CRYSTALLIZATION KINETICS OF GLASS-CERAMICS BY DIFFERENTIAL THERMAL ANALYSIS

The crystallization behavior of fluorphlogopite, a glass-ceramic in the MgO–SiO2–Al2O3–K2O–B2O3–F system, was studied by substitution of Li2O for K2O in the glass composition. DTA, XRD and SEM were used for the study of crystallization behavior, formed phases and microstructure of the resulting glass-ceramics. Crystallization kinetics of the glass was investigated under non-isothermal conditions, using the formal theory of transformations for heterogeneous nucleation. The crystallization results were analyzed, and both the activation energy of crystallization process as well as the crystallization mechanism were characterized. Calculated kinetic parameters indicated that the appropriate crystallization mechanism was bulk crystallization for base glass and the sample with addition of Li2O. Non-isothermal DTA experiments showed that the crystallization activation energies of base glasses was in the range of 234-246 KJ/mol and in the samples with addition of Li2O was changed to the range of 317-322 KJ/mol

Comprehensive thermodynamic and operational optimization of a solar-assisted LiBr/water absorption refrigeration system

Absorption cooling systems have been investigated for many years due to their ability to use low-grade heat instead of electricity as the energy source. The aim of this work is to advance the performance of a single-effect Lithium bromide/water absorption cooling system. Taking the generator and evaporator temperatures as variables, the system is optimized to maximize exergetic and energetic efficiencies at different operational conditions using a multi-objective–multi-variable Genetic Algorithm. The Group Method of Data Handling neural network approach is adopted to derive correlations between the design variables and operational parameters. Finally, the system is coupled to evacuated tube solar collectors and compared to a similar system. The results reflect a maximum improvement in energetic and exergetic efficiencies of about 9.1% and 3.0%, respectively. This translates into savings of 187 dollars for every square meter of solar collector at present time. This improvement is achieved by decreasing the mean temperature of the generator by 6.2 °C and increasing the mean temperature of the evaporator by 1.6 °C. In the case of applying low-grade heat such as solar energy, it brings about both an improvement in the thermodynamic performances and a reduction in the generator temperature

Electronic bandgap miniaturized UWB antenna for near-field microwave investigation of skin

Near-field microwave investigation and tomography has many practical applications, especially where the trend of fields and signals in different environments is vital. This article shows an elliptical patch ultra-wideband antenna fed by a transmission line for the near-field characterization of cancerous cells in the skin. The antenna comprises an elliptical patch, stub loading to shift the band to lower bands, and an electronic bandgap structure on the ground side. Even though the antenna has a low profile of 15 × 15 mm2, the proposed antenna has more promising results than recent studies. Furthermore, both simulated near-field and far-field results show a broad bandwidth of 3.9–30 GHz and a resonance at 2.4 GHz applicable for industrial, scientific, and medical band applications. The proposed antenna also illustrates a peak gain of 6.48 dBi and a peak directivity of 7.09 dBi. Free space and skin (on a layer of breast fat and a tumor with a diameter of 4 mm at the boundary of skin and breast) are used as test environments during the simulation and measurement of near-field and far-field investigations while considering a phantom breast shape. Both far-field and near-field microwave investigations are performed in Computer Simulation Technology studio, and results are then compared with the measured data. The simulated and measured results are in good agreement, and the focused energy around the tumor is completely reconstructed. Therefore, the proposed antenna can be an adequate candidate for the differentiation of breast skin and tumor to reconstruct the tumor’s image

Non-allergic rhinitis: a case report and review

Rhinitis is characterized by rhinorrhea, sneezing, nasal congestion, nasal itch and/or postnasal drip. Often the first step in arriving at a diagnosis is to exclude or diagnose sensitivity to inhalant allergens. Non-allergic rhinitis (NAR) comprises multiple distinct conditions that may even co-exist with allergic rhinitis (AR). They may differ in their presentation and treatment. As well, the pathogenesis of NAR is not clearly elucidated and likely varied. There are many conditions that can have similar presentations to NAR or AR, including nasal polyps, anatomical/mechanical factors, autoimmune diseases, metabolic conditions, genetic conditions and immunodeficiency. Here we present a case of a rare condition initially diagnosed and treated as typical allergic rhinitis vs. vasomotor rhinitis, but found to be something much more serious. This case illustrates the importance of maintaining an appropriate differential diagnosis for a complaint routinely seen as mundane. The case presentation is followed by a review of the potential causes and pathogenesis of NAR

SDCOR Synthetic Datasets

SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale Datasets Link to arXiv e-print: https://arxiv.org/pdf/2006.07616.pdf Link to ResearchGate e-print: https://www.researchgate.net/publication/342197681_SDCOR_Scalable_Density-based_Clustering_for_Local_Outlier_Detection_in_Massive-Scale_Datasets This paper presents a method for local outlier detection in massive-scale datasets, which is based on a batch-wise density-based clustering approach. SDCOR consists of three major phases: 1) Sampling; 2) Scalable Clustering; and 3) Scoring. In the Sampling phase, a preliminary random sampling is conducted to obtain an abstraction of the entire data, named temporary clustering model; and also to acquire some information over the necessary parameters for the clustering procedures. Then, the Scalable Clustering phase will commence and the input data will be processed in chunks; as by processing successive chunks, the temporary clustering model gets gradual updates, till it turns into the final clustering model after processing the last chunk. Ultimately, at the last phase of the algorithm, regarding the final clustering model attained through the batch-wise clustering, and by employing the Mahalanobis distance criterion, each object is given an outlying score called SDCOR, which is equal to its local Mahalanobis distance. Each synthetic dataset in this repository is made of some Gaussian clusters with arbitrary mean vectors, far enough from each other, to impede probable overlappings among multidimensional clusters. For each of these artificial datasets, a specific amount of outliers are added around every cluster in the corresponding data; and moreover, the outliers "truth" is available along with each synthetic data. For every artificial dataset, there is a n-by-p matrix of dataset X (as n and p stand for the cardinality and dimensionality of the input data, respectively), along with the n-by-1 vector y of outlier labels, all together as a single binary MAT-file. We have implemented our code in MATLAB 9, which due to becoming reproducible, is accessible through our GitHub page (https://github.com/sana33/SDCOR). Finally, if you are interested in the idea or you are using this data for your research, please cite our paper as: @article{naghavi2021sdcor, title={SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets}, author={Naghavi Nozad, Sayyed Ahmad and Amir Haeri, Maryam and Folino, Gianluigi}, journal={Knowledge-Based Systems}, pages={107256}, year={2021}, publisher={Elsevier} } Thanks a lot ..

SDCOR Synthetic Datasets

SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale Datasets Link to arXiv e-print: https://arxiv.org/pdf/2006.07616.pdf This paper presents a method for local outlier detection in massive-scale datasets, which is based on a batch-wise density-based clustering approach. SDCOR consists of three major phases: 1) Sampling; 2) Scalable Clustering; and 3) Scoring. In the Sampling phase, a preliminary random sampling is conducted to obtain an abstraction of the entire data, named temporary clustering model; and also to acquire some information over the necessary parameters for the clustering procedures. Then, the Scalable Clustering phase will commence and the input data will be processed in chunks; as by processing successive chunks, the temporary clustering model gets gradual updates, till it turns into the final clustering model after processing the last chunk. Ultimately, at the last phase of the algorithm, regarding the final clustering model attained through the batch-wise clustering, and by employing the Mahalanobis distance criterion, each object is given an outlying score called SDCOR, which is equal to its local Mahalanobis distance. Each synthetic dataset in this repository is made of some Gaussian clusters with arbitrary mean vectors, far enough from each other, to impede probable overlappings among multidimensional clusters. For each of these artificial datasets, a specific amount of outliers are added around every cluster in the corresponding data; and moreover, the outliers "truth" is available along with each synthetic data. For every artificial dataset, there is a n-by-p matrix of dataset X (as n and p stand for the cardinality and dimensionality of the input data, respectively), along with the n-by-1 vector y of outlier labels, all together as a single binary MAT-file. We have implemented our code in MATLAB 9, which due to becoming reproducible, is accessible through our GitHub page (https://github.com/sana33/SDCOR). Finally, if you are interested in the idea or you are using this data for your research, please cite our paper as: @article{naghavi2020sdcor, title={SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale Datasets}, author={Naghavi-Nozad, Sayyed-Ahmad and Haeri, Maryam Amir and Folino, Gianluigi}, journal={arXiv preprint arXiv:2006.07616}, year={2020} } Thanks a lot ..