Correlation of Stress Hyperglycemia with Barthel Index in Acute Non-hemorrhagic Stroke Patients at Neurology Ward of RSUD Arifin Achmad Pekanbaru

The raising of blood glucose that occurs due to disruption of the regulation of blood glucose which is part of the non-specific reaction to the occurrence of stress or tissues damage called stress hyperglycemia. Stress hyperglycemia is common in acute diseases such as stroke, which significantly affect patient outcomes that can be assessed with the Barthel Index. The aim of this study was to find out the relationship between stress hyperglycemia with Barthel index in patients with acute non-hemorrhagic stroke. This study using analytic observational method with cross sectional design. The sample are acute non-hemorrhagic stroke patients totaling 38 patients who conform the inclusion criterias. Results of the study of 38 patients, is the prevalence of stress hyperglycemia prevalence cases totaled 23 people (60.52%) in non-acute hemorrhagic stroke. Showed that Barthel Index acute non-hemorrhagic stroke patients are heavy dependence (21-61) of 21 patients (55.26%), followed by 3 patients (7, 89%) with a full dependence (0-20), 13 patients ( 34.21%) with moderate dependence (62-90) and 1 patient (2.63%) with mild dependence (91-99). There is no correlation between stress hyperglycemia with Brthel Index with the value of (r = 0.059) and the value of (p = 0.654). So, based on this research most of the respondents in this study suffered from stress hyperglycemia but there is no correlation between stress hyperglycemia with Barthel Index and most of the patients has a bad interpretation of Barthel Index

Strengthening of self-compacting reinforces concrete slabs using CFRP strips subjected to punching shear

This research is conducted to investigate the behavior of self-compacting reinforced concrete slabs strengthened by CFRP laminates in a stitching way under the impact of punching shear strength. In this study, trail mixes are carried out to perform high strength self-compacting concrete (f_c^'=72.3 MPa). Two groups of specimens were assessed in this study. The first group (A) involves three solid slab specimens, while the second group (B) includes three slab specimens with an opening in the shear zone. Two variables were included in these experiments, namely, the effect of strengthening by CFRP laminate and the effect of high strength self-compacting concrete. The outcomes were discussed based on cracking load, ultimate punching shear capacity, crack patterns, and load-deflection response. The results showed that strengthening by CFRP enhanced the ultimate punching shear capacity by (23%-65%) and increased the deflection by (33%-79.5%)

Ultra-high performance steel fibers concrete corbels: Experimental investigation

In the present paper, results of testing eleven ultra-high performance steel fibers concrete (UHPSFC) corbels with concrete compressive strength 150 MPa and under vertical loading are reported. The main test variables were shear span-to depth ratio, main tension reinforcement ratio and the provision of secondary reinforcement (closed stirrups). In all corbels, except one, the main steel bars yielded before failure and corbels failed in a consistent manner. Whereas the validity from provision the secondary reinforcement in the UHPSFC corbels represents by a significant increase in the corbels stiffness with taking into consideration the corbels failure modes. The test results in terms of load versus deflection curves, stiffness, ductility and crack patterns show the effectiveness of using ultra high performance steel fibers concrete to ensure a superior strength and deformation capacity in reinforced concrete corbels. Experimental results have been compared with diverse prediction methods. The truss model can provide accurate strength predictions in comparison with the ACI 318-14 cod’s procedure

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

We propose an automated postconstruction quality assessment robot system for crack, hollowness, and finishing defects in light of a need to speed up the inspection work, a more reliable inspection report, as well as an objective through fully automated inspection. Such an autonomous inspection system has a potential to cut labour cost significantly and achieve better accuracy. In the proposed system, a transfer learning network is employed for visual defect detection; a region proposal network is used for object region proposal, a deep learning network employed as feature extractor, and a linear classifier with supervised learning as object classifier; moreover, active learning of top-N ranking region of interest is undertaken for fine-tuning of the transfer learning on convolutional activation feature network. Extensive experiments are validated in a construction quality assessment system room and constructed test bed. The results are promising in a way that the novel proposed automated assessment method gives satisfactory results for crack, hollowness, and finishing defects assessment. To the best of our knowledge, this study is the first attempt to having an autonomous visual inspection system for postconstruction quality assessment of building sector. We believe the proposed system is going to help to pave the way towards fully autonomous postconstruction quality assessment systems in the future.NRF (Natl Research Foundation, S’pore)Published versio