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High-energy diets increase the risk of Sub-Acute Ruminal Acidosis (SARA) inruminants. The behaviour of ruminants is modified under SARA however the impact onbehaviour of a high-energy diet and those of SARA per se are often confounded. Weaimed at distinguishing the effects of the diet and of SARA on cow behaviour. We fed28 Holstein cows a low-starch diet (low starch diet, 10.5% starch) or a high-starch diet(high starch diet, 31.5% starch). Control cows (n = 14) received the low starch diet for60 days, Challenge cows (n = 14) received the same diet except for a 2-week periodduring which they received the high starch diet and the 10 preceding days to ensurethe transition between diets. We monitored ruminal pH and activity of cows thanks tosensors, dry matter intake (DMI), and milk yield of each cow on a daily basis. TheSARA status was defined according to the relative decrease in ruminal pH and to pHvariability. High starch diet induced SARA more often than low starch diet (81% dayswhen cows received high starch diet vs. 8% when they received low starch diet. Highstarch diet also decreased milk yield and made cows spending less time eating buteating more quickly (Challenge vs. Control cows during the challenge period: milkyield, 20.0 vs. 18.2 L/d; % time spent eating, 22.5 vs. 27.6; eating rate, 77.1 vs. 69.6 gDMI/min; P (diet x period) 0.50). In conclusion, an increase ineating rate, especially combined with a decrease in milk yield should alert farmers tothe risk of ruminal acidosis. International audience

Authenticated key exchange protocols play a crucial role in the communication security stack of an Internet-of-Things (IoT) device: they authenticate the communicating parties and establish a shared symmetric secret between them.Following a large debate in the community, the Internet Engineering Task Force (IETF) has recently standardized a new protocol called EDHOC for authenticated key exchange targeting IoT environments.The EDHOC protocol performs a compact Diffie-Hellman key exchange handshake, requiring several times less bytes-over-the-air than the de-facto solution used in the Internet, the (D)TLS protocol.In this paper, we study how this reduction in message size correlates with the usage of other scarce resources in IoT environments: time, energy, and memory.We evaluate EDHOC and DTLS with different authentication configurations over two IoT radio technologies.First, we measure the EDHOC and DTLS handshakes on constrained hardware over an IEEE 802.15.4 radio.We observe that EDHOC achieves ×6 to ×14 reduction in packet sizes, ×1.44 improvement in handshake duration and ×2.79 reduction in energy consumed.Next, we simulate time on air on LoRaWAN networks and find that, in the most restrictive configuration (SF=12), DTLS uses at least ×7 more time on air than EDHOC.Finally, we measure flash memory and RAM usage, with the EDHOC implementation achieving a ×4 reduction in both. International audience

International audience; Weed recognition is an essential step for automatic weed control systems. Identifying weeds enables targeted control measures to be implemented, minimizing the use of chemicals and reducing the impact on the environment. Deep learning-based approaches proved to be e↵ective for addressing various complex classification problems. However, to benefit fully from their capabilities, large amounts of labeled data are required, which represents a limitation for agricultural applications, consequence of the tedious and time-consuming process of data labeling. Conversely, unlabeled data could be acquired in large quantities, with relative ease. Hence, our aim is to develop robust and precise deep learning models, to carry-out the recognition and identification of weed species, using both types of data. To this end, we propose a method, that adopts the semi-supervised learning paradigm, to optimally combine labeled and unlabeled data. The method is based on a new deep neural networks architecture, which consists of a modernized convolutional encoder belonging to the family ConvNeXt and a thoroughly designed deep decoder network. This architecture, enables a successful integration of consistency regularization. The conducted experiments on DeepWeeds and 4-Weeds, showed that the semi-supervised models trained through our proposed method provide a stable and high classification performance, compared to other state-of-the-art deep learning models, which were a↵ected negatively by the amount of labeled data available, and the presence of noise during inference. Furthermore, the e↵ectiveness of the proposed method was demonstrated in comparison to other semi-supervised learning methods. The results obtained demonstrate the benefits of adopting the semi-supervised learning paradigm, especially in scenarios with very limited labeled data.

African savanna elephants (Loxodonta africana) can substantially modify their habitat through their interactions with woody vegetation. Nonetheless, the scale, intensity and characteristics of these relations are not yet fully understood. Consequently, it is unclear how vegetation-megafauna interactions can be disrupted by external factors, such as land management. This study attempted to quantify and characterize structural changes in vegetation caused by elephants, from landscape to tree level scales. We applied multi-scale geospatial tools, including airborne (ALS) and terrestrial laser scanning (TLS), to address the following questions: (1) How do elephants shape landscape level vegetation structure in conservation areas? (2) Are the impacts of elephants evident on individual tree architecture? Our study area was located at the Taita Hills Wildlife Sanctuary in South-eastern Kenya. The occurrence of elephants was estimated using elephant observation records and proximity to elephant tracks. Landscape level structure was assessed using tree density maps calculated based on individually detected treetops from ALS data. Next, TLS measurements of 72 trees were processed using quantitative structural modelling to characterize their architecture. Our results demonstrate a widespread influence of elephants on both landscape and tree level structural characteristics. This influence was strongly mediated by management, as we observed differences in vegetation structure inside and outside conservation areas. Tree density was up to 42% lower (5.84 trees/ha) in conservation areas than in non-conservation areas (10.17 trees/ha). Trees were relatively larger with closer proximity to elephant tracks, while smaller trees were more often observed in areas further away from elephants. At an architectural level, trees closer to elephant tracks had lower ratio between the crown length and the tree height, demonstrating a substantial influence of elephants on the morphological characteristics of trees. Our results highlight the importance of accounting for vegetation fauna interactions when planning conservation areas in African savannahs. Peer reviewed

International audience

Monitoring pollution plumes is a key issue, given the harmful effects they cause. The dynamic of these plumes, which may be important due to meteorological conditions, makes their study difficult. Real-time monitoring in order to obtain an accurate mapping of the pollution dispersion is helpful and valuable to mitigate risks. In this work, we consider a fleet of cooperative drones carrying pollution sensors and operating in order to assess a pollution plume. The latter is assumed to follow a Gaussian Process (GP) with varying parameters. For this use case, we propose an efficient approach to characterize spatially and temporarily the plume while optimizing the path planning of drones. In our approach, drones are guided by a Deep Reinforcement Learning (DRL) model called Categorical Deep Q-Network (Categorical DQN) to maximize the plume coverage while considering budget constraints. Specifically, we develop a scalable Independent Q-Learning (IQL) scheme that shares team rewards based on each drone's deployment relevance and therefore ensures cooperation. We evaluate the performance of the plume parameter estimation as well as the maps generated by the GP regression. By testing our framework on several plume scenarios, we show that it offers good results in terms of both estimation quality and run-time efficiency. International audience

Digital 3D technologies are emerging methods for recording and visualizing forests. Therefore, it is not surprising that these technologies have seen many applications and developments in recent years. In this study, we conducted a comprehensive review of existing 3D technologies within the context of forestry and how they interact with users and stakeholders. We present a summary of the requirements, visualization, and application of virtual forests. This includes an overview of state-of-the-art 3D reconstruction and visualization tools, which have seen a major increase in interest in the past few years, as evidenced by a preliminary analysis on research keywords. Based on the reviewed studies, we present the current trend and emerging questions, as well as challenges in the field of virtual forests. Further, we discuss the identified trends and challenges related to data acquisition, along with existing and potential future interactions between the 3D data and more specific demands from the forestry sector. We conclude that the use of digital 3D data in forestry is on the rise and that such novel methods show great potential and merit further attention. Peer reviewed

National audience

Wildfires are critical socio-ecological features in the Mediterranean basin. In a context of global changes (climate, land use), we questioned whether the wildfire regime was altered in the mountains of Corsica, France. Using tree- ring analysis of fire-scarred trees, we tested for changes in frequency, seasonality, and area. We hypothesized that the fire regime changed during the middle-20th century due to human activities, as observed elsewhere in the Mediterranean. We sampled fire-scarred trees, geolocated for mapping fire areas, in a forest of black pine (Pinus nigra laricio). The oldest fire was in 1684 but the fire chronology with adequate sample depth for analysis covered 202 years [1820–1921]. Between 1820–2012, 15 fires were recorded, 8 of which scarred at least 25% of the sampled trees. The mean fire interval was 14 years, corresponding to a high fire frequency with 4 major fires per century. Most fires occurred between 1931–1970. On average, about 50% of trees were scarred by fires before 1931, but this percentage decreased thereafter. The exception was the 2000 fire that impacted 100% of living trees. Mapping showed spatially heterogenous fire areas. These results match other Mediterranean studies showing longer fire intervals since the late-20th century, and wildfires generally occurred during the period of late-earlywood or latewood formation, i.e., summer or early autumn, which is the season of contemporary fires. Although fires were recurrent for more than 200 years with no change in the fire season, the regime changed twice in frequency. These changes likely result from a combination of land use and warmer summer conditions. While pines survived most past fires of low intensity through the last large fire in the 1970’s, the severe fire of 2000, following ca 30 years of fire suppression, killed large patches of mature trees. Such chronology provides rational arguments for black pine ecosystem management, notably to consider seeking to recover the sustainable fire regime of the 19th century, for instance by using prescribed fires to manage surface fuel. International audience