A fuzzy-stochastic OWA model for robust multi-criteria decision making

All realistic Multi-Criteria Decision Making (MCDM) problems face various kinds of uncertainty. Since the evaluations of alternatives with respect to the criteria are uncertain they will be assumed to have stochastic nature. To obtain the uncertain optimism degree of the decision maker fuzzy linguistic quantifiers will be used. Then a new approach for fuzzy-stochastic modeling of MCDM problems will be introduced by merging the stochastic and fuzzy approaches into the OWA operator. The results of the new approach, entitled FSOWA, give the expected value and the variance of the combined goodness measure for each alternative. Robust decision depends on the combined goodness measures of alternatives and also on the variations of these measures under uncertainty. In order to combine these two characteristics a composite goodness measure will be defined. The theoretical results will be illustrated in a watershed management problem. By using this measure will give more sensitive decisions to the stakeholders whose optimism degrees are different than that of the decision maker. FSOWA can be used for robust decision making on the competitive alternatives under uncertainty.     

Maximizing the minimum completion time on parallel machines

We propose an exact branch-and-bound algorithm for the problem of maximizing the minimum machine completion time on identical parallel machines. The proposed algorithm is based on tight lower and upper bounds as well as an effective symmetry-breaking branching strategy. Computational results performed on a large set of randomly generated instances attest to the efficacy of the proposed algorithm.      

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.     

Incorporation of Sulfate or Selenate Groups into Oxotellurates(IV): I. Calcium,Cadmium, and Strontium Compounds

Seven new mixed oxochalcogenate compounds in the systems M^II/X^VI/Te^IV/O/(H), (M^II = Ca, Cd, Sr; X^VI = S, Se) were obtained under hydrothermal conditions (210 °C, one week). Crystal structure determinations based on single‐crystal X‐ray diffraction data revealed the compositions Ca₃(SeO₄)(TeO₃)₂, Ca₃(SeO₄)(Te₃O₈), Cd₃(SeO₄)(Te₃O₈), Cd₃(H₂O)(SO₄)(Te₃O₈), Cd₄(SO₄)(TeO₃)₃, Cd₅(SO₄)₂(TeO₃)₂(OH)₂, and Sr₃(H₂O)₂(SeO₄)(TeO₃)₂ for these phases. Peculiar features of the crystal structures of Ca₃(SeO₄)(TeO₃)₂, Ca₃(SeO₄)(Te₃O₈), Cd₃(SeO₄)(Te₃O₈), Cd₃(H₂O)(SO₄)(Te₃O₈), and Sr₃(H₂O)₂(SeO₄)(TeO₃)₂ are metal‐oxotellurate(IV) layers connected by bridging XO₄ tetrahedra and/or by hydrogen‐bonding interactions involving hydroxyl or water groups, whereas Cd₄(SO₄)(TeO₃)₃ and Cd₅(SO₄)₂(TeO₃)₂(OH)₂ crystallize as framework structures. Common to all crystal structures is the stereoactivity of the Te^IV electron lone pair for each oxotellurate(IV) unit, pointing either into the inter‐layer space, or into channels and cavities in the crystal structures.     

Microdroplet size prediction in microfluidic systems via artificial neural network modeling for water-in-oil emulsion formulation

In this paper, an experimental study and modeling by artificial neural networks were carried out to predict the generated microdroplet dimensionless size in a microfluidic system in order to formulate a water-in-oil emulsion. The various parameters that affect the size of microdroplets (flow rates, viscosities, surface tensions of both the two phases and the diameter of the microchannel) are studied and further grouped into dimensionless numbers; we used these numbers as input to the neural network and the dimensionless length as output. The better neural network architecture has 10 neurons in the hidden layer with a mean square error of 1.4 10^?6 and a determination’s coefficient near 1 value. The relative importance of inputs on the size of the microdroplets has been determined using the Garson algorithm and the results are in good agreement with other works.     

Synthesis,structural characterization and alcohol oxidation activity of a new mononuclear manganese(II) complex

A manganese(II) complex of 2,4,6-tris(2-pyridyl)-1,3,5-triazine (tptz) has been synthesized and characterized by single-crystal X-ray diffraction, elemental analyses, IR, and UV–Vis spectroscopic techniques. Oxidation of alcohols to their corresponding aldehydes and ketones was conducted by this catalyst using oxone (2KHSO₅·KHSO₄·K₂SO₄) as an oxidant under biphasic reaction conditions (CH₂Cl₂/H₂O) and tetra-n-butylammonium bromide as phase transfer agent under air at room temperature. Easy preparation, mild reaction conditions, high yields of the products, short reaction times, no further oxidation to the corresponding carboxylic acids, high selectivity and inexpensive reagents make this catalytic system a useful oxidation method for aliphatic and benzylic alcohols.     

Electrocatalytic oxidation of nitrite at a terpyridine manganese(II) complex modified carbon past electrode

A carbon past electrode modified with [Mn(H₂O)(N₃)(NO₃)(pyterpy)], ( \textpyterpy = 4￠- ( 4 - \textpyridyl ) - 2,2￠:\text6￠,\text2￠￠- \textterpyridine ) \left( {{\text{pyterpy}} = 4\prime - \left( {4 - {\text{pyridyl}}} \right) - 2,2\prime:{\text{6}}\prime,{\text{2}}\prime\prime - {\text{terpyridine}}} \right) complex have been applied to the electrocatalytic oxidation of nitrite which reduced the overpotential by about 120 mV with obviously increasing the current response. Relative standard deviations for nitrite determination was less than 2.0%, and nitrite can be determined in the ranges of 5.00 × 10⁻⁶ to 1.55 × 10⁻² mol L⁻¹, with a detection limit of 8 × 10⁻⁷ mol L⁻¹. The treatment of the voltammetric data showed that it is a pure diffusion-controlled reaction, which involves one electron in the rate-determining step. The rate constant k′, transfer coefficient α for the catalytic reaction, and diffusion coefficient of nitrite in the solution, D, were found to be 1.4 × 10⁻², 0.56× 10⁻⁶, and 7.99 × 10⁻⁶ cm² s⁻¹, respectively. The mechanism for the interaction of nitrite with the Mn(II) complex modified carbon past electrode is proposed. This work provides a simple and easy approach to detection of nitrite ion. The modified electrode indicated reproducible behavior, anti-fouling properties, and stability during electrochemical experiments, making it particularly suitable for the analytical purposes.     

New coumarin-hemiterpene ether glucosides and a structurally related phenylpropanoic acid derivative from Artemisia armeniaca

Two new coumarin-hemiterpene ether glucosides, 4'-O-(beta-D-glucopyranosyl) desoxylacarol (1), and 5-O-(beta-D-glucopyranosyl) lacarol (2), were isolated from the methanolic extract of the aerial parts of Artemisia armeniaca Lam. Their structures were established by means of spectroscopic analysis including 1H- and 2D-NMR, HRESIMS, and acid hydrolysis. In addition, a structurally related phenylpropanoic acid derivative (3) was obtained in small amounts, and its structure tentatively assigned as 3-(4-hydroxy-3-methylbutoxy)-4-methoxy melilotic acid.     

Theoretical calculation of excitonic binding energies and optical absorption spectra for Armchair graphene nanoribbons

In this paper the excitons of armchair graphene nanoribbons with layers of different width and thickness have been investigated. In this investigation, the band structure and energy gap of armchair graphene nanoribbons have been calculated using a tight-binding model including edge deformation effects (all edge atoms have been passivated with hydrogen atoms). Also, by calculating the conductance in armchair graphene nanoribbons (A-GNRs) optical absorption of armchair graphene nanoribbon in the single-electron approximation has been obtained. Finally, the binding energy of excitons in armchair graphene nanoribbons has been calculated using the Wannier model, Hartree-Fock approximation and the Bethe-Salpeter equation.