This work proposes sustainability criteria for the selection or design of restoration mortars based on their physical and mechanical properties, durability, price in the French market, and the environmental impact estimated by the global warming potential. A score is assigned to the mortars based on normalized values of their physical and mechanical properties. A total of 24 formulations of restoration mortars were characterized, and their scores were compared. A case study showing the application of the proposed selection method is presented, focused on the restoration of historical monuments in Paris, France, built with Lutetian and Euville stones. In this case, hydraulic lime mortars were the most sustainable options. The application of the method is also projected for global application, as showcased for the restoration of Mayan stones in Southern Mexico.
Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R2p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R2p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R2p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R2p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.
Eva Neumann; Filippa Schreeck; Jethro Herberg; Evelyne Jacqz Aigrain; Anke H. Maitland‐van der Zee; Antonio Pérez‐Martínez; Daniel B. Hawcutt; Elke Schaeffeler; Anders Rane; Saskia N. Wildt; +1 more
Eva Neumann; Filippa Schreeck; Jethro Herberg; Evelyne Jacqz Aigrain; Anke H. Maitland‐van der Zee; Antonio Pérez‐Martínez; Daniel B. Hawcutt; Elke Schaeffeler; Anders Rane; Saskia N. Wildt; Matthias Schwab;
Countries: Netherlands, United Kingdom, Netherlands
Project: EC | c4c (777389), EC | c4c (777389)
The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates and infants, is limited by a paucity of good-quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OMICs technologies may support these efforts by complementary information about targeted and nontargeted molecules through systematic characterization and quantitation of biological samples. OMICs technologies comprise at least genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics in addition to the patient's phenotype. OMICs technologies are in part hypothesis-generating, allowing an in depth understanding of disease pathophysiology and pharmacological mechanisms. Application of OMICs technologies in paediatrics faces major challenges before routine adoption. First, developmental processes need to be considered, including a subdivision into specific age groups as developmental changes clearly impact OMICs data. Second, compared to the adult population, the number of patients is limited as are the type and amount of necessary biomaterial, especially in neonates and preterms. Thus, advanced trial designs and biostatistical methods, noninvasive biomarkers, innovative biobanking concepts including data and samples from healthy children, as well as analytical approaches (eg liquid biopsies) should be addressed to overcome these obstacles. The ultimate goal is to link OMICs technologies with innovative analysis tools, such as artificial intelligence at an early stage. The use of OMICs data based on a feasible approach will contribute to the identification complex phenotypes and subpopulations of patients to improve the development of medicines for children with potential economic advantages.
Proper monitoring of quality-related but hard-to-measure effluent variables in wastewater plants is imperative. Soft sensors, such as dynamic neural network, are widely used to predict and monitor these variables and then to optimize plant operations. However, the traditional training methods of dynamic neural network may lead to poor local optima and low learning rates, resulting in inaccurate estimations of parameters and deviation of predictions. This study introduces a general Kalman-Elman method to monitor the effluent qualities, such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (TN). The method couples an Elman neural network with the square-root unscented Kalman filter (SR-UKF) to build a soft-sensor model. In the proposed methodology, adaptive noise estimation and weight constraining are introduced to estimate the unknown noise and constrain the parameter values. The main merits of the proposed approach include the following: First, improving the mapping accuracy of the model and overcoming the underprediction phenomena in data-driven process monitoring; second, implementing the parameter constraint and avoid large weight values; and finally, providing a new way to update the parameters online. The proposed method is verified from a dataset of the University of California database (UCI database). The obtained results show that the proposed soft-sensor model achieved better prediction performance with root mean square error (RMSE) being at least 50% better than the Elman network based on back propagation through the time algorithm (Elman-BPTT), Elman network based on momentum gradient descent algorithm (Elman-GDM), and Elman network based on Levenberg-Marquardt algorithm (Elman-LM). This method can give satisfying prediction of quality-related effluent variables with the largest correlation coefficient (R) for approximately 0.85 in output suspended solids (SS-S) and 0.95 in BOD and COD.
The post-synthesis procedure for cyclic amine (morpholine and 1-methylpiperazine) modified mesoporous MCM-48 and SBA-15 silicas was developed. The procedure for preparation of the modified mesoporous materials does not affect the structural characteristics of the initial mesoporous silicas strongly. The initial and modified materials were characterized by XRD, N2 physisorption, thermal analysis, and solid-state NMR. The CO2 adsorption of the obtained materials was tested under dynamic and equilibrium conditions. The NMR data revealed the formation of different CO2 adsorbed forms. The materials exhibited high CO2 absorption capacity lying above the benchmark value of 2 mmol/g and stretching out to the outstanding 4.4 mmol/g in the case of 1-methylpiperazin modified MCM-48. The materials are reusable, and their CO2 adsorption capacities are slightly lower in three adsorption/desorption cycles.
Publisher: Multidisciplinary Digital Publishing Institute
Project: EC | TAILOR (952215)
Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.
Supporting local and central authorities in decision-making processes pertaining to environmental planning requires the adoption of scientific methods and the submission of proposals that could be implemented in practice. Taking into consideration the dual role that honeybees play as honey producers and crop pollinators, the aim of the present study is to identify and utilize a number of indicators and subsequently develop priority thematic maps. Previous research has focused on the determination of, and, on certain occasions, on mapping, priority areas for apiculture development, based mainly on the needs of honeybees, without taking into consideration the pollination needs of crops that are cultivated in these areas. In addition, research so far has been carried out in specific spatial entities, in contrast to the current study, in which the areas to be comparatively assessed are pre-chosen based on their geographical boundaries. The information derived from this process is expected to help decision-makers in local and regional authorities to adopt measures for optimal land use and sound pollination practices in order to enhance apiculture development at a local scale. To achieve this target, the study incorporates literature about the attractiveness of crops and plants to pollinating honeybees as well as the pollination services provided by honeybees, in combination with detailed vegetative land cover data. The local communities of each municipality were comparatively evaluated, by introducing three indicators through numerical and spatial data analysis: Relative Attractiveness Index (RAI), Relative Dependence Index (RDI), and Relative Priority Index (RPI). Based on these indicators, attractiveness, dependence, and priority maps were created and explained in detail. We suggest that a number of improvement measures that will boost pollination or honey production or both should be taken by decision-makers, based on the correlations between the aforementioned indicators and the exanimated areas. In addition, dependence maps can constitute a powerful tool for raising awareness among both the public and the farmers about the value of honeybees in pollination, thus reinforcing bee protection efforts undertaken globally. Attractiveness maps that provide a thorough picture of the areas that are sources of pollen and nectar can serve as a general guide for the establishment of hives in areas with high potential for beekeeping.
Silke Voigt-Heucke; Claudia Fabó Cartas; Kim Mortega;
Silke Voigt-Heucke; Claudia Fabó Cartas; Kim Mortega;
Project: EC | CS-SDG (101000014)
This is the abstract volume of the Citizen Science SDG Conference titled "Knowledge for Change: A decade of Citizen Science (2020-2030) in support of the SDGs" that took place on 14.-15. October 2020 as a hybrid conference (in Berlin and online). This conference was an official event of Germany’s 2020 EU Council presidency. The conference took place as part of the CS-SDG project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101000014.
This is the conference programme of the Citizen Science SDG Conference titled "Knowledge for Change: A decade of Citizen Science (2020-2030) in support of the SDGs" that took place on 14.-15. October 2020 as a hybrid conference (in Berlin and online). This conference was an official event of Germany’s 2020 EU Council presidency. The conference took place as part of the CS-SDG project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101000014.