In this research, AA2024 aluminum alloy-based surface composites was fabricated using ex-situ titanium boride particles (TiB2) as reinforcement using friction stir processing technique. Microstructural and mechanical variation with respect to addition of TiB2 onto AA2024 surface was studied and evaluated. Results proposed an increase in mechanical strength and hardness with respect to TiB2 addition when compared with the substrate metal. Dry sliding wear characteristics of aluminum surface composites at varying sliding distances (500m, 1000m, 1500m and 2000m) was analyzed using pin-on-disc apparatus. Wear resistance of developed surface composites improved comparatively with respect to substrate metal due to the combined effect of particle inclusion and friction stir processing. Characterization of worn out surface composites proposed that wear mechanism happens due to combination of abrasive and adhesive wear, while the major material removal happens due to abrasive wear.
“Malignant mesothelioma (MM)” is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is “malignant mesothelioma.” Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination “epithelial/mesenchymal appearance of MM,” however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful “Malignant Mesothelioma illness” identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support “Vector Machine, Neural Network, and Decision Tree” are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.
Software reliability is one of the important factors of software quality. Many mathematical models are proposed in literature to predict the software quality and related reliability. Generally during testing many factors are considered like effort, time and resources. Testing effort can be better described by time, person hours and number of test cases. During testing many resources are being consumed. In this paper an analysis is done based on incorporating the Bass diffusion testing-effort function in to NHPP Software reliability growth model and also observed its release policy. Experiments are performed on the real datasets. Parameters are calculated and observed that our model is best fitted for the datasets. Index Terms-Software reliability, software testing, testing effort, non-homogeneous poisson process (NHPP), software cost. NOTATIONS m (t): Expected mean number of faults detected in time (0,t] λ (t) : Failure intensity for m(t) n (t) : Fault content function m d (t): Cumulative number of faults detected up to t m r (t): Cumulative number of faults isolated up to time t. W (t): Cumulative testing effort consumption at time t. W*(t): W (t)-W (0) W (t): Cumulative testing effort consumption at time t. W*(t) : W (t)-W (0) A : Expected number of initial faults W (t): Cumulative testing effort consumption at time r (t) : Failure detection rate function r : Constant fault detection rate function.
Software come to be an important element in recent times, from small residence hold gadgets to large machinery wishes fine software. software development is a technical oriented system where range of quantitative and qualitative duties have been completed parallel a good way to meets the needs of the consumer. Many people play a vital role within the improvement of software program product, consequently there is chance of committing errors by way of these humans and these errors becomes faults in later stages. Computing software program cost for the duration of software development can facilitate us predicting the time of release of the software. In this paper we have investigated release time of software program by way of considering the imperfect debugging software program reliability growth model and cost model.
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