Oil Families and Their Potential Sources in the Southern Gulf of Suez, Egypt

Four oil families are identified in the southern Gulf of Suez, through high-resolution geochemical studies including gas chromatography, gas chromatography-mass spectrometry, and carbon isotope analyses. Biological features characterize oils in family 1a, suggesting tertiary carbonate source rocks for these oils, rich in type II organic matter and deposited under anoxic depositional environment. Family 1b oil shows minor variations in the source of organic matter and the depositional environment, as it was derived from carbonate source rock with more algal and bacterial contribution and minor input of terrestrial organic sources, deposited under less saline condition compared to family 1a oil. Family 2 oil, although genetically related to family 1a oil, has some distinctive features, such as diasterane to sterane and pristane to phytane ratios, which suggest clay-rich source rocks and a more oxic depositional environment. Also, the lack of oleanane indicates pre-tertiary source rocks for this oil. In contrast, family 3 oil is of mixed sources (marine and non-marine), generated from low sulfur and clay-rich source rock of tertiary and/or younger age. Family 4 oil seems to be mixed from family 1b and family 3 oils, sourced mainly from carbonate source rocks rich in clay minerals with algal and bacterial contributions. Family 4 oil is highly mature, family 1b oil lies within equilibrium values (peak oil generation stage), while the other families are more or less near equilibrium.     

Novel hybrid DNN approaches for speaker verification in emotional and stressful talking environments

Neural Computing and Applications - In this work, we conducted an empirical comparative study of the performance of text-independent speaker verification in emotional and stressful environments....     

Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.     

Noble Metal Ion-Directed Assembly of 2D Materials for Heterostructured Catalysts and Metallic Micro-Texturing

Assembling 2D-material (2DM) nanosheets into micro- and macro-architectures with augmented functionalities requires effective strategies to overcome nanosheet restacking. Conventional assembly approaches involve external binders and/or functionalization, which inevitably sacrifice 2DM's nanoscale properties. Noble metal ions (NMI) are promising ionic crosslinkers, which can simultaneously assemble 2DM nanosheets and induce synergistic properties. Herein, a collection of NMI–2DM complexes are screened and categorized into two sub-groups. Based on the zeta potentials, two assembly approaches are developed to obtain 1) NMI-crosslinked 2DM hydrogels/aerogels for heterostructured catalysts and 2) NMI–2DM inks for templated synthesis. First, tetraammineplatinum(II) nitrate (TPtN) serves as an efficient ionic crosslinker to agglomerate various 2DM dispersions. By utilizing micro-textured assembly platforms, various TPtN–2DM hydrogels are fabricated in a scalable fashion. Afterward, these hydrogels are lyophilized and thermally reduced to synthesize Pt-decorated 2DM aerogels (Pt@2DM). The Pt@2DM heterostructures demonstrate high, substrate-dependent catalytic activities and promote different reaction pathways in the hydrogenation of 3-nitrostyrene. Second, PtCl₄ can be incorporated into 2DM dispersions at high NMI molarities to prepare a series of PtCl₄–2DM inks with high colloidal stability. By adopting the PtCl₄–graphene oxide ink, various Pt micro-structures with replicated topographies are synthesized with accurate control of grain sizes and porosities.     

Modulation Based Combination of High Level Features Generated from SCCF & Contourlet Transforms for CBIR Applications

The need for suitable and cost-effective technologies rise with the growth of the internet of things (IoT) applications. These aim at handling voluminous data transmission in addition to minimum energy and latency cost constraints. LoRa networks are recommended for applications in confined spaces, long ranges, and less battery consumption requirements. However, the end devices in these networks communicate to all gateways in their ranges, thereby expediting energy unproductively in redundant transmissions. In our article, we explore the possibilities of whether LoRa networks could employ the advantages of clustering and propose two algorithms, path-based and data-centric, for such networks. We suggest that LoRaWAN technology with clustering can be apt for long-range, low power consumption IoT applications in the future. We study the impact of network density, node range, and cluster range on the energy consumption in data transmissions. The algorithms are compared with the inherent star-based communication of LoRa networks based on energy consumed, and our results show that, for dense deployments, clustering becomes advantageous.

     

An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

     

Dynamic estimation of the modeling error statistics in Diffuse Optical Tomography

Diffuse Optical Tomography (DOT) is a non-invasive imaging technique that suffers from a typical large-scale and ill-posed inverse problem with low spatial resolution. In DOT, the inverse problem is computationally intensive and decreasing the computation complexity and making it well-posed is the one of the most challenging research areas. More precisely, one of the well-known complexity reduction techniques is defined as applying modelling error originated from discretization of forward problem. Applying the discretization error in Bayesian inference has already been discussed; the method in which the likelihood is modified by an off-line prior density estimation. This paper implements a new method to enhance the modelling error approach using an iterative scheme to update statistical parameters of modelling discrepancy in DOT. The algorithm is very similar to Ensemble Kalman Filter. Moreover, the reconstruction process in the applied method is conducted by a small sample size rather than off-line method. Hence, the computation complexity is decreased and the algorithm converges in few iterations. The efficiency of the proposed method is illustrated by simulations.     

Ranking alternatives in a preferential voting system using fuzzy concepts and data envelopment analysis

In this paper, a new method for aggregating the opinions of experts in a preferential voting system is proposed. The method, which uses fuzzy concept in handling crisp data, is computationally efficient and is able to completely rank the alternatives. Through this method, the number of votes for certain rank position that each alternative receives are first grouped together to form fuzzy numbers. The nearest point to a fuzzy number concept is then used to introduce an artificial ideal alternative. Data envelopment analysis is next used to find the efficiency scores of the alternatives in a pair-wise comparison with the artificial ideal alternative. Alternatives are rank based on these efficiency scores. If the alternatives are not completely ranked, a weight restriction method also based on fuzzy concept is used on the un-discriminated alternatives until they are completely ranked. Two examples are given for illustration of the method.     

Probabilistic design of ground improvement by vertical drains for soil of spatially variable coefficient of consolidation

The design of soil consolidation via prefabricated vertical drains (PVDs) has been traditionally carried out deterministically and thus can be misleading due to the ignorance of the uncertainty associated with the inherent variability of soil properties. To treat such uncertainty in the course of design of soil improvement by PVDs, more rational probabilistic methods are necessary. In this paper, a simplified probabilistic method is proposed in which the inherent variability of the coefficient of consolidation, which is the most significant uncertain soil parameter that affects the consolidation process, is considered. An easy-to-use design procedure and charts are provided for routine use by practitioners.