Alexandre Caux-Dedeystère

Philippe Derambure

Hervé Devanne
email: herve.devanne@chru-lille.fr

Late cortical disinhibition in relaxed versus active hand muscles

Keywords: motor cortex, late cortical disinhibition, long-interval intracortical facilitation, voluntary activity

Recent research suggests that long-interval intracortical inhibition (LICI) is followed by a transitory period of late cortical disinhibition (LCD) that can even lead to a net increase in cortical excitability. The relationship between LICI/LCD and voluntary drive remains poorly understood. Our study aims at investigating the influence of index abduction on LICI and LCD in an actively engaged muscle and a neighboring muscle, while varying the intensity of the conditioning stimulus. Motor-evoked potentials (MEPs) were recorded from the first dorsal interosseus (FDI) and abductor digiti minimi (ADM) muscles in 13 subjects. Paired-pulses were delivered with 10 different interstimulus intervals (ranging from 60 ms to 290 ms). Whatever the condition (relaxed or active FDI), the test stimulus was set to evoke an MEP of 1 mV. The time course of conditioned MEPs amplitude was compared for relaxed and active conditions when the conditioning stimulus (CS) intensity was set to (i) 130% of the rest motor threshold (RMT) or (ii) to evoke the same size of MEP under both conditions. LICI lasted longer (i.e. disinhibition occurred later) at rest than during abduction when evoked either by similar or matched conditioning stimuli. No post-LICI facilitation was observed at rest -even when the CS intensity was set to 160% RMT. In contrast, long-interval intracortical facilitation was observed in the quiescent ADM when the FDI was active.

Long-interval intracortical facilitation may then be associated with voluntary activity albeit with a lack of topographic specificity.

Abbreviations: EMG, electromyogram; MEP, motor-evoked potential; FDI, first dorsal interosseus; GABA, gamma aminobutyric acid; ISI, interstimulus interval, LCD, late cortical disinhibition; LICF, long-interval intracortical facilitation; LICI, long-interval intracortical inhibition; SICI, short-interval intracortical inhibition; MVC, maximal voluntary contraction; TMS, transcranial magnetic stimulation; CS, conditioning stimulus; ADM, abductor digiti minimi; RMT, resting motor threshold.

Introduction

Over the past two decades, many studies have used paired-pulse transcranial magnetic stimulation (TMS) protocols to study intracortical inhibitory and facilitatory networks and the latter's underlying relationship with motor behavior. It has been suggested that intracortical inhibitory and excitatory mechanisms have a role in the modulation of cortical excitability prior to and during voluntary movements, so that corticospinal drive meets the task's requirements [START_REF] Floeter | Releasing the brakes before pressing the gas pedal[END_REF]. For example, the shortinterval intracortical inhibition (SICI) that can be observed when a subthreshold conditioning stimulus is delivered 1 to 4 ms before the test pulse has been extensively investigated. After SICI was first evidenced at rest [START_REF] Kujirai | Corticocortical inhibition in human motor cortex[END_REF], it was found that this intracortical inhibition decreases before movement onset [START_REF] Reynolds | Inhibition in the human motor cortex is reduced just before a voluntary contraction[END_REF] and during tonic voluntary contraction [START_REF] Ridding | The effect of voluntary contraction on corticocortical inhibition in human motor cortex[END_REF][START_REF] Ortu | Effects of volitional contraction on intracortical inhibition and facilitation in the human motor cortex[END_REF]. Furthermore, SICI is weaker during complex tasks [START_REF] Kouchtir-Devanne | Taskdependent changes of motor cortical network excitability during precision grip compared to isolated finger contraction[END_REF] than during simple isometric contraction and thus is thought to correspond to a mechanism by which cortical motor neuron activity is fine-tuned to allow precise movements. The mechanism referred to as long-interval intracortical inhibition (LICI) was observed initially by [START_REF] Valls-Solé | Human motor evoked responses to paired transcranial magnetic stimuli[END_REF] and later by Wassermann et al [START_REF] Wassermann | Responses to paired transcranial magnetic stimuli in resting, active, and recently activated muscles[END_REF] with interstimulus intervals (ISIs) ranging from 50 and 200 ms. Although LICI was not as extensively investigated as SICI, several studies have highlighted its potential significance in motor control. For example, LICI evoked in the first dorsal interosseus (FDI) muscle is weaker when the muscle is engaged in a thumb-index precision grip than during index abduction [START_REF] Kouchtir-Devanne | Taskdependent changes of motor cortical network excitability during precision grip compared to isolated finger contraction[END_REF]. LICI also appears to be shorter and weaker during active contraction than at rest [START_REF] Wassermann | Responses to paired transcranial magnetic stimuli in resting, active, and recently activated muscles[END_REF][START_REF] Chen | Impaired inhibition in writer's cramp during voluntary muscle activation[END_REF]. More recent studies (using paired TMS protocols) have also revealed that LICI is progressively suppressed at longer ISIs and may even be followed by a period of long-interval intracortical facilitation (LICF), corresponding to a net increase of cortical excitability. Late cortical disinhibition (LCD) is a newly reported phenomenon that is thought to reflect the period of time during which long-interval intracortical inhibition (which decreases more or less rapidly) is combined with the possible recruitment of facilitatory cortical interneurons responsible for the subsequent LICF.

The LCD has been reported to occur at rest [START_REF] Rfh | Late cortical disinhibition in human motor cortex: a triple-pulse transcranial magnetic stimulation study[END_REF][START_REF] Cash | Inhibitory and disinhibitory effects on Iwave facilitation in motor cortex[END_REF] and during voluntary activity [START_REF] Caux-Dedeystère | Task-dependent changes in late inhibitory and disinhibitory actions within the primary motor cortex in humans[END_REF], and may reflect post-movement synchronization of facilitatory cortical interneurons [START_REF] Rfh | Late cortical disinhibition in human motor cortex: a triple-pulse transcranial magnetic stimulation study[END_REF]. Recent evidence suggests that the induction of long-term potentiation plasticity is facilitated when repetitive TMS pulses are delivered during the disinhibition period [START_REF] Rfh | Late cortical disinhibition in human motor cortex: a triple-pulse transcranial magnetic stimulation study[END_REF].

Better knowledge of the LCD's time-dependent profile is therefore of particular value for further improving the efficacy of repetitive TMS protocols.

The time profiles of LICI/LCD at rest and during active contraction have not previously been compared. The primary objective of the present study was thus to analyze the influence of index tonic voluntary abduction on LICI and LCD in the FDI at different conditioning stimulus intensities. We also investigated the topographic specificity of LCD and LICF by recording the responses of a neighboring muscle (the abductor digiti minimi, ADM) not engaged in the index abduction task. Throughout the text, the conditioned MEP's return to baseline i.e. the weakening of LICI will be referred to as LCD. Post-LCD facilitation (corresponding to the net increase in cortical excitability after LCD, when present) will be referred to as LICF (see figure 1).

Methods

Participants

Thirteen subjects (including six females) aged between 20 and 46 (mean ± SD age: 24.92 ± 7.26) participated in the study after providing their written, informed consent. None of the subjects was taking any medication or was suffering from a neurological or psychiatric disorder. The study was approved by the local independent ethics committee (Comité Consultatif de Protection des Personnes Nord Ouest, Amiens, France) and complied with the tenets of the Declaration of Helsinki.

Electromyographic recordings

Participants were seated in a comfortable chair, with their forearms resting on a heightadjustable table. Ag-AgCl surface electrodes were used to record the electromyographic (EMG) activity of the FDI and ADM muscles of the dominant hand in a belly-tendon montage. The electrodes were positioned over the body of each the two muscles and (for the ADM and FDI, respectively) the first metacarpophalangeal joint of the fifth finger or the thumb. A large ground electrode was attached to the wrist. The EMG signals were amplified (x1000), high-pass filtered at 10 Hz and low-pass filtered at 1000 Hz (Digitimer, Hertfordshire, UK) prior to sampling at 2 kHz with a 1401 MicroMKII device (Cambridge Electronic Design, Cambridge, UK). The data were stored on a computer for subsequent off-line analysis using customized SIGNAL software (Cambridge Electronic Design). The subjects first produced maximal voluntary contraction (MVC) of the FDI (abduction of the index finger) in three successive trials. A 1401+ device and customized SPIKE2 software (both from Cambridge Electronic Design) were used to record and display the root mean square of the low-pass filtered (100 Hz) EMG signal from the FDI as a percentage of the MVC EMG signal.

Stimulation procedures

TMS was performed using a figure-of-eight focal coil (external diameter: 9.5 cm) connected to two Magstim 200 stimulators (The Magstim Company Ltd, Whitland, UK) via a Bistim module. Stimulation was applied over the optimal scalp point for the FDI of the dominant hand, i.e. the site that yield the strongest MEPs for the FDI at a given suprathreshold intensity. This scalp point was determined by moving the coil while the subject relaxed his/her hand muscles. To ensure constant coil positioning throughout the series, the FDI's hotspot was marked on a swimming cap worn by the subject. The coil was held tangentially to the scalp, with the handle pointing backwards and laterally (at a 45° angle from the midline). We then measured the resting motor threshold (RMT) of the FDI, defined as the lowest possible stimulus intensity capable of eliciting MEPs >50 µV in at least 5 of 10 trials in the muscle at rest. In all experimental series, single pulses and dual pulses were separated by an inter-trial interval of random duration (between 4 and 6 s).

Experimental design

In the first two series (one at rest and one with index abduction, in randomized order), the intensity of the conditioning stimulus (CS) was set to 130% RMT (CS130). The mean amplitude of the motor response evoked by CS130 was measured after each of the two series. Given that motor thresholds are lower during active motor tasks, a fixed conditioning stimulus (CS130) is likely to recruit more neurons during the active task (as suggested by greater MEPs) than at rest. We thus sought to determine, if exist, whether the differences between active and relaxed muscles were due (at least in part) to a differential effect of the conditioning stimulus on the neural circuits. To this end, the participants performed two additional series. In the first series, the intensity of the CS in the abduction task was lowered (CS*Lower), so that it evoked an MEP whose amplitude matched that induced by CS130 at rest. In the second series, we increased the CS intensity in the rest condition (CS*Higher) so that it induced an MEP whose amplitude matched that induced by CS130 in the abduction task (Figure 2). Whatever the series, the test stimulus intensity was set to induce an MEP of about 1 mV (measured peak-to-peak) when delivered alone. On the basis of our previous results (Caux-Dedeystere et al, 2014), ISIs were chosen in order to study post-LICI disinhibition with accuracy. Ten ISIs (60, 90, 120, 140, 150, 165, 180, 195, 230 and 290 ms) were applied in random order in two sets of five. This was performed in order to shorten the duration of the block and avoid muscle fatigue during the tasks. In summary, a total of eight series (two series of 5 ISIs x two conditioning intensities x two conditions) were performed. In each of the series, eight conditioned responses were recorded in pseudorandom order for each ISI, together with eight non-conditioned responses delivered pseudo-randomly throughout the block. Each series lasted for about three minutes, and none of the subjects reported fatigue or difficulties in maintaining the level of muscle contraction (10% of MVC) during index abduction. In all series, the subject was instructed to fully relax the ADM muscle. The ADM's activity was monitored throughout the experiment; when necessary, the subject was reminded that the ADM should not be contracted.

Data analysis

The mean conditioned peak-to-peak MEP amplitude was measured offline and expressed as a percentage of the non-conditioned MEP. Statistical analysis was carried out with SPSS v16.0 software (IBM Corp., Armonk, NY). The effects of ISI and task were evaluated in a two-way repeated-measures analysis of variance (RM-ANOVA), with two factors: task (rest, abduction) and ISI (10 levels). Three comparisons were made: (i) abduction with CS130 vs. rest with CS130; (ii) abduction with CS130 vs. rest with CS*Higher and (iii) abduction with CS*Lower vs. rest with CS130. If the RM-ANOVA revealed a significant effect of one or both factors and/or a significant interaction between the two factors, a post hoc test with Bonferroni correction was performed to determine the ISIs for which the two conditions differed. If the effect of ISI was significant, the difference from baseline (i.e. the non-conditioned MEP) was tested for each ISI in a one-sample ttest. Unconditioned MEPs evoked either by CS130, CS*Lower, CS*Higher or Test stimulus were compared with a paired t-test (or a Wilcoxon signed-rank test depending on the distribution and variance of the data distribution). The same test was used to study the influence of CS intensity on conditioned MEPs at given ISIs. In all analyses, the threshold for statistical significance was set to p<0.05.

Results

Comparison of LICI/LCD profiles in the resting condition versus index abduction, with

CS130

The mean RMT across all subjects was 40.61±4.56%. The mean MEP amplitude elicited in the FDI by CS130 was 2.08±1.34 mV at rest and 5.53±1.73 mV in the abduction task (Figure 2). The mean TS MEP amplitude was 1.12±0.33 mV at rest and 1.15±0.32 mV during abduction.

Figure 3A shows the time course of LICI and LCD in the FDI at rest and during abduction, with CS130. The RM-ANOVA revealed (i) significant effects of both task (F = 24.36, p<0.01) and ISI (F = 17.13, p<0.01) and (ii) a significant interaction between the two (F = 5.80, p<0.01). Two main intercondition differences are illustrated in figure 3A. Firstly (and in line with Wasserman et al.'s findings [START_REF] Wassermann | Responses to paired transcranial magnetic stimuli in resting, active, and recently activated muscles[END_REF])), we found that LICI decreased more rapidly in the abduction task than at rest. In the former condition, LICI began to decrease from 90 ms and the conditioned MEP crossed the baseline around 165 ms. At rest, inhibition was still significantly present and lasted until 180 ms.

Secondly, the amplitude of the conditioned MEPs was significantly greater during abduction than at rest at the longest ISIs (from 165 ms to 290 ms). Although disinhibition was followed by marked facilitation when the FDI was activated, the conditioned MEP never exceeded the baseline level when the muscle was at rest (table 1). In the latter condition, LICI was followed by clear disinhibition from 195 ms to 230 ms. However the conditioned MEP was significantly lower than baseline (p<0.01) at the longest ISI (290 ms).

The nature of the relationship between LICI/LCD and ISI was almost the same for the quiescent ADM (Figure 3B) as it was for the FDI. At the shortest ISIs, LICI was observable (regardless of the intensity of FDI contraction) and disinhibition of the ADM also occurred earlier when the FDI muscle was tonically activated (although the ADM remained quiescent under both conditions). In line with the data for the FDI, disinhibition in the ADM was not followed by a phase of MEP facilitation when the muscles were at rest. The most striking observation was that although the ADM muscle was always kept at rest, disinhibition was followed by strong facilitation when the FDI muscle was tonically activated during index abduction (Figure 3B,Table2). When comparing FDI activation and rest, the ADM's conditioned MEPs differed significantly at all ISIs between 165 ms and 230 ms.

Comparison of LICI/LCD profiles with CS130 in the resting condition and adjusted CS intensity (CS*Lower) in the abduction task.

For the study participants as a whole, the mean±SE CS*Lower intensity was 102.17±3.21% RMT (41.62±2.01% of the stimulator output). The mean MEP amplitude evoked by CS*Lower in the abduction task was 2.15±1.22 mV and did not differ significantly from that obtained at rest with CS130 (2.08±1.34mV) (Figure 2). The TS MEP amplitude was respectively 1.12±0.33 mV and 1.10±0.21 mV in resting and abduction conditions.

Reducing the CS intensity did not appear to greatly modify the curve obtained in the abduction task (Figure 4A). The curves obtained with matched CS under the two conditions were clearly distinct following the initial LICI phase observed at the lowest ISIs. This was confirmed by the RM ANOVA, which revealed (i) significant effects of both task (F = 24.36, p<0.01) and ISI (F = 17.13, p<0.01) and (ii) a significant interaction between the two (F = 5.80, p<0.01). Although the use of a lower CS intensity resulted in less facilitation than with CS130 during abduction, the facilitation remained statistically significant (relative to baseline intensity) at 180 ms and 195 ms (table 1). When compared with the resting condition, disinhibition in abduction again occurred earlier, and LICI had disappeared by 140 ms with CS*Lower. The two conditions differed significantly in terms of the conditioned MEPs at all ISIs between 120 ms and 290 ms.

The time-dependent profile of the conditioned MEP obtained from the ADM was very similar to that of the FDI when CS*Lower was used (Figure 4B). Compared with the rest condition, disinhibition also occurred earlier in the ADM and was followed by a phase of facilitation being significant at ISI 180 ms (Table 2).

Comparison of LICI/LCD profiles with CS130 in the abduction task and an adjusted CS intensity (CS*Higher) in the resting condition.

In 5 of the 13 subjects, a ceiling effect prevented us from adjusting the FDI MEP amplitude evoked by CS*Higher to that obtained during abduction with CS130. In the remaining 8 subjects, the mean FDI MEP amplitude evoked by CS*Higher was 4.34±1.85 mV and did not differ significantly from that obtained during abduction with CS130 (4.58±1.46 mV) (Figure 2). For the 8 participants, the mean CS*Higher intensity was 161.79±9.01% RMT (65.88±2.47% of the stimulator output). The TS MEP amplitude was respectively 1.16±0.26 mV and 1.25±0.29 mV in resting and abduction conditions.

Figure 5A shows the time course of LICI and LCD under each of the two conditions; the abduction condition was the same as that in figure 3A but the curve was drawn with data from 8 of the 13 subjects). Adjusting the CS intensity in the rest condition did not lead to significant changes in the time-dependent profile of conditioned FDI MEPs. As in the comparisons described above, the RM ANOVA indicated (i) significant effects of both task (F = 20.58, p<0.01) and ISI (F = 11.87, p<0.01) and (ii) a significant interaction between the factors (F = 4.60, p<0.01). While the use of CS intensity as high as 1.6 times the RMT delayed disinhibition, we did not notice any facilitation at the longest ISIs in the rest condition; this contrasted with the strong facilitation observed during abduction (Figure 5A, table 1).

In the neighboring ADM muscle, the application of CS*Higher was associated with longer LICI and greater disinhibition (Figure 5B). Again, there was no facilitation of the ADM's conditioned MEPs when both muscles were at rest.

Influence of CS intensity

In the quiescent FDI, increasing the CS intensity from 130% RMT to 160% RMT only induced subtle changes: (i) the LICI was deeper, but the difference was only significant at ISI 165ms, and (ii) the LCD tended to occur later (Figure 6A). Although conditioned MEPs tended to be greater with CS*Higher at longest ISIs (i.e. 230 ms and 290 ms) there was no difference due to the CS intensity. In contrast, marked changes were observable in the active FDI muscle when the CS intensity was decreased from 130% RMT to just above threshold (102% RMT)(Figure 6B). At first, the LICI was significantly lower, and the LCD occurred earlier. Furthermore, although LICF was still present with CS*Lower, it lasted shorter and the amount of LICF was weaker than with CS130. The influence of CS intensity on the ADM muscle was quite similar. Increasing the CS intensity did not induce significant change in LICI and LCD when the muscles were at rest. The reduction of CS intensity when the FDI was active was accompanied by a decrease in the amount of LICI between 60 ms and 120 ms in the ADM muscle.

Discussion

The purpose of this study was to compare the time profiles of LICI/LCD at rest and during active contraction. We thus analysed the influence of index abduction on LICI and LCD in the FDI and in a neighboring muscle, the ADM, at different conditioning intensities. We have shown that (i) the duration of LICI was longer at rest than during contraction; (ii) LCD was observed at rest and during tonic voluntary contraction, whereas LICF only occurred during the active task; (iii) the overall time-dependent evolution from LICI to LCD/LICF was not influenced by CS intensity; (iv) but the "size" of LICI, LCD, or LICF may be influenced by CS intensity; and finally (v) similar results were observed in a muscle adjacent to the contracted muscle, but not formally involved in the contraction task. In the discussion that follows, we begin by addressing the changes of duration and amount of LICI that accompany voluntary FDI activation. We then consider the neural mechanisms that may underlie the changes in LCD and LICF occurring when a muscle becomes active and when the conditioning intensity increases. Lastly, we consider the late phase of inhibition that was observable in the quiescent FDI muscle at longest interstimulus intervals.

LICI in active versus relaxed muscle

To the best of our knowledge, the time course of LICI (both at rest and during voluntary contraction) has only been reported on twice [START_REF] Wassermann | Responses to paired transcranial magnetic stimuli in resting, active, and recently activated muscles[END_REF][START_REF] Chen | Impaired inhibition in writer's cramp during voluntary muscle activation[END_REF] but has never been explored in the post-LICI period during which LCD and LICF are observable. Our data are consistent with these previous observations in that (regardless of the amount of inhibition) the LICI lasted longer when the muscle was relaxed than during tonic, voluntary condition. The data recorded during FDI voluntary contraction show more obviously than at rest that increasing the conditioning intensity is accompanied by more ample, earlier disinhibition. Although LICI appeared to be most intense when the CS intensity increased (from 130% to 160% RMT in the relaxed FDI and from 102% to 130% RMT during index abduction), the degree of inhibition was much the same, at least until ISI 90 ms in the active and quiescent FDI. This finding suggests that unlike SICI which falls during voluntary muscle contraction [START_REF] Ridding | The effect of voluntary contraction on corticocortical inhibition in human motor cortex[END_REF][START_REF] Ortu | Effects of volitional contraction on intracortical inhibition and facilitation in the human motor cortex[END_REF] LICI does not participate in the down-regulation involved in the recruitment of new cortical units. At first sight, these results appear to contrast with certain literature data showing that LICI is lower during voluntary activity than at rest and decreases even further as the level of muscle contraction increases [START_REF] Hammond | Modulation of long-interval intracortical inhibition and the silent period by voluntary contraction[END_REF]. In the latter study, however, LICI was only measured at an ISI of 100 ms, and it is likely that disinhibition already occurs at this interval -as suggested by the report that LICI can turn into facilitation when subthreshold conditioning stimuli are applied [START_REF] Vallence | Long-interval facilitation and inhibition are differentially affected by conditioning stimulus intensity over different time courses[END_REF].

LCD in active versus relaxed muscle

To date, very few studies have explored excitatory and inhibitory phenomena in the post-LICI period. Regardless of the amplitude of the preceding LICI, the disinhibition process began earlier in the active task. The subsequent, conditioned MEPs returned to the baseline level more rapidly and were even clearly facilitated during index abduction.

The mechanisms responsible for LCD (i.e. a decrease in LICI and the conditioned MEP's return to baseline) have not yet been fully characterized. One of the most convincing hypothesis proposed to date holds that the conditioning pulse induces (i) immediate, inhibitory effects (SICI and LICI) mediated by postsynaptic gamma aminobutyric acid (GABA) receptors and (ii) delayed disinhibition of SICI neurons mediated by activation of presynaptic GABA-B receptors, which may be responsible (at least in part) for the LCD [START_REF] Rfh | Late cortical disinhibition in human motor cortex: a triple-pulse transcranial magnetic stimulation study[END_REF]. Interaction between LICI and SICI circuits [START_REF] Sanger | Interactions between two different inhibitory systems in the human motor cortex[END_REF] may constitute the physiological substrate for this type of disinhibition. However, this hypothesis holds that disinhibition would not occur (or would occur to a lesser extent) when SICI neurons have been depressed as during voluntary activation [START_REF] Ridding | The effect of voluntary contraction on corticocortical inhibition in human motor cortex[END_REF][START_REF] Ortu | Effects of volitional contraction on intracortical inhibition and facilitation in the human motor cortex[END_REF]. In such a case, and given that SICI neurons are less active, the removal of their inhibition should be accompanied by weaker disinhibition. In contrast, our data show earlier disinhibition with more rapid decay during the active task compared with rest. We thus consider that another mechanism is likely to be involved in the rapid decrease in LICI (and the increase in LICF) during voluntary contraction. One possible candidate is contraction-induced afferent input. During index abduction, many proprioceptive endings are stimulated and may then modulate corticospinal excitability.

A large body of literature data suggests that information from skin and muscle afferents can interact with inhibitory networks in the primary motor cortex and thus modify motor output [START_REF] Hess | Task-dependent modulation of inhibitory actions within the primary motor cortex[END_REF][START_REF] Ridding | Afferent input and cortical organisation: a study with magnetic stimulation[END_REF][START_REF] Rosenkranz | The effect of sensory input and attention on the sensorimotor organization of the hand area of the human motor cortex[END_REF]. Although speculative, our hypothesis is supported by the finding that median nerve stimulation may decrease the intensity of LICI [START_REF] Sailer | Effects of peripheral sensory input on cortical inhibition in humans[END_REF][START_REF] Udupa | Interactions between short latency afferent inhibition and long interval intracortical inhibition[END_REF] and that in half of the subjects with LICI and long-latency afferent inhibition, LICI turned into facilitation when paired with peripheral median nerve stimulation [START_REF] Sailer | Effects of peripheral sensory input on cortical inhibition in humans[END_REF].

LICF in active versus relaxed muscle

Our most surprising finding (and in contrast to the report by [START_REF] Rfh | Late cortical disinhibition in human motor cortex: a triple-pulse transcranial magnetic stimulation study[END_REF][START_REF] Cash | Inhibitory and disinhibitory effects on Iwave facilitation in motor cortex[END_REF] was the absence of LICF in all subjects when both muscles were at rest. This result was especially unexpected because we used much the same stimulation parameters as [START_REF] Rfh | Late cortical disinhibition in human motor cortex: a triple-pulse transcranial magnetic stimulation study[END_REF], namely a conditioning intensity set to 130% RMT, a test intensity set to evoke an MEP of about 1 mV, the same range of ISIs, and a similar muscle. Furthermore, increasing the CS intensity to 160% RMT in the resting condition was not effective in inducing LICF in our experiments, whereas increasing the conditioning stimulus intensity from 110% to 140% in Cash et al.'s study delayed the inhibition-to-facilitation inflection point and increased net cortical excitability above baseline [START_REF] Rfh | Late cortical disinhibition in human motor cortex: a triple-pulse transcranial magnetic stimulation study[END_REF]. The reasons for these discrepancies remain unclear. Even though differences between subject populations are unlikely, they cannot be ruled out. However, given the clear facilitation observed in the quiescent ADM muscle when the FDI became active (see Figures 3B &4B), an alternative hypothesis could be that the recruitment of a muscle is accompanied by diffuse effects in neighboring muscles -leading to the LICF observed in both hand muscles even when only one muscle was active. If this hypothesis is confirmed by additional experiments, it would mean that LICF might arise in a resting muscle as soon as other neighboring muscles are activated even slightly. Lastly, it is noteworthy that only 8 subjects were involved in this part of the study; hence, the power of the statistical test was reduced accordingly. However, when looking at individual datasets, only 4 of the 8 subjects having received the highest CS intensity showed facilitation of the conditioned MEPs at an ISI of 230 ms. The remaining 4 subjects had less intense conditioned than unconditioned MEPs. At the previous ISI (195 ms) and following ISI (290 ms), the conditioned MEP was facilitated in only 1 of the 8 subjects.

Even though LICF might conceivably be present at rest in some subjects at an ISI around 230 ms, it is not likely to be intense.

Influence of CS intensity

In line with some previous studies, our data seems at first sight to confirm that LICI is affected by CS intensity. The amount of LICI was de facto greater when the CS intensity increased in both relaxed and active FDI muscle and LICI tended to last longer with increasing CS, at least in the active condition. This is in line with Vallence et al's finding that the CS threshold for evoking LICI with an interstimulus interval of 100ms is lower than that for evoking LICI with an ISI of 150ms [START_REF] Vallence | Long-interval facilitation and inhibition are differentially affected by conditioning stimulus intensity over different time courses[END_REF]. The corollary of LICI prolongation with increasing CS intensity was that LCD occurred later, as already reported in a previous study showing that the inflection point (i.e. the change of slope during the transition between inhibition and facilitation) was delayed [START_REF] Rfh | Late cortical disinhibition in human motor cortex: a triple-pulse transcranial magnetic stimulation study[END_REF], or, in the present work, by the rightward shift of the curve with more intense CS intensity. In the active condition but not at rest, we found that the amount of facilitation following LCD was dependent on CS intensity with LICF being observable with CS just above the resting motor threshold. Taken together, these data suggest that the conditioning stimulus exerts some complex effects on both inhibitory circuits responsible for LICI, LCD and most likely on excitatory neurons that could be recruited preferentially during voluntary contraction.

Delayed inhibition in relaxed muscles

When both the ADM and FDI were at rest, we observed a late inhibition phase in the FDI muscle (as evidenced by the significant inhibition of the conditioned MEPs at the longest ISI, i.e. 290 ms). A quite similar profile was observed for the ADM curve; after disinhibition occurred, the conditioned MEP slightly decreased at an ISI of 290 ms. To the best of our knowledge, conditioned MEPs have never been measured at such a long ISI. The late inhibition observed here is unlikely to be an artifact, since it was present in all the participants and varied little from one participant to another. Although the nature of this late inhibition remains to be assessed, we hypothesize that LICI lasts longer (>290 ms) than the generally assumed limit of around 200 ms and is attenuated in the time window corresponding to disinhibition (i.e. between 150 ms and 300 ms). Very slow phenomena of this type have been evidenced in rat and human cortical neurons (Deisz, 1999a). They are mediated by GABAB receptors, the activity of which increases the potassium conductance underlying slow, inhibitory postsynaptic potentials lasting up to 400 ms (Deisz, 1999b). Our results are thus consistent with slow, long-lasting LICI that is mediated by a post-synaptic GABAB effect and attenuated by a disinhibition phase due to presynaptic inhibition of GABAA terminals. Nevertheless, as evoked earlier, the net increase in cortical excitability (the LICF) observed during index abduction is unlikely to be only due to SICI disinhibition.

In conclusion, our study is the first to show that rest and voluntary hand muscle activity differ in terms of the time course of LICI, LCD and LICF. Given the lack of LICF and the slower disinhibition observed at rest, we speculate that an intracortical excitatory component (tonically inhibited at rest but released when the muscle becomes active) contributes to the reduction in the late part of LICI and to the strong increase in net cortical excitability. Although speculative, the involvement of sensorial inputs in LCD and LICF merits to be investigated.

Figure 1 .

 1 Figure 1. Theoretical curve describing the time-dependent modulation of the

Figure 2 .

 2 Figure 2. Mean amplitude of the motor response evoked by the conditioning stimulus in

Figure 3 .

 3 Figure 3. Change over time in the conditioned MEP amplitude at rest (circles, solid line)

Figure 4 .

 4 Figure 4. Change over time in the conditioned MEP amplitude at rest (circles, solid line)

Figure 5 .

 5 Figure 5. Change over time in the conditioned MEP amplitude at rest (diamonds, dashed

Figure 6 .

 6 Figure 6. Influence of CS intensity on the overall time-dependent evolution of LICI , LCD

Table 1 :

 1 Results of the one-sample t-test used to compare the conditioned MEP amplitude with the control MEP (100%) for the FDI muscle (* : p<0.05 ; NS : non significant)

	Tables
	ISI (ms)	Rest	Rest	Abduction	Abduction
		CS130	CS*Higher	CS130	CS*Lower
	60	*	*	*	*
	90	*	*	*	*
	120	*	*	*	*
	140	*	*	*	NS
	150	*	*	*	NS
	165	*	*	NS	NS
	180	*	*	NS	*
	195	NS	*	*	*
	230	NS	NS	*	NS
	290	*	NS	*	NS

Table 2 :

 2 Results of the one-sample t-test used to compare the conditioned MEP amplitude with the control MEP (100%) for the ADM muscle (* : p<0.05 ; NS : non significant)

Acknowledgements

We thank Jean-Louis Bourriez and François Cassim for helpful comments and David Fraser for editorial support.


Frédéric Lalanne

Pierrick Bedouch

Cyril Simonnet

Vincent Depras

Georgeta Bordea

Romain Bourqui

Thierry Hamon

Frantz Thiessard

Fleur Mougin

Visualizing food-drug interactions in the Thériaque database

Keywords: food-drug interactions, data visualization, hierarchical data

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

Introduction

The French ANR PRCE MIAM project 2 (2017-2020) aims to identify and model fooddrug interactions. It focuses on the problems that can emerge when an interaction occurs between a food and a drug. In practice, the consumption of a food associated with the intake of a drug can have three effects: (i) the attenuation or suppression of the effect of a drug, (ii) the increase of the effect of a drug, and (iii) the occurrence of unknown adverse effects. This information, when available, is described in scientific publications or knowledge bases in an unstructured manner. Thus, MIAM partners have used natural language processing methods to extract such information and made it available within the Thériaque database 3 . This database has been approved by the French National Authority for Health (HAS) and aims at providing health professionals and patients with exhaustive and reliable information about the drugs available in France. Thériaque is integrated into many French hospital computerized prescription order entry systems in order to propose drug decision systems and it is accessible through a website. In addition to drug-drug interaction and drug recommended dosage, Thériaque will therefore be able to provide information on food-drug interactions in the near future.

In this frame, a prototype has been created in order to show and visually explore interactions between foods and drugs. To visually represent foods, drugs and their interactions, we have taken advantage of the fact that foods and drugs can both be organized hierarchically. Each interaction may be displayed as a link between a node from each tree and may have a type and a degree of confidence as well as a source reference.

In their survey of multi-tree visualization approaches, the authors listed five types of representations for visualizing two trees: edge-drawing, matrix, colouring, animation, and agglomeration [START_REF] Graham | A survey of multiple tree visualisation[END_REF]. The last three approaches did not suit our needs because they do not allow to link a node of a tree with several nodes of the other tree. On the other hand, the use of the matrix did not seem appropriate given the low density of the edges between the trees and the size of the trees as this would have led to a too large drawing (i.e. most of the boxes of the matrix would have been empty).

We thus chose the edge-drawing approach, and more precisely the technique presented by Holten et al. [START_REF] Holten | Visual comparison of hierarchically organized data[END_REF] as a starting point for the design of the visualization prototype. More specifically, the goal is to visualize two hierarchies and matching elements between those hierarchies. This is achieved by displaying both hierarchies using icicle plots [START_REF] Graham | Extending taxonomic visualisation to incorporate synonymy and structural markers[END_REF][START_REF] Sifer | Filter co-ordinations for exploring multi-dimensional data[END_REF] on opposite sides of the window. This layout method consists in displaying children nodes beneath their common parent. The horizontal abutment of the children nodes next to their parent gives the hierarchical relations. Matched elements of the hierarchies are linked by a line drawn between them using hierarchical edge bundles [START_REF] Holten | Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data[END_REF] to decrease visual clutter. Hierarchical edge bundles help to assess the proximity of extremities of links in the hierarchical structure: a couple of links connected to nodes with a common parent will join a same bundle close to their extremities. On the opposite, if link extremities have no non-trivial common ancestor, they will not form a bundle.

Methods

Materials

First of all, we collected food-drug interactions from the 2008 and 2016 versions of the Stockley's Drug interactions compendium 4 , which include interactions involving foods. As described in [START_REF] Bordea | Automatic query selection for acquisition and discovery of food-drug interactions[END_REF], we manually extracted the food-drug interactions and automatically retrieved the corresponding PubMed identifiers from the title and year of each reference listed for each interaction.

Visualization approach

In this work, the hierarchical structures displayed in the icicle plots represent the hierarchy of drugs (using the Anatomical Therapeutic Chemical (ATC) Classification System) and a hierarchy of foods, based on the alphabetical order. Links between hierarchies depict interactions between foods and drugs (Figure 1).

The main adaptations we had to make to the work of Holten et al. [START_REF] Holten | Visual comparison of hierarchically organized data[END_REF] are as follows:

• visualization of supplementary information: (i) the type of food-drug interactions, being one of Contraindication, Discouraged association, Precaution of use and To be taken into account (four types, ordered from the most harmful to the least damaging, as recommended by the French Agency for the Safety of Medicines and Health Products), and (ii) the number of articles reporting the interaction, • multiple links between elements of the two hierarchies as each food might interact with several drugs, and the other way around. Thus, we both needed to represent elements with multiple links and a number of links that might be, for matching the size of hierarchies, substantially higher than in [START_REF] Holten | Visual comparison of hierarchically organized data[END_REF]. Thus, we use different colors to represent the type of each interaction. If multiple types are associated with a given food-drug interaction (thus described in more than one article), the chosen color is the one of the more harmful type. On the other hand, the nodes in the same hierarchy have the same color, from the darkest color for the root to the lightest color for the leaves.

In addition to the graphical view of hierarchies and interactions between them, a more traditional view has been created on the right side of the interface. The right view provides a representation of hierarchies in the form of trees ("Arbre d'interactions" tab) and details about the interaction ("Détail" tab), i.e. the type of interaction, the number of articles reporting it and the list of corresponding articles (abstract and the hyperlink to access the article).

Results

A total of 560 interactions were acquired from the 2008 and 2016 versions of the Stockley Drug interactions compendium. Only 444 were selected for use in the prototype because a match had to be found between the drug name and a corresponding label in the ATC. The prototype allows users to perform their analysis by looking for interactions based on: (i) a drug or a set of drugs (including drugs classes), (ii) a food or a set of foods, or (iii) a combination of both.

Figure 1 presents the left-hand side of the interface, that is the graphical view of food-drug interactions implemented in the prototype. Figure 2 exhibits the right-hand side of the interface, that is either the tab showing the tree view of hierarchies or the tab displaying details about the interactions described hereafter.

We provide a system of on-demand details, as well as an element-based filtering of the interactions to allow users to focus on a limited part of the hierarchies. To cope with the potentially high number of links, hindering both the tool's performance and visual perception, we choose to first display an overview of the data by displaying only nodes of depth one at most. In this state, instead of being connected to leaf nodes of the hierarchies, links can be connected to the nearest ancestor of an undisplayed leaf node. Users can then choose to unfold some of the hierarchy elements to get a higher level of detail on the specific part of the hierarchies they are interested in. Alternatively, users can choose to fold a previously unfolded inner node to hide its subtree. If users want more or less detail on an entire hierarchy, the same operation can be performed levelwise: all nodes of the same depth will be folded or unfolded. In addition, a mouse-over of the link gives details of the interaction in a small grey window. Specifically, the two connected nodes, the type(s) of interaction and the number of articles reporting it are displayed. Finally, the graphical (left side of the interface) and more traditional (right side of the interface) views are automatically updated based on user actions in either view. In particular, users can filter interactions according to their type by clicking on the desired type in the right view, which results in only that type of interaction being displayed in the graphical view.

Discussion

The proposed prototype provides an original and graphical way to explore food-drug interactions. It will be integrated within the Thériaque database in the coming weeks.

It should be noted that the exploration of the drug hierarchy requires familiarity with the ATC system, and in particular its codes. This is of course a limitation to the use of the prototype by patients, who are also a target audience for Thériaque. An obvious perspective is to proceed to the evaluation of the prototype with the different user groups to which the Thériaque database is addressed. Moreover, the food hierarchy is based on the alphabet whereas the use of a hierarchy with semantic meaning would be more relevant. In this framework, the food hierarchy described in FIDEO [START_REF] Bordea | FIDEO: Food Interactions with Drugs Evidence Ontology[END_REF] could be used.

In our future work, we also plan to recover other food-drug interactions. This is the broader goal of the MIAM project and one of the avenues currently being explored is the contents of DrugBank. The latter is a knowledge base in which 1195 food-drug interactions are described [START_REF] Wishart | DrugBank 5.0: a major update to the DrugBank database for 2018[END_REF]. This information is provided in free text and thus needs to be processed with NLP algorithms so that the food(s) mentioned as interacting with the drug of interest are identified.

Figure 1 .

 1 Figure 1. Graphical view of interactions existing between Atorvastatin and Grapefruit juice. Red, orange, purple and blue links correspond respectively to the following types of interaction: contraindication, discouraged association, precaution of use and to be taken into account. The broader links correspond to interactions involving the element(s) initially searched for (here, Grapefruit juice).

Figure 2 .

 2 Figure 2. Right hand side of the interface. On the left, the tab listing details about the food-drug interactions.On the right, the tab presenting the drug and food hierarchies (only the drug hierarchy is visible here).

Corresponding author: Pierrick Bedouch; E-mail: PBedouch@chu-grenoble.fr

https://miam.limsi.fr

http://www.theriaque.org

https://about.medicinescomplete.com/publication/stockleys-drug-interactions

Acknowledgements. This work was supported by the projects MIAM through grantANR-16-CE23-0012 and kANNa through grant H2020 MSCA-IF-217 number 800578.


domain: spi info

text:
Khalil Aloui

Amir Guizani
email: amir.guizani@live.fr

Moncef Hammadi
email: moncef.hammadi@supmeca.fr

Thierry Soriano
email: thierry.soriano@supmeca.fr

Mohamed Haddar
email: mohamed.haddar@enis.rnu.tn

System level

Keywords: Manufacturing systems, Multi-agent simulation, Systems Modeling Language (SysML), Simulation tool (Anylogic)

published or not. The documents may come

Introduction

Thanks to multi-agent systems (MAS), the design and manufacturing of control solutions have become more flexible, adaptive, and reconfigurable compared to traditional systems [START_REF] Wooldridge | An introduction to multiagent systems[END_REF]. These systems are used to solve emerging challenges in the design and manufacture of industrial systems in terms of adaptation, flexibility and reconfigurability [START_REF] Barbosa | Simulation of multi-agent manufacturing systems using agent-based modelling platforms[END_REF]. [START_REF] Marik | Industrial adoption of agent-based technologies[END_REF] have shown that MAS are characterized by the decentralization of the control of its distributed structures which provide robustness, modularity, and autonomy of the processes and solve at least 25% of manufacturing problems. For example, (Guizani et al, 2014a) have developed a new approach based on multi-agent technologies for the optimal design of mechatronic systems.

The proposed approach is further improved by the development of a coordination and negotiation process allowing agents to facilitate the collaborative design of distributed systems [START_REF] Guizani | Agent-based approach for collaborative distributed mechatronic design[END_REF]. They subsequently validated this work through a test case to optimize the preliminary design of an electric vehicle [START_REF] Guizani | Multi-agent approach based on a design process for the optimization of mechatronic systems[END_REF].

The multi-agent swarm framework can adequately represent characteristics of supply chains, such as multiple levels of abstraction and separation of concerns. Through this multi-agent model, individual agents are integrated with trust mechanisms to identify trusted partners to fulfill customer orders [START_REF] Lin | Effects of trust mechanisms on supply-chain performance: A multi-agent simulation study[END_REF]. In addition, the study of swarm robotic systems has been considered as a study of multi-agent systems. Developers of swarm robot systems use realistic simulators to test and accelerate the development of new design methods [START_REF] Aloui | On the continuity of the swarm robot design using MBSE method and simulation[END_REF]. They use simulators to model the interactions between robots and the interactions of robots with their environment (Sahin et al, 2008). The software required to develop agents is simpler and shorter than the software required by centralized approaches [START_REF] O'hare | Foundations of distributed artificial intelligence[END_REF]. However, analyzing, testing, and validating the behavior of multi-agent systems is generally difficult and time-consuming. It is necessary to use tools that support the correction of errors during the design phase before its deployment in the real operation; These are the tools of multi-agent simulation that simplify the representation, the testing, and therefore the understanding of the behavior of the system.

In manufacturing, developers reduce the time and cost of developing control systems by using simulation which allows for the detection of errors, mistakes, and misunderstandings during the design phase and before moving on to implementation [START_REF] De Vin | Why we need to offer a modeling and simulation engineering curriculum[END_REF]. In this article, we study the steel converter process with the aim of developing a model that focuses on the crane management algorithm to minimize discontinuities in the operation of continuous casting machines. In the first part, we model this process using Systems Modeling Language [START_REF] Mhenni | A SysMLbased methodology for mechatronic systems architectural design[END_REF]. In fact, several SysML diagrams are used such as requirements diagram to specify system requirements, sequence diagram and state machine diagram to model system behavior, and block definition diagrams to identify structural architecture. In the second part, we use a multi-agent tool called "Anylogic" to simulate the modeled version and check the performances through the results obtained.

Steel converter process

Today, we use steel to fabricate everything from sewing needles to oil tankers. It is the most used material for building industries and the world's infrastructure [START_REF] Tossavainen | Characteristics of steel slag under different cooling conditions[END_REF]. In addition, all the tools required to build and manufacture are made of steel. The process of converting steel is very complicated [START_REF] Kovalev | Improving production technology of tube steel grades in converter process[END_REF]. It can be simplified as follows: at first, the hot metal was treated in a converter where all secondary metallurgical work is done. Secondly, the hot metal is poured into a large refractory-lined container called a ladle and transported to the cranes. Then the filled ladles loaded through cranes and carried to the continuous casting machines (CCM). These machines are used to pour hot metal into molds. [START_REF] Ives | Everyday Brave: Media Nomads: Thaiday Brothers-Ep 5 Of 6[END_REF]. In this paper, to develop a model of the steel converter process, the work mainly focused on the crane management algorithm to minimize discontinuities in the operation of continuous casting machines (CCM).

System requirements

To minimize discontinuities in the operation of continuous casting machines (CCMs), there are several constraints that cause difficulties. The requirements diagram shown in Figure 1 presents the main system requirements.

Fig. 1 System requirements

The critical timing is imposed by the CCMs and additional constrains come from the steel converters and the ladle transportation vehicles. For a good productivity, the number of cranes should correspond to the number of other elements in the system. In addition, the model should be highly scalable: it can work with any number of converters, cranes, CCMs, etc. and any timing, distance and capacity parameters.

Behavior modeling

Modeling behavior requires the identification of the main components of the system. As mentioned, the steel conversion process consists of converters where all secondary metallurgical work is done, ladles to transfer hot metal, cranes and continuous casting machines (CCM). Figure 2 represents the sequence diagram which describes the chronological order of the functional scenario of the system.

Fig. 2 Sequence diagram

At the beginning of the process, the empty ladles move to the converters for filling. The converters pour the hot metal into the ladles. The filled ladles are then moved to the cranes for loading onto the continuous casting machine (CCM). If the ladles connect to the continuous casting machines (CCM), these machines pour the hot metal into the molds and the process is repeated each time.

Figure 3 shows the state machine diagram of the hot metal transport activity throughout the process. The ladle waits to be full at the converter. If the filling is carried out, the ladle moves to the crane for loading. Then the empty ladle returns to the converter for refilling.

Fig. 3 State machine diagram of transportation activity

The state machine diagram shown in Figure 4 describes the crane activity. The crane awaits the arrival of the ladle; it moves towards target 1 where it checks whether there is a ladle or not. If there is a ladle, the crane loads it and moves towards target 2 to unload, otherwise, it returns to the wait step.

Fig. 4 State machine diagram of crane activity

The continuous casting of liquid steel into the mold is a very important step in the steel conversion process. This step is performed by a continuous casting machine (CCM). This machine is constituted by two right and left ladle holders. Its operating algorithm is illustrated in Figure 5. At first, the machine waits for the arrival of a ladle. The crane checks whether one of the two CCM ladle holders is filled or not. If the right ladle holder is empty, the crane loads the ladle into this ladle holder; otherwise, the CCM rotates 180 °so that the crane loads the ladle into the left ladle holder.

Fig. 5 State machine diagram of CCM activity

The simulation of the steel converter process requires the identification of the structural architecture of the system. That's why the next section discusses structural modeling of the system using block definition diagrams.

Structural modeling

In this section, the structural architecture of the process has been studied. Figure 6 represents the activity diagram which describes the general structure of the steel converter process. This process generally consists of four subsystems; two subsystems for pouring activity at converter and CCM level, and two subsystems for loading activity at converter and crane level. The block definition diagram shown in Figure 7 describes the components of the pouring system at the converter level. This subsystem consists of three converters, ten steel ladles and five rails. In fact, each component is characterized by a set of values that ensure the functioning of the system. Likewise, the block definition diagram illustrated in Figure 8 describes the pouring system at the CCM level. This subsystem is made up of three CCM's and two ladles.

Fig. 8 Pouring system (CCM level)

The loading system at the converter level is shown in Figure 9. This subsystem consists of two ladles and five rails. To ensure the continuity and consistency of the modeling approach, we use the allocation matrix and the traceability diagram. In fact, the allocation matrix illustrated in Figure 12 links the functions with its system components; and the traceability diagram illustrated in Figure 11 represents the links and the interactions between the different levels of the system.

Multi-agent simulation

In this section, we use a multi-agent simulation tool called "AnyLogic". This simulation tool has a graphical modeling language and also makes it easy to extend the simulation model with Java code [START_REF] Borshchev | Distributed simulation of hybrid systems with AnyLogic and HLA[END_REF].

Generally, the model of steel converter process focuses on crane management algorithm that should minimize discontinuities in the continuous casting machines (CCM) operation. Figure 13 represents the simulated model of the steel converter process.

Fig. 13 Simulated model of the steel converter process

With the model developed in the previous sections, defining the simulation becomes easier. You just need to create the necessary components through the library found in the software (cranes, converters, CCM's, and ladles).

The aim of this work is to minimize discontinuities in the operation of continuous casting machines (CCM) to avoid solidification of the metal. Figure 14 represents the simulation results before and after the modification of the crane management algorithm.

Fig. 14 Simulation results

Before the intervention, the crane management algorithm is insufficient; which causes discontinuities in the operation of continuous casting machines and solidification of the metal. That's why the model focuses on the crane management algorithm and modify it to minimize these discontinuities. Finally, the discontinuities in the operation of continuous casting machines are minimized.

Conclusion

In the field of manufacturing, the use of multi-agent systems facilitates the design of processes and the improvement of their performances. In fact, a multiagent simulation is a tool for analyzing, understanding, and optimizing manufacturing processes such as the steel converter process.

This paper discusses the use of SysML languages in modeling and the multi-agent tool in the simulation of agent-based manufacturing systems. Aiming to illustrate the applicability and benefits of using these tools in the modeling and simulation of agent-based systems, a steel converter process study was considered. For this purpose, the Anylogic tool was used as a simulation platform to focus on the crane management algorithm which should minimize discontinuities in the operation of continuous casting machines (CCM).

As a future work, the developed model will continue to be used to finalize the specification of the steel converter process. Also, and due to some Anylogic limitations, the logical step is to move into a more powerful software.

Fig. 6

 6 Fig. 6 The steel converter process

Fig. 7

 7 Fig. 7 Pouring system (Converter level)

Fig. 9

 9 Fig. 9 Loading system (Converter level)

Fig. 10

 10 Fig. 10 Loading system (Crane level)

Fig

  Fig. 11 Traceability diagram