ALBERT

Description: Future Internet, Vol. 10, Pages 93: Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks Future Internet doi: 10.3390/fi10100093 Authors: Anwar Said Syed Waqas Haider Shah Hasan Farooq Adnan Noor Mian Ali Imran Jon Crowcroft Caching close to users in a radio access network (RAN) has been identified as a promising method to reduce a backhaul traffic load and minimize latency in 5G and beyond. In this paper, we investigate a novel community detection inspired by a proactive caching scheme for device-to-device (D2D) enabled networks. The proposed scheme builds on the idea that content generated/accessed by influential users is more probable to become popular and thus can be exploited for pro-caching. We use a Clustering Coefficient based Genetic Algorithm (CC-GA) for community detection to discover a group of cellular users present in close vicinity. We then use an Eigenvector Centrality measure to identify the influential users with respect to the community structure, and the content associated to it is then used for pro-active caching using D2D communications. The numerical results show that, compared to reactive caching, where historically popular content is cached, depending on cache size, load and number of requests, up to 30% more users can be satisfied using a proposed scheme while achieving significant reduction in backhaul traffic load.

Description: Sensors, Vol. 18, Pages 3037: Fecal Volatile Organic Compounds in Preterm Infants Are Influenced by Enteral Feeding Composition Sensors doi: 10.3390/s18093037 Authors: Sofia el Manouni el Hassani Hendrik J. Niemarkt Hager Said Daniel J. C. Berkhout Anton H. van Kaam Richard A. van Lingen Marc A. Benninga Nanne K. H. de Boer Tim G. J. de Meij Fecal volatile organic compound (VOC) analysis has shown great potential as a noninvasive diagnostic biomarker for a variety of diseases. Before clinical implementation, the factors influencing the outcome of VOC analysis need to be assessed. Recent studies found that the sampling conditions can influence the outcome of VOC analysis. However, the dietary influences remains unknown, especially in (preterm) infants. Therefore, we assessed the effects of feeding composition on fecal VOC patterns of preterm infants (born at <30 weeks gestation). Two subgroups were defined: (1) daily intake >75% breastmilk (BM) feeding and (2) daily intake >75% formula milk (FM) feeding. Fecal samples, which were collected at 7, 14 and 21 days postnatally, were analyzed by an electronic nose device (Cyranose 320®). In total, 30 preterm infants were included (15 FM, 15 BM). No differences in the fecal VOC patterns were observed at the three predefined time-points. Combining the fecal VOC profiles of these time-points resulted in a statistically significant difference between the two subgroups although this discriminative accuracy was only modest (AUC [95% CI]; p-value; sensitivity; and specificity of 0.64 [0.51–0.77]; 0.04; 68%; and 51%, respectively). Our results suggest that the influence of enteral feeding on the outcome of fecal VOC analysis cannot be ignored in this population. Furthermore, in both subgroups, the fecal VOC patterns showed a stable longitudinal course within the first month of life.

Description: Sensors, Vol. 18, Pages 2332: Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles Sensors doi: 10.3390/s18072332 Authors: Vincent Judalet Sébastien Glaser Dominique Gruyer Saïd Mammar The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.

Description: Symmetry, Vol. 10, Pages 213: Medical Diagnosis Based on Single-Valued Neutrosophic Probabilistic Rough Multisets over Two Universes Symmetry doi: 10.3390/sym10060213 Authors: Chao Zhang Deyu Li Said Broumi Arun Kumar Sangaiah In real-world diagnostic procedures, due to the limitation of human cognitive competence, a medical expert may not conveniently use some crisp numbers to express the diagnostic information, and plenty of research has indicated that generalized fuzzy numbers play a significant role in describing complex diagnostic information. To deal with medical diagnosis problems based on generalized fuzzy sets (FSs), the notion of single-valued neutrosophic multisets (SVNMs) is firstly used to express the diagnostic information in this article. Then the model of probabilistic rough sets (PRSs) over two universes is applied to analyze SVNMs, and the concepts of single-valued neutrosophic rough multisets (SVNRMs) over two universes and probabilistic rough single-valued neutrosophic multisets (PRSVNMs) over two universes are introduced. Based on SVNRMs over two universes and PRSVNMs over two universes, single-valued neutrosophic probabilistic rough multisets (SVNPRMs) over two universes are further established. Next, a three-way decisions model by virtue of SVNPRMs over two universes in the context of medical diagnosis is constructed. Finally, a practical case study along with a comparative study are carried out to reveal the accuracy and reliability of the constructed three-way decisions model.

Description: Remote Sensing, Vol. 10, Pages 669: Full-Waveform LiDAR Pixel Analysis for Low-Growing Vegetation Mapping of Coastal Foredunes in Western France Remote Sensing doi: 10.3390/rs10050669 Authors: Patrick Launeau Manuel Giraud Antoine Ba Saïd Moussaoui Marc Robin Françoise Debaine Dimitri Lague Erwan Le Menn The monitoring of coastal sand dunes requires regular high-resolution aerial photography along hundreds of kilometers of coastal strips. Light detection and ranging (LiDAR) is now the most widely used method for detailed topographic and vegetation studies. The aim of this work is to show how the full-waveform shapes returned from single or multiple targets can carry information relating to low-vegetation cover and ground roughness of dunes. This work focuses on marram grass, widely involved in the development of mobile dunes. Low-growing plants often exhibit identical pigmentary composition and can only be distinguished by the height of their foliage, which modifies the shape of the LiDAR waveform around the main returns at the top of the foliage. We show that ray tracing of full LiDAR waveforms on the regular grid of pixels of hyperspectral images, acquired synchronously, can resolve the confusion between low-vegetation gradients and bare sand by analyzing the waveform damping induced by cumulating microdiffusion on foliage height, but also with glint effects on the surface roughness of compact materials. Analysis of successive shorelines of wet to dry sand, sand to pioneer couch grass, and couch grass to consolidating marram grass can thereby be conducted routinely.

Description: Symmetry, Vol. 10, Pages 190: Some Results on the Graph Theory for Complex Neutrosophic Sets Symmetry doi: 10.3390/sym10060190 Authors: Shio Gai Quek Said Broumi Ganeshsree Selvachandran Assia Bakali Mohamed Talea Florentin Smarandache Fuzzy graph theory plays an important role in the study of the symmetry and asymmetry properties of fuzzy graphs. With this in mind, in this paper, we introduce new neutrosophic graphs called complex neutrosophic graphs of type 1 (abbr. CNG1). We then present a matrix representation for it and study some properties of this new concept. The concept of CNG1 is an extension of the generalized fuzzy graphs of type 1 (GFG1) and generalized single-valued neutrosophic graphs of type 1 (GSVNG1). The utility of the CNG1 introduced here are applied to a multi-attribute decision making problem related to Internet server selection.

Description: Remote Sensing, Vol. 10, Pages 974: Combining a Two Source Energy Balance Model Driven by MODIS and MSG-SEVIRI Products with an Aggregation Approach to Estimate Turbulent Fluxes over Sparse and Heterogeneous Vegetation in Sahel Region (Niger) Remote Sensing doi: 10.3390/rs10060974 Authors: Bouchra Ait Hssaine Jamal Ezzahar Lionel Jarlan Olivier Merlin Said Khabba Aurore Brut Salah Er-Raki Jamal Elfarkh Bernard Cappelaere Ghani Chehbouni Estimates of turbulent fluxes (i.e., sensible and latent heat fluxes H and LE) over heterogeneous surfaces is not an easy task. The heterogeneity caused by the contrast in vegetation, hydric and soil conditions can generate a large spatial variability in terms of surface–atmosphere interactions. This study considered the issue of using a thermal-based two-source energy model (TSEB) driven by MODIS (Moderate resolution Imaging Spectroradiometer) and MSG (Meteosat Second Generation) observations in conjunction with an aggregation scheme to derive area-averaged H and LE over a heterogeneous watershed in Niamey, Niger (Wankama catchment). Data collected in the context of the African Monsoon Multidisciplinary Analysis (AMMA) program, including a scintillometry campaign, were used to test the proposed approach. The model predictions of area-averaged turbulent fluxes were compared to data acquired by a Large Aperture Scintillometer (LAS) set up over a transect about 3.2 km-long and spanning three vegetation types (millet, fallow and degraded shrubs). First, H and LE fluxes were estimated at the MSG-SEVIRI grid scale by neglecting explicitly the subpixel heterogeneity. Moreover, the impact of upscaling the model’s inputs was investigated using in-situ input data and three aggregation schemes of increasing complexity based on MODIS products: a simple averaging of inputs at the MODIS resolution scale, another simple averaging scheme that considers scintillometer footprint extent, and the weighted average of inputs based on the footprint weighting function. The H and LE simulated using the footprint weighted method were more accurate than for the two other aggregation rules despite the heterogeneity of the landscape. The statistical values are: correlation coefficient (R) = 0.71, root mean square error (RMSE) = 63 W/m2 and mean bias error (MBE) = −23 W/m2 for H and an R = 0.82, RMSE = 88 W/m2 and MBE = 45 W/m2 for LE. This study opens perspectives for the monitoring of convective and evaporative fluxes over heterogeneous landscape based on medium resolution satellite products.

Description: Symmetry, Vol. 10, Pages 236: An Extended Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with Maximizing Deviation Method Based on Integrated Weight Measure for Single-Valued Neutrosophic Sets Symmetry doi: 10.3390/sym10070236 Authors: Ganeshsree Selvachandran Shio Gai Quek Florentin Smarandache Said Broumi A single-valued neutrosophic set (SVNS) is a special case of a neutrosophic set which is characterized by a truth, indeterminacy, and falsity membership function, each of which lies in the standard interval of [0, 1]. This paper presents a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with maximizing deviation method based on the single-valued neutrosophic set (SVNS) model. An integrated weight measure approach that takes into consideration both the objective and subjective weights of the attributes is used. The maximizing deviation method is used to compute the objective weight of the attributes, and the non-linear weighted comprehensive method is used to determine the combined weights for each attributes. The use of the maximizing deviation method allows our proposed method to handle situations in which information pertaining to the weight coefficients of the attributes are completely unknown or only partially known. The proposed method is then applied to a multi-attribute decision-making (MADM) problem. Lastly, a comprehensive comparative studies is presented, in which the performance of our proposed algorithm is compared and contrasted with other recent approaches involving SVNSs in literature.

Description: Entropy, Vol. 20, Pages 76: Maximum Entropy Expectation-Maximization Algorithm for Fitting Latent-Variable Graphical Models to Multivariate Time Series Entropy doi: 10.3390/e20010076 Authors: Saïd Maanan Bogdan Dumitrescu Ciprian Giurcăneanu This work is focused on latent-variable graphical models for multivariate time series. We show how an algorithm which was originally used for finding zeros in the inverse of the covariance matrix can be generalized such that to identify the sparsity pattern of the inverse of spectral density matrix. When applied to a given time series, the algorithm produces a set of candidate models. Various information theoretic (IT) criteria are employed for deciding the winner. A novel IT criterion, which is tailored to our model selection problem, is introduced. Some options for reducing the computational burden are proposed and tested via numerical examples. We conduct an empirical study in which the algorithm is compared with the state-of-the-art. The results are good, and the major advantage is that the subjective choices made by the user are less important than in the case of other methods.