This paper proposes a two-stage texture synthesis algorithm. At the first stage, a structure tensor map carrying information about the local orientation is synthesized from the exemplar's data and used at the second stage to constrain the synthesis of the texture. Keeping in mind that the algorithm should be able to reproduce as faithfully as possible the visual aspect, statistics, and morphology of the input sample, the method is tested on various textures and compared objectively with existing methods, highlighting its strength in successfully synthesizing the output texture in many situations where traditional algorithms fail to reproduce the exemplar's patterns. The promising results pave the way towards the synthesis of accurately large and multi-scale patterns as it is the case for carbon material samples showing laminar structures, for example.

The internet of things (IoT) and Industry 4.0 technologies are becoming widely used in the field of apiculture to enhance honey production and reduce colony losses using connected scales combined with additional data, such as relative humidity and internal temperature. This paper exploits beehive weight measurements and builds appropriate business rules using two instruments. The first is an IoT fixed scale installed on one hive, taking rich continuous measurements, and used as a reference. The second is a portable nomad scale communicating with a smartphone and used for the remaining hives. A key contribution will be the run and triggering of a business process model based on apicultural business rules learned from experience and system observed events. Later, the evolution of the weight of each individual hive, obtained by either measurement or inference, will be associated with a graphical workflow diagram expressed with the business process model and notation (BPMN) language, and will trigger events that inform beekeepers to initiate relevant action. Finally, the BPMN processes will be transformed into executable models for model driven decision support. This contribution improves amateur and professional user-experience for honeybee keeping and opens the door for interoperability between the suggested model and other available simulations (weather, humidity, bee colony behavior, etc.).

Business processes (BPs) have become extremely complex with thousands of tasks to be completed. These processes should be modelled to manage their complexity, but deviations from these models can occur during execution. To account for the potential outcomes, models are often over-specified, but in fact, it is impossible to anticipate every scenario. Therefore, the issue of how to model the response to these problems arises. The approach put forward in this study entails the creation of a primary model with the aid of domain experts. If a deviation occurs during execution, the system searches for compatible patterns in a collaborative library of workflows, rather than relying on dedicated solutions for specific problems. Nevertheless, the main challenges lie in selecting the most appropriate set of candidates from a vast number of patterns, as well as identifying the optimal injection points on the primary model to correct the deviation. One main objective is to rapidly eliminate incompatible patterns by employing simple mathematical techniques to narrow down the pool of candidate solutions. This serves to minimize the number of patterns that should be considered where more sophisticated methods can then be utilized to rank and select the best solution from this smaller set.

This paper proposes an image processing algorithm for the creation of images of arbitrary shapes with predefined size. This method uses an arbitrary image to construct a structure layer, which is then used to constrain the creation of the output image. The structure layer is represented by the field of matrices of second moment. Many experiments with different constraint images are conducted to demonstrate the high efficiency of the proposed approach in terms of accurately reproducing the visual aspects of the input image. The obtained results show that the proposed method can accurately create images of arbitrary shapes while respecting the local structure of the input image. This paves the way toward the creation of 3D images exhibiting arbitrary shapes from 2D images, which has many engineering applications.