Application of an Interior-Point Algorithm For Optimization of a Large-Scale Reservoir System

Optimization of a multi-reservoir system operation is challenging due to the non-linearity, stochasticity, and dimensionality involved in such a problem. In this research, a long-term planning model is presented for optimizing the operation of Iranian Karoon-Dez reservoir system using an interior-point algorithm. The system is the largest multi-purpose reservoir system in Iran with hydropower generation, water supply, and environmental objectives. The focus is on resolving the dimensionality of this problem while considering hydropower generation and water supply objectives. The weighting and constraints methods of multi-objective programming are used to assess the trade-off between water supply and hydropower objectives so as to find noninferior solutions. The computational efficiency of the proposed approach is demonstrated using historical data taken from Karoon-Dez reservoir system.     

Ultra-trace determination of silver in water samples by electrothermal atomic absorption spectrometry after preconcentration with a ligand-less cloud point extraction methodology

A very simple and ligand-less cloud point extraction (CPE) methodology for the preconcentration of ultra-trace amounts of silver as a prior step to its determination by electrothermal atomic absorption spectrometry (ETAAS) has been developed. The method is based on the extraction of silver at pH 9 by using non-ionic surfactant polyethyleneglycolmono-p-nonylphenylether (PONPE 7.5) without adding any chelating agent. Several important variables that affect the CPE efficiency and ETAAS signal were investigated and optimized. The preconcentration of 15 ml sample solution allowed us to achieve an enhancement factor of 60. The calibration graph using the preconcentration system was linear in the range of 5-100 ngl(-1) with a correlation coefficient of 0.9991. The lower limit of detection (3s) obtained in the optimal conditions was 1.2 ngl(-1). The relative standard deviation (R.S.D.) for eight replicate determinations at 30 ngl(-1) Ag level was 4.2%. The proposed method was successfully applied to the ultra-trace determination of silver in water samples.     

Multi-Objective Optimization-Simulation for Reliability-Based Inter-Basin Water Allocation

A simulation-optimization framework is presented for reliability-based optimal sizing, operation, and water allocation in the Bashar-to-Zohreh inter-basin water transfer project. The problem was formulated as a mixed integer nonlinear program (MINLP), for which two solution approaches were tested, including gradient-based nonlinear programming and simulation-optimization (SO). The SO framework linked the water evaluation and planning system (WEAP) simulation module, benefiting from fast, single-period linear programming, to the multi-objective particle swarm optimization (MOPSO) for multiperiod optimization. The objective functions were minimizing the sizes of the project’s infrastructures and maximizing the reliability of supplying water to agricultural lands. The combination of nonlinear programming and the branch-and-bound algorithm was not able to solve the resulting MINLP. The results of the MOPSO-WEAP framework indicated that the project can supply water for land development in the Dehdasht and Choram agricultural plains, located in the less developed Kohgiluye and Boyer-Ahmad Province of Iran at an acceptable reliability level. For example, for one of the Pareto solutions found corresponding to maximum land development (30,000 ha), an average volume of 237 million cubic meter (MCM) is transferred annually at a 73.2% reliability level with average sizes of water transfer and storage elements. Further, design-operation and hydropower scenarios were also evaluated, and the Pareto solutions were obtained.     

Cross‐linking gold nanoparticles aggregation method based on localised surface plasmon resonance for quantitative detection of miR‐155

MiR‐155 plays a critical role in the formation of cancers and other diseases. In this study, the authors aimed to design and fabricate a biosensor based on cross‐linking gold nanoparticles (AuNPs) aggregation for the detection and quantification of miR‐155. Also, they intended to compare this method with SYBR Green real‐time polymerase chain reaction (PCR). Primers for real‐time PCR, and two thiolated capture probes for biosensor, complementary with miR‐155, were designed. Citrate capped AuNPs (18.7 ± 3.6 nm) were synthesised and thiolated capture probes immobilised to AuNPs. The various concentrations of synthetic miR‐155 were measured by this biosensor and real‐time PCR method. Colorimetric changes were studied, and the calibration curves were plotted. Results showed the detection limit of 10 nM for the fabricated biosensor and real‐time PCR. Also, eye detection using colour showed the weaker detection limit (1 µM), for this biosensor. MiR‐133b as the non‐complementary target could not cause a change in both colour and UV–visible spectrum. The increase in hydrodynamic diameter and negative zeta potential of AuNPs after the addition of probes verified the biosensor accurately fabricated. This fabricated biosensor could detect miR‐155 simpler and faster than previous methods.Inspec keywords: RNA, molecular biophysics, biochemistry, cancer, nanoparticles, gold, aggregation, surface plasmon resonance, molecular configurations, nanosensors, enzymes, calibration, ultraviolet spectra, visible spectra, eye, hydrodynamics, electrokinetic effects, biosensors, nanofabricationOther keywords: cross‐linking gold nanoparticles aggregation method, localised surface plasmon resonance, quantitative detection, cancers, diseases, biosensor, miR‐155 detection, miR‐155 quantification, SYBR green real‐time polymerase chain reaction, thiolated capture probes, citrate capped AuNPs, synthetic miR‐155, real‐time PCR method, colorimetric changes, calibration curves, eye detection, colour, detection limit, MiR‐133b, noncomplementary target, UV‐visible spectrum, hydrodynamic diameter, negative zeta potential, Au     

The finite cell method for nearly incompressible finite strain plasticity problems with complex geometries

In this paper, the performance of the Finite Cell Method is studied for nearly incompressible finite strain plasticity problems. The Finite Cell Method is a combination of the fictitious domain approach with the high-order Finite Element Method. It provides easy mesh generation capabilities for highly complex geometries; moreover, this method offers high convergence rates, the possibility to overcome locking and robustness against high mesh distortions. The performance of this method is numerically investigated based on computations of benchmark and applied problems. The results are also verified with the $h$- and $p$-version Finite Element Method. It is demonstrated that the Finite Cell Method is an appropriate simulation tool for large plastic deformations of structures with complex geometries and microstructured materials, such as porous and cellular metals that are made up of ductile materials obeying nearly incompressible $J_2$ theory of plasticity.     

A hybrid genetic algorithm–adaptive network-based fuzzy inference system in prediction of wave parameters

An important issue in application of fuzzy inference systems (FISs) to a class of system identification problems such as prediction of wave parameters is to extract the structure and type of fuzzy if–then rules from an available input–output data set. In this paper, a hybrid genetic algorithm–adaptive network-based FIS (GA–ANFIS) model has been developed in which both clustering and rule base parameters are simultaneously optimized using GAs and artificial neural nets (ANNs). The parameters of a subtractive clustering method, by which the number and structure of fuzzy rules are controlled, are optimized by GAs within which ANFIS is called for tuning the parameters of rule base generated by GAs. The model has been applied in the prediction of wave parameters, i.e. wave significant height and peak spectral period, in a duration-limited condition in Lake Michigan. The data set of year 2001 has been used as training set and that of year 2004 as testing data. The results obtained by the proposed model are presented and analyzed. Results indicate that GA–ANFIS model is superior to ANFIS and Shore Protection Manual (SPM) methods in terms of their prediction accuracy.     

Mathematical Modelling of Non‐Isothermal Venturi Scrubbers

A mathematical model consisting of differential equations for energy, momentum and material exchange is developed for a non‐isothermal Venturi‐type scrubber. By this model, the effects of heat and mass transfer on droplets concentration distribution and removal efficiency of particulate in a non‐isothermal Venturi scrubber can be investigated. In order to approach a realistic model, the liquid film flow on the walls and droplet size distribution are considered. The model is validated by comparing the results of mathematical model by plant and experimental data reported in the literature. The Results section of this work reveals that the inlet humidity and temperature of the gas can affect the removal efficiency of the scrubber.     

A Mathematical 3D Solution to Efficiently Locate Drones in 5G Wireless Networks

Wireless Personal Communications - One of the most important issues in wireless networks is the proper coverage of users as well as providing quality of service (QoS). The fifth generation of...     

Extension of VIKOR method based on interval-valued fuzzy sets

Decision making is the process of finding the best option among the feasible alternatives. In classical multiple-criteria decision-making (MCDM) methods, the ratings and the weights of the criteria are known precisely. However, if decision makers cannot reach an agreement on the method of defining linguistic variables based on the fuzzy sets, the interval-valued fuzzy set theory can provide a more accurate modeling. In this paper, the interval-valued fuzzy VIKOR method is presented, aiming at solving MCDM problems in which the weights of criteria are unequal, using interval-valued fuzzy set concepts. For application and verification, this study presents a numerical example and builds a practical maintenance strategy selection problem to verify our proposed method. Moreover, a comparison is made between the interval-valued fuzzy VIKOR and other adapted MCDM interval-valued fuzzy number-based.     

Improving solubility and refolding efficiency of human V(H)s by a novel mutational approach

The antibody V(H) domains of camelids tend to be soluble and to resist aggregation, in contrast to human V(H) domains. For immunotherapy, attempts have therefore been made to improve the properties of human V(H)s by camelization of a small set of framework residues. Here, we have identified through sequence comparison of well-folded llama V(H) domains an alternative set of residues (not typically camelid) for mutation. Thus, the solubility and thermal refolding efficiency of a typical human V(H), derived from the human antibody BT32/A6, were improved by introduction of two mutations in framework region (FR) 1 and 4 to generate BT32/A6.L1. Three more mutations in FR3 of BT32/A6.L1 further improved the thermal refolding efficiency while retaining solubility and cooperative melting profiles. To demonstrate practical utility, BT32/A6.L1 was used to construct a phage display library from which were isolated human V(H)s with good antigen binding activity and solubility. The engineered human V(H) domains described here may be useful for immunotherapy, due to their expected low immunogenicity, and in applications involving transient high temperatures, due to their efficient refolding after thermal denaturation.