Search results for: Reusability.

Authors: Bianca Schweiger, Lucile Bahnweg, Barbara Palm, Ute Steinfeld

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The occurrence and removal of trace organic contaminants in the aquatic environment has become a focus of environmental concern. For the selective removal of carbamazepine from loaded waters molecularly imprinted polymers (MIPs) were synthesized with carbamazepine as template. Parameters varied were the type of monomer, crosslinker, and porogen, the ratio of starting materials, and the synthesis temperature. Best results were obtained with a template to crosslinker ratio of 1:20, toluene as porogen, and methacrylic acid (MAA) as monomer. MIPs were then capable to recover carbamazepine by 93% from a 10-5 M landfill leachate solution containing also caffeine and salicylic acid. By comparison, carbamazepine recoveries of 75% were achieved using a nonimprinted polymer (NIP) synthesized under the same conditions, but without template. In landfill leachate containing solutions carbamazepine was adsorbed by 93-96% compared with an uptake of 73% by activated carbon. The best solvent for desorption was acetonitrile, with which the amount of solvent necessary and dilution with water was tested. Selected MIPs were tested for their reusability and showed good results for at least five cycles. Adsorption isotherms were prepared with carbamazepine solutions in the concentration range of 0.01 M to 5*10-6 M. The heterogeneity index showed a more homogenous binding site distribution.

Keywords: Carbamazepine, landfill leachate, removal, reuse

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Authors: Manjeet Bansal, Diwan Singh, V.K.Garg, Pawan Rose

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The potential of economically cheaper cellulose containing natural materials like rice husk was assessed for nickel adsorption from aqueous solutions. The effects of pH, contact time, sorbent dose, initial metal ion concentration and temperature on the uptake of nickel were studied in batch process. The removal of nickel was dependent on the physico-chemical characteristics of the adsorbent, adsorbate concentration and other studied process parameters. The sorption data has been correlated with Langmuir, Freundlich and Dubinin-Radush kevich (D-R) adsorption models. It was found that Freundlich and Langmuir isotherms fitted well to the data. Maximum nickel removal was observed at pH 6.0. The efficiency of rice husk for nickel removal was 51.8% for dilute solutions at 20 g L-1 adsorbent dose. FTIR, SEM and EDAX were recorded before and after adsorption to explore the number and position of the functional groups available for nickel binding on to the studied adsorbent and changes in surface morphology and elemental constitution of the adsorbent. Pseudo-second order model explains the nickel kinetics more effectively. Reusability of the adsorbent was examined by desorption in which HCl eluted 78.93% nickel. The results revealed that nickel is considerably adsorbed on rice husk and it could be and economic method for the removal of nickel from aqueous solutions.

Keywords: Adsorption, nickel, SEM, EDAX.

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Authors: Parvinder S. Sandhu, Roma Jaswal, Sandeep Khimta, Shailendra Singh

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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.

Keywords: Neural Network, Software faults, Software Metric.

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Authors: J. Barbarán, M. Díaz, I. Esteve, D. Garrido, L. Llopis, B. Rubio

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Wireless Sensor and Actor Networks (WSANs) constitute an emerging and pervasive technology that is attracting increasing interest in the research community for a wide range of applications. WSANs have two important requirements: coordination interactions and real-time communication to perform correct and timely actions. This paper introduces a methodology to facilitate the task of the application programmer focusing on the coordination and real-time requirements of WSANs. The methodology proposed in this model uses a real-time component model, UM-RTCOM, which will help us to achieve the design and implementation of applications in WSAN by using the component oriented paradigm. This will help us to develop software components which offer some very interesting features, such as reusability and adaptability which are very suitable for WSANs as they are very dynamic environments with rapidly changing conditions. In addition, a high-level coordination model based on tuple channels (TC-WSAN) is integrated into the methodology by providing a component-based specification of this model in UM-RTCOM; this will allow us to satisfy both sensor-actor and actor-actor coordination requirements in WSANs. Finally, we present in this paper the design and implementation of an application which will help us to show how the methodology can be easily used in order to achieve the development of WSANs applications.

Keywords: Sensor networks, real time and embedded systems.

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Authors: Homa Torabizadeh, Mohaddeseh Mikani

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Inulinase from Aspergillus niger was covalently immobilized on magnetic nanoparticles (MNPs/Fe₃O₄) covered with soy protein isolate (SPI/Fe₃O₄) functionalized by bovine serum albumin (BSA) nanoparticles. MNPs are promising enzyme carriers because they separate easily under external magnetic fields and have enhanced immobilized enzyme reusability. As MNPs aggregate simply, surface coating strategy was employed. SPI functionalized by BSA was a suitable candidate for nanomagnetite coating due to its superior biocompatibility and hydrophilicity. Fe₃O₄@SPI-BSA nanoparticles were synthesized as a novel carrier with narrow particle size distribution. Step by step fabrication monitoring of Fe₃O₄@SPI-BSA nanoparticles was performed using field emission scanning electron microscopy and dynamic light scattering. The results illustrated that nanomagnetite with the spherical morphology was well monodispersed with the diameter of about 35 nm. The average size of the SPI-BSA nanoparticles was 80 to 90 nm, and their zeta potential was around −34 mV. Finally, the mean diameter of fabricated Fe₃O₄@SPI-BSA NPs was less than 120 nm. Inulinase enzyme from Aspergillus niger was covalently immobilized through gluteraldehyde on Fe₃O₄@SPI-BSA nanoparticles successfully. Fourier transform infrared spectra and field emission scanning electron microscopy images provided sufficient proof for the enzyme immobilization on the nanoparticles with 80% enzyme loading.

Keywords: High fructose syrup, inulinase immobilization, functionalized magnetic nanoparticles, soy protein isolate.

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: Computer-aided system, detection, image segmentation, morphology.

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Authors: D. Mowla, N. Rasti, P. Keshavarz

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Reduction of fossil fuels sources, increasing of pollution gases emission, and global warming effects increase the demand of renewable fuels. One of the main candidates of alternative fuels is biodiesel. Biodiesel limits greenhouse gas effects due to the closed CO₂ cycle. Biodiesel has more biodegradability, lower combustion emissions such as CO, SO_x, HC, PM and lower toxicity than petro diesel. However, biodiesel has high production cost due to high price of plant oils as raw material. So, the utilization of waste cooking oils (WCOs) as feedstock, due to their low price and disposal problems reduce biodiesel production cost. In this study, production of biodiesel by transesterification of methanol and WCO using modified sodic potassic (SP) clinoptilolite zeolite and sodic potassic calcic (SPC) clinoptilolite zeolite as heterogeneous catalysts have been investigated. These natural clinoptilolite zeolites were modified by KOH solution to increase the site activity. The optimum biodiesel yields for SP clinoptilolite and SPC clinoptilolite were 95.8% and 94.8%, respectively. Produced biodiesel were analyzed and compared with petro diesel and ASTM limits. The properties of produced biodiesel confirm well with ASTM limits. The density, kinematic viscosity, cetane index, flash point, cloud point, and pour point of produced biodiesel were all higher than petro diesel but its acid value was lower than petro diesel. Finally, the reusability and regeneration of catalysts were investigated. The results indicated that the spent zeolites cannot be reused directly for the transesterification, but they can be regenerated easily and can obtain high activity.

Keywords: Biodiesel, renewable fuel, transesterification, waste cooking oil.

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Authors: H. Ferfera-Harrar, T. Benhalima, D. Lerari

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Contamination of water, due to the discharge of untreated industrial wastewaters into the ecosystem, has become a serious problem for many countries. In this study, bioadsorbents based on chitosan-g-poly(acrylamide) and montmorillonite (MMt) clay (CTS-g-PAAm/MMt) hydrogel nanocomposites were prepared via free‐radical grafting copolymerization and crosslinking of acrylamide monomer (AAm) onto natural polysaccharide chitosan (CTS) as backbone, in presence of various contents of MMt clay as nanofiller. Then, they were hydrolyzed to obtain highly functionalized pH‐sensitive nanomaterials with uppermost swelling properties. Their structure characterization was conducted by X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) analyses. The adsorption performances of the developed nanohybrids were examined for removal of methylene blue (MB) cationic dye from aqueous solutions. The factors affecting the removal of MB, such as clay content, pH medium, adsorbent dose, initial dye concentration and temperature were explored. The adsorption process was found to be highly pH dependent. From adsorption kinetic results, the prepared adsorbents showed remarkable adsorption capacity and fast adsorption rate, mainly more than 88% of MB removal efficiency was reached after 50 min in 200 mg L^-1 of dye solution. In addition, the incorporating of various content of clay has enhanced adsorption capacity of CTS-g-PAAm matrix from 1685 to a highest value of 1749 mg g^-1 for the optimized nanocomposite containing 2 wt.% of MMt. The experimental kinetic data were well described by the pseudo-second-order model, while the equilibrium data were represented perfectly by Langmuir isotherm model. The maximum Langmuir equilibrium adsorption capacity (q_m) was found to increase from 2173 mg g⁻¹ until 2221 mg g⁻¹ by adding 2 wt.% of clay nanofiller. Thermodynamic parameters revealed the spontaneous and endothermic nature of the process. In addition, the reusability study revealed that these bioadsorbents could be well regenerated with desorption efficiency overhead 87% and without any obvious decrease of removal efficiency as compared to starting ones even after four consecutive adsorption/desorption cycles, which exceeded 64%. These results suggest that the optimized nanocomposites are promising as low cost bioadsorbents.

Keywords: Chitosan, clay, dye adsorption, hydrogels nanocomposites.

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Authors: G. Candel, D. Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embedding. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic, and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n2) to O(n2/k), and the memory requirement from n2 to 2(n/k)2 which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: Concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning.

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