Exploranative Code Quality Documents

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.Comment: IEEE VIS VAST 201

Multiobjective Optimization in 5G Hybrid Networks

The increasing adoption of the Internet of Things has led to the need for systems with higher spectral and energy efficiency (EE) in order to enable communication. Larger data rate demands had led researchers to look at millimeter wave (mmWave) bands to boost network rates. This paper investigates the downlink performance of a three-tier heterogeneous network that consists of sub-6 GHz macrocells overlaid with small cells operating on both the mmWave and sub-6 GHz bands. A model is developed using tools from stochastic geometry to analyze the coverage, rate, area spectral efficiency, and EE of such a network. Various deployment strategies and their impacts on the considered metrics are studied. Simulation results are used to verify the validity of the proposed model

Contract-Based Resource Allocation for Low-Latency Vehicular Fog Computing

Low-Iatency communication is crucial to satisfy the strict requirements on latency and reliability in 5G communications. In this paper, we firstly consider a contract-based vehicular fog computing resource allocation framework to minimize the intolerable delay caused by the numerous tasks on the base station during peak time. In the vehicular fog computing framework, the users tend to select nearby vehicles to process their heavy tasks to minimize delay, which relies on the participation of vehicles. Thus, it is critical to design an effective incentive mechanism to encourage vehicles to participate in resource allocation. Next, the simulation results demonstrate that the contract-based resource allocation can achieve better performance

Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism

The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively

Performance Enhancement in P300 ERP Single Trial by Machine Learning Adaptive Denoising Mechanism

The P300-based lie detection scheme is yet another and advantageous tactic for unadventurous Polygraphy. In the proposed scheme, the raw electroencephalogram (EEG) signals are assimilated from 15 subjects during deception detection. After the assimilation, EEG signals are separated using an independent component analysis (ICA). The proposed adaptive denoising approach, extracts three kinds of features from denoised wave to reproduce P300 waveform and identify the P300 components at the Pz electrode. Finally, in order to enhance the performance, four classifiers are used, i.e., support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and back propagation neural network (BPNN), achieving the accuracy of 74.5%, 79.4%, 97.9% and 89%, respectively

Biogenic fabrication of iron oxide nanoparticles from Leptolyngbya sp. L-2 and multiple in vitro pharmacogenetic properties

Metallic nanoparticles have received a significant amount of reflection over a period of time, attributed to their electronic, specific surface area, and surface atom properties. The biogenic synthesis of iron oxide nanoparticles (FeONPs) is demonstrated in this study. The green synthesis of metallic nanoparticles (NPs) is acquiring considerable attention due to its environmental and economic superiorities over other methods. Leptolyngbya sp. L-2 extract was employed as a reducing agent, and iron chloride hexahydrate (FeCl3·6H2O) was used as a substrate for the biogenic synthesis of FeONPs. Different spectral methods were used for the characterization of the biosynthesized FeONPs, ultraviolet-visible (UV-Vis) spectroscopy gave a surface plasmon resonance (SPR) peak of FeONPs at 300 nm; Fourier transform infrared (FTIR) spectral analysis was conducted to identify the functional groups responsible for both the stability and synthesis of FeONPs. The morphology of the FeONPs was investigated using scanning electron microscopy (SEM), which shows a nearly spherical shape, and an X-ray diffraction (XRD) study demonstrated their crystalline nature with a calculated crystallinity size of 23 nm. The zeta potential (ZP) and dynamic light scattering (DLS) measurements of FeONPs revealed values of −8.50 mV, suggesting appropriate physical stability. Comprehensive in-vitro pharmacogenetic properties revealed that FeONPs have significant therapeutic potential. FeONPs have been reported to have potential antibacterial and antifungal properties. Dose-dependent cytotoxic activity was shown against Leishmania tropica promastigotes (IC50: 10.73 µg/mL) and amastigotes (IC50: 16.98 µg/mL) using various concentrations of FeONPs. The cytotoxic potential was also investigated using brine shrimps, and their IC50 value was determined to be 34.19 µg/mL. FeONPs showed significant antioxidant results (DPPH: 54.7%, TRP: 49.2%, TAC: 44.5%), protein kinase (IC50: 96.23 µg/mL), and alpha amylase (IC50: 3745 µg/mL). The biosafety of FeONPs was validated by biocompatibility tests using macrophages (IC50: 918.1 µg/mL) and red blood cells (IC50: 2921 µg/mL). In conclusion, biogenic FeONPs have shown potential biomedical properties and should be the focus of more studies to increase their nano-pharmacological significance for biological applications

Prolonged Repellent Activity of Plant Essential Oils against Dengue Vector, Aedes aegypti

Repellents are effective personal protective means against outdoor biting mosquitoes. Repellent formulations composed of EOs are finding increased popularity among consumers. In this study, after an initial screening of 11 essential oils (EOs) at the concentration of 33 μg/cm2, five of the most repellent EOs, Perovskia atriplicifolia, Citrus reticulata (fruit peels), C. reticulata (leaves), Mentha longifolia, and Dysphania ambrosioides were further investigated for repellent activity against Aedes aegypti mosquitoes in time span bioassays. When tested at the concentrations of 33 μg/cm2, 165 μg/cm2 and 330 μg/cm2, the EO of P. atriplicifolia showed the longest repellent effect up to 75, 90 and 135 min, respectively, which was followed by C. reticulata (peels) for 60, 90 and 120 min, M. longifolia for 45, 60 and 90 min, and C. reticulata (leaves) for 30, 45 and 75 min. Notably, the EO of P. atriplicifolia tested at the dose of 330 μg/cm2 showed complete protection for 60 min which was similar to the commercial mosquito repellent DEET. Gas chromatographic-mass spectrometric analyses of the EOs revealed camphor (19.7%), limonene (92.7%), sabinene (24.9%), carvone (82.6%), and trans-ascaridole (38.8%) as the major constituents of P. atriplicifolia, C. reticulata (peels), C. reticulata (leaves), M. longifolia, and D. ambrosioides, respectively. The results of the present study could help develop plant-based commercial repellents to protect humans from dengue mosquitoes