APPLICATION OF RESPONSE SURFACE METHODOLOGY FOR THE MAXIMIZATION OF CONCORA CRUSH RESISTANCE OF PAPERBOARD

Response surface methodology is often used by researchers in different fields to determine the optimum values for controlled variables to maximize or minimize the response variables. Either maximization or minimization might be necessary depending on the response property. For example, if the response variable represents the yield of a process, maximization could be necessary; on the other hand, if the response variable is the biological oxygen demand (BOD) of an effluent the aim would definitely be minimization

Response surface methodology can be used two ways. It can be applied to the full-scale production or it can be scaled to a laboratory or the pilot plant. When applied to the full-scale production, the method is known as evolutionary operation (EVOP). EVOP is the continuous optimization of a process. The optimum conditions in a production plant can change depending on many factors such as raw material, ambient temperature, and equipment wear. Therefore, controlled variables should be optimized continuously to keep the response variable as close as possible to the maximum or minimum value. Hence, controlled variables are systematically changed around a center point to depict any shift of the response variable from the extreme. A thorough discussion of EVOP is given by Box, Evolutionary Operation: A Method for Increasing Industrial Productivity, Appl. Statist., 6, 81-101 (1957).     

Dyes from the leaves of deciduous plants with a high tannin content for wool

The aqueous leaf extracts of five different deciduous plants, namely, silver oak, flame of the forest, tanner’s senna, wattle and serviceberry, were used on their own and in combination with aluminium sulphate, stannous chloride and ferrous sulphate to dye wool by a simultaneous mordanting technique. The washing and light fastness properties of the developed shades were moderate to good. Based on the CIE 2000 spectral colour coordinate values (K/S, ΔL, Δa, Δb and ΔE), the developed shades were classified into four groups: yellow/brown, yellow, orange and dark grey. The use of aluminium sulphate gave medium shades (K/S = 8.24), while the stannous chloride and ferrous sulphate mordants provided deep shades (K/S = 30.5). Statistical analyses have shown that only the type of mordant and not the dye source significantly influenced the development of colour on wool. Hence, it was theoretically possible to use five selected leaves as a single mixture to produce four different colours on wool.     

A Novel DUF569 Gene Is a Positive Regulator of the Drought Stress Response in Arabidopsis

In the last two decades, global environmental change has increased abiotic stress on plants and severely affected crops. For example, drought stress is a serious abiotic stress that rapidly and substantially alters the morphological, physiological, and molecular responses of plants. In Arabidopsis, several drought-responsive genes have been identified; however, the underlying molecular mechanism of drought tolerance in plants remains largely unclear. Here, we report that the “domain of unknown function” novel gene DUF569 (AT1G69890) positively regulates drought stress in Arabidopsis. The Arabidopsis loss-of-function mutant atduf569 showed significant sensitivity to drought stress, i.e., severe wilting at the rosette-leaf stage after water was withheld for 3 days. Importantly, the mutant plant did not recover after rewatering, unlike wild-type (WT) plants. In addition, atduf569 plants showed significantly lower abscisic acid accumulation under optimal and drought-stress conditions, as well as significantly higher electrolyte leakage when compared with WT Col-0 plants. Spectrophotometric analyses also indicated a significantly lower accumulation of polyphenols, flavonoids, carotenoids, and chlorophylls in atduf569 mutant plants. Overall, our results suggest that novel DUF569 is a positive regulator of the response to drought in Arabidopsis.     

Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model

This paper is the first one of the two papers entitled “modeling and solving mixed-model assembly line balancing problem with setups”, which has the aim of developing the mathematical programming formulation of the problem and solving it with a hybrid meta-heuristic approach. In this current part, a mixed-integer linear mathematical programming (MILP) model for mixed-model assembly line balancing problem with setups is developed. The proposed MILP model considers some particular features of the real world problems such as parallel workstations, zoning constraints, and sequence dependent setup times between tasks, which is an actual framework in assembly line balancing problems. The main endeavor of Part-I is to formulate the sequence dependent setup times between tasks in type-I mixed-model assembly line balancing problem. The proposed model considers the setups between the tasks of the same model and the setups because of the model switches in any workstation. The capability of our MILP is tested through a set of computational experiments. Part-II tackles the problem with a multiple colony hybrid bees algorithm. A set of computational experiments is also carried out for the proposed approach in Part-II.     

Towards an Environmentally Acceptable Heterogeneous Catalytic Method of Producing Adipic Acid by the Oxidation of Hydrocarbons in Air

A survey is given of the catalytic methods potentially available for the production of adipic acid by the oxidation of readily available hydrocarbon precursors under environmentally benign conditions. Encouraging results are reported using H₂O₂ as oxidant and microporous FeAlPO-5 as catalyst at moderate temperatures.     

Toward an efficient and scalable feature selection approach for internet traffic classification

There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques.     

Opposition Based Joint Grey Wolf-Whale Optimization Algorithm Based Attribute Based Encryption in Secure Wireless Communication

Wireless Personal Communications - One of the applications of Sparse Linear Wireless Sensor Networks is environmental monitoring. In these networks, sensors are deployed in sensitive and strategic...     

Design of backpropagated neurocomputing paradigm for Stuxnet virus dynamics in control infrastructure

Neural Computing and Applications - In the present study, a novel application of backpropagated neurocomputing heuristics (BNCH) is presented for epidemic virus model that portrays the Stuxnet...     

Laser-treated austenitic steel and nickel alloy for human implants

The recent research in biocompatible materials has been useful in replacing and supporting the fractured natural human bones/joints. Under some condition, negative reaction like release of ions from the bare metal toward the human body fluid leads to corrosion. In this proposed research paper, the biocompatibility of the laser surface-modified austenitic stainless steel (SS316L) and nickel-based superalloy (Inconel 718) was studied. The investigation on laser-modified surfaces is evaluated through electrochemical polarization analysis using simulated body fluid (SBF). The samples subjected to electrochemical polarization analysis were characterized by optical image analysis, SEM, EDS, and XRD analysis. It was inferred that laser surface-modified materials provided enhanced corrosion resistance and bare nickel alloy is more susceptible to corrosion by SBF.