Artykuły w czasopismach na temat „Risky mountain trail detection”

AbstractCrop losses to foraging elephants are one of the primary obstacles to the coexistence of elephants and people. Understanding whether some individuals in a population are more likely to forage on crops, and the temporal patterns of elephant visits to farms, is key to mitigating the negative impacts of elephants on farmers’ livelihoods. We used camera traps to study the crop foraging behaviour of African elephants Loxodonta africana in farmland adjacent to the Udzungwa Mountains National Park in southern Tanzania during October 2010–August 2014. Camera traps placed on elephant trails into farmland detected elephants on 336 occasions during the study period. We identified individual elephants for 126 camera-trap detections. All were independent males, and we identified 48 unique bulls aged 10–29 years. Two-thirds of the bulls identified were detected only once by camera traps during the study period. Our findings are consistent with previous studies that found that adult males are more likely to adopt high-risk feeding behaviours such as crop foraging, although young males dispersing from maternal family units also consume crops in Udzungwa. We found a large number of occasional crop-users (32 of the 48 bulls identified) and a smaller number of repeat crop-users (16 of 48), suggesting that lethal control of crop-using elephants is unlikely to be an effective long-term strategy for reducing crop losses to elephants.

<strong>Abstract</strong> Outdoor recreational activities are increasing worldwide and occur at high frequency especially close to cities. Forests are a natural environment often used for such activities as jogging, hiking, dog walking, mountain biking, or horse riding. The mere presence of people in forests can disturb wildlife, which may perceive humans as potential predators. Many of these activities rely on trails, which intersect an otherwise contiguous habitat and hence impact wildlife habitat. The aim of this study was to separate the effect of the change in vegetation and habitat structure through trails, from the effect of human presence using these trails, on forest bird communities. Therefore we compared the effects of recreational trails on birds in two forests frequently used by recreationists with that in two rarely visited forests. In each forest, we conducted paired point counts to investigate the differences between the avian community close (50 m) and far (120 m) from trails, while accounting for possible habitat differences, and, for imperfect detection, by applying a multi-species N-mixture model. We found that in the disturbed (i.e., high-recreation-level forests) the density of birds and species richness were both reduced at points close to trails when compared to points further away (&minus;13 and &minus;4% respectively), whereas such an effect was not statistically discernible in the forests with a low-recreation-level. Additionally we found indications that the effects of human presence varied depending on the traits of the species. These findings imply that the mere presence of humans can negatively affect the forest bird community along trails. Visitor guidance is an effective conservation measure to reduce the negative impacts of recreationists. In addition, prevention of trail construction in undeveloped natural habitats would reduce human access, and thus disturbance, most efficiently.

Background: Myelodysplastic syndromes (MDS) consist of a spectrum of myeloid malignancies typified by regular genetic abnormalities, clonal hematopoiesis, dysfunctional myelopoiesis, inefficient blood cell production, peripheral-blood cytopenia, and an elevated risk for progressing to acute myeloid leukemia (AML). Mounting evidence suggests that the dysregulation of immune checkpoints (ICs) plays critical roles in immune evasion of MDS, which thus led to a series of trials using HMAs in combination with immune checkpoints inhibitors (ICIs). Of these, programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathway has been studied most extensively. Recently, forms of cell-free soluble PD-1/PD-L1 (sPD-1/sPD-L1) detection in plasm of patients with solid tumors has open a new paradigm for PD-1/PD-L1 investigations. However, within the context of MDS, the potential dysregulation and prognostic impact of sPD-1/sPD-L1 remains uncertain. This study was aimed to assess roles of sPD-1/sPD-L1 in newly diagnosed MDS patients. Method: Between July 2020 and March 2023, 130 MDS/sAML patients matched the inclusion criteria and 55 healthy individuals with no familial or personal history of autoimmune or cancer disorders were included. The criteria for patient inclusion included: (1) a diagnosis of MDS confirmed pathologically according to 2016/2022 World Health Organization (WHO) classification, or sAML patients with a verified prior MDS history; (2) absence of other conditions like autoimmune diseases or different types of tumors; (3) availability of complete clinical information and survival data. MDS patients were categorized by IPSS-R as: lower-risk (very low risk/ low risk; higher-risk (intermediate risk/high risk/very high risk. Follow-up was conducted until June 16th, 2023. The median follow-up was 14.32 months (range 3.2-34.63 months). Profile of sICs (sPD-L1, sPD-1, sCTLA-4, sGITR, sTIM-3, sOX40, sLAG-3, s4-1BB, sICOS) were tested by ELISA, cytokines (IFN-γ, IFN-α2, IL-2, IL-2α, IL-6, IL-7, IL-10, IL-15, IL-17, TNF-α) were detected using Luminex analysis, and the expression levels of PD-1 on T cells (CD3 +, CD4 +, CD8 +, DPT, DNT）were measured using flow cytometry (FCM). Result: In the plasma of 98 newly diagnosed MDS patients, sPD-L1 levels (median 66.80 pg/ml, range 25.00-219.5 pg/ml) were significantly elevated compared to 55 healthy blood donors (median 49.22 pg/ml, range 12.5-218.1 pg/ml). sPD-L1 level was higher in higher-risk IPSS-R groups ( P=0.0380). In addition, higher sPD-L1 levels also showed a correlation with lower hemoglobin concentration, implying a potential link between higher sPD-L1 levels and a more advanced MDS disease state. The AUC for sPD-1 was found to be 0.6333 [95% CI 0.5331-0.7334, P=0.0100] and for sPD-L1, it was 0.6785 [95% CI 0.5836-0.7733, P=0.0002]. The optimal cut-off values were established at 187.5 pg/mL for sPD-1 and 55.08 pg/mL for sPD-L1. Based on these cut-off values, patients with increased sPD-L1 experienced shorter OS ( P=0.0305). By using multivariate Cox model, high expression of sPD-L1 ( P=0.019, HR = 4.172, 95% CI: 1.265-13.755) and high IPSS-R scores ( P=0.030, HR = 2.898, 95% CI: 1.111-7.565) were found to be independent risk factors for newly diagnosed MDS patients. In addition, compared to healthy controls, the PD-1 + T cell subpopulations in MDS patients were significantly reduced, sPD-1 levels were positively correlated with proportion of CD4 + PD-1 + T cells, absolute count of CD4 + PD-1 + T cells and PD-1 + DPT cells, suggesting that CD4 + T cells and DPT cells may be potential sources of sPD-1 in plasma. Subsequently, we analysed the potential roles of cytokines and HMAs in sPD-1/sPD-L1 regulation. It was found that sPD-1 levels had a positive correlation with sPD-L1, IL-2Rα, IP-10, MIG, TNFα, GROα, and TRAIL. sPD-L1 was positively correlated with IL-2Rα, IP-10, MCP-1, MIG, MIP1α, and SCF (Figure 1 A). Notably. We found a reduced sPD-L1 levels in MDS patients who achieved remission after HMA treatment compared to treatment-naïve patients. ( P=0.0302) (Figure 1 B). Conclusions: In conclusion, we found sPD-L1 is an independent risk factors for overall survival in newly diagnosed MDS patients. The elevation of plasma sPD-L1 levels is associated with disease progression in MDS, and may be a potential target for MDS immunotherapy.

We provide mountaineer's motion data to various mountain trail surface conditions. Mountain trail surface conditions classified into risky and non-risky segments based on the slope and height of the rock surface. Motion data was collected using the smartphone's built-in sensor. We perform two experiments to collect data. In the first experiment, data collected along the trail are alternating between the risky and non-risky segments. In the second experiment, we collected the data that is collected separately from where there are only risky segments and non-risky segments exist. In first experiments, we collected sensor data from 14 participants (7 males and 7 females) whose ages ranged from 22 to 32 years (Mean: 27.4, Std: 2.17). The experiment site was located in Gyeryongsan National Park, Daejeon, South Korea. We chose five different zones; each was chosen to reflect various mountain trails in real mountain climbing scenarios. The first zone represents an easy path without risky segments, and the second and third zones represent traces of alternating risky and non-risky segments. Also, the fourth zone represents a large rocky trail, and finally the fifth zone represents a high slope trails. In second experiments, we hired five participants (one female), and their mean age was 26.2 (SD=2.3). We proceeded second experiment on the other trails of the mountain where the first experiment was conducted. In the second experiment, data from risky trails and non-risky trails were collected separately. Data were collected from 9 risky trails and 0.9 km long, non-risky trails We used the Nexus 5 smartphones with Android OS version 5.00 (lollipop) and collected the three-axis accelerometer data, three-axis gyroscope data, three-axis magnetic sensor data and GPS coordinates. To collect data, we developed a custom sensor-data logging program that uses the Android sensor API and recorded data with the sample rate set to FASTEST, which is approximately 50 Hz. Experimenters take the smartphones in the front pockets on pants since they are natural places for holding smartphones during climbing. For ease of collection, all phone screens faced toward the body with the camera on top. In this orientation, X-axis means the left-right axis, Y-axis means the vertical axis and Z-axis means the inward-outward axis (see Figure 2 in the paper). The name of each file contains the information of the data. The data for the experiment is in the form "zoneX_UserY". "ZoneX_UserY" indicates that data collected from user Y in Zone X. The second experiment data is named in the format "Second_labelX_UserY". Label indicates whether the data was collected from risky trails or non-risky trails and User Y indicates that data collected from user Y. Each coulmn of the data file represents the record of 3-axis accelerometer, magnetic sensor, gyroscope sensor data and total magnitude of each 3-axis sensor data. Also, 'Label' column in first experiment data represents the trail surface condition. '0' reprensents that data collcetd in non-risky segments and '1' represents that data collected in risky segments. The X, Y, and Z axes are defined as above. For example, Accel X refers to the acceleration sensor X-axis data, and Accel Mag refers to the total amount of acceleration calculated from the 3-axis acceleration sensor data. Please refer to 'TrailSense: A Crowdsensing System for Detecting Risky Mountain Trail Segments with Walking Pattern Analysis' published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1 Issue 3, September 2017 Article No. 65 for details.

AbstractThe management objectives of many protected areas must meet the dual mandates of protecting biodiversity while providing recreational opportunities. It is difficult to balance these mandates because it takes considerable effort to monitor both the status of biodiversity and impacts of recreation. Using detections from 45 camera traps deployed between July 2019 and September 2021, we assessed the potential impacts of recreation on spatial and temporal activity for 8 medium‐ and large‐bodied terrestrial mammals in an isolated alpine protected area: Cathedral Provincial Park, British Columbia, Canada. We hypothesised that some wildlife perceive a level of threat from people, such that they avoid ‘risky times’ or ‘risky places’ associated with human activity. Other species may benefit from associating with people, be it through access to anthropogenic resource subsidies or filtering of competitors/predators that are more human‐averse (i.e., human shield hypothesis). Specifically, we predicted that large carnivores would show the greatest segregation from people while mesocarnivores and ungulates would associate spatially with people. We found spatial co‐occurrence between ungulates and recreation, consistent with the human shield hypothesis, but did not see the predicted negative relationship between larger carnivores and humans, except for coyotes (Canis latrans). Temporally, all species other than cougars (Puma concolor) had diel activity patterns significantly different from that of recreationists, suggesting potential displacement in the temporal niche. Wolves (Canis lupus) and mountain goats (Oreamnos americanus) showed shifts in temporal activity away from people on recreation trails relative to off‐trail areas, providing further evidence of potential displacement. Our results highlight the importance of monitoring spatial and temporal interactions between recreation activities and wildlife communities, in order to ensure the effectiveness of protected areas in an era of increasing human impacts.

Abstract Detecting shifts in trait values among populations of an invasive plant is important for assessing invasion risks and predicting future spread. Although a growing number of studies suggest that the dispersal propensity of invasive plants increases during range expansion, there has been relatively little attention paid to dispersal patterns along elevational gradients. In this study, we tested the differentiation of dispersal-related traits in an invasive plant, Galinsoga quadriradiata, across populations at different elevations in the Qinling and Bashan Mountains in central China. Seed mass–area ratio (MAR), an important seed dispersal-related trait, of 45 populations from along an elevational gradient was measured, and genetic variation of 23 populations was quantified using inter-simple sequence repeat (ISSR) markers. Individuals from four populations were then planted in a greenhouse to compare their performance under shared conditions. Changing patterns of seed dispersal-related traits and populations genetic diversity along elevation were tested using linear regression. Mass–area ratio of G. quadriradiata increased, while genetic diversity decreased with elevation in the field survey. In the greenhouse, populations of G. quadriradiata sourced from different elevations showed a difference response of MAR. These results suggest that although rapid evolution may contribute to the range expansion of G. quadriradiata in mountain ranges, dispersal-related traits will also likely be affected by phenotypic plasticity. This challenges the common argument that dispersal ability of invasive plants increases along dispersal routes. Furthermore, our results suggest that high-altitude populations would be more effective at seed dispersal once they continue to expand their range downslope on the other side. Our experiment provides novel evidence that the spread of these high-altitude populations may be more likely than previously theorized and that they should thus be cautiously monitored.

Abstract Background We aimed to determine whether and how the combination of acetazolamide and remote ischemic preconditioning (RIPC) reduced the incidence and severity of acute mountain sickness (AMS). Methods This is a prospective, randomized, open-label, blinded endpoint (PROBE) study involving 250 healthy volunteers. Participants were randomized (1:1:1:1:1) to following five groups: Ripc (RIPC twice daily, 6 days), Rapid-Ripc (RIPC four times daily, 3 days), Acetazolamide (twice daily, 2 days), Combined (Acetazolamide plus Rapid-Ripc), and Control group. After interventions, participants entered a normobaric hypoxic chamber (equivalent to 4000 m) and stayed for 6 h. The primary outcomes included the incidence and severity of AMS, and SpO2 after hypoxic exposure. Secondary outcomes included systolic and diastolic blood pressure, and heart rate after hypoxic exposure. The mechanisms of the combined regime were investigated through exploratory outcomes, including analysis of venous blood gas, complete blood count, human cytokine antibody array, ELISA validation for PDGF-AB, and detection of PDGF gene polymorphisms. Results The combination of acetazolamide and RIPC exhibited powerful efficacy in preventing AMS, reducing the incidence of AMS from 26.0 to 6.0% (Combined vs Control: RR 0.23, 95% CI 0.07–0.70, P = 0.006), without significantly increasing the incidence of adverse reactions. Combined group also showed the lowest AMS score (0.92 ± 1.10). Mechanistically, acetazolamide induced a mild metabolic acidosis (pH 7.30 ~ 7.31; HCO3− 18.1 ~ 20.8 mmol/L) and improved SpO2 (89 ~ 91%) following hypoxic exposure. Additionally, thirty differentially expressed proteins (DEPs) related to immune-inflammatory process were identified after hypoxia, among which PDGF-AB was involved. Further validation of PDGF-AB in all individuals showed that both acetazolamide and RIPC downregulated PDGF-AB before hypoxic exposure, suggesting a possible protective mechanism. Furthermore, genetic analyses demonstrated that individuals carrying the PDGFA rs2070958 C allele, rs9690350 G allele, or rs1800814 G allele did not display a decrease in PDGF-AB levels after interventions, and were associated with a higher risk of AMS. Conclusions The combination of acetazolamide and RIPC exerts a powerful anti-hypoxic effect and represents an innovative and promising strategy for rapid ascent to high altitudes. Acetazolamide improves oxygen saturation. RIPC further aids acetazolamide, which synergistically regulates PDGF-AB, potentially involved in the pathogenesis of AMS. Trial registration ClinicalTrials.gov NCT05023941.

AbstractMonitoring of drug use in athletes is of interest both for health and competition‐related issues. Considering the advantages of Dried Blood Sampling (low invasiveness, easy sampling, long term storage), we have validated a quantitative LC–MS/HRMS method for the screening of 16 nonsteroidal anti‐inflammatory drugs. For all drugs, accuracy and imprecision were within 15% for the 3 levels of quality control and lower than 20% for the lower limit of quantification. Application was performed from samples obtained for Ultra‐Trail du Mont‐Blanc® 2021 and 2022. A focus on ibuprofen and its metabolites (hydroxyibuprofen, carboxyibuprofen, ibuprofen glucuronide and hydroxyibuprofen glucuronide) was made because the results showed that it was the most detected nonsteroidal anti‐inflammatory drug. Further, an interpretation of the ibuprofen concentrations was proposed either from experimental data obtained after an intake of ibuprofen by 10 control subjects, or from a pharmacokinetic modelling and simulations. Depending on the analytical performances of the method, we proposed possible detection windows for ibuprofen in runners. The pharmacokinetic model made it possible to consider two scenarios with and without modification of the total clearance of ibuprofen linked to a modification of the pharmacokinetics of the drugs due to the practice of a long and intense physical activity.

In this article we consider the ways that parents and children construct an object that has long been associated with North American childhood: the bicycle. We ask the question: is the bicycle a toy or a tool? At first glance, this seems like a straightforward distinction. For example, if an object serves a useful purpose, we classify it a tool. Hammers are tools because they help drive nails into wood. If the object serves no apparent purpose other than our own intrinsic enjoyment, it is a toy. Kites are toys because we gain no instrumental benefit by flying them; kites offer us only amusement and entertainment. Of course, it is not as clear as this. Sometimes toys become tools as ingenious and resourceful people find new uses for them. Tools become toys as we discover other objects that fulfill a function more efficiently or affordably. At times, we engage in public debates about the classification of objects as toys or tools. We saw this recently, when educators debated whether the fidget spinner was a toy that distracted students from learning or a tool that helped students focus on learning (Silver). These examples show that the meanings that objects hold are not inherent to the object but are actively constructed through social processes and situated in specific historical, geographical, and political contexts. Understanding how we make meaning of objects is important because meanings impact on how objects circulate through everyday life and how they are used and valued. In a culture that values work over leisure, tools are socially valued yet ‘toy’ is a word loaded with judgment; although toys are objects of delight they are also associated with superficiality, consumerism, and a desire for status (Whitten). As manufacturers of ‘educational toys’ certainly understand, the construction of an object as a tool or toy shapes when, where, and by whom that object should be used, including the spaces they are allowed to occupy and the ways that children are permitted to engage with and use them (Brougère). The bicycle is many things at once: it moves through space, it requires physical effort of the rider, and it is self-propelled. As an object, the bicycle has also held different meanings depending on the cultural, historical, and political context. Hoffman (6) calls the bicycle a ‘rolling signifier’ in that ‘it carries a diversity of signification depending on its location in time and space’. Throughout its 150-year history in North America, the bicycle has been a leisure-based status symbol of the progressive urban elite, a symbol of women’s liberation, and a transportation vehicle of the working poor, and the focus of a fitness craze (Turpin). Starting out as an adult leisure activity, the bicycle began to be associated with childhood in the 1950s, when the bicycle manufacturing industry began to turn its attention to selling bicycles to children rather than adults (Turpin 1). Through the 1950s and 1970s, advertisements and television shows began to represent the bicycle as a vehicle for childhood freedom, highlighting the bicycle as the quintessential childhood gift and the moment of learning to ride a bicycle a childhood milestone (Turpin; McDonald). Although still constructed as an “indelible part of childhood” (Turpin, 1), the bicycle, and childhood, have changed since the days when the bicycle first gained its iconic status. Although new styles of bicycling (e.g., BMX, mountain) have emerged, the actual bicycling of children in terms of the amount of time spent riding and distance travelled has been on the decline for a generation (Cox; McDonald). Changing ideas about children’s health, development, and parental responsibilities to prepare children for their future have also raised anxieties about how, where, and how much time children spend engaged with ‘toys’ versus ‘tools’, and whether children should be playing or moving around outdoors, in the streets, unsupervised and alone (Alexander et al.; Valentine). A growing body of research highlights the ways in which childhood has become increasingly contained, immobilised, and institutionalised (Karsten; Rixon, Lomax, and O’Dell). In this context the object of the bicycle becomes more problematic given its features of mobility, physicality, and rider autonomy. In this article we investigate the ways that children and parents construct meanings of the bicycle in childhood. We draw on data collected in 2019 and 2020 when we interviewed 24 bicycle-riding children (aged 10-16, rode independently at least once per week) and 19 bicycle-supportive parents about their perspectives and experiences of bicycling in the downtown and suburban areas of the small Canadian city in which they lived. As we elaborate below, children constructed the bicycle as a toy that allowed physical and environmental exploration. For parents, these meanings produced anxiety because they relied on children moving through space unsupervised. In the article we will show how parents managed their desires and worries in ways that at times reconfigured the meaning of ‘bicycle’. We point to the central role of emotion in enabling and limiting children’s bicycling opportunities. We close with a discussion of the implications of these findings in the construction and promotion of children’s bicycling. Children's Constructions of the Bicycle in Childhood Our interviews with children revealed that while it was appreciated as a vehicle that could get them places faster than walking, children primarily constructed the bicycle as a toy. In fact, children constructed the bicycle as two different types of toys. First, the bicycle was a physical toy that afforded riders the opportunity to connect with their environment in novel ways and in so doing, to experiment with their physicality. For MK (boy, 11) the best part about riding was practicing ‘tricks’, or small manoeuvres with a bicycle like popping it up on one wheel, or jumping the bike over an obstacle. He had a number of favourite ‘trick spots’ – curbs, steps, benches, small hills – spread through the city that he would stop at as he made his way across town. Ross (383) sees this as ‘discipline and disorder’, noting, with respect to children’s unaccompanied school journeys, “the potential for impromptu play responding to features along the route”. She adds that “such free-play can only occur when children are able to set their own agenda, making decisions along the way”, implying that journeys may be more ‘playful’ – and bicycles more ‘toylike’ – when adults are not co-present. MK explained that he liked tricks because there was nothing at stake, other than possibly being teased by his brother. At the same time, friends were a source of inspiration and creativity as kids worked together to test out tricks and record their performances: Q: What do you like about tricks? MK: They’re easy to learn. If you mess one up, no one makes fun of you for it, no one laughs at you. Q: What is your least favourite part about riding? MK: When I do miss a trick, my brother makes fun of me. Alternatively, GL (boy, 15) sought out trails in nearby wooded areas on his mountain bike where he would engage with the rocky and rooted terrain at different speeds. For GL, the fun of mountain biking was that anything could happen: Q: What it's like to do the trails? What happens and what do you like about it? GL: Just the craziness of the unexpected sometimes. And like, the downhill obviously, not [to] have to do anything and just roll down the hill through all these roots and rocks and stuff. It is quite challenging. Second, the bicycle was an adventure toy that afforded children the opportunity to explore the local environment with no agenda other than to take in the surroundings and see what’s there. Whereas with riding for transportation “you’re trying to get somewhere, maybe going faster to try to get there faster, obviously, but for leisure you're just having fun enjoying it and just looking around, you see what's around you” (GL). Perhaps less risky than trick riding, adventure riding still required some bravery as it required the rider to venture into the unknown. Given this, it was the experience of exploring and discovering their surroundings that engendered joy and exhilaration. Children enthusiastically described their journeys and the special spots and surprising moments they experienced along the way. Whereas trick and trail riding required focus and intensity, adventure riding encouraged openness and receptivity. NT (boy, 10) explained, “there's no rules that you [need] to go here. It's, just, you can bike wherever you want. And do whatever. Like it's not somebody pushing you to go a certain speed or slow down or anything. I really like that.” Being afforded the autonomy to move as they wanted through space was the most treasured aspect of bicycle-riding. TL (girl, 12) explained, “I get to go places that I wouldn't normally get to go when I’m with other people. And then I get to choose where we go”. SG (girl, 12) related her experience of freedom on a bicycle to her right to autonomy: “you can do whatever you want and however you want, and its your own opinion and you don't have to follow anybody else's. You can be free.” In a culture that values productivity and improvement, toys are sometimes dismissed as objects with little value other than to provide amusement or fill time. This is why we often see toy manufacturers working to establish associations between toys and various improvement-oriented or utilitarian purposes, as this helps legitimise toys as good, valuable, and necessary (Brougère). However, the descriptions above highlight the richness of experience that comes from engaging with objects as toys. Commonalities across these two uses of the bicycle were the elements of creativity, curiosity, and low-stakes outcome, and an emotional experience of joy, satisfaction, and exhilaration. Parents’ Constructions of the Bicycle in Childhood Among parents, the construction of the bicycle as a childhood toy provoked a wider array of emotions that included joy and exhilaration but also fear and worry. For parents, the lesser worry of the two uses of the bicycle was of the bicycle as a physical toy. Parents appreciated the physical skills that their children learned on the bike and acknowledged, with relatively little concern, that injury might result. One parent (LL) described “falling off the bike or a slip, I mean, it happens to the best of bikers. I'm not worried about my kids in terms of their skill, it would just be an accident”. Vastly more troubling to parents was the construction of the bicycle as an adventure toy as the activity produced by this kind of toy – adventuring on bike – involved children moving greater distances through their environment and without adult supervision. Although parents could understand the joy and exhilaration of adventure riding, they were concerned about the dangers posed by the riding environment. Parents were fearful of cars for how they moved quickly and, speaking from their positionality as drivers, how car drivers paid little attention to bicycles. MM lamented that in her suburban neighbourhood drivers didn’t look for bicycles as they backed out of a driveway. This meant that children on bicycles had to assume responsibility for their own safety, and parents worried whether their child had the decision-making and social capabilities for this: Probably getting hurt would be the biggest [fear], even. If we're out and on a busier road, and he were to wipe out or not be paying attention or something. He's not really in any situations right now where he would be. I'd worry about him being approached by anyone or anything like that. (NT) Concerns related to children travelling alone in public space are longstanding. In the 1990s, Valentine reported that parents feared that their children lacked the capabilities to travel safety on their own in public space, and that these fears inhibited children’s autonomous mobilities. Since then, notions of the ‘vulnerability’ of childhood have worked to intensify and expand parenthood to include ‘risk management’ through supervision and monitoring (Lee et al.). Through this, time spent with children, including time spent chauffeuring children from place to place, has also become associated with parental care. McLaren and Parusel argue that this form of “parental mobility care” is one of the ways in which mothers (and fathers to a lesser degree) implement ‘good mothering’ (1426). One parent (NF) noted that although she was comfortable with her child biking alone, she worried about “feedback I might get from neighbours or whatever, right, judging”. Another parent (MM) illustrates the association between knowing your child’s whereabouts and good parenting: The mom’s let them [friend and brother] already go on the bikes together, right. So, he's got that confidence already built with his brother, and by himself. He shows up at my door and rings the door bell and there he is, waving at me, and I'm like, ‘Oh my god, does his mom know where he is?’ (laughs). Managing Feelings and Reconfiguring Meanings Parents simultaneously desired to support their child’s biking and worried about their child travelling alone through public space. They sought ways to manage these competing feelings. Some parents achieved this by reconfiguring their construction of the bicycle in ways that made parental accompaniment more sensible and acceptable. For example, EK, who always accompanied her son on bike rides, highlighted the physical effort required to ride a bicycle and the benefits that resulted from riding, such as greater physical endurance, strength, and skill. In other words, to her the bicycle was less a toy and more a piece of equipment that helped people achieve self-improvement goals. When the focus of riding is fitness, the context of riding – where one travels and with whom – matters only in relation to the achievement of fitness goals. She discussed how she rode with her son so they could fulfill fitness goals together: EK: I want to ride a bike with [son, 12] because I want to have, like, exercise to do, and it’s better. We have YMCA membership, but I prefer outdoors. In the wintertime last year we we were biking at the YMCA on those stationary ones. I enjoy those ones as well. Q: But not the same as going outside? EK: No, we prefer outside. We prefer outdoors. TS, who also accompanied her children on bicycle rides, reconfigured bicycling as an adventurous activity for the family, rather than solely for children. In her interviews, she highlighted bicycling as a way to strengthen family bonds and build great memories from their bike rides together: TS: It's brought us closer together now that we all have a bike. Like, my boyfriend is pretty physical, and he's already got planned out trails he wants to take them on in the summer. So, I think it has brought up some exciting new adventures for us to look forward to and nobody can feel left out because we all can bike together. Certainly, the joy and thrill of riding can be a shared experience for parents and children (McIlvenny). Children did indicate their appreciation for these rides, particularly because they ventured further with parents than they were permitted when riding alone. However, family biking also produced a different kind of bike-riding experience for children, with a shift in position from ‘pilot’ to ‘crew’ and their attention directed inward, toward others in the group: GL (boy, 15): when I'm biking with my friends and family I am always watching out for them, like making sure they're keeping up, or if you're keeping the right pace if you're in the front. When you're by yourself, just like focused on doing, you're not really thinking about anything else. Our intent is not to dismiss the value of the bicycle as child exercise equipment or a family adventure toy. But we do wish to point out the ease with which the bicycle can be made sense of as a range of different-use objects in the context of contemporary childhood. Indeed, in this context, concerns about children’s physical health, development, and preparation for the future have been transforming – both ‘healthifying’ (Alexander et al. 78), and instrumentalising – children’s play for a generation. That said, there were parents who continued to support their children’s engagement with the bicycle as a toy, and their autonomous bike-riding. Although these parents certainly had worries, they connected bicycling to an array of positive emotions – joy, exuberance, pride, calm – and drew on these emotions to bolster their support. Parents often associated these positive emotions with memories of their own childhood biking experiences, which they wanted their children to experience. They also directly observed them in their children, after they returned from a ride. These moments offered parents ‘feedback’ that helped bolster their commitment to holding space for their children’s adventure riding: LL: They're pretty proud when they come home, muddy and dirty. Yeah, they'll tell me things that they saw or just things that would stand out like, ‘oh, the bugs are really bad’, or ‘oh, we found this cool part of a trail’ or [they] don't really meet people that they know on the trail. But yeah, they’ll give me some feedback. ‘RL almost ran into a tree’. ‘JL almost fell off trying to jump a log’: the highlights. The shared experience of the COVID-19 pandemic also connected parents to the emotional experience of bike-riding, bolstering parental support for children’s autonomous bike-riding because the pandemic made the emotional experience of bike-riding so much more apparent to parents. At the time of our spring 2020 interviews, children were just beginning to surface from a three-month lockdown period in which schooling was online, extra-curricular activities had been cancelled, and a public health order had drastically curtailed their movements outside the home. Although now we better understand the extent of the psychological impact of the lockdown on children (Panchal et al.), at that time parents were seeing its impacts on their children first-hand. In this context, the bicycle took on a new meaning as a vehicle that afforded a way for children escape the home and have some time and space to themselves: KK: For [daughter, age 12], definitely there are times that with two younger siblings, she'll just need to go. ‘I'm done. I need space.’ She'll go for a bike ride and that’s a little bit of a calm downtime for her. Right. Anyway, she says she enjoys it, it's healthy and gets her outside and away from your younger siblings. Parents increasingly supported children’s independent riding, again based on their observations of the emotional experience of children’s biking experiences. Both parents and children described these bike rides as mood-changing. Parents were able to recognise how biking offered children a time and space to “cool down” or “unwind from other things that are going on.” JJ [girl, age 13] explained: When I go on bike rides, I was like, kind of in a bad mood. If I'm angry at someone, if I'm sad, if I'm frustrated. Just flick a switch. Like, frustrated to happy; or angry to confident; or something like that. I don't know how it works, but it just boosts my mood every time I go on a bike ride. And then it is a great day. Conclusion This article illustrates the different ways that parents and children construct and negotiate meanings of the bicycle in childhood. It highlights the connections between meaning and use, and the ways that different meanings encourage different ways of thinking about how the bicycle should be used, where, with whom, and for what reasons. The analysis also points to the centrality of emotions in the process of meaning-making. In doing so, it builds on previous research that has illustrated now negative emotions (reluctance, worry, fear, anxiety) work to limit children’s mobilities (Fotel and Thomsen; Rixon et al.). At the same time, it also builds on recent research that illustrates the ways that attention to positive emotions (joy, pride, exhilaration, calm) can enable children’s bicycling (Silonsaari et al.) while centring children’s experiences in conversations about play and toys in contemporary childhood. References Alexander, Stephanie A., Katherine L. Frohlich, and Caroline Fusco. Play, Physical Activity and Public Health: The Reframing of Children’s Leisure Lives. Routledge, 2018. Brougère, Gilles. "Toys: Between Rhetoric of Education and Rhetoric of Fun." Toys and Communication (2018): 33-46. Cox, Peter. Cycling: A Sociology of Vélomobility. Routledge, 2019. Fotel, Trine, and Thyra Uth Thomsen. “The Surveillance of Children’s Mobility.” Surveillance &amp; Society 1.4 (2003). Furness, Zack. One Less Car: Bicycling and the Politics of Automobility. Temple UP, 2010. Hoffmann, Melody L. Bike Lanes are White Lanes: Bicycle Advocacy and Urban Planning. U of Nebraska P, 2016. Karsten, Lia. "It All Used to Be Better? Different Generations on Continuity and Change in Urban Children's Daily Use of Space." Children's Geographies 3.3 (2005): 275-290. Lee, Ellie, et al. Parenting Culture Studies. Springer, 2014. McDonald, Noreen C. “Children and Cycling.” City Cycling 487 (2012): 211-234. McIlvenny, Paul. "The Joy of Biking Together: Sharing Everyday Experiences of Vélomobility." Mobilities 10.1 (2015): 55-82. Panchal, Urvashi, et al. "The Impact of COVID-19 Lockdown on Child and Adolescent Mental Health: Systematic Review." European Child &amp; Adolescent Psychiatry (2021): 1-27. Rixon, Andy, Helen Lomax, and Lindsay O’Dell. "Childhoods Past and Present: Anxiety and Idyll in Reminiscences of Childhood Outdoor Play and Contemporary Parenting Practices." Children's Geographies 17.5 (2019): 618-629. Ross, Nicola J. "‘My Journey to School…’: Foregrounding the Meaning of School Journeys and Children's Engagements and Interactions in Their Everyday Localities." Children's Geographies 5.4 (2007): 373-391 Silonsaari, Jonne, et al. "Unravelling the Rationalities of Childhood Cycling Promotion." Transportation Research Interdisciplinary Perspectives 14 (2022): 100598. Silver, Erin. "Kids Love Those Fidget Spinner Toys. But Are They Too Much of a Distraction?" The Washington Post (2017). Turpin, Robert. First Taste of Freedom: A Cultural History of Bicycle Marketing in the United States. Syracuse UP, 2018. Valentine, Gill. Public Space and the Culture of Childhood. Routledge, 2017. Valentine, Gill. "'Oh Yes I Can.' 'Oh No You Can't': Children and Parents' Understandings of Kids' Competence to Negotiate Public Space Safely." Antipode 29.1 (1997): 65-89. Whitten, Sarah. "Adults Are Buying Toys for Themselves, and It's the Biggest Source of Growth for the Industry." NBC News, 19 Dec. 2022. &lt;https://www.nbcnews.com/business/business-news/adults-are-buying-toys-s-biggest-source-growth-industry-rcna62354&gt;.

General information and background on AI 1.1 The three battlefield revolutions The digitisation of the battlefield is a major revolution in combat, which needs to be assessed on a long-term scale as it will profoundly change military operating methods. First of all, it will mean that all the equipment deployed in the field will be interconnected with a tactical bubble that enables secure data exchanges to reduce the fog of war. What is already true for many armoured vehicles* will be true in the future for the dismounted soldier himself, who will be carrying advanced technologies. Processing these data will optimise military action. Firstly, through the speed with which information is processed, enabling greater respon­siveness, and secondly, through the consistency with which information is processed, enabling omni-surveillance of the battlefield. But this revolution of the digitisation of the battlefield is coupled with a second one, that of military robotics. Among its advantages, of course, we have the removal of the combatant from the danger zone, a high-risk area where we would rather risk a robot than a human life (cave reconnais­sance, mine clearance). If their energy autonomy can be guaranteed, these machines can also remain omnipresent in the field, where humans are subject to fatigue and climatic constraints (surveillance), or particularly in the 3rd dimension (flying over areas). Embedded technologies also enable them to be more responsive and more precise than human beings when carrying out a task. The enormous advantage of these last two qualities is immediately apparent if countermeasures need to be triggered to face a sudden threat, or if a favourable opportunity arises. More specifically, the use of robotic resources extends a military unit’s range of action beyond its traditional limits, traditionally established by its firing range. Carrying functional modules on robotic platforms will extend the unit’s information-gathering capabilities (remote cameras, sensors for CBRN threats detection) or its identification capabilities (algorithmic image processing), thereby extending the limits of its information-gathering range. Nevertheless, the use of these robotic systems requires providing a high degree of autonomy in their movements in order to reduce the human cognitive load induced by their control. This autonomy will be a factor of power, whatever the environment in which these systems operate (land, air, sea, submarine, space or cyber) and a factor in levelling the asymmetry of military potential. Recent conflicts (the 44-Day War of the Nagorno Karabakh conflict, more recently Gaza, Yemen and the Red Sea, and above all the Russian-Ukrainian conflict) have marked a turning point in the way UAVs (Unmanned Aerial Vehicles) are perceived. They have gradually highlighted the inevitability of war between robots, that autonomy will amplify in the years to come, whether in high or low-intensity, or in symmetrical or asymmetrical conflicts. A third revolution, that goes hand in hand with the first two aforementioned, is to be dealt with by this article: Artificial Intelligence (AI). This is a veritable tool at the service of the military that will enable them to manage some of the complexity of tomorrow’s battlefield, and in particular the multiplication of operational data, interconnection of deployed equipment with remote support systems, at combined, joint, or even allied levels. Such an abundance of military data to process is accompanied by a cognitive overload that is too significant for the military leader, requiring automatic data processing. AI is a response because, with its computational capabilities, it will enable heterogeneous multi-source data to be processed, real-time analysis and rapid responses, allowing for advanced automation within systems, priority management, optimisation of available resources, etc1. In more practical terms, it can be divided into two main categories: a) decision support for the military commander when preparing or conducting a mission, b) management, coordination and interconnection of multifunctional robotic systems. 1.2 The different types of AI This article will not deal with the difference between the various types of AI. Indeed Artificial Intelligence is a term that encompasses two very different notions: symbolic AI first of all, which can be described as a top-down model, in the sense that it simulates or describes a formal representation of thought, whatever the substrate on which it is based2. It is a transcription of human decision-making mechanisms into algorithms, which thus execute a thought formalised by the designer, but which therefore cannot deviate from the original framework in which it was conceived. The solutions found by these systems are therefore logical, but do not deviate from the rules that have been set. Then the connectionist AI, or neural networks, based on a simplified model of the biological neuron and its links with the synapses send to the AI information to be processed. Such a neural network must be trained to perform a specific task or to acquire new skills, the performance of which can be improved with experience. This is known as machine learning. We are entering a new range of AI here, one that moves away from the manual writing of computer programmes. A connectionist AI can be discriminative and focus on classifying the data it analyses, or it can generate content, such as images, videos, music, texts or 3D models. Unlike symbolic AI, it raises the question of the trust that a military leader can place in such a system. 2. The benefits of AI for the military With data being set to be ubiquitous on the battlefield in the future, the opportunities offered by AI to process data from the military world are manifold. We will attempt here a general functional approach to its possible uses. 2.1 Mission preparation AI will help military leaders to make better decisions in increasingly complex tactical environments. It will be able to study several alternative solutions and propose decision options based on the analysis of multiple parameters. Our brains are not efficient at making decisions when more than five or seven factors are taken into account depending on the person and the context; beyond that limit they go into cognitive overload, which often translates into an emotional burden for the military leader. AI has no such limits and can take hundreds or even thousands of factors into account! Prior to operations, AI will thus help the leader to prepare the mission and plan the operations: by analysing the 3D terrain mapping (lines of sight, radio coverage, hydrography, soil survey, inhabited areas), by reading the history of enemy operating methods in the area, by taking into account the expected meteorology, etc. It will be a decision-making aid and will be able to propose a choice of route according to the weather conditions. It can be used as a decision-making aid, proposing an optimal itinerary based on these elements and the history of the area (mapping of IED hot spots), the light and shade for movements, checking accessible high points and listing possible areas for UAVs landing or for searches, etc. Above all, the AI’s computational capabilities will enable it to compare the military commander’s courses of action with the enemy’s possible courses of action, incorporating a host of possible non-compliant cases (complex enemy attacks such as drones swarms or combat helicopters, jamming effects, electromagnetic attacks, deception, etc.). It will enable a set of candidate solutions to be proposed to the commander, who will then be able to decide on the best course of action. 2.2 Mission conduct In the conduct of a mission, by capturing and analysing data from the battlefield in real time, AI will give military commanders a better understanding of the tactical situation. There are many ways of doing this, including tracking people (facial recognition) or vehicles (shape recogni­tion), and detecting enemy attacks (source of sound or light flashes). This requires sensors to be autonomous in their data processing, indepen­dently of networks involving on-board computing capabilities (i. e., edge computing). 2.3 Detection / Prediction The proliferation of cameras integrated into camouflaged and abandoned sensors on the field, or mounted on drones or microsatellites at altitudes that allow them to monitor the entire battlefield, will partially lift the fog of war for those who control them. AI will enable the detection of aerial stealth targets, and the spotting and identification of objects on images or videos taken by these various types of equipment, using conventional, IR or thermal vision. Data produced by these cameras and sensors are legion in theatres of operation, but armies suffer from a chronic lack of human resources to analyse them. Detection and identification will therefore be based on AI-assisted remote surveillance systems. Before deployment and under supervision, it will also have to learn how filter out false alarms, such as the rustling of leaves in trees due to the wind or the falling of dead leaves, which must not trigger alerts on images used to detect enemy movements. For detected and identified threats, AI will be able to predict and calculate the trajectory of targets in real time, and suggest priorities to deal with the fastest (missiles or remotely operated munitions). It will determine likely modes of progression for enemy vehicles or armed groups. AI will also be able to optimise radio transmission and coverage capacities according to the constraints of the terrain (mountains, relief, weather) and the resources available (positioning of communication relay drones). 2.4 Collaborative Combat Provided data is effectively shared between equipment, AI could encourage the emergence of collaborative combat as it makes it possible to optimise the distribution and availability of critical resources and effectors (i.e., means having an effect on its environment: jammer, smoke bomb, grenade etc.). Collaboration can be seen here on three main levels: a) the inter-environment availability of resources and effectors available in a given air-land tactical zone, b) cooperation between combat units and robotic systems during combat phases, and c) the organisation of logistical support by anticipating supplies as close as possible to the units, depending on the conduct of the manoeuvre. The necessary fusion of various types of data and the capacity for geo-distributed processing nevertheless requires very strong connectivity between the sensors deployed to carry out collaborative combat and a permanent flow of data. This requires an unjammed tactical network, backed up by connections to low-earth orbit satellites. 2.5 Equipment customisation In the future, AI will make it possible to customise the equipment worn by the soldier, i.e., his weaponry or the objects he wears, such as the exoskeleton. The exoskeleton will be able to adapt to the individual’s specific movement characteristics: each soldier having his own gait, AI will be able to learn it and optimise the exoskeleton’s muscular support accordingly. AI will provide cognitive assistance to the soldier through cognitive interfaces that are easy to use and present contextualised and adaptive information with a mental representation tailored to each individual, based on natural interaction between the soldier and the interface. Given the influx of operational and terrain data, it will be necessary to determine beforehand those of the soldier and personalise them: for example, with an intelligent filter adapted to the individual’s hierarchical level (group leader, platoon leader, captain) and his military speciality. Here again, AI can play a role in this filtering. Finally, AI can offer instant language translation capabilities for soldiers in the midst of a foreign population, adapting to local dialects and accents. It can also offer a “Speech to Text” capability for transmitting digital orders or chatting, adapting to the language of each person and its potential distortion depending on the context (as with the effects of stress, or as for pilots at high altitude subject to pressure variations). 2.6 Predictive maintenance In the field, equipment is subject to severe constraints. For any military equipment or weapons system, AI will help to improve their Maintenance in Operational Condition through predictive maintenance. It will enable self-diagnosis of vehicles or equipment, with access to external databases for diagnostic assistance in the event of breakdowns, but above all on a preventive basis. To achieve this, integrated HUMS (Health &amp; Usage Monitoring System) will enable equipment to observe its own operating status. 3. The indispensable contribution of AI to robotics 3.1 Navigation AI will gradually be integrated into mobile platforms, and more specifically into robotic systems that include some form of autonomy (UAV air/ UGV land/ USV and UUV sea). Primarily for navigation functions to avoid a teleoperator being constantly dedicated to piloting and having consequently his cognitive load being dedicated solely to this function. It will enable robots to adapt to spatial configuration and unknown environments whenever necessary. It will also enable trajectory adjustments to be made under time pressure, particularly when unexpected obstacles appear along the way. Finally, it will enable these platforms to dodge threats and to position their effectors quickly and reactively. AI will also make it possible to overcome jamming constraints. While this article is being written, in the context of the Russian-Ukrainian conflict, we are close to observing remotely operated munitions that will be automated in their last trajectory section to track and neutralise the target, without direct human control. This also raises the question of a prior validation of the system’s activation by the military commander, who is responsible under International Humanitarian Law. Remotely operated munitions are currently heavily jammed in the last few hundred metres, and AI target identification functions will soon be developed to ensure the success of strikes in heavily jammed environments. 3.2 Managing multiple robotic platforms Swarms of multi-function robots, which can be multi-environments too, represent the next step in the introduction of robotics into the battlefield. Swarms can effectively occupy aerial or land spaces, ensure saturation effects thanks to redundancy of action and their sheer number. Several robotic platforms will be able to be coordinated by a collective intelligence, which adapts to external events and enables a “group behaviour” capable of reconfiguring itself and reallocating tasks internally to achieve a common objective. Their move will adapt dynamically, reactively and rapidly to the spatial configuration and to unknown environments, depending on the collective resources available. This will have multiple advantages: piloting will be supervised by a single operator, requiring less cognitive effort, and the swarm will be assigned a mission whose various components will be carried out by each of the specialised platforms (observation/detection, neutralisation, jamming, etc.). AI will grant them with a global strategy in the action, defining the expected characteristics of the swarm (speed and 3D device positioning), and the coordination of effects (observation, jamming, neutralisation, etc.). However, these strategies require modelling that takes into account the potential attrition of resources, but also the best configuration to generate a strong psychological impact on the enemy. Digital simulation is the technological solution that will make it possible to test on a larger scale various options to configure swarms and their possible uses, and to select the most appropriate configurations3. It will allow for testing vast combinations on the basis of several parameters: the rules of engagement laid down by the operational situation, the principles of the Law of Armed Conflicts, but also the types of swarm formations, the automation capabilities, and so on. 3.3 AI creates innovative robotic behaviours AI will also be innovative for robotic systems to which the military commander has delegated the execution of certain tasks. It will enable them to adapt their behaviour according to criteria that are no longer the classic criteria of an operation mounted with human partners, but mounted solely with machines whose loss in the field is entirely acceptable. In this way, it is possible to conceive a use centred on a main effect, whatever the attrition of robotic resources. It could be noted that these robotics systems are expendable and therefore have to be low cost and considered as consumable munitions, which in itself is already a conceptual evolution in military thinking. This gives rise to a number of exclusively robotic for which new doctrines of use could be devised, with some freedom of manoeuvre entrusted to the AI. For example, missions to deceive the enemy to provide support for a manoeuvre carried out by ground units. This can be done by deliberately misleading the enemy as to the direction of the friendly manoeuvre, with robots moving in the area where the enemy’s attention is required, or by disrupting them with trajectories that appear erratic or even incoherent. All this combined with the advantages offered by land-based robotics, such as responsiveness and precision, the effects of submerging by sheer numbers, and the ability to remain in the area 24 hours a day, this, provided that the robots have sufficient energy autonomy or that they carry out norias between their launch base and the action zone. While respecting IHL, we can imagine some AI whose objective will be to constantly harass enemy units by creating a feeling of constant observation and stalking, with the effect of depriving the enemy of the feeling of security that is essential to avoid any psychological collapse in the long term. On a more offensive level, AI will make it possible to seize opportunities in military action, in particular with the use of lethal assets integrated into larger, multifunctional robotic systems. The example of remotely operated munitions is very significant here, as they can be the assets around which the AI will organise the manoeuvre to detect and neutralise potential targets. For example, AI will be a particular component of future air raids in hostile territory for trajectory optimisation, in day and night conditions, and also for the training and positioning of robotic carriers and their effectors according to the potential risks detected. Compliance with IHL is of paramount importance in the execution of these missions, but its application to AI requires a specific development that could be the subject of another article, given the complexity of the subject. 3.4 Delegating tasks to AI The military leader will be able to delegate tasks to systems with a certain degree of autonomy, enabling them to carry out their mission 24 hours a day in the field, which is impossible for human beings, and will have the capacity to be more reactive than humans and therefore better able to react to saturating threats. The example of robot swarms is particularly telling, because with the advent of these sets of multifunctional robots, the leader and his subordinates will no longer be able to operate each of them remotely. He will delegate to a collective intelligence the piloting of each of the robots in the swarm, reserving for himself the piloting and control of the whole entity. Furthermore, since command performance is linked to respon­siveness, and since machines are more responsive than humans, AI will be better suited to immediately seize opportunities or react to threats, especially saturating ones and to attrition. However, this delegation of tasks to machines is a new concept for the military that raises the question of subsidiarity and the trust placed in these machines. We will come back to this in the command chapter. 4. AI to help the weak against the strong Military superiority often remains the prerogative of States benefitting from technological advances over their adversaries. But how can a State protect itself if its technologies are less developed than those of a Nation with great technological and industrial military capabilities? To answer this question, it appears that AI can be a factor in levelling asymmetry on the battlefield, by making appropriate use of the capabilities it offers. The creativity of AI can indeed offer innovative solutions to a military leader to counter the doctrines of employment of enemy military equipment. An AI trained in knowledge of friendly and enemy doctrines can develop, as an example, surprise strategies with the assets that a leader has at his disposal in a given tactical situation. We will consider here two types of assets: military equipment served by human assets and those served by robot assets. For the former, AI will be limited to being a decision aid. For the latter, the AI can manage the robot assets on its own, if this task has been delegated by the military leader. 4.1 AI as a decision-making aid for daring doctrines AI can revolutionize military tactics through daring reasoning that can surprise the adversary. However, this requires excellent knowledge of the tactical situation, provided by a comprehensive overall view of the battle­field, often called God’s eye, thanks to cameras embedded in micro­satellites as well as tactical drones. This knowledge of a precise tactical situation becomes entirely possible through the interconnection of one’s own or allied observation systems, which ensures a centralized vision of friendly and enemy troops movements. This is currently the case of the digital platform used by Ukraine in the Ukrainian theater of war, which is used to centralize all images emitted by drones to make them available to their allies. The military genius will then be able to refer to it and propose daring manoeuvres focusing on the detected enemy’s weaknesses and based on the knowledge of its modes of action in order to constrain its posture. 4.2 AI as a manager of robot assets Technological developments will very soon enable the development of coordinated systems of different robots, with a relatively low acquisition cost compared to traditional military systems, thanks to the reuse of civilian robots facilitating a “low cost” effect. AI will allow them to ensure the autonomy necessary to carry out the tasks that the military leader delegates to them, while maintaining constant supervision. Armed forces will therefore be able to benefit from their use. The responsiveness of these systems, in constant flight in the sky or easily deployable from a platform or from a truck, will enable them to counter saturating threats in real time. As a result, a battle for the occupation of 3D space by robotized machines is taking shape, setting drones against drones, swarms against drones and swarms against swarms. 4.3 Digital deception by AI While camouflage remains a basic rule for protecting your units, the introduction of AI brings a new discipline that will have to be integrated into deployed or embarked combat units: digital deception. Digital deception is a new discipline whose aim is to prevent the enemy from collecting exploitable data on our forces, but also to deceive opposing AI, which will thus be disrupted in their process of analyzing captured images. In addition to classic concealment measures, notably to conceal our forces and equipment from the omni-surveillance from the sky (satellites, drones), deceiving the enemy will also involve camouflaging our military data captured on the battlefield. This will involve breaking down shapes (characteristic points) and electromagnetic signatures. Indeed, the capture of imagery intelligence (IMINT) is now increasingly delegated to remote systems, using cameras that most often only render a 2D image, i.e., with no relief effect, on which the precise detection of details remains complex. Mirrors, for example, can become a simple way of deceiving, which is very difficult for AI to detect. The latter usually relies on detected shapes. For connectionist AI, certain neurons will specialize in the recognition of specific shapes. Let’s take the example of the tank: an AI specialized in tank recognition will have learned specific shapes characteristic of a combat tank (turret, cannon, tracks, and so on). Adding whacky shapes that are incompatible with a tank structure will most likely confuse the enemy AI. It is no longer a question of breaking lines as in classic camouflage, but of adding patterns that will lower the AI’s statistical detection of a tank. For example, by adding wooden panels with painted window shutters to the tank’s superstructure, on all 4 sides and on the top of the tank. This may seem very incongruous, but the AI will be completely confused and will not be able to conclude that an enemy tank is present, as the risk of error is too high. Of course, the enemy will quickly become aware of this, but by the time he has performed a new training for his AI to get around the problem, the friendly forces will have time to modify the paintwork on the wooden panels, and replace the shutters with … other motifs such as slates! It may be objected that this type of superstructure paint will not be compatible with traditional camouflage, the aim of which is to deceive the human eye. Not necessarily, since the primary aim of visual camouflage is to break the classic shapes detectable to the human eye. The solutions outlined above are admittedly simplistic, but further study will enable us to reconcile these two constraints: breaking the lines, and adding structural elements unlikely to be seen on military equipment. Another example is the use of fake images, with deliberately modified dimensions. These can deceive an enemy AI into not making the correlation between an object’s size and its environment, because AI simply hasn’t learned perspective in the sense that we humans manage it with our binocular vision, which gives us an idea of distances in 3D and a perception of relief. In the same vein, a virtuous AI will have learned the basic constraints of international humanitarian law (IHL), implying the non-aggression of civilian populations during combat. This represents an ethical and technical challenge, which requires AI to respect these unbreakable rules. Consequently, simulating civilian personnel on the same tank (by adding mannequins placed on the vehicle), will once again disturb an ethical AI trained not to open fire on suspicion of firing on defenseless populations. Reasoning can be applied to electromagnetism. The difference is that it is difficult to reduce an electromagnetic signature, which indicates emission on the battlefield, and therefore active participation in the conflict. This makes it difficult to mask one emission with a different electromagnetic signature. 5. The impact of AI on command For the military leader, the issue raised by the introduction of AI on the battlefield lies in the constant need to retain his responsibility for decision-making. Nevertheless, the three revolutions outlined in the introduction will gradually call into question traditional military command, which until now has been reserved for Man, the only one capable of apprehending the context and the environment and synthesizing the information4. 5.1 From vertical to horizontal command structure Primarily, since the dawn of military history, the strength of the armed forces has always been their hierarchical command structure. Soldiers place their trust in their leader, who in turn is aware of the overall tactical situation and decides on the idea of manoeuvre. Subordinates carry out orders and trust their leader’s tactical analysis. However, with the digitization of the battlefield, data is now more widely available than ever before. What is known as the verticality of command is thus disrupted by the horizontal nature of the information disseminated to all. As a result, the leader will have to formulate his orders keeping in mind that his subordinates also share the same information, and may even have received and analyzed it before him. He must therefore take their opinions into account and co-construct his thinking with them, at the risk of otherwise cutting himself off from a host of advice from his staff deployed in the field as close to the action as possible. 5.2 Optimising the decision-making process duration At the same time, AI and its ability to process information in real time means that military decision-making duration can be shortened. In a conventional decision-making process, which is traditionally broken down into four steps (information acquisition, analysis, decision based on certain rules or constraints, then action or lack of action), immediate access to information means that the decision-making process is extremely shortened. Besides, the decision-making process itself can be delegated to a machine, whose computing capacity and reaction time are far superior to those of human beings. 5.3 Hybrid subsidiarity Finally, command is exercised through subsidiarity. This will involve two components: the traditional one with the soldier team member under his command, and the future one with AI-integrated systems to which he will delegate tasks to carry out. The leader will therefore have to proceed in two phases. The first phase consists in defining precisely the tasks he delegates to these systems, while ensuring that he controls the framework within which their actions are carried out. He defines the actions that have to be validated at his level, the others being subject to a regular report. He also controls the temporal and spatial framework in which these systems evolve. Once these systems are active, the second phase implies to command by reaction. This takes place at a more global level, as in the example of the swarms mentioned above. It can be both a) a command by reframing if the action carried out by these systems deviates from the spirit of the manoeuvre intended by the leader, or b) a command by veto that temporarily or definitively stops the triggering of actions considered critical. It should be noted that the ethical question raised here is that of the trust we can place in machines that are potentially more efficient than humans, but which are not moral agents in the sense that they will never be aware of the scope of their actions and decisions. They are simply algorithms that execute. In contrast, the soldier exercises discernment and free will. 5.4 Maintaining the demands of command However, whatever the assets at his disposal, the military leader must take responsibility for the military action he leads. This principle is both structuring and reassuring. Structuring, because it ensures the credibility of the command, which takes responsibility for its own actions despite the fog of war. Reassuring, because the leader retains the need for discernment before making any decisions, and avoids offloading his responsibility onto the behavior of the machines at his disposal. Moreover, the military leader takes decisions according to the context. He is the only one able to take into account the global situation of a military action, to see beyond the initial data of the mission he is leading, and beyond the data emitted on the battlefield. Besides, he is the only one who is aware of the moral implications of his actions ­­– something, let us not forget, any machine would never possess. Nevertheless, he will have to train himself to avoid the new challenge of the significant reduction in the time allowed to make a decision, as outlined above. AI will certainly enable systems to react more quickly, but this advantage is the same for the enemy: “He who shoots first wins”, as Lieutenant-Colonel Rommel wrote in his memoirs. He will therefore have to train himself to discern and decide promptly, in order to maintain his superiority over his adversary, while restraining himself from the temptation of excessive confidence, or even fascination, in the AI’s performance. Consequently, AI, as a built-in tool in military systems that can allow for a degree of autonomy, must not cause the military leader to lose the possibility of regaining control over the machine. Here we quote Professor Dominique Lambert, who lists the conditions of supervision required to ensure that the human sense of the action is preserved5. According to him, human supervision must be: sufficient, which means that humans introduce into the management of the weapons system sufficient conditions (and not just a few necessary conditions) to ensure that ethical principles are preserved and that the rules of International Humanitarian Law and the rules of engagement are satisfied; meaningful, meaning that it is ultimately always a reference to the human sense that must guide the design, development and use of weapons systems […]; coherent, which means that at no point can the weapons system contradict what human authority has prescribed as the goal of action. In fact, it would be incoherent if a weapons system deployed to fulfill a certain mission began to behave in a way that is inconsistent with the prescribed aims. 5.5 Defining a national strategy for a sovereign military AI Given the risks inherent to this new technology, every country needs to set a strategy that respects the ethical constraints it has defined. We can cite here as an example the French Army which, in its AI Task Force report of September 2019, indicates the need to: rely on trusted, controlled and responsible AI; maintain the resilience and scalability of its systems; preserve national sovereignty; maintain freedom of action and interoperability with its allies. These general principles for a strategic policy for the implementation of a sovereign AI can be taken up or adapted by Armenia, partner country of France. The fact is that connectionist AI relies on the data that feeds its machine learning process, and then for the data processing to be carried out. But, if the data with which the AI of the civilian world is trained (Gemini, LLaMA, ChatGPT, etc.) is plethoric, it is because they have been generously and freely made available to GAMMA by their owners, without them even realizing it. The same cannot be said of military data. Under no circumstances should military data be distributed to the whole world on an open-access basis, and it must remain the priority of sovereign states. Military data is of vital importance! This means knowing how to retrieve and preserve it: it is a challenge to national sovereignty. As a result, these same states need to develop their own sovereign AI, which are the only ones to be authorized to use their military data. While the help of allied nations is invaluable in this respect, the use of military data by foreign AI will have to be agreed on between the countries concerned. It is also conceivable to start with neural networks developed by foreign nations and specialized in a given function, and then to enrich them by learning new additional classes which will remain the property of the sovereign country. 6. Conclusions As a logical consequence of the digitization of the battlefield, the interconnection of systems and the drastic increase in the amount of data to be processed, AI offers a host of opportunities that every nation must seize by adopting a development strategy that enables it to retain sovereignty over its own military data. In terms of command, AI is not just another technique. It requires every military leader who uses it to master its use, to seize the opportunities it offers, while understanding its risks. This implies having leaders capable of grasping the complexity, and not simply delegating its management to technical specialists. While AI military experts, engineers and technicians are obviously needed, so too are military personnel and officers trained in these techniques. To achieve this, the latter will be able to draw on simulation tools to help establish new doctrines of use for AI-built-in systems (detection, counter-threat, robotic systems of systems, swarms), as well as tactical situation exercises in which AI is used as a decision-support tool for manoeuvre preparation, or in conduct. We however must bear in mind that, while AI enables us to express a new type of military genius in the service of our forces, the enemy can also be inventive and changeable, with the sole aim of surprising us in order to win. References 1 See Gérard de Boisboissel. Déclinaisons et applications possibles de l’IA dans le domaine militaire. “Moroccan National Defence review”. First edition, Juin 2023. 2 See Dominique Lambert. Que penser de… ?: la robotique et l’intelligence artificielle. Fidélité/Lessius. Editions Jésuites, 2019, N 100. 3 See Thierry Berthier, Gérard de Boisboissel. Du drone au essaim de drones: une nécessaire modélisation des comportements au profit de la simulation. Conference CAID DGA 2023. 4 See Gérard de Boisboissel. Intelligence artificielle et commandement. “Défense et Sécurité Internationale”, Janvier-Février 2024, N 169. 5 See Dominique Lambert. Fondements éthiques d’un approche humaine­ment signifiante du problème des SALAS. “Les enjeux de l’autonomie des systèmes d’armes létaux”, Pedone, 2022, P. 138. * The SICS (Système d’Information du Combat de SCORPION) is an on-board operational information system all French Army vehicles are gradually being equipped with.