Islam Hassan

Environmental life cycle assessment and techno-economic analysis of triboelectric nanogenerators

As the world economy grows and industrialization of the developing countries increases, the demand for energy continues to rise. Triboelectric nanogenerators (TENGs) have been touted as having great potential for low-carbon, non-fossil fuel energy generation. Mechanical energies from, amongst others, body motion, vibration, wind and waves are captured and converted by TENGs to harvest electricity, thereby minimizing global fossil fuel consumption. However, only by ascertaining performance efficiency along with low material and manufacturing costs as well as a favorable environmental profile in comparison with other energy harvesting technologies, can the true potential of TENGs be established. This paper presents a detailed techno-economic lifecycle assessment of two representative examples of TENG modules, one with a high performance efficiency (Module A) and the other with a lower efficiency (Module B) both fabricated using low-cost materials. The results are discussed across a number of sustainability metrics in the context of other energy harvesting technologies, notably photovoltaics. Module A possesses a better environmental profile, lower cost of production, lower CO2 emissions and shorter energy payback period (EPBP) compared to Module B. However, the environmental profile of Module B is slightly degraded due to the higher content of acrylic in its architecture and higher electrical energy consumption during fabrication. The end of life scenario of acrylic is environmentally viable given its recyclability and reuse potential and it does not generate toxic gases that are harmful to humans and the environment during combustion processes due to its stability during exposure to ultraviolet radiation. Despite the adoption of a less optimum laboratory manufacturing route, TENG modules generally have a better environmental profile than commercialized Si based and organic solar cells, but Module B has a slightly higher energy payback period than PV technology based on perovskite-structured methyl ammonium lead iodide. Overall, we recommend that future research into TENGs should focus on improving system performance, material optimization and more importantly improving their lifespan to realize their full potential.

Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.

Design guidelines of triboelectric nanogenerator for water wave energy harvesters

Ocean waves are one of the cleanest and most abundant energy sources on earth, and wave energy has the potential for future power generation. Triboelectric nanogenerator (TENG) technology has recently been proposed as a promising technology to harvest wave energy. In this paper, a theoretical study is performed on a duck-shaped TENG wave harvester recently introduced in our work. To enhance the design of the duck-shaped TENG wave harvester, the mechanical and electrical characteristics of the harvesters overall structure, as well as its inner configuration, are analyzed, respectively, under different wave conditions, to optimize parameters such as duck radius and mass. Furthermore, a comprehensive hybrid 3D model is introduced to quantify the performance of the TENG wave harvester. Finally, the influence of different TENG parameters is validated by comparing the performance of several existing TENG wave harvesters. This study can be applied as a guideline for enhancing the performance of TENG wave energy harvesters.

From K-space to Nucleotide: Insights into the Radiogenomics of Brain Tumors

Abstract Radiogenomics is a relatively new and exciting field within radiology that links different imaging features with diverse genomic events. Genomics advances provided by the Cancer Genome Atlas and the Human Genome Project have enabled us to harness and integrate this information with noninvasive imaging phenotypes to create a better 3-dimensional understanding of tumor behavior and biology. Beyond imaging-histopathology, imaging genomic linkages provide an important layer of complexity that can help in evaluating and stratifying patients into clinical trials, monitoring treatment response, and enhancing patient outcomes. This article reviews some of the important radiogenomic literatures in brain tumors.