From colloidal dispersions to colloidal pastesthrough solid–liquid separation processes

Solid–liquid separation is an operation that starts with a dispersion of solid particles in a liquid and removes some of the liquid from the particles, producing a concentrated solid paste and a clean liquid phase. It is similar to thermodynamic processes where pressure is applied to a system in order to reduce its volume. In dispersions, the resistance to this osmotic compression depends on interactions between the dispersed particles. The first part of this work deals with dispersions of repelling particles, which are either silica nanoparticles or synthetic clay platelets, dispersed in aqueous solutions. In these conditions, each particle is surrounded by an ionic layer, which repels other ionic layers. This results in a structure with strong short-range order. At high particle volume fractions, the overlap of ionic layers generates large osmotic pressures; these pressures may be calculated, through the cell model, as the cost of reducing the volume of each cell. The variation of osmotic pressure with volume fraction is the equation of state of the dispersion. The second part of this work deals with dispersions of aggregated particles, which are silica nanoparticles, dispersed in water and flocculated by multivalent cations. This produces large bushy aggregates, with fractal structures that are maintained through interparticle surface– surface bonds. As the paste is submitted to osmotic pressures, small relative displacements of the aggregated particles lead to structural collapse. The final structure is made of a dense skeleton immersed in a nearly homogeneous matrix of aggregated particles. The variation of osmotic resistance with volume fraction is the compression law of the paste; it may be calculated through a numerical model that takes into account the noncentral interparticle forces. According to this model, the response of aggregated pastes to applied stress may be controlled through the manipulation of interparticle adhesion

AI4SmallFarms: A data set for crop field delineation in Southeast Asian smallholder farms

Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers. However, the limited availability of training labels poses a challenge in developing supervised methods to accurately delineate field boundaries using Earth observation (EO) data. This letter introduces an open dataset for training and benchmarking machine learning methods to delineate agricultural field boundaries in polygon format. The large-scale dataset consists of 439 001 field polygons divided into 62 tiles of approximately 5× 5 km distributed across Vietnam and Cambodia, covering a range of fields and diverse landscape types. The field polygons have been meticulously digitized from satellite images, following a rigorous multistep quality control process and topological consistency checks. Multitemporal composites of Sentinel-2 (S2) images are provided to ensure cloud-free data. We conducted an experimental analysis testing a state-of-the-art deep learning (DL) workflow based on fully convolutional networks (FCNs), contour closing, and polygonization. We anticipate that this large-scale dataset will enable researchers to further enhance the delineation of agricultural fields in smallholder farms and to support the achievement of the Sustainable Development Goals (SDGs). The dataset can be downloaded from https://doi.org/10.17026/dans-xy6-ngg6.Management Suppor

Yield stress, heterogeneities and activated processes in soft glassy materials

The rheological behavior of soft glassy materials basically results from the interplay between shearing forces and an intrinsic slow dynamics. This competition can be described by a microscopic theory, which can be viewed as a nonequilibrium schematic mode-coupling theory. This statistical mechanics approach to rheology results in a series of detailed theoretical predictions, some of which still awaiting for their experimental verification. We present new, preliminary, results about the description of yield stress, flow heterogeneities and activated processes within this theoretical framework.Comment: Paper presented at "III Workshop on Non Equilibrium Phenomena...", Pisa 22-27 Sep. 200

Exploring the role of smartphone technology for citizen science in agriculture

Citizen science is the involvement of citizens, such as farmers, in the research process. Citizen science has become increasingly popular recently, supported by the proliferation of mobile communication technologies such as smartphones. However, citizen science methodologies have not yet been widely adopted in agricultural research. Here, we conducted an online survey with 57 British and French farmers in 2014. We investigated (1) farmer ownership and use of smartphone technologies, (2) farmer use of farm-specific management apps, and (3) farmer interest and willingness to participate in agricultural citizen science projects. Our results show that 89 % respondents owned a smartphone, 84 % used it for farm management, and 72 % used it on a daily basis. Fifty-nine percent engaged with farm-specific apps, using on average four apps. Ninety-three percent respondents agreed that citizen science was a useful methodology for data collection, 93 % for real-time monitoring, 83 % for identification of research questions, 72 % for experimental work, and 72 % for wildlife recording. Farmers also showed strong interest to participate in citizen science projects, often willing to commit substantial amounts of time. For example, 54 % of British respondents were willing to participate in farmland wildlife recording once a week or monthly. Although financial support was not always regarded as necessary, experimental work was the most likely activity for which respondents thought financial support would be essential. Overall, this is the first study to quantify and explore farmers' use of smartphones for farm management, and document strong support for farm-based citizen science projects. (Résumé d'auteur

Novel semi-supervised classification method based on class certainty of samples

The traditional classification method based on supervised learning classifies remote sensing (RS) images by using sufficient labeled samples. However, the number of labeled samples is limited due to the expensive and time-consuming collection. To effectively utilize the information of unlabeled samples in the learning process, this paper proposes a novel semi-supervised classification method based on class certainty of samples (CCS). First, the class certainty of unlabeled samples obtained based on multi-class SVM is smoothed for robustness. Then, a new semi-supervised linear discriminant analysis (LDA) is presented based on class certainty, which improves the separability of samples in the projection subspace. Ultimately, we extend the semi-supervised LDA to nonlinear dimensional reduction by combining class certainty and kernel methods. Furthermore, to assess the effectiveness of proposed method, the nearest neighbor classifier is adopted to classify actual SAR images. The results demonstrate that the proposed method can effectively exploit the information of unlabeled samples and greatly improve the classification effect compared with other state-of-the-art approaches

Electrorheological properties and microstructure of silica suspensions

We present experimental and theoretical results on the electrorheological response and microstructure of colloidal suspensions composed of silica nanoparticles dispersed in a silicon oil, as a function of electric field strength and silica water content. Using small-angle neutrons scattering experiments, we determined the evolution of the static structure factor of the suspensions when an electric field is applied. Experimental data were fitted with model calculations using the Percus-Yevick solution for Baxter's hard-sphere adhesive potential. The obtained stickiness parameter is directly related to the polarization interactions that depend on the water content of silica particles. The influence of the polarization interparticle potential on the rheology of the silica dispersions was investigated in a second time. A microscopic theory for the shear viscosity of adhesive hard-sphere suspensions was successfully used which describes the steady shear viscosity of suspension in terms of the fractal concept