Correction to: Impact of visual saliency on multi-distorted blind image quality assessment using deep neural architecture

Multimedia Tools and Applications -     

Adsorptive potential of Acacia nilotica based adsorbent for chromium(Ⅵ) from an aqueous phase

The objective of this research was to enhance adsorption capacity of Acacia nilotica(keekar) sawdust for the abatement of chromium bearing wastewater and to investigate the effect of process parameters on adsorption capacity. The sawdust was activated by acid wash and functionalized subsequently with formaldehyde.Functionalization of activated sawdust raised its chromium removal efficiency of almost 10% as compared to its adsorption removal efficiency of HCl treated sawdust in a batch adsorption study. Adsorption kinetic data provided better fitting with pseudo second order model. Maximum adsorption capacity calculated through the best fitting Langmuir model was 6.34 mg·g~(-1) and 8.2 mg·g~(-1) for HCl treated and formaldehyde functionalized sawdust adsorbents, respectively. The adsorption of Cr(Ⅵ) was endothermic when studied by varying temperature from 20 °C to 50 °C for both activated and functionalized adsorbents.     

Two-group,three-dimensional model based study of reactivity induced transients in upgraded LEU material test reactors

A two-group, three-dimensional diffusion theory based methodology coupled with one-dimensional single-phase heat transfer calculations has been developed for the transient analysis of typical material test reactors (MTRs). This methodology has been implemented in a FORTRAN based computer program MTRAP3. It uses the CITATION computer program as a subroutine for static neutronic calculations while the group constant generation is performed by employing the WIMS-D/4 code. The MTRAP3 program uses Cranck–Nicolson (CN) based numerical scheme for solution of time dependant neutron diffusion calculations while time-implicit strategy is employed for detailed heat-transfer calculations. The CN-scheme has been found to remain stable for much larger time steps (Δt ∼ 10⁻⁵ s) as compared with the time-explicit scheme which is limited to very small time steps only (Δt ∼ 10⁻¹⁰ s). For step as well as for ramp reactivity induced transients, the predicted values of core integrated reactor power and core average temperatures has been found to agree well with the corresponding values found by using the PARET computer program. The assembly-wise power profile as found by the MTRAP3 program has been found consistent with the corresponding experimental measurements.     

Sustainable charcoal production from biomass

Charcoal products can be produced from biomass sources such as charcoal from wood, woody agricultural products, the biogenic fraction of municipal wastes, nut shells, etc. The liquid and gaseous fractions obtained from biomass are a valuable fuel source; however, the solid fraction (charcoal) has the recovery potential of carbon black or as carbon adsorbent after applying an activation step. Charcoal is produced by slow heating wood (carbonization) in airtight ovens or retorts, in chambers with various gases, or in kilns supplied with limited and controlled amounts of air. Charcoal has the potential to improve soil properties, crop productivity, and carbon sequestration in soil. The most interesting temperature range for the production of the pyrolysis products is between 625 and 775 K. The charcoal yield decreased gradually from 43.5 to 31.0% for the walnut shell and from 38.3 to 25.4% for the spruce wood with an increase of temperature from 550 to 1150 K. The charcoal yield decreases as the temperature increases. The ignition temperature of charcoal increases as the carbonization temperature increases. The charcoal briquettes that are sold on the commercial market are typically made from a binder and a filler.     

Comparative study of actinide and fission product inventory of HEU and potential LEU fuels for MNSRs

A comparative study of fuel burnup and buildup of actinides and fission products for potential LEU fuels (UO₂ and U–9Mo) with existing HEU fuel (UAl₄–Al, 90% enriched) for a typical Miniature Neutron Source Reactor (MNSR) has been carried-out using the WIMSD4 computer program. For the complete burnup, the UAl₄–Al, UO₂ and U–9Mo based systems show a total consumption of 6.89, 6.83 and 6.88 g of ²³⁵U, respectively. Relative to 0.042 g ²³⁹Pu produced in case of UAl₄–Al HEU core, UO₂ and U–9Mo based cores have been found to yield 0.793 and 0.799 g, respectively, indicating much larger values of conversion ratios and correspondingly high values of fuel utilization factor. The end-of-cycle activity of the HEU core has been found 2284 Ci which agrees well with value found by Khattab where as for UO₂ based and U–9Mo based LEU cores show 1.8 and 4.8% increase with values 2326 and 2394 Ci, respectively.     

Evaluation of fortran vector compilers and preprocessors

Many scientific codes can achieve significant performance improvement when executed on a computer equipped with a vector processor. Vector constructs in source code should be recognized by a vectorizing compiler or preprocessor. This paper discusses, from a general point of view, how a vectorizing compiler/preprocessor can be evaluated. The areas discussed include data dependence analysis, IF loop analysis, nested loops, loop interchanging, loop collapsing, indirect addressing, use of temporary storage, and order of arithmetic. The ideas presented are based on vectorization of over a million lines of production codes and an extensive test suite developed to evaluate preprocessors under varying degrees of code complexity. Areas for future research are also discussed.     

Machine Learning with Dimensionality Reduction for DDoS Attack Detection

With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS attacks. However, the challenge in using these techniques is the limitations on capacity for the volume of data and the required processing time. In this research work, we propose the framework of reducing the dimensions of the data by selecting the most important features which contribute to the predictive accuracy. We show that the ‘lite’ model trained on reduced dataset not only saves the computational power, but also improves the predictive performance. We show that dimensionality reduction can improve both effectiveness (recall) and efficiency (precision) of the model as compared to the model trained on ‘full’ dataset.     

An adaptive doctor-recommender system

ABSTRACT

Recommender systems use machine-learning techniques to make predictions about resources. The medical field is one where much research is currently being conducted on recommender system utility. In the last few years, the amount of information available online that relates to healthcare has increased tremendously. Patients nowadays are more aware and look for answers to healthcare problems online. This has resulted in a dire need of an effective reliable online system to recommend the physician that is best suited to a particular patient in a limited time. In this article, a hybrid doctor-recommender system is proposed, by combining different recommendation approaches: content base, collaborative and demographic filtering to effectively tackle the issue of doctor recommendation. The proposed system addresses the issue of personalization through analysing patient's interest towards selecting a doctor. It uses a novel adoptive algorithm to construct a doctor's ranking function. Moreover, this ranking function is used to translate patients’ criteria for selecting a doctor into a numerical base rating, which will eventually be used in the recommendation of doctors. The system has been evaluated thoroughly, and result show that recommendations are reasonable and can fulfil patient's demand for reliable doctor's selection effectively.     

Toward scalable cloud data center simulation using high-level architecture

Existing simulators are designed to simulate a few thousand nodes due to the tight integration of modules. Thus, with limited simulator scalability, researchers/developers are unable to simulate protocols and algorithms in detail, although cloud simulators provide geographically distributed data centers environment but lack the support for execution on distributed systems. In this paper, we propose a distributed simulation framework referred to as CloudSimScale. The framework is designed on top of highly adapted CloudSim with communication among different modules managed using IEEE Std 1516 (high-level architecture). The underlying modules can now run on the same or different physical systems and still manage to discover and communicate with one another. Thus, the proposed framework provides scalability across distributed systems and interoperability across modules and simulators.