Effects of Steel and Polypropylene Fiber Addition on Interface Bond Strength between Normal Concrete Substrate andSelf-Compacting Concrete Topping

Based on facts that the composite action in semi-precast and strengthened structural system depends on the bond strength of the interface between concrete faces of different ages, this preliminary research is aimed to investigate effects of mixed polypropylene (PPF) and steel fiber (SF) addition on the hardened properties of Self-Compacting Concrete (SCC) and its bond strength when used as topping layer on normal concrete substrate. Effects of hybrid fiber addition on the hardened properties of SCC were investigated based on the compressive, splitting tensile and flexural strength of concrete specimens which is tested in 28 days of age. In the next step, the tensile and shear strength of the interface were evaluated using indirect splitting tensile and bi-surface shear test method. In this research, fiber addition were prepared using 1 kg/m PPF and various SF addition ranging from 15 kg/m3, 20 kg/m3, 25 kg/m3 and 30 kg/m3. Test results indicate that hybrid fiber addition does not affect the compressive strength significantly but it leads ositive improvement to the splitting tensile and flexural strength of hardened SCC and also improve the bond strength between SCC and normal concrete. Hybrid fiber addition of 1 kg/m3 PPF which is combined with 20 kg/m3 SF can be suggested as optimum composition for Hybrid Fiber Reinforced Self-Compacting Concrete (HyFRSCC) that will be used as topping or overlay material based on its hardened properties and interface strength

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Finite Element Modeling of the Transition Zone between Aggregat and Mortar in Concrete

Visual observations to the Interfacial Transition Zone (ITZ) between aggregate and mortar in concrete showed that this area differs significantly to the bulk mortar, further away from the ITZ. This ITZ has a higher porosity with a dissimilar crystal formation, therefore becoming the weak link in the material. In the past, concrete was seen as a two-phase material consisting of mortar and aggregates only. However, analyzing the material as a three-phase composite including the ITZ, will give a more realistic representation to its behavior. A Finite Element Model (FEM) was developed. The ITZ is modeled as a linkage element having a double spring, perpendicular and parallel to the ITZ surface. The individual load-deformation responses of these springs were obtained from laboratory tested specimens. Non-linearity is generated by evaluating the principal stresses at Gauss points, using the Kupfer-Hilsdorf-Rusch (1969) failure envelope and the CEB-FIB 2010 code. Iteration is conducted by the arc-length method developed by Riks-Wempners. The load-displacement curves resulted by the FEM were validated to laboratory tested specimens curves, to compare its effectiveness and asses the sensitivity of the model

Optimal Clustering under Uncertainty

Classical clustering algorithms typically either lack an underlying probability framework to make them predictive or focus on parameter estimation rather than defining and minimizing a notion of error. Recent work addresses these issues by developing a probabilistic framework based on the theory of random labeled point processes and characterizing a Bayes clusterer that minimizes the number of misclustered points. The Bayes clusterer is analogous to the Bayes classifier. Whereas determining a Bayes classifier requires full knowledge of the feature-label distribution, deriving a Bayes clusterer requires full knowledge of the point process. When uncertain of the point process, one would like to find a robust clusterer that is optimal over the uncertainty, just as one may find optimal robust classifiers with uncertain feature-label distributions. Herein, we derive an optimal robust clusterer by first finding an effective random point process that incorporates all randomness within its own probabilistic structure and from which a Bayes clusterer can be derived that provides an optimal robust clusterer relative to the uncertainty. This is analogous to the use of effective class-conditional distributions in robust classification. After evaluating the performance of robust clusterers in synthetic mixtures of Gaussians models, we apply the framework to granular imaging, where we make use of the asymptotic granulometric moment theory for granular images to relate robust clustering theory to the application.Comment: 19 pages, 5 eps figures, 1 tabl

Analysis and optimization of material flow inside the system of rotary coolers and intake pipeline via discrete element method modelling

There is hardly any industry that does not use transport, storage, and processing of particulate solids in its production process. In the past, all device designs were based on empirical relationships or the designer's experience. In the field of particulate solids, however, the discrete element method (DEM) has been increasingly used in recent years. This study shows how this simulation tool can be used in practice. More specifically, in dealing with operating problems with a rotary cooler which ensures the transport and cooling of the hot fly ash generated by combustion in fluidized bed boilers. For the given operating conditions, an analysis of the current cooling design was carried out, consisting of a non-standard intake pipeline, which divides and supplies the material to two rotary coolers. The study revealed shortcomings in both the pipeline design and the cooler design. The material was unevenly dispensed between the two coolers, which combined with the limited transport capacity of the coolers, led to overflowing and congestion of the whole system. Therefore, after visualization of the material flow and export of the necessary data using DEM design measures to mitigate these unwanted phenomena were carried out.Web of Science117art. no. 184

X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM

The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed. First, all potential point sources are located by peak detection on the image. The image and spectral features of these potential point sources are then extracted. Finally, a classifier to recognize the true point sources is build through the extracted features. Experiments and applications of our approach on real X-ray astronomical images are demonstrated. comparisons between our approach and other SVM-based classifiers are also carried out by evaluating the precision and recall rates, which prove that our approach is better and achieves a higher accuracy of around 89%.Comment: Accepted by ICSP201