Experimental study of turbulent single-phase flow and heat transfer inside a micro-finned tube

Single-phase heat transfer and pressure drop characteristics of a commercially available internally micro-finned tube with a nominal outside diameter of 7.94 mm were studied. Experiments were conducted in a double pipe heat exchanger with water as the cooling as well as the heating fluid for six sets of runs. The pressure drop data were collected under isothermal conditions. Data were taken for turbulent flow with 3300 ≤ Re ≤ 22,500 and 2.9 ≤ Pr ≤ 4.7. The heat transfer data were correlated by a Dittus–Boelter type correlation, while the pressure drop data were correlated by a Blasius type correlation. The correlation predicted values for both the Nusselt number and the friction factors were compared with other studies. It was found that the Nusselt numbers obtained from the present correlation fall in the middle region between the Copetti et al. and the Gnielinski smooth tube correlation predicted Nusselt number values. For pressure drop results, the present correlation predicted friction factors values were nearly double that of the Blasius smooth tube correlation predicted friction factors. It was also found that the rough tube Gnielinski and Haaland correlations can be used as a good approximation to predict the finned tube Nusselt number and ffriction factor, respectively, in the tested Reynolds number range.     

A comprehensive system for image scene classification

In recent years, image scene classification based on low/high-level features has been considered as one of the most important and challenging problems faced in image processing research. The high-level features based on semantic concepts present a more accurate and closer model to the human perception of the image scene content. This paper presents a new multi-stage approach for image scene classification based on high-level semantic features extracted from image content. In the first stage, the object boundaries and their labels that represent the content are extracted. For this purpose, a combined method of a fully convolutional deep network and a combined network of a two-class SVM-fuzzy and SVR are used. Topic modeling is used to represent the latent relationships between the objects. Hence in the second stage, a new combination of methods consisting of the bag of visual words, and supervised document neural autoregressive distribution estimator is used to extract the latent topics (topic modeling) in the image. Finally, classification based on Bayesian method is performed according to the extracted features of the deep network, objects labels and the latent topics in the image. The proposed method has been evaluated on three datasets: Scene15, UIUC Sports, and MIT-67 Indoor. The experimental results show that the proposed approach achieves average performance improvement of 12%, 11% and 14% in the accuracy of object detection, and 0.5%, 0.6% and 1.8% in the mean average precision criteria of the image scene classification, compared to the previous state-of-the-art methods on these three datasets.

     

Differential Evolution Based Reliable Cooperative Spectrum Sensing in the Presence of Malicious Users

Wireless Personal Communications - Investors who intend to execute large orders have to always a trade-off between price impact and opportunity cost in Big Data. In this study, reinforcement...     

Analyzing COVID-19 Impact on the Researchers Productivity through Their Perceptions

Context: Since the end of 2019, the COVID-19 pandemic had a worst impact on world’s economy, healthcare, and education. There are several aspects where the impact of COVID-19 could be visualized. Among these, one aspect is the productivity of researcher, which plays a significant role in the success of an organization. Problem: There are several factors that could be aligned with the researcher’s productivity of each domain and whose analysis through researcher’s feedback could be beneficial for decision makers in terms of their decision making and implementation of mitigation plans for the success of an organization. Method: We perform an empirical study to investigate the substantial impact of COVID-19 on the productivity of researchers by analyzing the relevant factors through their perceptions. Our study aims to find out the impact of COVID-19 on the researcher’s productivity that are working in different fields. In this study, we conduct a questionnaire-based analysis, which included feedback of 152 researchers of certain domains. These researchers are currently involved in different research activities. Subsequently, we perform a statistical analysis to analyze the collected responses and report the findings. Findings: The results indicate the substantial impact of COVID-19 pandemics on the researcher’s productivity in terms of mental disturbance, lack of regular meetings, and field visits for the collection of primary data. Conclusion: Finally, it is concluded that researcher’s daily or weekly meetings with their supervisors and colleagues are necessary to keep them more productive in task completion. These findings would help the decision makers of an organization in the settlement of their plan for the success of an organization.     

Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks

Software-defined networking (SDN) plays a critical role in transforming networking from traditional to intelligent networking. The increasing demand for services from cloud users has increased the load on the network. An efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service (QoS). The multiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best algorithms. Moreover, the requests need to be transferred to reliable network elements. To address SDN’s current and future challenges, there is a need to know how artificial intelligence (AI) optimization techniques can efficiently balance the load. This study aims to explore two artificial intelligence optimization techniques, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), used for load balancing in SDN. Further, we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load balancing. Finally, we propose a conceptual framework for SDN futurology by evaluating node and link reliability, which can balance the load efficiently and improve QoS in SDN.     

Multi-objective path planning in discrete space

Finding a path for a robot which is near to natural looking paths is a challenging problem in motion planning. This paper suggests two single and multi-objective optimization models focusing on length and clearance of the path in discrete space. Considering the complexity of the models and potency of evolutionary algorithms we apply a genetic algorithm with NSGA-II framework for solving the problems addressed in the models. The proposed algorithm uses an innovative family of path refiner operators, in addition to the standard genetic operators. The new operators intensify explorative power of the algorithm in finding Pareto-optimal fronts in the complicated path planning problems such as narrow passages and clutter spaces. Finally, we compare efficiency of the refiner operators and the algorithm with PSO and A* algorithms in several path planning problems.     

Mathematical Modelling of Non‐Isothermal Venturi Scrubbers

A mathematical model consisting of differential equations for energy, momentum and material exchange is developed for a non‐isothermal Venturi‐type scrubber. By this model, the effects of heat and mass transfer on droplets concentration distribution and removal efficiency of particulate in a non‐isothermal Venturi scrubber can be investigated. In order to approach a realistic model, the liquid film flow on the walls and droplet size distribution are considered. The model is validated by comparing the results of mathematical model by plant and experimental data reported in the literature. The Results section of this work reveals that the inlet humidity and temperature of the gas can affect the removal efficiency of the scrubber.     

Security modeling for service-oriented systems using security pattern refinement approach

Security is one of the critical aspects of current systems, which are based on loosely coupled and technology-agnostic service-oriented architectures (SOA). Though SOA is the driving force for enterprises to open their ends for global business collaborations, nevertheless it evolves many challenges for modeling and enforcing security. One of the main problems for designing secure systems is the lack of consistent frameworks and methodologies for modeling security concerns. Traditional approaches consider security at the end of system development, which evolves inflexible and un-configurable systems, which are too difficult to maintain and manage. The other major problem with current approaches is that they assume pre-defined and hard-coded security patterns and mechanisms for secure system design. Whereas, the evolving SOA systems require configurable security to realize different security patterns and security policies in a variety of business scenarios. To solve these problems, it is necessary to model security concerns from the beginning of system modeling in a platform-independent way. This paper proposes a pattern refinement approach for security modeling to achieve configurable and declarative security, based on the principles of abstraction, refinement, separation-of-concerns and maintainability to achieve flexible configurations of SOA security. In the proposed approach, a Domain Expert defines abstract policies using common security vocabulary and a Security Expert models security with patterns and refines them for a target architecture in successive systematic refinements. Furthermore, it facilitates the transformation of abstract security models into executable security policies for the target platforms.     

Synthesis of polyaniline/mesoporous carbon nanocomposites and their application for CO<Subscript>2</Subscript> sorption

Ordered mesoporous carbons (OMC), were synthesized by nanocasting using ordered mesoporous silica as hard templates. Ordered mesoporous carbons CMK-1 and CMK-3 were prepared from MCM-48 and SBA-15 materials with pore diameters of 3.4 nm and 4.2 nm, respectively. Mesoporous carbons can be effectively modified for CO₂ adsorption with amine functional groups due to their high affinity for CO₂. Polyaniline (PANI)/mesoporous carbon nanocomposites were synthesized from in-situ polymerization by dissolving OMC in aniline monomer. The polymerization of aniline molecules inside the mesochannels of mesoporous carbons has been performed by ammonium persulfate. The nanocomposition, morphology, and structure of the nanocomposite were investigated by nitrogen adsorption-desorption isotherms, Fourier Transform Infrared (FT–IR), X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM) and thermo gravimetric analysis (TGA). CO₂ uptake capacity of the mesoporous carbon materials was obtained by a gravimetric adsorption apparatus for the pressure range from 1 to 5 bar and in the temperature range of 298 to 348 K. CMK-3/PANI exhibited higher CO₂ capture capacity than CMK-1/PANI owing to its larger pore size that accommodates more amine groups inside the pore structure, and the mesoporosity also can facilitate dispersion of PANI molecules inside the pore channels. Moreover, the mechanism of CO₂ adsorption involving amine groups is investigated. The results show that at elevated temperature, PANI/mesoporous carbon nanocomposites have a negligible CO₂ adsorption capacity due to weak chemical interactions with the carbon nanocomposite surface.