ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Making Aqueously Processed LiNi0.5Mn0.3Co0.2O2-Based Electrodes Competitive in Performance: Tailoring Distribution and Interconnection of Active and Inactive Electrode Materials through Paste Surfactants

Enabling aqueous processing of positive active materials to replace toxicologically critical N-methyl-2-pyrrolidone could significantly reduce the ecologic and economic footprint of lithium ion battery production. Processing additives are key to elevate the performance of aqueously processed electrodes beyond the state of the art. A mostly neglected factor during aqueous processing is electrostatic repulsion of active/inactive materials due to their zeta potentials, which can be compensated for by applying optimized amounts of surfactants like hexadecyltrimethylammonium bromide. The notably improved distribution and interconnection of active/inactive materials lead to superior rate capability and similar capacity retention during long-term cycling compared to state-of-the-art processing.
Mapping the Binding Energy of Layered Crystals to Macroscopic Observables

Van der Waals (vdW) integration of two dimensional (2D) crystals into functional heterostructures emerges as a powerful tool to design new materials with fine-tuned physical properties at an unprecedented precision. The intermolecular forces governing the assembly of vdW heterostructures are investigated by first-principles models, yet translating the outcome of these models to macroscopic observables in layered crystals is missing. Establishing this connection is, therefore, crucial for ultimately designing advanced materials of choice-tailoring the composition to functional device properties. Herein, components from both vdW and non-vdW forces are integrated to build a comprehensive framework that can quantitatively describe the dynamics of these forces in action. Specifically, it is shown that the optical band gap of layered crystals possesses a peculiar ionic character that works as a quantitative indicator of non-vdW forces. Using these two components, it is then described why only a narrow range of exfoliation energies for this class of materials is observed. These findings unlock the microscopic origin of universal binding energy in layered crystals and provide a general protocol to identify and synthesize new crystals to regulate vdW coupling in the next generation of heterostructures.
Efficient low-order system identification from low-quality step response data with rank-constrained optimization

In the presence of low-quality industrial process data, generic step response identification methods typically show unsatisfactory performance and heavily rely on manual intervention of technical personnel. This erects obvious obstacles for the advancement of intelligent manufacturing in process industries. To address these challenges, we propose a novel rank-constrained optimization approach to low-order system identification from step response data, which yields much more accurate and robust estimates than existing modeling methods. By exploiting the inherent low-rank structure of the Hankel matrix of ideal step response, parameters of a low-order process can be accurately recovered by solving a rank-constrained program, which effectively bypasses the two-step procedure in some state-of-the-art algorithms involving significant error accumulation. The alternating direction method of multipliers is adopted to effectively solve the nonconvex error minimization problem and circumvent poor local optima. Case studies on both numerical examples and industrial datasets demonstrate that, the proposed method not only gives much better modeling accuracy, but also secures reliable and robust estimates even for raw low-quality industrial data. This is particularly helpful for automated execution of the identification routine without human intervention, with success percentage over 99% that is remarkably higher than the state-of-the-art.
Role of the Chemical Environment beyond the Coordination Site: Structural Insight into Fe(III) Protoporphyrin Binding to Cysteine-Based Heme-Regulatory Protein Motifs

The importance of heme as a transient regulatory molecule has become a major focus in biochemical research. However, detailed information about the molecular basis of transient heme-protein interactions is still missing. We report an in-depth structural analysis of Fe(III) heme-peptide complexes by a combination of UV/Vis, resonance Raman, and 2D-NMR spectroscopic methods. The experiments reveal insights both into the coordination to the central iron ion and into the spatial arrangement of the amino acid sequences interacting with protoporphyrin IX. Cysteine-based peptides display different heme-binding behavior as a result of the existence of ordered, partially ordered, and disordered conformations in the heme-unbound state. Thus, the heme-binding mode is clearly the consequence of the nature and flexibility of the residues surrounding the iron ion coordinating cysteine. Our analysis reveals scenarios for transient binding of heme to heme-regulatory motifs in proteins and demonstrates that a thorough structural analysis is required to unravel how heme alters the structure and function of a particular protein.
Science-fiction literature as inspiration for social theorizing within sustainability research

As commonly accepted, the stakes of sustainable development (SD) are altogether highly serious, complex, and diverse, spanning from changing living standards to the very future of mankind. Although it has long been argued that such complexity warrants radical change in the way "good" knowledge is conceived of and produced, it appears that much of sustainability research including its grounding in social sciences has yet to extend beyond the institutionalized precepts of natural science with regard to validity and credibility. In this context, this paper argues that crucial insights can be developed by opening social sciences' theorizing process to undervalued forms of knowledge including art in general, and science-fiction literature in particular. To support this argument, current non-scientific productions of knowledge are reviewed in light of alternate conceptions of epistemological value. The potential of science fiction as thought experiments and inspiration for both problematizing and theory building in the social sciences is explored. In particular, certain science-fiction texts are thoroughly examined to illustrate how current problems pertaining to firm theories and management practice may be "discovered" (or made visible) only through science-fiction hindsight. Ultimately, the research shows how this hidden potential may be employed by social research to radically stimulate theoretical imagination and benefit sustainability research. (C) 2017 Elsevier Ltd. All rights reserved.
Energy management using non-intrusive load monitoring techniques - State-of-the-art and future research directions

In recent years, the development of smart sustainable cities has become the primary focus among urban planners and policy makers to make responsible use of resources, conserve the environment and improve the well-being of the society. Energy management is an integral part of the smart sustainable cities development programme which involves conscious and efficient use of available energy resources towards attaining sustainability and self-reliance on energy systems. Building sector is one of the key sectors that utilize more energy. Therefore, efforts are being made to monitor and manage energy consumption effectively in residential and commercial buildings. In recent years, non-intrusive load monitoring (NILM) technique has become a popular and emerging approach to monitor events (on/off) and energy consumption of appliances/electrical utilities in buildings using single energy meter. The information about the energy consumption at the appliance level would help consumers to understand their appliance usage behavior and take necessary steps for reducing energy consumption. In this paper, we present the comprehensive review of state-of-the-art algorithms that have been explored by the researchers towards developing an accurate NILM system for effective energy management. Finally, potential applications of NILM in different domains and its future research directions are discussed.
An Efficient Protocol of Queries for Large and Small Categories in RFID Systems

One of the most important problems for categorized radio-frequency identification systems is to find large categories and small categories. In this article, we propose a protocol that can find the large categories and the small categories quickly. The protocol uses multiple hash pairs to allocate as many tag categories as possible to distinct slots to estimate the sizes of the tag categories. Specifically, we optimize the parameters by minimizing the execution time of the protocol. Extensive simulation results demonstrate that the proposed protocol is superior to the state-of-the-art protocols. Particularly, when the length of single-one-geometric (SOG) string is 32 b, it reduces nearly 24% of the required execution time compared with the Top-k protocol and reduces 77% of the required execution time compared with the ART protocol.
Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling

To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VES-SEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4(+/- 1.0%,) 2.2(+/- 1.0) mm, 0.42(+/- 0.10)%) and the VESSEL12 database (99.0(+/- 0.5)%, 2.1(+/- 1.6) mm, 0.39(+/- 0.20)%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.
H-Patches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors

In this paper, a novel benchmark is introduced for evaluating local image descriptors. We demonstrate limitations of the commonly used datasets and evaluation protocols, that lead to ambiguities and contradictory results in the literature. Furthermore, these benchmarks are nearly saturated due to the recent improvements in local descriptors obtained by learning from large annotated datasets. To address these issues, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and verification. This allows for more realistic, thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors is able to boost their performance to the level of deep learning based descriptors once realistic benchmarks are considered. Additionally we specify a protocol for learning and evaluating using cross validation. We show that when training state-of-the-art descriptors on this dataset, the traditional verification task is almost entirely saturated.
Efficient dc-free RLL codes for optical recording

We will report on new dc-free runlength-limited codes (DCRLL) intended for the next generation of DVD. The efficiency of the newly developed DCRLL schemes is extremely close to the theoretical maximum, and as a result, significant density gains can be obtained with respect. to prior art coding schemes. With a newly developed DCRLL (d = 2) code we can achieve a 9% higher overall rate than that of DVD's EFMPlus.
Boom, Bust and Beyond: Arts and Sustainability in Calumet, Michigan

Cycles of boom and bust plague mining communities around the globe, and decades after the bust the skeletons of shrunken cities remain. This article evaluates strategies for how former mining communities cope and strive for sustainability in the decades well beyond the bust, using a case study of Calumet, Michigan. In 1910, Calumet was at the center of the mining industry in the Upper Peninsula of Michigan, but in the century since its peak, mining employment steadily declined until the last mine closed in 1968, and the population declined by over 80%. This paper explores challenges, opportunities, and progress toward sustainability associated with arts-related development in this context. Methods are mixed, including observation, interviews, document review, a survey, and secondary data analysis. We follow Flora and Flora's Community Capitals Framework to analyze progress toward sustainability. Despite key challenges associated with the shrunken city context (degraded tax base, overbuilt and aging infrastructure, diminished human capital, and a rather limited set of volunteers and political actors), we find the shrunken city also offers advantages for arts development, including low rents, less risk of gentrification, access to space, and political incentive. In Calumet, we see evidence of a spiraling up pattern toward social sustainability resulting from arts development; however impacts on environmental and economic sustainability are limited.
Use of free radial forearm and pronator quadratus muscle flap: Anatomical study and clinical application

The authors present an anatomical study and clinical experience with radial forearm flap (RFF) and pronator quadratus muscle (PQM) application in the reconstruction of various body areas. The aim was to describe the anatomical placement and proportions of the PQM, the anatomical location of the major arterial branch of the radial artery supplying the PQM, and the application of this knowledge in clinical practice. The anatomical study was based upon an analysis of 13 fresh adult cadaver upper extremities, of which nine were female and four male; both arms from the same donors were used in four cases. The study of the PQM was performed using a dye-containing intraarterial injection, standard macro- and micro-preparation techniques, and chemical digestion. The data on the PQM size in males and females, thickness of the radial artery branch (the principal artery nourishing the muscle), and its position were analysed. The radial artery branch nourishing the PQM was identified in all cadaveric specimens of the anatomical study. In addition, 12 patients underwent reconstructions of soft and bony tissue defects using a RFF + PQM (pedicled or free flap). The radial artery branch perfusing the PQM was identified in all cases. The flap was used for the management of defects of the head (seven cases), arm (three cases) and lower leg (two cases). The harvest site healed well in all cases and, with the exception of one case in which a partial necrosis of the flap was observed, all flaps remained viable, which demonstrated the safety of the method.
Method to Design General RNS Reverse Converters for Extended Moduli Sets

In recent years, research on residue number systems (RNS) has targeted larger dynamic ranges (DRs) in order to further explore their inherent parallelism. In this brief, we start from the traditional three-moduli set {2(n), 2(n) - 1, 2(n) + 1}, with an equivalent 3n-bit DR; propose horizontal and vertical extensions to scale the DR; and improve the parallelism according to the requirements. This brief also introduces a method to design general reverse converters for extended moduli sets with the desired DRs, whereas the existing state of the art allows to achieve at most (8n + 1) bit. The experimental results suggest that the proposed moduli set extensions allow for larger and more balanced moduli sets, in comparison with the state of the art, resulting in an improvement of the overall RNS performance at the cost of a slower reverse conversion operation.
Design of an efficient Scalable Vector Graphics player for constrained devices

The mobile industry, and in particular the 3(rd) Generation Partnership Project (3GPP) Consortium, has selected the "Tiny" profile of the Scalable Vector Graphics (SVG) specification as a basis for the Rich Media format for mobile applications, leading the way to consumer electronics. Among the foreseen applications for SVG on constrained consumer electronics devices, maps, clip arts, animated cartoons and user interfaces are often cited. However, such devices are memory-constrained and have limited processing capabilities whereas vector graphics clip arts, animated cartoons or maps are described by large SVG documents. Such content may require heavy computations and important memory consumption, especially when applying models for animation and inheritance. In this paper, we present the design of a low-footprint and compulationally efficient player for large and animated SVG documents. We describe the structures of the scene objects which enable low memory consumption and the compositing and rendering algorithms enabling fast playback. Finally, we evaluate the limitations of our proposal and compare our results with publically available desktop SVG players.
Genetic programming for multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture ... One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities ... ). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, *, -, ... ). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art. (C) 2011 Elsevier Ltd. All rights reserved.
Up-sampling of YCbCr4:2:0 Image Exploiting Inter-color Correlation in RGB Domain

This paper introduces a practical inter-color up-sampling algorithm for images with decimated chrominance components. Most of the existing and emerging image and video compression coding standards such as JPEG, MPEG and H.26x families employ; YCbCr4:2:0 chrominance format. After reconstructing the YCbCr4:2:0 image at a decoder, one must up-sample the decoded chrominance signals by a factor of two in both horizontal and vertical directions to generate the YCbCr4:4:4 format image, which is eventually transformed to RGB color space especially for displaying the reconstructed image. Then, we focus on generating high-fidelity RGB image, (but not an intermediate YCbCr image), and propose an innovative up-sampling algorithm that exploits the RGB inter-channel cot-relation. This is based on the fact that the RGB inter-channel correlation is usually higher than the YCbCr inter-channel correlation. The simulation results show that the proposed method outperforms the state-of-the-art method and a bi-linear method with regard to the objective quality measures. In addition, the proposed algorithm is less expensive than the state-of-the-art technology in terms of the resource requirements.
Simplified Solutions for Activity Deposited on Moving Filter Media

Simplified numerical solutions for particulate activity viewed on moving filter continuous air monitors are developed. The monitor configurations include both rectangular window (RW) and circular window (CW) types. The solutions are demonstrated first for a set of basic airborne radioactivity cases, for a series of concentration pulses, and for indicating the effects of step changes in reactor coolant system (RCS) leakage for a pressurized water reactor. The method is also compared to cases from the prior art. These simplified solutions have additional benefits: They are easily adaptable to multiple radionuclides, they will accommodate collection and detection efficiencies that vary in known ways across the collection area, and they also ease the solution programming.
SensorFormer: Efficient Many-to-Many Sensor Calibration With Learnable Input Subsampling

Accurate calibration of low-cost environmental sensors is a prerequisite for their successful use in many monitoring applications. State-of-the-art calibration methods vary from simple linear regression to sophisticated deep models based on LSTMs and GRUs. The latter take past measurements to improve calibration accuracy. In this article, we argue that both recent past and close future measurements help to achieve accurate calibration, whereas accuracy improvements beyond the past come with a delay introduced by the occurrence of the future. We propose a generalized many-to-many calibration scheme called SensorFormer based on the successful Transformer model which takes both past and future raw measurements into account. We show that the proposed approach: 1) outperforms other methods by improving calibration accuracy by 16.5%-20.4% on public data sets and own field data and 2) can efficiently run on low-power microcontrollers with very limited computational and storage capabilities. The latter is achieved by a novel optimization technique based on learnable input subsampling taking advantage of the properties of typical sensor data. We manage to reduce the model size by 20%-33% and minimize the overall floating point operations per second (FLOPs) by 65% while maintaining superior accuracy than state-of-the-art methods.