D3FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging

Applied Intelligence - Providing accurate and speedy diagnosis and, in turn, treatment, automated medical image analysis plays a significant role in survival rate improvement. Inherent different...     

Simulation Studies on Growth and Death of Microorganisms Using the Oil‐Degrading Bacteria Petrotoga sp.

Three models were developed to estimate the potential of the selected bacteria Petrotoga sp., a thermophilic anaerobic oil‐degrading microorganism. Fourteen data sets of these bacteria were simulated by a multilayer feed‐forward neural network and an adaptive neuro‐fuzzy interference system. Twelve data sets served for training and two for testing these models. A simplified numerical model was performed assuming two phases in the growth process of oil‐degrading microorganisms, the logarithmic growth phase and the death phase. Comparison between these models in predicting bacterial cell concentration for different data sets indicates little difference between the overall average relative errors of the three methods and that all can be applied for prediction. Effects of salinity concentration, amount of yeast extract, and temperature on bacterial cell concentration were simulated by numerical and neural network models.     

Environmental management in Urmia Lake: thresholds approach

Urmia Lake is a vivid example of negligence in providing the environmental water requirement rooted in prolonged droughts and excessive upstream water withdrawal. The aim of this study is to propose a reclamation strategy for the lake by focusing on estimation of environmental requirement thresholds under various drought severity conditions. On the average, the lake will remain in a moderate hydrological drought condition if the business-as-usual scenario prevails. The shares of natural drought, water withdrawals and water resources development projects (i.e. dams) in recent lake inflow reduction are estimated at 45%, 40% and 15%, respectively; thus, the aggregated contribution of human-induced factors has a more significant impact on the current lake condition than that of natural drought, and a firm and consistent approach to reduce water consumption by the agricultural sector is required for lake reclamation.     

Post-weld Tempered Microstructure and Mechanical Properties of Hybrid Laser-Arc Welded Cast Martensitic Stainless Steel CA6NM

Manufacturing of hydroelectric turbine components involves the assembly of thick-walled stainless steels using conventional multi-pass arc welding processes. By contrast, hybrid laser-arc welding may be an attractive process for assembly of such materials to realize deeper penetration depths, higher production rates, narrower fusion, and heat-affected zones, and lower distortion. In the present work, single-pass hybrid laser-arc welding of 10-mm thick CA6NM, a low carbon martensitic stainless steel, was carried out in the butt joint configuration using a continuous wave fiber laser at its maximum power of 5.2 kW over welding speeds ranging from 0.75 to 1.2 m/minute. The microstructures across the weldment were characterized after post-weld tempering at 873 K (600 °C) for 1 hour. From microscopic examinations, the fusion zone was observed to mainly consist of tempered lath martensite and some residual delta-ferrite. The mechanical properties were evaluated in the post-weld tempered condition and correlated to the microstructures and defects. The ultimate tensile strength and Charpy impact energy values of the fully penetrated welds in the tempered condition were acceptable according to ASTM, ASME, and industrial specifications, which bodes well for the introduction of hybrid laser-arc welding technology for the manufacturing of next generation hydroelectric turbine components.     

Ablation behavior of elastomeric insulator based on nitrile rubber containing silica or silica-clay aerogels

Iranian Polymer Journal - Nitrile-based nanocomposite heat insulators are very attractive materials, due to their higher deformation bearing in special applications such as high temperature and...     

Scate: A Scalable Time and Energy Aware Actor Task Allocation Algorithm in Wireless Sensor and Actor Networks

In many applications of wireless sensor actor networks (WSANs) that often run in harsh environments, the reduction of completion times of tasks is highly desired. We present a new time‐aware, energy‐aware, and starvation‐free algorithm called Scate for assigning tasks to actors while satisfying the scalability and distribution requirements of WSANs with semi‐automated architecture. The proposed algorithm allows concurrent executions of any mix of small and large tasks and yet prevents probable starvation of tasks. To achieve this, it estimates the completion times of tasks on each available actor and then takes the remaining energies and the current workloads of these actors into account during task assignment to actors. The results of our experiments with a prototyped implementation of Scate show longer network lifetime, shorter makespan of resulting schedules, and more balanced loads on actors compared to when one of the three well‐known task‐scheduling algorithms, namely, the max‐min, min‐min, and opportunistic load balancing algorithms, is used.