Effect of Different Steel Fiber Type and Content in Flexural Behavior of Ultra High Performance Fiber Reinforced Concrete

In the research study, the effect of different fiber contents to flexural behavior of the Ultra-High Performance Fiber Reinforced Concrete (UHPFRC) was investigated experimentally. Various prismatic beam specimens with a dimension of 100×100×400 mm including two types of end-hooked steel fibers (aspect ratios: 30/0.55 and 60/0.75) in macro forms and one short straight steel fiber (aspect ratio: 13/0.16) in micro form were produced. The beam specimens corresponding to a total of 18 mixtures having two different volume fractions (1% and 1.5%) were subjected to series of four-point bending tests in accordance with the ASTM standard C 1609. The experimental test results were discussed in terms of the cracking patterns, flexural strengths and toughness (energy absorption ability). In addition, a parametric research was conducted to ensure an appropriate homogenous UHPFRC mixture as well as good workability for the steel fiber volume fraction of 1.0%. Hence the prism and cubic samples were produced by modified of the composition of matrix mixtures (i.e. aggregate, water/binder, cement, superplasticizer). The performance of mixtures was evaluated in terms of the slump flow, T 500, compressive strength and workability. It is apparent from the test results, the use of micro steel fiber significantly improves the flexural performance of the UHPFRC comparing to that of the macro form. It was also noted that the fiber type is decisive in characteristic of the load- deflection curve while the volume content amplifies it with an increasing trend after the first cracking region. When evaluating all UHPFRC matrixes, some of the mixtures under consideration ensured good fiber distribution, workability as well as target compressive strength

Expression of vascular endothelial growth factor and transforming growth factor alpha in rat testis during chronic renal failure

Introduction. Vascular endothelial growth factor (VEGF) is known to influence testis function. Transforming growth factor alpha (TGF-α) is expressed in the postnatal testis, and has been demonstrated to stimulate testis development. Systemic diseases such as chronic renal failure (CRF) interfere with hypothalamic-pituitary-go­nadal axis, which may cause defective steroidogenesis and gonadal functions. The aim of this study was to inve­stigate the expression and localization of VEGF and TGF-α in testicular tissues of experimental CRF model. Material and methods. Experimental CRF was induced in rats by the resection of more than 85% of renal mass. The expression of VEGF and TGF-α in testicular tissues were assessed by immunohistochemistry on paraffin sections of control, CRF-nondialysed and CRF-dialysed rats. Results. The microscopic evaluation of the testicular structure showed that CRF did not affect testicular histology. Immunohistochemical evaluation showed that VEGF was expressed in the cytoplasm of primary and secondary spermatocyte series as well as the early spermatids. Staining intensity was lower in sperma­tocytes going through the first meiotic division. TGF-α was expressed in the nuclei of spermatogonia and primary spermatocytes with stronger staining intensity in spermatogonia. The intensity of VEGF staining was similar in control and experimental animals, however, TGF-α expression was lower in the CRF group.Conclusions. The continuous expression of VEGF in spermatocytes and spermatids suggests that the applied model of CRF does not directly disrupt morphology of seminiferous epithelium, thus also spermiogenesis. However, difference between control rats and CRF group in TGF-α immunopositivity, which was localised in spermatogonial mitosis step, may suggest the interference of CRF with early stages of spermatogenesis.

Hybrid deep feature generation for appropriate face mask use detection

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time

Super Neurons

Self-Organized Operational Neural Networks (Self-ONNs) have recently been proposed as new-generation neural network models with nonlinear learning units, i.e., the generative neurons that yield an elegant level of diversity; however, like its predecessor, conventional Convolutional Neural Networks (CNNs), they still have a common drawback: localized (fixed) kernel operations. This severely limits the receptive field and information flow between layers and thus brings the necessity for deep and complex models. It is highly desired to improve the receptive field size without increasing the kernel dimensions. This requires a significant upgrade over the generative neurons to achieve the “non-localized kernel operations” for each connection between consecutive layers. In this article, we present superior (generative) neuron models (or super neurons in short) that allow random or learnable kernel shifts and thus can increase the receptive field size of each connection. The kernel localization process varies among the two super-neuron models. The first model assumes randomly localized kernels within a range and the second one learns (optimizes) the kernel locations during training. An extensive set of comparative evaluations against conventional and deformable convolutional, along with the generative neurons demonstrates that super neurons can empower Self-ONNs to achieve a superior learning and generalization capability with a minimal computational complexity burden. PyTorch implementation of Self-ONNs with super-neurons is now publically shared.Peer reviewe

Applicability of pressure retarded osmosis power generation technology in Istanbul

In this study, the applicability of pressure retarded osmosis power generation was investigated in order to meet the electricity demand in Turkey. Pressure retarded osmosis (PRO) is a method that converting salinity gradients to power using a semi-permeable membrane against an applied pressure and PRO is one of the promising candidates to reduce fossil fuel dependency. In PRO, water is transported from a low concentrated feed solution to a high-concentrated draw solution. According to the literature findings, in order to produce 1MW of electricity 1m3/s fresh water flow is needed. Turkey is surrounded on three sides by water and has a big potential to develop this technology. Riva River is investigated in the scope this study. Currently Turkey’s total installed power capacity reached 85.200 MW at the end of 2017.Calculations of PRO power generation reveals that it is possible to generate 25,45 MW, If using 5% of total river flow

The impact of corporate social responsibility disclosure on financial performance : evidence from the GCC Islamic banking sector.

This paper examines the relationship between corporate social responsibility (CSR) and financial performance for Islamic banks in the Gulf Cooperation Council (GCC) region over the period 2000–2014 by generating CSR-related data through disclosure analysis of the annual reports of the sampled banks. The findings of this study indicate that there is a significant positive relationship between CSR disclosure and the financial performance of Islamic banks in the GCC countries. The results also show a positive relationship between CSR disclosure and the future financial performance of GCC Islamic banks, potentially indicating that current CSR activities carried out by Islamic banks in the GCC could have a long-term impact on their financial performance. Furthermore, despite demonstrating a significant positive relationship between the composite measure of the CSR disclosure index and financial performance, the findings show no statistically significant relationship between the individual dimensions of the CSR disclosure index and the current financial performance measure except for ‘mission and vision’ and ‘products and services’. Similarly, the empirical results detect a positive significant association only between ‘mission and vision’ dimension and future financial performance of the examined banks