Conceptual Framework for SDSS Development with an Application in the Retail Industry

Spatial information is becoming crucial for strategic decision making, but accessing and understanding this information is not easy. Dedicated tools can support the decision process in many ways, such as visualization interfaces or data analyses. Numerous Decision Support System (DSS) development methodologies exist along with dedicated Spatial Decision Support System (SDSS). Unfortunately, for multiple reasons, these tools andmethodologies are not easily adaptable for the development of another SDSS. This paper proposes a framework for the development of a flexible SDSS that is built on open source software, allowing for low cost implementation. To support the efficiency of our approach, the design of a specific SDSS that is currently in use will be presented. This SDSS was developed for a company that distributes products through various retail networks. The multiple capabilities of the resulting SDSS will be revealed through an explanation of the different development steps. The complete framework is applied to a real data set that will be detailed in a demonstration

Methodology for multi-temporal prediction of crop rotations using recurrent neural networks

ABSTRACT: In a context of growing demand for food and the scarcity of natural resources, the development of more sustainable agriculture is imperative. This means it is necessary to limit the environmental impact of agricultural activities on soil and water and to be mindful of the carbon footprint, while maintaining crop yields and economic benefits for producers. Crop rotation is a valuable tool in sustainable agriculture, but this technique has to be appropriately coupled with sustainable fertilization plans to optimize crops. The proposed methodology uses recurrent neural networks (RNN); more precisely, LSTMs, in a Seq2Seq architecture, to predict the most probable scenarios of crop rotations to be exploited in a field in subsequent growing seasons, according to cropping habits. The output can be used in crop models to build a decision support system for greater sustainability in agricultural production by allowing producers to choose the strategy that offers the best compromise between profitability and environmental impact

Competence maps using agglomerative hierarchical clustering

Knowledge management from a strategic planning point of view often requires having an accurate understanding of a firm’s or a nation’s competences in a given technological discipline. Knowledge maps have been used for the purpose of discovering the location, ownership and value of intellectual assets. The purpose of this article is to develop a new method for assessing national and firm-level competences in a given technological discipline. To achieve this goal, we draw a competence map by applying agglomerative hierarchical clustering on a sample of patents. Considering the top levels of the resulting dendrogram, each cluster represents one of the technological branches of nanotechnology and its children branches are those that are most technologically proximate. We also assign a label to each branch by extracting the most relevant words found in each of them. From the information about patents inventors’ cities, we are able to identify where the largest invention communities are located. Finally, we use information regarding patent assignees and identify the most productive firms. We apply our method to the case of the emerging and multidisciplinary Canadian nanotechnology industry

Discovering and assessing fields of expertise in nanomedicine: a patent co-citation network perspective

Discovering and assessing fields of expertise in emerging technologies from patent data is not straightforward. First, patent classification in an emerging technology being far from complete, the definitions of the various applications of its inventions are embedded within communities of practice. Because patents must contain full record of prior art, co-citation networks can, in theory, be used to identify and delineate the inventive effort of these communities of practice. However, the use patent citations for the purpose of measuring technological relatedness is not obvious because they can be added by examiners. Second, the assessment of the development stage of emerging industries has been mostly done through simple patent counts. Because patents are not all valuable, a better way of evaluating an industry’s stage of development would be to use multiple patent quality metrics as well as economic activity agglomeration indicators. The purpose of this article is to validate the use of (1) patent citations as indicators of technological relatedness, and (2) multiple indicators for assessing an industry’s development stage. Greedy modularity optimization of the ‘Canadian-made’ nanotechnology patent co-citation network shows that patent citations can effectively be used as indicators of technological relatedness. Furthermore, the use of multiple patent quality and economic agglomeration indicators offers better assessment and forecasting potential than simple patent counts

Distant recombination and the creation of basic inventions: An analysis of the diffusion of public and private sector nanotechnology patents in Canada

This article explores whether the relationship between the breath of technological integration (recombination distance) and the breath of an invention׳s subsequent application (basicness) is moderated by the sector of activity (private or public), science-linkage strength and industry characteristics. Our analysis of Canadian nanotechnology patents granted between 1990 and 1997 shows that although private organizations generally yield smaller rates of basic inventions than public organizations, increases to recombination distance by the former increases invention basicness at a higher rate; increasing reliance upon basic science moderates the relationship between recombination distance and basicness; and increases to recombination distance in emerging science-based industries increases invention basicness at a higher rate. These findings have implications regarding the debate around the efficiency of the academic enterprise model

Along-strike variations of P-T conditions in accretionary wedges and syn-orogenic extension, the HP-LT Phyllite-Quartzite Nappe in Crete and the Peloponnese

International audienceSyn-orogenic detachments in accretionary wedges make the exhumation of high-pressure and low-temperature metamorphic rocks possible with little erosion. The velocity of exhumation within the subduction channel or the accretionary complex, and thus the shape of P-T paths, depend upon the kinematic boundary conditions. A component of slab retreat tends to open the channel and facilitates the exhumation. We document the effect of slab retreat on the shape of P-T paths using the example of the Phyllite-Quartzite Nappe that has been exhumed below the Cretan syn-orogenic detachment during the Miocene in Crete and the Peloponnese. Data show a clear tendency toward colder conditions at peak pressure and during exhumation where the intensity of slab retreat is larger. This spatial evolution of P-T gradient is accompanied with an evolution from a partly coaxial regime below the Peloponnese section of the detachment toward a clearly non-coaxial regime in Crete

Future trends in organic flour milling: the role of AI

The milling of wheat flour is a process that has existed since ancient times. In the course of history, the techniques have improved, the equipment modernized. The interest of the miller in charge of the mill is still to ensure that a mill is functional and profitable, as well as to provide a consistent quality of flour. The production of organic flour means that methods of adding chemicals and unnatural agents are not possible. In organic flour production, it is necessary to work with the raw material. A grain of wheat is a living material, and its quality varies according to a multitude of factors. Challenges are therefore present at each stage of the value chain. The use of artificial intelligence techniques offers solutions and new perspectives to meet the different objectives of the miller. A literature review of artificial intelligence techniques developed at each stage of the value chain surrounding the issues of quality and yield is conducted. An analysis of a large number of variables, including process factors, process parameters and wheat grain quality from data collected on the value chain enables the development and training of artificial intelligence models. From these models, it is possible to develop decision support tools and optimize the wheat flour milling process. Several major research directions, other than constant quality, are to be studied to optimize the process and move towards a smart mill. This includes energy savings, resource optimization and mill performance

Low-dimensional space modeling-based differential evolution for large scale global optimization problems

Large-Scale Global Optimization (LSGO) has been an active research field. Part of this interest is supported by its application to cutting-edge research such as Deep Learning, Big Data, and complex real-world problems such as image encryption, real-time traffic management, and more. However, the high dimensionality makes solving LSGO a significant challenge. Some recent research deal with the high dimensionality by mapping the optimization process to a reduced alternative space. Nonetheless, these works suffer from the changes in the search space topology and the loss of information caused by the dimensionality reduction. This paper proposes a hybrid metaheuristic, so-called LSMDE (Low-dimensional Space Modeling-based Differential Evolution), that uses the Singular Value Decomposition to build a low-dimensional search space from the features of candidate solutions generated by a new SHADE-based algorithm (GM-SHADE). GM-SHADE combines a Gaussian Mixture Model (GMM) and two specialized local algorithms: MTS-LS1 and L-BFGS-B, to promote a better exploration of the reduced search space. GMM mitigates the loss of information in mapping high-dimensional individuals to low-dimensional individuals. Furthermore, the proposal does not require prior knowledge of the search space topology, which makes it more flexible and adaptable to different LSGO problems. The results indicate that LSMDE is the most efficient method to deal with partially separable functions compared to other state-of-the-art algorithms and has the best overall performance in two of the three proposed experiments. Experimental results also show that the new approach achieves competitive results for non-separable and overlapping functions on the most recent test suite for LSGO problems

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online