Comparative study of the factors affecting the generativity of office spaces

This study investigated the categories of generative and conservative offices by using syntactic theories and conducting a comparativean alysis of two different layout configurations. Generativity depends on several features that include but are not limited to spatial criteria. Generativity is also affected by aspects that extend beyond the physical, such as the symbolic relationship between the social organism of an organization and the internal structure of a workplace. Results show that no consistent relationship exists between spatial variables and behaviors even though the examined layouts satisfy an organization's basi ccultural require-ments, including privacy, territoriality, and hierarchy. Spatial parameters increase the potential for generativity, but such increase is not guaranteed without necessary changes in the attitudes, programs, and policies of organizations. Recognizing the limits of spatial variables requires evidence-based field research on office settings to determine operational and tailor-made solutions.     

A symmetric rank-one method based on extra updating techniques for unconstrained optimization

In this paper, we present a new symmetric rank-one (SR1) method for the solution of unconstrained optimization problems. The proposed method involves an algorithm in which the usual SR1 Hessian is updated a number of times in a way to be specified in some iterations, to improve the performance of the Hessian approximation.In particular, we discuss how to consider a criterion for indicating at each iteration whether it is necessary to employ extra updates. However it is well known that there are some theoretical difficulties when applying the SR1 update. Even for a current positive definite Hessian approximation, it is possible that the SR1 update may not be defined or the SR1 update may not preserve positive definiteness at some iterations. We then employ a restarting procedure that guarantees that updated matrices will be well-defined while preserving positive definiteness of updates. Numerical results support these theoretical considerations. They show that the implementation of the SR1 method using extra updating techniques improves the performance of the SR1 method substantially for a number of test problems from the literature.     

Fault Diagnosis Improvement Using Dynamic Fault Model in Optimal Sensor Placement: A Case Study of Steam Turbine

Health data are collected dominantly through sensors mounted on different locations in the system. Optimization of sensor network has a significant influence on the reliability of system health prognostics process. In this research, the effect of sensors reliability is studied on their placement optimization. Sensors are considered in this study as components in system failure model. This study is aimed to use ‘Priority AND’ gate for evaluating the effect of time dependencies of sensors as well as components failure on the optimal sensor placement. Because of that, PAND gate is added to the fault tree between all sensors and their corresponding components to develop the failure model of each sensor placement scenario. For calculating the probability of top event, a Monte Carlo‐based algebraic approach is proposed. In algebraic approach, temporal operator ‘BEFORE’ is proposed for calculating the probability of ‘PAND’ gate. The result of using ‘BEFORE’ operator is an analytical solution for probability of each cut sequence. Because of the complexity of analytical solution in practical problems, a Monte Carlo simulation is utilized on the solution in this research. Then the probability of each cut sequence is calculated. Consequently, the probability of top event for each scenario is obtained. Finally, all scenarios are ranked based on top event probabilities. As a case study, optimization of sensor placement has been demonstrated on steam turbine and results are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Efficient desulfurization of fuel with functionalized mesoporous carbon CMK-3-O and comparison its performance with mesoporous carbon CMK-3

In this work, a functionalized mesoporous carbon (CMK-3-O) was synthesized after oxidation with nitric acid and was used to adsorb dibenzothiophene (DBT) from model oil for the first time. Then, its performance was compared with that of CMK-3. The functionalized mesoporous carbon, CMK-3-O, showed better a capacitance performance for DBT adsorption than that of CMK-3. The maximum adsorption capacity was obtained for functionalized mesoporous carbon at optimum conditions with 6 M HNO₃ aqueous solution and 30 min contact time. The physical and structural properties of CMK-3-O and CMK-3 were investigated with X-ray diffraction method (XRD), N₂ adsorption–desorption isotherm, FT-IR, and elemental analysis (CHNO). Results of the elemental analysis showed that the oxygen and nitrogen content has increased and the carbon content has decreased through oxidation treatment. The effects of various factors on the adsorption process (such as temperature, amount of adsorbent, contact time, and concentration) of DBT were studied. CMK-3-O showed a maximum adsorption capacity of 86.96 mg DBT g^?1 of CMK-3-O at optimized conditions (temperature, 25°C; adsorbent dosage, 20 g L^?1; contact time, 60 min), which was a higher adsorption capacity of that observed for CMK-3 (57.47 mg DBT g^?1 of CMK-3). Kinetic studies have revealed that the adsorption of DBT can be described by a pseudo-second-order rate equation. Equilibrium data showed that adsorption process was best represented by the Langmuir model. The results also illustrated the fact that the regenerated adsorbent afforded 64.3% of the initial adsorption capacity after the two regeneration cycles.     

Fingerprinting of Peptides with a Large Channel of Bacteriophage Phi29 DNA Packaging Motor

Nanopore technology has become a highly sensitive and powerful tool for single molecule sensing of chemicals and biopolymers. Protein pores have the advantages of size amenability, channel homogeneity, and fabrication reproducibility. But most well‐studied protein pores for sensing are too small for passage of peptide analytes that are typically a few nanometers in dimension. The funnel‐shaped channel of bacteriophage phi29 DNA packaging motor has previously been inserted into a lipid membrane to serve as a larger pore with a narrowest N‐terminal constriction of 3.6 nm and a wider C‐terminal end of 6 nm. Here, the utility of phi29 motor channel for fingerprinting of various peptides using single molecule electrophysiological assays is reported. The translocation of peptides is proved unequivocally by single molecule fluorescence imaging. Current blockage percentage and distinctive current signatures are used to distinguish peptides with high confidence. Each peptide generated one or two distinct current blockage peaks, serving as typical fingerprint for each peptide. The oligomeric states of peptides can also be studied in real time at single molecule level. The results demonstrate the potential for further development of phi29 motor channel for detection of disease‐associated peptide biomarkers.     

Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models

The study applies kidney algorithm for the optimization of reservoir operation for hydropower generation. The objective function defined for optimization is to minimize the hydroelectric power deficiency. Results of kidney algorithm are compared with those of bat algorithm (BA), water cycle algorithm (WCA), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSOA), and scatter matters search algorithm (SMSA). All algorithms are evaluated by Complex proportional assessment (COPRAS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), modified TOPSIS, and Weighted Aggregated Sum Product Assessment (WASPAS), as well as Borda count social choice theory. Then, vulnerability, time and volumetric reliability, as well as resiliency indices are used for comparison and multi-criteria decision-making indicators for selecting the best algorithm. It is found that no algorithm is ranked uniformly the best. Results indicate that kidney and particle swarm algorithms are ranked higher than other algorithms by most indices. Results of 10 random implementations of the objective function indicate that KA has a lower coefficient of variation and is computationally moe efficient. Further, most of the multi-criteria decision making models allocate the first rank to KA.     

Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm

This article shows an application of a new algorithm, called kidney algorithm, for reservoir operation which employs three different operators, namely filtration, secretion, and excretion that lead to faster convergence and more accurate solutions. The kidney algorithm (KA) was used for generating the optimal operation of a reservoir namely; Aydoghmoush dam in eastern Azerbaijan province in Iran whose purpose was to decrease irrigation deficit downstream of the dam. Results from the algorithm were compared with those by other evolutionary algorithms, including bat (BA), genetic (GA), particle swarm (PSO), shark (SA), and weed algorithms (WA). The results showed that the kidney algorithm provided the best performance against the other evolutionary algorithms. For example, the computational time for the KA was 3 s, 2 s, 4 s, 6 s and 3 s less than BA, SA, GA PSA and WA, respectively. Also, the objective function for the optimization problem was the minimization of the irrigation deficits and its value for the KA was 55%, 28%, 52%, 44 and 54% less than GA, SA, WA, BA and PSA, respectively. Also, the different performance indexes showed the superiority of the KA compared to the other algorithms. For example, the root mean square error for the KA was 74%, 61%, 68%, 33 and 54% less than GA, SA, WA, BA and PSA, respectively. Different multi criteria decision models were used to select the best models. The results showed that the KA achieved the first rank for the optimization problem and thus, it shows a high potential to be applied for different problems in the field of water resources management.     

A New Method for Flood Routing Utilizing Four-Parameter Nonlinear Muskingum and Shark Algorithm

Water Resources Management - Considering the great importance of flood prediction, flood routing based on Shark Algorithm (SA) and Four-Parameter Nonlinear Muskingum (FPNM) has been proposed in the...     

Compact microstrip lowpass filter with very sharp roll-off using meandered line resonators

In this paper, a new compact size microstrip lowpass filter (LPF) with a very sharp roll-off is presented to apply in the modern wireless networks. The proposed LPF is designed using the series main resonators with meandered lines based on inductor-capacitor (LC) equivalent circuit analysis. The main goal is to achieve maximum-sharp roll-off by maintaining a wide stopband bandwidth and high return loss (RL). The main resonator of the proposed filter is consisted of two meandered line hairpin resonators (MLHR), and a meandered line T-shaped resonator (MLTR). The designed suppressor is composed of two coupled radial stubs to create a wide stopband. Low return loss in the passband, which has been created by the main resonator, is resolved by the suppressor structure with high return loss. The measured results show a ??3 dB cut-off frequency of 1.93 GHz. The very sharp transition band starts at 1.93 to 1.97 GHz (from ??3 to ??20 dB). The stopband is from 1.97 to 19.9 GHz (with the suppression level of ??20 dB). Also, the total size of the proposed LPF is only 13.3?×?10.1 mm².

     

Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters

Water Resources Management - For the first time, a novel hybrid machine learning model named the least-squares support vector machine-arithmetic optimization algorithm (LSSVM-AOA) was proposed. The...