Application of ion-engineered Persian Gulf seawater in EOR:effects of different ions on interfacial tension,contact angle,zeta potential,and oil recovery

In this study,we initially performed interfacial tension(IFT)tests to investigate the potential of using the Persian Gulf sea-water(PGSW)as smart water with dif...     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

On the use of Buchberger criteria in G2V algorithm for calculating Gröbner bases

It has been experimentally demonstrated by Faugère that his F₅ algorithm is the fastest algorithm for calculating Gröbner bases. Computational efficiency of F₅ is due to not only applying linear algebra but also using the new F₅ criterion for revealing useless zero reductions. At the ISSAC 2010 conference, Gao, Guan, and Volny presented G²V, a new version of the F₅ algorithm, which is simpler than the original version of the algorithm. However, the incremental structure of G²V used in the algorithm for applying the F₅ criterion is a serious obstacle from the point of view of application of Buchberger’s second criterion. In this paper, a modification of the G²V algorithm is presented, which makes it possible to use both Buchberger criteria. To experimentally study computational effect of the proposed modification, we implemented the modified algorithm in Maple. Results of comparison of G²V and its modified version on a number of test examples are presented.     

Critical aspects of sinter-hardening of prealloyed Cr–Mo steel

In recent years, growing demand for greater mechanical properties of PM steel components with competitive fabrication cost has led to significant innovations in different fields of powder metallurgy. Recent research has been focused on reaching higher performance with lower cost. To this end, the possibility of combining the conventional sintering and post-sintering processes for a particular powder composition has been introduced. Sinter-hardening is a result of the research conducted along this line. Elimination of any secondary operation such as quench-hardening by incorporating it in the sintering process (i.e. sinter-hardening) is of great interest, as it will lead to lower processing costs and equal, if not higher mechanical performance. However, to ensure the desired mechanical properties of the final component and robustness of the performance, critical aspects of the sinter-hardening process should be rigorously studied.Hence with specific attention to a Cr–Mo steel powder (FL-5305), this study deals with the influence of density on cooling rate, the effect of different sintering temperatures (e.g. 1120 °C and 1250 °C) on austenite grain size and consequently, hardenability. The microstructure development in sinter-hardened FL-5305 material has been analyzed and predicted by means of the available literature for solid steel and also using the commercial software (JMatPro 5.0) for materials assessment based on thermodynamic and kinetics modeling. Finally, inaccurate carbon control and its adverse impact on excessive formation of cementite have been addressed.     

An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic

Clustering is an effective approach for organizing a network into a connected hierarchy, load balancing, and prolonging the network lifetime. On the other hand, fuzzy logic is capable of wisely blending different parameters. This paper proposes an energy-aware distributed dynamic clustering protocol (ECPF) which applies three techniques: (1) non-probabilistic cluster head (CH) elections, (2) fuzzy logic, and (3) on demand clustering. The remaining energy of the nodes is the primary parameter for electing tentative CHs via a non-probabilistic fashion. A non-probabilistic CH election is implemented by introducing a delay inversely proportional to the residual energy of each node. Therefore, tentative CHs are selected based on their remaining energy. In addition, fuzzy logic is employed to evaluate the fitness (cost) of a node in order to choose a final CH from the set of neighboring tentative CHs. On the other hand, every regular (non CH) node elects to connect to the CH with the least fuzzy cost in its neighborhood. Besides, in ECPF, CH elections are performed sporadically (in contrast to performing it every round). Simulation results demonstrate that our approach performs better than well known protocols (LEACH, HEED, and CHEF) in terms of extending network lifetime and saving energy.     

Task scheduling mechanisms in cloud computing: A systematic review

Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.     

Cardiac motion and deformation recovery from MRI: a review

Magnetic resonance imaging (MRI) is a highly advanced and sophisticated imaging modality for cardiac motion tracking and analysis, capable of providing 3D analysis of global and regional cardiac function with great accuracy and reproducibility. In the past few years, numerous efforts have been devoted to cardiac motion recovery and deformation analysis from MR image sequences. Many approaches have been proposed for tracking cardiac motion and for computing deformation parameters and mechanical properties of the heart from a variety of cardiac MR imaging techniques. In this paper, an updated and critical review of cardiac motion tracking methods including major references and those proposed in the past ten years is provided. The MR imaging and analysis techniques surveyed are based on cine MRI, tagged MRI, phase contrast MRI, DENSE, and SENC. This paper can serve as a tutorial for new researchers entering the field.     

Analytical formulation of transient oscillation amplitude in rotary traveling wave oscillators

This paper presents an accurate analysis for deriving transient oscillation amplitude of Rotary Traveling Wave Oscillators (RTWOs) as a transmission-line based and high frequency circuit. The procedure of the paper is based on considering the nonlinear behavior of negative transconductors that are used for loss compensation of the transmission line. Finally, a closed-form expression for the time-domain amplitude of the RTWOs is obtained. The proposed useful and accurate expression could be used for designing the RTWOs for high performance-high speed systems. Also, it enables us to analyze and synthesize the oscillators with the desired transient behavior. This aspect of the RTWOs is not studied in previous works. The proposed theoretical results are then compared with accurate simulations. Simulations have been done in 0.18 μm CMOS technology with 1.8 V supply voltage. Results show less than 10% difference in steady state oscillation amplitude of theoretical expression and simulations. Considering the nonlinear equation of the RTWOs amplitude, complicated type of solving, simplifications and its numerical solution, the proposed derived expression has a good agreement with simulations.     

Superior Thermoelectric Performance of Textured Ca3Co4−xO9+δ Ceramic Nanoribbons

Calcium cobaltite Ca₃Co_4−xO_9+δ (CCO) is a promising p-type thermoelectric (TE) material for high-temperature applications in air. The grains of the material exhibit strong anisotropic properties, making texturing and nanostructuring mostly favored to improve thermoelectric performance. On the one hand multitude of interfaces are needed within the bulk material to create reflecting surfaces that can lower the thermal conductivity. On the other hand, low residual porosity is needed to improve the contact between grains and raise the electrical conductivity. In this study, CCO fibers with 100% flat cross sections in a stacked, compact form are electrospun. Then the grains within the nanoribbons in the plane of the fibers are grown. Finally, the nanoribbons are electrospun into a textured ceramic that features simultaneously a high electrical conductivity of 177 S cm⁻¹ and an immensely enhanced Seebeck coefficient of 200 µV K⁻¹ at 1073 K are assembled. The power factor of 4.68 µW cm⁻¹ K⁻² at 1073 K in air surpasses all previous CCO TE performances of nanofiber ceramics by a factor of two. Given the relatively high power factor combined with low thermal conductivity, a relatively large figure-of-merit of 0.3 at 873 K in the air for the textured nanoribbon ceramic is obtained.