Determination of inorganic arsenic species (As and As) in muscle tissues of fish species by electrothermal atomic absorption spectrometry (ETAAS)

Arsenic speciation was carried out in muscle tissues of freshwater fish species. Inorganic arsenic species (As³⁺and As⁵⁺) were extracted with chloroform, prior to microwave assisted digestion with concentrated HClO₄ and Fe₂(SO₄)₃. The extracted As³⁺ and As⁵⁺ were determined by electrothermal atomic absorption spectrometry (ETAAS). The accuracy of the technique was evaluated by using certified reference material DORM-2. The limit of detection of the method was 0.004 and 0.005 μg/g for As³⁺ and As⁵⁺, respectively. The mean relative standard deviation values (RSD) in real sample analysis were 1.90 and 3.92 for As³⁺ and As⁵⁺, respectively. The results demonstrated the suitability of the procedure for screening and quantification of As species in biological samples. The mean concentration of As³⁺ and As⁵⁺ in muscle tissues of studied fish species ranged from 1.19 to 2.05 and 0.17 to 0.46 μg/g, respectively. The contribution of the daily intake of inorganic As, based on the consumption of 250 g fresh fish muscles/body weight/day was found in the range of 1.21–1.91 μg/kg/day.     

Synthesis and characterization of a cation‐exchange resin based on vinylpyrrolidone‐co‐divinylbenzene: Effect of nature and amount of diluent on different resin properties

Fundamental studies regarding the synthesis of a porous copolymer synthesized from 1‐vinyl‐2‐pyrrolidone and divinylbenzene in the presence of different diluents were carried out. A series of porous copolymer resins was synthesized by suspension polymerization using the following diluents: dimethylphthalate (DMT), diethylphthalate, dibutylphthalate, and bis‐2‐ethyl hexylphthalate [dioctylphthalate (DOT)]. It was observed that the porosity of resin increases with increase in length of the methylene group in the phthalate ester of DMT to DOT. In another series of experiments the amount of solvent was increased from 30 to 60% while the crosslinkage was kept constant at 30% and the diluent used was DOT. It was observed that the porosity of resins increased as the fraction of solvent increased. However, surprisingly, the Na⁺ capacity did not show any appreciable change by varying the amount or nature of diluent, remaining approximately 4 meq/g. The resin remained mechanically strong despite higher porosity. The mechanical strength also did not show any significant change by varying the diluent or amount of diluent. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 3917–3920, 2004     

The superconducting transition temperature for La1−x Gd x

The deviation of the superconducting transition temperature from the Abrikosov-Gorkov behavior in the case of La_1−xGd _x is explained considering the presence of short range spin-glass at low temperature. The theoretical results show good agreement with the experimental data.     

Enhancement of CO hydrogenation activity on incompletely reduced nickel wire

Enhanced activity of Ni wire in CO hydrogenation has been observed when the metal surface is incompletely reduced. The catalytic behavior has been studied in a flow reactor for different H₂/CO ratios, and the carbon species accumulated on the metal surface during reaction have been analyzed by Tempera-ture-Progranimed-Surface-Reaction(TPSR). It has been concluded that the surface or sub-surface oxygen remaining on the catalyst due to incomplete reduction plays an important role m determining the metal catalyst behavior. The extent of metal reduction in addition to the metal surface area has to be considered in estimation of the intrinsic activity of Ni catalysts.     

Effect of cobalt nanoparticles on mechanical properties of Sn–58Bi solder joint

Brittle phases are responsible for crack formation and propagation in tin–bismuth (Sn–58Bi) solder material. The purpose of this work is to investigate the effects of various cobalt (Co) nanoparticle (NP) concentrations on the tensile properties of the Sn–58Bi solder matrix. Different aging times were studied to find out the effect of Co NP on ultimate tensile strength. Tin–bismuth solder joints of different Co NP concentrations of 0%, 0.5%, 1%, and 2% were prepared. The reflow process was done at 180 °C for 1 min. Scanning Electron Microscopy and Energy-Dispersive X-ray spectroscopy were used to analyze the solder joints. The tensile test was carried out for the Sn–58Bi and Sn–58Bi–xCo (x?=?0.5, 1, and 2) solder joints. The tensile test was run before and after aging time. The tensile results reveal that the addition of Co NP increased the tensile strength significantly at different concentrations of Co NP. The Tensile test revealed that ductility was improved as the temperature was increased. As the aging time increased, the ultimate tensile strength of all samples decreased.

     

Multimodal Fuzzy Downstream Petroleum Supply Chain: A Novel Pentagonal Fuzzy Optimization

The petroleum industry has a complex, inflexible and challenging supply chain (SC) that impacts both the national economy as well as people’s daily lives with a range of services, including transportation, heating, electricity, lubricants, as well as chemicals and petrochemicals. In the petroleum industry, supply chain management presents several challenges, especially in the logistics sector, that are not found in other industries. In addition, logistical challenges contribute significantly to the cost of oil. Uncertainty regarding customer demand and supply significantly affects SC networks. Hence, SC flexibility can be maintained by addressing uncertainty. On the other hand, in the real world, decision-making challenges are often ambiguous or vague. In some cases, measurements are incorrect owing to measurement errors, instrument faults, etc., which lead to a pentagonal fuzzy number (PFN) which is the extension of a fuzzy number. Therefore, it is necessary to develop quantitative models to optimize logistics operations and supply chain networks. This study proposed a linear programming model under an uncertain environment. The model minimizes the cost along the refineries, depots, multimode transport and demand nodes. Further developed pentagonal fuzzy optimization, an alternative approach is developed to solve the downstream supply chain using the mixed-integer linear programming (MILP) model to obtain a feasible solution to the fuzzy transportation cost problem. In this model, the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number. Furthermore, defuzzification is performed using an accuracy function. To validate the model and technique and feasibility solution, an illustrative example of the oil and gas SC is considered, providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.     

Handling Class Imbalance in Online Transaction Fraud Detection

With the rise of internet facilities, a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction. However, the fraud cases have also increased causing the loss of money to the consumers. Hence, an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time. Generally, the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem. In this research work, an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e., fraud transactions. A novel loss function named Weighted Hard- Reduced Focal Loss (WH-RFL) has been proposed which has achieved maximum fraud detection rate i.e., True Positive Rate (TPR) at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate (TNR) in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional datasets. Also, Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results.     

The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test

In this study, artificial neural networks (ANNs) were used to predict the settlement of one-way footings, without a need to perform any manual work such as using tables or charts. To achieve this, a computer programme was developed in the Matlab programming environment for calculating the settlement of one-way footings from five traditional settlement prediction methods. The footing geometry (length and width), the footing embedment depth, the bulk unit weight of the cohesionless soil, the footing applied pressure, and corrected standard penetration test varied during the settlement analyses, and the settlement value of each one-way footing was calculated for each traditional method by using the written programme. Then, an ANN model was developed for each method to predict the settlement by using the results of the analyses. The settlement values predicted from each ANN model developed were compared with the settlement values calculated from the traditional method. The predicted values were found to be quite close to the calculated values. Additionally, several performance indices such as determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to check the prediction capacity of the ANN models developed. The constructed ANN models have shown high prediction performance based on the performance indices calculated. The results demonstrated that the ANN models developed can be used at the preliminary stage of designing one-way footing on cohesionless soils without a need to perform any manual work such as using tables or charts.     

Adsorption of cationic platinum complex onto carbon support

Adsorption of cationic Pt complex, Pt(NH₃)₄²⁺, in aqueous solution onto carbon was carried out by varying the initial pH of the solution. The Pt uptake showed a maximum as a function of the uptake time and afterward decreased gradually, showing that the adsorbed species is desorbed from the carbon surface. It was found that the degree of desorption was strongly dependent on the solution pH, as well as on the IEP (Isoelectric point) of the carbon support. In the solution pH lower than the IEP of the carbon, the desorption was almost complete. The desorption was found to be caused by a gradual decomposition of the ion-exchanged and/or electrostatically adsorbed Pt amine complex after giving the ligand ammonia to the acidic sites of the carbon surface, most probably, positive sites like protonated carboxyl group. The desorption could be prevented to a considerable extent by using a carbon support treated by nitric acid, because lowering the IEP of the carbon by acid treatment must result in a decrease in the interaction of basic ligand ammonia and positive surface sites, which eventually leads to the desorption of Pt complex. Measurements of the Pt dispersion of the catalysts that have undergone desorption indicate that the desorption of the adsorbed Pt complex occurs on relatively weak adsorption sites like carboxyl group, rather than strong adsorption sites (e.g., phenolic hydroxyl or lactonic groups.).     

Synergistic effect of organic and inorganic nano fillers on the dielectric and mechanical properties of epoxy composites

Titanium oxide TiO₂/epoxy and TiO₂ with detonation nano-diamond (DND)/epoxy nanocomposites were prepared by using ultrasonication method. TiO₂ and DND particles as reinforcement species and epoxy as matrix were used to produce nanocomposites. The addition of DND particles into TiO₂/epoxy composite improved the dielectric and mechanical properties of nanocomposites in significant amount. The dielectric properties of TiO₂-DND/epoxy nanocomposite demonstrated increase in permittivity and conductivity after addition of the DND particles. The maximum and minimum reflection losses of TiO₂-DND/epoxy nanocomposite for 0.6 and 0.2 wt% DND loading were detected at ?14.5 and ?1.3 dB, respectively. The flexural and tensile strength of TiO₂-DND/epoxy nanocomposites with the addition of 0.4 wt% DNDs were enhanced to 220% and 223%, respectively. Additionally, the energy to break and percent break strain were 3.9 J and 3.86, respectively for 0.4 wt% DND loading in TiO₂-DND/epoxy nanocomposite. Therefore, the present work findings claim that DND particles are well suitable to enrich the dispersion of TiO₂ nanoparticles in epoxy matrix, which develops a strong load transfer interface between the nanoparticles and epoxy matrix and consequently leads to superior properties.