Quality of life in dialysis: A Malaysian perspective

There is a growing interest to use quality of life as one of the dialysis outcome measurement. Based on the Malaysian National Renal Registry data on 15 participating sites, 1569 adult subjects who were alive at December 31, 2012, aged 18 years old and above were screened. Demographic and medical data of 1332 eligible subjects were collected during the administration of the short form of World Health Organization Quality of Life questionnaire (WHOQOL‐BREF) in Malay, English, and Chinese language, respectively. The primary objective is to evaluate the quality of life among dialysis patients using WHOQOL‐BREF. The secondary objective is to examine significant factors that affect quality of life score. Mean (SD) transformed quality of life scores were 56.2 (15.8), 59.8 (16.8), 58.2 (18.5), 59.5 (14.6), 61.0 (18.5) for (1) physical, (2) psychological, (3) social relations, (4) environment domains, and (5) combined overall quality of life and general health, respectively. Peritoneal dialysis group scored significantly higher than hemodialysis group in the mean combined overall quality of life and general health score (63.0 vs. 60.0, P < 0.001). Independent factors that were associated significantly with quality of life score in different domains include gender, body mass index, religion, education, marital status, occupation, income, mode of dialysis, hemoglobin, diabetes mellitus, coronary heart disease, cerebral vascular accident and leg amputation. Subjects on peritoneal dialysis modality achieved higher combined overall quality of life and general health score than those on hemodialysis. Religion and cerebral vascular accident were significantly associated with all domains and combined overall quality of life and general health.     

油气藏井间监测新技术及其应用

目前,井间监测技术正朝着将地层测量从近井区域推向储层深处这一方向发展。油气藏管理人员可以利用根据测量结果获得的层析成像来监测地层深处流体的流动情况。     

Generalized multilayer anisotropic dielectric resonators

Modeling of the generalized multilayer cylindrical anisotropic dielectric loaded resonator by a rigorous mode-matching method is presented in this paper. Eigenmodes of the multilayer two parallel-plate waveguides are obtained. By cascading the radial discontinuities of the structure, resonant frequency, field distribution, unloaded Q, and the frequency sensitivity of the resonator are obtained. The method can be used to analyze and design resonators and filters of multilayer structure. The computed results are compared with measured results and with experimental data, and shown to be in good agreement     

Mechanical properties of recycled kenaf/polyethylene terephthalate (PET) fiber reinforced polyoxymethylene (POM) hybrid composite

Environmental concerns have attracted researchers to study the recycling of composite materials and thermoplastics due to the desire not to waste materials and reduce disposal of scraps that may eventually pollute the environment. The main objective of this article is to study the effect of recycling on the mechanical properties of kenaf fiber/PET reinforced POM hybrid composite. The virgin hybrid composite was produced by compression molding and later subjected to mechanical testing. The scraps obtained after the mechanical testing were shredded, granulated and subjected to compression molding to produce samples for mechanical testing. Tensile strength of 27 MPa was obtained and (after second recycling process) which is lower compared to 73.8 MPa obtained for the virgin hybrid composite. There was a significant increase in flexural modulus (4.7 GPa) compared to the virgin hybrid composite. The impact strength dropped to 4.3 J cm^?1 as against 10.5 J cm^?1 for the virgin hybrid composite. The results of thermal degradation showed about 80% weight loss for kenaf fiber between 300 and 350°C. The weight loss may be due to the degradation of cellulose and hemicellulose content of the fiber. The percentage water absorption of the recycled composite dropped by about 80% compared to the virgin hybrid composite. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 39831.     

Chemical activation of calcium aluminate cement composites cured at elevated temperature

The influence of sodium sulfate, as an activator, on the hydration of calcium aluminate cement (CAC)–fly ash (FA)–silica fume (SF) composites was investigated. Different mixes of CAC with 20% pozzolans (20% FA, 20% SF and 10% FA + 10% SF) were prepared and hydrated at 38 °C for up to 28 days. The hydration products were investigated by XRD, DSC and SEM. The results showed that sodium sulfate accelerated the hydration reactions of calcium aluminate cement as well as the reactions of FA and SF with CAH₁₀ and C₂AH₈ to form the strätlingite (C₂ASH₈). The later reactions prevent the strength loss by preventing the conversion of CAH₁₀ and C₂AH₈ to the cubic C₃AH₆ phase. The acceleration effect of Na₂SO₄ on the reactivity of fly ash was more pronounced than on the reactivity of silica fume with respect to reaction with CAH₁₀ and C₂AH₈ phases.     

Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate more highlights from the medical images. Other than that, we have identified the loss function by utilizing several dice measurements approach and received Dice Result on top of a specific test case. The average mean score of dice coefficient and soft dice loss for three test cases was 0.0980. At the same time, for two test cases, the sensitivity and specification were recorded to be 0.0211 and 0.5867 using patch level predictions. On the other hand, a software integration pipeline was integrated to deploy the concentrated model into the webserver for accessing it from the software system using the Representational state transfer (REST) API. Eventually, the suggested models were validated through the Area Under the Curve–Receiver Characteristic Operator (AUC–ROC) curve and Confusion Matrix and compared with the existing research articles to understand the underlying problem. Through Comparative Analysis, we have extracted meaningful insights regarding brain tumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imaging modalities.     

Graph mining for discovering infrastructure patterns in configuration management databases

A configuration management database (CMDB) can be considered to be a large graph representing the IT infrastructure entities and their interrelationships. Mining such graphs is challenging because they are large, complex, and multi-attributed and have many repeated labels. These characteristics pose challenges for graph mining algorithms, due to the increased cost of subgraph isomorphism (for support counting) and graph isomorphism (for eliminating duplicate patterns). The notion of pattern frequency or support is also more challenging in a single graph, since it has to be defined in terms of the number of its (potentially, exponentially many) embeddings. We present CMDB-Miner, a novel two-step method for mining infrastructure patterns from CMDB graphs. It first samples the set of maximal frequent patterns and then clusters them to extract the representative infrastructure patterns. We demonstrate the effectiveness of CMDB-Miner on real-world CMDB graphs, as well as synthetic graphs.