ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Madhubani Art Classification using transfer learning with deep feature fusion and decision fusion based

Some traditional Indian art forms enjoy widespread popularity across the world. One of the most prominent among these is the Madhubani style. This art form's rich heritage and beauty enthrall the connoisseurs and continue to inspire new designs catering to the changing tastes of prevalent fashion. Preservation of these traditional art forms is the need of the hour. Modern technological advances can be utilized with great advantage for this purpose. Since a database of Madhubani art forms was hitherto unavailable, an attempt is made in this work to create one from scratch. Five different classes of Madhubani art, i.e., Bharni, Godna, Kachni, Kohbar, and Tantrik, are identified, and the collected images are annotated with these classes. Classification of the art images is attempted using the handcrafted Local Binary Pattern (LBP) texture descriptors and state-of-the-art Convolutional Neural Networks (CNNs). The Transfer Learning approach with CNNs is employed to classify the designs. An attempt is made to obtain a better classification accuracy than the one provided by standard CNNs. Towards this end, the current work proposes a fusion of features extracted from several deep CNNs, decision fusion-based classification based on averaging prediction score (FAVG), and maximum vote score (FMAX). The proposed method's performance is tested on our Madhubani art dataset and compared against several standard pre-trained CNNs available in the literature. The proposed approaches provide significantly higher classification accuracy for Madhubani art patterns, with decision fusion based on averaging prediction score (FAVG) approach being the best. The maximum accuracy, specificity, and error rate scores are 98.82%, 99.72%, and 1.18%, respectively. This is the first such attempt, and the excellent results motivate further work to develop content-based image retrieval tools and evolutionary design-based tools for automating the development of new designs. These endeavors are expected to go a long way in preserving precious art heritage and fostering its rapid growth in the world market. The dataset will be made publically available for further experimentation.
Rapid Weight Loss and Dietary Inadequacies among Martial Arts Practitioners from Poland

Healthy nutrition and maintaining a normal body weight are very important for reducing the risk of various diseases not only among the general population, but also among athletes, especially combat sport athletes. The aim of the study was to evaluate the prevalence of rapid weight loss (RWL) and to indicate eventual dietary inadequacies among professional martial arts practitioners. Sixty-two male athletes (aged: 23 +/- 4) completed a questionnaire (i.e., frequency of food group consumption, questions about training and RWL) and single 24-h dietary recall. This study confirmed the high prevalence of RWL in athletes (58%) for two to three days before the competition, which allowed for reduction of 3.4 +/- 1.0 kg (4.3 +/- 1.5%) of their body weight. Many dietary inaccuracies were found such as: lower than recommended by nutrition experts, level of consumption of dairy products, grain products, fruit, and vegetables, and insufficient intake of energy, carbohydrates, minerals (especially iodine, potassium, calcium) and vitamins (especially D, folate, C, E) during the training day. Adequate nutrition is a natural support for the whole training process, and may allow for regulation of body weight in a longer period and in a safer manner; thus, there is a need for nutrition education dedicated to athletes and their trainers.
Factors influencing nursing professional identity development: A qualitative study

This research focuses on approaches that best support nursing professional identity formation, particularly by providing the insights of nursing students in their own words. This report reflects qualitative phenomenological research on nursing professional development from the perspective of both associate degree and baccalaureate degree student nurses in their final semester of study and describes factors that support or detract from the experience of nursing professional identity development. Participants were guided through individual interviews using semi-structured interview questions and later invited to facilitated focus groups with other students to clarify and elaborate on previous comments. The approach participants most often described as helping them develop into a professional nurse was clinical experience and the ability to practice independently. It was mentioned far more than the next most common response: role models and mentors. Other methods include reflection, critical thinking, and confidence. This research adds to a limited body of literature on the factors that influence nursing professional identity.
Residual Networks of Residual Networks: Multilevel Residual Networks

A residual networks family with hundreds or even thousands of layers dominates major image recognition tasks, but building a network by simply stacking residual blocks inevitably limits its optimization ability. This paper proposes a novel residual network architecture, residual networks of residual networks (RoR), to dig the optimization ability of residual networks. RoR substitutes optimizing residual mapping of residual mapping for optimizing original residual mapping. In particular, RoR adds levelwise shortcut connections upon original residual networks to promote the learning capability of residual networks. More importantly, RoR can be applied to various kinds of residual networks (ResNets, Pre-ResNets, and WRN) and significantly boost their performance. Our experiments demonstrate the effectiveness and versatility of RoR, where it achieves the best performance in all residual-networklike structures. Our RoR-3-WRN58-4 + SD models achieve new state-of-the-art results on CIFAR-10, CIFAR-100, and SVHN, with the test errors of 3.77%, 19.73%, and 1.59%, respectively. RoR-3 models also achieve state-of-the-art results compared with ResNets on the ImageNet data set.
Ten years of art imaging research

This paper describes a decade of work on digital imaging for museums. From 1989 to 1992, the visual arts system for archiving and retrieval of images (VASARI) project produced a digital-imaging system that made color-calibrated images of up to 20000 x 20000 pixels directly from paintings. It used seven color-separation bands in the visible region, resulting in an average color error of around I DeltaE(ab)* unit. These images have since been used to monitor the condition of paintings, document paintings during conservation treatment, including predicting appearance after cleaning, reconstruct the original appearance of paintings in which pigments have faded, and assess whether paintings have been damaged during transportation, in estimations of the surface reflectance spectra and in the printing of high-quality reproductions. We have applied similar techniques to museum infrared and X-ray imaging. To manage the images produced by the VASARI system, an image-processing package has been developed that is tailored for very large colorimetric images. This package has since been used in several other projects. including a remote image viewer designed to provide internet access to high-resolution images. The paper explores these developments and gives details of the current generation of VASARI-derived systems. set in the context of the state of the art for museum imaging.
Copredication and Complexity Revisited: A Reply to Löhr and Michel

Human language affords the ability to attribute semantically distinct concepts to a single nominal, a process now commonly termed 'copredication'. If we describe a lunch as being delayed but also filling , senses of distinct semantic categories (event, physical object) are simultaneously being accessed. Copredication is relevant to major debates in cognitive science, since it cuts to the core of how the lexicon is formatted, and how distinct lexico-semantic representations relate to each other. The apparent scope and limits of copredication licensing can be explored via acceptability judgment and processing experiments, exposing certain replicable and generalizable patterns that apply across lexical types, syntactic structures, and different languages (Murphy 2021a, 2021b). As such, laying out the psycholinguistic terrain in which to address this phenomenon is crucial - and accounts that lack a valid psycholinguistic and empirical basis should be highlighted as problematic if they are to be accommodated and refined. Löhr and Michel (2022) claim that copredication acceptability is determined by a "set of expectations that are influenced by higher-order priors associated with discourse context and world knowledge". I will show that their model encounters a number of obstacles, and ends up unintentionally supporting an alternative model in Murphy (2019, 2021a, 2021b, 2021c), which they attempt to critique.
Towards Human-centric Digital Twins: Leveraging Computer Vision and Graph Models to Predict Outdoor Comfort

Conventional sidewalk studies focused on quantitative analysis of sidewalk walkability at a large scale which cannot capture the dynamic interactions between the environment and individual factors. Embracing the idea of Tech for Social Good, Urban Digital Twins seek AI-empowered approaches to bridge humans with digitally-mediated technologies to enhance their prediction ability. We employ GraphSAGE-LSTM, a geo-spatial artificial intelligence (GeoAI) framework on crowdsourced data and computer vision to predict human comfort on the sidewalks. Conceptualising the pedestrians and their interactions with surrounding built and unbuilt environments as human-centric dynamic graphs, our model captures such spatio-temporal variations given by the sequential movements of human walking, enabling the GraphSAGE-LSTM to be spatio-temporal-explicit. Our experiments suggest that the proposed model provides higher accuracy by more than 20% than a traditional machine learning model and two state-of-art deep learning frameworks, thus, enhancing the prediction power of Urban Digital Twin. The source code for the model is shared openly on GitHub.
Development of biomimetic triazine-based affinity ligands for efficient immunoglobulin G purification from human and rabbit plasma

Immunoglobulin purification from different biological fluids is considered one of the most critical steps in antibody production for diagnostic, therapeutic, and research purposes. The current study aimed to elucidate the role of the different aryl substituents in triazine-based affinity ligands on the performance of an affinity chromatography purification media to separate immunoglobulin G (IgG). The biomimetic triazine-based affinity ligand was chosen as a varied containing fix spacer and support. The sepharose beads were activated by epichlorohydrin, and five types of aryl substituents were replaced in the triazine ring and covalently immobilized to the resin surface by 1, 4-diaminobutane spacer. All affinity resins with various ligands were characterized and validated using FTIR, SEM, EDX, and microscopic images. The findings revealed that using R1=3-aminophenol and R2=3-aminophenol substituents in the triazine ring, as affinity ligands attached to the sepharose surface with a 10-atom linker CAES-6B-Cl@R1= MAF, R2= MAF (No. 4), leads to better purification of IgG from human and rabbit plasma with 22.8 mg/mL resin binding capacity in 73±5% yield and 95% of purity. All results confirmed that the designed triazine-based affinity ligands could effectively purify IgG compatible with a fast and low-cost approach.
A new diterpenoid from the leaves and twigs of Croton lachnocarpus Benth

A new clerodane diterpenoid, crotolanin A (1), along with three known clerodane diterpenoids, crotoeurin B (2), teucvidin (3) and teucvin (4), was isolated from the ethanol extract of the leaves and twigs of Croton lachnocarpus Benth. Their structures were identified by extensive NMR spectroscopic and HRESIMS analyses. The dopaminergic neuroprotective activity of compounds 1-4 was tested by using transgenic Caenorhabditis elegans pathological model. Compound 2 alleviated dopaminergic neuron degeneration of worms induced by 6-hydroxydopamine (6-OHDA) that represented a potential therapy for Parkinson's disease (PD).
Decentralized Active Demand Response (DADR) system for improvement of frequency stability in distribution network

The concepts of decentralized demand response systems are present in the state of the art literature, however, the material is limited to general ideas and possible system services. In this paper the original and application ready proposition of Decentralized Active Demand Response (DADR) system realization is presented in the form of a stochastic control algorithm. Such an approach to the system description enables simulation investigations of the DADR system as an element of dynamic stability improvement of an electrical energy distribution network. The results obtained from simulations have confirmed that the proposed DADR solution, because of its high dynamic response in dealing with disturbance phenomena, might be used as part of both primary and secondary Load Frequency Control in electrical power systems. (C) 2016 Elsevier B.V. All rights reserved.
Fronthaul Load Balancing in Energy Harvesting Powered Cloud Radio Access Networks

Enhanced with wireless power transfer capability, cloud radio access network (C-RAN) enables energy-restrained mobile devices to function uninterruptedly. Beamforming of C-RAN has potential to improve the efficiency of wireless power transfer, in addition to transmission data rates. In this paper, we design the beamforming jointly for data transmission and energy transfer, under finite fronthaul capacity of C-RAN. A non-convex problem is formulated to balance the fronthaul requirements of different remote radio heads (RRHs). Norm approximations and relaxations are carried out to convexify the problem to second order cone programming (SOCP). To improve the scalability of the design to large networks, we further decentralize the SOCP problem using the alternating direction multiplier method (ADMM). A series of reformulations and transformations are conducted, such that the resultant problem conforms to the state-of-the-art ADMM solver and can be efficiently solved in real time. Simulation results show that the distributed algorithm can remarkably reduce the time complexity without compromising the fronthaul load balancing of its centralized counterpart. The proposed algorithms can also reduce the fronthaul bandwidth requirements by 25% to 50%, compared with the prior art.
Multi-Part Modeling and Segmentation of Left Atrium in C-Arm CT for Image-Guided Ablation of Atrial Fibrillation

As a minimally invasive surgery to treat atrial fibrillation (AF), catheter based ablation uses high radio-frequency energy to eliminate potential sources of abnormal electrical events, especially around the ostia of pulmonary veins (PV). Fusing a patient- specific left atrium (LA) model (including LA chamber, appendage, and PVs) with electro-anatomical maps or overlaying the model onto 2-D real-time fluoroscopic images provides valuable visual guidance during the intervention. In this work, we present a fully automatic LA segmentation system on nongated C-arm computed tomography (C-arm CT) data, where thin boundaries between the LA and surrounding tissues are often blurred due to the cardiac motion artifacts. To avoid segmentation leakage, the shape prior should be exploited to guide the segmentation. A single holistic shape model is often not accurate enough to represent the whole LA shape population under anatomical variations, e. g., the left common PVs vs. separate left PVs. Instead, a part based LA model is proposed, which includes the chamber, appendage, four major PVs, and right middle PVs. Each part is a much simpler anatomical structure compared to the holistic one and can be segmented using a model-based approach (except the right middle PVs). After segmenting the LA parts, the gaps and overlaps among the parts are resolved and segmentation of the ostia region is further refined. As a common anatomical variation, some patients may contain extra right middle PVs, which are segmented using a graph cuts algorithm under the constraints from the already extracted major right PVs. Our approach is computationally efficient, taking about 2.6 s to process a volume with 256 x 256 x 245 voxels. Experiments on 687 C-arm CT datasets demonstrate its robustness and state-of-the-art segmentation accuracy.
Innovative Upscaling of Architectural Elements for Strengthening Building Structures

For conservation of heritages or life prolongation of aged buildings that contributes to environmental sustainability, there is a global need of structural strengthening or upgrading so as to restore their original functions or fulfil more stringent performance requirements stipulated in modern design codes of practice. However, the actual implementation is usually met with resistance from the property owner; hence, it is desirable to adopt an effective, economical and less invasive technique. In order to provide a further incentive, this article explores an innovative idea of upscaling decorative architectural elements, such as brackets, knee braces and corbels, in order that they also possess adequate strength capacity to resist extreme loadings such as earthquake actions. The required dimensions of architectural brackets for seismic retrofitting of concrete beam-column joints are calculated for different levels of seismicity through a parametric study. It is demonstrated that the proposed design can enhance both the aesthetics and structural performance of a building. This exemplifies how art can be integrated into engineering design for solving real-world problems.
Structural Analysis of Oligosaccharides and Glycoconjugates Using NMR

Carbohydrate chains play critical roles in cellular recognition and subsequent signal transduction in the nervous system. Furthermore, gangliosides are targets for various amyloidogenic proteins associated with neurodegenerative disorders. To better understand the molecular mechanisms underlying these biological phenomena, atomic views are essential to delineate dynamic biomolecular interactions. Nuclear magnetic resonance (NMR) spectroscopy provides powerful tools for studying structures, dynamics, and interactions of biomolecules at the atomic level. This chapter describes the basics of solution NMR techniques and their applications to the analysis of 3D structures and interactions of glycoconjugates in the nervous system.
PolSAR Coherency Matrix Optimization Through Selective Unitary Rotations for Model-Based Decomposition Scheme

In this letter, a special unitary SU(3) matrix group is exploited for coherency matrix transformations to decouple the energy between orthogonal states of polarization. This decoupling results in the minimization of the cross-polarization power along with the removal of some off-diagonal terms of coherency matrix. The proposed unitary transformations are utilized on the basis of the underlying dominant scattering mechanism. By doing so, the reduced power from the cross-polarization channel is always concentrated on the underlying dominant copolar scattering component. This makes it unique in comparison to state-of-the-art techniques. The proposed methodology can be adopted to optimize the coherency matrix to be used for the model-based decomposition methods. To verify this, pioneer three-component decomposition model is implemented using the proposed optimized coherency matrix of two different test sites. The comparative studies are analyzed to show the improvements over state-of-the-art techniques.
Self-Similarity and Spectral Correlation Adaptive Algorithm for Color Demosaicking

Most common cameras use a CCD sensor device measuring a single color per pixel. The other two color values of each pixel must be interpolated from the neighboring pixels in the so-called demosaicking process. State-of-the-art demosaicking algorithms take advantage of interchannel correlation locally selecting the best interpolation direction. These methods give impressive results except when local geometry cannot be inferred from neighboring pixels or channel correlation is low. In these cases, they create interpolation artifacts. We introduce a new algorithm involving nonlocal image self-similarity in order to reduce interpolation artifacts when local geometry is ambiguous. The proposed algorithm introduces a clear and intuitive manner of balancing how much channel-correlation must be taken advantage of. Comparison shows that the proposed algorithm gives state-of-the-art methods in several image bases.
Scale-Aware Visual-Inertial Depth Estimation and Odometry Using Monocular Self-Supervised Learning

For real-world applications with a single monocular camera, scale ambiguity is an important issue. Because self-supervised data-driven approaches that do not require additional data containing scale information cannot avoid the scale ambiguity, state-of-the-art deep-learning-based methods address this issue by learning the scale information from additional sensor measurements. In that regard, inertial measurement unit (IMU) is a popular sensor for various mobile platforms due to its lightweight and inexpensiveness. However, unlike supervised learning that can learn the scale from the ground-truth information, learning the scale from IMU is challenging in a self-supervised setting. We propose a scale-aware monocular visual-inertial depth estimation and odometry method with end-to-end training. To learn the scale from the IMU measurements with end-to-end training in the monocular self-supervised setup, we propose a new loss function named as preintegration loss function, which trains scale-aware ego-motion by comparing the ego-motion integrated from IMU measurement and predicted ego-motion. Since the gravity and the bias should be compensated to obtain the ego-motion by integrating IMU measurements, we design a network to predict the gravity and the bias in addition to the ego-motion and the depth map. The overall performance of the proposed method is compared to state-of-the-art methods in the popular outdoor driving dataset, i.e., KITTI dataset, and the author-collected indoor driving dataset. In the KITTI dataset, the proposed method shows competitive performance compared with state-of-the-art monocular depth estimation and odometry methods, i.e., root-mean-square error of 5.435 m in the KITTI Eigen split and absolute trajectory error of 22.46 m and 0.2975 degrees in the KITTI odometry 09 sequence. Different from other up-to-scale monocular methods, the proposed method can estimate the metric-scaled depth and camera poses. Additional experiments on the author-collected indoor driving dataset qualitatively confirm the accurate performance of metric-depth and metric pose estimations.
Graded Applications of NQS Theory for Modeling Correlated Noise in SiGe HBTs

In this paper, we develop a correlated noise model for bipolar transistors from an accurate nonquasi-static model. The proposed noise model includes the signal delay through base-collector space-charge region and is implemented using four extra nodes. We also present a simplified version of the same model that requires only two extra nodes. A further simplified version that uses only one extra node is found to be identical with a state-of-the-art correlated noise model. When compared with the device simulation data, our proposed models show improved accuracy compared with the existing state-of-the-art noise models.