Foam-like phantoms for comparing tomography algorithms

Tomographic algorithms are often compared by evaluating them on certain benchmark datasets. For fair comparison, these datasets should ideally (i) be challenging to reconstruct, (ii) be representative of typical tomographic experiments, (iii) be flexible to allow for different acquisition modes, and (iv) include enough samples to allow for comparison of data-driven algorithms. Current approaches often satisfy only some of these requirements, but not all. For example, real-world datasets are typically challenging and representative of a category of experimental examples, but are restricted to the acquisition mode that was used in the experiment and are often limited in the number of samples. Mathematical phantoms are often flexible and can sometimes produce enough samples for data-driven approaches, but can be relatively easy to reconstruct and are often not representative of typical scanned objects. In this paper, we present a family of foam-like mathematical phantoms that aims to satisfy all four requirements simultaneously. The phantoms consist of foam-like structures with more than 100000 features, making them challenging to reconstruct and representative of common tomography samples. Because the phantoms are computer-generated, varying acquisition modes and experimental conditions can be simulated. An effectively unlimited number of random variations of the phantoms can be generated, making them suitable for data-driven approaches. We give a formal mathematical definition of the foam-like phantoms, and explain how they can be generated and used in virtual tomographic experiments in a computationally efficient way. In addition, several 4D extensions of the 3D phantoms are given, enabling comparisons of algorithms for dynamic tomography. Finally, example phantoms and tomographic datasets are given, showing that the phantoms can be effectively used to make fair and informative comparisons between tomography algorithms.

A GENERIC FUZZY-BASED RECOMMENDATION APPROACH (GFBRA)

Recommender Systems aim to automatically provide users with personalized information in an overloaded search space. To dual with vagueness and imprecision problems in RS, several researches have been proposed fuzzy based approaches. Even though, these works have incorporated experimental evaluation; they were used in different recommendation scenarios which makes it difficult to have a fair comparison between them. Also, some of them performed an items and/or users clustering before generating recommendations. For this reason they need additional information such as item attributes or trust between users which are not always available. In this paper, we propose to use fuzzy set techniques to predict the rating of a target user for each unrated item. It uses the target user's history in addition with rating of similar users which allows to the target user to contribute in the recommendation process. Experimental results on several datasets seem to be promising in term of MAE (Mean Average Error), RMSE (Root Mean Square Error), accuracy, precision, recall and F-measure.

On the Convergence of the Quasi-Periodic Approximations on a Finite Interval

We investigate the convergence of the quasi-periodic approximations in different frameworks and reveal exact asymptotic estimates of the corresponding errors. The estimates facilitate a fair comparison of the quasi-periodic approximations to other classical well-known approaches. We consider a special realization of the approximations by the inverse of the Vandermonde matrix, which makes it possible to prove the existence of the corresponding implementations, derive explicit formulas and explore convergence properties. We also show the application of polynomial corrections for the convergence acceleration of the quasi-periodic approximations. Numerical experiments reveal the auto-correction phenomenon related to the polynomial corrections so that utilization of approximate derivatives surprisingly results in better convergence compared to the expansions with the exact ones.

Screening of Gas Substrate and Medium Effects on 2,3-Butanediol Production with C. ljungdahlii and C. autoethanogenum Aided by Improved Autotrophic Cultivation Technique

Gas fermentation by acetogens of the genus Clostridium is an attractive technology since it affords the production of biochemicals and biofuels from industrial waste gases while contributing to mitigate the carbon cycle alterations. The acetogenic model organisms C. ljungdahlii and C. autoethanogenum have already been used in large scale industrial fermentations. Among the natural products, ethanol production has already attained industrial scale. However, some acetogens are also natural producers of 2,3-butanediol (2,3-BDO), a platform chemical of relevant industrial interest. Here, we have developed a lab-scale screening campaign with the aim of enhancing 2,3-BDO production. Our study generated comparable data on growth and 2,3-BDO production of several batch gas fermentations using C. ljungdahlii and C. autoethanogenum grown on different gas substrates of primary applicative interest (CO2 · H2, CO · CO2, syngas) and on different media featuring different compositions as regards trace metals, mineral elements and vitamins. CO · CO2 fermentation was found to be preferable for the production of 2,3-BDO, and a fair comparison of the strains cultivated in comparable conditions revealed that C. ljungdahlii produced 3.43-fold higher titer of 2,3-BDO compared to C. autoethanogenum. Screening of different medium compositions revealed that mineral elements, Zinc and Iron exert a major positive influence on 2,3-BDO titer and productivity. Moreover, the CO2 influence on CO fermentation was explored by characterizing C. ljungdahlii response with respect to different gas ratios in the CO · CO2 gas mixtures. The screening strategies undertaken in this study led to the production of 2.03 ± 0.05 g/L of 2,3-BDO, which is unprecedented in serum bottle experiments.

A Comparison Between The (Design-Build DB) and (Design-Bid-Build DBB) Approaches Used in the Jordanian Construction Sector and Clarify the Risks Associated with Each Approach

The continual need for building in developing economies and countries such as Jordan increases the obstacles and problems faced by Project Management Units in completing projects on time, with quality assurance, and at lower prices. However, as more and more new types of project management approaches such as (Design-Bid DB) are introduced, the binding conditions of contracts are growing tougher in order to reduce risks during project execution. In this paper, the researcher tries to provide a fair comparison between (Design-Bid-Build DBB) and (Design-Build DB) approaches which are already used in the construction project in Jordan and offering a good vision for both owners and contractors equally

VirIoT: A Cloud of Things That Offers IoT Infrastructures as a Service

Many cloud providers offer IoT services that simplify the collection and processing of IoT information. However, the IoT infrastructure composed of sensors and actuators that produces this information remains outside the cloud; therefore, application developers must install, connect and manage the cloud. This requirement can be a market barrier, especially for small/medium software companies that cannot afford the infrastructural costs associated with it and would only prefer to focus on IoT application developments. Motivated by the wish to eliminate this barrier, this paper proposes a Cloud of Things platform, called VirIoT, which fully brings the Infrastructure as a service model typical of cloud computing to the world of Internet of Things. VirIoT provides users with virtual IoT infrastructures (Virtual Silos) composed of virtual things, with which users can interact through dedicated and standardized broker servers in which the technology can be chosen among those offered by the platform, such as oneM2M, NGSI and NGSI-LD. VirIoT allows developers to focus their efforts exclusively on IoT applications without worrying about infrastructure management and allows cloud providers to expand their IoT services portfolio. VirIoT uses external things and cloud/edge computing resources to deliver the IoT virtualization services. Its open-source architecture is microservice-based and runs on top of a distributed Kubernetes platform with nodes in central and edge data centers. The architecture is scalable, efficient and able to support the continuous integration of heterogeneous things and IoT standards, taking care of interoperability issues. Using a VirIoT deployment spanning data centers in Europe and Japan, we conducted a performance evaluation with a two-fold objective: showing the efficiency and scalability of the architecture; and leveraging VirIoT’s ability to integrate different IoT standards in order to make a fair comparison of some open-source IoT Broker implementations, namely Mobius for oneM2M, Orion for NGSIv2, Orion-LD and Scorpio for NGSI-LD.

Semantics Aware Dynamic SLAM Based on 3D MODT

The idea of SLAM (Simultaneous Localization and Mapping) being a solved problem revolves around the static world assumption, even though autonomous systems are gaining environmental perception capabilities by exploiting the advances in computer vision and data-driven approaches. The computational demands and time complexities remain the main impediment in the effective fusion of the paradigms. In this paper, a framework to solve the dynamic SLAM problem is proposed. The dynamic regions of the scene are handled by making use of Visual-LiDAR based MODT (Multiple Object Detection and Tracking). Furthermore, minimal computational demands and real-time performance are ensured. The framework is tested on the KITTI Datasets and evaluated against the publicly available evaluation tools for a fair comparison with state-of-the-art SLAM algorithms. The results suggest that the proposed dynamic SLAM framework can perform in real-time with budgeted computational resources. In addition, the fused MODT provides rich semantic information that can be readily integrated into SLAM.

STUDYING THE EFFECT OF DIFFERENT ADDITIVES ON THE THERMAL CONDUCTIVITY AND MECHANICAL CHARACTERISTICS OF EPOXY-BASED COMPOSITE MATERIALS

The current paper deals with investigating the effect of two different fillers on the thermal and mechanical characteristics of epoxy-based composite. The filler used throughout the study are: charcoal and Pyrex, both of them are different in nature and have not been investigated thoroughly or even compared fairly in terms of their effect on polymer matrix. Further, they can be considered as a cheap filler, charcoal can be obtained from a simple pyrolysis process of plants (charcoal) and Pyrex waste can be collected easily. Both types are added to the selected matrix with volume percent ranged from 10 to 60 with increments of 10. To ensure a fair comparison, the particle size is fixed (is about 1.7 micrometer). The results showed that the epoxy thermal conductivity has enhanced by about two orders of magnitudes over the studied range of filler. In terms of mechanical properties, the charcoal improves the tensile strength about 84% at 60% volume fraction while the Pyrex effect is about 40% at the same filler level. On the contrast, the results of compressive strength do not show an appreciable improvement overall. It decreases by about 12% at 60% volume fraction of charcoal while increases about the same percent with Pyrex at the same filler level.