J J Liu

M G Kong

Sub-60 o C Atmospheric Helium-Water Plasma Jets: Modes, Electron Heating and Downstream Reaction Chemistry

For plasma treatment of many heat labile materials (e.g. living tissues) that are either moist or contain a surface layer of liquid, it is desirable that the gas plasma is generated at atmospheric pressure for process convenience and with a gas temperature ideally no more than 60 o C for mitigating permanent damage to the integrity of the test material. This implies that the liquid-containing plasma needs to be of low energy and that plasma treatment should be based largely on non-equilibrium reaction chemistry. In this contribution, a class of sub-60 o C atmospheric helium-water plasma jets is studied in terms of their main physiochemical properties. It is shown that there are five distinct modes appearing in the sequence of, with increasing voltage, the first chaotic mode, the plasma bullet mode, the second chaotic mode, the abnormal glow mode, and the nonthermal arc mode. Its chaotic modes may be sustained over a wide range of water vapour concentration (0 -2,500ppm). Compared to other liquid-containing plasmas, the He-H 2 O plasma jet operated below its nonthermal arc mode has several distinct advantages, namely very low energy consumption (2 -10 J per pulse), sub-60 o C gas temperature, electron-modulated production of He, N 2 , N 2 + , O * , H and OH(A-X), and low ozone production (0.1 -0.4 ppm). These results provide a first attempt at the landscape of the physiochemical characteristics in atmospheric He-H 2 O plasma jets.

Introduction:

Liquid-containing gas plasmas generated at or around atmospheric pressure have been studied for many decades, driven by many industrial and environmental applications such as electrical insulation, electrochemical analysis, waste water treatment, and nano-structure fabrication [1]- [5]. For most liquidcontaining gas plasmas used in these applications, electrical energy sustaining their generation is usually very large, sometimes in excess of 25 kJ per electrical pulse [3]. As a result, they create an array of highly interacting and complex physiochemical processes including dynamic deformation of the gas-liquid-plasma interface, liquid vaporization, shock and acoustic wave, and considerable heat generation. These strongly coupled processes are at the root of some of the most intriguing phenomena, for example, dynamic phase transition [6] and single-bubble sonoluminescence [7].

Recent times have witnessed growing interest in biomedical applications of liquid-containing plasmas, due to a combination of biological effects of ionized gases and the aqueous environment of living tissues that is always present in plasma-tissue interactions [8]. Nonthermal atmospheric plasmas, so far interpreted broadly with a gas temperature as high as 7,000 K [3] [9], have indeed found applications in medicine and these include blood and tissue coagulation [10] [11], lithotripsy [12], and tissue removal [13], some of which have already become a routine clinical procedure. A common feature of these successful medical applications is that liquid-containing plasmas are used as a destructive step to break down an unwanted solid matter (e.g. kidney stone [14]) or colloidal matter (e.g. malignant tissue). This requires a physical force to be established by plasma in the form of localized heat release and/or shock wave. These plasma-based surgical procedures take up to a few tens of minutes to complete. More recent interest has been to mitigate damages to healthy tissues by reducing the reliance on plasma-established physical forces [15]. Provision of localized physical forces represents only part of a wide range of biological effects of nonthermal atmospheric plasmas [8]. Due to their antimicrobial and antitumor properties [8] [16], they are increasingly studied and developed for low-temperature treatment of many diseases including skin and wound infection, chronic and acute wounds, and cancers and tumors [8] [16] [17]. Here reactive plasma chemistry provides the dominant biological effect with a timescale of as short as a few seconds, and the destructive physical force of the plasma is no longer beneficial. Low-energy liquid-containing atmospheric plasmas are therefore desired. Permanent damage and collagen denaturation are known to start to occur to skin at approximately 55 o -65 o C [18] [19]. Therefore the ceiling of the gas temperature should ideally be not much above 60 o C, as an indicator of the 55 o -65 o C range, for safe plasma treatment of living tissues. This choice of the ceiling temperature is appropriate also for other heat-labile materials such as plant tissues, organic matters, and plastics [20].

Most liquid-containing atmospheric plasmas reported so far have a much higher gas temperature than 60 o C at the core of the plasma [3] [9], and as such is not directly amendable for treatment of heat-labile materials. Although gas temperature may be reduced by increasing the plasma-sample distance or the flow rate of the plasma-forming gas, such methods can reduce considerably concentrations of reactive plasma species arrived at the surface of the heat-labile sample. For plasma treatment of living tissues, it is an important consideration since short-living reactive plasma species such as hydroxyl radicals and singlet oxygen are known to be important in plasma-mediated bactericidal effects [21]- [26]. This consideration is also true for plasma treatment of plastics and organic materials. Clearly, there is a general need for sub-60 o C atmospheric liquid-containing plasmas for treatment of many heat-labile materials. At present, this is a largely unexplored area partly because the benefits of liquid plasma reaction chemistry (e.g. those mediated by hydroxyl radicals and hydrogen peroxides) are only beginning to become clear [8]. Yet despite of few reported studies so far, sub-60 o C atmospheric liquid-containing plasmas should be feasible since near room-temperature atmospheric plasmas in other electronegative gases (e.g. air) have been realized successfully using, for example, nanosecond-pulsed excitations [27] [28] or microplasma jets [29] [30].

At present, it is little known what reaction chemistry may be most desirable for any given application and this uncertainty extends to the chemical composition of the plasma-forming gas (e.g. liquid-containing helium, argon, nitrogen or air) [31]. For treatment of living human and plant tissues, the consideration of efficacy, safety and selectivity are more important than that of the cost of the plasma treatment. Given these, we consider in this contribution helium-water mixture as the plasma-forming gas with which to realize a sub-60 o C atmospheric plasma jet using a conventional AC power supply in the 10 -100 kHz range. This study is intended to lead to similar studies of sub-60 o C atmospheric plasmas in other water-containing gases, for which the use of pulsed excitation may become necessary [32], so that similarities and contrasts of reaction chemistry provided by different plasma-forming gases could be unraveled to inform their use for different applications. For the convenience of discussion thereafter, this study is placed in a loose context of plasma treatment of living tissues. However its results should be directly applicable to other heat-labile materials.

To explore as broadly as possible the scope of reaction chemistry of atmospheric He-H 2 O plasma jets, we consider varying both the applied voltage and the water content. The latter is controlled by splitting the helium flow into two channels with one fed through a water bubbling system and the other uninterrupted before being recombined as the plasma-forming gas with a fixed total flow rate of 4 slm (standard liter per minute). The primary goal here is to unravel main physical and chemical characteristics of the atmospheric He-H 2 O plasma jet around the critical gas temperature of 60 o C so that the study lays down a solid scientific foundation with which to explore its low-temperature applications. From a plasma science standpoint, such He-H 2 O plasma jets bridge the traditional pin-to-plate liquid-containing plasmas [3][33] and the cold atmospheric plasma (CAP) jets formed in a dry gas flow [34]. It is of interest whether their plasma characteristics resemble those of the two more studied atmospheric gas discharges and whether they may exhibit some unique properties of their own. As will be shown below, the atmospheric He-H 2 O plasma jet has five modes of operation, each having distinct physical and chemical characteristics.

Experimental Setup:

The experimental setup is illustrated in Fig. 1 with an AC power supply having a peak-to-peak voltage (V p-p ) of up to 7kV at 20 kHz. The electrode unit consists of a powered capillary electrode, through which the helium-water gas is fed, and a grounded ring electrode and a downstream grounded stainlesssteel plate electrode. The outer diameter of the capillary electrode is only slightly smaller than the inner diameter of the quartz tube, and the plate electrode is 10 mm downstream from the nozzle of the quartz tube. Since the main component of the electric field is in parallel to the gas flow, this is a linear-field plasma jet [35] and it can be up-scaled in an array of many parallel plasma jets for treatment of large and threedimensionally structured objects (e.g. surgical forceps and human hands) [36] [37]. Its electrode unit is detailed elsewhere [37]. The flow rate of the moist helium channel, F moist , varies from 0 (i.e. dry helium) to 400 sccm (standard cubic centimeter per minute). The maximum F moist corresponds to a water concentration of 2,500 ppm, estimated using data in literature [38]. The plasma image in Figure 1 shows a visually streamerfree discharge formed between the powered capillary electrode and the ground downstream plate electrode. This plasma stability is in sharp contrast to that in high-energy liquid-containing plasmas [3], but similar to jet plasmas formed in a dry He-containing gas flow [37].

For treatment of living tissues, the upstream introduction of large quantity of water vapor is motivated by the need to achieve reproducible plasma-mediated non-equilibrium chemistry on the site of a malignant tissue. One source of variations comes from the liquid layer on the surface of an infected skin or an open wound, which varies significantly from one case to another. This introduces variation to the plasma interaction with the liquid layer covering the living tissue and indeed the resulting biological effects. It may be mitigated by, for example, firstly wiping and/or gas drying before plasma treatment. Then the remaining water content on the tissue surface can be made negligible by introducing larger quantity of water vapor into the upstream plasma generation region some distance above the tissue surface. As a result, plasma chemistry involving water molecules takes place predominately in the upstream electrode region (see Fig. 1), affected little by the inevitably variable liquid content on the tissue surface.

It is worth comparing the atmospheric He-H 2 O plasma jet in Fig. 1 with several similar liquid-containing jet plasma sources. Atmospheric plasma jets in a dry gas flow and with a downstream aqueous ground electrode is clearly different, since the H 2 O-participating chemistry takes place in the downstream liquid layer and vaporization of the liquid layer is common [39]- [42]. Sharing some similarities with that in Fig. 1, atmospheric plasmas having a carrier gas pre-mixed with liquid aerosol are often used as a detector for gas chromatography [43], for analysis of liquid sample by means of atomic absorption and emission [44], and for surface modification in the plasma generation region [45]. With the liquid aerosol containing precursor chemicals, these plasmas have very different plasma chemistry from the atmospheric He-H 2 O plasma jet. It is worth noting that similar setup to that in Fig. 1 has been used in studies of atmospheric Ar-H 2 O plasma jets excited at 13.56 MHz [46] and at 71 kHz [47] with rotational temperature being 1,100K and 620 -1,125K, respectively. These argon plasmas have much greater gas temperature than 60 o C and as such are more appropriate to applications for which high gas temperature is not hindering. Argon is known to lead to high gas temperature [48][49], though this could be mitigated with sub-microsecond pulsed excitation [48].

Electrical Characterization and Plasma Modes:

Using common electrical techniques for cold atmospheric plasmas [37], the applied voltage is measured together with the discharge currents flowing through the ring electrode (marked in blue in Fig. 2) and through the downstream plate electrode (in red). Fig. 2 shows the case of F moist = 20 sccm over a wide voltage range from shortly after plasma ignition to the point where the plasma becomes unstable with a significant temperature rise. The peak discharge current is in the range of 2 -10 mA, clearly less than those of the Ar-H 2 O plasma jets (>50mA) [46] [47] and much lower than those of traditional nonthermal liquid-containing plasmas (1 -1000 A) [3][9]. This comparison remains true for the current density also, and so the He-H 2 O plasma jet of Fig. 1 is likely to possess different properties to traditional nonthermal liquid-containing plasmas. It is evident that the current waveform undergoes a sequence of distinct changes as the applied voltage increases, indicating the presence of different operation regimes or modes and their transition. These are examined here using electrical properties of the plasma and its nanosecond images. Figure 2a shows a weak discharge shortly after breakdown and with non-periodic current waveform. The cycle-to-cycle evolution of its discharge current shows clear features of chaos (data now shown), and is very similar to that in the chaotic mode of an atmospheric dry-helium plasma jet also at low discharge currents [50]. This is the first chaotic mode of the He-H 2 O plasma jet. In Fig. 2b, the discharge current becomes periodic and its waveform is typical of that when a discrete train of bullet-like and moving emission spots, the so-called plasma bullet, is formed on a nanosecond scale [34]. As will be confirmed with nanosecond imaging (see Fig. 4 and its discussion below), this is the bullet mode. With increasing applied voltage, the discharge current again becomes non-periodic in Fig. 2c and2d and their cycle-to-cycle behaviors are similar to that of the chaos mode observed in dry-helium flow [50] and that in Fig. 2a. This is the second chaotic mode at higher dissipated power than that in Fig. 2a (see discussion of Fig. 3 below). For both chaotic modes, their power spectra are continuous function of frequency and peaks in the spectra do not disappear with increasing data points. Their phase portraits over a few hundreds of excitation cycles do not resemble limited cycles but a complex structure and their Poincare sections appear as a horseshoe structure (data not shown), both indicative of deterministic chaos and both similar to the characters of the chaos mode in a dry He atmospheric plasma jet [50]. Detailed analysis of chaos requires full consideration of phase portrait and Lyapunkov exponent. This is outside the scope of the present study and will be addressed in a future note. It is worth noting that the presence of two chaotic modes on either side of the bullet mode was not discussed in the first report of chaos in atmospheric plasma jets in a dry gas flow [50]. This has since been observed in atmospheric dry helium plasma jets [START_REF] Walsh | submitted and currently under review[END_REF]. So they are generic to atmospheric helium-containing plasmas.

Further increase in the applied voltage leads to the current waveforms returning to periodic in Fig. 2e -2f, where the current is no longer pulse-like but persists continuously throughout one entire period of the applied voltage. Similar to those observed in atmospheric helium plasma jets [50], these are known as the continuous mode in atmospheric plasmas in dry gases [50] with that in Fig. 2f having unstable plasma filaments and non-sinusoidal voltage waveform. As will be shown below, the discharge current increases with increasing voltage in Fig. 2e whereas it increases with decreasing voltage in Fig. 2f. These are similar to properties of the abnormal glow and the nonthermal arc modes in low-pressure dc plasmas. Therefore they are referred to as the abnormal glow mode (Fig. 2e) and the nonthermal arc mode (Fig, 2f), respectively. Electrical characteristics of the five plasma modes of Fig. 2 and their mode transition are more clearly seen in the voltage dependence of the dissipated plasma power, obtained from averaging the product of the applied voltage and the total current. The use of the dissipated power in the place of the discharge current is due to considerable distortion to the current waveform. However if a suitable value is used to represent the discharge current, the current-voltage dependence is likely to be very similar to the power-voltage dependence. As illustrated in Fig. 3, all five modes are present in an appearance sequence of, with increasing voltage, the first chaotic mode, the bullet mode, the second chaotic mode, the abnormal glow mode and the nonthermal arc mode when F moist < 100 sccm. Under such low H 2 O-content conditions particularly when F moist < 40 sccm, the power-voltage relationship is very similar to that with dry helium flow (i.e. F moist = 0 sccm). In the two chaotic modes, the bullet mode, and the abnormal glow mode, the dissipated power increases with the applied voltage. By contrast, the dissipated A,B,C,D, and E are used to refer to the first chaotic mode, the bullet mode, the second chaotic mode, the abnormal glow mode and the nonthermal arc modes, respectively. power increases with decreasing voltage in the nonthermal arc mode. It should be noted that the use of water vapour inevitably deposits a water layer on the surface of the ground electrode and that the ring electrode is dielectrically insulated from the power electrode by the quartz tube. This suggests that the plasma formed with the setup in Fig. 1 is in fact dielectric barrier discharges (DBD). With parallel-plate electrodes and atmospheric helium, the Townsend and the normal glow modes are the common DBD modes that can be observed with cycle-to-cycle reproducibility [START_REF] Zhang | [END_REF] [53]. The use of the flowing gas in the DBD configuration appears to have broadened the available discharge modes.

Further increase in water concentration towards F moist = 120 sccm sees the disappearance of the first chaotic mode and the abnormal glow mode. Now there are only three modes (i.e. the bullet, the second chaotic and the nonthermal arc mode). As the moist helium flow rate increases, the second chaotic mode is seen to persist over a wider range of the applied voltage and its dissipated power reduces to become comparable to that of the bullet mode. The presence of a chaotic mode in liquid-containing atmospheric plasmas has not been reported, nor has its persistence over a large water content range (0 -2,500 ppm). In many ways, this is counter-intuitive since chaos is in general difficult to observe experimentally in a strongly driven system (e.g. periodic electrical excitation) that is open to persistent perturbations (e.g. ambient condition fluctuations). The introduction of large quantity of water vapour could readily smear or disrupt the evolution of necessary nonlinear processes required for the development of chaos. Yet Fig. 3 suggests a persistent chaotic mode over a larger F moist range, and this needs to be further studied to identify possible mechanisms. Also indicated in Fig. 3 is a co-existence between the chaotic and the arc modes at a given applied voltage. As the arc mode consumes much higher electrical power, a power-limiting technique may be used effectively to control transition to the arc mode. In other words, mode selection and locking is achievable.

Plasma jet formation is often considered to be linked to photon ionization, and its explanation is often based on the seminal work of Dawson and Winn in 1965 [54]. In the Dawson-Winn model of photon ionization [54]- [56], a plasma bullet is viewed as the positively charged head with n + positive ions of a cathodedirected streamer. Photon emission from the streamer produces new electrons and positive ions in front of itself. To form a plasma bullet, it is necessary that the number of the newly produced positive ions by photon ionization must be equal to n + [54]. At a fixed applied voltage, water molecules are likely to reduce the level of gas ionization. Being electronegative, water molecules introduce an added electron loss mechanism. Both effects are expected to reduce the amount of the positive ions in the head of a plasma bullet at a given applied voltage. While photon ionization is also expected to become less effective and produces less new electron-ion pairs, it is not immediately clear that the new ions produced would remain balanced out by n + . Yet plasma bullets are formed at the same applied voltage, for example at V p-p = 5kV as shown in Fig. 4, over a wide range of water concentration from 0 to 1,950 ppm (F moist = 0 -300 sccm). These results appear to suggest that the Dawson-Winn model [54] would need further examination and other possible mechanisms should not be ruled out. With increasing H 2 O concentration and at a fixed voltage, the dissipated power in the bullet mode is seen to decrease in Fig. 3 and this is consistent with the decreasing optical emission of the plasma bullet in Fig. 4a and4b.

The nonthermal arc mode exists for all water concentrations studied and its dynamics are considered here. Fig. 5 shows its plasma appearance on a nanosecond scale of 10 ns over one complete cycle of the applied voltage. The plasma is no longer bullet-like, and its light emission appears over the entire length of the inter-electrode gap. Its discharge current through the downstream plate electrode persists over the whole period of the applied voltage (Fig. 2f). In the positive half cycle, the development of the plasma jet seems to suggest a cathode-directed propagation with the images at 2.8, 3.2 and 3.6 s showing a clear evolution of plasma dynamics. In the negative half-cycle, on the other hand, images show less clear evolution pattern of plasma dynamics.

Modes of the atmospheric He-H 2 O plasma jet and their transition are discussed above in terms of its dissipated power and its implication for gas ionization, electron production and electron heating (see discussion below on electron density and electron temperature). For other atmospheric liquid-containing plasmas, modes in terms of whether the plasma is formed within water bubbles are recently reported [57].

It is of interest to discuss the dissipated electrical energy in the plasma. In the bullet mode, the current flowing through the plate electrode has a sharp pulse of 10 -12 s (Fig. 2b) and the dissipated power of 0.2 -0.8 W (Fig. 3) is mostly consumed during the current pulse. The electrical energy is therefore 2 -10 J per pulse. In the nonthermal arc mode, the current waveform is continuous and so the half period is used as an effective pulse width. At 20kHz, the half period is 25 s with which the consumed electrical energy is estimated to be 200 J per pulse at a dissipated power of 8W (Fig. 3). Therefore the energy consumption of the atmospheric He-H 2 O plasma jet is at most 2 -200 J per pulse, some 4 -6 orders of magnitude below that of the liquid-containing corona discharges and 7 -10 orders below of the liquid-containing pulsed arcs [3]. In its bullet mode, the energy consumption of the He-H 2 O plasma is slightly less than 5 -35 J per pulse estimated for the 71 kHz Ar-H 2 O plasma jet [47] and comparable to 5 J per half cycle estimated for the 13.56 MHz Ar-H 2 O plasma jet [46]. These plasma jets represent a relatively new class of low-energy atmospheric liquid-containing plasmas appropriate for low-temperature applications.

Plasma Temperatures and Electron Density:

Non-equilibrium plasmas are usually characterized by at least five temperatures, namely the electron temperature (T e ), the rotational temperature (T rot ), the vibrational temperature (T vib ), the translational temperature (T tran ), and the electronic excitation temperature (T exc ). These are useful to describe electron kinetic properties and their implications to plasma chemical reactions and to the global plasma properties (e.g. gas temperature). At atmospheric pressure, the rotational temperature may be used to approximate the gas temperature since the collision frequency of molecules with heavy particle is very high (~ THz) and rotational relaxation is fast [58]. For non-equilibrium plasmas sustained by an externally applied electric field, reliable diagnostics of electron temperature such as Thomson's scattering are not easily accessible and as a result the vibrational or excitation temperatures of certain molecular species (OH or N 2 ) is sometimes used as an indirect indicator of T e . For low-temperature atmospheric plasmas, T e > T vib > T rot = T tran [59] is usually true and T e = T exc may not be universally assumed [58]. For T rot and T vib , they may be measured together from comparing a measured molecular spectrum of the plasma with its synthetic spectrum.

Here we use the N 2 second positive system (N 2 C 3 u → X 2 g , 360-388nm) to estimate T rot and T vib with the synthetic spectrum obtained using SPECAIR [58]. For T e of non-equilibrium atmospheric plasmas, the most accurate technique is perhaps laser Thomson scattering [60] [61]. Since this and other similarly sophisticated diagnostics are not widely accessible, it is desirable to employ easily applicable techniques and obtain some indication T e of by means of, for example, T exc . For non-equilibrium plasmas, T exc can be used only as an indicator of the ability of electrons to excite atoms to their excited states. This work is based on the work of van der Mullen and co-workers [62]- [64], to which the reader is referred. Here, a brief discussion is provided to describe the basic principle and its application. Consider the Boltzmann balance that describes the exchange of the internal energy of an atom with free electrons in an inelastic atom-electron collision. This leads to either an excitation of the atom or its de-excitation between two energy levels, j and k. If these two energy levels equilibrate and the free electrons may be assumed to have a Maxwellian energy distribution, the atomic state distribution function (ASDF) obeys the Boltzmann relation [62]- [64] (1) where j,k B is the Boltzmann population density of atom A in its energy level j or k, and E k and E j are the excitation energy of the atom at level j and k, respectively, k is the Boltzmann constant, and T e is the electron temperature. Consider now the Saha balance that describes production and destruction of free electrons through ionization of an atom and electron-ion recombination. Involving an ionic state of the atom, A + , and assuming that the ionization and recombination balance, the ASDF is described by the Saha formula [64] (2) Here j S is the Saha population density of atom A at level j, + is the population density of ion A + in the ground state, E + is the ionization energy of the atom, n e is the electron density, m e is the mass of electron, and h is the Planck constant. By assuming η e =η + (hence n e = n + ) and by taking the natural logarithm of the above equation, we arrive at (3) where .

(

) 4
Therefore if ln (η j

S

) is plotted as a function of (E + -E j ), the value of T e may be obtained from the slope of the plot and that of η may be obtained by a linear extrapolation of η j S . With the value of T e , the value of η may be used to deduce n e . As indicated above, T e obtained this way is more close to T exc rather than the actual electron temperature. When the ASDF applicable to all the energy levels obeys the Saha formula, the plasma is said to be in Local Saha Equilibrium (LSE) [65]. If only a part of the atomic system (i.e. some energy levels) is populated according to the Saha formula, this part of the atomic system is said to be in partial LSE (pLSE) [65]. These atomic levels are interrelated to each other by the Boltzmann relation. The excitation temperature determined from the relative or absolute population of these levels equals the electron temperature. In other words, T e determined from the above method may be considered to be close to T exc .

In a plasma experiment, the atomic population density on one particular energy level can be measured or inferred from optical emission spectroscopy. This is usually done in a plasma volume-averaged and timeaveraged fashion, with which the electron density and electron temperature may be assumed as constants for all energy levels (i.e. mean electron energy and mean electron temperature). Similarly the ionic population density may be assumed as constant as well. Therefore, ∞ may be considered as constant, and may be plotted as a function of energy levels. This ln j ~ E j relation should appear as a straight line with a negative slope of -1/kT exc , from which T exc may be deduced by linear extrapolation. The above discussion is based on the assumption of local thermodynamic equilibrium (LTE) [62], which is not satisfied in low-temperature atmospheric plasmas. An alternative indicator of the electron temperature may be obtained under partial LTE (pLTE) and pLSE conditions, by considering an excitation temperature, T 13 , for the transition to the first easily measurable excited state [63]. In our experiments, this is associated with the helium emission at 706 nm to from level 2 to level 3. Clearly T 13 should be less than T e . Here the ASDF is constructed with the absolute density of the ground state and four excited helium state (i.e. 447.2, 587.6, 667.8, 706.5nm), and the elementary occupation of level k is given by [66] (5)

where I kj is the transition integrated absolute intensity of relevant optical emission, A kj is the spontaneous transition probability, and E jk = E k -E j is the difference between the lower and higher atomic energy levels. D is the plasma depth in the line of sight (i.e. the diameter of the plasma plume), and g k is the statistical weight of level k. Values of g k , E jk and A kj are estimated from the NIST data for the four excited helium states. The population density of the ground state, , can be calculated by,

1 1 1 1 // g n g p k T g
and the population density at level 3, η 3 , satisfies eq.( 1), or (6) Here the population density is obtained from eq.( 5) with I kj = I 32 corresponding to the transition integrated absolute intensity of optical emission at 706nm. Therefore by plotting ln(η 3 ) as a function of the excitation energy E 13 , the resulting curve should be a straight line and its slope should be (-1/kT 13 ). This is known as the ASDF plot of an ionizing plasma. Figure 6 shows the ASDF in the bullet mode. The plasma volume whose light emission was picked up by the optical fibre of the spectrometer had a diameter of 0.64mm, across which the plasma emission intensity was largely uniform. The solid angle of the optical pickup was approximately 3x10 -4 rad. On the other hand, self-absorption effects of the helium emission lines were not considered, and therefore the result shown in figure 6 is an approximate. Fig. 7 shows the moist helium flow rate dependence of T rot , T vib , T exc and T 13 for one case each in the bullet mode and in the nonthermal arc mode. In the case of the bullet mode at V pp = 5.0kV (Fig. 7a and7b), rotational temperature, T rot , increases slightly from 300 K at F moist = 0 sccm to 325 K at F moist = 60 sccm after which it undergoes a small increase to ~ 335K at 300 sccm. This corresponds to 27 -62 o C for all moist helium flow rates studied, similar to those of atmospheric dry-He plasmas [32][67]. This temperature range is in general appropriate for sub-60 o C applications to heat-labile materials. Similar to the T rot -F moist dependence, the vibrational temperature also undergoes a small increase from 3,400 to 3,550 K in 0 -40 sccm. Then it decreases to 3,200 K at F moist = 80 sccm before levelling off at approximately 3,100K. For T exc and T 13 , the initial trend is also a small increase till F moist = 40 sccm when they reach their maximum values of 3,632K and 8,265K, respectively. After that, they both decreases monotonically to reach 1,000K and 7,988 K at 300 sccm, respectively. For F moist > 60 sccm however, helium emission lines at 447.2 nm and 587.6 nm are weak in the bullet mode thus affecting the reliability of the T exc measurement. This is marked with dashed line in Fig. 7b. Its value of 3,200 -3,632 K for 0 -60 sccm is similar to those of atmospheric dry helium plasmas [68]. Despite of the uncertainty of T exc for F moist > 60 sccm, its trend of an initial rise and then decay is unambiguous in Fig. 7b. This suggests an effective electron heating with increasing water concentration till 40 sccm (or 250 ppm), perhaps benefited from a similar trend of the dissipated power (see Fig. 8a), in the bullet mode.

In the 0 -60 sccm range, T exc = 3,200 -3,632 K and T vib = 3,400 -3,550 K and they are therefore comparable. For atmospheric dry helium plasmas, the mean electron temperature is reported to be about 1 -5 eV [69] [70]. Therefore T 13 of 7,988 -8,265 K in Fig. 7b is closer to T e in value than T exc . It is worth reemphasizing that T 13 is obtained under pLSE conditions and for the first measurable excited state of the helium atom. It should therefore be smaller than T e . It is interesting to note that T vib , T exc and T 13 all increase first to reach their maximum values at 40 sccm and then start to decrease. At a constant applied voltage (V p-p = 5.0 kV), this appears to suggest that electrons efficiently exchange energy with helium atoms with increasing moist flow rate from 0 to 40 sccm. The subsequent fall in T vib , T exc and T 13 may indicate a reduced energy exchange, possibly related to an increase in electron attachment to water molecules and to a reduced level of gas ionization at a constant applied voltage.

In the nonthermal arc mode at V p-p = 5.8kV (Fig. 7c and7d), the rotational temperature initially remains at 445K for 0 -120 sccm, then from 120 sccm increases to reach its maximum value of 480K at 200 sccm, and finally from 200 sccm undergoes a gradual reduction to 450K at 400 sccm. The trend of an initial increase followed by a gradual decay in 120 -400 sccm is similar to that in the bullet mode, but the transition point in the arc mode occurs at a much higher moist flow rate of 200 sccm. The rotational temperature of less than 480K confirms the nonthermal character of the arc mode in Fig. 2f. Its value is nevertheless 145K above the rotational temperature in the bullet mode and is well above 60 o C thus inappropriate for treatment of living tissues. Yet it is still considerably below those observed in Ar-H 2 O plasma jets of 620 -1,125 K [46] [47]. Even with comparable energy consumption of 2 -35 J per pulse, this low gas temperature in the helium plasma is not surprising because of the better thermal conductivity of helium.

Of interest is that the vibrational temperature in the nonthermal arc mode follows an identical trend to its corresponding rotational temperature with the transition point also at 200 sccm. The value of the vibrational temperature in the arc mode is between 4,800 and 5,200 K, some 400K above that in the bullet mode. The similarity in the F moist dependence of T rot and T vib in the bullet mode is less but nevertheless remains close.

Similar to the F moist dependence of T 13 in the bullet mode (Fig. 7b), T 13 in the arc mode reaches its maximum value also at 40 sccm. Its value range varies in the range of 8,570 -8,940 K, and is about 520 -580K above its corresponding value in the bullet mode. In the arc mode, the electronic excitation temperature, T exc , evolves initially as a constant of 4,900 K in the range of F moist = 0 -40 sccm before climbs to its maximum value of 5,200K at 120 sccm, where its corresponding T rot and T vib start to increase. Subsequently in the 120 -200 sccm range, T exc , undergoes a monotonic decrease to 4,920 K at 200 sccm after which it changes little between 4,850 and 5,000 K. Its final plateau in the 200 -400 sccm range may not be compared to that in the bullet mode due to the weak helium emission intensities of the latter. The maximum value of T exc in the arc mode is about 1,550K above its value in the bullet mode.

To explain the moist flow rate dependence of T vib , T exc and T 13 in Fig. 7, the dissipated power, P, is obtained from averaging the product of the applied voltage and the total current over time and is shown in Fig. 8a. Electron density n e is also estimated from calculating the total charge flowing to the plate electrode from the discharge current and dividing it by the volume of the plasma plume. The results are shown in Fig. 8b with n e = 4.9 -15.4 x10 12 cm -3 in the bullet mode and 1.8 -3x10 13 cm -3 in the nonthermal arc mode. Alternatively, electron density may be obtained from ∞ in eq.( 2), for which the electron temperature at the surface of the plate electrode may be assumed to be ~ 1eV. This method results in a maximum n e of 0.4 -6 x10 13 cm -3 , similar to the n e = 0.5 -3 x10 13 cm -3 range for both the bullet and the arc modes in Fig. 8. These estimates are also consistent with n e ≤ 2x10 13 cm -3 for a discharge current <10mA, measured using laser heterodyne interferometry [71].

In the bullet mode, n e = 4.9 -15.4 x10 12 cm -3 and P = 0.01 -0.9 W. Both the dissipated power and the electron density are seen to follow a very similar trend, namely an initial increase to reach their perspective peak value at 40 sccm (or ~ 250 ppm) followed by an exponential-like fall. Resembling closely the F moist dependence of T vib , T 13 and T exc with the same transition point at 40 sccm (see Fig. 6a -6b), Fig. 8a and 8b suggest that electron heating and electron production increase with increasing dissipated power till 40 sccm. An increase in the dissipated power is likely to increase ionisation via Penning ionization processes by helium metastables, particularly He * +H 2 O → He+H 2 O + +e, He * +H 2 O → He+ OH + + H +e, He * +H 2 O 2 → He+OH+OH + +e [72]. A recent global modeling of atmospheric He-H 2 O plasmas suggests that electron generation is predominately due to Penning ionization of H 2 O, electron impact ionization of helium, and metastable pooling of helium species and that the electron generation rate peaks between 30 -100 ppm [72]. In the bullet mode, the simultaneous reach to the maximum value of n e , T exc and T 13 , and P at 40 sccm or 250 ppm is therefore consistent with the prediction of the global model.

In the nonthermal arc mode, the initial increase in n e (1.8 -3.0 x10 13 cm -3 ) and P (5.1 -6.3 W) is very modest and their subsequent fall from 60 sccm is also more gradual in comparison to the bullet mode. As n e = 3 x10 13 cm -3 is close to ~5×10 13 cm -3 above which Stark broadening may be used to measure electron density, we consider the latter method for the arc mode. Specifically n e may be evaluated from the line-shape of the Balmer β transition (4-2) of atomic hydrogen with the related emission at 486nm (H β line) [73]. Although the H α line (at 656nm) has higher intensity than H β , it is rarely used to measure low electron density due to its sensitivity for self-absorption and strong broadening by ion dynamics [74]. We first estimate various broadenings including Van der Waals, Doppler, resonance, natural and instrumental broadenings, and their combination broadening is 0.035 -0.045 nm, which can be resolved using our spectrometer. Results using Stark broadening are shown in Fig. 8c and indicate a peak electron density of 1.7x10 14 cm -3 in the 40 -60 sccm range (or 250 -400 ppm). The moist helium rate at which n e reaches its peak value is the same regardless of its measurement techniques, but that estimated using the H β line is higher.

It is worth noting that electron density estimated for a comparable atmospheric Ar-H 2 O plasma jet is about 1.5x10 13 cm -3 [47], similar to the upper end of the electron density range of the atmospheric He-H 2 O plasma jet in its bullet mode. Given that the maximum energy consumption is 10 J per pulse in the He-H 2 O plasma jet and 35 J per pulse in the Ar-H 2 O plasma jet, it appears that the former is more efficient in electron production at a lower gas temperature (i.e. less energy conversion into heat).

Reactive plasma species:

The key benefit of sub-60 o C atmospheric liquid-containing plasmas is to deliver controlled reaction chemistry to a downstream location. To this end, it is important to characterize main reactive plasma species arrived at the surface of the ground electrode (see Fig. 1) where the sample is usually placed. So far there has been little characterization of downstream reaction chemistry for low-energy atmospheric liquid-containing plasmas, and the only information in literature appears to be a monotonic decrease of optical emission intensities, with increasing water content, of excited atomic oxygen at 777nm, excited OH (A-X) at 309 nm, and exited argon atom at 696, 763 and 801 nm, respectively, in an atmospheric Ar-H 2 O plasma jet [47]. This is therefore a considerably under-studied area. To provide comparison with future studies, absolution emission measurement is made in the 350 -850 nm range and simultaneous relative emission measurement in the 200 -900 nm. As absolution emission measurement below 350nm is unavailable at the point of this investigation, the relative intensity data provide an indirectly calibrated extrapolation of absolute emission intensities spanning across the visible range. Fig. 9 shows the H 2 O dependence of optical emission intensities of atomic oxygen lines at 777 and 845 nm, helium line at 706 nm, N 2 line at 358, 317, 337 nm, N 2 + line at 391 nm, and H line at 656 nm, in the bullet mode at V pp = 5kV. The strongest emission is from N 2 line at 358nm, and it reaches 0.6 W/cm 2 . In Fig. 9a, all emission intensities increase first then decrease and all have their maximum intensity at F moist = 40 sccm. This is identical to the point where both the electron density (see Fig. 8b) and the electron excitation temperatures (see Fig. 7b) reach their respective maxima. The same trend is seen in Fig. 9b for two other N 2 lines, the H line at 656 nm, and the OH line at 309 nm, indicating a strong correlation to electrons. This suggests that production of these species is directly correlated to electron density and/or electron temperature, or directly related to electron heating.

For F moist > 40 sccm, the emission intensities are seen to decrease for all excited species considered. This may be related to the decrease of the dissipated power and hence that of the electron density. To see this more clearly, the emission intensities are divided by the dissipated power to obtain a measure of energy efficiency of plasma species. As shown in Fig. 9c and9d, this energy efficiency increases with increasing water content. This may be explained by that the increase of water content increases the efficiency of the Penning ionization of water molecules by helium metastables via He * +H 2 O →He+H 2 O + +e, He * +H 2 O →He+ OH + + H +e, and He * +H 2 O 2 →He+OH+OH + +e [72]. There appears to be an optimal window of operation for energy efficiency of plasma species production.

Similarly in the nonthermal arc mode, the optical emission intensity increases first, though more gradual than that in the bullet mode, and then undergoes a monotonic reduction in Fig. 10a. The change-over point is around F moist = 40 sccm, and is consistent with the H 2 O dependences of both electron density and dissipated power in Fig. 8. The exception appears to be the H line at 656 nm and helium line at 706 nm, which remain little changed. When considering energy efficiency using the dissipated power to divide into the emission intensity, the trends do not change much due to the dissipated power increasing only 24% from 6.3 to 5.1 W. In Fig. 10c and10d, the two N 2 lines follow similar trend as the electron density whereas the OH line intensity behaves different to all others. The latter peaks at F moist = 100 sccm after which the energy efficiency of its production remains approximately constant. In Fig. 7d, T exc reaches its peak at 120 sccm and T 13 reaches its maximum at 40 sccm. When related to Fig. 10, it seems that OH(A-X) is more directly related to T exc whereas all other excited species are related to T 13 or electron density. If this is confirmed directly in future studies, it may provide methods to enable preferential reaction chemistry. The water vapour dependence of OH in Fig. 10d also suggests that the loss channel of OH through OH+OH = H 2 O 2 and that of N 2 (358nm) are not as effective as other species that are seen to undergo fast decay.

Finally the ozone concentration is measured chemically [31] and shown as a function of the water content in Fig. 11. It is clear that the ozone production is low with its peak value at 0.7 ppm in the bullet mode and 8.4ppm in the arc mode. With increasing water content, the ozone concentration reduces significantly to below 1 ppm in both cases. The US National Institute for Occupational Safety and Health (NIOSH) recommends that the maximum ozone level is 0.1 ppm in an industrial working area (i.e. 8 hours per day and 6 days per week). The use of reasonably large quantity of water vapour would reduce the ozone concentration to 0.1ppm above 120 sccm in the bullet mode and to 0.4 ppm above 200 sccm in the arc mode. At these low levels, plasma exposure at a timescale of less than one minute per day may be become safe. Fig. 11 shows that reaction chemistry provided by the He-H 2 O plasma jet is characterised by those based on OH, H 2 O 2 and electrons with ozone playing a minor role having a concentration at a potentially safe level. Figure 9 -11 show the water content dependence of many key excited species and ozone. It is possible to adjust the water content and alter the relative proportion of different plasma species and the associated plasma chemistry. This is useful not only for manipulating plasma chemistry but also for minimising plasma species that may potentially be harmful to mammalian cells. For example water content above 120 sccm or 750ppm would reduce the ozone content to less than 0.4ppm. These data are useful in assisting the choice of the chemical composition of the plasma-forming gas for biomedical applications. An obvious related question is the specific production and loss mechanisms for each of many plasma species such as OH, H 2 O 2 , singlet O 2 , O, and O 3 . In atmospheric He-O 2 plasmas, atomic oxygen is produced mainly collisional excitation by helium metastables and their depopulation is dominated through quenching by He and O 2 [31]. With water molecules involved, production and loss mechanisms are altered and in general become dependent on the water content and the dissipated electrical power [72][76]. In general, simple conclusions true for the entire parametric space are no longer always possible, due to the strong coupling of many hundreds chemical reactions, and this is an area in need of further investigation. The case of OH radicals is however becoming clearer, for which main generation and loss mechanisms are likely to be electron impact dissociation and OH+OH → H 2 O 2 , respectively [72][75] [76]. Notwithstanding necessary in-vitro cell biology and in-vivo studies, the atmospheric He-H 2 O plasma jet appears to offer an efficient and effective way to deliver desirable reaction chemistry at sub-60 o C temperature and a safe level of ozone.

Conclusions:

Proposed as a possible candidate for treatment of living tissues and other heat-labile material, a class of sub-60 o C atmospheric helium-water plasma jets is studied. It is shown that there are five distinct modes in the atmospheric He-H 2 O plasma jet, namely the first chaotic mode, the bullet mode, the second chaotic mode, the abnormal glow mode, and the nonthermal arc mode. Mode selection and mode locking are possible by means of electrical control, thus offering a mechanism with which to operate the atmospheric He-H 2 O plasma jet in a preferred mode. The observation of a chaotic mode has not been reported before in atmospheric liquid-containing plasmas, and the bullet and the second chaotic modes persist for the water vapour range 0 -1,950ppm.

In the bullet mode, electron density, electron excitation temperature, and dissipated power simultaneously reach their maximum values all at about 40 sccm (~ 250ppm), and their water vapour dependence is mirrored in the H 2 O dependence of a panel of excited plasma species (e.g. N 2 , N 2 + , He, O * , H and OH(A-X)) and ozone. In the nonthermal arc mode, the water vapour dependence of OH intensity at 309 nm and N 2 intensity at 358 nm appear to be correlated to T exc whereas that of all other excited species and ozone seem to be corrected to electron density. This separate dependence may offer routes to tailor reaction chemistry of atmospheric liquid-containing plasmas. Energy efficiency of plasma species production increases with the water content, highlighting the importance of electrical power coupling, reaction chemistry, and the possibility of n e and T e based tailoring of downstream chemistry.

In comparison with other atmospheric liquid-containing plasmas, the He-H 2 O plasma jet operated below its nonthermal arc mode has several distinct advantages, namely very low energy consumption (2 -10 J per pulse), sub-60 o C gas temperature, electron-modulated production of He, N 2 , N 2 + , O * , H and OH(A-X), and low ozone production (0.1 -0.4 ppm). In addition, it can be operated in a chaotic mode with a robust independence of the water concentration. These results provide a first attempt at the landscape of the physiochemical characteristics in atmospheric He-H 2 O plasma jets.

Figure 1 :

 1 Figure 1: Schematic of the capillary-ring electrode configuration and an image of a plasma jet in an atmospheric He/H2O flow. The capillary, the ring, and the downstream plate electrodes are labelled as 1, 3 and 4, respectively, whereas the quartz tube is marked as 2.

Figure 2 :

 2 Figure 2: Waveforms of the applied voltage (black), the current flowing to the plate electrode (red), and the current flowing to the ring electrode (blue) in (a) the first chaotic mode; (b) the bullet mode; (c) the second chaotic mode near the bullet mode; (d) the second chaotic mode near the arc mode; (e) the abnormal glow mode with no voltage distortion; and (f) the nonthermal arc mode with voltage distortion.

Figure 3 :

 3 Figure 3: Dissipated power as a function of the applied voltage at different Fmoist. ModeA, B, C, D, and E are used to refer to the first chaotic mode, the bullet mode, the second chaotic mode, the abnormal glow mode and the nonthermal arc modes, respectively.

Figure 4 :

 4 Figure 4: Nanosecond images (10ns) of the He/H2O plasma jet in its bullet mode when Fmoist is (a) 0 ppm (at 0.7 W and Vp-p = 5kV); (b) 400 ppm (at 0.4 W and Vp-p = 5kV); and (c) 1950 ppm (at 0.24 W and Vp-p = 5.8kV). Label 1, 2, and 3 mark, respectively, the locations of the ring electrode, the tube nozzle, and the ground plate electrode.

Figure 5 :

 5 Figure 5: Nanosecond images of the atmospheric He/H2O plasma jet in its nonthermal arc mode at Vp-p=5.8kV, P = 4.5 W and Fmoist = 0 sccm. The left panel is for a positive half cycle, and the right is for the next negative half cycle. Exposure time is 10 ns.

Figure 7 :

 7 Figure 7: Fmoist dependence of rotational temperature, vibrational temperature, excitation temperature, and T13 (the excitation temperature between the ground and the first excited state) at Vp-p = 5kV within the bullet mode (in (a) and (b)) and at Vp-p = 5.8kV within the nonthermal arc mode (in (c) and (d)).

Figure 6 :

 6 Figure 6: Plot of the atomic state distribution function for the atmospheric He-H2O plasma jet in its bullet mode (Vpp = 5kV; Fmoist = 0sccm). E+ is the helium ionization energy.

Figure 8 :

 8 Figure 8: Fmoist dependence of (a) dissipated power; (b) electron density measured from discharge current; and (c) electron density measured from Sark broadening in the nonthermal arc mode. In the bullet mode (black), Vp-p = 5 kV; and Vp-p=5.8kV in the high-current arc mode (red).

Figure 9 :

 9 Figure 9: Fmoist dependence of (a) the absolute optical emission intensity above 350 nm; (b) the absolute intensities per watt of dissipated power; (c) the relative optical emission intensities; and (d) the relative optical intensity per watt, all in the bullet mode at Vp-p = 5 kV.

Figure 11 :

 11 Figure 11: Moist helium flow rate dependence of ozone concentration in the bullet mode (Vp-p = 5kV) and the nonthermal arc mode (Vp-p = 5.8 kV).

Figure 10 :

 10 Figure 10: Fmoist dependence of (a) the absolute optical emission intensity above 350 nm; (b) the absolute intensities per watt of dissipated power; (c) the relative optical emission intensities; and (d) the relative optical intensity per watt,, all in the nonthermal arc mode at Vp-p = 5.8 kV.

Corresponding author; e-mail: m.g.kong@lboro.ac.uk


Walid Fdhila

Aymen Baouab

Karim Dahman

Claude Godart

Olivier Perrin

Franc ¸ois Charoy

Change Propagation in Decentralized Composite Web Services

Keywords: decentralized orchestration, business process, change propagation, web service

Every company wants to improve the way it does business, or produce things more efficiently, and make greater profit. Therefore, business processes have become subject to evolutionary changes, which in turn increase the need for an efficient change support. In this sense, many researches were conducted to deal with business process adaptation to changes. The latter may result in the restructuring of the whole or a part of the process. Most of the proposed approaches focus on adaptation to changes in centralized processes. In sharp contrast to these works, our operation of change adaptation that we present in this paper, concerns decentralized orchestrations. Indeed, many recent approaches were proposed to decompose a composite web service into small partitions. Since the activities, the control and data flows are distributed over these partitions, it becomes difficult to specify the changes directly. Moreover, changing a derived partition may affect the way it interacts with others. In order to overcome these deficiencies, we propose a design-time methodology to support changes in decentralized business processes. We mainly demonstrate how to propagate the changes made on a centralized specification of composite web service to its resulting decentralized sub-processes.

I. INTRODUCTION

The Service-Oriented Architecture (SOA) is a proven collection of principles for structuring large-scale systems in order to improve manageability and to streamline change. One of the pillars of the SOA is its ability to rapidly compose multiple services into an added-value business process, and then to expose the resulting composition as a composite service [START_REF] Benatallah | The self-serv environment for web services composition[END_REF]. Despite the decentralized nature of the context of the B2B and B2C interactions, the conception and implementation of a typical business process rely on a centralized execution setting. The relevant research literature on business management confirms that the decentralization of business processes is a critical need for several reasons [START_REF] Benatallah | The self-serv environment for web services composition[END_REF]. In a previous work, we proposed an approach to partition a composite web service [START_REF] Fdhila | A flexible approach for automatic process decentralization using dependency tables[END_REF], [START_REF] Fdhila | Toward synchronization between decentralized orchestrations of composite web services[END_REF], [START_REF] Fdhila | Optimized decentralization of composite web services[END_REF]. The partitioning transforms the centralized process into behaviorally equivalent distributed sub-processes each of which related to a criteria. These partitions are executed independently at distributed locations and can be invoked remotely. They directly interact with each other using asynchronous messaging without any centralized control. The flexibility introduced by the derived decentralized processes on the other hand raises new requirements like adaptation to change. Neither our approach or similar approaches on decentralization deal with the problem of change propagation after partitioning. However, in today's dynamic business world, the economic success of an enterprise increasingly depends on its ability to react to changes within its environment in a quick and flexible way [START_REF] Hammer | The reengineering revolution: A handbook[END_REF]. Therefore, a critical challenge for the competitiveness of any enterprise is its ability to quickly react to business process changes and to adequately deal with them [START_REF] Van Der Aalst | Generic workflow models: How to handle dynamic change and capture management information?[END_REF]. Causes for these changes can be manifold and include the introduction of new laws, market dynamics, or changes in customers' attitudes [START_REF] Rinderle | Correctness criteria for dynamic changes in workflow systems -a survey[END_REF]. For these reasons, companies have recognized business agility as a competitive advantage, which is fundamental for being able to successfully cope with business trends. Changes may range from simple modifications of the process to a complete restructuring of the business process to improve efficiency. In the context of the decentralized service orchestrations, applying these changes in a straightforward manner on the derived orchestration partitions is a complex maintenance task, since the control and data flows are decomposed over multiple partitions. In this sense, this paper presents a method for adaptation to change in the decentralized composite web services. In sharp contrast to previous works [START_REF] Pesic | Constraint-based workflow models: Change made easy[END_REF], [START_REF] Reichert | Adept flex -supporting dynamic changes of workflows without losing control[END_REF], [START_REF] Adams | Dynamic, extensible and context-aware exception handling for workflows[END_REF], [START_REF] Weber | Change patterns and change support features -enhancing flexibility in processaware information systems[END_REF], our change adaptation concerns decentralized orchestrations. Given a well-behaved structural update on a centralized orchestration, our approach automates the change forward propagation that consistently propagates the update to the derived decentralized partitions. This includes the identification of the partitions concerned by the modification, and the incorporation of the necessary changes in each of them. The main advantage of this method, is that only concerned partitions by the change are affected, and there is no need to recompute the whole decentralization or redeploy all the partitions. The reminder of this paper is structured as follows. Section II introduces a motivating example to illustrate the importance of change adaptation in decentralized orchestrations. We adopt this example to explains the different steps of our approach. Section III presents the formal definitions needed to provide a generic approach, while section IV details our adaptation to change mechanism for the decentralized processes. After a reviewing of the properties of our approach in Section V and its evaluation, the section VI and VII we discuss the related works, summarize the contribution and outline future directions.

II. MOTIVATING EXAMPLE

The composite web services are generally captured by means of an orchestration model: a process model in which each activity represents either an intermediate work step (e.g. a data transformation) or an interaction with one of the services participating in the composition (the component services). The process model specifies the control-flow and data-flow relations between activities, using a specialized language such as the Business Process Execution Language (WS-BPEL) or the Business Process Modeling Notation (BPMN). To motivate and illustrate the method presented in this paper, we make use of a sample orchestration (cf. Figure 1). The corresponding process model is captured in the BPMN notation, and it includes both control and data dependencies. The process is represented by a directed acyclic graph where nodes are activities or control patterns and edges are data or control dependencies. Activities are depicted with boxes with the activity name inside and the web service it refers to. The arcs between boxes describe the dependencies. This leads to a well defined chronological execution of the activities. In this example, we consider that services are known in advance. In particular this process represents a collaboration with four services s 1 , s 2 , s 3 and s 4 . Data edges are represented by dashed arrows, and each control pattern has an identifier. After a 1 termination, the data d 1 is sent to a 4 , then a 2 or a 3 or both of them are executed. This execution terminates when it reaches the terminate state. Based on our partitioning techniques presented in [START_REF] Fdhila | A flexible approach for automatic process decentralization using dependency tables[END_REF], [START_REF] Fdhila | Toward synchronization between decentralized orchestrations of composite web services[END_REF], Figure 2 depicts a decentralized execution settings for the process example of Figure 1. The latter is partitioned into four partitions that are executed by four distributed orchestrators (preferably collocated with the web services). Each partition P s is responsible for all activities that are delegated to a given service and defines the relationships between them. The connectivity between activities of the centralized process is translated to that between activities of different partitions, through asynchronous message exchange mechanism. This includes data and control links (dashed arrows) represented by c 1 , c 2 , and d 1 , d 7 respectively. The schema, illustrates only a part of the interconnection mechanism. Now, consider that the designer wants to make some changes, which result in a restructuring of the process activities. Specifying these changes directly on the decentralized partitions is a complex and error-prone task. Indeed, the activities are distributed over partitions, and a local change on a partition may affect other partitions, since they are related. Hence, it is more easier to apply the changes on the centralized specification, and then propagate these modifications to the derived partitions with respect to control and data flows. Let's consider the example depicted in Figure 1, and suppose that the fragment F (gray box, consisting of OR sp5 , a 10 , a 11 , a 17 and OR j5 ) should be replaced by the fragment F ′ (consisting of AN D sp6 , a 25 , a 26 , a 27 and AN D j5 ). The data link, initially connecting a 17 and a 23 is modified by a data link between a 25 and a 23 . Obviously, this replacement would affect mainly the partitions P s2 and P s4 , since the modification includes only activities invoking s 2 and s 4 respectively (c.f. Figure 2). As can seen in the above example, the changes on the centralized specification, results in restructuring of the concerned derived partitions. They can either be done as an insertion, an update or a deletion of tasks. In Figure 2, the gray boxes in partitions P s2 and P s4 , represent the fragments which are affected by the change. These fragments are sub-processes with single entry/single exit and should be replaced by the updated fragments. To conclude, the main problem behind this change is: (i) How to identify the concerned partitions, and in each partition the fragments to be changed or restructured? (ii) How the concerned activities and control patterns are deleted or updated? (iii) Where to insert the new fragments and how? (iv) Finally, how to update the communications with other partitions to preserve the semantics of the initial process?

III. FORMAL MODEL

In order to provide a generic approach for change adaptation in the decentralized business processes, we adopt a high level reasoning using an abstract notation. A process is specified in an abstract way (e.g. using a graph based formalism), and mapped onto the implementation level. We do not presume any particular process modeling approach, but simply assume that the basic elements of a process can be specified in an abstract way to be translated to an executable process language (i.e. process structure in terms of atomic activities and sub-processes, dependencies between the steps of a process, etc). By definition, a process which specifies a web service composition defines the relationship between service invocations. This relationship may characterize either the control or data flow structure.

Definition 1 (Process): Formally, a process P is a tuple (O, D , Ec, Ed, S) where

• O is a set of objects which can be partitioned into disjoints sets of activities A, events (start and end) and control patterns CT R, • D is a set of data,

• Ec is a set of control edges where, Ec ⊂ O×O.

• Ed is a a set of data edges where, Ed ⊂ A×A×D, • S is the set of services invoked by the process. In this paper, we consider that the processes are structured [START_REF] Kiepuszewski | On structured workflow modelling[END_REF]. This means that different activities are structured through control elements such as AND-split, OR-split, AND-join, OR-join, etc., and for each split element, there is a corresponding join element of the same type. Additionally, the split-join pairs are properly nested. The process, has a single entry and a single exit. A process activity consists of a one-way or a bidirectional interaction with a service via the invocation of one of its operations. In conversational compositions, different operations of a service can be invoked with the execution of different activities. The set of activities that refer to the same service s is denoted A s | s ∈ S. A control edge characterizes the mapping relationship while a data edge characterizes the mapping relation of the output and the input values of two activities.

Definition 2 (Activity): An activity a i ∈A s is a tuple (In, Out, s) where In⊂D is the set of a i 's intputs and Out ⊂ D is the set of a i 's outputs, s is the service invoked by a i .

Activities are related to each other and are dependent on each other. These dependencies are intra-process. Dependencies may also exist across processes and are referred to as inter-process dependencies. A path between two objects (activity, event or control pattern) is defined by the set of edges which link them. We define the preset (postset) of an activity a i , denoted •a i (a i •), as the set of activities which may execute just before (after) a i and related to it by a set of control dependencies.

Definition 3 (Path):

The path between two objects o i and o j is defined by path ij =e 1 ,e 2 ,...e n ∈ E c where ∀k, 1≤k<n, target(e k ) = source(e k+1 ) ∧ source(e 1 )=o i ∧ cible(e n )=o j .

Definition 4 (Preset):

•a i ={a j ∈ A|∃ path ji = e 1 ,e 2 ,.
..e n ⊂ E c where ∀k, 1<k<n, target(e k ), source(e k ), target(e 1 ), source(e n ) ∈CT R}.

Definition 5 (Postset):

a i • ={a j ∈ A|∃ path ij = e 1 ,e 2 ,...e n ⊂ E c where ∀k, 1 <k<n, target(e k ),source(e k ), target(e 1 ), source(e n ) ∈CT R }.
The partitioning of a composite web service, leads to a set of interconnected partitions, each of which defines the relationship between the objects it includes. Each partition communicates with other partitions using the interaction patterns (i.e. send, receive..) [START_REF] Barros | Service interaction patterns[END_REF].

Definition 6 (Partition):

A sub-process or a partition is a tuple

P s = (O s , D s , Ec s , Ed s ) where • O s is a set of objects of P s . O s ⊂ O ∪ A dummy(i)
where A dummy(s) is a set of dummy activities. Dummy activity is an activity with zero execution time (used for synchronization). • D s ⊂D∪Sync, where Sync is a set of control data necessary for synchronization with other partitions. • Ec s is the set of control edges, Ec ⊂O s ×O s , • Ed s is the set of data edges, Ed ⊂A s ×A×D s . (control edges between partitions are transformed to data edges since they are routed in messages). • s ∈ S is the set of services invoked by the partition.

Example:

The definition of P s1 (c.f. figure 2) is the tuple Next, we define the transitive postset (resp., transitive preset) of an activity a i denoted T a i • (•T a i ), as the set of activities in the same partition as a i , which may execute just after (before) it, and linked to it by a set of control dependencies. In the centralized process, the path between a i and its transitive postset (resp., transitive preset) may include other activities of other partitions.

P s1 = (O s1 , D s1 , Ec s1 , Ed s1 ) where, • O s1 = {a 1,8,9,15,12 , a dummy1 , start 1 , end 1 , OR 1,2,3,4sp , OR 1,2,3,4j }. • D s1 = {d 1 ,d 5 ,..} • Ec s1 = {e start,

Definition 7 (Transitive postset):

Formally, for an activity

a i ∈ P ck , is T a i • = {a j ∈ P ck | path ij = e 1 ,

IV. CHANGE PROPAGATION

In this section, we present our methodology for decentralized business processes adaptation to change. We remind that our approach concerns only already partitioned processes. This means that we do not seek to provide a change support for a centralized process. Instead, we demonstrate how to propagate the changes made on a centralized specification of a composite web service to its resulting decentralized sub-processes. The approach is structured as follows. First the designer specifies the changes using the centralized process specification, then we compute the new configurations of the decentralized fragments enclosing the changes. Finally, we propagate the changes to the concerned partitions. In this way, only the fragments which are concerned by the changes would be affected. Moreover, there is no need to re-partition the

A. Change operations

In general, process models can be decomposed into SESE fragments [START_REF] Vanhatalo | Faster and more focused control-flow analysis for business process models through sese decomposition[END_REF]. A SESE fragment is a non-empty subgraph in the process model with a single entry and a single exit edge [START_REF] Küster | Dynamic computation of change operations in version management of business process models[END_REF]. For every change in the process model, there is at least one enclosing fragment. Here, we consider only the smallest fragment that encloses the changes. This can be achieved using the process structure tree (PST) [START_REF] Vanhatalo | Faster and more focused control-flow analysis for business process models through sese decomposition[END_REF]. In the following, we consider that the fragments enclosing the changes are already identified (the identification issue is out of scope of this paper). Formally, a fragment has the same definition as a process (c.f. definition 1), except it has no start and end events, instead it has one entry and one exit edges. We can resume the changes that can be made on a process model, by three formal operations as follows:

• Insert(fragment, entry, exit): this operation is used to insert a new fragment into the process. This fragment should be inserted between the entry and exit edges in the centralized process model. • Delete(entry,exit): this operation is used for the deletion of the fragment between the entry and exit edges in the centralized process model. • Update(fragment,entry,exit): this operation updates the existing fragment between entry and exit edges in the centralized process model, and replace it by f ragment. This operation can also be replaced by the two consecutive operations delete(entry, exit) and insert(f ragment, entry, exit).

It should be noted that a fragment ranges from a simple activity to a enhanced structured sub-process with a single entry/single exit. Let's consider the example of figure 1. The fragment F ′ to add in the process model is defined by F ′ = (O F ′ , D F ′ , Ec F ′ , Ed F ′ , S F ′ ) and the fragment to delete is defined by F = (O F , D F , Ec F , Ed F , S F ). Then, the change operation is defined by Update(F ′, F .entry, F .exit) where F .entry = e a5,OR5sp and F .exit = e a17,OR5j ∈Ec B . This operation can also be transformed as follows:

Update(F ′, F .entry, F .exit) ⇒ Delete(F .entry, F .exit) ∧ Insert(F ′, F .entry, F .exit).

B. Change adaptation

This section describes the different steps to propagate the changes made on the centralized process model to the derived decentralized partitions. To have a better understanding, we refer to the motivating example depicted in Figure 1) to illustrate the adaptation process, then we detail our approach using a generic process model. We remind that we want to replace the fragment F in the centralized process by the fragment F ′. For this purpose, we first identify the partitions affected by this change using activities identifiers. For instance, the deletion of activity a 10 : s 4 in F or the insertion of the new activity a 25 : s 2 in F ′ means the deletion or the insertion of the same activities from P s4 or to P s2 respectively. The second step, is to identify the blocs of activities to change inside the affected partitions. Indeed, a simple change in a partition may result in other modifications (including interactions with other partitions or control patterns). For example (c.f. figure 2), the deletion of the activity a 11 in P s2 results in the deletion of all the fragment enclosed between OR 5sp and OR 5j since the other activity in parallel with it, is dummy. For each identified fragment, we notice the entry and the exit edges. Two use cases are possible: the fragment is updated and replaced by another fragment, or deleted. Using the new fragment F ′, the next step is to determine what to insert in each partition. For this purpose, we have to partition F ′.

P

Update(F , F.entry, F.exit) As can seen in Figure 3, the partitioning of the fragment F ′, leads to two fragments F ′ 1 and F ′ 2 , connected by a control edge c 4 (for more details about the decentralization process refer to [START_REF] Fdhila | A flexible approach for automatic process decentralization using dependency tables[END_REF]). The affected fragments by the change are respectively, F 1 and F 2 , and therefore, the initial change operation is transformed into two updates on P s2 and P s4 respectively. Figure 4 illustrates the transformation operation, where F 1 is updated by F ′ 1 in P s2 and F 2 is updated by F ′ 2 in P s4 . Now, consider a general example of a centralized process model P, as depicted in Figure 5. The process model is structured through split and join patterns, and enclosed with a start / end events. The partitioning of this process, results in |S| interconnected partitions P I , P J , P K , etc, each of which executed by a separate orchestrator (c.f. Figure 6). The steps toward change propagation are as follows: 1-Change specification: the designer specifies the changes to do using change operations: Insert, Delete and Update. If the operation is a delete, then he has to indicate the concerned fragment (i.e. in figure 5 Delete(Entry edge , Exit edge )). If the operation is an Insert, then he has to specify the fragment to add and in which place in the process model (i.e. in figure 5 Insert(F ′, Entry edge , Exit edge )). Otherwise, he has to specify both the fragment to update and the new fragment to insert (i.e. in Figure 5 U pdate(F ′, F .entry, F .exit)). Next, we consider the Update operation since it is more general and includes the two other operations.

P S4 Update(F 2 , F 2 .entry, F 2 .exit) P S2 Update(F 1 , F 1 .entry, F 1 .exit)

2-Partitions identification:

Using the fragments F and F ′, we identify all the partitions that would be affected by the change. Indeed, during partitioning, each activity is assigned to a partition upon to a certain criteria. If the activity responds to the criteria of the partition then it would be assigned to it (i.e. activities having the same role, or invoking the same service). So, using the criteria assigned to each activity we can determine the partition it would belong to. By this way, each partition having a criteria of one of F or F ′ activities would be affected by the change.

3-Fragments partitioning:

this step consists in decentralizing separately the fragments F ′ and F into interconnected sub-fragments, using partitioning techniques for structured processes. In figure 6, we take into consideration only the sub-fragments F P J ′, F P K ′, F P I and F P J , since they cover the three possible scenarios: insert, delete or update a sub-fragment into a partition.

4-Change translation:

After F and F ′ partitioning, change operation for the process model is decomposed into one or more change operations. Each operation represents the change to make on the corresponding partition. The generic formula for operation transformation is operation(x, y, ..) ⇒ operation 1 (x 1 , y 1 , ..) ∧ ...∧ operation 2 (x 2 , y 2 , ...), where operation i is the change to apply to partition P i . For instance, the generated change operations on partitions P I , P J and P K are as follows:

U pdate P (F ′, F .entry, F .exit) ⇒ U pdate P J (F ′ P J , F P J .entry, F P J .exit) ∧ Delete P I (F P I .entry, F P I .exit) ∧ Insert P K (F ′ P K , entry, exit).

It should be noticed, that the entry and the exit of the Insert construct should be computed (next step).

5-Partitions adaptation to change:

This step consists in applying the changes to the corresponding partitions. For this purpose, we first, have to determine exactly where to insert the sub-fragments F P J ′, F P K ′. The first scenario related to the update of F P J ′ in P J is simple, since we already know the entry and exit edges of F P J . So, we have just to look for these edges in the partition and replace all the fragment between them by the fragment F P J ′.

The deletion of the latter, implies the deletion of all the interactions with other activities in the same partition or other partitions. The partitions which interact with any activity concerned by the change is also concerned by the change, since we have to update its corresponding interaction edges. Formally, the update of F P J in a P J by F P J ′ corresponds to the deletion of all objects o ∈ O FP J , edges e ∈ Ec FP J ∪ Ed FP J , and data, and their substitution by the objects, edges, and data of F ′ P J . The Delete operation application is similar to the update, except that we do not insert a new sub-fragment. We simply look for the entry and exit of F P I in the partition P I . Then we delete the sub-fragment between them. If the entry edge of the sub-fragment to delete, is linked to a (choice or parallel) split control patterns (outside the sub-fragment), and the exit edge is linked to its corresponding join element, then we look if the other branches linking these two elements include only dummy activities or not. If yes then we delete these two patterns. We iterate this operation on each nested constructs linking the sub-fragment F P I to its transitive preset •T FPI and postset T FPI • (c.f. definition 7, 8) (we extend the definition of transitive postset (transitive preset) to that between a fragment and its subsequent (previous) activities). Otherwise, we replace the sub-fragment to delete, by a dummy activity. Now, to insert sub-fragment F K ′ in the partition P K , we have to identify the entry and exit edges. For this purpose, we first compute the transitive preset and postset of

F K ′ in P K (•F K ′, F K ′•).
Then, we identify all the control patterns that link them in the centralized process model.

Next, we identify each split pattern ctr in this control path linking it to its F K ′•, such as ctr is in the path linking it to its •F ′ K (ctr is the correspondent join element of ctr). For each choice ctr found, we look if it already exists in the partition. If yes, we just add a new branch linking ctr to ctr in which we put F ′ K . If no, we add it and its corresponding ctr, then we put the F ′ K between them (in parallel with a dummy activity). In some cases, the update or the insertion of a fragment may result in the creation of a new partition or the deletion of an existing partition.

The propagation of the changes made on the centralized process to the derived decentralized partitions are formally described by three rules. These rules include all the steps mentioned previously. According to the change operation (update, delete or insert), we execute the corresponding actions. These actions represent the identification of the affected partitions by the change, the identification of the fragments to be changed in these partitions and the modifications to apply. The modifications range from the change operations to apply on the concerned partitions to the update of the interactions with other partitions. In these rules dec(F ) is a function that returns the decentralized sub-fragments of the fragment F .

Rule 1

Operation: Delete p (F .entry, F .exit), F ⊂ p Actions:

∀ F i ∈ dec(F ), p i ←Identif yP artitionOf (F i ) ∀ F i ∈ dec(F ), (entry i , exit i )←Identif yIn(F i , p i ) ∀ F i ∈ dec(F ), Delete pi (entry i , exit i ) ∀a j ∈ •F, ∀a k ∈ F • update connection(a j , a k ) Rule 2 Operations: Insert p (F ′, entry, exit) Actions: ∀ F i ′ ∈ dec(F ′), p i ←Identif yP artitionOf (F ′ i ) ∀ F i ′ ∈ dec(F ′), (entry i , exit i )←Identif yIn(F i ′, p i ) ∀ F i ′ ∈ dec(F ′), Insert pi (F i ′, entry i , exit i ) ∀a j ∈ •F ′, update connection(a j , F ′) ∀a k ∈ F ′• update connection(F ′, a k ) Rule 3 Operations: U pdate p (F ′, F .entry, F .exit), F ⊂ p Actions: ∀F i ′ ∈ dec(F ′), p i ←Identif yP artitionOf (F ′ i ) ∀F j ∈ dec(F ), p j ←Identif yP artitionOf (F j ) ∀ F i ∈ dec(F ), (entry i , exit i )←Identif yIn(F i , p i ) ∀F i ∈ dec(F ), ∀F j ′∈ dec(F ′) s.t. p i =p j , U pdate pi (F j ′, entry i , exit i ) ∀F j ′∈ dec(F ′) s.t. ∄F i ∈ dec(F )∧p i = p j , Insert pj (F j ′, entry j , exit j ) where (entry j , exit j )←Identif yIn(F j ′, p j ) ∀F i ∈ dec(F ) s.t. ∄F j ′∈ dec(F ′)∧p i = p j , Delete pj (F i , entry i , exit i ) where (entry i , exit i )←Identif yIn(F j , p i ) ∀a j ∈ •F, update connection(a j , F ′) ∀a k ∈ F • update connection(F ′, a k ) V.

EVALUATION OF THE APPROACH

This section presents the properties of our change propagator. Given a well-behaved structural update on the centralized process model and the derived decentralized subprocesses, our approach automates the change forward propagation that consistently transforms the update on the source into the related target partitions, as presented in Section IV. Then we show the applicability of our approach by reviewing the motivating example and presenting an implementation.

A. The properties of the approach

In order to describe this approach following our work in [START_REF] Dahman | Towards consistency management for a business-driven development of soa[END_REF] and defined in definitions 1 and 6, the process partitioning is a total function of the type dec : P → {P s } s∈S that takes a source centralized process in P = (O, D, Ec, Ed, S) and produces a target set of decentralized partitions P s = (O s , D s , Ec s , Ed s ) where s ∈ S. It establishes a consistency relation, denoted C ⊆ P × {P s } s∈S between the source and the target process models. This relation captures the mapping between the centralized process logic in P and the (same) decentralized process logic described in {P s }s ∈ S. Since our decentralization algorithm is idempotent as we have presented in [START_REF] Fdhila | A flexible approach for automatic process decentralization using dependency tables[END_REF]: it can be applied multiple times without changing the result, then C is a total function. Now, consider a source centralized process model p ∈ P and a target partition set dec(p) that are consistent with respect to the relation C, i.e., (p, dec(p)) ∈ C means that p was previously decentralized to dec(p). Given a source change δ P that alters p to δ P (p), the problem is to translate the well-behaved change of the source process δ P into a well-behaved changes on the target partitions (δ Ps ) s∈S , such that the application of both updates results in consistent process models. The change propagator that provides this function is of the type prop : P × ∆ P × {P s } s∈S → {P s } s∈S . For p ∈ P, δ P ∈ ∆ P and dec(p) ∈ {P s } s∈S , it determines (δ Ps ) s∈S ∈ ∆ PS and then computes the changes on the partitions (δ Ps ) s∈S such that the updated models are consistent, i.e., (δ P (p), (δ Ps (dec(p))) s∈S ) ∈ C . We use ∆ P : P ⇀ P and ∆ PS : {P s }s ∈ S ⇀ {P s } s∈S as an abbreviation for the update types respectively on the processes and on the partitions. They represent the space of all partial functions describing the changes on each of the centralized and decentralized process models and which can be described by productions, i.e. the change operation defined in Section IV.

In our semantics, a process and its decentralization result (i.e., the derived partitions) are specified with graphs as introduced in Section II. Then, a change on a process implies a modification on the graph structure which can be expressed by graph rewriting rules [START_REF] Biermann | Precise Semantics of EMF Model Transformations by Graph Transformation[END_REF]. Formally, given a graph G, a graph rewriting rule (i.e., also called production) consists of injective morphisms of the form δ G : L → R that transform a source graph L into a target graph R. In order to apply this rewrite rule to the initial graph G, a match m : L → G is needed to specify which part of G is being updated. Then, the application of δ G to G via a match m for δ G is uniquely defined by the graph rewriting G ⇒ δG ,m H. This rule application induces a co-match m ′ : R → H which specifies the embedding of R in the result graph H. For more details on the so called forward satisfaction associated to the graph rewriting refer to [START_REF] Biermann | Precise Semantics of EMF Model Transformations by Graph Transformation[END_REF]. Generally, the problem of a graph rewriting technique is that it is considered to be undecidable. However, if its meets certain criteria, then, we can conclude that it is terminating and locally confluent.

Firstly, the most important criteria is the change propagation correctness: a graph-based change propagator must return consistent process models. In this paper, we suppose that when applying a rewriting rule to a given graph G, it is enough to consider the case where the morphisms that matches L to G is injective [START_REF] Antkiewicz | Design space of heterogeneous synchronization[END_REF], and that the match m is a total label-preserving, type-preserving and root-preserving [START_REF] Biermann | Precise Semantics of EMF Model Transformations by Graph Transformation[END_REF] graph morphism. However, to be correctly applied, the productions must satisfy the structural consistency of the centralized process constraints. Note that we assume the well-behavedeness of the updates propagated by the designers. It means that the graph production on a centralized process is consistent with the behavioral requirements, and after the production the process remains structured. Moreover, the fragment or process partitioning preserves by definition the well-behaved process semantic. Secondly, a fundamental law is that the change propagation should be deterministic [START_REF] Xiong | Towards automatic model synchronization from model transformations[END_REF]: for each centralized process model input there is a unique decentralization result. In our case, the change propagator is modeled by a mathematical function. It means that given the same pair of the centralized and its decentralized models, and a finite set of changes (i.e., bounded within the SESE fragment) on the source centralized process model, our propagator produces the same changes on the target partitions.

Finally, our propagator takes as a parameters the previous decentralization, i.e., (p, dec(p)) ∈ C, and the update of the source process δ P . In order to adapt dec(p) the potential changes induced by δ P , and rather than executing the decentralization afresh on the entire updated centralized process model, i.e., dec(δ P (p)), our change propagator enforces an in-place synchronization between δ P (p) and dec(p) by translating the updates δ P into well-behaved target updates (δ Ps ) s∈S to get (δ Ps (dec(p))) s∈S ) consistent with δ P (p). The change translation can be seen as a partial function of the type ∆ P ⇀ ∆ T . The reason is that in the case where δ P is small, generally, it corresponds to a small update of the partitions: (δ Ps ) s∈S , and the performance savings for those computations are expected to be high in comparison with a diff-based method [START_REF] Alanen | Difference and union of models[END_REF], [START_REF] Biermann | Precise Semantics of EMF Model Transformations by Graph Transformation[END_REF]. Taking into account a fragment in the source process p and a fragment in the previously obtained partitions dec(p), the effort to determine (δ Ps ) s∈S with the partition identification and to compute (δ Ps (dec(p))) s∈S should be much less than to apply a new decentralization dec(δ P (p)) and a diff between the new result and the previous dec(p). The speedup of this incremental decentralization and the partitions adaptation to change results in a reasonable decoupling from the processes and partitions size [START_REF] Giese | From model transformation to incremental bidirectional model synchronization[END_REF].

B. Proof of concepts prototype

The change propagator has been successfully implemented and integrated with our previous development of the partitioning algorithm [START_REF] Fdhila | A flexible approach for automatic process decentralization using dependency tables[END_REF] as an extension to a BPMN Editor [START_REF]Yaoqiang bpmn editor[END_REF]. This BPMN editor is based on a graph visualization library, and it is used to model a source centralized process model, for instance the structured process of Figure 1. After applying our partitioning algorithm, we obtain the partitions depicted in Figure 2 using the graph library. Moreover, we have developed a filter that logs the process model editing operations presented in Section IV. Actually, the specification of the entry and the exit of a fragment is performed manually, for example as depicted in Figure 3, but it can be easily automated. The change propagation algorithm is implemented in the DROOLS [START_REF][END_REF] inference engine, and it automatically computes the graph editing operation sequence that manipulates the partitions. The experimental results are encouraging, but still needs to be validated in a real-scale case. We refer the reader to [START_REF] Giese | From model transformation to incremental bidirectional model synchronization[END_REF] for further discussions on the scalability measurements of a similar incremental model synchronization technique.

VI. RELATED WORK

Several issues related to change management have been addressed in business process management, and workflow literature [START_REF] Van Der Aalst | Exterminating the dynamic change bug: A concrete approach to support workflow change[END_REF], [START_REF] Rinderle | Correctness criteria for dynamic changes in workflow systems -a survey[END_REF], [START_REF] Sadiq | Managing change and time in dynamic workflow processes[END_REF]. These proposals deal with adaptive process management. For instance, the ADEPT proposal enables controlled changes at the process type as well as the process instance level [START_REF] Reichert | Adept flex -supporting dynamic changes of workflows without losing control[END_REF]. The authors, present a formal foundation for the support of dynamic structural changes of running workflow WF instances. Based upon a formal WF model (ADEPT). They also define a set of change operations (ADEPTflex) that support users in modifying the structure of a running WF.

In [START_REF] Pesic | Constraint-based workflow models: Change made easy[END_REF], Van der Aalst and Jablonski present important issues related to process changes and discuss organizational structures. In [START_REF] Weber | Change patterns and change support features -enhancing flexibility in processaware information systems[END_REF], the authors motivate the need for the controlled change of organizational models. In particular, they present different adaptations models to be supported by respective components (e.g. to extend, reduce, replace, and re-link model elements).

Alanen and Porres describe in [START_REF] Alanen | Difference and union of models[END_REF] an algorithm to compute elementary change operations. In [START_REF] Kolovos | Merging models with the epsilon merging language (eml)[END_REF], Kolovos et al. describe the Epsilon merging language. The latter is used to specify how models are merged. Kelter et al. present in [START_REF] Kelter | A generic difference algorithm for uml models[END_REF] a generic model differencing algorithm. In [START_REF] Corradini | Algebraic approaches to graph transformation -part i: Basic concepts and double pushout approach[END_REF], Cicchetti et al. propose a metamodel for the specification and detection of syntactical and semantical conflicts. Rinderle et al. [START_REF] Vanhatalo | Faster and more focused control-flow analysis for business process models through sese decomposition[END_REF] have studied disjoint and overlapping process model changes in the context of the problem of migrating process instances.

All the mentioned approaches, address change adaptation in a centralized process. They also deal, with how to dynamically adapt running instances to changes. This, may be complementary to our work.

In the decentralized setting, [START_REF] Vanhatalo | Faster and more focused control-flow analysis for business process models through sese decomposition[END_REF] presents a formal model for a distributed workflow change management (DWFCM) that uses a rules topic ontology and a service ontology to support the needed run-time flexibilty. A system architecture and the workflow adaptation process are presented. The approach aims to generate a new workflow that is migration consistent with the original workflow. This work is different from our proposal, since they do not seek to propagate a pre-defined changes on a centralized process to that on the derived partitions. Their work is more focused on run-time adaptation using the migration rules.

In [START_REF] Dahman | Towards consistency management for a business-driven development of soa[END_REF] the authors present a unidirectional model incremental transformation approach. Its central contribution is the definition and the realization of an automatic synchronizer for managing and re-establishing the structural consistency of heterogeneous source and target models. From a design-time perspective, an evolution describes the update on model's internal structures that can be assimilated to a graph. They express the model evolutions and their transformation using conditional graph rewriting techniques.

VII. CONCLUSION

Lifecycle support of BPM solutions is becoming more and more important. In order to achieve the goals of BPM implementation, it is required to support process analysis and improvement, which necessitates a framework for managing business process changes. In this paper, we have presented an approach to adapt decentralized orchestrations to changes specified on the corresponding centralized process. The proposed approach is based on three change patterns Insert, U pdate and Delete. The method consists in partitioning the fragment to change into sub-fragments, which in turn, are integrated into the corresponding partitions. To the best of our knowledge, this is the first work that takes on changes adaptation in decentralized composite web services. Evidently, there are some limitations inherently implied by the usage of only the structured processes. Thus, as a perspective we further to extend our approach to handle nonstructured processes. Moreover, the introduced operations can be composed to give rise to process update types with enhanced semantics for the users (e.g., move of fragment, refactoring of fragments: splitting and merging). However, we restricted our presentation to the structural correctness. The integration of our update translation operator in a previously developed ATLAS Transformation Language chain [START_REF] Dahman | Generation of Component Based Architecture from Business Processes: Model Driven Engineering for SOA[END_REF] for the Eclipse SOA Tools Platform is in development.

Figure 1 .

 1 Figure 1. Process example

Figure 2 .

 2 Figure 2. Decentralized partitions

Figure 3 .

 3 Figure 3. Change adaptation example

Figure 4 .

 4 Figure 4. Operation transformation

  ...............

Figure 5 .

 5 Figure 5. generic process example

Figure 6 .

 6 Figure 6. generic example for change management

  a1 , e a1,OR1sp , e OR1sp,OR2sp , e OR1sp,OR3sp , ...}.• Ed s1 = {c 1 , c 2 , e a1:Ps1,a4:Ps4 (i.e. d 1 )}

  e 2 ,...e n ⊂ E c st ∀ 1<k<n, target(e k ), source(e k ), target(e 1 ), source(e n ) / ∈A ck }. Definition 8 (Transitive Preset): Formally, for an activity a i ∈ P ck •T a i = {a j ∈ P ck | path ji = e 1 ,e 2 ,...e n ⊂ E c st ∀ 1<k<n, target(e k ), source(e k ), target(e 1 ), source(e n ) / ∈A ck }.