate the corresponding response. 4-Repeat steps 2 and 3 to obtain N s realisations and their corresponding responses.

Then, evaluate the corresponding values of the performance function to obtain the vector G 0 ={g 1 , …, g k , ..., g Ns }. 5-Propose an intermediate failure probability P(F j ) and evaluate the first failure threshold C 1 which corresponds to the failure region F 1 where C 1 is equal to the [(N s xP(F j ))+1] th value in the increasing list of elements of the vector G 0 . This ensures that the value of P(F 1 ) wi equal to the proposed value of P(F j ). 6-Among the N s realisations, there are [N s xP(F j )] ones whose values of the performance function is less than C 1 (i.e. they are located in the failure region F 1 ). The corresponding vectors {ξ 1 , …, ξ k , ..., ξ M } of these realisations are used as 'seeds' to generate an additional [(1-P(F j ))N s ] vectors of {ξ 1 , …, ξ k , ..., ξ M } using Markov chain method based on Metropolis-Hasting algorithm. These new vectors are used to obtain the random field realisations of level 1 using K-L expansion. The corresponding responses are evaluated using the deterministic model and the corresponding vector of performance function G 1 ={g 1 , …, g k , ..., g Ns } is computed.

7-Evaluate the second failure threshold C 2 as the [(N s xP(F j ))+1] th value in the increasing list of the vector G 1 . 8-Repeat steps 6-7 until reaching the value of the last failure threshold (i.e. C m =0).

For the last failure threshold, thowing equation may be used: otherwise. 9-Finally, the failure probability P(F) is evaluated according to Equation (3).

∑ = - = Ns k k F m s I F P m 1 1 m ) ( ) (F (4)

EXAMPLE PROBLEM

In this section, the proposed procedure for the computation of the failure probability is illustrated through an example problem. In this example, the failure probability at SLS (i.e. the probability of exceeding a tolerable vertical displacement) of a strip footing resting on a soil having spatially varying Young modulus and subjected to a central vertical load (P v ) is calculated. The Young modulus was considered as a log-normal random field with mean value μ=60MPa and coefficient of variation COV=15%. An exponential covariance function was used in this paper. The random field was discretized using K-L expansion. Although an isotropic random field is often assumed in theature; in this paper, different values of the horizontal and vertical autocorrelation distances were studied and analyzed. The performance function used to calculate the failure probability is defined as follows:

G=v max -v ( 5 )
where v max is a prescribed tolerable vertical displacement and v is the footing vertical displacement due to the applied load. The deterministic model used to calculate the footing vertical displacement (v) is based on the commercial numerical code FLAC 3D . Due to the non-symmetrical footing movement created by the soil heterogeneity, the entire soil domain shown in Fig. 2 was considered to calculate the footing displacement. The values of the parameters of the soil, footing and interface are given in Table (1).

It should be mentioned here that a normal PDF was used as a target probability distribution P t . However, a uniform PDF was used as a proposal probability distribution P p . Notice also that the intermediate failure probability P(F i ) was chosen equal to 0.1. Notice finally that v max was assumed equal to 4cm and P v was taken equal to 1000kN/m throughout the paper. As mentioned before, the accuracy of the approximated random field depends on the number M of terms of the K-L expansion. For the most critical configurations of the autocorrelation distances used in this paper [(L x =10m and L y =0.5m), (L x =5m and L y =1m)], the eigenvalue vanishes when the number of terms is 100 ≈ M terms. As a conclusion, the number of terms will be set to M=100 terms for all the probabilistic calculations presented in this paper.

Comparison between subset simulation and MCS methodologies

In this section, the failure probabilities calculated at each level of the subset simulation were compared to those obtained by MCS. To perform this comparison, a random field with L x =10m and L y =1m (called hereafter the reference case) was considered. For each level j of the subset simulation, the corresponding failure threshold C j was calculated and presented in Table (2) for different values of the number of realisations N s .

0 0 0 C 5 0 - - - -
This table indicates that the failure threshold decreases with the successive levels until reaching zero at the last level. This means that the realisations generated by the proposed procedure successfully progress towards the limit state surface G=0 which indicates the validity of the proposed procedure. Notice that the value of the failure threshold remains almost constant for N s ≥200. This means that 200 realisations at each level are sufficient to calculate the failure probability. To confirm the validity of the present approach, a comparison with MCS was carried out. The number of realisations N of MCS should be adequate for a rigorous calculation of the failure probability. The accuracy of MCS can be estimated by calculating the coefficient of variation of the failure probability as follows:

N/A 30 o ψ s =2/3 φ 20 o N/A 20 o E 60MPa 25GPa N/A ν 0.3 0.4 N/A K n N/A N/A 1GPa K s N/A N/A 1GPa ) 6 ( / ) 1 ( f f P NP P COV f - =
Using this equation, the coefficient of variation of the failure probability was calculated and presented in Fig. 3. It was found to decrease with the increase of the number of realisations. It reaches an asymptote when the number N of realisations is equal to 20,000. Hence, 20,000 realisations were used to calculate the failure probabilities by MCS. The comparison between the subset simulation method (using 4 levels with N s =200, i.e. 800 realisations) and the MCS method is presented in Fig. 4.

Notice that to calculate the failure probability at level j using MCS, the performance function should be set equal to C j . In this case, the failure region is defined as G≤C j and the safety region is defined as G>C j . The failure probability can then be calculated as:

∑ = = N k k F i s I F P 1 ) ( ) ( (7)
where 1 I F = if s k is located in the failure region F j and 0 I F = otherwise. Fig. 4 shows that for the case where N s =200 realisations, the failure probabilities calculated by subset simulation were found to be very close to those computed by MCS. For smaller values of N s , there is some discrepancy between both approaches (results not shown in this paper). From Fig. 4, one can conclude that the 20,000 realisations required by MCS methodology can be reduced to only 800 realisations by applying the proposed procedure based on subset simulation methodology.

Parametric study

In order to investigate the effect of the anisotropy of the random field, the failure probability was plotted against the vertical and horizontal autocorrelation distance in Figs. 5 and 6 respectively for the prescribed footing breadth. (i.e. B=2m).

Both figures show that the failure probability presents a maximum when the ratio of L x to L y is equal to 10 for B=2m. This observation can be explained as follows: The small value of the autocorrelation distance (L x or L y ) induces a large soil heterogeneity which results in a large variety (i.e. a great number of high and small values) of the Young modulus beneath the footing. This variety leads to small footing displacement and consequently to small failure probability. The small footing displacement occurs because the rigid footing is prevented to settle due to the presence of a high number of small strong zones beneath this footing; the high number of small weak zones being of little effect in this case; the increase in the autocorrelation distance increases the footing displacement and consequently the failure probability. On the other hand, when the autocorrelation distance is very large in either the vertical or the horizontal direction, the problem becomes similar to that of the one-dimensional random field for which the failure probability is smaller than that of the two-dimensional isotropic case. For medium autocorrelation distances, the soil contains a number of strong zones adjacent to a number of weak zones whose areas are greater than those corresponding to the case of the one-dimensional random field. The soil movement can easily develop throughout the weak zones. As a result, the failure probability increases in such cases with respect to the case of the onedimensional random field. The values of the autocorrelation distance corresponding to the maximum can be used to define a conservative design case for probabilistic analysis when rigorous information about the autocorrelation distance is not available. Notice finally that for the same ratio of L x /L y but greater values of L x and L y , the maximum failure probability was found to be higher. This is due to the simultaneous increase of the autocorrelation distances in both the vertical and the horizontal directions which makes the failure probability tend to that of the homogeneous soil. The numerical results of Figs 5 and 6 also indicate that the failure probability is more sensitive to the vertical autocorrelation distance. This is because the rate of change in the failure probability (i.e. rate of increase or decrease) when increasing the vertical autocorrelation distance by a certain percentage is larger than that when increasing the horizontal autocorrelation distance by the same percentage.

CONCLUSION

This paper presents an alternative procedure to Monte Carlo Simulation (MCS) methodology for the computation of the failure probability of a strip footing resting on a soil with spatially varying properties. This procedure is based on the subset simulation approach. The proposed procedure was applied to calculate the failure probability at the serviceability limit state of a strip footing resting on a soil with a spatially varying Young modulus and subjected to a central vertical load. The random field was discretized into a finite number of random variables using K-L expansion. The vertical displacement of the footing center was used to represent the system response. The deterministic model used to compute the system response is based on numerical simulations using the commercial software FLAC 3D . The failure probability calculated by the proposed procedure was compared to that computed by MCS methodology and was found to be very close with a significant decrease in the number of calls of the deterministic model. For the case of a non-isotropic random field, the failure probability presents a maximum value. The maximum was obtained at a fixed value of L x /L y =10 for a footing breadth equal to 2. Notice however that for the same ratio of L x /L y but greater values of L x and L y , the maximum failure probability was found to be higher. It has also been shown that the failure probability is more sensitive to the vertical autocorrelation distance than the horizontal one.

Figure 2 .

 2 Figure 2. Soil domain and mesh used in the numerical simulation

  Figure 3. Effect of number of realisations of MCS on COV(P f )

Figure 4 .

 4 Figure 4. Comparison between P f obtainedat at each level of subset simulation and that computed by MCS

Figure 6 .

 6 Figure 5. Effect of L y on the failure probability for different values of L x

Table 2 . Evolution of the performance function with the different levels of the subset simulation and with the number of realisations (N s ) at each level

 2

	Performance function C j	Number of realisations at each level (N s )
	for each level j	50	100	150	200	250
	C 1	0.0086	0.0077	0.0080	0.0076	0.0076
	C 2	0.0058	0.0048	0.0050	0.0041	0.0040
	C 3	0.0044	0.0015	0.0019	0.0011	0.0011
	C 4	0.0017	0

Table 1 . Shear Strength and Elastic Properties of Soil, Footing, and Interface

 1

	Variable	Soil	Footing Interface
	c	20kPa	N/A	20kPa
	ϕ	30 o


Mohammad Amin Bozorgmanesh
email: mohammadamin.bozorgmanesh@gmail.com

Farshad Honar

Fatemeh Kowkabi

Investigating the Effect of Mobile Phone Electromagnetic Waves on the Human Eye

Keywords: Electromagnetic waves, Electric field, Human eye, Mobile phone, Specific absorption rate (SAR)

Rapid progress in various fields of science and technology in recent years has caused various adverse and predictable negative effects on the environment. Today, one of the most important biological problems is environmental electromagnetic pollution. The intensity of its electromagnetic radiation has become so widespread that it is currently known as one of the hidden and silent forms of pollution. One means of communication that is an inseparable part of daily life is the mobile phone. Research and surveys show that mobile phone waves have a negative effect on the human body. Our goal in this article is to study the effects of mobile phone electromagnetic waves on the human eye. In order to check the effect of these waves, the specific absorption rate (SAR) criterion is used. Because the rate of specific absorption in the eye is determined according to the absorption of energy in the eye tissues and the conversion of this absorbed energy into thermal energy, they increase the temperature of the eye tissues. In other words, the temperature distribution in the eye directly correlates with the rate of specific absorption. The results have shown that the tissue of the eye is susceptible. When the eye is exposed to waves, the eye's temperature increases, and the antenna angle relative to the eye is effective in the rate of specific absorption. According to studies, the conclusion of this research indicates that without observing safety points, including maintaining distance, they can cause dangerous bodily complications.

Introduction

Electromagnetism is a branch of physics that studies electric and magnetic phenomena and their relationship. Electromagnetic phenomena are investigated in classical physics under Maxwell's laws. According to Maxwell's theory, every moving charged particle emits energy. For the first time, Maxwell obtained the magnetic wave equations and showed that electric and magnetic fields could behave like waves; According to his theory, electric fields that change with time causes the creation of magnetic fields [START_REF] Theerman | A Biography[END_REF].

EMF electromagnetic fields, called electric smog pollution, is one of the types of industrial pollution [START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF][START_REF] Mobasseri | Impact of driving style on fuel consumption[END_REF] that are also known as one of the hidden and silent forms of pollution. Electromagnetic radiation (EMR) is widely used in modern technology and communication [START_REF] Sivani | Impacts of radio-frequency electromagnetic field (RF-EMF) from cell phone towers and wireless devices on biosystem and ecosystem-a review[END_REF].

The increasing use of radio, television, radar devices, and telecommunication towers and high pressure has increased the amount of electromagnetic radiation in the environment [START_REF] Kundi | Electromagnetic fields and the precautionary principle[END_REF]. A large amount of magnetic radiation in the environment is caused by telecommunication towers. A mobile phone is produced.

The eye [START_REF] Bozorgmanesh | The Effect of Mobile Phone Electromagnetic Waves on Honey Bee[END_REF] is one of the most sensitive organs of the human body to environmental factors such as electromagnetic fields and high-temperature conditions. This is due to the crystalline nature of the lens, the lack of blood vessels in the cornea, and the absence of thermal sensors [START_REF] Mobasseri | Intelligent Circuit Application for Monitoring the Employees Attendance[END_REF].

It is a protective reaction in the eyes [START_REF] Wessapan | Influence of ambient temperature on heat transfer in the human eye during exposure to electromagnetic fields at 900 MHz[END_REF]. The intensity of the radio waves, the distance of the emitter to the eye, the frequency of the radio waves, the type of the radiating antenna, and the angle of the antenna to the eye are effective on the amount of the effect of the radio waves on the eye [START_REF] Hirata | Temperature rises in the human eye exposed to EM waves in the frequency range 0.6-6 GHz[END_REF].

In order to check the impact of these waves, the specific absorption rate (SAR) is used because the specific absorption rate in the eye is determined according to the absorption of energy in the eye tissues and the conversion of this absorbed energy into energy. Thermal, increase the temperature of the eye tissues; in other words, the temperature distribution in the eye has a direct relationship with the specific absorption rate [START_REF] Wessapan | Specific absorption rate and temperature increase in the human eye due to electromagnetic fields exposure at different frequencies[END_REF].

1-Biological effects of RF energy

Radio waves sent through transmitter antennas are a form of electromagnetic energy [START_REF] Mobasseri | Intelligent Circuit Application for Detecting the amount of Air in Automobile Tires[END_REF]. These waves are known as RF energy or radio frequency. The term electromagnetic field is often used for the presence of electromagnetic energy or radio frequency energy in a place. Radio waves have a frequency between 30 kHz and 300 GHz.

Biological effects from tissue heating by RF energy are often referred to as "thermal" effects [START_REF] Bozorgmanesh | Thermal Effects of Electromagnetic Waves on the Human Body[END_REF]. Exposure to high levels of RF radiation can be dangerous due to its ability to heat biological tissue rapidly. Under certain conditions, contact with RF energy at power intensity values of 1-10 mW/(cm 2 ) and above can lead to measurable heating of biological tissue. The amount of this heating depends on several factors, including radiation frequency, duration of contact, the direction of the object exposed to radiation, environmental conditions, and heat dissipation efficiency at relatively low levels of exposure to RF radiation, field intensities below those that will produce significant and measurable heating. Such effects are sometimes referred to as "non-thermal" effects [START_REF] Kumar | Cell tower radiation[END_REF].

In Figure 1, the temperature increase in different eye tissues is given for different antenna distances from the eye. In this figure, it is shown that the largest temperature change in the eye occurs in the vitreous region, and its value is less than 0.3 at the frequency of 1800 MHz and for the radiated power of 0.2 W using the PIFA antenna, which is a standard antenna in mobile phone applications [START_REF] Liu | Evaluation of the specific absorption rate and the temperature rise in the human eyes with account for resonance[END_REF]. The antenna angle compared to the eye is examined in Figure 2. In this study, a dipole antenna with a length of half the wavelength at a frequency of 1800 MHz was used. For 10W radiation power and placement angles. Different antennae relative to the eye, the value of the specific absorption rate was calculated according to the different distances of the antenna relative to the eye. This study shows that the value of the specific absorption rate also increases with the angle of the antenna in relation to the eye [START_REF] Liu | Evaluation of the specific absorption rate and the temperature rise in the human eyes with account for resonance[END_REF].

The effect of electromagnetic waves on the human eye

Human eyes are exposed daily to radiation from mobile phones and other electromagnetic fields around humans. These electromagnetic fields in the eye cause heat in deeper tissues, thus damaging their tissue. The amount of specific absorption rate and temperature increase caused by exposure of the human eye to different frequencies of radio waves has been investigated [START_REF] Fesharaki | Investigating the effect of RF radio waves on the human eye and its reduction methods. The second national conference on crisis management and HSE in vital arteries, industries, and urban management[END_REF].

Has been studied. In Figure 3, the increase in the intensity of the electric field results from the value of the specific absorption rate in the eye is evident. In addition to the intensity of the electric field, the amount of dielectric and thermal properties in each tissue directly.

The value of the specific absorption rate in the eye will influence it. For both frequencies, the highest value of the specific absorption rate was obtained in the area of the corneal surface. In this frequency area of 900 and 1800 MHz, the specific absorption rate values of 135.2 and 45.9 w/kg are shown. The eye's cornea has a much higher electrical conductivity than the lens, iris, and cornea's location.

It is close to eye level and has a strong electric field [START_REF] Wessapan | Specific absorption rate and temperature increase in the human eye due to electromagnetic fields exposure at different frequencies[END_REF]. Figure 5 shows the SAR distribution evaluated on the vertical cross-section of the human eye exposed to an electromagnetic frequency of 900 MHz at different power densities. It is clear from the figure that the results of SAR values in the human eye increased according to the intensity of the electric field (Figure 4). In addition to the intensity of the electric field, the amount of dielectric properties and thermal properties in each tissue directly affects the amount of SAR in the human eye. For all power densities, the highest SAR values are obtained only in the cornea region but not in lenses and irises as electric field distribution [START_REF] Wessapan | Specific absorption rate and temperature increase in human eye subjected to electromagnetic fields at 900 MHz[END_REF]. On average, the electromagnetic energy absorbed by W7 per kilogram of the human body increases the temperature by three degrees Celsius. The average absorption energy threshold that causes a noticeable fever in humans in 6 minutes is equal to 4 W/Kg. Therefore, on September 3, 1982, the American National Standards Institute determined one-tenth of this value as 0.4 W/Kg as the limit of the specific absorption rate. By observing this point, the increase in temperature or fever is no longer visible in humans, but it has gradual effects that should be considered [START_REF] Aghaei | The Effect of Electromagnetic Waves on Humans[END_REF].

Conclusion

In modern society, it is completely impossible to prevent electromagnetic pollution. Therefore, we must take adequate measures to prevent electromagnetic pollution and protect human health. Using a mobile phone for one minute increases the eye temperature by 0.02 degrees Celsius.

If the eyeglass frame of people who suffer from cataracts is made of metal, it can be very dangerous for them when they use mobile phones; Because the metal form of the glasses can lead to an increase in the absorption of radiation from the mobile phone due to electromagnetic fields and increase it by 63% so that this issue is more serious for people who suffer from eye damage. We suggest using wired hands-free to reduce the absorption of electromagnetic waves during mobile phone conversations. One of the ways to protect against magnetic radiation is to have a safe distance from the source of electromagnetic fields and different frequencies; Also, with the help of daily food consumption, such as some foods containing vitamin A, vitamin C, and protein such as carrots, cabbage, and animal liver, etc., it seems important to strengthen the body's ability to resist electromagnetic radiation.

Figure 1 -

 1 Figure 1 -The level of temperature distribution in the eye model before and after the radiation of electromagnetic waves by the PIFA antenna at a frequency of 1800 MHz. The distance of the PIFA antenna from the eye is 20 cm, and the radiation power is 0.2 W (Liu et al, 2009).

Figure 2 -

 2 Figure 2 -The results of the dependence of the specific absorption rate on the placement angle of the dipole antenna with a length of half the wavelength (frequency 1800 MHz and radiation power 10 watts). (Liu et al., 2009)

Figure 3 -

 3 Figure 3 -The distribution of the specific absorption rate in the vertical section of the eye exposed to the frequencies of (a) = 900 and (b) = 1800 MHz in COMSOL Finite Element Method [16] Software [10].

Figure 4

 4 Figure4shows the simulation of the electric field pattern inside the human eye under the electromagnetic field in TM mode with a frequency of 900 MHz along the vertical section of the human eye where the power density is different. Due to the different dielectric properties of different tissue layers, the human eye absorbs a different part of the electromagnetic energy provided in each layer[START_REF] Wessapan | Specific absorption rate and temperature increase in human eye subjected to electromagnetic fields at 900 MHz[END_REF].

Figure 4 -

 4 Figure 4-Electric field distribution (V/m) in the human eye exposed to an electromagnetic frequency of 900 MHz in power density (a) 5 mW/cm2, (b) 10 mW/cm2, (c) 50 mW/cm2 and (d) 100 mW/cm

Figure 5 -

 5 Figure 5 -Distribution of SAR (W/kg) in human eyes exposed to an electromagnetic frequency of 900 MHz in power density 5 mW/cm2, (b) 10 mW/cm2, (c) 50 m/cm2 and (d) 100 mW/cm

Funding information:

The authors received no financial support for this research, authorship, and/or publication.

Declaration of interests:

The authors declares that there is no conflict of interest in this work.

Ethical approval: This article does not contain any studies with human participants or animals performed by the authors.


Mathieu Guglielmino

Francesco Bronzino

Sébastien Monnet

Outil interactif pour l'alignement de la vue client-opérateur sur les architectures de réseau

Keywords: topologie, réseau, visualisation, interfaces humain-machine, modèle de données

LIP

). Afin de réduire les délais de déploiement, qui peuvent venir d'une distance entre les clients et l'opérateur-compétences techniques variées, vocabulaire-, nous proposons (a) un module pour décrire les données nécessaires à la visualisation des topologies, et (b) une mise en oeuvre sous forme d'une interface web pour la visualisation interactive de graphes multi-couches, adaptée aux topologies réseaux * .

Introduction

La communication entre les opérateurs et leurs clients est essentielle pour assurer que les réseaux soient configurés et maintenus correctement. Cependant, cette communication peut s'avérer complexe car les clients et les opérateurs ont souvent des besoins opérationnels variés, et ne partagent pas le même vocabulaire technique. Cette distance se traduit par des incompréhensions ou des impossibilités techniques dans ce qui est proposé, et résulte en un temps de déploiement plus long et des coûts supplémentaires.

De plus, les attentes de ces profils sont différentes, et nécessitent de représenter dans une même vue divers aspects du réseaux. Pour cela, nous nous basons sur un formalisme récent issu du domaine des systèmes complexes, les graphes multi-couches. Un même objet, ou noeud, peut apparaître dans plusieurs couches pour représenter des aspects différents. Dans le contexte des réseaux les couches peuvent correspondre aux différents protocoles du data-plane mais n'y sont pas limitées, et prises dans leur ensemble forment la topologie du réseau.

Nous proposons ici un outil pour manipuler ces topologies de réseaux multi-couches. Basé sur la sémantique visuelle des réseaux, cet outil repose sur les modules de l'IETF qui servent à décrire les topologies de façon générique. Parmi les fonctionnalités développées, il est possible de fusionner les différentes couches dans une même vue, de choisir la méthode de disposition des noeuds, de grouper certains objets ou encore de choisir les icônes pour représenter les noeuds. Il est également possible d'exporter la topologie dans un format déclaratif comme XML ou JSON qui pourra être utilisé pour le déploiement de la topologie virtuelle par un contrôleur SDN.

Nos contributions sont les suivantes :

1. Un module YANG qui augmente ietf-network-topology pour décrire les données nécessaires à la représentation visuelle d'une topologie réseau, 2. Une interface web interactive pour manipuler dynamiquement les objets du graphe multi-couches qui en résulte et l'extraire dans un format déclaratif.

La suite de ce document est organisée comme suit: l'état de l'art sur la visualisation et la standardisation des données de topologies réseau avec le langage YANG (Section 2) et les outils que nous avons développés (Section 3).

2 Les modules de l'IETF pour la topologie réseau En 2010, l'IETF publie le langage de modélisation YANG, aujourd'hui largement partagée par l'industrie (cf Figure 1), pour standardiser les données échangées pour la configuration des réseaux. Avec le protocole NETCONF, les données exprimées avec YANG peuvent être utilisées par les équipements pour configurer des de base (IP, interfaces, routage), ou plus complexes, comme des VPN couche 2 ou 3.

Ce langage fournit une structure de données normalisée pour les équipements et facilite la communication entre les équipements de différents fabricants.

Les modules YANG forment la colonne vertébrale de nombreux outils d'automatisation et sont un des concepts clés de nombreux contrôleurs SDN (Software Defined Networks) tels qu'OpenFlow et OpenDaylight. 3 La visualisation standard des topologies réseaux

Le modèle multi-couches des topologies réseaux

Un module YANG pour visualiser la topologie: network-topo-viz

Les démarches de standardisation des données de topologie réseau ont abouti à une structure de données qui correspond à un réseau multi-couches (cf figure 2), exprimée via les modules ietf-network et ietf-network-topology.

Design interactif de topologies réseaux

Afin rendre propice l'utilisation des modules de par des profils variés, des contrôleurs des de graphes, il nécessaire d'y des capacités de visualisation. Aussi, nous nous proposons d'augmenter ces modules grâce à une fonctionnalité du langage YANG pour décrire les données nécessaires leur visualisation dans un nouveau module : network-topo-viz (cf. Figure 4).

Groups force directed circular linear

Nodes layout Network layout width h e ig h t merge

Networks and supporting networks layout 3D Geographic View Icons Fig. 4: Fonctionnalités de base du module network-topo-viz Ce module inclut des options pour décrire les couleurs (des couches, des noeuds ou des liens), les icônes ou formes des noeuds et des liens dans un graphe de topologie, ou la disposition spécifique des noeuds ou des couches. Nous nous inspirons largement du langage visuel des réseaux pour adapter les fonctionnalités souvent génériques des outils existants d'édition de topologies réseaux qui en général produisent des images, avec l'objectif de l'intégrer dans un écosystème automatisé.

Afin de prouver la validité de la méthode, nous proposons une plateforme web afin de manipuler les éléments de la topologie, définir comment elle s'affiche à l'écran et extraire l'instance de la topologie au format XML ou JSON.

L'édition interactive de topologies de réseau

Les réseaux multi-couches ont la caractéristique d'avoir un objet qui n'existe pas dans les graphes simples : la couche-ou layer-, et qui nécessite la mise en oeuvre d'interactions particulières: le choix du layout sur une ou plusieurs couches, des commandes de navigation pour la réorganisation des noeuds ou des couches, ainsi que le choix des couleurs. En outre, certaines couches peuvent être masquées ou fusionnées pour mieux correspondre à l'objectif de l'utilisateur.

Du déclaratif au visuel, et vice-versa

Les instances de topologie peuvent s'exprimer en XML, ou en JSON de façon équivalente. La manipulation de ces fichiers peut rapidement s'avérer complexe, en plus de ne pas être une solution viable pour des profils non-techniques comme le client final, ou un commercial.

2. 1 Fig. 1 :

 11 Fig. 1: Modules YANG publiés depuis 2015

  and ietf-network-topology

Fig. 2 :

 2 Fig. 2: Schéma défini par les modules YANG de topologie La visualisation de la topologie réseau se heurte à la grande diversité des équipements, et les solutions de visualisation existantes sont limitées au contexte des équipements du même fournisseur, ou à des images éditées manuellement. Des tentatives de standardisation des données de réseaux ont bien vu le jour pour faciliter la visualisation des topologies, comme NetJSON [C + ] ou NevML [Wan10], mais leur utilisation reste limitée car ne résulte pas du consensus de la communauté.Le module ietf-network de l'IETF (cf figure2) décrit les différents réseaux qui interviennent dans la topologie et leurs réseaux sous-jacents. Générique, cette structure permet de décrire les topologies qui peuvent être rencontrées dans des contextes variés (topologies L3 [CMV + 18b]) ou de centre de données[START_REF] Zhuang | A yang data model for fabric topology in data-center networks[END_REF]).Toutefois, si des outils dédiés à la visualisation de la structure des modèles YANG (figure3(a)) existent, rien a notre connaissance, n'est dédié à l'édition interactive d'une instance de données (Figure 3 (b)) et les utilisateurs se heurtent à la complexité des topologies de réseaux multicouches.

Fig. 3 :

 3 Fig. 3: Utilisation du modèle ietf-network pour représenter la topologie d'un réseau. (a) La représentation du modèle sous forme d'arbre. (b) Son utilisation pour la représentation d'une instance particulière (ici en XML).

  Fig. 5: Pipeline


Mohammad Ilchinezhad Firouz Abadi

Mohammad Amin Bozorgmanesh
email: mohammadamin.bozorgmanesh@gmail.com

Kowkabi

The Effect of Mobile Electromagnetic Waves on the Human Brain

Keywords: Electromagnetic waves, Human brain, Cell phone. 1-Introduction

Rapid progress in various fields of science and technology in recent years has led to various adverse and anticipated negative effects in the environment. One of the most important biological problems today is electromagnetic pollution in the environment. The intensity of electromagnetic radiation is so pervasive that it is now known as one of the forms of hidden and quiet contamination. One of the means of communication that are an integral part of human daily life is a mobile phone. Research shows that mobile waves have a negative effect on the human body. Putting the human brain near a source of electromagnetic waves can damage the normal activity of the brain and disrupt its normal function. Our goal in this article is to investigate the effects of mobile electromagnetic waves on the human brain.

The increasing use of radio, television, radar and telecommunications, monitoring the attendance [START_REF] Mobasseri | Intelligent Circuit Application for Monitoring the Employees Attendance[END_REF] and high pressure towers has increased the amount of electromagnetic radiation in the environment [START_REF] Kundi | Electromagnetic fields and the precautionary principle[END_REF].

Following the scientific and technology advances and the construction of new devices and the presence of human beings exposed to these waves, much research has been conducted on the impact of these waves on biological systems [START_REF] Winker | Chromosomal damage in human diploid fibroblasts by intermittent exposure to extremely low-frequency electromagnetic fields[END_REF][START_REF] Moulder | Power-frequency fields and cancer[END_REF]. Some devices that emit electromagnetic waves and humans are exposed to mobile phones, wireless phones, TVs, computers, microwaves, telecommunications and highpressure rigs, detecting the amount of air in Automobile tires that working with signal etc. [START_REF] Palicot | On the use of cognitive radio for decreasing the electromagnetic radiations[END_REF][START_REF] Mobasseri | Intelligent Circuit Application for Detecting the amount of Air in Automobile Tires[END_REF].

Research by scientists shows that prolonged presence of these waves can cause some diseases such as tumors, cancer, infertility, cell destruction and DNA, some of which will be mentioned in the next sections of the article. These radiations are divided by parameters such as signal changes, the intensity of radiation, radiation rate, with the highest amount of radiation waves being for mobile [START_REF] Hutter | Subjective symptoms, sleeping problems, and cognitive performance in subjects living near mobile phone base stations[END_REF]. Germany is one of the countries that are in residential areas under the radiation of high -frequency GSM phone rigs [START_REF] Haumann | HF-Radiation levels of GSM cellular phone towers in residential areas. 2nd International Workshop on Biological effects of EMFS[END_REF]. Mobile phones have a frequency of 900 to 1800 MHz with 218 Hz pulse. This frequency range is used in most European and Asian countries, including Iran [START_REF] Sicard | Introduction to GSM. 5th[END_REF].

Figure 1 shows that the GSM mobile masts have the highest effect on brain activity compared to the CDMA. Figure 1. Charts of PSD value values in four channels [START_REF] Hamblin | Effects of mobile phone emissions on human brain activity and sleep variables[END_REF] Many studies have been reported that radio frequency and electromagnetic radiation (EMR) create biological misleading effects that disrupt the functioning of biological systems of living organisms [START_REF] Blank | Electromagnetic fields stress living cells[END_REF].

Putting the human brain near a source of electromagnetic waves can damage and disrupt the normal activity of the brain.

2-Biological Effects of Energy RF

There are different types of pollution in different industries, and EMF electromagnetic fields, which is called electric smog pollution, is also known as one of the types of hidden pollution. [START_REF] Sivani | Impacts of radio-frequency electromagnetic field (RF-EMF) from cell phone towers and wireless devices on biosystem and ecosystem-a review[END_REF][START_REF] Mobasseri | Impact of driving style on fuel consumption[END_REF][START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF].

Radio waves sent through transmitter antennas are an electromagnetic energy. These waves are known as RF energy or radio frequency. Often, the word electromagnetic field is used in the presence of electromagnetic or frequency radio energy in one place. Radio waves have a frequency of between 30 kHz and 300 GHz.

The biological effects caused by RF energy heating are often expressed as "thermal" effects.

Contact with high levels of RF radiation can be dangerous because of its ability to quickly heat biological texture.

Under certain conditions, contact with RF energy in power intensity values 𝟏 -𝟏𝟎 𝐦𝐖 𝐜𝐦^𝟐 and above can lead to measurable biological tissue heating. The amount of this heating depends on several factors, including radiation frequency, contact time, direction of exposure to radiation, environmental conditions, and heat scattering efficiency. At relatively low levels of exposure to RF radiation, the intensity of the fields will produce significant and measurable heating. Such effects are sometimes referred to as "non -emotional" effects [START_REF] Kumar | Cell tower radiation[END_REF].

3-Literature review

In 1996, Sami Gabrie et al. performed numerous tests in the frequency range of 10 to 20 Hz [START_REF] Gabriel | The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz[END_REF][START_REF] Gabriel | The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues[END_REF].

Stephen B. Bauman et al. in 1997 examined the effect of frequency change and temperature on the electrical conductivity of the human brain [START_REF] Baumann | The electrical conductivity of human cerebrospinal fluid at body temperature[END_REF].

G. Schmind et al. In 2000 conducted experiments on dielectric properties on the human brain [START_REF] Schmid | Dielectric properties of the human brain measured less than 10 hours post mortem[END_REF].

In 2002, T. V. Yioutsis conducted their research on the effects of electromagnetic waves and temperature analysis on the body caused by a variety of antennas [START_REF] Yioultsis | A comparative study of the biological effects of various mobile phone and wireless LAN antennas[END_REF].

Nurulhuda binti Ismail and mohd. Zarar Bin Mohd Jenu in 2007 model the propagation of mobile electromagnetic waves in the human head with FDTD numerical method in two -dimensional environment [START_REF] Binti Ismail | Modeling of electromagnetic wave penetration in a human head due to emissions from cellular phone[END_REF].

Faruque compared some types of antennas, including Helical, Monopole, PIFA, PATCH at different frequencies in 2010, using the FDTD numerical method [START_REF] Faruque | SAR analysis in human head tissues for different types of antennas[END_REF].

In 2011, Boyan Tomovs obtained the effects of electromagnetic fields in two -dimensional form and by numerical FDTD on the human head in four different modes [START_REF] Tomovski | Effects of electromagnetic field over a human body, sar simulation with and without nanotextile in the frequency range 0.9-1.8 ghz[END_REF].

4-The effect of cellular electromagnetic waves on the human brain

The human brain is made up of dyspsery tissues. Brain tissues are 1) skin) 2) skulls 3) fat 4) Dura 5) Cerebral fluid 6) gray matter 7) white matter and 8) blood.

In Fig. 2, the location of each of the above components is shown [START_REF] Ireland | Modeling human head at microwave frequencies using optimized Debye models and FDTD method[END_REF]. The human brain is made up of important parts such as scalp, skull and brain, and each part has different dioxide properties. To examine the effect of cell waves on different parts of the brain, such as the scalp and skull, the size of the electric and magnetic field can be drawn from each part of the head. For example, the effect of these waves on the skin and skull parts is shown in Figures 3 and4. In Fig. 5, the size of the electrical field in the human brain exposed to a bipolar antenna (mobile) is the Finite Element Method [START_REF] Mobasseri | A Comparative Study Between ABS and Disc Brake System Using Finite Element Method[END_REF] border integral components. As can be seen, the numerical method used well simulates the penetration of the electromagnetic field into the brain.

Figure 5. The size of the electric field in the human brain, which is adjacent to a cell, [START_REF] Jafari | Simulation of Mobile Radiations in Vicinity of Adult and Child Head[END_REF].

5-Prevention and Recreation of Electromagnetic Waves

A) Reducing the duration of the conversation: Studies show that if the cell phone is spoken for more than two minutes, the brain will be affected by cellular waves.

B) The cell phone is placed on the ear after the call is made; Because in the early moment of communication, the amount of waves emitted is very high. C) Using Handsfree: One of the best ways to be suggested is that instead of direct use of mobile phones, the use of a "handsfree" or "headset" is used; This reduces the amount of possible damage to these three vital organs due to the increased distance of cellular waves from the ear, brain and skull D) use antioxidants: Using a cell phone is an inevitable thing; One of the suggested options to eliminate and reduce the effects of electromagnetic waves on the human body is to use antioxidants.

6-Conclusion

According to the research, we conclude that when using the mobile phone, we need to take the utmost care and shorten the conversations as much as possible. Things such as headaches, dizziness, seizures, changes in heart rate rhythm, anxiety in sleep, reduced concentration and memory, migraine, distraction, and hypertension are among the non -thermal complications of radio frequencies. In order to safeguard the potential effects of these waves, it is suggested that a number of safety methods including shortening the phone call, keeping the phone away from the body, using anti -wave cover coatings, using free handsets, be considered. Therefore, it is essential to examine the effects of electromagnetic waves on the human body.

Funding information:

The authors received no financial support for this research, authorship, and/or publication.

Figure 2 .

 2 Figure 2. The components of the human brain[START_REF] Ireland | Modeling human head at microwave frequencies using optimized Debye models and FDTD method[END_REF]

Figure 3 .

 3 Figure 3. a) Electric field size b) size of the magnetic field caused by mobile waves on the skull of the head [30].

Figure 4 .

 4 Figure 4. a) Electric field size b) size of the magnetic field caused by mobile waves on the scalp [30].

Declaration of interests:

The authors declares that there is no conflict of interest in this work.

Ethical approval: This article does not contain any studies with human participants or animals performed by the authors.