A comparative study of rate monotonic schedulability tests

With the increased penetration of real-time systems into our surroundings, the selection of an efficient schedulability test under fixed priority system from a plethora of existing results, has become a matter of primary interest to real-time system designers. The need for a faster schedulability tests becomes more prominent when it applies to online systems, where processor time is a sacred resource and it is of central importance to assign processor to execute tasks instead of determining system schedulability. Under fixed priority nonpreemptive real-time systems, current schedulability tests (in exact form) can be divided into: response time based tests, and scheduling points tests. To the best of our knowledge, no comparative study of these test to date has ever been presented. The aim of this work is to assist the system designers in the process of selecting a suitable technique from the existing literature after knowing the pros and cons associated with these tests. We highlight the mechanism behind the feasibility tests, theoretically and experimentally. Our experimental results show that response time based tests are faster than scheduling points tests, which make the response time based tests an excellent choice for online systems.     

Improved mechanical properties of magnesium–graphene composites with copper–graphene hybrids

New magnesium nanocomposites reinforced with copper–graphene nanoplatelet hybrid particles have been prepared through the semipowder metallurgy method. Compared with the monolithic Mg, the Mg–1Cu–xGNPs nanocomposites exhibited higher tensile and compressive strength. In tension, nanocomposites revealed substantial enhancement in elastic modulus, 0.2% yield strength, ultimate tensile strength and failure strain (up to +89, +117, +58 and +96% respectively) compared to monolithic Mg. In compression, the nanocomposites showed the greatest improvement in 0.2% yield strength, and the ultimate compressive strength and failure strain (%) (up to +34, +59 and +61% respectively), whilst the compressive elastic modulus first increases and then decreases with an increase in the graphene nanoplatelets (GNPs) contents. The enhanced strength of the composites is likely to result from strengthening mechanisms invoked by the addition of Cu–GNPs hybrids.     

High-efficiency remediation of cadmium (Cd2+) from aqueous solution using poultry manure– and farmyard manure–derived biochars

Farmyard manure (FYM-BC) and poultry manure (PM-BC) derived biochars were applied as adsorbents to remove Cd²⁺ from water. Results indicated that PM-BC was a more efficient adsorbent than FYM-BC at all experimental conditions. Maximum Cd²⁺ adsorption was observed at pH 4, temperature 318 K and contact time 1 h, regardless of biochar type. The Langmuir model predicted maximum adsorption capacity of 90.09 mg g^?1 for PM-BC. The data fitting to pseudo-second-order model proposed chemisorption of Cd²⁺ onto biochars. Thermodynamics indicated that adsorption was spontaneous and endothermic. Post-adsorption analysis provided evidences of strong chemical interactions between biochars’ functional groups and Cd²⁺ ions.     

Synthesis,Characterization and Surface Properties of Amidosulfobetaine Surfactants Bearing Odd-Number Hydrophobic Tail

Three amidosulfobetaine surfactants were synthesized namely: 3-(N-pentadecanamidopropyl-N,N-dimethyl ammonium) propanesulfonate (2a); 3-(N-heptadecanamidopropyl-N,N-dimethyl ammonium) propanesulfonate (2b), and 3-(N-nonadecanamidopropyl-N,N-dimethyl ammonium) propanesulfonate (2c). These surfactants were prepared by direct amidation of commercially available fatty acids with 3-(dimethylamino)-1-propylamine and subsequent reaction with 1,3-propanesultone to obtain quaternary ammonium salts. The synthesized surfactants were characterized by IR, NMR and mass spectrometry. Thermogravimetric analysis (TGA) results showed that the synthesized surfactants have excellent thermal stability with no major thermal degradation below 300 °C. The critical micelle concentration (CMC) values of the surfactants 2a and 2b were found to be 2.2 × 10^?4 and 1.04 × 10^?4 mol/L, and the corresponding surface tension (γ_CMC) values were 33.14 and 34.89 mN m^?1, respectively. The surfactants exhibit excellent surface properties, which are comparable with conventional surfactants. The intrinsic viscosity of surfactant (2b) was studied at various temperatures and concentrations of multi-component brine solution. The plot of natural logarithm of relative viscosity versus surfactant concentration obtained from Higiro et al. model best fit the surfactant behavior. Due to good salt resistance, excellent surface properties and thermal stability, the synthesized surfactant has potential to be used in various oil field applications such as enhanced oil recovery, fracturing, acid diversion, and well stimulation.     

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F‐score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.