Fatemeh Mohammadi

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

Guiding the Honeybee Navigation System Using the Second-Harmonic Line Width of 456 nm Nd:GdVO4 Laser to Prevent CCD Phenomenon

Keywords: Electromagnetic Waves, Colony Collapse Disorder (CCD) Phenomenon, Bee Navigation System, Nd:GdVO4 Laser, Fluorescent Blue Light. 1

Today, one of the biological problems is electromagnetic pollution in the environment. The intensity of electromagnetic radiation is so pervasive that it is now known as a form of latent and silent pollution. The study of the biological effects of these waves on the growth and development of living insects -such as bees -are considered by many scientific communities around the world. However, the response of honey/wild bees to traps designed to selectively stimulate bee light receptors by fluorescent light indicated that selective arousal of the blue light receptor type was highly attractive. In this paper, to selectively stimulate the blue light receiver in bee species in order to guide the bee navigation system to return to its hive and prevent the colony collapse disorder (CCD) phenomenon, high-efficiency continuous laser emission by second-harmonic line width of 456 nm Nd:GdVO4 laser is proposed.

INTRODUCTION

Increasing use of radio, television, radar and telecommunication towers and high pressure stations has increased the amount of electromagnetic radiation in the environment [START_REF] Vargas-Chaves | Contaminación electromagnética y principio de precaución: de norma crepuscular a criterio jurídico vinculante[END_REF][START_REF] Bozorgmanesh | The Effect of Mobile Phone Electromagnetic Waves on Honey Bee[END_REF][START_REF] Bozorgmanesh | Investigating the Effect of Mobile Phone Electromagnetic Waves on the Human Eye[END_REF][START_REF] Bozorgmanesh | Thermal Effects of Electromagnetic Waves on the Human Body[END_REF]. Large amounts of magnetic radiation in the environment are generated by mobile telecommunication towers. Insects and small creatures are the first living organisms to be affected by this radiation. Some bee species are, however, becoming extinct due to not returning to their hive, which is called colony collapse disorder (CCD). Studies show that radio frequencies and electromagnetic waves have deceptive biological effects and in turn, disrupt the functioning of biological systems of living organisms and lead to their destruction [START_REF] Halašová | Effect of acute intermediate frequency electromagnetic field exposure on human neural cells[END_REF]. If the number of masts and cell phones increases, honey bees may become extinct in the coming decades. Studies have shown that radiation at 900 MHz is very bioactive and changes the physiological function of living organisms [START_REF] Erdoğan | Effect of Electromagnetic Field (EMF) and Electric Field (EF) on Some Behavior of Honeybees (Apis mellifera L.)[END_REF]. Bees, in general, are arthropods that are key species in the ecosystem that pollinate more than 90 commercial products around the world [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 increase in deaths in bee colonies around the world has led to a phenomenon in which the number of bees in the hive decreases rapidly without leaving any signs of disease [START_REF] Gallai | Economic valuation of the vulnerability of world agriculture confronted with pollinator decline[END_REF]. According to literatures, it is reported that radio frequency and electromagnetic radiation have misleading biological effects that disrupt the functioning of biological systems of living organisms [START_REF] Gultekin | Absorption of 5G radiation in brain tissue as a function of frequency, power and time[END_REF]. However, to selectively stimulate the blue light receiver in honey bee in order to guide the bee navigation system to return to its hive and prevent the colony collapse disorder (CCD) phenomenon, high-efficiency continuous laser emission by second-harmonic line width of 456 nm Nd:GdVO4 laser is proposed. The beam is conducted By SHG process with a nonlinear crystal BiB3O6 (𝐵𝑖𝐵𝑂) under laser diode 880 nm pumping with emission level of

The Effect of Electromagnetic Waves on Honey Bees

EMF electromagnetic fields, also known as electric fog pollution, are known as a form of latent and silent pollution. Electromagnetic waves are widely used in modern technology and communication [START_REF] Sutherland | A 2018 horizon scan of emerging issues for global conservation and biological diversity[END_REF]. Due to various mobile phone sources, however, radio and microwave frequency pollution is considered an important factor in reducing the population of insects such as honey bees [START_REF] Hou | Enhanced flexibility and microwave absorption properties of HfC/SiC nanofiber mats[END_REF][START_REF] Kalayci | The role of viral and parasitic pathogens affected by colony losses in Turkish apiaries[END_REF]. The CCD phenomenon occurs when worker bees leave the hive to collect food and never return to the hive; this occurs when the hive population suddenly disappears [START_REF] Tesovnik | Exposure of honey bee larvae to thiamethoxam and its interaction with Nosema ceranae infection in adult honey bees[END_REF]. Some scientists believe that the new CCD phenomenon occurs due to electromagnetic radiation. Recent studies show that the mortality of many animals and insects, such as bees, birds and bivalves, is due to exposure to the EMR field [START_REF] Hayes | A smart curtailment approach for reducing bat fatalities and curtailment time at wind energy facilities[END_REF][START_REF] Sahib | Impact of mobile phones on the density of honeybees[END_REF][START_REF] Sharma | Changes in honeybee behaviour and biology under the influence of cellphone radiations[END_REF]. In his study on the effect of pollution caused by electromagnetic waves of mobile phone antennas on honeybee, [START_REF] Balmori | Electromagnetic pollution from phone masts. Effects on wildlife[END_REF] found that microwave and radio frequency contamination creates a weird potential to reduce the population of animals and plants near mobile towers [START_REF] Balmori | Electromagnetic pollution from phone masts. Effects on wildlife[END_REF].

Magnetic Navigation System in Honey Bees

Honey bees sometimes fly more than 12 km from the hive to collect food [START_REF] Ratnieks | Have suitable experimental designs been used to determine the effects of neonicotinoid insecticides on bee colony performance in the field?[END_REF]. Using the sense of sight and smell, they mark the environment around the hive and use them to find and track the way back to the hive [START_REF] Collett | Memory use in insect visual navigation[END_REF]. In addition to being signaled by sight and smell, the bees are able to detect the earth's electromagnetic fields and waves and use them as navigation means [START_REF] Parke | Electric and Magnetic Fields in Life. Biophysics: A Student's Guide to the Physics of the Life Sciences and Medicine[END_REF][START_REF] Pall | Wi-Fi is an important threat to human health[END_REF]. However, there are two types of magnetic navigation systems in honey bees: A) Magnetic navigation system based on magnetic structure [START_REF] Natan | Symbiotic magnetic sensing: raising evidence and beyond[END_REF]: due to the presence of a type of bacterium with magnetic properties called that is the bee's abdomen and reacts to the magnetic field [START_REF] Natan | Symbiotic magnetic sensing: raising evidence and beyond[END_REF][START_REF] Wajnberg | Titanium and iron titanium oxide nanoparticles in antennae of the migratory ant Pachycondyla marginata: an alternative magnetic sensor for magnetoreception?[END_REF]. This property gives the bees the ability to sense even minor changes in the field lines. If these delicate structures are exposed to the least amount of magnetic pollution, they will disrupt the bee's navigation system [START_REF] Stanimirović | Looking for the causes of and solutions to the issue of honey bee colony losses[END_REF]. B) Chemical magnetic navigation system [START_REF] Shao | Genomic and phenotypic analyses reveal mechanisms underlying homing ability in pigeon[END_REF]: also known as light-dependent magnetic sense, it conducted by cryptochrome (a receiver of blue ultraviolet rays in plants and animals [START_REF] Mao | Photoexcited CRY1 and phyB interact directly with ARF6 and ARF8 to regulate their DNA-binding activity and auxin-induced hypocotyl elongation in Arabidopsis[END_REF]). Honey/wild bees benefit from a composite imaging system consisting of ultraviolet, blue, and green light receptors. Color space models show details of how the visual signal received by these light receptors is processed at the neural surface by the bee's brains [START_REF] Heath | Circuit mechanisms underlying chromatic encoding in Drosophila photoreceptors[END_REF]. Ostroverkhova et al. studied the response of bees to traps with different spectral characteristics under certain outdoor conditions. Extraordinary absorption of a wide range of honey bee species is reported in a highly unrefined blue fluorescent trap in the 480-420 nm spectral range of UV rays. Analyzing the spectral characteristics of fluorescent and non-fluorescent traps and their relationship with the number of honey bees absorbed in each trap, they found that fluorescent blue light in the range of 480-430 nm is particularly attractive to bees. Figure 1 below shows the average number of bees adsorbed to each trap per day within the range of light receptor sensitivity. The properties of the traps used by Ostroverkhova et al., are summarizes in Table 1.

Guiding Honeybee Navigation System Using the Second-Harmonic Line Width of 456 nm Nd:GdVO4 Laser

High efficiency continuous wave emission in the second harmonic line width of 456 nm Nd:GdVO4 laser is generated by direct pumping of a diode with a wavelength of 808 nm to the level of and 880 nm to the level of By modifying the water performance under pumping 880 nm, it was observed that approximately 3.8 watt is generated at a wavelength of 456 nm from Nd:GdVO4 laser with and at laser medium with %0.4 thickness and a BiBO crystal with dimensions of 3×3× 12 mm 3 is produced as a frequency doubler in a Z-type cavistrap; which is totally unique source of excitation of the blue light receiver type. Citing studies on the inherent superiority of color in honey bees -which showed that targets with higher spectral purity and visual stimuli at 480-430 nm are stronger absorbers than other wavelengths [START_REF] Ostroverkhova | Understanding innate preferences of wild bee species: responses to wavelength-dependent selective excitation of blue and green photoreceptor types[END_REF] -the Second-Harmonic line width of 456 nm Nd:GdVO4 laser is proposed to use the blue laser light to guide the bees track back to the hive and prevent the CCD phenomenon since the beam is a more efficient source than fluorescent traps. In order to collect, analyze and study a subject, there are different methods such as finite element method [START_REF] Mobasseri | A Comparative Study Between ABS and Disc Brake System Using Finite Element Method[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][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], finite volume method (numerical methods for approximate solution of differential equations), field research, statistical methods, case study, review paper . Review paper, however, is used in the current paper.

CONCLUSIONS

The suggestion for controlling the honey bee colony population is to use laser light at the bee colony site. These findings could be a powerful tool for beekeepers and farmers. For example, if farmers need to pollinate their crops, they can use laser blue light to attract the bees for pollination. The technique is also used by beekeepers to guide bees to track the path back to the hive and prevent CCD. Honey bees have shown a stronger response to fluorescent traps in the range of 430 to 480 nm. The paper aimed to stimulate blue light receptors in honey bees by high efficiency continuous wave emission in the second harmonic line width of 456 nm Nd:GdVO4 laser which is generated by direct pumping of a diode with a wavelength of 808 nm to the level of and 880 nm to the level of

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.

Fig. 1 .

 1 Fig. 1. Average number of honey bees absorbed per trap per day (bars) in the range of light receptor sensitivity [28]

Fig. 2 .

 2 Fig. 2. Schematic diagram of experimental settings for the doubled frequency of the 456 nm blue laser; the distance from S1 to M1, from M1 to M2 and from M2 to M3 mirrors are 110, 400 and 56 mm, respectively [29].


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

Mojtaba Sadeghi
email: mojtaba.sadeghi@iau.ac.ir

Optimal Antenna Design in the Cancer Tissues Treatment by Hyperthermia: A Review Study

Keywords: Hyperthermia, Cancer, Single and Double Aperture Antenna, Microstrip Antenna, Interstitial Antenna

Although cancer is a recognized disorder worldwide, its treatment is a great challenge; and new and effective treatment methods become one of the fundamental demands. However, one of the new treatment approaches for cancer is hyperthermia, which is considered an assistant to surgery, chemotherapy, and radiotherapy. Despite the proven effectiveness of the method, there are numerous challenges in applying it that require recognizing the effective parameters and optimizing them to achieve better and more effective performance. Considering this, the review paper aims to study the optimization of antennas utilized in the cancerous tissue treatment by hyperthermia. However, four different antennas with different purposes are studied and optimized for this purpose. Furthermore, studies show that recognizing and optimizing the design parameters of hyperthermia antennas is likely to greatly improve the performance of the method.

Introduction

Cancer is one of the leading causes of death in the world. Many people die each year from various cancers. In some countries, however, cancer is the second leading cause of death after heart disease [START_REF] Jemal | Global cancer statistics. CA: a cancer journal for clinicians[END_REF][START_REF] Ferlay | Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012[END_REF]. Moreover, approx. 13% of the reported deaths worldwide are because of various types of cancer, the most common of which are lung, stomach, and breast cancers. By the end of 2030, however, the number of cancer deaths is projected to increase to twelve million [START_REF] Issels | Hallmarks of hyperthermia in driving the future of clinical hyperthermia as targeted therapy: translation into clinical application[END_REF]. Cancer is caused by genetic changes in cells; a normal cell turns into a cancer cell following changes in the genes. Normally, cells grow in a controlled manner according to the body's needs. In the abnormal condition, however, when genetic changes occur, it is unmanageable, and extra cells form that form a mass called a tumor, which may malignant or benign [START_REF] Yoshioka | Development of cancer-initiating cells and immortalized cells with genomic instability[END_REF]. Over the years, various approaches are identified for numerous cancers treatment, the most common of which are surgery, chemotherapy, and radiotherapy. Furthermore, the surgical treatment is likely to use when the cancerous tumor is confined to one area. However, it is impossible to use surgery if the tumor has spread too far and spread to other organs in the body, as well as in some types of cancer, such as leukemia; in such cases, two other wellknown methods, chemotherapy, and radiotherapy are usually used for treatment. Chemotherapy, unlike surgery, is a non-invasive procedure with a known function. Nevertheless, since the drugs used in chemotherapy affect other tissues in the cancerous tumor pathway, the treatment provides various side effects such as hypersensitivity to infection, infertility, hair loss, anemia, fatigue, and nausea. Another recognized method is radiotherapy, which as a non-invasive method, despite its effective function, in addition to tumor tissue, also affects adjacent healthy tissues. Additionally, chemotherapy and radiotherapy are often applied as adjunctive therapy to surgery ]5[. Along with surgery, chemotherapy and radiotherapy, additional methods of adjuvant treatment that are sometimes based on artificial intelligence [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] can help; One of those cases is "hyperthermia". Hyperthermia consists of two parts: "hyper" meaning high and "term" meaning heat, and generally means an increase in body temperature above normal. High body temperature is commonly known as fever or heat shock and is considered a disease; however, a controlled rise in temperature, known as hyperthermia, is likely to apply to accelerate the healing of some cancers along with surgery, chemotherapy, and radiotherapy [START_REF] Paulides | Recent technological advancements in radiofrequencyandmicrowave-mediated hyperthermia for enhancing drug delivery[END_REF]. Although the potential of the hyperthermia method has been demonstrated in many clinical studies, the exact mechanism of action is not yet well understood. In some preclinical studies, such as cell culture and animal experiments, the dependence of cell death on the very high temperature required in the tissue has been shown; however, in many studies, the appropriate response to the hyperthermia method is yielded by applying the range of 41 -47 °C. On the other hand, in constant hyperthermia settings, we report a completely different response to hyperthermia between different types of cancer and disease progression. Furthermore, minor adjustments in the hyperthermia treatment -such as changes in applied temperature, duration of application, and heat transfer method-are yielded different results under the same disease conditions [START_REF] Ribeiro | Nanomaterials in cancer: Reviewing the combination of hyperthermia and triggered chemotherapy[END_REF].

The additional challenge is to raise the temperature of a specific area of the body from systemic temperature, and the optimal temperature distribution value for the clinical applications of hyperthermia is generally unknown. However, in different methods of hyperthermia, the temperature distribution obtained is homogeneous and the amount of heat produced is limited; this is due to the physiological and physical properties of the electrical boundaries of the tissue, the different velocities, and the order of blood flow in different areas and other factors (in hyperthermia, the minimum temperature difference is usually around 39.5-40.5 ° C). Furthermore, roughly half of deep tumors are likely to be heated to 42 °C in specific areas, thanks to the advances in hyperthermia instruments and methods. Therefore, since reaching a certain temperature in different tumors that have clinical applications is not yet known, there is a necessity for a high and homogeneous temperature in the tumor [START_REF] Datta | Local hyperthermia combined with radiotherapy and-/or chemotherapy: Recent advances and promises for the future[END_REF].

The process of heat transfer in living tissue occurs by the effect of blood flow in the tissue and by influencing the local temperature distribution of the tissue. When there is a clear difference between the temperature of the blood and the tissue, a convective current occurs and changes the temperature of the blood and the tissue. However, tissue and blood heat reactions are a function of several factors, such as blood flow rate and vascular anatomy, which are fundamentally different in different tissues. Moreover, blood flow rates in different tissues during the day are also different from normal activity. Additionally, some diseases and their treatment methods have a decreasing or increasing effect on blood flow. The biothermal transfer equation applies to specific models that are developed to explore potential approaches to sensitive therapeutic applications, such as destroying cancer cells and tumors.

In August 1984, Harry Pennes published an article entitled "Analysis of Arterial Tissue and Blood Temperature in the Resting Human Arm."; subsequently in 1998, Whistler revised the paper and referred to the proposed model as the first analytical model of heat transfer in living tissue. In this paper, Pence proposed a model for describing the metabolic and blood flow effects of energy balance in tissues, which is expressed in the following equation [START_REF] Park | Antennas for hyperthermia therapy[END_REF][START_REF] Rabienejhad | Design and optimization of nano-antenna for thermal ablation of liver cancer cells[END_REF]:

(𝛿 𝑡𝑠 𝜌𝑐 𝜕𝑇 𝜕𝑡 + ∇. (-𝑘 ⃗ ∇𝑇) = 𝜌 𝑏 𝐶 𝑏 𝜔 𝑏 (𝑇 𝑏 -𝑇) + 𝑄 𝑚𝑒𝑡 + 𝑄 𝑒𝑥𝑡 (1)
The amount of perfusion is the amount of fluid (such as blood) that flows through a particular type of tissue. However, the type of tissue (such as the liver, heart, and muscle) and fluid are of particular importance in this process. Although the equation is configurated for blood flow, it may be applied for fluids and other fluid compounds as well, The biothermal transfer equation is comparable to the conduction heat transfer equation. In a steady-state heat transfer, the first left term in Eq.( 1) is set to zero:

𝛿 𝑡𝑠 𝜌𝑐 𝜕𝑇 𝜕𝑡 = 0 (2)
In the biothermal transfer equation, the only term of the heat source (Q) is divided into three terms, namely:

1. Metabolic heat (𝑄 𝑚𝑒𝑡 ) 2. Perfusion [𝜌 𝑏 𝐶 𝑏 𝜔 𝑏 (𝑇 𝑏 -𝑇)] 3. External heat source (𝑄 𝑒𝑥𝑡 )
The only heat source divided the biothermal transfer equation into three terms, and the purpose is to easily formulate and convert the equation to partial differential equations (PDE).

In recent years, finally, efforts have led to heat application in cancer treatment. The reason for such a delay was the inability to raise the right temperature in the right place to protect the healthy cells and tissue. However, the lack of uniform heat distribution, insufficient heat production, and unwanted heat treatment of healthy cells, as well as the creation of hot spots on the surface of the skin are still the most important challenges and problems in modern methods of hyperthermia. In a study, "Antennas for Hyperthermia Therapy", Park et al. [START_REF] Mattoso | Pointwise antennas design in hyperthermia therapy[END_REF] designed and optimized 1, 2, and 3 aperture coaxial antennas at 2.45 GHz applying the impedance matching technique. However, optimization is based on geometric characteristics. The results show that the optimization performed increases the thermal effect. In 2021, in a paper, " Design and optimization of nano-antenna for thermal ablation of liver cancer cells", Rabieinejad et al. [START_REF] Karampatzakis | Antenna design and tissue parameters considerations for an improved modelling of microwave ablation in the liver[END_REF] while designing an L-shaped nano antenna, further optimized the model by changing the parameters of near and far-field intensity, orientation and sensitivity to geometric dimensions. In 2021, Matoso and Newton [START_REF] Gas | Transient analysis of interstitial microwave hyperthermia using multi-slot coaxial antenna[END_REF] proposed a new method for designing and optimizing point antennas using the steady-state heat equation and the Helmholtz equation. They seek to minimize the cost function based on measuring the difference between the target temperature and the temperature yielded from the modeled problem concerning the current permittivity through the antenna.

In 2013, Karampatzakis et al. [START_REF] Curto | Compact patch antenna for electromagnetic interaction with human tissue at 434 MHz[END_REF] studied the effect of coaxial slot antenna design features on the dimensions and shape of the erosion zone. The results, however, showed that the values of permeability and relative conductivity are less significant for predicting the erosion region. In 2015, Gus Piotr [START_REF] Pisa | Power density and temperature distributions produced by interstitial arrays of sleeved-slot antennas for hyperthermic cancer therapy[END_REF] studied the effect of the number of air gaps in the structure of a microwave antenna on the transient temperature distribution in human tissue; and the results show that the highest temperature is with a slot antenna and the lowest for antennas 2 and 3. Considering the effectiveness of hyperthermia and its increasing use as a new method in the treatment of cancerous tumors as well as the challenges in using this method, it seems necessary to study the parameters affecting the performance of hyperthermia, which are mainly related to the design of the antennas; therefore, in this paper, four different antennas are studied and optimized for different purposes.

Modeling

The first antenna is a small, lightweight, broadband microstrip antenna with flexibility for non-flat surfaces of body tissue, optimized based on a study by Curto et al. [START_REF] Saito | A proposition on improvement of a heating pattern of an antenna for microwave coagulation therapy: Introduction of a coaxial-dipole antenna[END_REF] by reducing the thickness of the dielectric layer. Moreover, the overall size of the proposed antenna is 1.93×140×140 mm for treatment at 433 MHz. The dielectric layer with a thickness of 1.93 mm is made of Teflon with a permeability of 2.2 and a loss tangent of 0.0002 and a thickness of 1.93 mm and holds a soft texture, the flexibility of which is considered an advantage since antennas required to be easily placed on different parts of the body. Fig. 1 shows the proposed antenna structure and Table 1 displays the dimensions. The simulation of this antenna was performed in CST Microwave Studio software on 5 layers of biological tissue according to Table 2. Moreover, A 50 Ω coaxial cable is used for the power supply. The second antenna is the 2.54 GHz coaxial cable-based interstitial antenna provided by Pisa et al. [START_REF] Park | Antennas for hyperthermia therapy[END_REF]. The optimization of this antenna is based on geometric specifications, which include changing the length of the pods and changing the distance between the gaps and the opening circuit of the upper pod. The purpose is to achieve a pattern of specific absorption rate (SAR) distribution in a spherical shape selected more compatible with most tumors and to cause less damage to healthy tissue. Fig. 2 shows the antenna properties. However, the antenna consists of a coaxial cable in which the material inside is Teflon with a dielectric constant of 1.2, and the antenna at the end of the connection is shorted. Power is applied from the top of the coaxial cable. Furthermore, the antenna is simulated utilizing HFSS in a material equivalent to brain tissue with a dielectric constant of 33.5, a conductivity of 1.23 Siemens per meter, and a density of 1000 kg / m 3 at a frequency of 2.45 GHz. Fig. 2 shows the applied dimensions. The dimensions considered for the texture are likely to be significant compared to the dimensions of the antenna. Fig. 3 shows the simulated antenna geometry. In the third antenna optimization, the goal of SAR distribution along the axis is in a limited and concentrated area; such a focus distribution is used for specific medical cases and does not damage healthy tissue by concentrating heat at a certain distance from the antenna axis is likely to be the location of the tumor.

The antenna provided is a coaxial cable-based antenna that has a check, a slot, and a conductor cap. The conductor of the antenna is made of copper and its dielectric is Teflon. In addition to the 18 mm long check, which is shorted at the beginning, this antenna also provides a conductor cap and a slot at the end. The antenna at a frequency of 2.45 GHz in a tissue equivalent to the bovine liver with a dielectric constant of 43.03 and a conductivity of 1.69 Siemens per meter with an input depth of 80 mm has been simulated in HFSS software. Moreover, the power supply is applied from the top of the coaxial cable. Fig. 4. Shows the overall dimensions of the antenna. In the fourth antenna, which is an axial aperture antenna, the change in aperture length to achieve the optimal state is studied. For this purpose, two single-and two-aperture antennas are utilized; the dimensions of which are shown in Table 3. [START_REF] Habibi | Drug delivery with therapeutic lens for the glaucoma treatment in the anterior eye chamber: a numerical simulation[END_REF][START_REF] Mobasseri | Approximated 3D non-homogeneous model for the buckling and vibration analysis of femur bone with femoral defects[END_REF][START_REF] Mobasseri | A Comparative Study Between ABS and Disc Brake System Using Finite Element Method[END_REF][START_REF] Mobasseri | Bending and Torsional Rigidities of Defected Femur Bone using Finite Element Method[END_REF] was utilized for modeling. In the simulation of this model in 10 minutes, the input power is 10 watts and the frequency is 2.45 GHz. Furthermore, the length of the aperture is 0.8, 1, and 1.2 mm and the distance between the two apertures is 5 mm.

Discussion and results

In the first antenna, upon optimization, the return antenna loss characteristic, which is a measure of the energy return from the antenna to the operator, is yielded in Figure 6. However, the antenna bandwidth at the central frequency of 433 MHz is approximately 120 MHz, which is equivalent to 26%, which is a 5-time increase compared to the antenna worked by Corto et al. [START_REF] Curto | Compact patch antenna for electromagnetic interaction with human tissue at 434 MHz[END_REF] with a bandwidth of approx. 25 MHz. Fig. 6. Characteristic of the return loss of the antenna with its position in the biological tissue of the body In Figure 7, however, the effect of different thicknesses of the silicon layer on the rate of energy penetration into the tissue is studied. As the Figure shows, the choice of silicon thickness has a great effect on the initial penetration force into the tissue, so that at a thickness of 0.5 mm, the initial penetration strength is 0.15 and at a thickness of 2.9 this value is equal to 0.83. Furthermore, the penetration decreases with the increasing thickness of silicon and reaches 0.57 energy at a thickness of 12.5 mm.

Fig. 7. The effect of different silicon thicknesses on linear SAR and penetration depth

In the second antenna, firstly, the local SAR distribution in W/Kg per 1 watt of input power in a plane parallel to and permittivity through the antenna axis is compared with the result reported in [START_REF] Park | Antennas for hyperthermia therapy[END_REF] (Fig. 8). Additionally, the SAR antenna distribution pattern presented in the two-dimensional space is close to the circle due to symmetry in the threedimensional space in a sphere shape and is more compatible with the spherical shape of most tumors, resulting in less damage to healthy tissues; therefore, the optimization performed has made the SAR distribution pattern better than the study [START_REF] Park | Antennas for hyperthermia therapy[END_REF]. The effect of changing the distance between the gaps on changing the distribution of the SAR pattern is studied in Fig. 9. However, reducing and increasing the distance between the gaps causes the SAR distribution pattern to deviate from the circular shape of the permittivity plate; therefore, the 8 mm distance for the gaps is the optimal distance to yield the spherical SAR distribution pattern. In Fig. 10, the output of the third antenna simulation, the SAR distribution pattern is limited to an area approximately 20 mm long, resulting in a rapid fall and ascent of the distribution pattern to the peak point. Furthermore, for a normalized SAR of 20%, the length of the SAR distribution area for the simulated antenna is 12 mm, while the same value is 29 mm for the antenna [START_REF] Saito | A proposition on improvement of a heating pattern of an antenna for microwave coagulation therapy: introduction of a coaxial-dipole antenna[END_REF]. Consequently, 80% of the power is consumed for the antenna provided at 12 mm of the antenna length, while this amount of power is consumed at 29 mm for the antenna. In the fourth antenna, firstly, the ratio of degraded tissue at points 5, 10, 15, and 20 mm from the antenna axis and a height of 22 mm from the bottom of the tissue is studied as Fig. 11 shows. However, in the single-aperture antenna, with increasing the aperture length, tissue damage begins faster and in contrast, in a two-aperture antenna, tissue damage occurs faster as the length of the aperture decreases. Figure 12 is yielded along a line parallel to the axis of the microwave antenna at a distance of 2.5 mm. However, the SAR distribution diagram obtained along the longitudinal axis of the antenna is gradually increased until it reaches its maximum point where the aperture is located and then decreases rapidly and reaches its minimum value at the end of the antenna. Moreover, the diagram has reached the maximum point in two-aperture antennas at the location of the upper aperture. Furthermore, the step mode created in the two-aperture antenna is because of the existence of a second aperture, while in the single-aperture antenna, the diagram without stairs and with a steeper slope is reduced. Nevertheless, in a single aperture antenna, increasing the aperture length from 8 mm to 1 and 1.2 mm increases the SAR distribution at points above the aperture; however, there is no significant change from the bottom of the aperture to the tip of the antenna. Since the same power is utilized, the maximum SAR distribution in both antennas is not different. In order to collect, analyze and study a subject, there are various methods such as laboratory methods, experimental tests, field research, software simulations, statistical methods, case studies and review articles [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] Bozorgmanesh | The Effect of Mobile Phone Electromagnetic Waves on Honey Bee[END_REF][START_REF] Mohammadi | Guiding the Honeybee Navigation System Using the Second-Harmonic Line Width of 456 nm Nd: GdVO4 Laser to Prevent CCD Phenomenon[END_REF][START_REF] Bozorgmanesh | Investigating the Effect of Mobile Phone Electromagnetic Waves on the Human Eye[END_REF][START_REF] Bozorgmanesh | Thermal Effects of Electromagnetic Waves on the Human Body[END_REF]. In this article, the review paper technique is used.

Conclusion

Unquestionably, hyperthermia is a practical and effective method in combination with other methods of cancer treatment, which according to the predictions made on the increasing growth of various cancers, is likely to play an important role in the future. However, the challenges in applying this method, and in the face of significant progress, make the further research inevitable to overcome the existing ones. This paper studies the optimal design of antennas applied in hyperthermia treatment as one of the main components affecting the performance of the method. The simulation results of four different antennas show that by recognizing and optimizing the design parameters of hyperthermia antennas, the performance of the method is likely to be greatly improved. Moreover, because of the multiplicity of parameters affecting the performance of the hyperthermia treatment, part of which is related to the characteristics of biological tissues, further study in this area appears essential.

Fig. 1 .

 1 Fig. 1. Antenna dimensions -(a) Antenna front view: Radiant element (b) Rear view of the antenna: Slot ground and antenna feeding point. The highlighted are the copper parts in the dielectric layer.

Fig. 2 .

 2 Fig. 2. The coaxial antenna of the sheath. Black color represents the conductor and white represents Teflon; (a) the longitudinal section of the antenna and (b) the transverse section of the antenna in the permittivity plane of AB

Fig. 3 .

 3 Fig. 3. The geometry of the antenna in the simulation tissue: (a) the transverse section of the tissue and antenna and (b) the longitudinal section of the tissue and antenna. The dashed line in (a) represents the SAR distribution pattern.

Fig. 4 .

 4 Fig. 4. Coaxial antenna with three conductors; the black color represents the conductor and white represents Teflon. (a) the longitudinal section of the antenna and (b) the cross-section of the antenna on the AB plate

Fig. 5 .

 5 Fig. 5. The geometric shape of the model

Fig. 8 .

 8 Fig. 8. Distribution of local SAR in W / Kg per 1 watt of input power in a "plane parallel to and permittivity through the antenna axis"; (a) antenna simulation results (b) the result reported in reference [17].

Fig. 9 .

 9 Fig. 9. Distribution of local SAR in W / Kg per 1 watt of input power in a "plane parallel to and permittivity through the antenna axis"; (a) by 5 mm distance of gaps (b) by 12 mm distance of gaps

Fig. 10 .

 10 Fig. 10. Normalized SAR distribution (antenna direction length); (a) the result of the simulation, (b) the result reported in reference [18].

Fig. 11 .

 11 Fig. 11. The fraction of destroyed tissue diagram in the presence of blood vessels in single-and double-aperture antennas

Fig. 12 .

 12 Fig. 12. SAR distribution with a linear length parallel to the axis of the microwave antenna at a distance of 2.5 mm

Table 1 .

 1 Dimensions of the proposed antenna (mm) Also, the thickness and electrical characteristics of body tissue at a frequency of 434 MHz are given in the table below (

	Parameter	Size	Parameter	Size
	R1	17.22	L1	114.14
	R2	33.66	L2	93.55
	R3	9.35	W	9.35
	R4	18.71	W1	4.67

Table 2 ) . Table 2 .

 2.2 Thickness and electrical characteristics of body tissue at 434 MHz

	Body layer	Density	Conductivity	Permittivity	Thickness
		(𝜌, 𝐾𝑔/𝑚 3 )	(𝜎, 𝑠/𝑚)	(𝜀 𝑟 )	(mm)
	Sponge bone	1920	0.241	22.257	15
	Cortical	1990	0.094	13.071	5
	Fat	916	0.041	5.566	10
	Muscle	1046.9	0.805	56.866	37.4
	Skin	3150	0.709	46.059	2.6

Table 3 .

 3 Dimensions of the axial aperture antenna

	Parameters	Dimensions (mm)
	Central conductor diameter	0.29
	Internal diameter of external conductor	0.94
	External diameter of external conductor	1.19
	Surgery needle Diameter	1.79

Table 4

 4 displays the material properties and the geometric shape of the model is shown in Figure5.

Table 4 .

 4 Material properties

	Properties	Surgery needle	Dielectric	Liver tissue
	Relative dielectric coefficient	2.60	2.03	43.03
	Conductivity			1.69	s/m

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.


Jessica López Espejel

Gaël De Chalendar
email: gael.de-chalendar@cea.fr

Jorge Garcia Flores
email: jgflores@lipn.univ-paris13.fr

Ivan Vladimir
email: ivanvladimir@turing.iimas.unam.mx

Meza Ruiz

Thierry Charnois
email: charnois@lipn.univ-paris13.fr

Saucissonnage

Jessica López Espejel
email: jessica.lopez-espejel@cea.fr

Ivan Meza

Saucissonnage of Long Sequences into a Multi-encoder for Neural Text Summarization with Transformers

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Recently, Transformer neural networks [START_REF] Vaswani | Attention is all you need[END_REF] have become the predominant model for a wide variety of Natural Language Processing (NLP) tasks. Transformers are based on an attention mechanism that allows focusing on the positional value of each element of a sequence. Contrarily to Long Short Term Memory (LSTMs) proposed by [START_REF] Hochreiter | Long short-term memory[END_REF], Transformers are not recurrent and allow reading input sequences in a parallel way. Transformers help avoiding the vanishing gradient problem, thus outperforming LSTMs and many other architectures for Deep NLP tasks [START_REF] Wood | Transformer neural network[END_REF]. The initially proposed transformer architecture is based on an encoder and a decoder. The encoder represents both the input words sequence and the positional information in a single encoding vector to be fed later into the decoder. The latter transforms the encoded vector into a sequence of per-word probabilities.

In this work, we propose an improvement of the Transformers model for Neural Text Summarization (NTS), addressing two main problems in this task. The first one is the training time of the model. Depending on the number of available GPUs, this phase could take from few days to few weeks, even with transformers' ability to process sequential input parallelly. The second one is the size of the encoded document (i.e., input word sequence). With the birth of deep learning, the length of the input sequence (L input ) fed into the model has been significantly reduced compared to extractive predecessor systems from the 2000s [START_REF] García Flores | Summarizing through sense concentration and contextual exploration rules: the CHORAL system at TAC 2009[END_REF]. For example, some LSTM-based summarization systems [START_REF] Cohan | A discourse-aware attention model for abstractive summarization of long documents[END_REF][START_REF] See | Get to the point: Summarization with pointer-generator networks[END_REF] truncate the source document to 2000 and 400 tokens, respectively. The maximum input length for a Transformer-based NTS system is L input = 1024 tokens [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF]. However, scientific articles are much larger. For instance, the biomedical dataset built by [START_REF] Cohan | A discourse-aware attention model for abstractive summarization of long documents[END_REF] has, on average, 3016 tokens in each article.

Our main contribution is to modify the transformers model to reduce the training time while increasing the input document size for summarization. To do so, we propose improving the original Transformer architecture by increasing the number of encoders from 1 to 4 and secondly splitting the input sequence between them. Experiments show that our architecture reduces training time without penalizing the quality of generated summaries. Our second contribution intervenes at the decoding level. Instead of presenting the gold summary to the decoder all at once, we present it chunk by chunk to learn progressively. This contribution is inspired by [START_REF] Hoang | Efficient adaptation of pretrained transformers for abstractive summarization[END_REF] that presents the gold summary to the decoder token by token. Our approach makes a compromise between learning progressively and fast.

In French, saucissonner means to "slice" a dry-cured sausage. In their original Transformer paper [START_REF] Vaswani | Attention is all you need[END_REF], the authors noted that self-attention models tend to perform better with shorter input sequences. Our approach's underlying rationale is to "slice" (saucissonner) longer sequences. It distributes the slices between the encoders to reduce the training time and consider a larger part of the documents to summarize. Our results show that our approach:

(1) summarizes documents having up to L input = 2000 tokens, (2) reduces the training time process, and (3) produces summaries with higher scores than the original Transformer architecture.

The rest of the paper is organized as follows. In Section 2, we describe the most popular recurrent and transformers based approaches for the NTS task. Section 3 presents our improved version of the original Transformer to train faster by processing longer source documents. Section 4 presents the dataset used for evaluation, baselines, experimental framework, and the implementation details. Section 5 provides an insight into the results, with a comprehensive discussion. Finally, we conclude in Section 6 and provide some perspectives.

Related work

Automatic summarization is an NLP task whose objective is to automatically produce a summary concentrating the most important information from a long source document or a document collection [START_REF] Mani | Automatic Summarization[END_REF]. Extractive summaries simply copy the most relevant sentences from the source document, while abstractive summaries reformulate and paraphrase the main information from the source document into new sentences.

The first approaches based on extractive summarization, assume that the most important words are those repeated most frequently [START_REF] Luhn | The automatic creation of literature abstracts[END_REF][START_REF] Sparck | A statistical interpretation of term specificity and its application in retrieval[END_REF]. The most important such methods include probabilistic models such as Probabilistic Context-Free Grammars (PCFG) [START_REF] Rahman | Automatic summarization of web content to smaller display devices[END_REF][START_REF] Knight | Summarization beyond sentence extraction: A probabilistic approach to sentence compression[END_REF], Markov Models (MM), and Hidden Markov Models (HMM) [START_REF] Chen | The use of emphasis to automatically summarize a spoken discourse[END_REF][START_REF] Jing | The decomposition of human-written summary sentences[END_REF][START_REF] Conroy | Text summarization via hidden markov models[END_REF].

Automatic summarization evolved later, and extractive based methods relied on machine learning to tackle the NTS as a classification problem, where new techniques were used, such as Naive Bayes [START_REF] Thu | An optimization text summarization method based on naive bayes and topic word for single syllable language[END_REF][START_REF] Ramanujam | An automatic multidocument text summarization approach based on naïve bayesian classifier using timestamp strategy[END_REF], Clustering (ShivaKumar and Soumya, 2015), and Support Vector Machine (SVM) [START_REF] Schilder | Fastsum: Fast and accurate query-based multi-document summarization[END_REF][START_REF] Begun | Automatic text summarization using support vector machine[END_REF]. However, work is still needed to improve the automatic generation of summaries, especially with the new challenges that arose along with neural networks.

The Deep Learning methodological leap has shifted research efforts into abstractive models based on neural architectures. Deep models require a considerable amount of data for efficient training and high GPU computational resources. Therefore, abstractive NTS has been gaining momentum [START_REF] See | Get to the point: Summarization with pointer-generator networks[END_REF][START_REF] Cohan | A discourse-aware attention model for abstractive summarization of long documents[END_REF] with the introduction of Recurrent Neural Networks (RNNs), in particular LSTMs [START_REF] Hochreiter | Long short-term memory[END_REF] and Gated Recurrent Units (GRUs) [START_REF] Cho | Learning phrase representation using rnn encoder-decoder for statistical machine translation[END_REF]. For instance, [START_REF] Rush | A neural attention model for sentence summarization[END_REF] introduced pioneer research to generate abstractive summarization at the sentence level. Further, [START_REF] Nallapati | Abstractive text summarization using sequence-to-sequence rnns and beyond[END_REF] adapted an off-the-shelf attentional encoder-decoder RNN that was initially proposed for translation [START_REF] Rush | A neural attention model for sentence summarization[END_REF] to tackle the summarization task. In recent years, Transformers Neural Networks [START_REF] Vaswani | Attention is all you need[END_REF] has been gaining increased interest for summarization in the scientific community. Therefore, a new wave of pre-trained NTS systems saw the light [START_REF] Narayan | Don't give me the details, just the summary! Topic-aware convolutional neural networks for extreme summarization[END_REF][START_REF] Kim | Abstractive summarization of Reddit posts with multilevel memory networks[END_REF][START_REF] Zhang | This email could save your life: Introducing the task of email subject line generation[END_REF][START_REF] Fabbri | Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model[END_REF][START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF]. Inspired by Transformers, several language models usable for automatic summarization have been developed, such as BERT (Bidirectional Encoder Representations from Transformers) [START_REF] Devlin | BERT: Pre-training of deep bidirectional transformers for language understanding[END_REF], MASS [START_REF] Song | Mass: Masked sequence to sequence pre-training for language generation[END_REF], UniML [START_REF] Dong | Unified language model pre-training for natural language understanding and generation[END_REF], T5 [START_REF] Raffel | Exploring the limits of transfer learning with a unified text-to-text transformer[END_REF], etc.

As stated above, our concern in this work is to reduce the training time while making it possible to summarize longer source documents in the context of a basic Transfomer NTS system without any pre-training whatsoever. Therefore, we compare our system with two state-ofthe-art approaches. The first one was proposed by [START_REF] Cohan | A discourse-aware attention model for abstractive summarization of long documents[END_REF]. It is an LSTM-based approach with the longest size of summarized source documents (L input = 2000 tokens). Authors of the same paper introduced the PubMed dataset of biomedical documents, having each an average of 3016 tokens. We used this dataset for our experiments insofar length of documents is convenient to test our multi-encoder model on long sequences. The second approach is called PEGASUS (Pre-training with Extracted Gap-sentences for Abstractive Summarization) [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF]. PEGASUS is a Transformer Neural Network pre-trained on massive text corpora. It is based on an encoder and a decoder architecture. We compare our approach with the non-pre-trained T ransf ormer BASE model from the PEGASUS paper. For the sake of comparability, we also do not pre-train our model in order to explicitly evaluate the modified Transformer architecture. 1) and is based on an encoder and a decoder. The encoder is a set of six layers, where each layer contains two sub-layers: a multi-head attention layer and a feed-forward network. The decoder also has six layers but is different from the encoder in two aspects. First, it has an additional multihead attention sub-layer, and second, the self-attention sub-layer is modified to avoid attending subsequent positions. Each sub-layer of the encoder and the decoder is followed by a residual connection and a normalization layer. Below, we call this baseline T ransf ormer ORIGIN AL .

Saucissonnage of Long

FIG. 1 -Transformer architecture [START_REF] Vaswani | Attention is all you need[END_REF])

Attention(Q, K, V ) = Sof tmax( QK T √ d k )V (1)
where Q are queries, K are keys, V are values, and d k is the dimension of Q and K.

There are three different ways to handle multi-head attention in Transformers:

(1) at the encoding level only, (2) at the decoding level only, or (3) at both the encoding and the decoding levels. Multi-head attention is a set of self-attentions (heads) concatenated as shown in Equation 2.

M ultiHead(Q, K, V ) = Concat(head 1 , ..., head h )W O (2)
where:

• h is the number of heads • W O is the weight matrix of Queries (Q), Values (V), or Keys (K).

•

head i = Attention(QW Q i , KW K i , V W V i )

The multi-encoder Transformer model

Our contributions to the Transformers model are organized in two parts. First, we explain the slicing of long sequences for multi encoding (Section 3.2.1), and later, we present the end-chunk task training improvement (Section 3.2.2). This improvement tries to cope with Transformer's attention under-performance with long input sequences. Four encoders with eight multi-attention heads each would improve the Transformer processing of long sequences and reduce the training time without penalizing the quality of generated summaries (see Section 5). As it is clearly stated in the original Transformer paper [START_REF] Vaswani | Attention is all you need[END_REF], self-attention models tend to perform better with shorter token sequences. Thus, we suppose that splitting long sequences and distributing them in the multi-encoder layer would reduce the training time and improve the processing of long source documents. Our goal here is to measure the impact of a substantial modification in the Transformer architecture before escalating to an intensive pre-training scenario, like in the PE-GASUS approach [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF]. The underlying rationale of both improvements from this section is to better focus the model's attention on shorter sequences with this saucissonnge process.

Slicing long sequences for multi-encoding

FIG. 3 -Multi-head attention in each encoder

Figure 3 shows multi-head attention layers in each of the four encoders of our multiencoder architecture. This input of multi-head attention encoders is a slice of the source text's embeddings and positional encodings. This input can be linearly transformed to get queries (Q), keys (K), and values (V) matrices from the original mono-encoder Transformer model. Each attention head uses different linear transformations to represent words. For this reason, different heads can learn different relationships between words. As a consequence of using a multi-encoding schema with four encoders, our model contains 32 attention heads compared to 8 in the original Transformer. We hypothesize that this increase in the number of attention heads would lead our multi-encoder Transformer to learn different relationships between words. While most of the Transformer models read 512 or at most 1024 [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF] tokens, we use input sequences having L input = 2000 tokens, which is closer to the average size of 3016 tokens of medical documents from the PubMed dataset [START_REF] Cohan | A discourse-aware attention model for abstractive summarization of long documents[END_REF]. However, this multi-encoding schema is only applied at the encoder level, not at the decoder level, where sequences are short enough to generate summaries of the desired length (216 tokens long).

End-chunk task training

End task training was introduced by [START_REF] Hoang | Efficient adaptation of pretrained transformers for abstractive summarization[END_REF]. The goal of their approach was to adapt a generic pre-trained text generation Transformer to the NTS task. End task training is an extra training step that aims to constrain the neural network's loss function to maximize the log-likelihood probability of generating pertinent summary given the reference summary. We adapted the equation from [START_REF] Hoang | Efficient adaptation of pretrained transformers for abstractive summarization[END_REF] to receive sequences of tokens (or chunks) instead of a token-by-token flow. We call this improvement end-chunk task training (ECTT). While the approach of [START_REF] Hoang | Efficient adaptation of pretrained transformers for abstractive summarization[END_REF] feeds increasing 1-token differential sentences, we feed chunks of cs (chunk size) tokens. The new loss function L ECT T is provided in Equation 3.

L ECT T = - cn-1 i=0 log P ({x s } (i+1)×cs-1 0 |{x a } M -1 0 ) (3)
where:

• M is the number of tokens in the article • cn is the number of chunks into which the summary is divided • cs is the chunk size, such that cs = N/cn, where N is the number of tokens in the summary • x s is a token from the summary • x a is a token from the article • {x s } (i+1)×cs-1 0 is the summary chunk, counting from the first token (0) until the token number

(i + 1) × cs -1 • {x a } M -1 0 is the input article 4 Experimental framework 4.

Evaluation dataset

Experiments were done using PubMed, a dataset collected by [START_REF] Cohan | A discourse-aware attention model for abstractive summarization of long documents[END_REF] from the well-known PubMed scientific repository (PubMed.gov). This dataset is composed of 130397 documents, where 117108 are in the training set, 6631 documents are in the validation set, and 6658 documents are in the test set. We used the validation set to tune hyper-parameters during the training process and the test set to get the final summaries.

Baselines

Experiments have been conducted with strong baselines, described as follows:

• Transformer ORIGINAL -This baseline is a mono-encoder architecture described in Subsection 3.1. Inspired by [START_REF] Gehrmann | Bottom-up abstractive summarization[END_REF], we use 4 layers contrarily to the initially proposed 6-layers architecture [START_REF] Vaswani | Attention is all you need[END_REF].

• Transformer BASE -is the version of P EGASU S BASE without pre-training [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF]. The architecture of this model has L = 12, H = 768, F = 4096, and A = 16, where L is the number of layers in the encoder and the decoder, H is the hidden size, F is the feed-forward layer size, and A is the number of self-attention heads.

• LSTM -is an NTS system proposed by [START_REF] Cohan | A discourse-aware attention model for abstractive summarization of long documents[END_REF]. It is based on a hierarchical encoder to model the discourse structure of documents. Both the encoder and the decoder are implemented as Long Short Term Memory (LSTM) networks.

Implementation details and evaluation metric

In our experiments, we truncate scientific documents to their first 2000 tokens to evaluate our model's performance on long sequences. Generated summaries are 216 tokens long, and only the most frequent 100,000 tokens are kept in the vocabulary.

We implemented T ransf ormer ORIGIN AL and our multi-encoder Transformer with Keras [START_REF] Chollet | Keras[END_REF]. However, we report results of T ransf ormer BASE from the Table 2 of the PEGASUS paper. We trained the models on 8 GPUs of Nvidia Quadro P5000 with 16GB of RAM capacity. We used sinusoidal positional encoding following [START_REF] Vaswani | Attention is all you need[END_REF]. For optimization, we used Adam algorithm with β 1 = 0.9, β 2 = 0.98, and batch size = 32. Besides, we used beam search with α = 0.8 and beam size = 6.

The T ransf ormer ORIGIN AL and our multi-encoder Transformer were trained for 300 epochs. The end-chunk stage lasts ten epochs in both approaches. The optimal chunk size in this stage was empirically set to cs = 27 tokens.

We used ROUGE [START_REF] Lin | Rouge: A package for automatic evaluation of summaries[END_REF] approach to evaluate generated summaries and thus compare the proposed multi-encoder architecture with our implementation of T ransf ormer ORIGIN AL and T ransf ormer BASE from the PEGASUS paper. The ROUGE method is based on lexical overlaps between tokens and phrases in the reference (human-written) summaries and the generated ones. We report scores with ROUGE-1 (unigrams), ROUGE-2 (bigrams), and ROUGE-L (longest common sub-sequence). Equally, we report the training time of the multi-encoder architecture versus the mono-encoder one to compare their performance in terms of computational time. Note that we do not report the training time of T ransf ormer BASE [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF] since it was trained on TPUs instead of GPUs, and the authors did not provide any information about execution time in their paper.

Results and discussion

Results in Table 1 show that our multi-encoder model outperforms the T ransf ormer BASE from the PEGASUS paper [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF] and T ransf ormer ORIGIN AL from Vaswani et al. (2017) in terms of ROUGE-1 and ROUGE-2 scores, while it gives slightly lower scores in terms of ROUGE-L compared to T ransf ormer BASE .

Our model can read sequences with L input = 2000 tokens. This number is closer to the average size of a standard biomedical article (3016 tokens) than the input length used by other state-of-the-art approaches. To the best of our knowledge, there is no Transformer-based NTS system able to read such length in the biomedical domain. The longest input text was used by P EGASU S LARGE [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF], a pre-trained model with L input = 1024 tokens. Note that the non-pre-trained T ransf ormer BASE uses sequences of length L input = 512.

The LSTM-based approach [START_REF] Cohan | A discourse-aware attention model for abstractive summarization of long documents[END_REF] (called LST M in Subsection 4.2) is still the best NTS system producing summaries with the highest ROUGE scores. This system can process 2000 tokens source documents, with ROUGE scores of R1/R2/RL = 39.93/15.37/35.21.

T ransf ormer

Multi-encoder T ransf ormer Automatic summary generated with our approach urothelial carcinoma of the bladder is a rare and aggressive malignancy with a poor prognosis . there are only a few reports of radical cystectomy for bladder carcinoma . we report the case of a 61 year old male patient who presented to our urology clinic with an initial diagnosis of bladder cancer , the patient was treated with radical nephroureterectomy and adjuvant chemotherapy . the outcome was excellent for all patients and there was no evidence of local recurrence or distant metastases . in this article we review the current literature on the use of neoadjuvant chemotherapy as the first line treatment for patients presenting with bladder carcinomas

Human reference summary for the same scientific article small cell carcinoma of the urinary bladder is an extremely aggressive and rare tumor . even though small cell carcinoma most commonly arises from the lungs there are several reports of small cell carcinoma in extrapulmonary sites . due to its low frequency there is no well -established management for this disease . we report the case of a 61 year -old man with small cell carcinoma of the bladder who underwent radical cystectomy following neoadjuvant chemotherapy . we also reviewed the literature for the optimal treatment strategy Our multi-encoder Transformer model runs almost two times faster than the original Transformer baseline (T ransf ormer ORIGIN AL ). According to our quantitative results and our qualitative analysis of the generated summaries, using a multi-encoder layer in the transformer neural network architecture helps capturing the most important tokens from the input. This finding is intuitive insofar we increase the number of head attentions when using more than an encoder at a time. In general, each encoder has eight self-attention heads. However, in our experiments, we have four encoders, leading to a total of 32 self-attention heads. Therefore, with more self-attentions, the encoders of the model are able to capture the most relevant tokens from the article, while we use a different number of self-attentions in the decoder.

Equally important, the second stage of training, called end-chunk task training, improves our summaries' quality. Indeed, learning chunk by chunk helps feeding information progressively and relatively fast to the decoder. This training is inspired by how humans get knowledge incrementally over time.

Conclusion and further work

In this work, we proposed two improvements for Neural Text Summarization using Transformers [START_REF] Vaswani | Attention is all you need[END_REF], intended to improve the model's performance with long input sequences and reduce training time without penalizing the quality of the generated summaries. The first contribution is to use a multi-encoder and to saucissonner (or slice) a long input sequence to process each slice with one of the four encoders composing our multi-encoder architecture. The second contribution is to modify the End Task Training technique [START_REF] Hoang | Efficient adaptation of pretrained transformers for abstractive summarization[END_REF] at the decoder level to process phrase chunks instead of individual words.

We evaluate these improvements on the medical articles summarization task, using the PubMed [START_REF] Cohan | A discourse-aware attention model for abstractive summarization of long documents[END_REF] dataset. Results show that our improved model was able to: 1. Reduce the training time to almost half compared to the original Transformer model, 2. Extend the size of the input source document to almost the double of the state-of-the-art Transformer-based summarization system [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF] 3. Slightly improve the ROUGE scores of lexical similarity between the generated summaries and their corresponding human reference abstracts Pre-training was intentionally left aside to analyze the exact impact of our improvements in the model. Further experiments will aim to pre-train our model with a large medical dataset (such as the Covid-19 virus corpus [START_REF] House | Covid-19 open research dataset challenge (cord-19)[END_REF]), and feed 2000 tokens long source documents to establish a comparison with other pre-trained models such as P EGASU S BASE and P EGASU S LARGE [START_REF] Zhang | Pegasus: Pre-training with extracted gapsentences for abstractive summarization[END_REF], whose longest length source document is 512, and 1024 tokens, respectively. Both PEGASUS models are pre-trained on large text corpora. Furthermore, a more detailed ablation analysis will be done to evaluate each contribution independently.

  Sequences into a Multi-encoder with Transformers 3 Segmentation of long sequences into a multi-encoder 3.1 The original Transformer network Our Transformer baseline (presented in Figure 1) was introduced by Vaswani et al. (2017). It is a deep neural network based on an attention mechanism (Equation

FIG. 2 -

 2 FIG. 2 -Segmentation of a long input sequence (L input = 2000 tokens) and feeding it to the multi-encoder layer

  ROUGE scores of the T ransf ormer ORIGIN AL , the multi-encoder Transformer (ours), and T ransf ormer BASE from the PEGASUS paper. Best results are in bold.

		ORIGIN AL	Transformer	BASE
		+ end-chunk	+ end-chunk (ours)
	Number of encoders	1	4	1
	Number of layers	4	4	12
	Input length (L input )	2000 tokens	2000 tokens	512 tokens
	Training time per epoch	58mn21s	33mn15s	NA
	ROUGE-1	32.7	34.11	33.94
	ROUGE-2	7.11	7.68	7.43
	ROUGE-L	18.14	18.56	19.02
	TAB. 1 -


Mohammad Nazarifar
email: mohammadnazarifar210@gmail.com

Hamidreza Fahham

The Relationship between Force and Length Change with Temperature Changes in the Synthetic Muscle of Complex Nylons

Keywords: Nylon Artificial Muscles, Length Changes, Force Changes, Biomechanics

Artificial muscle benefits from outstanding properties and is widely used in numerous industries. The new type of this muscle is made of nylon and provides more advantages than the previous ones. The mechanism is such that by giving heat, it causes a change in the length of the muscle. The study analyzes the muscle and calculates the changes in length and force by giving heat, and the relationship between changes in length and force with temperature changes is determined. For this purpose, three diameters are considered and their efficiency is compared.

Introduction

Muscles can be divided into two types, biological and artificial. Biological muscles can withstand a wide range of pressures and forces and are inherently adaptable to the environment and are optimal dampers. The stimulation of this type of muscle is very fast and often unlimited [START_REF] Pratt | Series elastic actuators[END_REF]. However, artificial muscle (AM) is a term used for all materials and instruments that are capable of reversing, stretching, and rotating around a component based on an external stimulus. Due to their high flexibility, versatility, and power-to-weight ratio compared to rigid actuators, these muscles have technology with high potential for replacement in current systems. Artificial muscles come in many forms; including artificial pneumatic muscles, artificial muscles with sponge hydrogel, artificial muscles with carbon nanotubes, artificial muscles with carbon nanotube threads, and artificial muscles made of nylon. This study considers the nylon one (Figure 1).

Figure 1. Artificial muscles made of the nylon yarn

Nylon-coil synthetic muscles are created by the interlocking of polymer fibers. These polymer fibers, with their subsequent rotations, form a heavy twisted band, producing a muscle that contracts when heated and returns to its original size when cooled. This is while the force required by these muscles is created by temperature changes. These types of muscles behave like actuators, so it called nylon-coil actuators (NCA).

NCA is a new type of operator that has advantages over conventional operators such as electric motors or pneumatic systems such as easy construction, less space occupied than other operators, as well as cost-effectiveness of production. These muscles are energized by temperature changes that can be caused by electrical, light absorption, or chemical reactions of fuels. The twisting of the polymer fibers turns them into a twisting muscle that contrasts dramatically with increasing temperature. When it cools, however, it returns to its initial state. If the initial twist of the fibers is in the opposite direction, these fibers expand when heated and the process occurs upside down [START_REF] Lima | Electrically, chemically, and photonically powered torsional and tensile actuation of hybrid carbon nanotube yarn muscles[END_REF].

The warm-up muscle twists and turns as it warms up to produce the necessary force, and when it cools down, it relaxes and relaxes. In this way, they are activated by changing the temperature [START_REF] Seok | Meshworm: a peristaltic soft robot with antagonistic nickel titanium coil actuators[END_REF].

Nylon filaments do not lose their efficiency after 100,000 cycles of resizing and have the ability to contract and expand 17 times per second. However, the mechanical strength of these muscles is 100 times greater than that of human muscles of similar length and weight. The artificial muscles produced have a power of 7.1 horsepower per kilogram, which is equal to the mechanical power of the jet engine. Twisting the polymer fibers creates AM that can spin a heavy rotor at more than 10,000 rpm. Compared to human muscles, which have a %20 contractility, artificial muscles can contract up to %50 of their length and contract and expand millions of times in heavy mechanical operations. Produced in a relatively simple process using nylon, the synthetic muscles respond to heat by changing thickness and length.

Artificial muscles have countless uses. Many advanced humanoid robots, prosthetic surgeries, and skeletons are limited by motors and hydraulic systems. Therefore, due to their size and weight, these systems have limited skills, power generation and activity capability. Artificial muscles can be used for robots and exoskeletons. Another function that scientists have come up with is to use the exterior of cars to change themselves aerodynamically. In general, using this new technology in advanced science [START_REF] Mobasseri | Intelligent Circuit Application for Monitoring the Employees Attendance[END_REF] and vehicles [START_REF] Mobasseri | Intelligent Circuit Application for Detecting the amount of Air in Automobile Tires[END_REF], while reducing fuel consumption and noise pollution, can also positively affect their speed [START_REF] Mobasseri | Impact of driving style on fuel consumption[END_REF][START_REF] Mobasseri | Traffic noise and it's measurement methods[END_REF] In the figure below, Shahinpour illustrates another application of gel stimuli, which uses an electric field change to bend gel fibers.

In the proposed model, creating consecutive and inverted bends in the fishtail causes it to move in the fluid (Figure 5) [START_REF] Shahinpoor | Conceptual design, kinematics and dynamics of swimming robotic structures using ionic polymeric gel muscles[END_REF]. Furthermore, they used a stimulus in a robotic arm to demonstrate its applicability as a low-cost, high-performance robotic muscle [START_REF] Yip | High-performance robotic muscles from conductive nylon sewing thread[END_REF]. Mir Vakili et al. used a simple electrical coating that is used as a means of heating an electrical stimulus to achieve relatively high energy [START_REF] Mirvakili | Simple and strong: twisted silver painted nylon artificial muscle actuated by Joule heating[END_REF].

This study is intended to model the NCA operators and calculate the relationship between force and heat, as well as the change in length and heat, using the Finite Element Method (FEM) and Abaqus [START_REF] Mobasseri | Approximated 3D non-homogeneous model for the buckling and vibration analysis of femur bone with femoral defects[END_REF][START_REF] Mobasseri | A Comparative Study Between ABS and Disc Brake System Using Finite Element Method[END_REF][START_REF] Mobasseri | Bending and Torsional Rigidities of Defected Femur Bone using Finite Element Method[END_REF][START_REF] Habibi | Drug delivery with therapeutic lens for the glaucoma treatment in the anterior eye chamber: a numerical simulation[END_REF]. Therefore, the relevant variables include changes in different temperatures, changes in length due to temperature changes, as well as the force exerted by the muscle.

The study aims to find the relationship between the change in length and the heat transferred to the AM made of nylon and find the relationship between the force exerted by the AM and the heat transferred. Furthermore, two basic assumptions are raised:



The heat transfer propagated through the muscle is uniform.



The rings created by the brain are interconnected and in complete contact with each other.

Methodology

Using the Finite Element Method (FEM) and Abaqus software, the relationship between length change by temperature change as well as temperature change with the applied force of the muscle is examined. For the study purpose, three different diameters are considered for nylon artificial muscles. However, for each diameter measure, temperature changes will be applied in five different intervals. First, we model the muscles using SolidWorks software (Figure 6). However, the muscles are made of nylon and their length is 10 cm. Given that for nylon AM so far only practical experiments have been performed to examine changes in length and only for an approximate diameter of 0.8 mm, therefore, simulation and analysis are performed at this diameter and the lower diameter, i.e. 0.6 mm, and the higher diameter, i.e. 1 mm, to compare the results with practical experiments. Upon modeling the muscles, the data was input to Abaqus for further analysis. Five constant temperatures, namely 30, 40, 50, 60, and 70 ° C was applied to each muscle to get the output of strain and force.

Figure 6. Spring model of nylon muscle in SolidWorks

Three-dimensional and free elements have been used to mesh the muscles. The following figure illustrates the meshing process of the muscles.

Figure 7. Muscle meshing output in Abaqus

Results and Discussion

The following figures show the strain contours for a diameter of 0.6 mm at each temperature. As the strain contours, the output of the force contours is correspondingly obtained, which is exhibited as an example of a force contours for a diameter of 0.8 at a temperature of 50 ° in Figure 11.

Strain

As the previous muscle, the maximum amount of strain occurs at the last temperature, which is equivalent to 7.744. In the diagram, the upward trend is similarly well-maintained. Here, the amount of strain obtained for a muscle with a diameter of 0.8 mm and the amount obtained in practical experiments are compared.

Diagram 3. Comparison between the maximum amount of muscle strain with a diameter of 0.8 mm in the analysis mode by software and practical analysis

Diagram 3 displays that the values obtained by software analysis are slightly higher than the practical analysis. The reason for this difference can be because the software cannot provide the complete conditions in which the practical test was performed in that environment, and only by applying the boundary conditions can it be approached to that environment. Therefore, there is always some difference between theoretical and practical results. As the results show, all the three force Diagrams are ascending, indicating that force changes are directly related to temperature changes. However, in the order of the diagrams, we conclude that the smallest diameter provides the most force changes. In other words, the change in force is directly related to the change in length (strain) and temperature but is inversely related to the diameter of the muscle.

Conclusion Remarks



Length changes were calculated according to temperature changes during the presented diagrams and the relationship between them was suggested for later uses;  Force changes with temperature were correspondingly calculated and the results were presented in graphs and appropriate formulas;  Changes in length and strength in terms of temperature changes in the synthetic muscles of complex nylons with a diameter of 0.8 mm were modeled using the software. Upon comparing with the practical results of other projects, it was found that the modeling is in good agreement with the practical results;  For artificial muscles with diameters of 0.6 and 1 mm, modeling was performed and the relationship between changes in length and force in terms of temperature was proposed as a graph and formula for later uses in dynamic and control design.

  Moghaddari et al. (1995) investigated the use of polymer gels and artificial muscles. They described the role of gel stimuli in artificial muscles and how to prepare an AM from polyacrylonitrile fibers and compared the function of AM with natural muscle in the human body. The following figure illustrates an example of the use of artificial muscles in cybernetic hands.

Figure 2 .Figure 3 .

 23 Figure 2. Synthetic muscle from polyacrylonitrile fibers Moghaddari et al. (1995) placed AM against two molar HCL currents and showed that the muscle reacts rapidly to this current and its volume and length decrease. However, the flow of NaOH demonstrates the opposite effect. The figure below illustrates the function of this experimental muscle as a simple stimulus in an artificial arm.

Figure 4 .

 4 Figure 4. Artificial muscle using a type of contractile gel [10]

Figure 5 .

 5 Figure 5. Gel-like stimuli in artificial muscle[START_REF] Shahinpoor | Conceptual design, kinematics and dynamics of swimming robotic structures using ionic polymeric gel muscles[END_REF] Heinz et al. showed that fishing lines can play the same role and effect in artificial muscles[START_REF] Haines | Artificial muscles from fishing line and sewing thread[END_REF]. In their paper, Michael C. Yip et al. developed a thermomechanical and thermoelectric model of supercoiled polymer (SCP) actuators and tested the controllability. Furthermore, they used a stimulus in a robotic arm to demonstrate its applicability as a low-cost, high-performance robotic muscle[START_REF] Yip | High-performance robotic muscles from conductive nylon sewing thread[END_REF]. Mir Vakili et al. used a simple electrical coating that is used as a means of heating an electrical stimulus to achieve relatively high energy[START_REF] Mirvakili | Simple and strong: twisted silver painted nylon artificial muscle actuated by Joule heating[END_REF].

Figure 8 .

 8 Figure 8. Muscle strain contour with a diameter of 0.6 mm; a) 30°C; b) 40°C; c)50°C; d) 60°C and e) 70°C

Figure 9 Figure 9 .Figure 10 .

 9910 Figure9illustrates the strain contours for a diameter of 0.8 mm at each temperature.

Figure 11 .

 11 Figure 11. Muscle force contour with a diameter of 0.8 at a temperature of 50 ° C

Diagram 4 .Diagram 6 .Diagram 7 .Diagram 9 .

 4679 The maximum amount of strain at any temperature for a muscle with a diameter of 1 mmThe maximum strain obtained in this diameter is at a temperature of 70 ° C and is equal to 6.447.The strain diagrams are compared in three diameters in Diagram 5.Diagram 5. Comparison between the maximum amount of strain in three diameters of 0.6, 0.8, and 1 mm y = -7E-05x 2 + 0.0718x + 1Diagrams, the strain for different diameters illustrates that the graph is ascending: the changes in length and finally the strain is directly related to changes in temperature. However, as the results of the analysis show (the numbers given for each diameter that represented the highest amount of strain) we conclude that at the smallest diameter of 0.6 mm, we have the largest change in length and strain. In other words, the change in length is inversely related to the diameter of the muscle. The maximum amount of force in a muscle with a diameter of 0.6 mm is 798.2 m at a temperature of 70 °C, which is displayed in Diagram 6. The maximum amount of force per temperature for a muscle with a diameter of 0.6 mm y = -0.0161x 2 + 16.994x -300.39 R² = for muscle 0.8 mm in diameter is 715.9 mN, at a temperature of 70 ° C (Diagram 7). The maximum amount of force per temperature for a muscle with a diameter of 0.8 mm Diagram 8 compares the force on a muscle with a diameter of 0.8 mm in the state of practical experimentation and theoretical analysis, and the values of force in the theory are greater than in the state of practice. Diagram 8 .Comparison between the maximum amount of force in a muscle with a diameter of 0.8 mm in the analysis mode by software and practical analysis y = 0.0064x 2 + 14.233x -299.41 R² = displays, the maximum force for a muscle with a diameter of 1 mm is 679.8 mN at a temperature of 70 ° C. The maximum amount of force at the temperatures for a muscle with a diameter of 1 mm Finally, Diagram 10, compares the forces in all three diameters. Diagram 10. Comparison between the maximum amount of force in three diameters of 0.6, 0.8, and 1 mm y = 0.0284x 2 + 11.439x -250.21 R² = 0.0161x 2 + 16.994x -300.39 R² = 0.9928 y = 0.0064x 2 + 14.233x -299.41 R² = 0.9935 y = 0.0284x 2 + 11.439x -250.21 R² =