An adaptive plan-oriented and continuous software migration to cloud in dynamic enterprises

Replacing existing software/hardware components with their equivalent cloud services is an important decision faced by IT managers in today's enterprises. A variety of possible migration targets and cloud services with too many configurations and cost models, disparate and changing strategic objectives of the enterprise management that triggers the migration process, and the complex structure of the legacy applications make software migration to the cloud a challenging issue. In contrast to the existing approaches that model the migration process as an optimization problem to find the optimal deployment of software components on cloud services without presenting a practical migration plan, in this paper, a plan-oriented migration approach is proposed by which the enterprise management is able to follow migration steps of a valid plan. All valid plans are modeled using a labeled transition system, and a recommender engine directs the management through the possible migration paths using predefined fitness functions. It was observed that, particularly in dynamic and changing conditions that a flexible migration plan is essential, the proposed plan-oriented method is very much effective in satisfying the enterprise strategic objectives. Evaluations have been performed using two quality indicators: total cost of ownership and scalability index.     

An efficient data dissemination model for wireless sensor networks

Wireless Networks - In a wireless sensor network, one of the most important constraints on sensor nodes is their power source, which is a battery. Sensor nodes carry a limited and generally...     

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault‐tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is still a challenging problem. In this paper, an intelligent neural network‐based model predictive control structure is proposed, where the prediction model is updated in each iteration based on a novel proposed online sequential multimodel structure. A hybrid offline‐online learning algorithm is adopted in the introduced online sequential multimodel structure to identify the time‐varying dynamics of the system. The proposed control structure can satisfactorily deal with unexpected actuator faults and structural damages as well as unmodeled dynamics and wind turbulence. The stability of the closed‐loop system is proved under some realistic assumptions. The simulation results demonstrate the high capability of the proposed approach for trajectory tracking control of a conventional aircraft in the simultaneous presence of system faults and external disturbances.     

Multi-Objective Surface Water Resource Management Considering Conflict Resolution and Utility Function Optimization

In the present research, a multi-objective model is developed for surface water resource management in the river basin area which is connected to the lake. This model considers different components of sustainable water resource management including economic, social and environmental aspects, and simultaneously tries to resolve conflicts between different stakeholders by means of non-symmetric Nash bargaining, which is linked to the multi-objective optimization method. This study proposes a new methodology to improve Nash Conflict Resolution through finding the optimum degree of the utility function. The proposed model is examined in the Zarrineh River basin in Iran. The results show that the amount of available resources or volume of reservoirs play a significant role in determining the optimal degree of the utility function and efficiency of the proposed method in such a way that the higher amount of resources or the larger reservoirs will result in the higher optimal degree of the utility function. In the proposed multi-objective model, two different amounts of surface water inflow are considered. The first assumed amount is the long-term average flow rate and the second one is equal to 80% of the first mode, which is reduced based on the estimated impacts of climate changes. This multi-objective allocation model could supply 100 and 97.5% of the environmental demand of Lake Urmia in the first and second situations, respectively.     

Bismuthene Metallurgy: Transformation of Bismuth Particles to Ultrahigh‐Aspect‐Ratio 2D Microsheets

Ultrathin bismuth exhibits promising performance for topological insulators due to its narrow band gap and intrinsic strong spin–orbit coupling, as well as for energy‐related applications because of its electronic and mechanical properties. However, large‐scale production of 2D sheets via liquid‐phase exfoliation as an established large‐scale method is restricted by the strong interaction between bismuth layers. Here, a sonication method is utilized to produce ultrahigh‐aspect‐ratio bismuthene microsheets. The studies on the mechanism excludes the exfoliation of the layered bulk bismuth and formation of the microsheets is attributed to the melting of spherical particles (r = 1.5 µm) at a high temperature—generated under the ultrasonic tip—followed by a recrystallization step producing uniformly‐shaped ultrathin microsheets (A = 0.5–2 µm², t: ≈2 nm). Notably, although the preparation is performed in oxygenated aqueous solution, the sheets are not oxidized, and they are stable under ambient conditions for at least 1 month. The microsheets are used to construct a vapor sensor using electrochemical impedance spectroscopy as detection technique. The device is highly selective, and it shows long‐term stability. Overall, this project exhibits a reproducible method for large‐scale preparation of ultrathin bismuthene microsheets in a benign environment, demonstrating opportunities to realize devices based on bismuthene.     

The Synthesis and Characterization of Hard-Soft Mn52Al45.7C2.3-α-Fe Nanocomposite Magnets

We report on the magnetic exchange coupling behavior in hard-soft Mn₅₂Al_45.7C_2.3-α-Fe nanocomposite magnets synthesized by high-energy ball milling at room temperature followed by post-annealing treatment at temperatures 300 to 600 °C. The analysis of hysteresis loops showed effective exchange coupling Mn₅₂Al_45.7C_2.3-α-Fe nanocomposite particles with smooth demagnetizing curves when annealed at 400 °C. But higher annealing temperatures pose kink in the hysteresis loop highlighting a weak exchange coupling with more magnetostatic interaction between hard and soft components. This trend was confirmed by the results on (BH)_max, which had the highest value for nanocomposite particles annealed at 400 °C. More detailed information on magnetic exchange coupling in nanocomposite particles was obtained by derivative magnetic curves and Henkel plots. Hard-soft Mn₅₂Al_45.7C_2.3-α-Fe magnets showed the sharpest high-field maximum in derivate magnetic curves when annealed at 400 °C as a signature of effective exchange coupling between Mn₅₂Al_45.7C_2.3 and α-Fe grains. In addition, Henkel plots display the dominance of positive peak for nanocomposite particles annealed at 300 and 400 °C, indicative of magnetic exchange-coupling. But the negative-peak dominated curves of those annealed at higher temperatures as well as single-phase Mn₅₂Al_45.7C_2.3 imply a significant magnetostatic interaction in the components owing to non-magnetic phases formed at elevated temperatures. Also, quantitative information obtained from recoil curve measurements assigned a higher degree of exchange coupling to nanocomposite magnets when annealed at 400 °C.

     

The preparation of surfactant-free highly dispersed ethylene glycol-based aluminum nitride-carbon nanofluids for heat transfer application

In the present study, aluminum nitride-carbon (AlN-C) nanocomposites are synthesized through a green, facile and inexpensive mechanochemical route. Well-dispersed nanofluids are prepared by milling of nanocomposite in ethylene glycol (EG) without using any surfactants/ dispersants. The resulting nanofluids have an excellent stability with no obvious sedimentation for at least three months. The results confirm the in-situ polymerization of EG on AlN surface and the formation of hyperbranched glycerol upon milling which in turn stabilizes the particles through a steric effect. The working nanofluids with very low loadings of up to 0.22 vol% of powder exhibit an enhanced heat transfer coefficient (h) of about 24% compared to that of the base fluid in a laminar flow regime (Re = 160). Brownian motion and boundary layer thinning are known as the main mechanisms, causing for this enhancement.     

Electrochemical pseudocapacitors of different compounds and nanocomposite of manganese: an introduction and review to it in recent studies

Journal of Materials Science: Materials in Electronics - This review gives an overview of the synthesis, surface, and electrochemical investigations over Mn-based compounds in the development of...     

Pattern synthesize of a cylindrical conformal array antenna by PSO algorithm

Pattern synthesize of conformal array antennas is often a challenging problem. Various optimization algorithms such as genetic, particle swarm optimization (PSO), and invasive weed optimization have already been used for pattern synthesizing of conformal arrays. In this paper, a focused beam is synthesized for a quarter cylindrical conformal array antenna using the PSO algorithm with small computations. The desired pattern is a focused beam at θ = 90° and ? = 45° with 10° beamwidth in elevation and 15° beamwidth in azimuth with ?20 dB side‐lobe level. This method can be used in general for synthesizing arbitrary desired patterns and array geometries.     

Estimation of average contact number of carbon nanotubes (CNTs) in polymer nanocomposites to optimize the electrical conductivity

The present paper suggests an equation for the average contact number of carbon nanotubes (CNTs) in CNT-reinforced polymer nanocomposites (PCNT) by two developed equations for electrical conductivity. Several novel parameters in PCNT such as CNT size, CNT concentration, network fraction, interphase depth, tunneling effect, and CNT wettability by the polymer medium are considered to define the average contact number (m). “m” is calculated for some samples and the variation of “m” is explored over a range of parameters’ values. The results show that dense interphase, high fraction of networked CNTs, reedy and short CNTs, low CNT surface energy, high polymer surface energy, low tunneling distance, and small contact diameter increase the “m” improving the conductivity. Moreover, tunneling distance and CNT contact diameter have the greatest effects on the “m”. The optimized level for “m” is necessary to control the nanocomposite’s conductivity.