A comparison of three methods for preparing reversible hydrocolloid duplicating material

This study compared the effects of repeated microwave oven and double-boiler liquefactions and prolonged autoduplicator storage on four physical properties of one reversible hydrocolloid duplicating material. No significant differences were observed between the linear dimensional change and detail reproduction of the three liquefaction techniques. Tear strength differences most clearly distinguished the techniques and effects of aging. Fifteen double-boiler remeltings produced tear strength values lower than those recorded for material stored in an autoduplicator for 2 weeks. Thirty microwave melting cycles still produced tear strength values equivalent to that of the autoduplicator material. After 30 melting cycles the compressive strengths of the microwave and double-boiler materials were inferior to that of the stored autoduplicator material.     

Time prediction on multi-perspective declarative business processes

Process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predictions which is typically a very challenging task. Predictions should be (1) multi-dimensional and (2) not based on a single process instance. Furthermore, the prediction system should be able to (3) adapt to changing circumstances and (4) deal with multi-perspective declarative languages (e.g., models which consider time, resource, data and control flow perspectives). In this work, a novel approach for generating time predictions considering the aforementioned characteristics is proposed. For this, first, a multi-perspective constraint-based language is used to model the scenario. Thereafter, an optimized enactment plan (representing a potential execution alternative) is generated from such a model considering the current execution state of the process instances. Finally, predictions are performed by evaluating a desired function over this enactment plan. To evaluate the applicability of our approach in practical settings we apply it to a real process scenario. Despite the high complexity of the considered problems, results indicate that our approach produces a satisfactory number of good predictions in a reasonable time.     

Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming

Conventional farming still relies on large quantities of agrochemicals for weed management which have several negative side‐effects on the environment. Autonomous robots offer the potential to reduce the amount of chemicals applied, as robots can monitor and treat each plant in the field individually and thereby circumventing the uniform chemical treatment of the whole field. Such agricultural robots need the ability to identify individual crops and weeds in the field using sensor data and must additionally select effective treatment methods based on the type of weed. For example, certain types of weeds can only be effectively treated mechanically due to their resistance to herbicides, whereas other types can be treated trough selective spraying. In this article, we present a novel system that provides the necessary information for effective plant‐specific treatment. It estimates the stem location for weeds, which enables the robots to perform precise mechanical treatment, and at the same time provides the pixel‐accurate area covered by weeds for treatment through selective spraying. The major challenge in developing such a system is the large variability in the visual appearance that occurs in different fields. Thus, an effective classification system has to robustly handle substantial environmental changes including varying weed pressure, various weed types, different growth stages, changing visual appearance of the plants and the soil. Our approach uses an end‐to‐end trainable fully convolutional network that simultaneously estimates plant stem positions as well as the spatial extent of crop plants and weeds. It jointly learns how to detect the stems and the pixel‐wise semantic segmentation and incorporates spatial information by considering image sequences of local field strips. The jointly learned feature representation for both tasks furthermore exploits the crop arrangement information that is often present in crop fields. This information is considered even if it is only observable from the image sequences and not a single image. Such image sequences, as typically provided by robots navigating over the field along crop rows, enable our approach to robustly estimate the semantic segmentation and stem positions despite the large variations encountered in different fields. We implemented and thoroughly tested our approach on images from multiple farms in different countries. The experiments show that our system generalizes well to previously unseen fields under varying environmental conditions—a key capability to deploy such systems in the real world. Compared to state‐of‐the‐art approaches, our approach generalizes well to unseen fields and not only substantially improves the stem detection accuracy, that is, distinguishing crop and weed stems, but also improves the semantic segmentation performance.     

Hybrid disturbance rejection control of dynamic bipedal robots

This paper presents a disturbance rejection control strategy for hybrid dynamic systems exposed to model uncertainties and external disturbances. The focus of this work is the gait control of dynamic bipedal robots. The proposed control strategy integrates continuous and discrete control actions. The continuous control action uses a novel model-based active disturbance rejection control (ADRC) approach to track gait trajectory references. The discrete control action resets the gait trajectory references after the impact produced by the robot’s support-leg exchange to maintain a zero tracking error. A Poincaré return map is used to search asymptotic stable periodic orbits in an extended hybrid zero dynamics (EHZD). The EHZD reflects a lower-dimensional representation of the full hybrid dynamics with uncertainties and disturbances. A physical bipedal robot testbed, referred to as Saurian, is fabricated for validation purposes. Numerical simulation and physical experiments show the robustness of the proposed control strategy against external disturbances and model uncertainties that affect both the swing motion phase and the support-leg exchange.

     

Under canopy light detection and ranging‐based autonomous navigation

This paper describes a light detection and ranging (LiDAR)‐based autonomous navigation system for an ultralightweight ground robot in agricultural fields. The system is designed for reliable navigation under cluttered canopies using only a 2D Hokuyo UTM‐30LX LiDAR sensor as the single source for perception. Its purpose is to ensure that the robot can navigate through rows of crops without damaging the plants in narrow row‐based and high‐leaf‐cover semistructured crop plantations, such as corn (Zea mays) and sorghum ( Sorghum bicolor). The key contribution of our work is a LiDAR‐based navigation algorithm capable of rejecting outlying measurements in the point cloud due to plants in adjacent rows, low‐hanging leaf cover or weeds. The algorithm addresses this challenge using a set of heuristics that are designed to filter out outlying measurements in a computationally efficient manner, and linear least squares are applied to estimate within‐row distance using the filtered data. Moreover, a crucial step is the estimate validation, which is achieved through a heuristic that grades and validates the fitted row‐lines based on current and previous information. The proposed LiDAR‐based perception subsystem has been extensively tested in production/breeding corn and sorghum fields. In such variety of highly cluttered real field environments, the robot logged more than 6 km of autonomous run in straight rows. These results demonstrate highly promising advances to LiDAR‐based navigation in realistic field environments for small under‐canopy robots.     

Estimation of variable atmospheric parameters for the atmospheric correction of satellite images

The information about variable components of the atmosphere (aerosol, water vapour, and ozone) during acquisition is required for the atmospheric correction of spectral images acquired by shortwave sensors of the Earth observing remote-sensing satellites. The procedure to estimate aerosol optical depth and columnar water vapour by the inversion of the atmospheric radiative transfer model 6S using moderate-resolution spectra of incident solar radiation is proposed. Comparison to the results obtained by the Aerosol Robotic Network AERONET at an AERONET site at the distance of 50 km on days when both sensors were in the same air mass shows systematic overestimation both of aerosol optical depth and of columnar water vapour if aerosol optical depth is estimated in the wavelength range of 365–425 nm and columnar water in the range of 895–985 nm using spectra of total irradiance. If more wavelengths and diffuse-to-total spectral irradiance ratio are implemented in the inversion, the bias of estimated water vapour decreases, but aerosol optical depth is underestimated. The estimates at 50 km distance are well correlated. The modelled spectral irradiance using estimated atmospheric parameters matches the measured spectra with high accuracy. In the spectral bands of the Sentinel-2 MultiSpectral Instrument (MSI), the differences do not exceed 2%.     

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19

Machine Learning - The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The...     

Styrene/butyl acrylate copolymerization with layer morphology: Kinetics and viscoelastic properties

Styrene/butyl acrylate copolymers with layer morphology were synthesized using an emulsion copolymerization process, The kinetic parameters studied include monomers composition, initiator and transfer agent content, feeding time, and monomer addition sequence. The final product consists of a homopolymer nucleus surrounded by concentric shells of copolymers with different composition; the initial composition is quite rich in the monomer that forms the nucleus while the process ends with an enriched layer of the second homopolymer. Since the middle copolymer layer tends to increase the compatibility among the original homopolyers, we expected to have a set of core-shell products with completely different properties. However, the experimental results showed that this was not the case. The composition effect on the viscoelastic properties shows, on one hand, that an increase in the butyl acrylate content lowers the elastic response of the final product, and on the other hand, that the elasticity increases with copolymer content. As the initiator content in the reaction media increases, the viscosity of the coreshell products decreases because of the existence of a media flooded with free radicals. If the butyl acrylate is first added, a graft polymerization is favored because of the polar nature of this homopolymer and, therefore, the molecular weight level increases.     

DBToaster: higher-order delta processing for dynamic,frequently fresh views

Applications ranging from algorithmic trading to scientific data analysis require real-time analytics based on views over databases receiving thousands of updates each second. Such views have to be kept fresh at millisecond latencies. At the same time, these views have to support classical SQL, rather than window semantics, to enable applications that combine current with aged or historical data. In this article, we present the DBToaster system, which keeps materialized views of standard SQL queries continuously fresh as data changes very rapidly. This is achieved by a combination of aggressive compilation techniques and DBToaster’s original recursive finite differencing technique which materializes a query and a set of its higher-order deltas as views. These views support each other’s incremental maintenance, leading to a reduced overall view maintenance cost. DBToaster supports tens of thousands of complete view refreshes per second for a wide range of queries.