Efficient Resources Allocation in Technological Processes Using an Approximate Algorithm Based on Random

Abstract — one of the main challenges of the management of complex manufacturing processes is the development of hierarchical control systems. Effective control of technological processes can be achieved by using a distributed control system with multi-level hierarchical structure. The hierarchical structure is characterized by decomposition into interrelated local subsystems. These subsystems are controlled by local decision makers that require coordination. In this paper, the concept of coordination is understood as the act of making the right allocation of tasks and resources and management actions to meet the objectives of production. An optimization program, based on random walk methods, was applied to a dairy factory data for the optimization of the obtained profits

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement

Application of a modified evolutionary algorithm for the optimization of data acquisitionto improve the accuracy of a video-polarimetric system

The use of the polarimetry techniques for display and study of biological tissues has gained increasing interest in recent years. This interest is related mainly to the non-invasiveness, relatively low cost, and ease of application among other characteristics. However, for full use of these advantages, the calibration methods must ensure the minimization of the effects of uncertainties related to the optical element positioning and the noise in intensities measurements

Remote sensing detection of climate-smart practices: Enhancing farm resilience in Austria

Climate-smart agricultural practices are techniques that help crops to endure “extreme” weather events. Practices such as minimum or no tillage, crop rotations, and cover crops reduce wind and rain-driven erosion, enhance soil physical quality, and enable soil to store water for a longer time. Climate change has already led to an increased frequency of “extreme” weather events including prolonged dry spells and intense rain. From a farmer’s perspective, a clearer and more spatially explicit demonstration of how these practices can enhance the resilience of farms would support their accelerated uptake and thus result in increased food security. From a policy maker’s perspective, knowing the extent of adoption and location of these more resilient farms would enable them to produce policies that facilitate and promote the adoption of these practices, which can buffer the effects of climate change. The use of remote sensing to detect these practices would, therefore, benefit this process. Several existing remote sensing-derived indicators, such as the Normalized Difference Vegetation Index (NDVI), are already in use. They inform farmers and policy makers on, e.g., crop and nutrient status. A combination of existing and new remote sensing-derived indices is needed to facilitate and streamline the detection and promotion of climate-smart practices, but a lack of in-situ data to date has prevented the development and verification of new models of detection. The “SATFARM services” project, which brings together expertise in agriculture, remote sensing, and data analysis, aims to connect a large agricultural time-series data set, provided by the Austrian Chamber of Agriculture, with various remote-sensing derived indicators. The goal is to detect and track climate-smart practices and to display the results on a platform (https://apps.sentinel-hub.com/eo-browser/) accessible to farmers, researchers, and policy makers. This presentation will showcase the methodology employed, the initial results and the display of these indicators on the platform

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis

Clinical implications of serum neurofilament in newly diagnosed MS patients: a longitudinal multicentre cohort study

BACKGROUND: We aim to evaluate serum neurofilament light chain (sNfL), indicating neuroaxonal damage, as a biomarker at diagnosis in a large cohort of early multiple sclerosis (MS) patients. METHODS: In a multicentre prospective longitudinal observational cohort, patients with newly diagnosed relapsing-remitting MS (RRMS) or clinically isolated syndrome (CIS) were recruited between August 2010 and November 2015 in 22 centers. Clinical parameters, MRI, and sNfL levels (measured by single molecule array) were assessed at baseline and up to four-year follow-up. FINDINGS: Of 814 patients, 54.7% (445) were diagnosed with RRMS and 45.3% (369) with CIS when applying 2010 McDonald criteria (RRMS[2010] and CIS[2010]). After reclassification of CIS[2010] patients with existing CSF analysis, according to 2017 criteria, sNfL levels were lower in CIS[2017] than RRMS[2017] patients (9.1 pg/ml, IQR 6.2-13.7 pg/ml, n = 45; 10.8 pg/ml, IQR 7.4-20.1 pg/ml, n = 213; p = 0.036). sNfL levels correlated with number of T2 and Gd+ lesions at baseline and future clinical relapses. Patients receiving disease-modifying therapy (DMT) during the first four years had higher baseline sNfL levels than DMT-naïve patients (11.8 pg/ml, IQR 7.5-20.7 pg/ml, n = 726; 9.7 pg/ml, IQR 6.4-15.3 pg/ml, n = 88). Therapy escalation decisions within this period were reflected by longitudinal changes in sNfL levels. INTERPRETATION: Assessment of sNfL increases diagnostic accuracy, is associated with disease course prognosis and may, particularly when measured longitudinally, facilitate therapeutic decisions

Crowd-driven tools for the calibration and validation of Earth Observation products

In recent years there has been a rapid diffusion in open access Earth Observation (EO) data available at global scales to help scientists address planetary challenges including climate change, food security and disaster management. For example, since 2016 the European Space Agency (ESA), via its Sentinel-2 satellites, has been providing frequent (5 day repeat cycle) and fine-grained (10 meter resolution) optical imagery for open and public use. As such, the EO community is faced with the need to design methods for transforming this abundance of EO data into well-validated environmental monitoring products. To help facilitate the training and validation of these products (i.e. land cover, land use), several crowd-driven tools that engage stakeholders (within and outside the scientific community) in various tasks, including satellite image interpretation, and online interactive mapping, have been developed. This paper will highlight the new results and potential of a series of such tools developed at the International Institute for Applied Systems Analysis (IIASA), namely the Geo-Wiki engagement platform, the LACO-Wiki validation tool, and Picture Pile, a mobile application for rapid image assessment and change detection. Through various thematic data collection campaigns, these tools have helped to collect citizen-observed information to improve global maps of cropland and agricultural field size, to validate various land cover products and to create post natural disaster damage assessment maps. Furthermore, Picture Pile is designed as a generic and flexible tool that is customizable to many different domains and research avenues that require interpreted satellite images as a data resource. Such tools, in combination with the recent emergence of Citizen Observatories (i.e. LandSense, GROW, GroundTruth 2.0, SCENT funded by Horizon2020), present clear opportunities to integrate citizen-driven observations with established authoritative data sources to further extend GEOSS and Copernicus capacities, and support comprehensive environmental monitoring systems. In addition, these applications have considerable potential in lowering expenditure costs on in-situ data collection and current calibration/validation approaches within the processing chain of environmental monitoring activities both within and beyond Europe

Influence of socioeconomic status on community-acquired pneumonia outcomes in elderly patients requiring hospitalization: a multicenter observational study

The associations between socioeconomic status and community-acquired pneumonia outcomes in adults have been studied although studies did not always document a relationship. The aim of this multicenter observational study was to determine the association between socioeconomic status and community-acquired pneumonia outcomes in the elderly, in the context of a public health system providing universal free care to the whole population

Insights into Ligand–Protein Binding from Local Mechanical Response

Computational studies of ligand–protein binding are crucial for properly designing novel compounds of potential pharmacological interest. In this respect, researchers are increasingly interested in steered molecular dynamics for ligand–protein binding and unbinding studies. In particular, it has been suggested that analyzing the work profiles along the ligand–protein undocking paths could be fruitful. Here, we propose that small portions of work profiles, termed “local mechanical responses” of the system to a steering force, could serve as a universal measure for capturing relevant information about the system under investigation. Specifically, we first collected a high number of steering trajectories using two biological systems of increasing complexity (i.e., alanine dipeptide and (R)-roscovitine/CDK5 complex). Then, we devised a novel postprocessing tool to be applied to the local mechanical responses, to extract structural information related to the biological processes under investigation. Despite the out-of-equilibrium character of the trajectories, the analysis carried out on the work profiles provided pivotal information about the investigated biological processes. This could eventually be applied to drug design

Estimating the Global Distribution of Field Size using Crowdsourcing

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, e.g. automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture