Electrospun Mn1−xZnxMoO4 nanofibers (x = 2.5%, 5% and 10%), have been prepared by controlling the Zn concentration of a polymeric solution followed by calcination. The effects of Zn doping on ...structural, morphological, chemical characterization, and optical properties of the manganese molybdate nanofibers were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), Energy dispersive analysis of X-rays (EDAX), BJH pore size and volume analysis, Fourier Transform Infrared (FTIR), and Reflectance spectrum in the wave length range of 200–1200nm. XRD studies revealed the diffraction peaks are mainly assigned to the orthorhombic structure of the MoO3 phase. The microstructural investigation of the calcinated nanofibers has been achieved from the size-strain plot (SSP) model using X-ray diffraction profile analysis. The SEM images exhibit Zn-doped manganese molybdate ceramic nanofibers with diameters ranged from 60 ± 10 to 90 ± 10nm after calcination at 400°C. The optical property of the prepared nanofibers was also proposed according to the Kramers-Kronig model.
In the present research, the electrospinning technique was used successfully to fabricate MnMoO
4
nanofibers. The as-electrospun fibers were calcinated at 400 °C for 2 h. The fibrous webs of MnMoO
4
...were characterized by X-ray diffraction (XRD) measurement, scanning electron microscopy (SEM), Fourier-transform-infrared spectroscopy, and Z-scan technique. XRD analysis of MnMoO
4
nanofibers was presented by the Williamson-Hall method. The results indicate that the average diameters of the calcinated fibers obtained from the SEM analysis were about 100 ± 10 and 120 ± 20 nm when feed rates of the solution in electospining were 0.5 and 1 ml/h, respectively. The nonlinear optical behavior of viscous sol of MnMoO
4
has been observed using both open and closed aperture Z-Scan technique at 534 nm. The results related to the nonlinear optical behavior indicate that the MnMoO
4
nanofibers may have potential nonlinear optical properties.
In this position paper, we discuss the critical need for integrating zero trust (ZT) principles into next-generation communication networks (5G/6G). We highlight the challenges and introduce the ...concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components. While network virtualization, software-defined networking (SDN), and service-based architectures (SBA) are key enablers of 5G networks, operating in an untrusted environment has also become a key feature of the networks. Further, seamless connectivity to a high volume of devices has broadened the attack surface on information infrastructure. Network assurance in a dynamic untrusted environment calls for revolutionary architectures beyond existing static security frameworks. To the best of our knowledge, this is the first position paper that presents the architectural concept design of an i-ZTA upon which modern artificial intelligence (AI) algorithms can be developed to provide information security in untrusted networks. We introduce key ZT principles as real-time Monitoring of the security state of network assets, Evaluating the risk of individual access requests, and Deciding on access authorization using a dynamic trust algorithm, called MED components. To ensure ease of integration, the envisioned architecture adopts an SBA-based design, similar to the 3GPP specification of 5G networks, by leveraging the open radio access network (O-RAN) architecture with appropriate real-time engines and network interfaces for collecting necessary machine learning data. Therefore, this work provides novel research directions to design machine learning based components that contribute towards i-ZTA for the future 5G/6G networks.
The excess of the chemical fertilizers not only causes the environmental pollution but also has many deteriorating effects including global warming and alteration of soil microbial diversity. In ...conventional researches, chemical fertilizers and their concentrations are selected based on the knowledge of experts involved in the projects, which this kind of models are usually subjective. Therefore, the present study aimed to introduce the optimal concentrations of three macro elements including nitrogen (0, 100, and 200 g), potassium (0, 100, 200, and 300 g), and magnesium (0, 50, and 100 g) on fruit yield (FY), fruit length (FL), and number of rows per spike (NRPS) of greenhouse banana using analysis of variance (ANOVA) followed by post hoc LSD test and two well-known artificial neural networks (ANNs) including multilayer perceptron (MLP) and generalized regression neural network (GRNN). According to the results of ANOVA, the highest mean value of the FY was obtained with 200 g of N, 300 g of K, and 50 g of Mg. Based on the results of the present study, the both ANNs models had high predictive accuracy (R2 = 0.66-0.99) in the both training and testing data for the FY, FL, and NRPS. However, the GRNN model had better performance than MLP model for modeling and predicting the three characters of greenhouse banana. Therefore, genetic algorithm (GA) was subjected to the GRNN model in order to find the optimal amounts of N, K, and Mg for achieving the high amounts of the FY, FL, and NRPS. The GRNN-GA hybrid model confirmed that high yield of the plant could be achieved by reducing chemical fertilizers including nitrogen, potassium, and magnesium by 65, 44, and 62%, respectively, in compared to traditional method.
Summary
The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its ...scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization.
An energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. The ranking process using BWM‐TOPSIS methodology allows this objective to be more comprehensive and principled. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative.
The present study aims to investigate effects of nanofluid flooding on EOR and also compares its performance with water flooding in field scale using the published experimental data provided from ...core-scale studies. The nanofluid is based on water including silica nanoparticles. The relative permeability curves of water, nanofluid and oil for a light crude oil core sample obtained in an experimental study are used in this numerical investigation. A 2D heterogeneous reservoir model is constructed using the permeability and porosity of the last layer of SPE-10 model. It has been shown that nanofluid flooding can substantially improve the oil recovery in comparison with the water flooding case. Afterward, the operational parameters of the 13 injection and production wells have been optimized in order to meet the maximum cumulative oil production. First, pattern search (PS) algorithm was implemented which has a good convergence speed, but with a high probability of trapping in local optimum points. Particle swarm optimization (PSO) approach has also been employed, which requires a large number of population (to approach the global optimum) with so many simulations. Accordingly, a hybrid PSO–PS algorithm with confined domain is proposed. The hybrid algorithm starts with PSO and depending on the distribution density of the values of each parameter, confines the searching domain and provides a proper initial guess to be used by PS. It is concluded that the hybrid PSO–PS method could obtain the optimal solution with a high convergence speed and reduced possibility of trapping in local optimums.
Faces attract the observer’s attention toward objects and locations of interest for the other, thereby allowing the two agents to establish joint attention. Previous work has delineated a network of ...cortical “patches” in the macaque cortex, processing faces, eventually also extracting information on the other’s gaze direction. Yet, the neural mechanism that links information on gaze direction, guiding the observer’s attention to the relevant object, has remained elusive. Here we present electrophysiological evidence for the existence of a distinct “gaze-following patch” (GFP) with neurons that establish this linkage in a highly flexible manner. The other’s gaze and the object, singled out by the gaze, are linked only if this linkage is pertinent within the prevailing social context. The properties of these neurons establish the GFP as a key switch in controlling social interactions based on the other’s gaze.
In 3D printing technology, unwanted resin properties such as low mechanical, thermal, electrical, and biocompatibility can be enhanced by using proper fillers. In this study, sucrose urethane ...microparticles, acrylic modified are used to improve the properties of the final object without losing other properties such as printing resolution. Modification is confirmed by FTIR spectroscopy and hydrogen bonding index enhancement after modification of the urethane filler. Filler particles are reduced in sizes by modification followed by ball‐milling as is confirmed by scanning electron microscopy. When the filler content is 2.5 wt%, mechanical tensile and flexural modulus increase compared to the neat resin however, increases the brittleness of the photo‐cured samples. Thermal degradation temperature is lifted up for 2.5–5.0 wt% of the filler loading in the resin. Improved dimensional accuracy and detailed printing in higher resolution, due to the physical effect of the filler particles, can also be achieved in real 3D‐printed object as is explored.
Highlights
High‐resolution 3D printing, by random geometry of filler particles in the resin.
Scattering from polyurethane foam microparticles lead to high‐resolution printing.
Acrylate‐modified polyurethane filler particles provide resin compatibility.
Polyurethane filler leads to higher resolution with enhanced mechanical properties.
Overall process of using polyurethane foam particles in a 3D‐printable resin, the modification, grinding and the effect of the modified fillers on mechanical properties and the printing resolution.
PDF
