C Wheldon

Tz Kokalova

W Von Oertzen

B Gebauer

H G Bohlen

Ch Schulz

S Thummerer

G Efimov

C Beck

D Curien

P Papka

Particle-γ coincidences and coplanarity in the 32 S + 24 Mg binary reaction

Keywords: numbers: 23.20.Lv (Gamma transitions and level energies), 25.70.Hi (Transfer reactions) NUCLEAR REACTIONS 24 Mg + 32 S, E = 165.4 MeV; measured particle-γ coincidences, Doppler-shift correction, binary reactions, out-of-plane angular correlations, ternary fission Q 0 -values

The reaction 32 S (165.4 MeV) + 24 Mg is studied using the binary reaction spectrometer (BRS) coupled to the Euroball germanium array. Particle-particle-γ and particle-γ-γ coincidences have been examined. The Z-identification, position and energy information for binary reaction products are shown together with the Doppler-shift corrected γ-rays emitted from the fragments. Recent reports of evidence for hyper-deformation from angular correlations in similar data are also investigated. Analogous out-of-plane angular correlations are observed but attributed to reactions with the target contaminants 16 O and 12 C.

Introduction

In-beam γ-ray spectroscopy has been dominated by the use of fusion-evaporation reactions populating a few nuclei to high angular momentum and with relatively large cross-sections. To reach the shortest-lived states, thin targets are used and the γ-rays emitted from the recoiling product nuclei are corrected for the Doppler shift, assuming a particular forward momentum. In contrast, binary reactions, utilising the same beam and target nuclei, can access different regions of the N -Z plane and different excitation-energy regimes. However, the cross-sections for these reactions are generally smaller and due to the type of states populated (low-spin states) the γ-ray yield is dramatically lower, falling off sharply with multiplicity. Furthermore, the products are spread over a much larger proportion of the available solid angle (the products from fusion-evaporation reactions continue in the forward, approximately 0 • , direction) and can have much larger recoil velocities compared with those of fusion-evaporation reactions, making a precise Doppler-shift correction of the subsequently emitted γ rays more difficult. The advent of radioactive beams providing exotic ions has also brought about a renaissance in the use of binary/transfer reactions (see e.g. Refs. [1][2][3][4]) since transferring a small number of nucleons too/from an already exotic ion can create otherwise hard-to-reach species. To successfully exploit such reactions it is necessary to precisely identify the angles and energies of the recoiling products and also to identify the products themselves with good efficiency. Here, we present results from an experiment using the Berlin Binary Reaction Spectrometer (BRS) coupled to the Euroball germanium array. Particle-γ coincidences are measured for binary reactions, completely suppressing the fusion-evaporation events. The yields are discussed and compared to recent, related work [5,6], that claim evidence for hyper-deformed shapes in 60 Zn. An independent analysis/interpretation of the present data (particle information only), similar to that for the 60 Zn data, is to appear as Ref. [7].

Experimental method

An experiment was performed in April 2003 at the Vivitron Tandem facility of the IReS at Strasbourg. A 165. 4 MeV beam of 2-3 pnA, 32 S ions was used to bombard a 2×120 µg cm -2 24 Mg target each foil being backed with 10 µg cm -2 carbon. The target was located at the centre of the Euroball IV germanium detector array. In this case the array comprised 209 Ge-crystals: 15 cluster detectors at backward angles, each comprising 7 Ge crystals in one Compton suppression shield, and 25 clover detectors close to 90 • , each comprising 4 Ge crystals within one suppression shield. The forward-angle tapered germanium detectors of Euroball were replaced by the two-arm BRS detector. This set-up [START_REF] Gebauer | Proc. Int. Conf. on the future of nuclear spectroscopy[END_REF][START_REF] Gebauer | Achievements with the Euroball spectrometer[END_REF] is shown in Fig. 1. Three trigger conditions were set, requiring:

(i) ≥2-Ge detectors to fire; (ii) ≥1 BRS detector; or (iii) ≥1 BRS and ≥1 Ge detector.

Typical rates for the three triggers were: (i) 11 kHz (94%); (ii) 500 Hz (4.3%); and (iii) 200 Hz (1.7%). For both the BRS-gated and Euroball-only data, the Ge multiplicities peak at one, but for the Euroball-only data multiplicities two and three are relatively much larger than for the BRS events. Radioactive sources of 152 Eu, 133 Ba and 56 Co at the target position were used to obtain energy and efficiency calibrations for the germanium detectors. Note that the last calibration point for the germanium crystals is therefore at 3.45 MeV. Beyond this, energies and efficiencies are extrapolated leading to increased uncertainties. However, the 16 O peaks with published values of 6128.63±0.04 and 6915.5±0.6 keV are observed at 6130±3 and 6920±6 keV respectively, after performing the Doppler-shift correction. Similarly, the 12 C, 4438.0±0.3 keV energy [START_REF] Ajzenberg-Selove | [END_REF] is measured here at 4437±2 keV. Due to the large number of germanium crystals, the calibration was automated using a custom written routine [CW] based on the RadWare [11] analysis package. Output from the RadWare peak-finding routines was used to systematically calculate linear calibration coefficients by checking all possible pairs of identified peaks against all possible pairs of known source energies. These coefficients were then used to test the match between the remaining peaks and source energies not used in the calculation of the coefficients. The 'best fit' coefficients were taken which matched the largest number of source peaks having the lowest χ 2 . A similar procedure is used for the online calibration at the RISING setup at GSI [12].

Overview of the BRS

Here, a few details of the BRS detector system are given. The BRS was designed and built in Berlin by B. Gebauer et al. [START_REF] Gebauer | Proc. Int. Conf. on the future of nuclear spectroscopy[END_REF][START_REF] Gebauer | Achievements with the Euroball spectrometer[END_REF]. The complete technical specification will appear as Ref. [13]. Some details can also be found in Refs. [14][15][16][17]. The first stage of each BRS detector comprises a low-pressure multi-wire chamber (MWC) providing the position (x and y) information for the incoming ions. The second stage consists of a Bragg-curve ionisation chamber (BIC) delivering Z (i.e. element) identification and energy, E. MWCs to the BICs and the mounting at Euroball was such that the centre of the front plane of each telescope was 361.6 mm from the target position. This results in angular ranges for each telescope of: θ=12.5 • →45.5 • and φ=-16.5 • →16.5 • . In order to increase the count-rate capability, the resolution and the dynamic range, all detection planes of both detector stages are subdivided into four segments.

The telescopes have rectangular cross sections, widening conically from the

From each of the four BIC segments per telescope three signal types are derived:

(i) The Bragg-peak signal (BP ), representing the maxima of the stopping (Bragg) curves of the reaction products in the counting gas and corresponding to the maximum amplitudes of the respective anode current pulses. The BP delivers Z information, unless the kinetic energy of the ions reaching the BIC is below the Bragg-maximum.

(ii) The range signal (R), representing the range of ions in the active gas volume of the BIC, measured via the electron drift time, started with the quasi-prompt MWC timing signals and stopped with a timing signal derived from the anode at the back plane of the BIC, i.e. long ranges correspond to small R values (see Fig. 2).

(iii) The energy signal (E), representing the kinetic energy of the ions after penetrating the MWC and the BIC entrance window, obtained by integrating the anode signal from the BIC.

Treatment of the BRS data

Below, the treatment of the BRS data in the offline analysis is described.

The BRS positions were calibrated using elastic scattering data collected with a 210 µg cm -2 197 Au target and using the known positions of slits in movable masks in front of the BRS telescopes (see Fig. 3). Events with good positions were used to define a BRS event as opposed to a Euroball-only event. From the position signals, the in-plane, θ, and out-of-plane, φ, angles are calculated. The BIC energy signals were calibrated using the end-point energies observed in the E versus BP plots (Fig. 4). The maximum kinetic energy deposited by the ions in the BIC was calculated using an energy-loss code, taking into account the window foils and gases. Following this procedure, the four segments could be summed together. (Note, that segment four of the first detector telescope could not be summed due to a different response profile and is treated separately in the subsequent analysis.) Following the gain-adjustment and summing of the BIC segment signals, they are matched to the position segments in case of multiple hits in the BICs caused by light charged particles, i.e. when two BIC segments fire, the segment used is that corresponding to the position signal. Next, a linear calibration was applied to the BIC signals on a run-by-run basis to correct for the accumulation of water vapour in the CF 4 counting gas. This effect steadily decreased the amplitudes of these signals; an effect of 11% over the course of the experiment.

In addition to the above corrections, a series of gates have been applied to the data, the efficiencies of which are given in Table 1. This demonstrates that, of the events sorted, 94% are Euroball-only events as expected from the relative trigger rates in Section 2. BRS events with good position and BIC signals comprise ≈1.3% of the data whereas further demanding a γ-ray in coincidence with a 'good' BRS event reduces the data to ≈0.2%.

In addition to the above gates, events were also removed for which the total Z (Z tot. = Z 1 + Z 2 ) of the event was greater than the Z of the compound system, i.e. Z > 28. These were primarily events for which Z tot. exceeded the Z of the compound system by 8 units, caused mainly by scattered beam particles entering the BRS telescopes in random coincidence.

Gamma-ray analysis

Following the processing of the BRS data, the γ-ray data could be processed as outlined below.

After the energy calibration and alignment of the germanium time signals an 'addback' routine was employed to recover the full γ-ray energies for instances where multiple germanium crystals in one detector fired. For the 7-crystal cluster detectors, the energy signals from up to three neighbouring crystals were added together. (Note: the middle crystal is considered as a neighbour to all other crystals in the surrounding ring.) Similarly for the 4-crystal clover detectors, all of the energy signals from up to (any) three crystals were added together. When adding signals together, the angles of each crystal that fired where averaged in preparation for the Doppler-shift correction. The above procedure increased the full-peak efficiency by a factor of ≈1.4 for energies above ∼1 MeV.

The Doppler-shift correction part of the analysis fell into two parts, both of which use the following formula: where E lab. and E 0 are the observed and corrected γ-ray energies respectively, θ is the angle between the recoiling nucleus and the Ge detector, and v is the recoil velocity. For those γ-events for which a BRS trigger was also recorded, the γ rays were Doppler-shift corrected using the angle information (provided by the BRS position and Ge-detector angle), the energy signal (E) and Z gates. To calculate the velocity from the energy signal, N = Z was assumed for each element due to the absence of mass resolution in this measurement. (Note that the Doppler-shift correction for binary reactions (those without particle evaporation) could also be made using 2-body kinematics. The resulting FHWM for the γ rays is the same as from the method used here.) For those events containing no BRS trigger, only Euroball data, the events were Dopplershift corrected by assuming an average forward velocity of v/c = 0.0615. This velocity was obtained iteratively by minimising the peak widths in the final spectrum. Furthermore, in each case, the non-Doppler corrected energies were also stored to provide a check for the Ge-energy alignment and calibration via the intense 511 keV, e + e -annihilation peak.

E 0 = E lab.   1 -(v/c) • cos θ 1 -(v/c) 2   (
For the BRS-triggered γ rays the FHWM is 17.5 keV (0.99%) at 1.8 MeV, whereas for the Euroball-only data the value is ≈23 keV (1.3-1.4%) at 1.8 MeV.

After performing the above procedure using the graphical sorting-software DATA8M [START_REF]DATA8M graphical sorting software[END_REF], the γ rays were written out to a series of RadWare-format [11] Table 1 Gates and their efficiencies for a typical run: ≈1.5 % of the total data. For each gate the percentage transmission is quoted. "Euroball-only" means events with no BRS; "BRS-Position" means x and y signal in the BRS; "Good pos." means that the BRS positions lie within the segment limits; "Good BP " means BP and E signals in the same segment as the position signals; "Good Z", means that the BIC signals were such as to lie within a Z-gate i.e. on the E versus BP plot the Z was unambiguous. histograms. Of these the following are of primary interest here:

γγγ: a 3-dimensional γ-γ-γ cube containing Doppler-shift corrected γ rays for which no BRS trigger was present. brs-γγ: a 2-dimensional γ-γ matrix containing all Doppler corrected γ-ray events with BRS triggers. brs-γ:

1-dimensional singles spectra for each BRS telescope containing all γ rays with a BRS trigger.

The results from this analysis are discussed in the following section.

Results

BRS triggered data

Plotting E versus BP for each BRS telescope enabled a series of Z gates to be set on the data to define the element for both the Doppler-shift correction and for separating the γ rays from different product species. Following this step, the γ rays for each product can be examined under various conditions. Examples of BRS-γ coincidences are shown in Fig. 5. As can be seen, from the BRS-γ projection (Fig. 5 (top)), the most prominent decays originate from deformed nuclei close to the target and/or beam nuclei. The most intense are 24 Mg (target), 20 Ne and 28 Si. Indeed, one of the most intense γ-ray transitions observed in these data is the 1779 keV 2 + →0 + transition in 28 Si, clearly seen in Fig. 5 (middle). The cleanliness of the Z separation is demonstrated by contrasting this Si-gated spectrum with that of the corresponding Al-gated spectrum (Fig. 5 (bottom)). No leak-through of the intense silicon lines is seen in the Al spectrum. Furthermore, the Al spectrum demonstrates the power of the BRS to select relatively weak channels from a large background of events. One interesting point to note is the 417 keV 26 Al, 3 + →5 + γ-ray transition (the lowest in 26 Al) highlighted in Fig. 5 (bottom). The 417 keV level has a half-life of t 1/2 = 1.25±0.03 ns [START_REF] Gould | [END_REF]] and yet appears as a sharp line in Fig. 5 (bottom). This is because the recoil velocity at 30 • is v/c≈0.09, implying a flight time from the target to the front of the BRS of ≈13 ns. Therefore, any states with lifetimes of the order of ten nanoseconds or less will decay in-view of the germanium detectors and be visible as sharp lines in the BRS gated spectra. Decays exceeding several tens of nanoseconds will not be observed.

The spectra shown in Fig. 5 are γ-ray singles spectra gated by particles in the BRS. However, there are sufficient statistics for particle-γ-γ coincidences.

Figure 6 shows the total projection of the brs-γγ matrix and the spectrum gated by the 440 keV 5/2 + →3/2 + transition in 23 Na. The decays, in 23 Na are clearly visible up to the 11/2 + state at 5534 keV. This channel is 1-proton lighter than the 24 Mg target.

One further feature of the BRS data is the ability to define a binary reaction by setting two Z gates; one on each BRS telescope. This enables the precise definition of a reaction and is demonstrated in Section 5.

Euroball-only data

In Table 1 the events are broken down into the various triggers, 94% of which constitute Euroball-only (i.e. γ-only) data. The total projection of the γγγ histogram is shown in Fig. 7 (top). Two of the most intense products are 46 Ti and 50 Cr isotopes corresponding to 2α 2p and the α 2p evaporation channels respectively. Setting double gates on the γ-ray energies enables a very clean selection of a particular channel, the case for 50 Cr is shown in Fig. 7 (bottom).

The distribution of isotopes identified in these data is shown in Fig. 8 and falls into three groups. The main population is from the 32 S + 24 Mg reaction.

The second, to the neutron-rich side of the vertical dashed line are products from reactions with aluminium in the target frame and are relatively weak. The third population starting with calcium and lighter isotopes is from oxygen and carbon (backing) in the 24 Mg target. Examining the latter group of isotopes in more detail, it is apparent that the observed peaks lie systematically lower in energy than the literature values. E.g. for 41 K, the 1677.2 keV transition [20] is observed at 1662 keV. This shift implies a Doppler correction using a higher velocity of v/c≈0.08 is required (the germanium detectors lie at predominantly backward angles; the average in-plane Ge angle is 115.57 • ), consistent with fusion-evaporation reactions with a target ∼10 mass units lighter than 24 Mg, half way between that for 16 O (a shift of 8 mass units from 24 Mg) and 12 C (a shift of 12 mass units). More details on this aspect of the data are given in the next section.

Discussion

Recently, BRS have been used to claim evidence for hyper-deformation in 60 Zn [5,6]. There are many similarities between the 60 Zn data and the present 56 Ni data, including the use of the BRS coupled to a germanium detector array. Therefore, it is worthwhile considering the evidence for hyper-deformed nuclei in light of the new data.

Firstly, a summary of the analysis and interpretation presented in Refs. [5,6] is given below. In Ref. [5,6], the data were obtained with the BRS telescopes in coincidence with the OSIRIS γ-ray detector array [21]. The primary evidence in favour of hyper-deformed shapes was obtained from particle-particle coincidences following the reaction: 24 Mg( 36 Ar, 60 Zn ⋆ ) at E beam = 195 MeV. In particular, the out-of-plane correlations were examined by plotting φ 1-2 (=φ 1 -φ 2 ). For binary reactions with no 'missing' charge, this is a sharp correlation distributed around 180 • because the two products are coplanar. Where additional particles are emitted, for example an α-particle (∆Z = -2) or proton (∆Z = -1), this distribution becomes broad, as the two primary binary fragments are no longer coplanar. As more and more particles are emitted/evaporated it is expected that these out-of-plane correlations become broader and broader. However, in the 60 Zn ⋆ data this is not the case for all the distributions. In fact, examining successive channels with increasing 'missing' charge, the distributions are broad for the ∆Z = -2 case, then a sharp component above an increasingly broad background appears for the ∆Z = -4 and -6 channels before returning to a purely broad distribution for all subsequent channels.

In Refs. [5,6], the authors attribute this phenomenon to elongated compound nuclear shapes (hyper-deformation), with multiple α-particles forming in the neck before fission occurs. After fissioning the α-particles continue, essentially at rest in the centre-of-mass frame. Thus, the coplanarity of the two primary (heavy) binary products is preserved. It has been argued that due to the very negative Q-values for such ternary reactions (the ∆Z = -4 and -6 channels), these reactions are only able to compete with binary fission because of the very elongated shapes adopted by the compound nucleus ( 60 Zn ⋆ ) with contributions from the rotational energy at large angular momentum (44 [5,6]) and shellcorrections playing a role.

The first publication of correlations [15] explained the distributions as either arising from pre-scission evaporations, such that the out-of-plane correlations of the remaining fissioning system are undisturbed, or from the amalgamation of multiple α-particle clusters in the neck of the di-nuclear system occurring before fission takes place. The pre-scission evaporation is unlikely since the energy carried away by the α-particles would be large, leaving insufficient energy for the subsequent fission process [22,23]. Also, if pre-scission were responsible, the ∆Z = odd channels would also exhibit these out-ofplane correlations. The second process of strongly aligned angular momentum vectors is discounted in Refs. [5,6] because no sharp out-of-plane correlations are observed in the 1α channel. However, a sharp ∆Z = 2 peak ought to be present if the scenario in which one α particle is emitted from the neck can compete as fission takes place, despite it's lower deformation. Also, there is no a priori reason why only two or three α particles should exhibit this structure assuming highly deformed shapes.

In addition to the three processes outlined above, a fourth opportunity for causing sharp out-of-plane correlations exists. In Refs. [5][6][7]15] and the present data 24 Mg targets have been used which contain both oxygen and carbon (backing). Reactions on oxygen would simulate binary reactions in which ∆Z = -4 and carbon would simulate the ∆Z = -6 channels. Whilst such a mechanism has been discussed in Refs. [5][6][7] it was reported to be unable to produce sufficient yields in the relevant reaction channels. This conclusion was reached after a quantitative comparison of the fission yields on 24 Mg and then by assuming that these differential cross-sections will remain the same for reactions on an 16 O target. This analysis was carried out for several reaction channels. From such an analysis the estimated oxygen contents of the magnesium targets are ≈45% in the 56 Ni [7] data and ≈15% in the 60 Zn experiment [5,6]. (Aside: the oxidation levels are different because for the 60 Zn experiment the targets were transferred under vacuum from the target laboratory to the scattering chamber by means of a target-lock system [14]. The vacuum quality in the chamber was also high. Conversely for the 56 Ni Euroball experiment, the targets were stored unprotected prior to mounting and the subsequent vacuum was also significantly worse.) However, the Q-values, thresholds and centre-of-mass energies are different for reactions on 16 O and indeed for 12 C too. An independent way of obtaining these yields would be to run the experiment using pure 16 O and 12 C targets, without 24 Mg, as is routinely done in other charged-particle spectroscopy measurements. In light of the present data, this aspect of sharp out-of-plane correlations for the ∆Z = -4 and -6 channels will be investigated.

The current data for 56 Ni ⋆ will now be discussed in terms of the coplanarity of the fragments. Figure 9 shows the out-of-plane distributions for the present data for all reaction channels, from true binary reactions with no 'missing' charge (∆Z = 0), up to reactions with ∆Z = -8. (The yields corresponding to Fig. 9 are given in Table 2.) The binary, ∆Z = -4 and -6 channels all show sharp distributions peaked around 180 • , set on a broader background. (This broad background, is interpreted here (as for the 60 Zn data [5,6]) as originating from reactions on 24 Mg with increasing numbers of particles emitted/evaporated.) In addition to the presence of a narrow component in the φ 1-2 distributions, the highest yields are also observed for the ∆Z = -4 and -6 channels. Looking in more detail at the narrow distributions the yields of each element are extracted for the binary, ∆Z = -2, -4 and -6 channels and are shown in Table 3. The yields for the narrow component of the ∆Z = -4 and -6 channels are obtained by gating on a φ 1-2 versus E tot. plot using a rectangular gate from 177 • →183 • and all energies above 95 MeV (see inset of Fig. 10). Side-by-side with the Z distributions in Table 3, are the Q 0 -values. A strong dependence of yield on Q 0 -value is expected for binary reactions, the more negative Q 0 -values being strongly suppressed. Indeed this effect is seen from the extent of the binary channels. The Q 0 -values for stripping reactions are generally more favourable than the corresponding pick-up reactions, leading to Z distributions skewed towards products lighter than sulphur. For example, for α-pick-up, Q 0 = -2.67 MeV in contrast to α-stripping with Q 0 = +3.04 MeV. For the narrow out-of-plane components, two sets of Q 0 -values are given. The first assumes that the products result from reactions on the target contaminants ( 16 O and 12 C), the second assumes particle emission/evaporation and a 24 Mg target. Both sets of Q 0 -values are consistent with the odd-even scattering in the data, with highly negative Q 0 -values for odd-Z products. This is correlated with reduced yield in those channels. How-ever, the highest yield channels, for example the Si+O (∆Z = -6) and Si+Ne (∆Z = -4), can be easily explained from target contaminants, having positive or small negative Q 0 -values. In this scenario, these two channels correspond to α transfer on carbon and oxygen targets respectively. However in terms of ternary reactions, the Q 0 -values are highly negative; -21 and -16 MeV for the Si+O and Si+Ne channels respectively. Shell-correction energies [24] and rotational energies are each calculated to be ∼5 MeV for a hyper-deformed 56 Ni ⋆ nucleus and are insufficient to explain the large yields. Indeed the yields are quite different from the 24 Mg( 36 Ar, 60 Zn ⋆ ) reaction of Refs. [5,6] where the peak yield appears to occur for the ∆Z = -2 channel in contrast to the -6 channel in the present work. Again, this is most likely due to the different oxidation levels in the targets, as outlined earlier. Returning to the γ-ray data, spectra can be generated in coincidence with the narrow component of the out-of-plane correlation and Z = 9 (F) and 15 (P) in the BRS telescopes. The resulting coincidences can be seen (Fig. 10). These represent the ∆Z = -4 channel and correspond to proton and deuteron transfer on an oxygen target. The spin distributions of binary partner events have also been examined to see if there is a different spin population for nuclei populated in reactions on the 24 Mg target compared to the target contami-ulation has been observed as the partner Z is varied. This is perhaps not surprising since the γ rays examined here are from relatively low-spin states and are therefore easily populated in both the 32 S + 24 Mg reactions and the reactions on 16 O and 12 C. Finally, it is striking that the only sharp distributions after the binary reaction channel are those corresponding to target contaminants. Products from target contaminants lighter than magnesium have been observed in the Euroballonly data (Fig. 8); confirmed by the shift seen in the Doppler correction necessary to reproduce the published transition energies (Section 4.2). The small sharp peak in the out-of-plane correlation reported in Refs. [5,6] for the ∆Z = -8 channel is not seen here (see Fig. 9). Furthermore, this phenomenon has only been observed using 24 Mg targets. It would be elucidating to perform experiments using a similar detector set-up but with different beam and target combinations to firmly tie down the mechanism responsible.

Summary

In summary, data taken in coincidence with the Euroball germanium detector array and the Binary Reaction Spectrometer have been presented. The BRS was able to sensitively select binary reactions between a 165.4 MeV 32 S beam and a 24 Mg target representing only a small percentage of the total number of events. The treatment of the data has been outlined, and the quality of the Doppler-shift correction shown for events both with and without the BRS. Furthermore, the present data from the BRS have been used to examine the recent claims of hyper-deformation at high angular momentum. In contrast to the published BRS data showing tentative evidence for such an effect in 60 Zn ⋆ , the data here for 56 Ni ⋆ are interpreted as being compatible with target contaminants. Specifically, the out-of-plane angular correlations for coplanar reactions have been investigated for reactions with ∆Z≥0 and the narrow distributions for ∆Z = -4 and -6 found to be consistent with the yields expected from examining the Q-values for reactions on light, target contaminants, specifically 16 O and 12 C. In particular, the peak in the yields for ∆Z = -6 from reactions on 12 C, despite the smaller quantity of carbon backing, indicates that the positive Q-value has a dominant role. This is further supported by the observation in the Euroball-only data of fusion-evaporation products from a target with A∼14.

The BRS enabled both fusion-evaporation reaction products and binary reaction products to be studied in the same experiment. The ability to efficiently provide an accurate Doppler-shift correction for γ-ray events will form an important aspect at future radioactive beam facilities such as Spiral2 [START_REF]SPIRAL2 at GANIL: A world leading ISOL facility at the dawn of the next decade, S. Gales[END_REF] and Fair [START_REF]The Rare-Isotope-Beam facility at FAIR, H. Emling[END_REF]

Fig. 1 .

 1 Fig. 1. Scale drawing showing the set-up of the BRS detectors at Euroball. The two BRS telescopes are stacked in a vertical configuration around the 0 • beam axis. Close to 90 • are the clover germanium detectors and at backward angles are the larger cluster germanium detectors.

Fig. 2 .

 2 Fig. 2. A plot for telescope 2 showing range, R versus Bragg peak, BP . Each horizontal line corresponds to a different atomic charge, Z as labelled. The vertical axis is in units of ≈Z×100. The Bragg chamber is 125 mm deep. Relative intensity is shown on the side bar.

BRS telescope 1

 1 Position, y [mm]Position, x[mm]

Fig. 3 .

 3 Fig. 3. (Top): technical drawing of one half of the BRS mask used for position calibration; (middle): photograph showing the two sides of the mask in place, in front of a BRS telescope; (bottom): calibrated position data (x versus y in mm) for telescope 1 using the 197 Au target. The relative intensity (∝ counts) is shown on the side bar.

Fig. 4 .

 4 Fig. 4. A plot of energy, E versus Bragg peak, BP for BRS telescope 2. The horizontal scale is in MeV×10 and the vertical scale ≈Z×100. The relative intensity (∝ counts) is shown on the side bar. The different elements (labelled) are well separated enabling unambiguous Z gates to be set. The end-point/punch-through energies for each element can be clearly seen.

Fig. 5 .

 5 Fig.5. BRS-gated spectra. (Top): full singles spectrum gated by BRS events with well-defined Z; (middle): spectrum gated by Z = 14 (silicon); (bottom): Z = 13 (aluminium) gated spectrum. The isotope(s) contributing to the most prominent peaks are indicated. In the bottom spectrum the 417 keV peak energy is also quoted. See text for details. Both Z-gated spectra represent the sum from gating on each BRS telescope. A contaminant from neutron activation in the germanium detectors is labelled 'c'.

Fig. 6 .

 6 Fig.6. BRS-γ-γ coincidence spectra. (Top): total projection of the brs-γγ matrix;background-subtracted spectrum gated by the 440 keV 5/2 + →3/2 + transition in23 Na. Transitions in23 Na are labelled with energies. A contaminant at 202 keV is labelled 'c'.

Fig. 7 .

 7 Fig. 7. Spectra from the Euroball-only data. (Top): background-subtracted total projection of the γγγ cube; (bottom): background-subtracted spectrum, double gated by the 661 and 610 keV transitions in 50 Cr. Transitions up to the 14 + state at 9917 keV can be identified. Note the different vertical scales.

Fig. 8 .

 8 Fig. 8. Plot showing the isotopes observed in the Euroball-only data using a 'classic' Doppler correction (v/c = 0.0615). All of the labelled isotopes have been identified with the exception of the compound nucleus, 56 Ni ⋆ , shown here only for orientation. Black squares indicate naturally abundant isotopes. See text for details.

Fig. 9 .

 9 Fig. 9. Plot showing out-of-plane distributions, φ 1-2 for BRS coincidences gated by 'missing' charge, ∆Z. Note the different vertical scales for each row. Sharp correlations are observed for the ∆Z = -4 and -6 channels. A systematic error of -1.0 • is present in φ 1-2 .

Fig. 10 .

 10 Fig. 10. Coincident γ-ray spectra generated by gating on Z = 9 (fluorine, bottom) and 15 (phosphorus, top) in the BRS telescopes. Additionally a gate on the narrow component of the out-of-plane distribution is set (box in the inset) on the φ 1-2 versus E tot. (= E 1 + E 2 ) matrix gated by Z 1 = 9 and Z 2 15. A broad contaminant peak in the fluorine spectrum, indicated by 'c', arises from the strong coincident 709 keV transition in phosphorus being wrongly Doppler-shift corrected.

Corresponding author. E-mail: c.wheldon@bham.ac.uk, URL: http://www.hmi.de/people/wheldon/

Present address: iThemba Labs, PO Box 722, Somerset West 7129, South Africa.

) as a function of binary partner, i.e. Z. However, within the uncertainties a constant ratio was observed, the weighted mean of which is 0.27±0.01. Similarly for transitions in

Si, no significant change in spin pop-

Acknowledgements

The authors would like to thank the Vivitron accelerator staff for the sulphur beam. Clausi Beschorner is thanked for their numerous contributions to the graphical sorting software. G. Duchêne, F. Haas and D.G. Jenkins are thanked for their contribution to part of the data collection. C.W. is grateful for the support of the Alexander von Humboldt Foundation. Tz.K. acknowledges a DAAD scholarship.

Table 2

Yields (counts) as a function of ∆Z for the out-of-plane distributions shown in Fig. 9. For the ∆Z = -4 and -6 cases, the number of counts in the narrow component of the φ 1-2 distributions are also given. Z distributions nants. This was undertaken by plotting, for example for 28 Si, the γ-ray yield ratio:


M Boutinaud

A Bondon

P Debournoux

J Couedon

M Johan

A Narcy

C Hurtaud

A Suárez-Trujillo

A Argüello

M A Rivero

J Capote

N Castro

lacTaTiOn BiOlOgy ii 1232 (W141) Daylength affects simultaneously mammary epithelium integrity and mammary epithelial cell exfoliation in milk

Keywords: mammary epithelial cell, mammary epithelium integrity, photoperiod, feeding 1233

Some of the mammary epithelial cells (MEC) responsible for milk synthesis are exfoliated into milk during the lactation process. MEC exfoliation into milk could play a role in mammary epithelium integrity. A trial was performed to study the effects of daylength and type of diet on milk calcium content. Calcium is known to play a role of cement for tight junction closure between epithelial cells. The aim of this study was to identify a potential effect of daylength and dietary anion-cation differences on mammary epithelium integrity and MEC exfoliation in milk. A trial was performed according to a Latin square design using 8 dairy cows averaging 103 ± 44 DIM with two treatments in a factorial arrangement with 4 periods of 14 d. The cows received 2 levels of dietary anion-cation differences (DCAD; 0 mEq/kg DM for D0 and 400 mEq/kg for D400) and 2 d lengths (8 h of light/d for short days and 16 h/d for long days). The cows were only exposed to solarium lights providing UVA and UVB. Once per period, milk was collected to purify MEC from milk after centrifugation and immunocytochemical sorting. MEC exfoliation was evaluated using the determination of MEC concentration in milk. The percentage of apoptotic MEC was determined by flow cytometry after TUNEL labeling. Epithelium integrity was monitored using the determination of blood lactose sampled 1 hbefore morning milking, and the ratio Na:K in milk. Blood prolactin concentrations from samples collected at 0700 h and 1400 h were determined by RIA. Data were analyzed using PROC MIXED. There was no significant interaction between daylength and DCAD level. Milk yield did not vary with any treatments averaging 32.7 kg. d -1 . DCAD treatment did not affect any of the parameters. Blood lactose and Na:K ratio were higher with short compared with long days (P < 0.05) indicating that mammary epithelium integrity was more disrupted with short days. More MEC and more apoptotic MEC were exfoliated in milk with short days compared to long days (338 vs. 227 • 10 6 exfoliated MEC per day, for respectively short and long days; P < 0.05). As expected blood prolactin concentration was lower with short days (P < 0.05). Taken together these results suggest that MEC exfoliation could be induced by low prolactin concentration during short days.

The role of serotonin (5-HT) as a feedback inhibitor of lactation in humans, mice and cows has been previously reported. This action is mediated by 5-HT receptors, which are expressed in the mammary tissue. The 5-HTR subtype 7 is expressed in the three quoted species, but in bovine, subtypes 1B, 2A, 2B and 4 have been also found. The aim of this study was to identify the 5-HTR (subtypes 1A, 1B, 1D, 1E, 1F, 2A, 2B, 2C, 3A, 4, 5a, 6 and 7) expression in the goat and sheep mammary gland using Real Time PCR-RT. Lactating Holstein cows, Majorera dairy goats and Canarian dairy ewes were used to obtain hypothalamus and mammary gland samples. The sampling was performed immediately after slaughter. Samples were kept in RNA later, and subsequent RNA extraction was performed using a combination between TriPure reagent (Roche, Barcelona, Spain) and E.N.Z.A. total RNA kit (Omega, Nordic Naturals, inc) protocols. cDNA synthesis was conducted using iScript cDNA synthesis kit (BioRad, Madrid, Spain). The primers used in this study were obtained from published data (1B, 1F, 2A, 2B, 2C, 4, 5a and 7) or were designed using bovine CD sequences from GeneBank (1A, 1D, 1E, 3A and 6). Hypoxanthine phosphosibosyltransferase I (HPTR1), Ribosomal Protein (S18), β-Actin and Glyceraldehyde 3-Phosphate Dehydrogenase (GAPDH) genes were utilized as internal control. Hypothalamus samples were used as positive control to evaluate the primers in the three studied species. Real Time PCR was performed at 57.5°C of annealing temperature for all primers. The receptors found in bovine mammary gland were in agreement with previous studies. Receptors 1B, 1E, 2A, 2B, 4 and 7 were found in the three species. Additionally, receptors 1D and 5a were observed in goats and sheep. Moreover, 1A and 1F subtypes were only detected in ovine mammary gland. Nevertheless, subtypes 2C, 3A and 6 were found neither bovine, nor caprine or ovine mammary gland. In conclusion, the same 5-HTR subtypes previously described in cows are expressed in caprine and ovine mammary gland. In addition, in caprine mammary gland were identified two more (5-HTR 1D and 5-HTR 5a ), and the most number of 5-HTR were found in ovine mammary gland. Further studies will be necessary to study more in deep the role of serotonin as inhibitor of lactation in small ruminants.

Key Words: serotonin receptors, goat, sheep


Dirk Seifert
email: dirk.seifert@loria.fr

Jeanine Souquières
email: jeanine.souquieres@loria.fr

Using UML Protocol State Machines in Conformance Testing of Components

In previous works we designed a comprehensive approach for conformance testing based on UML behavioral state machines. In this paper we propose two extensions to this approach. First, we apply our approach in the context of a component-based development, and address the problem of checking the interoperability of two connected components. Second, we address the problem of selecting relevant input sequences. Therefore we use UML protocol state machines to specify restricted environment models. This means that we restrict the valid protocol at the provided interface of the component under test with respect to a specific test purpose. Based on these models we select relevant input sequences. We implemented both extensions presented here in our TEAGER tool suite to show their applicability. Both extensions address the behavior at the interfaces of components. We use UML state machines as a unified notation for behavioral and protocol conformance testing as well as for test input selection. This considerably eases the work of test engineers.

Introduction

In a model-based development approach, models of the system which have to be built guide and control the development process [START_REF]Model-Based Testing of Reactive Systems[END_REF]. There are various types of models differing in the level of abstractions or in their intended use. For example, the Unified Modeling Language (UML) comprises thirteen diagram types to specify the structure and the behavior of a system or a system component [START_REF]Unified Modeling Language: Infrastructure and Superstructure[END_REF]. In the first steps, the models are used to analyze the problem domain and to ease the information exchange among developers. Later on, they form the basis to design and implement the system, and serve as documentation. Nowadays, the models are also used for quality assurance purposes. Before implementing the system, required properties can be verified on the models, or they can be simulated to check the intended behavior. Finally, the models can be used for generating tests to check the implemented system. Hence, models of the system which shall be built allow early starting, continuous and automated quality assurance processes.

Testing means executing a system under test with selected but real data to evaluate its conformance, whereat conformance is evaluated on the basis of the observations made on the system under test. It aims in falsification, that means to show inconsistencies between the specification and the system under test. It benefits from the fact that the real system is brought to execution. Thus, the interaction of the real hardware and the real software can be evaluated. A further important advantage of testing is its applicability at different levels of abstraction and at different stages of the development. In [START_REF] Seifert | Automatisiertes Testen asynchroner nichtdeterministischer Systeme mit Daten[END_REF], we presented a conformance test approach based on UML state machines, where a state machine model [START_REF]Unified Modeling Language: Infrastructure and Superstructure[END_REF] serves as the specification of the system under test. We generate test cases from a state machine specification which include input sequences to stimulate the system under test as well as test oracles to automatically evaluate the test execution. Thus, we are able to automatically generate, execute, and evaluate test cases. The focus of our approach is on the level of unit testing.

In a component-based development approach [START_REF] Szyperski | Component Software, Beyond Object-Oriented Programming[END_REF][START_REF] Heineman | Component-Based Software Engineering[END_REF], the problem of building a system out of previously-existing software components from a variety of sources is addressed. Building a system out of components has the potential to reduce the development cost and, at the same time, to enhance its flexibility and maintainability. The components are considered as black-boxes described by interfaces expressing their visible behavior. Components are connected through required and provided interfaces. Interoperability is only guaranteed if the required interfaces correctly implement the provided interfaces of the connected components [START_REF] Chouali | Proving Component Interoperability with B Refinement[END_REF]. In most cases, an adapter (i.e., a piece of glue code, expressing the mapping between a required and a provided interface) has to be introduced [START_REF] Mouakher | Component Adaptation: Specification and Verification[END_REF]. In previous works, we have used the B method and its refinement and assembling mechanisms to model component interfaces as well as patterns for adapters, allowing the interoperability to be checked with tool support [START_REF] Lanoix | A Trustworthy Assembly of Components using the B Refinement[END_REF]. Our verification and testing techniques complement each other. Verification techniques enable early checks of important properties, whereat testing checks the real implementation. The application of both techniques ensures a high-quality development and a comprehensive quality assurance with reliable results.

The first problem we address in this paper is testing the input-output behavior of a system under test in the context of a component-based development. Now, the system under test becomes a component under test, and we assume it to be connected to other system components. In this setting, we additionally check if the component under test correctly implements the provided interfaces of the connected components. We accomplish this by checking the outputs of the component under test at its required interfaces against the specified protocols of the corresponding provided interfaces of connected components. The protocols are specified by protocol state machines. We do not address the problem of integration testing; we still focus on one component under test. Our extension allows early checks of interoperability on the level of unit testing with insignificant additional effort compared to the primary test approach.

Furthermore, we address the problem of selecting relevant input sequences during test case generation for testing reactive systems. In general, the set of possible input sequences for reactive systems is infinitely large. To generate test cases, we have to select a finite subset. Specifying the behavior at the interfaces of components provides a appropriate basis for input selection. The component under test must work correctly in environments behaving according to the specified behavior at the provided interfaces of the component. Thus, it is worthwhile to select test inputs on the basis of these descriptions. We use protocol state machines to specify restricted environment models and to select relevant input sequences. Moreover, we propose two extensions of protocol state machines. First, we extend them by the ability to specify probabilistic behavior. Second, we enable the use of feedback from the system under test when testing non-deterministic systems.

The contribution of this paper is the integration of protocol state machines into our existing test approach as a uniform notation and their use to address two important problems in testing, namely interoperability and input selection.

The rest of the paper is organized as follows. In Section 2, we briefly introduce both variants of UML state machines and review our test approach for conformance testing. In Section 3, we present our extension for checking the interoperability of two connected components. In Section 4, we present our approach to select relevant input sequences, including the two possible extensions to the notation of pro-tocol state machines. In Section 5, we conclude our work and discuss prospects for future work.

Foundations

UML state machines are used to model the discrete reactive behavior of a system or a system component through finite state transition systems [START_REF]Unified Modeling Language: Infrastructure and Superstructure[END_REF]. They come in two flavors: behavioral state machines and protocol state machines. Behavioral state machines specify the states a system or a system component can take and the actions it can execute during its lifetime in response to external and internal events. They are an object-oriented extension of the classical Harel-Statecharts [START_REF] Harel | Statecharts: A Visual Formulation for Complex Systems[END_REF]. The semantics is adapted from the STATEMATE semantics [START_REF] Harel | The STATEMATE Semantics of Statecharts[END_REF] to fit into the objectoriented paradigm. Protocol state machines are used to express usage protocols of a system or a system component by expressing legal interaction sequences, which consists of either events or method calls.

Behavioral State Machines

Behavioral state machines are mathematical models with a graphical representation: the nodes depict simple or composed states of a system and the labeled edges depict transitions between these states (see Figure 1 for an example). Composite states are used to hierarchically and orthogonally structure the model, thus reducing its graphical complexity. Labels express conditions under which transitions can be taken and the actions which will be executed when the transitions are taken. Events are used as triggers to activate transitions and can be parameterized to exchange data. Optionally, every behavioral state machine has a data space which can be read and manipulated by the state machine during execution. More precisely, it is possible to read data values to describe specific conditions when a transition can be taken or to manipulate data values and exchange information within the actions. A transition consists of a source state, a trigger event, an optional guard, an optional effect (which comprises a sequence of actions), and a target state. We also write a transition as follows:

source trigger[guard] /effect ------------→ target (1)
With the optional guard, a fine-grained condition to enable the transition can be described depending on the system's state. Hence, the activation of the source state, the trigger event and the guard condition evaluating to true constitute the condition which must be fulfilled to enable the transition. An action can either be a statement manipulating the data space or the generation of new events. The action sequence and the subsequently active target state constitute the effect of the transition. In opposite to the classical Statecharts [START_REF] Harel | Statecharts: A Visual Formulation for Complex Systems[END_REF], the event processing takes place in a so-called run-to-completion step [START_REF]Unified Modeling Language: Infrastructure and Superstructure[END_REF]. This asynchronous event processing demands the processing of the previous event to be completely finished before the next event can be processed.

Figure 1 shows a behavioral state machine for a component named C1 as an example. The top-level composite state BSM-C1 is refined into two simple states, namely A and B, whereat state A is marked as the default state. The four transitions specify the behavior of C1. There are two possible input events, namely a and b, and four possible output events, namely aa, ab, bb and ba. The output events indicate the source state and the target state of a taken transition. For example, an observation ba indicates a transition from state B to state A. For simplification, the state machine neither contains orthogonal regions nor complex guards and actions reading and manipulating data values. Furthermore, the state machine is completely deterministic. For more information on the syntax and semantics of behavioral state machines we refer the interested reader to [START_REF] Seifert | Automatisiertes Testen asynchroner nichtdeterministischer Systeme mit Daten[END_REF][START_REF]Unified Modeling Language: Infrastructure and Superstructure[END_REF].

The semantic model of behavioral state machines builds on the semantic steps a state machine can execute during its lifetime. Such a step moves the state machine from one semantic state to another semantic state while receiving events from and emitting events to the environment. A semantic state (a status) comprises three components: a configuration (a maximal set of active states), an event queue, and all variable assignments. We denote a semantic step as follows:

[[c, q, d ]] in,out ---→ [[c ′ , q ′ , d ′ ]] (2)
Based on this definition, we describe the execution runs of a state machine as the concatenation of semantic steps and call them computations:

[[c 1 , q 1 , d 1 ]] in 1 , out 1 ----→ . . . in n-1 , out n-1 -------→ [[c n , q n , d n ]] (3)

Protocol State Machines

Protocol state machines are attached to interfaces1 and specify their legal usage protocol. In most applications, protocol state machines are used to specify which operations (call events) can be called in which state, under which condition and what result is expected from their use. In our context of testing reactive systems, we use protocol state machines to specify legal event sequences (signal events) and pre-and postconditions. This involves to specify which events can be processed in which state, under which condition and what result is expected from their processing.

The notation of protocol state machines is very similar to that of behavioral state machines. The keyword {protocol} placed close to the name of the state machine differentiates protocol state machine diagrams graphically. The states of a protocol state machine present an external view of the component. The two differences that exist for states in protocol state machines are as follows: first, there exist no entry-, exit-or do-actions and second, invariants can be attached to states in protocol state machines. Protocol transitions specify that the referenced trigger event can be processed in the source state under the precondition, and that at the end of the transition, the target state will be reached under the postcondition. They are labeled with an optional guard (i.e., the precondition), the trigger event, and an optional postcondition. They do not comprise explicit actions:

source [precondition] trigger / [postcondition] ---------------------→ target (4)
Figure 2 shows a composite structure diagram for two components, namely C1 and C2. They are connected via the required interface of C1 and the provided interface of C2. The associated protocol state machines specify the legal behavior at the interfaces. For example, component C2 expects at its provided interface that when an event ab occurs, only the event sequence bb•ba•aa can follow.

If two components A and B are connected, then the protocol state machine of the required interface of A must conform to the protocol state machine of the provided interface of B. In other words, the specification given by a protocol state machine is a requirement to the environment external to that component: it is legal to send events to the component only under the conditions specified by this protocol state machine.

Conformance Testing

In previous works we designed a comprehensive approach for conformance testing based on UML state machines [START_REF] Seifert | Conformance Testing based on UML State Machines[END_REF][START_REF] Seifert | Automatisiertes Testen asynchroner nichtdeterministischer Systeme mit Daten[END_REF]. In this approach, a UML state machine model [START_REF]Unified Modeling Language: Infrastructure and Superstructure[END_REF] serves as the formal specification of the system under test. To enable an automated test case generation, we first formalized a substantial subset of UML state machines. This subset includes all relevant aspects to seriously study automated test case generation and evaluation based on state machines. In contrast to other approaches we use a precisely defined semantics for UML state machines including data. We do not restrict state machines to ease test case generation. Instead, we follow the semantics description of the UML standard [START_REF]Unified Modeling Language: Infrastructure and Superstructure[END_REF] as much as possible. Only misleading or conflicting statements are clarified. We address all semantic details which arise from the different sources of non-determinism. In particular we address the problem of asynchronous communication which is introduced to the run-to-completion semantics.

The precise semantics is a necessary prerequisite for test automation. Furthermore, we need to specify conformance in relation to state machine specifications (to enable automated test evaluation). A system under test conforms to its specification, if the observations for the input sequences on the system under test can be related to the possible observations on the specification. Therefore we compare the observed outputs of the system under test with the precalculated possible correct observations (the test oracle) of the specification:

I ≤ out S ⇔ ∀ σ : seq E S • out(I, σ ) ⊆ out(S, σ ) (5)
An implementation I conforms to its specification S, if and only if, for all input sequences σ over the event set E S of the specification S, the output sequences of the system under test, out(I, σ ), are included in the set of all possible output sequences of the specification, out(S, σ ). According to this notion of conformance we generate test cases on the basis of the computations and the corresponding observations (cf. Definition 3) calculated in a stepwise exploration of the state machine's state space for selected input sequences. Test execution includes stimulating the system under test with those input sequences, observing the outputs of the system under test and comparing them to the pre-calculated possible correct observations.

For example, with respect to the behavioral state machine presented in Figure 1, we could choose to test the system under test with the input sequence

!a!b!b!a!a!b 2 .
For this input sequence we calculate the possible correct observations. Due to the fact that the state machine in Figure 1 specifies only deterministic behavior, we obtain as the test oracle the single observation sequence ?aa?ab?bb?ba?aa?ab.

In general, the generated test cases include input sequences to stimulate the system under test as well as test oracles to automatically evaluate test execution. A test oracle is a deterministic acyclic acceptance graph, accepting all possible correct observation sequences. When a system under test is stimulated with an input sequence of a test case, it must show exactly one complete observation sequence of the test oracle to pass the test. Note that for the sake of simplicity, we did not illustrate all state machine features that make automated test case and test oracle generation a challenge. In particular, the various sources of non-determinism, mainly caused by the asynchronous event processing, by the multiple possible sets of firing transitions and different possible orders of firing transitions, introduce complex behaviors in state machine models and make the computation of test oracles a particular challenge [START_REF] Seifert | Conformance Testing based on UML State Machines[END_REF]. To evaluate and to show the practicability of our approach we implemented the TEAGER tool suite [START_REF] Seifert | The TEAGER Tool Suite. Test Execution and Generation Framework for Reactive Systems[END_REF][START_REF] Santen | Teager -Test Automation for UML State Machines[END_REF]. TEAGER consists of an environment to automatically generate and execute test cases, and of an environment to execute state machine specifications. The latter we use to analyze the execution behavior and the testability of a state machine, and to measure coverage on a state machine specification to evaluate the quality of generated test suites. The test execution includes both: stimulating the system under test and comparing the observation to the computed possible correct behavior in the acceptance graphs. The communication with the system under test takes place over a socket connection using pre-implemented adapters. This concept offers a flexible way to connect the system under test. It also offers the possibility to use our State Machine Executor as a system under test stub.

Testing Interface Interoperability

In the previous section we reviewed our approach to test the behavioral conformance of a system under test with respect to a behavioral state machine specification. Now we consider the system under test as a component of a larger system and we address the problem of additionally checking the interoperability of this component with other components (i.e., protocol conformance). Protocol conformance testing is mainly known from testing communication systems [START_REF] Brinksma | Testing Transition Systems: An Annotated Bibliography[END_REF][START_REF] Sun | Protocol Conformance Testing Using Unique Input/Output Sequences[END_REF][START_REF] Holzmann | Design and Validation of Computer Protocols[END_REF][START_REF] Fernandez | An Experiment in Automatic Generation of Test Suites for Protocols with Verification Technology[END_REF][START_REF] Sabnani | A Protocol Test Generation Procedure[END_REF] and SDL specifications [START_REF] Kerbrat | Automated Test Generation from SDL Specifications[END_REF][START_REF] Grabowski | SDL-and MSC-based Specification and Automated Test Case Generation for INAP[END_REF]. With our work we focus on interoperability and conformance of classes in an object-oriented programming environment based on UML descriptions.

For example, in Figure 2 the system under test (henceforth the component under test) is component C1. For testing C1 we embed it in a test environment. The test environment is connected to the provided interface of C1 to allow the sending of inputs to C1. The required interface of C1 is also connected to the test environment to allow the observing of outputs of C1. In this test setting, we assume that the required interface of C1 is intended to be connected to a provided interface of another component -in this example component C2. Without loss of generality, we demonstrate the approach with one interface for sending inputs and one interface for observing outputs of the component under test. The approach is also applicable to a larger number of interfaces.

The problem we address here is that component C2 may require a special protocol at its provided interface. Connecting C1 to C2 is only possible if C1 respects this protocol. The required protocol is specified by a protocol state machine -in Figure 2, namely PSM-PI-C2. To test whether C1 respects this protocol we extended our conformance test approach by checking the observations of C1 against the protocol state machine of C2. In particular, we test for every observation sequence made on C1, if the protocol state machine of C2 can process these observations (i.e., can fire transitions triggered by these observations).

To identify failures in the specified protocol we need to define some semantic variation points of protocol state machines. The interpretation of the reception of an event in an unexpected situation (unexpected current state, violated state invariant or precondition) is a semantic variation point: the event can be ignored, rejected, or deferred; an exception can be raised; or the application can stop on an error. It corresponds semantically to a precondition violation, for which no predefined behavior is defined in the UML standard. The interpretation of an unexpected resulting behavior, that is an unexpected result of a transition (wrong final state or final state invariant, or postcondition) is also a semantic variation point, that should be interpreted as an error of the implementation of the protocol state machine [START_REF]Unified Modeling Language: Infrastructure and Superstructure[END_REF]. We interpret both, event reception in unexpected situations and unexpected behavior as violations of the specified protocol. Thus, we can give a precise definition of protocol conformance:

I ≤ protocol (S b , S p ) ⇔ ∀ σ : seq E S b • ∀ ω : out(I, σ ) • S p ω = =⇒ (6)
An implementation I conforms to a protocol specification S p , if and only if for all input sequences σ over the event set E of a behavioral specification S b , all output sequences ω in out(I, σ ) of the implementation, can trigger the protocol specification S p . The fact that a sequence γ can trigger a state machine SM is defined as follows:

SM γ = =⇒ = def ∃ [[c 1 , q 1 , d 1 ]] in 1 , out 1 ----→ . . . in n-1 , out n-1 -------→ [[c n , q n , d n ]] • in 1 • • • in n-1 = γ (7)
Here, we identify SM with its initial status and require the existence of a computation of SM, such that the sequence of inputs of this computation is equal to the given sequence γ.

As an example, we demonstrate our approach to check protocol conformance by means of two exemplary test cases in the test set up presented in Figure 2 and Figure 1. First, we consider the test case from the previous example. We do not encounter a violation, since for the sequence ?aa?ab?bb?ba?aa?ab, there exists a valid computation in PSM-PI-C2. Second, we choose !a!b!b!b!a!b as input sequence to C1. With respect to this input sequence we observe the sequence ?aa?ab?bb?bb?aa?ab at the required interface of C1. If we send this sequence as input to PSM-PI-C2 we encounter a violation. The sequence ?aa?ab?bb is accepted by PSM-PI-C2, changing its state to state (3). In state (3), the state machine expects the reception of event ba. There is no transition that is triggered by the event bb. This is a violation of the specified protocol. Consequently, we can state that component C1 and C2 are not interoperable in an environment showing behavior (i.e., triggering C1) according to PSM-PI-C1.

Interpreting Test Results

If we encounter a violation of the required protocol during protocol testing of a component A under test against the required protocol of a component B, then the general consequence is that it is impossible to connect component A with component B in the assumed environment. There could exist several reasons why two components are not interoperable.

The simplest reason could be that the two interfaces do not fit. That means that a component A sends events to a component B which are not "understood" by B. In such cases, it is possibly feasible to use adapters to translate, abstract or put events into a concrete form [START_REF] Lanoix | A Trustworthy Assembly of Components using the B Refinement[END_REF]. Thus, from a technical point of view, interoperability could be made possible. More problematic is that a component A can show behavior at its required interface which is in general not interoperable with respect to the required protocol of a component B (independently from the way component A is used). Without changing the internal behavior of component A or without using more "intelligent" adapters, A and B cannot be connected. But the reasons could also be that the way component A is used leads to a violation of the required protocol of a component B. In the reverse, that means that for some inputs to A, interoperability with B is possible since the behavior of A for those inputs produces outputs that conform to the required protocol of B.

In particular from the latter reason it follows that the question of protocol conformance must always be seen in conjunction with the assumed environment. A component must not generally conform to the required protocol of another component. Only in the special situation that it should be connected to the other component, and only in the assumed environment. Consequently, the question whether we can restrict the general environment of a component A in such way that A meets the required protocol of a component B becomes immanent in this context. In general, an environment like that is not guaranteed to exist. Usually, domain experts must define which behavior at a provided interface must be or should be allowed, and thereby disallow input sequences that lead to a violation of a required protocol. This could be done by restricting the allowed protocol at provided interfaces (i.e., by restricting the behavior of the associated protocol state machines). Restricting a protocol state machine means restricting the set of valid input sequences.

For example, Figure 3 shows a possible restriction of PSM-PI-C1 in the protocol state machine PSM-PI-C1' (we will explain the remaining picture in the next section). This protocol state machine allows less input sequences that are still valid with respect to the protocol state machine PSM-PI-C1 (i.e., is a sub-behavior). The input sequence of our second test case (!a!b!b!b!a!b) is not a valid input sequence with respect to the protocol state machine PSM-PI-C1'. Instead, selecting inputs according to the restricted protocol description and testing a component under test only capable of showing behavior according to behavioral state machine BSM-C1, will not encounter a violation of the required protocol at the provided interface of C2. Note, PSM-PI-C1' does not describe the maximal set of valid input sequences. In this simple example, we could have used the protocol state machine PSM-PI-C2 at the provided interface of C1 to describe the maximal set of valid inputs, whereat we have to replace aa and ba with a, and bb and ab with b.

These considerations lead to two further applications of the results of protocol conformance checks. First, we can use the results of protocol conformance tests in analyzes to specify valid environments for connected components and thus, by explicitly requiring the specification of valid environments, to support the assembling of a system out of pre-fabricated components. Second, it improves the motivation for selecting inputs according to restricted environments (i.e., to exclude disallowed or unwanted input sequences from the test case generation process). We discuss this subject in the following section.

Input Selection

Testing consists of executing experiments with the system under test. For these experiments we have to choose the inputs for the stimulation of the system under test. If the domains of the inputs are not finite, or if the number of values in the domains is pretty large, it is impractical to test with all possible values. Even in our case, where the number of events is finite, we have to deal with sequences of inputs which are not restricted in their length. This is due to the fact that most reactive systems are designed as nonterminating systems which continuously process inputs.

Various strategies are studied in the literature for selecting a finite number of test cases [START_REF] Binder | Testing Object-Oriented Systems: Models, Patterns, and Tools[END_REF][START_REF] Offutt | Criteria for Generating Specification-based Tests[END_REF][START_REF] Beizer | Black-Box Testing: Techniques for Functional Testing of Software and Systems[END_REF][START_REF] Zhu | Software Unit Test Coverage and Adequacy[END_REF][START_REF] Goodenough | Towards a Theory of Testing: Data Selection Criteria[END_REF][START_REF] Duran | An Evaluation of Random Testing[END_REF]. They range from analyzes of the structure or the data-flow of systems under test, via dedicated fault models or explicit test case specifications to the idea of choosing test cases according to statistical data. All have their assets and drawbacks. Automated techniques allow selecting inputs in systematic and efficient way, while domain experts are able to select "interesting" or "relevant" inputs, but mostly less systematic and with more time needed [START_REF] Binder | Testing Object-Oriented Systems: Models, Patterns, and Tools[END_REF][START_REF] Beizer | Black-Box Testing: Techniques for Functional Testing of Software and Systems[END_REF].

We address the problem of selecting "interesting" or "relevant" input sequences within an automated input selection process. We use environment models to describe usage patterns of the system under test. A usage pattern can describe heavily used cases of the system under test, but also sequences of interest to achieve a special test purpose. The motivation for using such test case specifications is that we eventually use the results of testing the system under test to evaluate its quality. Therefore it is essential to execute adequate test cases. Since our environment models usually do not ensure finite behavior, we have to combine their usage with other test case selection strategies as cited at the beginning of this section.

In the previous sections we described that the protocol state machine associated with the provided interface of the component under test specifies the valid behavior of its environment. The component under test is assumed to or must work correctly in an environment behaving like this. Consequently, such protocol state machines specify the most general environments for which this component must work correctly. Hence, we can use it as a basis to select relevant input sequences. Benefits of doing so are that the graphical notation eases the understanding of the described behavior, and that it is possible to describe sequences of inputs. Compared to, for example, choosing input sequences only on the basis of the event set, invalid or unlikely input sequences can be avoided. This becomes especially necessary if it cannot be assumed that the system under test is input enabled (i.e., is not blocking for all inputs in all states). To use protocol state machines for input selection we execute them and select the next input according to the fire-able transitions. For example, in Figure 3, PSM-PI-C1 describes the behavior of the most general environment. In the initial state (1) both transitions can be fired. Hence we can choose for the next input either a or b and thus input sequences containing a's and b's in an arbitrary order.

Further on, we restrict the behavior of an environment to select test cases according to specific system uses (i.e., according to a specific test purpose). For example, PSM-PI-C1' restricts the behavior of PSM-PI-C1 in a way that a correct implementation of C1 can comply with the specified protocol of component C2. It also forms the basis to select input sequences to test for protocol conformance. In the initial state of PSM-PI-C1' we can only choose b as the next input followed by b, then a, then a, then b, and so on. It is not possible to generate input sequences starting with a. Thus, in the context of connecting C1 and C2, invalid input sequences for C1 are avoided. The initial protocol state machine describes the most general environment in which the system under test is assumed to work correctly. So it follows that all restrictions must be a sub-behavior of the initial one.

We extend the protocol state machine notation by two variants to allow a finer description of environments: first, by using probabilities for choosing the next input from the set of possible inputs, and second, by using feedback from the system under test to allow adapting the behavior of the environment according to this feedback. The latter is used when testing non-deterministic systems on-line.

Input Probabilities

A statistical test case generation usually aims at selecting data values for input variables using a statistical distribution. In model-based testing it is also used to generate input sequences from environment models. For example, Markov chain models are widely used to specify usage profiles [START_REF] Kemeny | Finite Markov Chains[END_REF]. This is especially useful as the system under test moves from one state to another one and thus, the probability of applying an input can change. Whittaker and Thomason [START_REF] Whittaker | A Markov Chain Model for Statistical Software Testing[END_REF] proposed an approach for test input selection based on usage profiles described by Markov chains. They use finite states, discrete parameters, time homogeneous Markov chains. We transfer the representation of Markov chains as finite state machines with probabilities attached to the transitions to our protocol state machines describing the behavior of the environment. This allows to express that in some states some inputs are more likely than others. It also allows to use all the theories around Markov chains to perform analyzes of the testing process [START_REF] Feller | An Introduction to Probability Theory and Its Applications[END_REF][START_REF] Kemeny | Finite Markov Chains[END_REF].

In the previous section we selected inputs according to fire-able transitions. There all transitions are equiprobable. We can only express that in some states it is not possible to choose some inputs (i.e., their probability to be chosen is equal to zero). To allow the expressing of varying probability distributions in different states, we extend the label notation of protocol state machine such that it is a tuple (p i , i), comprising a real value p i (i.e., the input's probability) and the input i. The value for a p i must be between zero and one (0 < p i ≤ 1) and the sum of all input probabilities in a state must be equal to one (∑ p i = 1). Thus, input sequences can be generated by traversing the protocol state machine, where the random choice of the next transition (i.e., input) is made using the probability distribution of the outgoing transitions.

For example, PSM-PI-C1'' in Figure 3 uses this extended labeling to specify different probability distributions. In state [START_REF] Beizer | Black-Box Testing: Techniques for Functional Testing of Software and Systems[END_REF], choosing as the next input a has a probability of 0.3. Choosing as the next input b has a probability of 0.7. Consequently, if we select input sequences for several test cases, input sequences starting with b are more likely than input sequences starting with a. Hence, this behavior is tested more intensively than others (which was the intention of using this profile). In particular, the la-bels of the two transitions in state (1) form two intervals, in fact [0, 0.3) for a and [0.3, 1) for b. Thus, for implementing this strategy we just need to choose a random number p with 0 ≤ p < 1. Given a random number of 0.56, we would choose for the next input a b. If the random number is uniformly distributed the order of the particular intervals does not influence the specified probability distribution. It is only required that the order at a state is fixed.

With this label extension, we are not only able to specify the valid behavior of environments, we are also able to specify which parts are more likely than others. Consequently, we are able to select "interesting" test cases with respect to our intended test purpose (e.g., a specific usage profile).

Using Feedback

Observing the behavior of environments and users in practice shows that their behavior changes depending on the reactions of the system. A common example for that is the behavior when doing a phone call. If you lift the receiver, the probability that you will dial a number is dependent of hearing the dial tone. Dialing a number is more likely if you hear the dial tone, or, hang up the receiver is more likely if you hear the busy tone. Therefore it would be advantageous to use such information for test input selection.

Note that if the system reaction to all inputs is deterministic, there is no need to analyze the system reactions for input selection. From the previous input it exactly follows in which state the system under test resides (related to the specification). Therefore, we use feedback information only for systems which are non-deterministic in their observable reactions to inputs.

To consider feedback from a system under test, we again slightly change the labels in protocol state machines. We differentiate two disjoint subsets among the label set. The first subset contains all inputs to the system under test, including input events as well as input events extended with probabilities. The second subset contains all reactions of the system under test (i.e., all possible observations at its required interface). When we traverse such extended protocol state machines to generate input sequences we have two options in each state. We can either choose to select the next input as described in the previous section or we can process output of the system under test.

Protocol state machine PSM-PI-C1''' in Figure 3 shows the principle of this strategy. In state (1) we could choose a or b as the next input. When we trigger the system under test and observe ab as reaction we change to state (2). If the system reaction is aa we change to state (3).

For the next input we can choose a or b as the next input depending on the actual state.

The described strategy requires to process system reactions during test input generation. However, a specific sys-tem reaction is only available during run-time (i.e., when executing the system under test). Our current off-line test generation approach calculates all possible correct system reactions for a given input sequence and then continues with the next input sequence. Considering feedback in this offline process would consequently require to consider all possible system reactions to each input separately. The resulting test case would have a tree structure with determined system reactions on each path. The effort needed to calculate such test cases would be enormous.

The idea of using feedback is similar to classical online-testing approaches (also known as on-the-fly testing) [START_REF] Fernandez | Using on-the-fly verification techniques for the generation of test suites[END_REF][START_REF] De Vries | On-the-fly Conformance Testing using SPIN[END_REF]. In these approaches, test cases are generated at runtime. The exploration of the specification's state space is controlled by the reactions of the system under test. Only these paths are further processed which show the system reactions so far. All the others are discarded. We carry this idea over to our test approach and the input selection with protocol state machines. In classical on-the-fly testing, the feedback is mainly used to avoid state space explosion in the computation of the test oracle, i.e., for test evaluation. In our approach, we do not only facilitate test evaluation, but also use feedback to generate valid input sequences. This is not straight forward for non-deterministic systems, as it may depend on system under test's behavior, which inputs are processable in the next step. With the extension presented here, we can generate more specific and valid input sequences in such a test set-up. Currently, we use feedback information during on-line testing of requirements [START_REF] Heisel | Using UML Environment Models for Test Case Generation[END_REF]. In this approach we continuously trigger the system under test and check the system reactions against the explicitly modeled requirements. The inputs to the system under test are selected on the basis of our extended (protocol) state machines.

Conclusion

In our approach UML behavioral state machines are used in quality assurance to serve as a formal specification for the desired reactive behavior of the system. It is possible to select relevant and interesting inputs for a test case and to calculate the possible correct observations for given inputs. They allow to automatically evaluate test executions which is in general a difficult and time consuming task. With both extensions to our test approach we still focus on conformance testing at the level of unit testing.

The first extension allows to check the interoperability of the component under test with other connected components based on a precise definition of protocol conformance. We use UML protocol state machines to specify the protocol at the provided interface of a connected component, and check the outputs of the component under test at its required interface against it. If the component under test respects the specified protocol of the connected component, we call them interoperable. During testing we check the observations of the component under test not only against the pre-calculated test oracle but also against the protocol state machine of a provided interface of a connected component. We only need to check whether there is a legal transition for the observed outputs. If not, a violation related to the interaction between these components can be reported. This is done fully automatically and with relatively small extensions to the current framework.

The second extension allows to use protocol state machines as test input specifications. By restricting the behavior at the provided interface of the component under test, the set of possible input sequences can be restricted and thus, relevant input sequences can be specified. Input sequences are then selected in combination with classical selection strategies. The two extensions of input probabilities and the interpretation of feedback of the system under test allow to describe the desired behavior on a more precise level. Thus, we can set the focus of the test process to a specific test purpose.

We use UML state machines as a unified notation for behavioral and protocol conformance testing as well as for test input selection. This considerably eases the work of test engineers. To show the general applicability we implemented both extensions in our TEAGER tool suite [START_REF] Seifert | The TEAGER Tool Suite. Test Execution and Generation Framework for Reactive Systems[END_REF][START_REF] Santen | Teager -Test Automation for UML State Machines[END_REF] and applied a case study of a sun blind control [START_REF] Heisel | Using UML Environment Models for Test Case Generation[END_REF].

Our approach is also applicable for more comprehensive protocol state machines. We do not restrict protocol state machines to a specific subset. But the interpretation of some notations is not straightforward and needs more experience with the presented approach. For example, in Section 2 we described that protocol state machines can also have orthogonal regions, or pre-and postconditions at transitions and state invariants. From orthogonal regions it follows that the protocol state machine is in multiple active states at a time. We interpret this in such a way that for the next step the behavior (i.e., input) enabled in every region can happen. This means for input selection that we can choose the next input depending on the possibilities in every region. But this is not clear in all situations. Further research must address this problem in more detail. Another problem is the interpretation of the mentioned predicates. They are defined on the basis of the state space of the component the protocol state machine is associated with, or on the basis of the data events or parameters can carry along. Since a protocol state machine only specifies these predicates and does not execute any code to define the resulting behavior when taking a transition, these predicates cannot always be evaluated (e.g., if they relate to the connected component). Thus, when these predicates should be taken into account, the manipulation of data (i.e., the evaluation of the associated behavior in the component) must also be taken into account. Therefore, we intent also to execute the component's behavior as far as possible for the future. In future research we also address the problem of automatically identifying the valid behavior of the environment of the component under test, if the component is connected to other components. Further research should also study input selection related to complex data. We want to select test cases including adequate data to cover as much as possible of this relevant behavior.

Figure 1 .

 1 Figure 1. Behavioral state machine for C1.

Figure 2 .

 2 Figure 2. Connected components C1 and C2.

Figure 3 .

 3 Figure 3. Specialized environments.

In this paper we do not differentiate between interfaces and ports and use the term interface for both meanings.

In test cases we mark inputs with exclamation marks and outputs with question marks. This may seem complementary to other literature notations. The reason for this annotation is that the outputs of a test case correspond to the inputs of a system under test and the outputs of a system under test correspond to the inputs of a test case.


Moutaz Alhamada

Nathalie Debus

Eliel González García

Francois Bocquier

Francois Bocquier Mating

Mating rams clearly prefer heavy ewes

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

Mating rams clearly prefer heavy ewes

Alhamada M. a , Debus N. a , González-García E a ., Bocquier F. a,b a INRA, UMR868 Systèmes d'élevage méditerranéens et tropicaux, F-34000 Montpellier, France b Montpellier SupAgro, Dept. MPRS, F-34000 Montpellier, France It is generally accepted that nutritional status of the ewe affects its fertility. It has however never been shown that ram's decision to mate a ewe may also contribute to this fertility. Such a ram's decision remains mostly unknown although even if many factors may influence its choice. Inversely, ewes' behaviour may also take part. Its behaviour has been characterised by using three criterions resulting from different pen-tests to be held during limited time. Attractiveness (A)[1] of an estrus ewe is defined as its relative ranking within a group of ewes considering the time spent by the ram when courting and mounting each of them. Considering time spent seeking for rams gives the criterion of Proceptivity of the ewe (P)[2] i.e. % of time spent close to rams in a situation of choice between rams and ewes. Finally, ewe Receptivity (R) [2] is the number of agonistic activities toward a unique ram placed with the ewe. Thirty-six Merinos d'Arles ewes with similar age, body weight (BW; 46.5±3.7kg) and sexual behaviour (A, R, P) were randomly assigned to long-term (3 months) contrasting diets in order to change their nutritional status and body condition score (BCS). All diets were made by mixing straw cap, beet pulp, alfalfa and long hay so that three different feeding regimes were obtained: High (H: 175% of Maintenance Energy Requirements), Medium (M: 105% MER) and Low (L: 72% MER). The average final attractiveness rank was compared by analysis of variance or compared to initial individual values using Spearman's rank correlation coefficient with R package. Mean final BW were significantly (P<0.002) different i.e. H: 55.0±2.7kg, M: 46.9±4.1kg and L: 36.3±3.1kg. Attractiveness of ewes was simultaneously evaluated on 2 oestrus induced ewes per diet which were joined, successively, to 6 active rams. The R and P tests were performed alternating ewes from each diet. Diets did not change (P>0.1) R and P behaviours of ewes, but induced a change in the average A scores i.e. H: 4.47±0.4(a), M: 3.63±0.6(b) and L: 2.42±0.7(c) (a, b, c; P<0.01). Furthermore, we found that rams can finely tune their choice according to the ewe' BW regardless of the diet: Attractiveness score (1-6) = +0.1002 BW(kg) -2.1 ; r=+0.82; n=36; P<0.001). Even if cues for these mating preferences were not identified, our results clearly prove that nutritional status of the ewe is of great influence on the rams' mating decision.

[1] Tilbrook AJ, Lindsay DR. Differences in the Sexual "Attractiveness" of Oestrous Ewes to Rams. Applied Animal Behaviour Science 1987; 17: 129-8.

[2] Fabre-Nys C, Venier G. Development and use of a method for quantifying female sexual behaviour in ewes. Applied Animal Behaviour Science 1987;17: 289-4.