Juridical Review On Notarical Testament In The Perspectives Of Islamic Inheritance Law

Most of Indonesian is aware of law in aspects of life, including inheritance distribution. Every parent with children does not want to let his heirs disagreed or conflicted in terms of inheritance after he passed away. Thus a testament is made to fairly distribute inheritance. Among reasons to compile testament deed are testator intentions to make his property useful for better purposes, i.e. to get closer to Allah SWT The Most Merciful. He also expects himself to perceive true faith and devotion to God, as well as to open fortune door to all recipients. However there may occur obstacle in distributing inheritance. Since civil law regarding testament is different from Islamic faraid law in some aspect.Keywords: Testament; Notary; Inheritance

Spinal cord gray matter segmentation using deep dilated convolutions

Gray matter (GM) tissue changes have been associated with a wide range of neurological disorders and was also recently found relevant as a biomarker for disability in amyotrophic lateral sclerosis. The ability to automatically segment the GM is, therefore, an important task for modern studies of the spinal cord. In this work, we devise a modern, simple and end-to-end fully automated human spinal cord gray matter segmentation method using Deep Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate our method against six independently developed methods on a GM segmentation challenge and report state-of-the-art results in 8 out of 10 different evaluation metrics as well as major network parameter reduction when compared to the traditional medical imaging architectures such as U-Nets.Comment: 13 pages, 8 figure

Increased lifetime of Organic Photovoltaics (OPVs) and the impact of degradation, efficiency and costs in the LCOE of Emerging PVs

Emerging photovoltaic (PV) technologies such as organic photovoltaics (OPVs) and perovskites (PVKs) have the potential to disrupt the PV market due to their ease of fabrication (compatible with cheap roll-to-roll processing) and installation, as well as their significant efficiency improvements in recent years. However, rapid degradation is still an issue present in many emerging PVs, which must be addressed to enable their commercialisation. This thesis shows an OPV lifetime enhancing technique by adding the insulating polymer PMMA to the active layer, and a novel model for quantifying the impact of degradation (alongside efficiency and cost) upon levelized cost of energy (LCOE) in real world emerging PV installations. The effect of PMMA morphology on the success of a ternary strategy was investigated, leading to device design guidelines. It was found that either increasing the weight percent (wt%) or molecular weight (MW) of PMMA resulted in an increase in the volume of PMMA-rich islands, which provided the OPV protection against water and oxygen ingress. It was also found that adding PMMA can be effective in enhancing the lifetime of different active material combinations, although not to the same extent, and that processing additives can have a negative impact in the devices lifetime. A novel model was developed taking into account realistic degradation profile sourced from a literature review of state-of-the-art OPV and PVK devices. It was found that optimal strategies to improve LCOE depend on the present characteristics of a device, and that panels with a good balance of efficiency and degradation were better than panels with higher efficiency but higher degradation as well. Further, it was found that low-cost locations were more favoured from reductions in the degradation rate and module cost, whilst high-cost locations were more benefited from improvements in initial efficiency, lower discount rates and reductions in install costs

Extracción y caracterización de nanocelulosa a partir de bambú

Treballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2020, Tutors: Javier Fernández González, José Antonio Padilla SánchezThere is a need for developing renewables materials due to the increasing demand of alternatives to the unrenewable petroleum supplies. Nanocrystalline cellulose (NCC) or cellulose nanocrystals (CNs), which derives from cellulose, the most abundant biopolymer, is one of the most promising materials. Cellulose is the most abundant organic polymer on earth which mainly provides structural reinforcement to the plants cell walls. Cellulose consists of a chain of thousands of bonded glucose units with β (1→4) links. It is mainly obtained from lignocellulosic biomass (plant dry biomass) to produce paperboard and paper. That lignocellulosic biomass is mostly composed by cellulose, which by strong hydrogen bonding networks forms microfibrils, hemicellulose, which binds together those microfibrils and lignin, which have various purposes like provide stiffness and cover the cellulose microfibrils. As will be seen later, several methods can be applied to extract the cellulose from the lignocellulose biomass. That cellulose contains crystalline and amorphous regions, which have different resistance to chemical attacks (crystalline regions have higher resistance while amorphous regions have lower), through different methods, which mainly involves sulfuric and hydrochloric acids, the amorphous regions are hydrolysed leaving the crystalline regions intact, those crystals that have a diameter between 5-70 nm and a length between 100-250 nm are called nanocrystalline cellulose. CNs are a renewable material with good mechanical properties and a nano-scaled dimension which opens a wide range of possible applications. CNs can be obtained from almost any source of cellulose, from plants to bacteria. This report is part of a collaboration with the University of Guayaquil in Ecuador, the possibility of using bamboo as a source of cellulose to obtain CNs material has been studied since it’s a material of great importance, widely used for its mechanical properties and with incredibly high growth rates. This report provides an overview of the CNs, an emerging nanomaterial, the different ways to isolate the cellulose from which the CNs are extracted, the process required to obtain those CNs and different techniques to characterize it

AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only few methods are publicly available. We introduce AxonDeepSeg, an open-source software that performs axon and myelin segmentation of microscopic images using deep learning. AxonDeepSeg features: (i) a convolutional neural network architecture; (ii) an easy training procedure to generate new models based on manually-labelled data and (iii) two ready-to-use models trained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results show high pixel-wise accuracy across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and 84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed and morphological metrics are extracted and compared against the literature. AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepsegComment: 14 pages, 7 figure

Equisummability Theorems for Laguerre Series

Here we prove results about Riesz summability of classical Laguerre series, locally uniformly or on the Lebesgue set of the function f such that (∫(1 + x)^(mp) |f(x)|^p dx )^(1/p) < ∞, for some p and m satisfying 1 ≤ p ≤ ∞, −∞ < m < ∞

SoftSeg: Advantages of soft versus binary training for image segmentation

Most image segmentation algorithms are trained on binary masks formulated as a classification task per pixel. However, in applications such as medical imaging, this "black-and-white" approach is too constraining because the contrast between two tissues is often ill-defined, i.e., the voxels located on objects' edges contain a mixture of tissues. Consequently, assigning a single "hard" label can result in a detrimental approximation. Instead, a soft prediction containing non-binary values would overcome that limitation. We introduce SoftSeg, a deep learning training approach that takes advantage of soft ground truth labels, and is not bound to binary predictions. SoftSeg aims at solving a regression instead of a classification problem. This is achieved by using (i) no binarization after preprocessing and data augmentation, (ii) a normalized ReLU final activation layer (instead of sigmoid), and (iii) a regression loss function (instead of the traditional Dice loss). We assess the impact of these three features on three open-source MRI segmentation datasets from the spinal cord gray matter, the multiple sclerosis brain lesion, and the multimodal brain tumor segmentation challenges. Across multiple cross-validation iterations, SoftSeg outperformed the conventional approach, leading to an increase in Dice score of 2.0% on the gray matter dataset (p=0.001), 3.3% for the MS lesions, and 6.5% for the brain tumors. SoftSeg produces consistent soft predictions at tissues' interfaces and shows an increased sensitivity for small objects. The richness of soft labels could represent the inter-expert variability, the partial volume effect, and complement the model uncertainty estimation. The developed training pipeline can easily be incorporated into most of the existing deep learning architectures. It is already implemented in the freely-available deep learning toolbox ivadomed (https://ivadomed.org)