Random poly(ε‐caprolactone‐L‐alanine) by direct melt copolymerization

A series of random polyesteramides (PEAs) with a range of molar composition from 90/10 to 50/50 were synthesized by direct melt polycondensation of ε‐caprolactone and l ‐alanine. Their structure was fully characterized by Fourier transform IR and NMR spectroscopy. The resulting copolymers are completely amorphous with the exception of PEA‐90/10 which possesses a semicrystalline structure. These PEAs present increasing glass transition temperatures at increasing l ‐alanine contents and exhibit fairly good thermal stability with 10% mass loss temperatures reaching 315 °C. © 2020 Society of Industrial Chemistry     

An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet.

The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.     

A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach.

     

Silhouette-based human action recognition using SAX-Shapes

Human action recognition is an important problem in Computer Vision. Although most of the existing solutions provide good accuracy results, the methods are often overly complex and computationally expensive, hindering practical applications. In this regard, we introduce the combination of time-series representation for the silhouette and Symbolic Aggregate approXimation (SAX), which we refer to as SAX-Shapes, to address the problem of human action recognition. Given an action sequence, the extracted silhouettes of an actor from every frame are transformed into time series. Each of these time series is then efficiently converted into the symbolic vector: SAX. The set of all these SAX vectors (SAX-Shape) represents the action. We propose a rotation invariant distance function to be used by a random forest algorithm to perform the human action recognition. Requiring only silhouettes of actors, the proposed method is validated on two public datasets. It has an accuracy comparable to the related works and it performs well even in varying rotation.     

Time optimization of ultraviolet–ozone pretreatment for improving wool fabrics properties

Wool fabrics were exposed to ultraviolet (UV)–ozone treatment for different periods. After exposure, the fabrics were analyzed by Fourior transform infrared spectroscopy (FTIR). Also, the crystallinity and amorphousity regions were followed by an X‐ray diffraction technique. The results were correlated by mechanical properties measurements. After a certain period of exposure, the dyeability and printability were improved because these abilities are strongly related to the percentage ratio of crystallinity regions to amorphousity regions in wool fabrics. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 1469–1476, 2002     

IESK-ArDB: a database for handwritten Arabic and an optimized topological segmentation approach

Even though a lot of researches have been conducted in order to solve the problem of unconstrained handwriting recognition, an effective solution is still a serious challenge. In this article, we address two Arabic handwriting recognition-related issues. Firstly, we present IESK-arDB, a new multi-propose off-line Arabic handwritten database. It is publicly available and contains more than 4,000 word images, each equipped with binary version, thinned version as well as a ground truth information stored in separate XML file. Additionally, it contains around 6,000 character images segmented from the database. A letter frequency analysis showed that the database exhibits letter frequencies similar to that of large corpora of digital text, which proof the database usefulness. Secondly, we proposed a multi-phase segmentation approach that starts by detecting and resolving sub-word overlaps, then hypothesizing a large number of segmentation points that are later reduced by a set of heuristic rules. The proposed approach has been successfully tested on IESK-arDB. The results were very promising, indicating the efficiency of the suggested approach.     

A branch and bound algorithm for extracting smallest minimal unsatisfiable subformulas

Explaining the causes of infeasibility of Boolean formulas has practical applications in numerous fields, such as artificial intelligence (repairing inconsistent knowledge bases), formal verification (abstraction refinement and unbounded model checking), and electronic design (diagnosing and correcting infeasibility). Minimal unsatisfiable subformulas (MUSes) provide useful insights into the causes of infeasibility. An unsatisfiable formula often has many MUSes. Based on the application domain, however, MUSes with specific properties might be of interest. In this paper, we tackle the problem of finding a smallest-cardinality MUS (SMUS) of a given formula. An SMUS provides a succinct explanation of infeasibility and is valuable for applications that are heavily affected by the size of the explanation. We present (1) a baseline algorithm for finding an SMUS, founded on earlier work for finding all MUSes, and (2) a new branch-and-bound algorithm called Digger that computes a strong lower bound on the size of an SMUS and splits the problem into more tractable subformulas in a recursive search tree. Using two benchmark suites, we experimentally compare Digger to the baseline algorithm and to an existing incomplete genetic algorithm approach. Digger is shown to be faster in nearly all cases. It is also able to solve far more instances within a given runtime limit than either of the other approaches.     

Dynamic storage and access load balancing for answering range queries in peer-to-peer networks

Recently, many applications have used Peer-to-Peer (P2P) systems to overcome the current problems with client/server systems such as non-scalability, high bandwidth requirement and single point of failure. In this paper, we propose an efficient scheme to support efficient range query processing over structured P2P systems, while balancing both the storage load and access load. The paper proposes a rotating token scheme to balance the storage load by placing joining nodes in appropriate locations in the identifier space to share loads with already overloaded nodes. Then, to support range queries, we utilize an order-preserving mapping function to map keys to nodes in order preserving way and without hashing. This may result in an access load imbalance due to non-uniform distribution of keys in the identifier space. Thus, we propose an adaptive replication scheme to relieve overloaded nodes by shedding some load on other nodes to balance the access load. We derive a formula for estimating the overhead of the proposed adaptive replication scheme. In this study, we carry simulation experiments with synthetic data to measure the performance of the proposed schemes. Our simulation experiments show significant gains in both storage load balancing and access load balancing.     

An efficient image-mosaicing method based on multifeature matching

Mosaicing is connecting two or more images and making a new wide area image with no visible seam-lines. Several algorithms have been proposed to construct mosaics from image sequence where the camera motion is more or less complex. Most of these methods are based either on the interest points matching or on theoretical corner models. This paper describes a fully automated image-mosaicing method based on the regions and the Harris points primitives. Indeed, in order to limit the search window of potential homologous points, for each point of interest, regions segmentation and matching steps are being performed. This enables us to improve the reliability and the robustness of the Harris points matching process by estimating the camera motion. The main originality of the proposed system resides in the preliminary manipulation of regions matching, thus making it possible to estimate the rotation, the translation and the scale factor between two successive images of the input sequence. This estimation allows an initial alignment of the images along with the framing of the interest points search window, and therefore reducing considerably the complexity of the interest points matching algorithm. Then, the resolution of a minimization problem, altogether considering the couples of matched-points, permits us to perform the homography. In order to improve the mosaic continuity around junctions, radiometric corrections are applied. The validity of the herewith described method is illustrated by being tested on several sequences of complex and challenging images captured from real-world indoor and outdoor scenes. These simulations proved the validity of the proposed method against camera motions, illumination variations, acquirement conditions, moving objects and image noise. To determine the importance of the regions matching stage in motion estimation, as well as for the framing of the search window associated to a point of interest, we compared the matching points results of this described method with those produced using the zero-mean normalized cross correlation score (without regions matching). We made this comparison in the case of a simple motion (without the presence of a rotation around optical axis and/or a scale factor), in the case of a rotation and in the general case of an homothety. For justifying the effectiveness of this method, we proposed an objective assessment by defining a reconstruction error.