Influence of Growth Conditions on Magnetite Nanoparticles Electro-Crystallized in the Presence of Organic Molecules

Magnetite nanoparticles were synthesized by electrocrystallization in the presence of thiourea or sodium butanoate as an organic stabilizer. The synthesis was performed in a thermostatic electrochemical cell containing two iron electrodes with an aqueous solution of sodium sulfate as electrolyte. The effects of organic concentration, applied potential and growth temperature on particle size, morphology, structure and magnetic properties were investigated. The magnetite nanoparticles were characterized by X-ray diffraction, electron microscopy, magnetometry and Mössbauer spectrometry. When the synthesis is performed in the presence of sodium butanoate at 60 °C, a paramagnetic ferric salt is obtained as a second phase; it is possible to avoid formation of this phase, increase the specific magnetization and improve the structure of the oxide particles by tuning the growth conditions. Room-temperature magnetization values range from 45 to 90 Am²kg⁻¹, depending on the particle size, type of surfactant and synthesis conditions. Mössbauer spectra, which were recorded at 290 K for all the samples, are typical of nonstoichiometric Fe_3−δO₄, with a small excess of Fe³⁺, 0.05 ≤ δ ≤ 0.15.     

Exploring network-level performances of wireless nanonetworks utilizing gains of different types of nano-antennas with different materials

Wireless nanonetworks are not a simple extension of traditional communication networks at the nano-scale. Owing to being a completely new communication paradigm, existing research in this field is still at an embryonic stage. Furthermore, most of the existing studies focus on performance enhancement of nanonetworks via designing new channel models and routing protocols. However, the impacts of different types of nano-antennas on the network-level performances of the wireless nanonetworks remain still unexplored in the literature. Therefore, in this paper, we explore the impacts of different well-known types of antennas such as patch, dipole, and loop nano-antennas on the network-level performances of wireless nanonetworks. We also investigate the performances of nanonetworks for different types of traditional materials (e.g., copper) and for nanomaterials (e.g., carbon nanotubes and graphene). We perform rigorous simulation using our customized ns-2 simulation to evaluate the network-level performances of nanonetworks exploiting different types of nano-antennas using different materials. Our evaluation reveals a number of novel findings pertinent to finding an efficient nano-antenna from its several alternatives for enhancing network-level performances of nanonetworks. Our evaluation demonstrates that a dipole nano-antenna using copper material exhibits around 51% better throughput and about 33% better end-to-end delay compared to other alternatives for large-size nanonetworks. Furthermore, our results are expected to exhibit high impacts on the future design of wireless nanonetworks through facilitating the process of finding the suitable type of nano-antenna and suitable material for the nano-antennas.

     

Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT‐framework

The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state‐of‐the‐art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256–264, 2015     

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F‐score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

A New Traffic Sign Recognition Technique Taking Shuffled Frog-Leaping Algorithm into Account

Everyday humans use cars to move faster, and the world is a chaotic place, and a little distraction or a mistake could be the reason for an accident and bring people great pain. An assistance system that can distinguish and detect signs on the roads and brings the driver's attention to road signs and make them aware of their meaning could be beneficial. The most important part of the Traffic Sign Recognition System is the algorithm. In this paper, a new way toward Traffic Sign Recognition algorithm taking the advantages of Color Segmentation, support vector machines, and histograms of oriented gradients on the GTSRB dataset is proposed. The unsupervised shuffled frog-leaping algorithm is employed for segmenting the images. The results show remarkable improvements by using meta-heuristic algorithms.

     

Toward an understanding of the impact of production pressure on safety performance in construction operations

It is not unusual to observe that actual schedule and quality performances are different from planned performances (e.g., schedule delay and rework) during a construction project. Such differences often result in production pressure (e.g., being pressed to work faster). Previous studies demonstrated that such production pressure negatively affects safety performance. However, the process by which production pressure influences safety performance, and to what extent, has not been fully investigated. As a result, the impact of production pressure has not been incorporated much into safety management in practice. In an effort to address this issue, this paper examines how production pressure relates to safety performance over time by identifying their feedback processes. A conceptual causal loop diagram is created to identify the relationship between schedule and quality performances (e.g., schedule delays and rework) and the components related to a safety program (e.g., workers’ perceptions of safety, safety training, safety supervision, and crew size). A case study is then experimentally undertaken to investigate this relationship with accident occurrence with the use of data collected from a construction site; the case study is used to build a System Dynamics (SD) model. The SD model, then, is validated through inequality statistics analysis. Sensitivity analysis and statistical screening techniques further permit an evaluation of the impact of the managerial components on accident occurrence. The results of the case study indicate that schedule delays and rework are the critical factors affecting accident occurrence for the monitored project.     

Fullerene: biomedical engineers get to revisit an old friend

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Production of bioethanol from sugarcane bagasse using yeast strains: A kinetic study

In this study, sugarcane bagasse (2 mm) was pretreated with 2.5% NaOH followed by steaming at 121°C for various time periods. Maximum cellulose content of 81% and delignification of 68.5% were achieved by soaking bagasse in 2.5% NaOH with a residence time of 1 h at room temperature followed by steaming at 121°C for 30 min residence time. The pretreated substrate was analyzed by SEM and FTIR to study the structural modification and functional group of the untreated and pretreated substrates. The pretreated substrate was saccharified by commercial cellulase enzyme depicting 106 µm mesh size of substrate yields maximum saccharification rate. The saccharified material was fermented by Saccharomyces cerevisiae and Pichia stipitis in mono- and co-culture modes. Maximum product yield (Y_p/s) was observed by monoculture using Saccharomyces cerevisiae after 96 h of fermentation period.     

Atmospheric pressure photoionization mass spectrometry

Atmospheric pressure photoionization (APPI) is the last arrival in the family of atmospheric pressure ionization (API) methods to couple mass spectrometry (MS) to liquid-phase separation techniques. The basic idea was to further extend the fields of application of liquid chromatography (LC)-MS to those molecules that are not, or are poorly amenable, to electrospray (ESI) or APCI. The present review explores the literature. After a short introduction with an historical background and the premises for its development, we describe the technique, its physical principles, and the factors that affect its efficiency. The review also presents a survey of applications in different fields.