A new approach to modification of an agro-based raw material for Pb(II) adsorption

This paper describes a relatively new and effective modification with thiourea of an agro-based raw material, sorghum biomass for adsorption of Pb(II) ions. The modification was done under microwave irradiation. Changes in characteristics of modified material were determined by FT-IR and CHNS analysis. Effect of adsorbent dosage, contact time, solution pH and temperature on equilibrium process was extensively studied. Famous equilibrium models (Langmuir, Freundlich, Temkin, Dubinin-Radushkevich) in non-linear fashion were applied to evaluate the equilibrium data using different initial concentrations of Pb(II) ranging from 20 mg/L to 300mg/L. The adsorption process followed the pseudosecond-order kinetics. This paper also discusses the thermodynamic parameters (free energy, enthalpy and entropy) and reveals that the adsorption process is spontaneous. An appreciable increase in adsorption capacity of the modified adsorbent was observed, depicting the advantages of thiourea modification of sorghum biomass under microwave irradiation.     

Enhancing Lipase Biosynthesis by Aspergillus Melleus and its Biocatalytic Potential for Degradation of Polyester Vylon-200

Catalysis Letters - Herein, response surface methodology (RSM) was employed to optimize the bioprocess parameters for improved production of extracellular lipase by Aspergillus melleus and...     

A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.     

Fast spatiotemporal MACH filter for action recognition

Human action recognition has been an active field of research in computer vision community for the last decade. The spatiotemporal MACH (maximum average correlation height) filter approach has proved to be a very efficient method to solve the problem. It captures the intra-class variability and produces a very high response at the spatiotemporal location $(x,y,t)$ where the action is present in a video. Its computation cost is significantly lower than any other action recognition approach. However, faster algorithm is always needed to perform a computer vision task in real-time. Therefore, we propose a very efficient algorithm for normalized spatiotemporal MACH filtering for action recognition. It is based on the computations performed both in the frequency domain as well as the spatiotemporal domain exploiting integral video. We compare its speed with that of the relevant traditional algorithms and show that our approach drastically outperforms all of them.     

Polymer‐Based Porous Microcapsules as Bacterial Traps

Exposure to live bacteria and accumulation of dead bacteria during bactericidal processes can cause bacterial infectious diseases, implant failure, and antibacterial surface deterioration. Microcapsules with asymmetrically distributed, funnel‐shaped pores, which are capable of capturing, retaining, and killing bacteria are developed, offering a solution to bacterial contamination in liquids. It is found that bacterial isolation inside microcapsules is mainly driven by the bacteria's own motility and the microcapsules' geometry. After entry into the microcapsule cavity, the bacteria are stably retained inside. The microcapsules shield surrounding cells from exposure to bacterial toxins, as demonstrated by the coculture of rat embryonic fibroblast cells with microcapsules loaded with live Escherichia coli. The microcapsules can be enhanced with a bactericidal coating covering only the interior cavity. This confines the bacteria‐killing process, thereby further increasing biocompatibility. The microcapsules may offer a viable bacteria combatant approach as a potentially advantageous method to eradicate bacterial contamination.     

A hierarchical congestion control method in clustered internet of things

The Journal of Supercomputing - Internet of things (IoT) is a modern technology where data can be transmitted to any things (human, animal, or object) over communications networks, whether internet...     

Parallel sentence generation from comparable corpora for improved SMT

A parallel corpus is an essential resource for statistical machine translation (SMT) but is often not available in the required amounts for all domains and languages. An approach is presented here which aims at producing parallel corpora from available comparable corpora. An SMT system is used to translate the source-language part of a comparable corpus and the translations are used as queries to conduct information retrieval from the target-language side of the comparable corpus. Simple filters are then used to score the SMT output and the IR-returned sentence with the filter score defining the degree of similarity between the two. Using SMT system output gives the benefit of trying to correct one of the common errors by sentence tail removal. The approach was applied to Arabic–English and French–English systems using comparable news corpora and considerable improvements were achieved in the BLEU score. We show that our approach is independent of the quality of the SMT system used to make the queries, strengthening the claim of applicability of the approach for languages and domains with limited parallel corpora available to start with. We compare our approach with one of the earlier approaches and show that our approach is easier to implement and gives equally good improvements.     

A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning

Telecom industry relies on churn prediction models to retain their customers. These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers. Retention not only contributes to the profit of an organization, but it is also important for upholding a position in the competitive market. In the past, numerous churn prediction models have been proposed, but the current models have a number of flaws that prevent them from being used in real-world large-scale telecom datasets. These schemes, fail to incorporate frequently changing requirements. Data sparsity, noisy data, and the imbalanced nature of the dataset are the other main challenges for an accurate prediction. In this paper, we propose a hybrid model, name as “A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning (HCPRs)” that used Synthetic Minority Over-Sampling Technique (SMOTE) and Particle Swarm Optimization (PSO) to address the issue of imbalance class data and feature selection. Data cleaning and normalization has been done on big Orange dataset contains 15000 features along with 50000 entities. Substantial experiments are performed to test and validate the model on Random Forest (RF), Linear Regression (LR), Naïve Bayes (NB) and XG-Boost. Results show that the proposed model when used with XGBoost classifier, has greater Accuracy Under Curve (AUC) of 98% as compared with other methods.     

The use of Neem biomass for the biosorption of zinc from aqueous solutions

An adsorbent was developed from mature leaves and stem bark of the Neem (Azadirachta indica) tree for removing zinc from water. Adsorption was carried out in a batch process with several different concentrations of zinc by varying pH. The uptake of metal was very fast initially, but gradually slowed down indicating penetration into the interior of the adsorbent particles. The data showed that optimum pH for efficient biosorption of zinc by Neem leaves and stem bark was 4 and 5, respectively. The maximum adsorption capacity showed that the Neem biomass had a mass capacity for zinc (147.08 mg Zn/g for Neem leaves and 137.67 mg Zn/g Neem bark). The experimental results were analyzed in terms of Langmuir and Freundlich isotherms. The adsorption followed pseudo-second-order kinetic model. The thermodynamic assessment of the metal ion-Neem tree biomass system indicated the feasibility and spontaneous nature of the process and DeltaG degrees values were evaluated as ranging from -26.84 to -32.75 (Neem leaves) kJ/mol and -26.04 to -29.50 (Neem bark) kJ/mol for zinc biosorption. Due to its outstanding zinc uptake capacity, the Neem tree was proved to be an excellent biomaterial for accumulating zinc from aqueous solutions.