PhD Mathieu Sanchez
email: mathieu.sanchez@univ-montp.fr

PhD Omer Ecker

MD Dominique Ambard

PhD Franck Jourdan

Franck Nicoud

PhD Simon Mendez

PhD J Lejeune

PhD L Thines

PhD H Dufour

H Brunel

PhD " F Nicoud

MD P Machi

MD K Lobotesis

Mbbs Bonafe

Intracranial aneurismal pulsatility as a new

Keywords: FSI, cerebral aneurysm, rupture risk, pulsatility"

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

%" "

Itemized list of Tables andFigures" Table 1: Material coefficients of the aneurysm wall used in the present study.

Table 2: Aneurysm geometry information, with V the diastolic volume, D the dome size, N the neck size, and D/N the ratio Dome/Neck." Table 3: Results of FSI computations for 12 different aneurysms for stiff and soft materials.

Table 4:

Comparison between volumetric flow rate variation and volume variation of the aneurismal sac.

Table 5: Results for R v and R d for the 12 cases." We certify that this manuscript has not been previously published, nor is being considered in whole or in part in any other primary scientific journal. All the authors are aware of and agree to the content of the manuscript. None of the authors have any conflict of interest to declare." Objective: This study was designed to highlight by means of numerical simulations, the correlation between aneurism sac pulsatility and the risk of rupture through the mechanical properties of the wall."

" Methods: In accordance to previous work suggesting a correlation between the risk of rupture and the material properties of cerebral aneurysms, twelve fluid-structure interaction (FSI) computations were performed on 12 "patient-specific" cases, corresponding to typical shapes and locations of cerebral aneurysms."

The variations of the aneurismal volume during the cardiac cycle (ΔV) are compared using wall material characteristics of either degraded and non-degraded tissues."

" Results: Aneurysms were located on 7 different arteries: Middle Cerebral Artery (4), Anterior Cerebral Artery (3), Internal Carotid Artery (1), Vertebral Artery (1), Ophthalmic Artery (1) and Basilar Artery (1).

Aneurysms presented different shapes (uniform or multi-lobulated) and diastolic volumes (from 18 to 392 mm 3 ). The pulsatility (ΔV/V) was significantly larger for a soft aneurismal material (average of 26 %) than for a stiff material (average of 4 %). The difference between ΔV, for each condition, was statistically significant: p = 0.005."

"

Conclusion:

The difference in aneurismal pulsatility as highlighted in this work might be a relevant patientspecific predictor of aneurysm risk of rupture." aneurysm, but no systematic screening of this disease exists at the moment. The main reason is that 2 to 6% of the general population lives with an aneurysm (Wardlaw et al. 1 ), but only 0.5% of them will rupture.

Screening of intracranial aneurysm is not justified unless being able to detect the vulnerable aneurysms.

Subarachnoid hemorrhage is the consequence of aneurysm rupture and approximately 12% of patients die before receiving medical attention, 40% of the patient will die within the first month and 30% will present a severe permanent disability. Nevertheless, with brain imaging being more frequently and widely used, a growing number of intracranial aneurysms are being diagnosed, raising the problem of which aneurysms harbour a sufficiently high risk of rupture to merit a prophylactic repair. This question is still unsolved at the moment and therapeutic decision for unruptured aneurysm still a challenging point discussed by the neurosurgeon/neuro-interventionist based on sparse epidemiological clinical data that cannot represent the specific individual risk of the patient.

Recent publications have addressed this issue and have demonstrated that, among other variables affecting the natural history of aneurysms, size and location represent independent predictors of both risk of rupture and surgical/endovascular repair outcomes 2,3 . Other parameters, such as irregular aneurysm shape and the presence of blebs are recognized as markers of weak wall structure and high risk of rupture. Rapid aneurysm growth is also likely a risk factor for rupture 4 .

From a mechanical point of view, the rupture of an aneurysm occurs when wall tension exceeds the strength of the wall tissue. Since these quantities cannot be assessed via conventional medical imaging, a natural approach is to compute the wall tension and set a rupture threshold.

Few studies consider the coupled fluid -structure interaction problem (FSI), where the flow equations for blood are solved together with the structural equations for the tissue 5 . The problem is the lack of data on

)" " mechanical properties of cerebral arteries and aneurysms, most of the studies based on a FSI 6-10 framework do not use experimental mechanical behavior of the aneurysm wall as input. A few exceptions exist for the case of the abdominal aorta aneurysms 11,12 but not for intracranial aneurysms, until recently where in a study by Costalat et al 13 , the aneurysm wall properties were characterized and a classification of aneurysm wall behavior was carried out. One of the main conclusions of this work was that the clinical status of the aneurysm (unruptured, pre-ruptured and ruptured) was strongly correlated with the mechanical behavior of the aneurysm wall and hence a classification was proposed, (stiff, intermediate and soft).

This was followed with FSI computations by Sanchez et al. 14 , who demonstrated for one specific aneurysm that the different mechanical properties of the aneurysm wall (stiff or soft) are responsible for significantly different variations in aneurism volume over the cardiac cycle (pulsatility). A parametric study was also achieved in this work, and demonstrated that uncertainties did not change the main conclusion.

The further application of these results to the in-vivo setting and in particular to cerebral aneurysms arising from the circle of Willis circle is an additional important step.

The aim of this work was to investigate and verify the correlation between wall biomechanical properties (stiff and soft) and aneurismal volume variation during the cardiac cycle for a variety of aneurysms which differ in shape and location in the circle of Willis.

This study was conceived and carried out as part of the IRRAs consortium which is a research project dedicated to the evaluation of patient-specific risk of rupture of cerebral aneurysms. The consortium brings together neurosurgeons, neuroradiologists and researchers in biomechanical engineering in a common translational research project. The details of the study establishing the correlation between the aneurysm status and tissue mechanical properties are given in the work of Costalat et al. 13 . The methodology and main results are briefly given in the following for the sake of completeness. A similar study has been done by Duprey and al. 15 for the thoracic aortic aneurysms."

Each sample was then studied as appropriate for biomaterials [16][17][18][19] . Only the meridional axis of the aneurysm was chosen in order to preserve maximum length of the aneurismal tissue in the sample, given the very small size of each specimen and the fragility of the tissue. Using theses measurement series, a model of the tissue behavior was proposed for large displacements in order to represent the evolution of the stress in the materials 20 . For this purpose the assumption that the material is isotropic and non-compressible was made and the hyperelastic model of Mooney-Rivlin with 3 parameters was selected (1). It reads:" " !"#"$ %& '( % ")"*+","$ &% '( -")"*+","$ %% '( % ")"*+'( -")"*+. An intracranial pressure of 20 mmHg [START_REF]/"#<==A>AT@MK=BMT!# %)" " ?="# c,%46#X+#Y-((%Bd&-3(#F+#X&%/4/#F+#03I3#*+[END_REF] was applied on the external surface of the geometries to recreate the in vivo conditions." 0505?(@49#/'&3(7,-23/+<( " The blood flow within the computational domain is described by the incompressible Navier-Stokes equations ( 2), (3)."

!!!!!!!!!!!!!!!"#$"
" ρ f = div σ f , (2)" -" " " div (v f ) = 0 , (3)"
" where v f is the time dependent flow velocity vector, ρ f is the density of blood and σ f is the stress tensor for the fluid part. The flow is assumed to be laminar, a reasonable assumption given the moderate value of the Reynolds number (Re ≈ 300). Blood is modeled as a Newtonian fluid (4), of density of ρ f = 1050 kg/m 3 and dynamic viscosity of µ = 0.004 Pa.s 9, 23-25 ."

"

σ f = -pI + 2µD, (4)
"

"
where p is the pressure, I is the identity tensor and D is the rate of deformation tensor."

For the parent artery, a linear elastic model was selected in order to focus the attention on the aneurismal sac and to reduce the computational time. The structural equation solved for both the artery and aneurismal sac reads (5):" " ρ s =div σ s (5)"

"
where σ s is the Cauchy strain for the structural part and v s stands for the structural velocity vector."

"
The aneurysm wall is modeled as an isotropic incompressible hyperelastic material (6):"

" σ s = ρ s F F t -pI , (6)" "
where F is the transformation gradiant tensor, E is the Green Lagrangian strain tensor and W is the strain energy potential defined in Eq.( 1) ."

The material used for the parent artery has the following properties: Young modulus E = 3 MPa, Poisson ratio ν = 0.49, specific mass ρ s = 2300 kg/m3 and thickness e pa = 0.6 mm 24,26 .The thickness of the aneurism

!." "

wall is e = 0.38 mm. A linear model was used for the arterial wall because the deformations are small enough in all cases (< 5%) to stay in the small deformation theory. Furthermore, the edge nodes of the arterial extremities are taken away of the displacement in the longitudinal direction " 0505A(B=*(',7>.$&$/,+#( " Computations were performed using the software Ansys V.13. The fluid motion equations were solved with the software CFX, which uses the finite volume approach and the Newton-Raphson method for solving the subsequent non-linear system. For the structural part, Ansys employed the finite-element method and the Newton-Raphson algorithm. The wall pressure resulting from the fluid was imported as boundary conditions in the structural analysis while wall displacement calculated by the structure solver was imported as a boundary condition for the fluid solver; this procedure was carried out in an iterative manner within each time step. It was an iterative implicit coupling. At the interface of the two physical domains, the element type differs and the nodes of the two meshes did not coincide, so surface interpolation was carried out 27 . For each FSI computation, a time step of 0.004 s was used. The structural meshes were composed by 2500 to 5000 shell elements and the fluid meshes by 100 000 to 300 000 tetrahedral elements. These meshes were fine enough to ensure the convergence of the computations. The two FSI computations of the reference case from Sanchez et al 14 were performed for mesh refinements multiplied by 10 (about 30 000 elements for structural mesh and 1 000 000 elements for fluid mesh). Despite a significant increase of the computation time, the results were similar. For example, in the soft case, the maximal mesh displacement was 0.78 instead of 0.77 mm (variation of 1.3%) and the volume variation was 44.3 mm 3 instead of 44 mm 3 (variation of 0.7%).

A structural computation was performed before the FSI computation to determine the stress state of the acquired geometry. All the details about the initial stress of the aneurysm geometry are given in Sanchez et al 14 ," !!" " " "

Wall motion analysis(

"

For each aneurismal geometry, two FSI computations were performed using the two different biomechanical properties (stiff and soft) defined in section 2.1. Volume variations and wall motions were calculated for each aneurysm. For each case, the systolo-diastolic variation of the volume of the aneurysm (ΔV) was computed from the results. The maximal mesh displacement on the aneurysm sac D max was also calculated.

ΔV is a global information about the aneurismal sac behaviour, whereas D max provides a local information (maximal displacement of a point of the aneurysm boundary)."

In order to compare ΔV and D max between soft and stiff material for different aneurysms, we defined the following ratio:"

" R v = ΔV soft / ΔV stiff " " R d = D max soft / D max stiff " "
A pulsatility index was also defined as: Pulsatility = ΔV/V, where V is the diastolic volume of the aneurismal sac." Geometric information is displayed in Table 2." 3 andTable 4. Two aneurysms (5 and 6) show unusual results. For aneurysm 5, the volume variation is 149 mm 3 . The important initial volume of the aneurysm sac, almost 400 mm3, can explain this important variation. This aneurysm could be considered as a giant aneurysm. Nevertheless, the pulsatility ratio is still in the range of 30% in comparison to an overall mean of 25% for the cases. For aneurysm 6, the pulsatility is much more important than in the other cases. That is the result of a volumetric flow rate more important in this location (vertebral artery) for this specific patient compare to the others (table 4). This due to the particularity of his cerebral arterial network."

A significant difference was observable between ΔV for a stiff material and for a soft material (p = 0.005).

Consequently, the pulsatility index (ΔV /V) was obviously more important for a soft aneurismal sac (average

!&" "
of 26 %) than for a stiff aneurismal sac (average of 4 %), in spite of the relatively small number of aneurysms considered." Naturally, we found the same difference for the ratio R v : R v varies from 4 (aneurysm 7 and 10) to 16 (aneurysm 2) for an average of 7. R d also showed differences between stiff and soft materials but to a lesser extent: the minimum is 1.5 (aneurysm 6) and the maximum is 5 (aneurysm 4) and the average was 3 (Table 5). The difference between ΔV and D max for each condition was statistically significant: p = 0.005 for ΔV and p = 0.02 for D max ."

A(E/#'.##/,+( " Our results clearly suggest that the wall displacement provides information about the level of degradation of the aneurysm wall and, thanks to the findings of Costalat et al 13 , about the rupture risk of the aneurysm sac.

Our study shows that whatever the location of the aneurysm on the Willis circle, the aneurismal pulsatility was about 7 times higher for soft/ruptured aneurysms in comparison with stiff/unruptured aneurysms (26 % versus 4 %). That is the result of a volumetric flow rate more important in this location (vertebral artery) compare to the others (table 4). These observations are consistent with our previous work: the study from Sanchez et al 14 , using FSI computations, where a significant difference between the displacements and volume variations corresponding to the soft (close to the rupture) and stiff (undegraded) tissues was observed in a particular case. This result is generalized to the entirety of the circle of Willis in this paper. " Interestingly, Hayakawa et al 28 highlighted in an observational study on 51 patients using 4D computed tomography angiography (CTA) 28 that the pulsation of the aneurysm sac (or volume variation) between ruptured aneurysms and unruptured aneurysms was significantly different. In their study, 12/51 cerebral aneurysms were ruptured (RCA). Among these 12 RCA, a pulsation was observed in nine cases (75%).

Thirty nine cerebral aneurysms were unruptured (URCA); among these 39 URCA, a pulsation was observed in 12 cases (30%). Two of these 12 pulsating URCA were treated by surgery because of the clinical background of the patients. They came to the conclusion that the detection of pulsation in an unruptured aneurysm could be therefore a clue of an important risk factor. Our results confirm this point of view."

!'" "

Furthermore, the precision needed to identify the aneurismal wall motion variation between unruptured/ruptured aneurysm would be about 0.1 mm (D max ) or 0.5 mm 3 (ΔV) according to our study. New imaging techniques may approach this high resolution level in recent literature (for example Ishida et al 29 and Zhang et al 30 )."

To date, the most relevant solution has been proposed by Karmonik et al 31 who used 2D phase contrast MRI (1.5 T MRI) to observe wall displacement over the cardiac cycle on 7 patients (7 aneurysms) and 3 different locations (ACom, BA and ICA). This non-invasive technique was accurate enough to measure wall displacements as small as 0.04 mm, in the range of the expected wall displacement reported in our study.

New generation MRI at 3T could offer a more accurate depiction of the wall motion in the next future."

As already stated, D max gives local information about the wall displacement and in some cases, this parameter is not accurate enough to clearly demonstrate a difference between degraded/ruptured and nondegraded/unruptured aneurysms. The variation of the maximal mesh displacement measured by the software depends strongly of the displacement of the artery. The deformation of the artery induces an additional displacement of the mesh of the aneurismal sac. Then, the bulge of the artery is added to the displacement of the sac in relation to the artery to obtain the total maximal mesh displacement.

As shown in Table 4, the value of the ratio R v is more important than R d and allow us to better characterize aneurysm status. Furthermore, ΔV is a global indicator and is less sensitive to the uncertainties of the input parameters of the computations 14 . ΔV is thus thought to be a better parameter to evaluate the risk of rupture.

A limitation of this study was the simplification of the aneurismal environment in the FSI computations, by assuming that the aneurysm lies in a fluid of a given pressure. This environment would probably influence the geometrical variations of the aneurysm depending on the location: friction and contact of the wall with other structures (bone, meninges, arachnoid trabeculae, cerebrospiral fluid) will certainly impact on the wall motion 32,33 . The assumptions made regarding the aneurysm wall is another limitation. In the present study, the aneurysm wall was considered as isotropic and homogenous but the anisotropy of this kind of biological material is well known 34 . Nevertheless, isotropy and homogeneity were assumed because they most probably do not change the trends observed when comparing soft and stiff materials in this range of physiological solicitation. To characterize the behavior of blood, more sophisticated models can be used in !(" " order to account for non-Newtonian effects, especially in the aneurismal sac (Cebral et al. 35 and Sforza et al. 36 ). However, these effects are expected to be prevalent only when dealing with local quantities like wall shear stress. This is not the case in the present study where the aneurismal volume, which is primarily influenced by the pressure forces generated by the blood flow, was analyzed.

Numerous parameters are not known precisely when computing the FSI problem for a specific patient: wall thickness, fluid boundary conditions, artery properties and intracranial pressure. Despite these last limitations, the parametric study achieved in previous work 14 showed a positive consistency of our results regarding the numerous sources of uncertainty involved in such FSI computations.

Therefore, the uncertainties of the input parameters did not change the main conclusion: whatever the values of the parameters (in a range of physiological variations), the soft/ruptured aneurysms deform more than the stiff/unruptured ones. " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " " %%" " " " " "

Figure 1 :Figure 2 :

 12 Figure 1: Nominal stress/engineering strain curves representing the average mechanical properties of the

  about 15 000 people in Europe each year. Most of these people are young, between 40 and 60 years old. New medical imaging techniques are now able to clearly depict intracranial

""

  where W is the strain energy potential, I 1 = tr(C) and I 2 = 1 /2 (tr 2 (C)-tr(C 2 )) are the first and second strain invariants of the right Cauchy-Green deformation tensor C and C 10 , C 01 , C 11 are the material coefficients.All unruptured aneurysms presented as stiffer tissue than the ruptured aneurysms."Their mechanical behavior was either stiff or intermediate. Conversely, all ruptured aneurysms correspond to a soft tissue. The corresponding parameters are given in Table1while the strain/stress curves are displayed in Fig.1." Aneurysm geometry was obtained from 3D rotational angiography. Using dedicated software ScanIP (Simpleware), the aneurismal geometries were clean up by keeping only the aneurysms and the parent vessels in order to reduce the region of interest. A low-pass filter was applied on the geometries to reduce noise-to-signal ratio. The results were exported as STL files in order to proceed to numerical analysis. " 05050(=/7.3&$/,+(>&%&72$2%#( " The flow boundary conditions imposed were obtained from specific patient examinations by using phase contrast MRI for each aneurysm location (SIEMENS 3T, Skyra, Hessen, Germany). The velocity profiles were considered as uniform, Outlet pressure was computed by computational fluid dynamic (CFD) and used as boundary conditions at the outlet of the computational domain. It had been obtained by resolving the 1D equations in the arterial network with boundary flow conditions from imaging measurement 21 .

"

  was performed to evaluate the influence of ΔV and D max in soft and stiff cases. The comparison was made by using non-parametric Wilcoxon rank test for continuous variables and Fisher exact test for categorical ones. Statistical significance threshold was set at 5%. Statistical analyses were performed using SAS version 9.1 (SAS Institute, Cary, North Carolina). " Twelve aneurysms from twelve different patients were computed with the stiff and the soft materials. These aneurysms were located on 7 different arteries: Middle Cerebral Artery right and left (MCA R and L) [4 cases], Anterior Cerebral Artery (ACA) [3 cases], Internal Carotid Artery (ICA) [1 case], Vertebral Artery (VA) [1 case], Ophthalmic Artery (OA) [1 case] and Basilar Artery (BA) [2 cases]. These had a variety of shapes: simple (uniform) or complex (multi-lobulated). The volume varied from 18 mm 3 to 392 mm 3 .

  Fig.2gives an example of the FSI results for 3 different cases, by displaying maximum displacement for the

"F""

  The difference in aneurismal pulsatility as highlighted in this work might be a relevant patient specific predictor of aneurysm rupture. These results are consistent with recent observational data in the literature to date. Development of new imaging technique would allow in the near future to accurately measure wall motion and therefore to characterise the intracranial aneurysm vulnerability for any given patient."The authors thank Philips Healthcare, Inc. for the financial support and Ansys, Inc. for their technical support. The authors are also grateful to Simpleware, Inc. for providing the software Scan IP and for their technical support."

Figure 1 :

 1 Figure 1:"Nominal stress/engineering strain curves representing the average mechanical properties of the

Figure 2 :

 2 Figure 2: FSI maximal mesh displacement results for the systolic pressure for the aneurysms 10 (top), 8

Table 1 :

 1 Material coefficients of the aneurysm walls used in the present study"

	Aneurysm"	C 10 (Mpa)"	C 01 (Mpa)"	C 11 (Mpa)"
	Soft"	0.024"	0.026"	0.42"
	Stiff"	0.39"	0"	22.14"

Table 2 :

 2 Aneurysm geometry informations, with V the diastolic volume, D the dome size," N the neck size and D/N the ratio Dome/Neck."

	aneurysm" location"	V (mm 3 )"	shape"	D (mm)"	N (mm)"	D/N"
	1"	MCA R"	61"	Simple"	4.8"	4"	1.2"
	2"	MCA L"	51"	Complex"	3.4"	3.75"	0.91"
	3"	ACA"	161"	Simple"	5.25"	5.6"	0.94"
	4"	MCA R"	188"	Complex"	5.5"	6.5"	0.85"
	5"	ICA"	392"	Complex"	9.2"	5"	1.84"
	6"	VA"	45"	Simple"	4"	2.3"	1.74"
	7"	MCA L"	212"	Complex"	7.8"	6.6"	1.18"
	8"	ACA"	232"	Complex"	8"	4.5"	1.78"
	9"	ACA"	79"	Complex"	6.85"	2.9"	2.36"
	10"	OA"	138"	Simple"	6.1"	4.3"	1.41"
	11"	BA"	68"	Complex"	4.4"	3.9"	1.13"
	12"	BA"	18"	Simple"	3"	3"	1"
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Table 3 :

 3 Results of FSI computations for 12 different aneurysms for stiff and soft materials."

	aneurysm" material"	V (mm 3 )"	ΔV (mm 3 )"	Pulsatility = ΔV/V (%)" D max (mm)"
	1"	Soft"	61"	10"	16"	0.35"
		Stiff"		1"	2"	0.12"
	2"	Soft"	51"	8"	16"	0.32"
		Stiff"		0.5"	1"	0.09"
	3"	Soft"	161"	44"	27"	0.7"
		Stiff"		6"	4"	0.26"
	4"	Soft"	188"	37"	20"	0.71"
		Stiff"		7"	4"	0.15"
	5"	Soft"	392"	149"	38"	1"
		Stiff"		18"	5"	0.45"
	6"	Soft"	45"	33"	73"	1.8"
		Stiff"		4"	9"	1.2"
	7"	Soft"	212"	60"	28"	1.05"
		Stiff"		14"	7"	0.48"
	8"	Soft"	232"	54"	23"	0.84"
		Stiff"		10"	4"	0.4"
	9"	Soft"	79"	15"	19"	0.48"
		Stiff"		3"	4"	0.15"
	10"	Soft"	138"	26"	19"	0.47"
		Stiff"		5"	4"	0.17"
	11"	Soft"	68"	11"	16"	0.42"
		Stiff"		2.5"	4"	0.12"
	12"	Soft"	18"	4"	22"	0.27"
		Stiff"		0.5"	3"	0.14"
	"

Table 4 :

 4 Comparison between volumetric flow rate variation and volume variation of soft aneurismal sacs.

	"
	Aneurysm	Systole flow rate (m 3 .s -1 )	Flow rate variation (m 3 .s -1 )	ΔV (mm 3 )
	1	3.21 E-06	1.78 E-06	10
	2	3.10 E-06	1.75 E-06	8
	3	2.85 E-06	1.95 E-06	44
	4	3.36 E-06	1.84 E-06	37
	5	4.21 E-06	2.15 E-06	149
	6	3.55 E-05	4.454E-05	33
	7	3.12 E-06	1.77 E-06	60
	8	1.28 E-06	1.27 E-06	54
	9	1.62 E-06	7.94 E-07	15
	10	2.92 E-07	1.36 E-07	26
	11	5.07 E-06	3.34 E-06	11
	12	5.07 E-06	3.34 E-06	4
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Table 5 :

 5 Results for R v and R d for the 12 cases"

	aneurysm"	Volume (mm 3 )"	Rv = ΔV soft / ΔV stiff "	Rd = D max	soft / D max	stiff "
			"		"
	1"	61"	10"	2.9"
	2"	51"	16"	3.5"
	3"	161"	7.3"	2.7"
	4"	188"	5.3"	4.7"
	5"	392"	8.3"	2.2"
	6"	45"	8.25"	1.5"
	7"	212"	4.3"	2.2"
	8"	232"	5.4"	2.1"
	9"	79"	5"	3.2"
	10"	138"	5.2"	2.8"
	11"	68"	4.4"	3.5"
	12"	18"	8"	1.9"
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Véronique Van Elewyck

Recent results from the ANTARES neutrino telescope

Keywords: ANTARES, neutrino telescopes, multimessenger astronomy

The ANTARES neutrino telescope is currently the largest operating water Cherenkov detector and the largest neutrino detector in the Northern Hemisphere. Its main scientific target is the detection of high-energy (TeV and beyond) neutrinos from cosmic accelerators, as predicted by hadronic interaction models, and the measurement of the diffuse neutrino flux. Its location allows for surveying a large part of the Galactic Plane, including the Galactic Centre.

In addition to the standalone searches for point-like and diffuse high-energy neutrino signals, ANTARES has developed a range of multi-messenger strategies to exploit the close connection between neutrinos and other cosmic messengers such as gamma-rays, charged cosmic rays and gravitational waves. This contribution provides an overview of the recently conducted analyses, including a search for neutrinos from the Fermi bubbles region, searches for optical counterparts with the TAToO program, and searches for neutrinos in correlation with gamma-ray bursts, blazars, and microquasars. Further topics of investigation, covering e.g. the search for neutrinos from dark matter annihilation, searches for exotic particles and the measurement of neutrino oscillations, are also reviewed.

Introduction

Neutrinos have long been proposed as an alternative to cosmic rays and photons to explore the high-energy sky, as they can emerge from dense media and travel across cosmological distances without being deflected by magnetic fields nor absorbed by ambient matter and radiation. High-energy (>TeV) neutrinos are expected to be emitted in a wide range of astrophysical objects. Along with gamma-rays, they originate from the decay of π's and K's produced in the interactions of accelerated protons and nuclei with matter and radiation in the vicinity of the source. Candidate neutrino emitters therefore include most known or putative cosmic ray accelerators, ranging from galactic sources such as supernovae remnants or microquasars to the most powerful extragalactic emitters such as Active Galactic Nuclei (AGNs) and Gamma-Ray Bursts (GRBs) (see [1] for a review on the subject). Apart from tracing the existence of hadronic processes inside astrophysical sources, the observation of cosmic neutrinos could also reveal new types of sources, yet unobserved in photons or cosmic rays. Due to oscillations, the expected flavor ratio at Earth for cosmic neutrinos is ν e : ν µ : ν τ = 1 : 1 : 1.

Neutrino astronomy is challenged both by the weakness of neutrino interactions and by the feebleness of the expected fluxes of cosmic neutrinos. Neutrino telescopes therefore require the instrumentation of large volumes of transparent medium (water or ice) with arrays of photosensors, in order to detect the Cherenkov light emitted by the charged leptons produced when a neutrino interacts with material in or around the detector. Muons are the most straightforward detection channel, but showers induced by electron-and tau-neutrino can also be detected. The timing, position and amplitude of the light pulses (or hits) recorded by the photosensors allow the reconstruction of the muon trajectory, providing the arrival direction of the parent neutrino and an estimation of its energy. Such detectors are installed at great depths and optimized to detect up-going muons produced by neutrinos which have traversed the Earth, in order to limit the background from down-going atmospheric muons. As a consequence, one single neutrino telescope can efficiently monitor only half of the sky in the TeV -PeV range. The ANTARES detector, located in the Mediterranean Sea, is well suited for the observation of Galactic sources; its layout and performances are described in Sec. 2.

Atmospheric neutrinos produced in cosmic-ray-induced air showers can also traverse the Earth and interact close to the detector, providing an irreducible source of background with energy spectrum ∝ E -3.7 ν . Diffuse fluxes of neutrinos from astrophysical origin, which are expected to be harder (typically ∝ E -α ν with α = 1 -2), can then be identified as an excess of events above a certain energy. Another approach consists in looking for localized excesses of events, either blindly over the full sky, or in the direction of a-priori selected candidate source locations that correspond e.g. to known gamma-ray emitters. Such searches are well suited to look for steady, pointlike sources, in particular in the Galaxy. Finally, multimessenger searches develop specific strategies to look for neutrinos with timing and/or directional correlations with other cosmic messengers such as photons, charged cosmic rays or gravitational waves. The outcome of such searches performed with Beyond its astrophysics goals, the science reach of a neutrino telescope such as ANTARES also includes an extended particle physics program, ranging from the study of neutrino oscillations and the indirect search for dark matter (in the GeV -TeV energy range) to the search for exotic particles (at the upper end of the energy spectrum). These topics are discussed in Sec. 6. Recent reviews on the techniques and challenges of neutrino astronomy can be found e.g. in [2,3].

The ANTARES detector

The ANTARES (Astronomy with a Neutrino Telescope and Abyss environmental Research) detector is the first undersea neutrino telescope and the only one currently operating [4]. Its deployment at a depth of 2475m in the Mediterranean Sea, about 40 km off the French coast near Toulon (Var), was completed in May 2008. It consists of an array of 12 lines anchored to the sea bed and connected to a junction box which distributes the electrical power and transmits the data to shore through an electro-optical cable (see Fig. 1). The detector lines are 450m high, with an inter-line spacing ranging from 60m to 75m. Each line supports 25 storeys made of triplets of 10-inch photomultipliers (PMTs) enclosed in 17-inch pressure-proof glass spheres. The detector comprises a total of 885 optical modules (OMs), corresponding to an instrumented volume of about 0,02 km 3 . Acoustic devices and inclinometers regularly spread along the lines allow to accurately monitor the position and orientation of the OMs [5], and time calibration is performed by means of an in situ array of laser and LED beacons [6].

The PMTs are orientated at 45 • downwards in order to maximize the sensitivity to Cherenkov light from upcoming muons. The time and charge of all PMT signals (or "hits") that pass a predefined threshold (typically 0.3 photoelectron) are digitized and sent to shore, where they are processed on the fly by a PC farm. The data flow to the shore amounts to about 1 to 10 Gb/s; it is dominated by random hits (∼ 60 -150 kHz per PMT) due to the background light produced by the radioactive decay of 40 K and by bioluminescence. Online filtering algorithms run on the PCs to select the physics events among the raw data and write them to disk to be analysed offline. Such events are then reconstructed using the hits time and position information, typically by means of a modified maximum likelihood method [7]. The median angular resolution achieved for muon tracks is 0.5 • , allowing good performance in the searches for neutrino point sources, as reported in Sec. 4.

The detector records a baseline rate of ∼10 Hz of muons. Those associated with upgoing (atmospheric) neutrinos can be identified by imposing both quality cuts on the track fit parameters, and a directional cut on the reconstructed zenith angle (θ > 90 • ). Within these conditions, the detector instantaneous sky coverage is 2π sr, and its integrated visibility covers about 3 4 of the sky. Its location in the Northern Hemisphere allows for surveying a large part of the Galactic Plane, including the Galactic Centre, thus complementing the sky coverage of the IceCube detector installed at the South Pole [8].

The main junction box of ANTARES also supports an additional instrumented line which provides measurements of environmental parameters such as sea current and temperature, and hosts part of the AMADEUS prototype array for ultrahigh energy neutrino acoustic detection [9]. Since April 2013, this line hosts a prototype OM, housing 31 3-inch PMTs, designed for the next-generation deep-sea neutrino telescope, KM3NeT [START_REF] Bagley | KM3NeT Collaboration[END_REF]. A secondary junction box dedicated to Earth and Marine Science projects was also connected to the main junction box and is operating since December 2010.

Searches for diffuse neutrino fluxes

Measurement of the atmospheric ν µ spectrum

The bulk of atmospheric neutrinos detected by ANTARES represent an irreducible background in the search for cosmic neutrinos. A precise measurement of this atmospheric neutrino flux is nevertheless of intrinsic interest both as a test of the good behaviour of the detector, and as a valuable source of information on the physics of cosmic-ray-induced atmospheric showers. Up to about 100 TeV, the dominant neutrino production channels are π ± and K ± decays, yielding an energy spectrum ∼ E -3,7 . At higher energies, an additional component originating from the prompt decay of charmed and bottomed particles takes over, with a spectrum which is expected to follow more closely that of the primary cosmic rays (∼ E -2,7 ). Its precise features are however sensitive to the hadronic interaction models, which are still poorly constrained at those high energies.

A measurement of the atmospheric ν µ spectrum has been performed using the ANTARES data collected between December 2007 and December 2011 (hereafter dubbed as the 2008-2011 data sample), for a total equivalent live time of 855 days. The cuts used to select a pure sample of atmospheric neutrinos were optimized using a run-by-run Monte Carlo simulation which reproduces the detail of the acquisition conditions of the detector. ν . The ANTARES measurement (black squares) is shown together with the results from AMANDA-II (red circles) [12] and and IceCube 40 (blue triangles) [13]. The black, solid line represents the conventional flux prediction from the Bartol group [14] ; the red and blue dashed lines include two prompt neutrino production models from [15] and [16], respectively. The red region corresponds to the older measurement from AMANDA-II [17], and the blue one the IC40 update from [18].

10% of the data were used as a burn sample to check the agreement between the observed and the expected distributions.

The determination of the neutrino energy is a crucial -and challenging -aspect of atmospheric neutrino spectrum studies. In the present analysis, two energy estimators were used. The first one is based on a maximum likelihood approach attempting to maximize the agreement between the observed and expected amount of detected light; the second one relies on the muon energy loss along its trajectory. Nevertheless, the limited resolution of these estimators make it impossible to reconstruct the neutrino energy spectrum on an even-by-event basis. Instead, an unfolding procedure based on Monte Carlo simulations is used; more detail on the analysis can be found in [START_REF] Adrin-Martnez | [END_REF].

The unfolded neutrino energy spectrum, averaged over zenith angles θ > 90 • , is shown in Fig. 2 as a function of the energy in the range 100 GeV -200 TeV. The decrease above E ν ∼ 100 TeV is expected from the change of the spectral index of the primary cosmic ray flux above the knee region (E knee ≃ 3 × 10 15 eV). It is about 25% higher than the prediction from the Bartol group [14], though still well within uncertainties, and intermediate between the spectra measured by the Antarctic neutrino telescopes AMANDA-II and IceCube-40. This good overall agreement provides confidence in the understanding of the absolute energy scale of the detector.

Search for a diffuse neutrino flux of astrophysical origin

Once the atmospheric neutrino flux has been measured, one can look for an excess of events, that would reflect the existence of a diffuse neutrino flux from the bulk of unresolved astrophysical sources. This excess would become apparent above a certain energy threshold, depending on the absolute normalization and spectral index of the cosmic neutrino flux.

An updated analysis using the 2008-2011 data sample has been performed with the same statistical method that was used for an E -2 spectrum, compared to 90% C.L. limits from other experiments, including IceCube in its 40 [18] and 59-string [20] configurations. Also shown are the Waxman-Bahcall benchmark flux predictions [21].

for the first (2008-2009) ANTARES limit [19]. The main difference lies in the use of an energy estimator based on the muon energy loss along its path, dE dx , as evaluated from the total collected charge of the hits that are selected to enter the track fitting procedure. An optimisation procedure based on the Model Rejection Factor (MRF) approach was then applied on Monte Carlo simulations (again validated on a burn sample of 10% of the data), in order to determine the position of the energy cut yielding the best sensitivity. In view of the ∼25% overall excess of the data with respect to the conventional atmospheric flux expectation (as described in Sec. 3.1, the background was conservatively normalized to data.

After normalization, 8 events were observed in the selected high-energy region, while 8.5 were expected from the atmospheric neutrino background. This result yields a 90% confidence level (C.L.) upper limit on a diffuse astrophysical component of

E 2 ν dN dE ν = 4.8 × 10 -8 GeV cm -2 s -1 sr -1 (1)
in the energy range 45 TeV -10 PeV. This updated limit, shown in Fig. 3 for an E -2 ν spectrum is only a factor of ∼ 4 above the limit obtained by IceCube in its 59-string configuration [20], and it is approaching the benchmark flux predictions from [21]. Complementarily to IceCube, it applies to the Southern sky.

Search for neutrinos from the Fermi bubbles

The Fermi-LAT Collaboration has recently reported the observation of an excess of gamma-rays from two extended, "bubble"-like regions above and below the Galactic Plane. The observed signal is characterized by a homogeneous intensity over the whole bubble regions and a hard (∼ E -2 ) spectrum with a probable cutoff at energies above 100 GeV. The origin of these so-called Fermi bubbles (FBs) is still debated. Several proposed scenarios invoke hadronic mechanisms in which accelerated cosmic rays interact with the interstellar medium, producing pions which would in turn generate neutrinos along the observed gamma-rays [22,23,24].

A dedicated search has been performed with the 2008-2011 data sample of ANTARES, taking advantage of the good sensitivity of the detector to the central region of our Galaxy; the detail of the analysis can be found in [25]. The rate of highenergy neutrino events observed in the FB region (ON zone) has been compared to that observed in equivalent (in size, shape and average detector efficiency) areas of the Galaxy excluding the FBs (OFF zones). No statistically significant excess of events above the chosen energy cut was observed in the ON zone with respect to the average expected number from the OFF zones. Corresponding upper limits on the neutrino flux from the FBs have therefore been derived for different assumptions on the energy cutoff at the source, as shown in Fig. 4. Some of these limits are relatively close to the expected fluxes, suggesting that at least part of the phase space for hadronic models of gamma-ray emission in the FBs could be probed with the full ANTARES dataset, or after about one year of operation of the next-generation KM3NeT neutrino telescope [START_REF] Adrián-Martinez | KM3NeT Collaboration[END_REF].

Searches for sources of cosmic neutrinos

Such searches aim at detecting significant excesses of events from particular spots (or small regions) of the sky. They can be performed either blindly over the full sky, or in the direction of a-priori selected candidate source locations that correspond e.g. to known gamma-ray emitters. They are well-suited to look for steady, point-like sources, in particular in the Galaxy.

These searches have first been performed on a data sample covering the period from February 2007 (when the detector was still under construction and operating with only 5 lines) until mid-November 2010, for a total equivalent live time of 813 days [27]. The studies presented here use an updated dataset extending until end of December 2012, for a total live time of 1339 days. The final neutrino sample is obtained after tight cuts on the reconstruction quality parameter, on the estimated angular resolution (< 1 • ) and on the zenith angle (θ > 90 • , in order to select only upgoing neutrino tracks. It contains 5516 neutrino candidates, with a predicted atmospheric neutrino purity of around 90%. The median uncertainty on the reconstructed neutrino direction, assuming an E -2 energy spectrum, is 0.4 • ± 0.1 • [START_REF] Schulte ; Coll | Update on the ANTARES full-sky neutrino point source search[END_REF].

Two different searches were performed:

-A time-integrated full-sky search looking for an excess of events over the atmospheric neutrino background in the declination range [-90 • ; +48 • ]. The search algorithm is based on an unbinned maximum likelihood which includes the information on the point-spread function and on the expected background rate as a function of the declination and of the number hits used in the track reconstruction. The most significant cluster of events is located at (RA = -47.8 • ; δ = -64.9 • ) and is the same as found in the 2007-2010 dataset; its post-trial p-value is 2.1%, equivalent to a 2.3 σ significance. This "warm spot" was also searched for counterparts in the electromagnetic band, without success [START_REF] Schüssler | Multiwavelength study of the region around the ANTARES neutrino excess[END_REF].

-A candidate-list search looking for events in the directions of a predefined list of 51 candidate sources of interest which are known gamma-ray emitters and potential sites for hadronic acceleration. No significant excess was found in any of these 51 directions, and upper limits have consequently been derived for these candidate sources. Figure 6: Sum of the muon neutrino spectra of all selected GRBs in the 2008-2011 period (solid lines) and limits set by this analysis [31] (short-dashed lines) as obtained from the NeuCosmA numerical simulation [32] (red) or from the model from [33] (blue). The experimental limits [START_REF] Adrián-Martinez | [END_REF]34] on the total flux are also shown in grey (ANTARES) and black (IC40+59) dashed lines. The righthand axis represents the inferred quasi-diffuse flux limit obtained assuming that each analysed sample represents an average burst distribution and that the annual rate of long bursts is 667 per year. fectively reduced.

The sensitivity of the ANTARES point source search to spatially extended objects such as the shell-type supernova remnant RXJ1713.7-3946 and the pulsar wind nebula Vela X were also studied. Assuming the neutrino flux to be related to the gammaray flux and taking into account the measured extensions, 90% C.L. upper limits on the neutrino flux were computed. They are respectively a factor of 8.8 (for RXJ1713.7-3946) and a factor of 9.1 (for Vela X) above the theoretical predictions (yet the most stringent ones for the emission models considered) [START_REF] Schulte ; Coll | Update on the ANTARES full-sky neutrino point source search[END_REF].

Multimessenger neutrino searches

Neutrino telescopes also develop specific strategies to look for neutrinos with timing and/or directional correlations with known (or potential) sources of other cosmic messengers. The limited space-time search window allows a drastic reduction of the atmospheric background, therefore enhancing the sensitivity to faint signals that would have remained undetected otherwise. This method has been used in ANTARES e.g. to put upper limits on the neutrino flux from known gamma-ray transient or flaring sources (see Secs. 5.1 and 5.2).

Alternatively, the occurrence of a special event in a neutrino telescope (such as the near-simultaneous arrival of two or more neutrinos from the same direction) could indicate that a highly energetic burst has occurred and may be used as a trigger for optical, X-ray, and gamma-ray follow-ups (see Sec. 5.3).

Coincident searches of neutrinos and gravitational waves are yet another example of a multi-messenger strategy that could help reveal hidden sources, so far undetected with conventional astronomy (see Sec. 5.4).

Neutrinos from GRBs

A first search for muon neutrinos in correlation with a sample of 40 GRBs that occurred in 2007, while the detector was still in a reduced, 5-line configuration, is described in [START_REF] Adrián-Martinez | [END_REF]. A second, more sophisticated search has been performed recently on the 2008-2011 data sample, using a set of 296 long GRBs representing a total equivalent live time of 6.6 hours [31]. The expected neutrino fluxes are calculated for each burst individually. In contrast to the previous analyses, this search has for the first time been optimised for a fully numerical neutrinoemission model dubbed as NeuCosmA, which includes Monte Carlo simulations of the full underlying photohadronic interaction processes [32]. The optimisation relied on an Extended Maximum Likelihood Ratio built with an a priori knowledge of the background as estimated using data off-window.

No coincident neutrino event is found within 10 • of any of the GRBs in the sample, and 90% C.L. upper limits are placed on the total expected flux according to the NeuCosmA model as well as to the commonly used model from [33], as shown in Fig. 6. Although not competitive with the IceCube limits [34], the ANTARES results concern a different sample of GRBs (only 10% of them being also in IceCube field of view), thus offering complementarity both in sky coverage and in energy range, as discussed in the previous Section.

Neutrinos from flaring sources

To further improve the sensitivity of the detector, specific searches have been performed during periods of intense activity of some sources, as reported by X-ray and/or gamma-ray observatories: -A search for neutrinos in coincidence with 10 flaring blazars chosen on basis of their Fermi-LAT light curve profiles was performed using data from 2008. By searching for neutrinos during the high state periods of the AGN light curve, the sensitivity to these sources was improved by about a factor of two with respect to a standard time-integrated point source search. For one of these AGNs (3C279), one neutrino event was found to be in spatial (0.56 • ) and time coincidence with a flare. The post-trial p-value is 10%, and the upper limits derived on the neutrino fluence for these AGNs are presented in [35]. A similar analysis is ongoing on the 2008-2012 period using data from Fermi and from the Cherenkov Telescopes HESS, MAGIC and VERITAS.

-A search for neutrinos in coincidence with X-ray or gammaray outbursts (as reported by the RXTE/ASM, Swift/BAT or Fermi/LAT satellites) of 6 microquasars was performed on 2007-2010 data. No neutrino events were detected in spacetime coincidence with those flares, and the inferred limits are close to predictions [36] which may be tested by ANTARES in the upcoming years, in particular for the two microquasars GX339-4 and CygX-3. More detail on these analyses can be found elsewhere in these Proceedings [START_REF] Losa | Transient Point Source Analyses in the ANTARES Neutrino Telescope[END_REF].

Follow-up of neutrino events

Thanks to the ability of its data acquisition system to filter events in real-time, ANTARES can also trigger an external detector on the basis of "golden" neutrino events selected by a fast, online reconstruction procedure [START_REF] Ageron | [END_REF]. These events can be neutrino doublets coincident in time (within 15 minutes) and space (within 3 • ) or single high-energy neutrinos (typically above 5 TeV). Neutrino events closer than 0.3 • to a local galaxy (within 20 Mpc) now also generate an alert.

This method was first proposed in [39]; the follow-up of interesting neutrino events possibly correlated to astrophysical transients can be done with fast-repositioning telescopes operating in different wavelength domains, from optical to X-rays and gamma-rays. The 2π sr instantaneous sky coverage and the high duty cycle of ANTARES are important assets for such neutrino-triggered multi-messenger programs.

Since 2009, alerts have been sent on a regular basis (≈25 per year) to a network of fast-response, wide-field (1.9 • × 1.9 • ) robotic optical telescopes (TAROT, ROTSE and ZADKO) and more recently also to the SWIFT/XRT telescope. The online event reconstruction allows a very short latency (< 1s) for the alert sending while maintaining a reasonable pointing accuracy (∼ 1 • ), which is compatible with the field of view of the telescopes. Due to the repositioning time of the telescope, the first optical image can typically be obtained after a few seconds. Successive sets of images can then be recorded minutes, hours and/or days after the alert, in order to follow the time profile of different transient sources (such as GRB afterglows or corecollapse supernovae) on appropriate time scales.

No optical counterpart has been observed so far in association with one of the ANTARES neutrino alerts, and limits on the magnitude of a possible GRB afterglow have been recently set [START_REF] Ageron | Search for νs from transient sources with the ANTARES telescope and optical follow-up observations[END_REF].

Joint searches for neutrinos and gravitational waves

Both gravitational waves (GW) and high-energy neutrinos (HEN) are cosmic messengers that can traverse very dense media and travel unaffected over cosmological distances, carrying information from the inner regions of astrophysical engines such as binary mergers or core-collapse supernovae. They could also reveal new, hidden sources that have not been observed by conventional astronomy, such as the putative choked or failed GRBs. This has motivated the development of joint searches between ANTARES and the last-generation GW interferometers VIRGO [START_REF] Acernese | [END_REF] and LIGO [42], which have been operating concurrently in the past few years. A review on the motivations and challenges of joint GW+HEN astronomy can be found in [START_REF] Ando | Multimessenger astronomy with gravitational waves and high-energy neutrinos[END_REF].

The first joint GW+HEN search uses concomitant data taken with the three detectors during 2007, during the VIRGO VSR1 and LIGO S5 science runs, while ANTARES was operating in a 5-line configuration. It consists in an event-per-event search for a GW signal correlating in space and time with a given HEN event considered as an external trigger. No coincident GW+HEN event was observed in this 2007 dataset, which allowed for the first time to place upper limits on the density of joint GW+HEN emitters [START_REF] Adrián-Martinez | [END_REF]. Those limits are presented in Fig. 7 and compared to the densities of mergers and corecollapse events in the local universe.

More stringent limits will soon become available from a second, optimized search using the data of the full ANTARES telescope in 2009-2010 taken in concomittance with the VIRGO VSR2/VSR3 and LIGO S6 science runs, where all the involved interferometers took data with improved sensitivities [START_REF] Pradier | Searches for coincident high energy neutrinos and gravitational wave bursts using the ANTARES and VIRGO/LIGO detectors[END_REF].

Particle physics with ANTARES

Beyond its astrophysics reach, a neutrino detector such as ANTARES also offers broad possibilities for particle physics.

Neutrino oscillations

At the lowest end of its energy sensitivity band, ANTARES can be used for neutrino oscillation studies involving the bulk of detected atmospheric neutrinos. A measurement of the neutrino mixing parameters in the atmospheric sector has been performed with the 2007-2010 data sample, for an overall live time of 863 days.

In the simplified, two-flavor oscillation framework in vacuum, the ν µ survival probability can be written as

P(ν µ -→ ν µ ) = 1 -sin 2 2θ 23 sin 2 16200 ∆m 2 23 cos θ E ν ( 2
)
where θ is the neutrino zenith angle (directly related to its path length across the Earth), ∆m 2 23 is the squared-mass difference of the eigenstates m 2 and m 3 and and θ 23 is the corresponding mixing angle. Considering values of ∆m 2 23 = 2.43 × 10 -3 eV 2 and sin 2 2θ 23 = 1, as measured by the MINOS experiment [START_REF] Adamson | [END_REF], the first oscillation maximum is found for vertical upgoing neutrinos at E ν = 24 GeV, which implies a suppression of vertical upgoing muon neutrinos of such energies.

The oscillation parameters are inferred by fitting the event rate as a function of the ratio of the neutrino energy to the reconstructed zenith angle, as can be seen from Eq. 2. At such low energies, the neutrino energy can be estimated from the observed muon range in the detector. Assuming maximal mixing, [48], MINOS (blue) [START_REF] Adamson | [END_REF] and SuperKamiokande (magenta) [50] for comparison. a squared-mass difference of ∆m 2 = (3.1 ± 0.9) × 10 -3 eV 2 is found [47]. The corresponding contour plot for different C.L.s is shown in Fig. 8 and compared to measurements by other dedicated experiments.

The compatibility of the ANTARES measurement with the world data gives confidence in the understanding of the detector, even close to the detection threshold. This measurement also opens the way to other studies of neutrino fundamental properties with neutrino telescopes. In particular, it has been recently pointed out that underwater/ice Cherenkov detectors could have competitive capabilities for the measurement of the neutrino mass hierarchy with atmospheric neutrinos (see e.g. [51,52]). A feasibility study for such a measurment with a deep-sea detector, dubbed ORCA (Oscillation Research with Cosmics in the Abyss), is currently being performed in the framework of the KM3NeT Collaboration and briefly discussed elsewhere in these Proceedings [START_REF] Bagley | KM3NeT Collaboration[END_REF].

Dark matter searches

ANTARES can also be used for searches of dark matter (DM) in the popularly advocated form of WIMPS (weakly interacting massive particles). Such particles get gravitationally trapped in massive bodies such as the Earth, the Sun or the Galactic Center, where they can subsequently self-annihilate, producing neutrinos with energy comparable to the WIMP mass. An excess of neutrinos in the 10 GeV -1 TeV energy range and in the direction of one such body could therefore provide an indirect signature of the existence of dark matter.

A search in the direction of the Sun has been performed using ANTARES data recorded in 2007 -2008, for a total live time of 294.6 days [53]. For each WIMP mass and annihilation channel considered, the quality cuts were designed as to minimize the average 90% C.L. upper limit on the WIMP-induced neutrino flux. No excess above the atmospheric background was found, yielding upper limits on the spin-dependent and spin-indepedent WIMP-proton cross-section; the latter ones are shown in Fig. 9 . An improvement factor of 3 to 5 in sensitivity is anticipated in the next update of this analysis. More detail on dark matter searches with ANTARES can be found elsewhere in these Proceedings [START_REF] De | Results and prospects of dark matter searches with ANTARES[END_REF].

Searches for exotics

Two searches for exotic particles have been performed so far: -A search for magnetic monopoles: such putative relic particles should leave a clear signature in a neutrino telescope as the intensity of light they emit is O(10 4 ) times greater than the conventional Cherenkov light, depending on their velocity β. A search for relativistic upgoing monopoles performed with 2007 -2008 data yielded one observed event, compatible with the background hypothesis. The corresponding limits on the magnetic monopole flux for relativistic velocities 0.625 ≤ β ≤ 0.995 are in the range 1.3 -8.9 × 10 -17 cm -2 s -1 sr -1 [START_REF] Adrián-Martinez | [END_REF].

-A search for nuclearites: these hypothetical massive particles made of lumps of u,d and s quarks could be present in the cosmic radiation either as relics of the early Universe or as debris of supernovae or strange star collisions [56]. They could be detected in a neutrino telescope through the blackbody radiation emitted by the expanding thermal shock wave along their path. A dedicated search, optimized for nonrelativistic, downgoing nuclearites, was performed with 2007-2008 data. As for monopoles, the light yield in the detector is expected to be much greater than the muon Cherenkov emission for nuclearite masses larger than a few 10 13 GeV. No significant excess of such events was found in the data, yielding upper limits on the flux of downgoing nuclearites between 7.1 × 10 -17 and 6.7 × 10 -18 cm -2 s -1 sr -1 , for masses 10 14 ≤ M N ≤ 10 17 GeV.

Conclusions

The ANTARES detector is currently the largest neutrino telescope that has ever operated in the Northern Hemisphere, and the first to be deployed in the deep sea. Since the connection of its first detection lines in 2007, it has been continuously monitoring the Southern Sky with unprecedented sensitivity, in particular to the TeV sources lying in the central region of our Galaxy. An extended multi-messenger program complements and expands its astrophysics reach. ANTARES also paves the way to a multi-km 3 neutrino telescope in the Northern Hemisphere, KM3NeT, to complement the IceCube detector deployed at the South Pole.

Beyond astrophysics, searches for dark matter and exotic particles are also performed, and ANTARES has open the way to the study of neutrino fundamental properties with neutrino telescopes. More competitive results are expected in the future as ANTARES will continue taking data at least until the end of 2016, when it gets eventually superseded by the next-generation KM3NeT detector.

Figure 1 :

 1 Figure 1: The ANTARES detector configuration, showing the 12 lines hosting standard storeys with optical modules (in black), as well as the instrumented line (IL07). The red storeys on L12 and IL07 correspond to the AMADEUS acoustic detection modules.

Figure 2 :

 2 Figure2: Zenith-averaged atmospheric neutrino spectrum multiplied by E3.5 ν . The ANTARES measurement (black squares) is shown together with the results from AMANDA-II (red circles)[12] and and IceCube 40 (blue triangles)[13]. The black, solid line represents the conventional flux prediction from the Bartol group[14] ; the red and blue dashed lines include two prompt neutrino production models from[15] and[16], respectively. The red region corresponds to the older measurement from AMANDA-II[17], and the blue one the IC40 update from[18].

Figure 3 :

 3 Figure3: Updated ANTARES upper limit at 90% C.L. for an E -2 spectrum, compared to 90% C.L. limits from other experiments, including IceCube in its 40[18] and 59-string[20] configurations. Also shown are the Waxman-Bahcall benchmark flux predictions[21].

Figure 4 :

 4 Figure4: Upper limits on the neutrino flux from the Fermi bubbles assuming different energy cutoffs at the source: no cutoff (black solid), 500 TeV (red dashed), 100 TeV (green dot-dashed), 50 TeV (blue dotted), together with the theoretical predictions for the case of a purely hadronic model (the same colours, areas filled with dots, inclined lines, vertical lines and horizontal lines respectively). The limits are drawn for the energy range where 90% of the signal is expected.

Figure 5 :

 5 Figure 5: ANTARES upper limits (at 90% C.L.) on the E -2 flux from the selected candidate sources (blue dots), and median sensitivity (blue line) as a function of the declination. Results from other experiments are also shown, in particular the IceCube sensitivity and flux limits with combined data from the IC40 and IC59 configurations.

Fig. 5

 5 summarizes the outcome of the above searches. The obtained limits are the most stringent ones for a large part of the Southern Sky for sources in the TeV -PeV energy range. IceCube limits in that region of the sky are indeed relevant only above ∼ 1 PeV, where the downgoing muon background is ef-

Figure 7 :

 7 Figure 7: GWHEN 2007 astrophysical limits on the density of two GRB populations thought to be potential GWHEN emitters, as compared to the local short/long GRB rates , to the rate of neutron star-neutron star mergers and of type II and type Ib/c supernovae. Also shown is the potential reach of future analyses with ANTARES (12 lines) and KM3NeT.

Figure 8 :

 8 Figure 8: 68% and 90% C.L. contour of the neutrino oscillation parameters ∆m 2 23 and sin 2 2θ 23 as derived from the fit of the reconstructed E/ cos θ distribution. The position of the best fit is indicated by the red triangle. The filled regions show 68% C.L. contours obtained by K2K (green)[48], MINOS (blue)[START_REF] Adamson | [END_REF] and SuperKamiokande (magenta)[50] for comparison.

Figure 9 :

 9 Figure 9: Solid lines are the ANTARES 90% C.L. upper limits on the spindependent WIMP-proton cross-section inside the Sun, as a function of the WIMP mass in the range M WI MP ∈ [10 GeV; 5 TeV], for the three annihilation channels: b b (green), W + W -(blue) and τ + τ -(red). Also shown are the limits obtained from other indirect DM search experiments: SuperKamiokande 1996-2008 (dotted lines), Baksan 1978-2009 (dash-dotted lines) and IceCube 79 (dashed lines). The black lines are the limits obtained from the direct search experiments KIMS 2007 (dot-dashed) and COUPP 2011 (dashed).


Keywords: Adaptation, context, mobile learning, semantic web

Recent developments on mobile devices and wireless technologies enable new technical capabilities for the learning domain. Nowadays, learners are able to learn anywhere and at any time. The dynamic and continually changing learning setting in learner's mobile environment gives rise to many different learning contexts. The challenge in context-aware mobile learning is to develop an approach building the best learning content according to dynamic learning situations. This paper aims to develop an adaptive system based on the semantic modeling of the learning content and the learning context. The behavioral part of this approach is made up of rules and metaheuristics to optimize the combination of pieces of learning content according to learner's context.

I. INTRODUCTION

OBILE learning (m-learning) is a natural extension of e-learning. It has the potential to make learning even more widely available, thanks to the rapid development on wireless technologies and the widespread use of mobile devices.

In the professional environment, training employees is at the heart of the concerns of human resources. Indeed, developing employees' skills has become a critical issue because of the continuing evolution of companies, to make sure that employees gain new knowledge. At the same time, there is an increasing number of employees working outside the office. Given the speed of business today and the problems due to information overload, employees require information and knowledge just when they need it, in their desired format and on the device of their choice, particularly as the use of mobile devices has become second nature. This is why the traditional training paradigm is shifting to just-in-time transmission of knowledge and information to boost employee performance. With personalized m-learning, an organization can deliver targeted pieces of content that help an employee on the spot, rather than heavyweight classroom or just computer-based training.

The dynamic and continual changing of learner settings in a mobile environment and the diversity of learner's characteristics as well as mobile devices and networks, give Manuscript received September 25, 2013. This work is partially funded by CrossKnowledge (www.crossknowledge.com), the European leader in distant learning solution.

F. Soualah Alila is with LE2I, UMR CNRS 6306, University of Bourgogne, Dijon, France (fayrouz.soualah-alila@checksem.fr) and CrossKnowledge (fayrouz.soualah-alila@crossknowledge.com).

F. Mendes is with LE2I, UMR CNRS 6306, University of Bourgogne, Dijon, France (florence.mendes@checksem.fr).

C. Nicolle is with LE2I, UMR CNRS 6306, University of Bourgogne, Dijon, France (christophe.nicolle@checksem.fr).

rise to different learning situations and therefore requires personalization for different cases. The search of educational content in a m-learning system can be defined as an activity whose purpose is to locate and deliver educational content to a learner according to his needs and the environment in which he is. So far, the learning environment was either defined by an educational setting (work, trainer, etc.), or imposed by the educational content (the learner must arrange his environment to receive training). In our approach we change the paradigm where the system adapts learning flow to the context of the learner.

The term context appeared the first time in 1994 [START_REF] Schilit | Disseminating active map information to mobile hosts[END_REF]. Here, location, identity, time, environment, and mobile technology have been suggested as primary types of context [START_REF] Brown | The stick-e document: a framework for creating context-aware applications[END_REF] [3] [4] [START_REF] Dey | Context-aware computing: the Cyber Desk project[END_REF]. Many previous studies in mobile computing provided various definitions of context. A commonly used one is: "any information that can be used to characterize the situation of an entity participating in the interaction between a user and a system" [START_REF] Dey | Providing architectural support for building context-aware applications[END_REF]. In the case of m-learning, location, time, identity and the mobile technology used to learn are the primary context types for characterizing the situation of a particular learner. These context types not only answer the questions of who, what, when, and where, but also act as indications for other sources of contextual information. For example, given a learner's identity, we can acquire many pieces of related information such as user tasks, roles, beliefs, desires, objectives, relationships with other users in the environment, etc. Furthermore, context can be information about devices (smartphone, tablet, connectivity, etc.), time (time of day, day of week, holidays, etc.), localization (in train, at home, at work, public place, etc.) and physical environment (lighting, noise level, etc.) since this may change the way users interact with any device they may be using. This set of information is useful to adapt the interaction and generally adapt the application behavior to the learner situation.

This paper presents ongoing research on an adaptive context-aware m-learning system that aims to offer a new approach for designing and adapting learning content as part of industrial training. This approach take into account the context of mobility related to the industrial training environment. To achieve this goal, it is necessary to improve the current e-learning systems with adaptation techniques to support the generation and management of m-learning environments so that, given a specific learner context, the system is able to suggest the most suitable learning activities to be accomplished in that specific situation. The objective of adaptive educational systems is to adapt the presentation of knowledge to learner. These systems have become very popular since 1990s, to allow users to access to personalized information [START_REF] Brusilovsky | Methods and techniques of adaptive hypermedia[END_REF]. In e-learning, learning content has witnessed high dropout rates as learners become increasingly dissatisfied with contents that do not engage them [START_REF] Frankola | Why online learners dropout[END_REF]. Such high dropout rates and lack of learner satisfaction are due to the "one size fits all" approach that most current learning content developments follow, delivering the same static learning experience to all learners, irrespective of their prior knowledge, experience, goals and context. Adaptive educational solutions have been used as possible approaches to address this dissatisfaction by attempting to personalize the learning experience for the learner. This learner empowerment can help to improve learner satisfaction with the learning experience.

In the recent years, many initiatives aimed at building educational resources to share and reuse them, have emerged. Learning Management Systems (LMS) are based on techniques of collaborative work, where communication processes come to support learning. These platforms should dispose of well-structured and organized pedagogical warehouses. Explaining a training course around items of knowledge offers advantages and opportunities to individualize training. In this case, the contribution of semantic web technologies is significant. We suggest the use of ontologies to allow the modeling of complex networks knowledge.

III. CONTEXT-BASED ADAPTATION MECHANISM

The aim of our adaptation mechanism described below is to satisfy learners' needs when connecting and interacting with the system.

Traditionally, adaptation systems in learning domain deal with applications that have two types of entities, which are users and items. To provide adaptation in a mobile environment incorporating contextual information, we propose a multidimensional adaptive model (MD model) based on the multiple dimensions of context (spatial, temporal, environment and device dimensions) and, therefore, extends the classical two-dimensional User × Items paradigm to a multi-dimensional paradigm.

To develop such a m-learning system, we have to bridge the gap of two different levels of heterogeneity: semantic heterogeneity and heterogeneity of use between the current design of the learning content and the willingness to adapt these resources to different learner profiles and context. On one hand, in e-learning, resources are designed and developed by different organizations and trainers, usually constituting semantically autonomous and heterogeneous data sources. Therefore, interoperability between these resources is complex: systems should be adapted to determine the required syntax and resource specific terminology to be able to combine relevant content and construct the final training result. On the other hand, learners have different prior knowledge and objectives and are located in different learning environments (heterogeneity of time, learning time, visual support, ambient noise, etc.). By having a better knowledge of these learners and of their learning environment, that we can efficiently query on pedagogical strategies, and set them up to respond to everyone needs.

To bridge this gap, our system is made up of a semantic level and a behavioral level (see Fig. 1.): the semantic level aims to express semantic characteristics of learning contents and learner context (what, how, when, where, via which device, etc.). Semantic modeling consists in describing the meaning of data by experts. This transfer of knowledge from experts to the computer enables our system to perform more intelligent reasoning according to changing constraints. The behavioral level is an adaptive system designed to overcome the problem of information overload by providing users with only the most relevant information. Here adaptation must be made considering the learner context while maximizing its benefit. The behavioral level contains the best learner practices (transformed in a set of logical rules) and algorithms of combinatorial optimization.

Combining the semantic and behavioral levels allows us not only to generate learning content, but also to generate learning methods adapted to the context of each learner. In what follows, we present these two levels. In the following, we focus respectively on the semantic level and behavioral level of our proposed architecture.

A. Semantic Level

The semantic web, envisioned as an extension of the current web [START_REF] Berners-Lee | The Semantic Web[END_REF], was proposed to provide enhanced access to information based on the use of machine-processable metadata annotating the web resources. To facilitate this process, RDF1 (Resource Description Framework) and OWL2 (Web Ontology Language) have been developed as standard formats for the sharing and integration of data and knowledge, the latter in the form of rich conceptual schemas called ontologies. Ontologies offer a way to cope with heterogeneous representations of resources on web and their interoperability. An ontology representing a model of a specific domain can be used as a unifying structure for giving information a common representation and semantics. Ontologies are becoming very popular due to their promise to allow a shared and common understanding of a domain that can be communicated between people and applications.

Realizing the potentials of semantic web technologies in education, initiatives using semantic web technologies in elearning started in late 90's [START_REF] Jin | An ontology-aware authoring tool -Functional structure and guidence generation[END_REF] [START_REF] Nejdl | Edutella: Searching and annotating resources within an RDF-based P2P network[END_REF]. The major argument for this is that the availability of massive information is of no use, unless the right information in the right context with the right level of details to the right person at the right time [START_REF] Holger | The topic maps handbook[END_REF] is delivered.

In the recent years, to build an approach of quality and to make learning platforms and their contents interoperable, international standards are developing in educational technologies 3 . Standardization initiatives do not seek to standardize teaching methods or multimedia technologies used. They just aim to set up rules that will help in sharing and reusing educational modules. These standards are still in constant evolution. The IEEE proposes the LOM (Learning Object Metadata) standard 4 . This standard specifies a conceptual data schema that defines the structure of a metadata instance for a Learning Object (LO). For this standard, a LO is defined as any entity, digital or non-digital, that may be used for learning, education or training. ADL (Advanced Distributed Learning) 5 has recognized the need for a model that aims to make learning platforms and their content interoperable. This model is the standard SCORM 6(Sharable Content Object Reference Model) which has become a major asset for distance learning platforms. It integrates a set of related technical standards, specifications, and guidelines designed to combine LOs around a package accessible, interoperable and reusable on other SCORM platforms.

To benefit from both the adaptive qualities of ontologies and the high scale interoperability brought by SCORM, we opt for an approach where the adaptive system output will be packaged as SCORM content. However, the learning content is modelled, indexed and manipulated by the system thanks to ontological models. This ontology is called domain ontology of m-learning. It presents the main concepts related to m-learning domain. It is largely based on concepts of LOM schema organization to describe LOs. Using a LOM model for indexing LOs enables a better understanding of learning contents, and therefore facilitates their descriptions. We followed some rules described in [START_REF] Ouafia | LOMonto : Une ontologie pour l'indexation d'objets pedagogiques[END_REF] to transform this schema into a set of ontological concepts and relations.

The second step to define the semantic level is modeling mobile learning context. We believe that ontologies are key requirements for building context for two reasons: first, a context can be considered as a specific kind of knowledge; and as such, ontologies are the state of the art for an efficient modification of context. Ontology-based models of context allow representing complex context knowledge and provide a formal semantic to context knowledge, which supports the sharing and integration of context information [START_REF] Villalon | A context ontology for mobile environments[END_REF]. Second, a common ontology enables knowledge sharing in an open and dynamic distributed system [START_REF] Peters | Using semantic networks for knowledge representation in an intelligent environment[END_REF]. To describe context, we model concepts related to different context dimensions: 1) Temporal dimension. In this dimension we try to answer two questions: "when?" to define the exact temporal localization of an event, and "how long?" to define the duration of an event. With the concept Laps we can measure the duration of an event (15 min, 2 days, etc.).

With the concept Time we can determine when an event should take place. We distinguish two sets of temporal localization type: Abstract_Time (in the morning, at the week-end, monday, etc.) and Concrete_Time (at 10 am, the 12/10/2012, etc.). 2) Spatial dimension. This dimension refers to a position or a place. A position is a Concrete_Location and refers to Geographic_Coordinates. A place is an Abstract_Location (at home, in a restaurant, in a train, etc.). We have also collected some characteristics of environment such as Location_Type (dynamic, public, etc.) in this dimension, and Location_Properties (comfort, noise level, etc.). 3) Device dimension. Information related to devices is generally divided into three sets depending on the type of information they provide [START_REF]FIPA Specifications[END_REF]: General_Description, Hardware_Description and Software_Description. General_Description contains basic information related to a device such as Device_Name, Device_Type (smartphone, tablet, etc.), etc. Hardware_Description regroups hardware properties of the device such as Connectivity, etc. and Software_Description regroups software properties of the device such as OS. 4) User dimension. This dimension regroups all data concerning LMS's user. To support the interactions of the various users intervening during the training and to propose LOs corresponding to their role, we suggest quoting them with the concept User_Type (learner, teacher, author, etc.). These users are described by a collection of information. A set of General_Information such as First_Name, Last_Name, Birth_Date, etc. is assigned to any type of user. Further, a set of Specific_Information is assigned to a specific user in the system. For example, an Author is assigned a Biography and a Reputation, whereas a Learner is assigned Goals and Centers_Of_Interest.

Once the content of the ontology is determined, we must consider the representation formalism we have to use to model it. RDF and RDFS are not powerful enough to define the complex relationships that exist between LOs and context elements. The proposed OWL recommendation actually consists of three languages of increasing expressive power: OWL-Lite, OWL-DL and OWL-Full. They are basically very expressive description logics (DLs) with RDF syntax. DLs are a family of knowledge representation languages that are widely used in ontological modeling. An important practical reason for this is that they provide one of the main underpinnings for the OWL. To model our ontology we need a set of constructors of DL. In particular, the DL SHOIQ, corresponding to OWL-DL, is used to define all complex relations and concepts of our model.

Once the ontological schema is achieved, we integrate data from CrossKnowledge database using Talend Data Integration tools 7 . Then, we use the OWLIM Sesame middleware 8 as a triple store, to store the corresponding ontology. The mobile context-aware ontology is stored in the triple store and is connected to the learning repository containing the physical data of learning modules.

B. Behavioral Level

To implement contextualized learning, each learner's context is saved into the m-learning ontology. Given the learner's activity at a given time, location, learning style, and the course of study; the learner can be offered corresponding LOs. Using the Semantic Web Rules Language (SWRL) 9 , specific rules are written. Then a reasoned tool can infer the list of LOs that will be offered to a given learner. These rules are defined by experts in the learning domain. As these experts do not necessarily have the technical competences to write SWRL rules, we developed a rule generator to easily manipulate m-learning ontology concepts and generate automatically SWRL rules. This rule generator is provided as a web application to experts.

When connected to the mobile learning system, the platform should propose an optimized panel of LOs corresponding to the current context of the learner. Optimization algorithms may improve various objectives such as minimizing learning time, minimizing the number of nonrequired LOs for training, maximizing learner satisfaction, etc. If each LO was accessible on every learning device, it would be easy to choose at any time the best support for training according to the learner's context. Actual cases that we studied showed us, on the contrary, a great heterogeneity of LOs available according to different devices. Training has different structure and different duration, depending on the device used. This forbids changing learning devices while training without risking redundancy of some LOs. In our case, the problem can be reduced to a variant of the wellknown shortest path problem called the multimodal shortest path problem.

This challenging problem extensively studied in recent years [START_REF] Gräbener | Time dependent multiobjective best path for multimodal urban routing[END_REF], consists of rallying a point B from a point A by taking various means of transport, with different traveling time, routes and transportation costs. We can make approximation considering that an optimized training is equivalent to the shortest path to join learning objectives by different means of transport (here different learning devices). Just like two paths may follow different routes depending on means of transport, two training courses may include different LOs. Similarly, just as traveling time between two points depends on the means of transport, the time needed to learn a set of LOs may vary depending on the broadcasting device. Finally, the availability of each learning device varies over time, like the availability of means of transport.

This problem cannot be resolved by an exact method, because of the exponential growth in complexity depending on the size of the problem; we propose to use metaheuristics in order to ensure the achievement of a solution in a reasonable time. The metaheuristics used must be adapted to take advantage of rules described by learning experts. We therefore link the semantic modeling techniques in the training offer and user profile with powerful algorithms derived from combinatorial optimization. The objective is to provide an adaptive system that maximizes the availability of m-learning. Various heuristics have been proposed for solving the problem of multimodal shortest path search [18] [19].

IV. CONCLUSION

In this paper, propose an approach for context-based adaptation for m-learning, making use of learning practices already deployed in e-learning systems and adopting them in m-learning. Our system is built around an ontology that both defines the learning domain and supports context-awareness. The use of this ontology facilitates context acquisition and enables a standard-based learning object metadata annotation. We also use a set of ontological rules to achieve personalized context-aware LOs by exploiting knowledge embedded in the ontology. The future adaptive system will offer an optimized panel of LOs matching with the current context of the learner.

In future work, we are going to compare the effectiveness of some heuristics for solving the problem of multimodal shortest path search with a metaheuristic inspired from simulated annealing that has been already successfully used in the TourismKM project [START_REF] Picot-Clémente | TOURISM-KM, A variant of MMKP applied to the tourism domain[END_REF].

MA

  Context-Based Adaptation In Mobile Learning F. Soualah Alila, F. Mendes, C. Nicolle, LE2I, UMR CNRS 6306 II. ADAPTIVE EDUCATIONAL SYSTEMS

Fig. 1 .

 1 Fig. 1. M-learning context-based adaptive system architecture.

http://www.w3.org/RDF/

http://www.w3.org/2004/OWL/

http://www2.tisip.no/quis/public_files/wp5-standards-for-elearning.pdf

http://ltsc.ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf

http://www.adlnet.org/

http://scorm.com/scorm-explained/

http://www.talend.com/

http://www.ontotext.com/owlim

http://www.w3.org/Submission/SWRL/

ACKNOWLEDGMENT

Authors thank Florian Orpelière, computer engineer in the LE2I team, for his help in the development of software tools.


Daniel Kermer

Guillaume Fromentin

Olivier Loutan

Kossi Agebebiade

K Agbebiade

J Giovanola
email: jacques.giovanola@epfl.ch

Micro-orthogonal Cutting of Metals

Industrial application of micro-milling faces several scientific, technological and economic issues. To address these issues, we have developed a micro-orthogonal cutting facility in which chip thickness can be controlled to within a micron and cutting forces can be measured. This paper presents the facility and preliminary results of micro-orthogonal cutting on copper, in the form of span observations, cutting ratio measurements and estimates of shear angles and cutting forces.

Introduction

Micro-cutting and in particular micro-milling of ductile metals and more brittle materials has attracted significant attention for its range of potential practical applications in manufacturing of high technology, miniaturized products [START_REF] Masuzawa | State of the Art of Micromachining[END_REF]. Its more wide-spread application hinges upon resolving several scientific, technological and economic issues. Economic and technological issues include the lack of 1) high speed high precision spindles with suitable tool spanning systems capable of achieving high speed cutting conditions and permitting an economical material removing rate, 2) tools with appropriate cutting edge quality and properties, and 3) well documented application databases to select tool geometry and cutting conditions for specific materials. Scientific issues involve among other questions 1) the understanding of the role of characteristic material microstructural dimensions and of cutting edge radius on the cutting size effect and 2) the integration of material damage and separation processes in models of metal cutting.

To address these issues, we are implementing an integrated approach covering from micro-modelling of fracture under predominantly shear loading to developing high speed, high precision spindles. In this paper, we present preliminary results of a basic investigation of micro-orthogonal cutting of metal aiming at 1) understanding and modelling micro-cutting processes, 2) identifying size effects by comparison with macro-cutting results and 3) assessing the transferability of macro-cutting data to micro-cutting.

2

Micro-orthogonal cutting facility. We performed orthogonal cutting experiments on copper specimens with the facility of Figure 1, varying depth of cut h c (5 and 10 μm), rake angle γ (20°, 0°, -20°) and cutting speed v c (440, 660, 880 mm/s). For these experiments, we characterized the morphology and dimensions of the span and the roughness of the specimen surface;

we determined the cutting ratio and estimated the cutting forces. In separate experiments we also performed proof-of-concept measurements of the Z thrust force and photographed the formation of the span. Figures 2 to 4 and Tables 1 and2 illustrate the results of these experiments. The thickness of material removed h c varies at most by 1 μm, independently of the cutting parameters investigated, showing that we have a good control of the depth of cut. The resulting roughness of the specimen surface does not follow any clear trend with varying cutting parameters and ranges between 0.39 and 0.94 (Table 1). Microspans look rather similar to their macroscopic counterparts, except for their small size. Spans produced by a tool with γ = 20° are strongly curled up whereas spans associated with the other 2 rake angles are almost flat (Figures 2 and3). The thickness of the cut span h s can vary by several μm along its length (Figure 2b). The reason for this variation is not known yet.

Spans vary in width too (Figure 3a). Whereas the surface sliding on the rake face is rather smooth -showing only the grooving pattern caused by the roughness of the Proceedings of the euspen International Conference -Zurich -May 2008 rake face -the free surface is strongly serrated with the size and the spacing of the serrations varying along the span length, and also as a function of cutting parameters (Figure 3a). The serrations or folds are more pronounced for a negative rake angle and their structure changes with cutting speed (Figure 3b).

Figure 1

 1 Figure1shows the micro-orthogonal cutting facility (a), the specimen (b), and the cutting tool (c). The X-axis consists of a hydrostatic linear guideway with a electrical linear motor. The detailed kinematic, static and dynamic characterizations of the system are still underway. Available results can be summarized as follows. The transverse stiffness of the X-axis is greater than 250 N/μm for a supply pressure of 10 bars. The stroke is 100 mm with a maximum speed of around 1.2 m/s (over 20 mm) with peak accelerations of approximately 3g. Estimates and measurements indicate maximum dynamic yaw and tilt displacements at the table edges on the order of ± 3 μm for an acceleration of 3 g and a supply pressure of 10 bars. A piezo-actuator controls the Z-axis position (± 250 nm) and the depth of cut by means of an Eddy current sensor and a feedback control loop. Currently the control of the span thickness is better than ± 1 μm, all error sources included. The Z-axis also provides a measurement of the Z cutting force through the control voltage of the piezo actuator or a separate force sensor. Another sensor, built, but not tested, will measure X cutting forces.

Figure 1 a

 1 Figure 1 a) Micro-orthogonal cutting facility b) specimen c) WC tool

Figure 2 SEM

 2 Figure 2 SEM photographs of a copper span a) overall view, b) edge view showing thickness variations (h c < 5 μm, γ = 20°).

Table 1 :

 1 Roughness R a and cutting depth accuracy as a function of cutting parameters

	Cutting depth 5 μm	Cutting depth 10 μm
	Rake angle	20°	Rake angle	20°
	Cutting speed, mm/s 880 660 440 Cutting speed, mm/s 880 660 440
	R a , μm Error on h c , μm	0.66 0.57 0.47 R a , μm -0.1 0.1 -0.3 Error on h c , μm	0.51 0.42 0.57 -1.0 0.2 -0.9
	Rake angle	0°	Rake angle	0°
	Cutting speed, mm/s 880 660 440 Cutting speed, mm/s 880 660 440
	R a , μm Error on h c , μm	0.72 0.78 0.58 R a , μm -0.7 -0.6 -1.0 Error on h c , μm	0.39 0.46 0.59 0.2 0.0 0.3
	Rake angle	-20°	Rake angle	-20°
	Cutting speed, mm/s 880 660 440 Cutting speed, mm/s 880 660 440 R a , μm 0.68 0.55 0.55 0.94 0.49 0.60 R a , μm Error on h c , μm -0.8 -0.8 -0.3 Error on h c , μm 0.6 0.9 0.1

Proceedings of the euspen International Conference -Zurich -May 2008

Summary and perspectives

An experimental platform for investigating micro-cutting has been developed and its performances have been partially validated. Encouraging preliminary cutting data have been obtained for copper. Work continues to characterize fully dynamic behaviour and precision of the facility, and to integrate reliable sensors for measuring cutting forces. Additional work will focus on developing cutting tools with sharper and more regular cutting edges and on demonstrating the importance of material separation processes in controlling cutting forces.