Removal of lead(II) from water using activated carbon developed from jujube stones,a low-cost sorbent

The preparation of activated carbon from jujube stones with H₂SO₄ activation and its ability to remove lead from aqueous solutions were reported in this study. The surface structure of the activated carbon was characterized by various physico-chemical methods. Sorption studies were carried out by varying the initial metal ion and the pH: the amount of sorbed Pb(II) ions increased with increasing pH and initial Pb(II) ions concentration. The removal of lead ions was rapid and the kinetic of sorption can be well described by pseudo-second order modelling. The Langmuir model conveniently fits the data of isotherm experiments and the monolayer sorption capacity of Pb(II) ions was determined as 71.43 mg/g at pH 6.0 and 25°C. These results showed that activated carbon prepared from jujube stones could be considered for application as a potential sorbent for the removal of lead from wastewaters.     

Transparent and thermally stable improved poly (vinyl alcohol)/Cloisite Na/ZnO hybrid nanocomposite films: Fabrication,morphology and surface properties

A series of poly(vinyl alcohol)/Cloisite Na⁺-Tyrosine/Zinc oxide (PVA/Cloisite Na⁺-Tyr/ZnO) bionanocomposites were prepared by dispersing ZnO nanoparticles in solution containing mixture of the PVA and modified Cloisite Na⁺. Structure of nanocomposite coatings was investigated by X-ray diffraction and Fourier-transform infrared spectroscopy. The thermal stability and optical properties of bionanocomposite were characterized by thermogravimetric analysis and UV–vis spectroscopy, respectively. The introduction of ZnO nanoparticles into PVA/Cloisite Na⁺-Tyr mixed solutions significantly increased the thermal stability of the obtained films. The results revealed that the high UV-shielding efficiency of the composites: for a film containing 6.0 wt% of ZnO nanocrystals, over 92% of UV light at wavelengths of 368 nm was absorbed while the optical transparency in the visible region was slightly below that of a PVA/Cloisite Na⁺-Tyr film.     

Properties of flowable sand concretes reinforced by polypropylene fibers

This paper presents an experimental research on fresh and hardened properties of flowable sand concretes (FSC) reinforced by polypropylene fibers (PF). Tests were conducted on both plain and fiber-reinforced FSC. The fresh properties are tested for viscosity using a programmable DV-II?+?viscometer. For the physical and mechanical properties, drying shrinkage, flexural, and compressive strengths, as well as micro-structural analyses have been studied. Results indicate that all studied mixtures have a pseudo-plastic behavior in fresh state and can be well fitted using power law model. Results also confirmed that PF incorporation increases the viscosity and reduces free shrinkage of FSC. In terms of mechanical strength, results show that incorporating PF would enhance flexural strength. However, a reduction in compressive strength is observed.     

Sensitivity analysis of using diethanolamine instead of methyldiethanolamine solution for GASCO’S Habshan acid gases removal plant

Sweeting natural gas processes are mainly focused on removing carbon dioxide (CO₂) and hydrogen sulfide (H₂S). The high-energy requirements and operational limitations make amine absorption process sensitive to any change in conditions. This paper presented a steady-state simulation using Hysys to reasonably predict removal amounts of carbon dioxide and hydrogen sulfide from natural gas with the diethanolamine (DEA) solvent. The product specifications are taken from the real plant (GASCO’S Habshan) which uses the methyldiethanolamine (MDEA) solvent, while this simulation uses DEA under the same operation conditions. First, the simulation validation has been checked with the data of the real plant. The results show accurate prediction for CO₂ slippage and accepted agreement for H₂S content compared with the data of the plant. A parametric analysis has been performed to test all possible parameters that affect the performance of the acid gases removal plant. The effects of operational parameters are examined in terms of carbon dioxide and hydrogen sulfide contents in clean gas and reboiler duty.     

Design theory of long-distance WDM dispersion-managed transmissionsystem

This paper describes a novel design theory of long distance wavelength division multiplexed (WDM) dispersion-managed optical transmission systems. Assuming that the transmission distance, bit rate, and number of WDM channels are initially known, we investigate the optimum dispersion allocation and input power per channel to achieve the minimum channel spacing. Based on the design guidelines for single-channel and multichannel systems, we establish the optimal design strategy. Details of the design procedure are demonstrated for 2.5-, 5-, and 10-Gb/s 10000 km WDM systems by using computer simulations. Next, we study the impact of the fiber dispersion slope on the usable wavelength span, and show that the attainable capacity of the representative 5-Gb/s 10000 km WDM system employing the postcompensation scheme can not exceed 100 Gb/s. Finally, we propose several techniques to approach the ultimate capacity of the WDM system and show that up to 1 Tb/s (200×5 Gb/s) 10000 km system can be implemented without utilizing the in-line dispersion slope compensation scheme. We also discuss the 10 Gb/s-10000 km WDM system employing in-line dispersion slope compensation     

DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks

Protein solubility is an important thermodynamic parameter that is critical for the characterization of a protein’s function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g., pharmaceutical) applications. Experimental approaches to predict protein solubility are costly, time-consuming, and frequently offer only low success rates. To reduce cost and expedite the development of therapeutic and industrially relevant proteins, a highly accurate computational tool for predicting protein solubility from protein sequence is sought. While a number of in silico prediction tools exist, they suffer from relatively low prediction accuracy, bias toward the soluble proteins, and limited applicability for various classes of proteins. In this study, we developed a novel deep learning sequence-based solubility predictor, DSResSol, that takes advantage of the integration of squeeze excitation residual networks with dilated convolutional neural networks and outperforms all existing protein solubility prediction models. This model captures the frequently occurring amino acid k-mers and their local and global interactions and highlights the importance of identifying long-range interaction information between amino acid k-mers to achieve improved accuracy, using only protein sequence as input. DSResSol outperforms all available sequence-based solubility predictors by at least 5% in terms of accuracy when evaluated by two different independent test sets. Compared to existing predictors, DSResSol not only reduces prediction bias for insoluble proteins but also predicts soluble proteins within the test sets with an accuracy that is at least 13% higher than existing models. We derive the key amino acids, dipeptides, and tripeptides contributing to protein solubility, identifying glutamic acid and serine as critical amino acids for protein solubility prediction. Overall, DSResSol can be used for the fast, reliable, and inexpensive prediction of a protein’s solubility to guide experimental design.     

Task partitioning,scheduling and load balancing strategy for mixed nature of tasks

Load balancing and task partitioning are important components of distributed computing. The optimum performance from the distributed computing system is achieved by using effective scheduling and load balancing strategy. Researchers have well explored CPU, memory, and I/O-intensive tasks scheduling, and load balancing techniques. But one of the main obstacles of the load balancing technique leads to the ignorance of applications having a mixed nature of tasks. This is because load balancing strategies developed for one kind of job nature are not effective for the other kind of job nature. We have proposed a load balancing scheme in this paper, which is known as Mixed Task Load Balancing (MTLB) for Cluster of Workstation (CW) systems. In our proposed MTLB strategy, pre-tasks are assigned to each worker by the master to eliminate the worker’s idle time. A main feature of MTLB strategy is to eradicate the inevitable selection of workers. Furthermore, the proposed MTLB strategy employs Three Resources Consideration (TRC) for load balancing (CPU, Memory, and I/O). The proposed MTLB strategy has removed the overheads of previously proposed strategies. The measured results show that MTLB strategy has a significant improvement in performance.     

Keyword-based patent citation prediction via information theory

ABSTRACT

Patent citation shows how a technology impacts other inventions, so the number of patent citations (backward citations) is used in many technology prediction studies. Current prediction methods use patent citations, but since it may take a long time till a patent is cited by other inventors, identifying impactful patents based on their citations is not an effective way. The prediction method offered in this article predicts patent citations based on the content of patents. In this research, Reconstructability Analysis (RA), which is based on information theory and graph theory, is applied to predict patent citations based on keywords extracted from the abstracts of selected patents. After applying three classes of RA (variable-based analysis without and with loops and state-based analysis), nine specific IV states of a predicting model are extracted. These states involve the four keywords of “chamber”, “hous”, “main”, and “return”. Lastly, the abstracts of the patents are examined to identify the technical subjects relevant to smart building technologies for which these keywords are proxies.     

An adaptive approach for decision making tactics in automated negotiation

In this paper, an adaptive decision making approach of three families of tactics has been proposed for bilateral negotiation: the time dependent tactics, behavior dependent tactics, and time independent tactics. These tactics are more adaptive to the environment’s changes (reservation interval, time deadline, opponent behavior). The suggested time dependent tactics take advantage from round based time continuity and dynamics aspects (features) integrated in their modelling. For suggested behavior dependent tactics, a new formalization based on the percentage of change was introduced, which helps agents to be more prudent in the environments with incomplete information comparing to previous behavior dependent tactics suggested by Faratin et al. (Int. J. Robotics Auton. Syst. 24(3–4):159–182, 1998). Concerning the new family of tactics which are completely independent from time, the agents compute their offers based on their reservation interval. These tactics are useful when there is no time deadline and, in addition, when the behavior of opponent agents doesn’t follow any negotiation equilibrium. Moreover, new experimental measures are suggested which are more useful for final evaluation. The experiments conducted in this paper, prove the applicability of all three families of tactic.     

From visual patterns to semantic description: A cognitive approach using artificial curiosity as the foundation

In this article, we present a cognitive system based on artificial curiosity for high-level knowledge acquisition from visual patterns. The curiosity (perceptual curiosity and epistemic curiosity) is realized through combining perceptual saliency detection and Machine-Learning based approaches. The learning is accomplished by autonomous observation of visual patterns and by interaction with an expert (a human tutor) detaining semantic knowledge about the detected visual patterns. Experimental results validating the deployment of the investigated system have been obtained on the basis of a humanoid robot acquiring visually knowledge from its surrounding environment interacting with a human tutor. We show that our cognitive system allows the humanoid robot to discover the surrounding world in which it evolves, to learn new knowledge about it and describe it using human-like (natural) utterances.