Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique

<p>Ammonia, a molecule that is gaining more interest as a fueling vector, has been considered as a candidate to power transport, produce energy, and support heating applications for decades. However, the particular characteristics of the molecule always made it a chemical with low, if any, benefit once compared to conventional fossil fuels. Still, the current need to decarbonize our economy makes the search of new methods crucial to use chemicals, such as ammonia, that can be produced and employed without incurring in the emission of carbon oxides. Therefore, current efforts in this field are leading scientists, industries, and governments to seriously invest efforts in the development of holistic solutions capable of making ammonia a viable fuel for the transition toward a clean future. On that basis, this review has approached the subject gathering inputs from scientists actively working on the topic. The review starts from the importance of ammonia as an energy vector, moving through all of the steps in the production, distribution, utilization, safety, legal considerations, and economic aspects of the use of such a molecule to support the future energy mix. Fundamentals of combustion and practical cases for the recovery of energy of ammonia are also addressed, thus providing a complete view of what potentially could become a vector of crucial importance to the mitigation of carbon emissions. Different from other works, this review seeks to provide a holistic perspective of ammonia as a chemical that presents benefits and constraints for storing energy from sustainable sources. State-of-the-art knowledge provided by academics actively engaged with the topic at various fronts also enables a clear vision of the progress in each of the branches of ammonia as an energy carrier. Further, the fundamental boundaries of the use of the molecule are expanded to real technical issues for all potential technologies capable of using it for energy purposes, legal barriers that will be faced to achieve its deployment, safety and environmental considerations that impose a critical aspect for acceptance and wellbeing, and economic implications for the use of ammonia across all aspects approached for the production and implementation of this chemical as a fueling source. Herein, this work sets the principles, research, practicalities, and future views of a transition toward a future where ammonia will be a major energy player. </p>

Abstract Aim We surveyed the empirical literature to determine how well six diversity hypotheses account for spatial patterns in species richness across varying scales of grain and extent. Location Worldwide. Methods We identified 393 analyses (‘cases’) in 297 publications meeting our criteria. These criteria included the requirement that more than one diversity hypothesis was tested for its relationship with species richness. We grouped variables representing the hypotheses into the following ‘correlate types’: climate/productivity, environmental heterogeneity, edaphics/nutrients, area, biotic interactions and dispersal/history (colonization limitation or other historical or evolutionary effect). For each case we determined the ‘primary’ variable: the one most strongly correlated with taxon richness. We defined ‘primacy’ as the proportion of cases in which each correlate type was represented by the primary variable, relative to the number of times it was studied. We tested for differences in both primacy and mean coefficient of determination of the primary variable between the hypotheses and between categories of five grouping variables: grain, extent, taxon (animal vs. plant), habitat medium (land vs. water) and insularity (insular vs. connected). Results Climate/productivity had the highest overall primacy, and environmental heterogeneity and dispersal/history had the lowest. Primacy of climate/productivity was much higher in large‐grain and large‐extent studies than at smaller scales. It was also higher on land than in water, and much higher in connected systems than in insular ones. For other hypotheses, differences were less pronounced. Throughout, studies on plants and animals showed similar patterns. Coefficients of determination of the primary variables differed little between hypotheses and across the grouping variables, the strongest effects being low means in the smallest grain class and for edaphics/nutrients variables, and a higher mean for water than for land in connected systems but vice versa in insular systems. We highlight areas of data deficiency. Main conclusions Our results support the notion that climate and productivity play an important role in determining species richness at large scales, particularly for non‐insular, terrestrial habitats. At smaller extents and grain sizes, the primacy of the different types of correlates appears to differ little from null expectation. In our analysis, dispersal/history is rarely the best correlate of species richness, but this may reflect the difficulty of incorporating historical factors into regression models, and the collinearity between past and current climates. Our findings are consistent with the view that climate determines the capacity for species richness. However, its influence is less evident at smaller spatial scales, probably because (1) studies small in extent tend to sample little climatic range, and (2) at large grains some other influences on richness tend to vary mainly within the sampling unit.

A wide-range experimental and theoretical investigation of ammonia gas-phase oxidation is performed, and a predictive, detailed kinetic model is developed.

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.

Locating moving objects in a video sequence is the first step of many computer vision applications. Among the various motion-detection techniques, background subtraction methods are commonly implemented, especially for applications relying on a fixed camera. Since the basic inter-frame difference with global threshold is often a too simplistic method, more elaborate (and often probabilistic) methods have been proposed. These methods often aim at making the detection process more robust to noise, background motion and camera jitter. In this paper, we present commonly-implemented background subtraction algorithms and we evaluate them quantitatively. In order to gauge performances of each method, tests are performed on a wide range of real, synthetic and semi-synthetic video sequences representing different challenges.

In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, unmanned aerial vehicles (UAVs) are becoming an interesting acquisition system for weed localization and management due to their ability to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. However, despite significant advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem because of their strong similarity to the crops. Recently, a deep learning approach has shown impressive results in different complex classification problems. However, this approach needs a certain amount of training data, and creating large agricultural datasets with pixel-level annotations by an expert is an extremely time-consuming task. In this paper, we propose a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images. The proposed method comprises three main phases. First, we automatically detect the crop rows and use them to identify the inter-row weeds. In the second phase, inter-row weeds are used to constitute the training dataset. Finally, we perform CNNs on this dataset to build a model able to detect the crop and the weeds in the images. The results obtained are comparable to those of traditional supervised training data labeling, with differences in accuracy of 1.5% in the spinach field and 6% in the bean field.

Bacteria‐inspired magnetic helical micro‐/nanoswimmers can be actuated and steered in a fuel‐free manner using a low‐strength rotating magnetic field, generating remotely controlled 3D locomotion with high precision in a variety of biofluidic environments. They are therefore envisioned for biomedical applications related to targeted diagnosis and therapy. In this article, a porous hollow microswimmer possessing an outer shell aggregated by mesoporous spindle‐like magnetite nanoparticles (NPs) and a helical‐shaped inner cavity is proposed. The fabrication is straightforward via a cost‐effective mass‐production process of biotemplated synthesis using helical microorganisms. Here, Spirulina ‐based fabrication is demonstrated as an example. The fabricated microswimmers are superparamagnetic and exhibit low cytotoxicity. They are also capable of performing structural disassembly to form individual NPs using ultrasound when needed. For the first time in the literature of helical microswimmers, a porous hollow architecture is successfully constructed, achieving an ultrahigh specific surface area for surface functionalization and enabling diffusion‐based cargo loading/release. Furthermore, experimental and analytical results indicate better swimming performance of the microswimmers than the existing non‐hollow helical micromachines of comparable sizes and dimensions. These characteristics of the as‐proposed microswimmers suggest a novel microrobotic tool with high loading capacity for targeted delivery of therapeutic/imaging agents in vitro and in vivo.

Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging. In addition, this study examines the role of data fusion for ongoing research in the context of disease detection. It highlights the advantage of intelligent data fusion techniques, from heterogeneous data sources, to improve plant health status prediction and presents the main challenges facing this field. The study concludes with a discussion of several current issues and research trends.

This paper assesses different butanol–gasoline blends used in a port fuel-injection, spark-ignition engine to quantify the influence of butanol addition on the emission of unburned hydrocarbons, carbon monoxide, and nitrogen oxide. Furthermore, in-cylinder pressure was measured to quantify combustion stability and to compare the ignition delay and fully developed turbulent combustion phases as given by 0%–10% and 10%–90% Mass Fraction Burned (MFB). The main findings are: 1) a 40% butanol/60% gasoline blend by volume (B40) minimizes HC emissions; 2) no significant change in NOx emissions were observed, with the exception of the 80% butanol/20% gasoline blend; 3) the addition of butanol improves combustion stability as measured by the COV of IMEP; 4) butanol added to gasoline reduces ignition delay (0%–10% MFB); and 5) the specific fuel consumption of B40 blend is within 10% of that of pure gasoline for stoichiometric mixture.

This paper deals with the image-based visual servoing (IBVS), subject to constraints. Robot workspace limitations, visibility constraints, and actuators limitations are addressed. These constraints are formulated into state, output, and input constraints, respectively. Based on the predictive-control strategy, the IBVS task is written into a nonlinear optimization problem in the image plane, where the constraints can be easily and explicitly taken into account. Second, the contribution of the image prediction and influence of the prediction horizon are pointed out. The image prediction is obtained due to a model. The latter can be a local model based on the interaction matrix or a nonlinear global model based on 3-D data. Its choice is discussed with respect to the constraints to be handled. Finally, simulations that were obtained with a 6-degree-of-freedom (DOF) free-flying camera highlight the potential advantages of the proposed approach with respect to the image prediction and the constraint handling.

Monitoring crops and weeds is a major challenge in agriculture and food production today. Weeds compete directly with crops for moisture, nutrients, and sunlight. They therefore have a significant negative impact on crop yield if not sufficiently controlled. Weed detection and mapping is an essential step in weed control. Many existing research studies recognize the importance of remote sensing systems and machine learning algorithms in weed management. Deep learning approaches have shown good performance in many agriculture-related remote sensing tasks, such as plant classification, disease detection, etc. However, despite the success of these approaches, they still face many challenges such as high computation cost, the need of large labelled datasets, intra-class discrimination (in growing phase weeds and crops share many attributes similarity as color, texture, and shape), etc. This paper aims to show that the attention-based deep network is a promising approach to address the forementioned problems, in the context of weeds and crops recognition with drone system. The specific objective of this study was to investigate visual transformers (ViT) and apply them to plant classification in Unmanned Aerial Vehicles (UAV) images. Data were collected using a high-resolution camera mounted on a UAV, which was deployed in beet, parsley and spinach fields. The acquired data were augmented to build larger dataset, since ViT requires large sample sets for better performance, we also adopted the transfer learning strategy. Experiments were set out to assess the effect of training and validation dataset size, as well as the effect of increasing the test set while reducing the training set. The results show that with a small labeled training dataset, the ViT models outperform state-of-the-art models such as EfficientNet and ResNet. The results of this study are promising and show the potential of ViT to be applied to a wide range of remote sensing image analysis tasks.

Understanding the chemistry behind the oxidation of ammonia/hydrogen mixtures is crucial for ensuring the flexible use of such mixtures in several applications, related to propulsion systems and power generation. In this work, the oxidation of ammonia/hydrogen blends was investigated through an experimental and kinetic-modeling study, where the low- and intermediate-temperature conditions were considered. An experimental campaign was performed in a flow reactor, at stoichiometric conditions and near-atmospheric pressure (126.7 kPa). The mole fraction of fuels, oxidizer and final products was measured. At the same time, a comprehensive kinetic model was set up, following a modular and hierarchical approach, and implementing the recently-available elementary rates. Such a model was used to interpret the experimental results, and to extend the analysis to literature data, covering several oxidation features. The reactivity boost provided by H2 addition was found to be approximately linear with its mole fraction in both flow- and jet-stirred-reactor conditions (except for the smallest H2 amounts in the flow reactor), in contrast with the more-than-linear increase in the laminar flame speed. The key role of HO2 in regulating fuel conversion and autoignition at low temperature was confirmed for binary mixtures, with H2NO being the bottleneck to the low-temperature oxidation of NH3-rich blends. On the other hand, the nitrogen fate was found to be mostly regulated by NHx + NO propagation and termination channels.

International audience

Teleoperated medical robotic systems allow procedures such as surgeries, treatments, and diagnoses to be conducted across short or long distances while utilizing wired and/or wireless communication networks. This study presents a systematic review of the relevant literature between the years 2004 and 2015, focusing on medical teleoperated robotic systems which have witnessed tremendous growth over the examined period. A thorough insight of telerobotics systems discussing design concepts, enabling technologies (namely robotic manipulation, telecommunications, and vision systems), and potential applications in clinical practice is provided, while existing limitations and future trends are also highlighted. A representative paradigm of the short-distance case is the da Vinci Surgical System which is described in order to highlight relevant issues. The long-distance telerobotics concept is exemplified through a case study on diagnostic ultrasound scanning. Moreover, the present review provides a classification into short- and long-distance telerobotic systems, depending on the distance from which they are operated. Telerobotic systems are further categorized with respect to their application field. For the reviewed systems are also examined their engineering characteristics and the employed robotics technology. The current status of the field, its significance, the potential, as well as the challenges that lie ahead are thoroughly discussed.

We explore a location based approach for behavior modeling and abnormality detection. In contrast to the conventional object based approach where an object may first be tagged, identified, classified, and tracked, we proceed directly with event characterization and behavior modeling at the pixel(s) level based on motion labels obtained from background subtraction. Since events are temporally and spatially dependent, this calls for techniques that account for statistics of spatiotemporal events. Based on motion labels, we learn co-occurrence statistics for normal events across space-time. For one (or many) key pixel(s), we estimate a co-occurrence matrix that accounts for any two active labels which co-occur simultaneously within the same spatiotemporal volume. This co-occurrence matrix is then used as a potential function in a Markov random field (MRF) model to describe the probability of observations within the same spatiotemporal volume. The MRF distribution implicitly accounts for speed, direction, as well as the average size of the objects passing in front of each key pixel. Furthermore, when the spatiotemporal volume is large enough, the co-occurrence distribution contains the average normal path followed by moving objects. The learned normal co-occurrence distribution can be used for abnormal detection. Our method has been tested on various outdoor videos representing various challenges.

After decades of development, microrobots have exhibited great application potential in the biomedical field, such as minimally invasive surgery, drug delivery, and bio‐sensing. Compared with conventional medical robotic systems, microrobots may be capable of reaching more narrow and vulnerable regions in the human body with minimal damage. However, limited by the small scale of microrobots, microprocessors, power supplies, and sensors can hardly be integrated on‐board. Thus, new strategies for the actuation and feedback for microrobots need to be explored. Furthermore, the open‐loop control method accomplished by operators may lack accuracy, and long‐duration operation could bring a severe physical challenge in many applications. Consequently, the automatic control of microrobots with the aid of control theories is developed to improve the control efficiency and precision. To further promote the automation level of microrobots, machine learning algorithms are expected to provide a solution to let microrobots adapt to more dynamic environments and undertake more complex medical tasks. Herein, a systematic introduction of the manipulation of microrobots from open‐loop to closed‐loop control is given in this review. It is envisioned that microrobots will play an important role in future biomedical applications.

The aim of this paper is to find out new alternative materials that respond to sustainable development criteria. For this purpose, an original utilization of straw for the design of lightweight aggregate concretes is proposed. Four types of straw were used: three wheat straws and a barley straw. In the present study, the morphology and the porosity of the different straw aggregates was studied by SEM in order to understand their effects on the capillary structure and the hygroscopic behavior. The physical properties such as sorption-desorption isotherms, water absorption coefficient, pH, electrical conductivity and thermo-gravimetric analysis were also studied. As a result, it has been found that this new vegetable material has a very low bulk density, a high water absorption capacity and an excellent hydric regulator. The introduction of the straw in the water tends to make the environment more basic; this observation can slow carbonation of the binder matrix in the presence of the straw.

This brief addresses the problem of controlling the planar position and orientation of an autonomous underactuated surface vessel. Under realistic assumptions, we show, first, that there exists a natural change of coordinates that transforms the whole dynamical system into a cascade nonlinear system, and, second, the control problem of the resulting system can be reduced to the stabilization of a third-order chained form. A time-invariant discontinuous feedback law is derived to guarantee global uniform asymptotic stabilization of the system to the desired configuration. The construction of such a controller is based on the backstepping design approach. A simulation example is included to demonstrate the effectiveness of the suggested approach

Does the advent of flash devices constitute a radical change for secondary storage? How should database systems adapt to this new form of secondary storage? Before we can answer these questions, we need to fully understand the performance characteristics of flash devices. More specifically, we want to establish what kind of IOs should be favored (or avoided) when designing algorithms and architectures for flash-based systems. In this paper, we focus on flash IO patterns, that capture relevant distribution of IOs in time and space, and our goal is to quantify their performance. We define uFLIP, a benchmark for measuring the response time of flash IO patterns. We also present a benchmarking methodology which takes into account the particular characteristics of flash devices. Finally, we present the results obtained by measuring eleven flash devices, and derive a set of design hints that should drive the development of flash-based systems on current devices.

We explore a location based approach for behavior modeling and abnormality detection. In contrast to the conventional object based approach where an object may first be tagged, identified, classified, and tracked, we proceed directly with event characterization and behavior modeling at the pixel(s) level based on motion labels obtained from background subtraction. Since events are temporally and spatially dependent, this calls for techniques that account for statistics of spatiotemporal events. Based on motion labels, we learn co-occurrence statistics for normal events across space-time. For one (or many) key pixel(s), we estimate a co-occurrence matrix that accounts for any two active labels which co-occur simultaneously within the same spatiotemporal volume. This co-occurrence matrix is then used as a potential function in a Markov random field (MRF) model to describe the probability of observations within the same spatiotemporal volume. The MRF distribution implicitly accounts for speed, direction, as well as the average size of the objects passing in front of each key pixel. Furthermore, when the spatiotemporal volume is large enough, the co-occurrence distribution contains the average normal path followed by moving objects. The learned normal co-occurrence distribution can be used for abnormal detection. Our method has been tested on various outdoor videos representing various challenges.