ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Simultaneous Neural and Movement Recording in Large-Scale Immersive Virtual Environments

Virtual reality (VR) allows precise control and manipulation of rich, dynamic stimuli that, when coupled with on-line motion capture and neural monitoring, can provide a powerful means both of understanding brain behavioral relations in the high dimensional world and of assessing and treating a variety of neural disorders. Here we present a system that combines state-of-the-art, fully immersive, 3D, multi-modal VR with temporally aligned electroencephalographic (EEG) recordings. The VR system is dynamic and interactive across visual, auditory, and haptic interactions, providing sight, sound, touch, and force. Crucially, it does so with simultaneous EEG recordings while subjects actively move about a space. The overall end-to-end latency between real movement and its simulated movement in the VR is approximately 40 ms. Spatial precision of the various devices is on the order of millimeters. The temporal alignment with the neural recordings is accurate to within approximately 1 ms. This powerful combination of systems opens up a new window into brain-behavioral relations and a new means of assessment and rehabilitation of individuals with motor and other disorders.
Fast and efficient contrast-enhanced super-resolution without real-world data using concatenated recursive compressor-decompressor network

The authors propose a novel model called concatenated recursive compressor-decompressor network (CRCDNet) for contrast-enhanced super-resolution. The characteristics of authors' model can be summarised as follows. First, a compression-decompression process reduces the computational complexity compared with the general fully convolutional model. Second, an internal/external skip-connection is used to preserve information of the preceding layers. Finally, by employing a recursive module, authors' model has a small number of parameters, yet is a deep and robust network. The authors apply authors' proposed network to license plate images. As a real application, license plates can provide important evidence for investigation of crimes and for security, but it is very difficult to collect the vast amounts of license plates required for analysis based on a data-driven approach. To solve this problem, the authors generated virtual datasets to train authors' model, while analysing the performance with real license plate datasets. Authors' method achieves better performance than the state-of-the-art models on license plate images.
Video Extrapolation Method Based on Time-Varying Energy Optimization and CIP

Video extrapolation/prediction methods are often used to synthesize new videos from images. For fluid-like images and dynamic textures as well as moving rigid objects, most state-of-the-art video extrapolation methods use non-physics-based models that learn orthogonal bases from a number of images but at high computation cost. Unfortunately, data truncation can cause image degradation, i.e., blur, artifact, and insufficient motion changes. To extrapolate videos that more strictly follow physical rules, this paper proposes a physics-based method that needs only a few images and is truncation-free. We utilize physics-based equations with image intensity and velocity: optical flow, Navier-Stokes, continuity, and advection equations. These allow us to use partial difference equations to deal with the local image feature changes. Image degradation during extrapolation is minimized by updating model parameters, where a novel time-varying energy balancer model that uses energy based image features, i.e., texture, velocity, and edge. Moreover, the advection equation is discretized by high-order constrained interpolation profile for lower quantization error than can be achieved by the previous finite difference method in long-term videos. Experiments show that the proposed energy based video extrapolation method outperforms the state-of-the-art video extrapolation methods in terms of image quality and computation cost.
Traffic surveillance camera calibration by 3D model bounding box alignment for accurate vehicle speed measurement

In this paper, we focus on fully automatic traffic surveillance camera calibration, which we use for speed measurement of passing vehicles. We improve over a recent state-of-the-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference method. The method is based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from arbitrary viewpoints, since it has no constraints on camera placement. We evaluate our method on the recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration method by detection of two vanishing points reduces error by 50% (mean distance ratio error reduced from 0.18 to 0.09) compared to the previous state-of-the-art method. We also show that our scene scale inference method is more precise, outperforming both state-of-the-art automatic calibration method for speed measurement (error reduction by 86 % - 7.98 km/h to 1.10 km/h) and manual calibration (error reduction by 19 % - 1.35 km/h to 1.10 km/h). We also present qualitative results of the proposed automatic camera calibration method on video sequences obtained from real surveillance cameras in various places, and under different lighting conditions (night, dawn, day). (C) 2017 Elsevier Inc. All rights reserved.
Numerical Methods With Engineering Applications and Their Visual Analysis via Polynomiography

Polynomiography is a fusion of Mathematics and Art, which as a software results in a new form of abstract art. Rendered images are through algorithmic visualization of solving a polynomial equation via iteration schemes. Images are beautiful and diverse, yet unique. In short, polynomiography allows us to draw unique and complex-patterned images of polynomials which be re-colored in different ways through different iteration schemes. In the modern age, polynomiography covers a variety of applications in different fields of art and science. The aim of this paper is to present polynomiography using newly constructed root-finding algorithms for the solution of non-linear equations. The constructed algorithms are two-step predictor-corrector methods. For reducing computational cost and making the algorithm more effective, we approximate the second derivative via interpolation technique. These methods have been derived by employing Householder's method, interpolation technique and Taylor's series expansion. The convergence criterion of the newly developed algorithms has been discussed and proved their sixth-order convergence which is higher than many existing algorithms. To analyze the accuracy, validity and applicability of the proposed methods, several arbitrary and engineering problems have been tested and the obtained numerical results certify the better efficiency of the suggested methods against the other well-known iteration schemes given in the literature. Finally, we present polynomiography through the constructed iteration schemes and give a detailed comparison with the other iteration schemes which reflects the convergence properties and graphical aspects of the constructed algorithms.
Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies

Metric learning is a key problem for many data mining and machine learning applications, and has long been dominated by Mahalanobis methods. Recent advances in nonlinear metric learning have demonstrated the potential power of non-Mahalanobis distance functions, particularly tree-based functions. We propose a novel nonlinear metric learning method that uses an iterative, hierarchical variant of semi-supervised max-margin clustering to construct a forest of cluster hierarchies, where each individual hierarchy can be interpreted as a weak metric over the data. By introducing randomness during hierarchy training and combining the output of many of the resulting semi-random weak hierarchy metrics, we can obtain a powerful and robust nonlinear metric model. This method has two primary contributions: first, it is semi-supervised, incorporating information from both constrained and unconstrained points. Second, we take a relaxed approach to constraint satisfaction, allowing the method to satisfy different subsets of the constraints at different levels of the hierarchy rather than attempting to simultaneously satisfy all of them. This leads to a more robust learning algorithm. We compare our method to a number of state-of-the-art benchmarks on k-nearest neighbor classification, large-scale image retrieval and semi-supervised clustering problems, and find that our algorithm yields results comparable or superior to the state-of-the-art.
Enhanced access and isolation by simple modifications of dental armamentarium

A good armamentarium facilitates the efficient working of the dentist which in turn improves the quality of treatment rendered to the patient. The present invention of the unit consisting of the flexible mirror attached to the suction and the dual suction tip aims at improving the clinical efficiency of dental treatments provided. This compact unit is designed to improve the visualization and isolation of the operating field. It is also easy to fabricate and alleviates the very relevant shortcomings of clinical work.
On Deep Reinforcement Learning for Static Routing and Wavelength Assignment

Deep Reinforcement Learning (DRL) is rising as a promising tool for solving optimization problems in optical networks. Though studies employing DRL for solving static optimization problems in optical networks are appearing, assessing strengths and weaknesses of DRL with respect to state-of-the-art solution methods is still an open research question. In this work, we focus on Routing and Wavelength Assignment (RWA), a well-studied problem for which fast and scalable algorithms leading to better optimality gaps are always sought for. We develop two different DRL-based methods to assess the impact of different design choices on DRL performance. In addition, we propose a Multi-Start approach that can improve the average DRL performance, and we engineer a shaped reward that allows efficient learning in networks with high link capacities. With Multi-Start, DRL gets competitive results with respect to a state-of-the-art Genetic Algorithm with significant savings in computational times. Moreover, we assess the generalization capabilities of DRL to traffic matrices unseen during training, in terms of total connection requests and traffic distribution, showing that DRL can generalize on small to moderate deviations with respect to the training traffic matrices. Finally, we assess DRL scalability with respect to topology size and link capacity.
Moving Urban Sculptures towards Sustainability: The Urban Sculpture Planning System in China

Following the continuous development characterized by large-scale constructions, Chinese urban development has shifted to the promotion of refined urban space quality. Urban sculpture, an important part of public arts, has been receiving increased attention in China as an important carrier for highlighting urban characteristics, culture, and history within cultural policies. As a type of cultural capital, it offers innovative methods to address the issues of economic, social, and environmental sustainability, in particular cultural sustainability. Interdisciplinary theories of urban planning are creatively applied to guide, coordinate, and improve the sustainable production of urban sculptures in China. This research was initiated to: (1) Illustrate how urban sculptures are produced through an urban planning system in the context of China; (2) explain what kind of influencing factors in relation to sustainability exist, mainly within the framework of planning strategies and cultural policies; and (3) put forward sustainable planning strategies to produce urban sculptures. To answer the above inquiries, we reviewed more than 100 articles, plans, and government documents, and we conducted several semi-structured interviews. The article argues that urban planning strategies and policies have been conceived as strategic instruments by the Chinese municipal governments to realize sustainable development of urban sculptures. Our findings would enrich knowledge on geographic studies of public art planning through the contextualized analysis of a Chinese urban sculpture planning system. It also fills the gap in the literature on the sustainability of urban sculptures by approaching the perspectives of planning strategies and cultural policies.
Ultralight, Elastic, Hybrid Aerogel for Flexible/Wearable Piezoresistive Sensor and Solid-Solid/Gas-Solid Coupled Triboelectric Nanogenerator

Aerogels have been attracting wide attentions in flexible/wearable electronics because of their light weight, excellent flexibility, and electrical conductivity. However, multifunctional aerogel-based flexible/wearable electronics for human physiological/motion monitoring, and energy harvest/supply for mobile electronics, have been seldom reported yet. In this study, a kind of hybrid aerogel (GO/CNT HA) based on graphene oxide (GO) and carboxylated multiwalled carbon nanotubes (CMWCNTs) is prepared which can not only used as piezoresistive sensors for human motion and physiological signal detections, but also as high performance triboelectric nanogenerator (TENG) coupled with both solid-solid and gas-solid contact electrifications (CE). The repeatedly loading-unloading tests with 20 000 cycles exhibit its high and ultrastable piezoresistive sensor performances. Moreover, when the obtained aerogel is used as the electrode of a TENG, high electric output performance is produced due to the synergistic effect of solid-solid, and gas-solid interface CEs (3D electrification: solid-solid interface CE between the two solid electrification layers; gas-solid interface CE between the inner surface of GO/CNT HA and the air filled in the aerogel pores). This kind of aerogel promises good applications for human physiological/motion monitoring and energy harvest/supply in flexible/wearable electronics such as piezoresistive sensors and flexible TENG.
Quantifying the state of the art of electric powertrains in battery electric vehicles: Range, efficiency, and lifetime from component to system level of the Volkswagen ID.3

With the rise of battery electric vehicles to mass production, many technical improvements have been realized to drastically increase the electric range, efficiency, and sustainability. However, insights into those valuable state-of-the-art solutions are usually not shared with researchers due to the strict nondisclosure policies of electric vehicle manufacturers. Many studies, therefore, rely on assumptions, best-guess estimates, or insider knowledge. This article presents an in-depth multi-scale analysis of the electric powertrain characteristics of a Volkswagen ID.3 Pro Performance. The focus is set on the range, power, and lifetime perceivable by the user. Thorough experimental tests are performed from the battery cell to vehicle level, following the energy conversion from source to sink. Energy densities are determined at all levels and the absolute electric range is quantified under varying operating conditions. Power capability and efficiency is evaluated at cell level by quantifying the battery cell and pack performance with current rate tests in charge/discharge scenarios and impedance determination, as well as by determining powertrain energy conversion efficiency with in-vehicle measurements. Moreover, accelerated aging tests of the lithium-ion battery cells are performed with close to real-world conditions and projected to vehicle level, demonstrating that the lithium-ion battery pack achieves mileages outperforming the warranty information of the manufacturer under real-world operation. Overall, the results provide valuable insights into the current state of the art and can serve as a reference for automotive engineering in academia. Over 10 GB of lithium-ion battery cell, pack, and overall powertrain measurement data from the lab and real-world environment is available as open source alongside the article. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Effect of ammonia stress on AMPK regulating-carbohydrate and lipid metabolism in Chinese striped-neck turtle (Mauremys sinensis)

In aquatic organisms, ammonia is one of the major factors that affect energy levels when it exceeds its optimal concentration. Numerous studies have examined the effects of ammonia on aquatic animals, but its effect on metabolism is still unknown. The effect of ammonia on carbohydrates and lipid metabolism in the Chinese striped neck turtle (Mauremys sinensis) was investigated in this study by exposing the turtle to two different ammonia concentrations (A100: 1.53 mg L-1) and (A200: 2.98 mg L-1) for 24 and 48 h, respectively. Our results showed that the mRNA expression of adenosine monophosphate-activated protein kinase α1 (AMPKα1) significantly increased only in A100 at 24 h, whereas its activity increased in both ammonia-exposed groups. The two AMPK-regulated transcription factors responsible for carbohydrate metabolism also exhibited changes in ammonia-treated groups, as hepatocyte nuclear factor-4-alpha (HNF4α) increased and forkhead box protein O1 (FoxO1) decreased. The expression of phosphofructokinase (PFK) and glucose-6-phosphatase (G-6-PAS) was subsequently downregulated. In addition, transcription factors, carbohydrate-responsive element-binding protein (ChREBP), and sterol regulatory element-binding protein 1c (SREBP-1c), which are known to be involved in lipogenesis, were suppressed. These downstream genes include fatty acid synthase, stearoyl CoA desaturase, and acetyl-CoA carboxylase (FAS, SCD-1 and ACC). Moreover, the glucose content decreased, whereas the triglyceride content increased significantly in A200 at 24 h. We concluded that AMPK signaling inhibits gluconeogenesis and lipogenesis, and promotes glycolysis to meet energy demand under stressful conditions in M. sinensis.
Recent advances and challenges in the early diagnosis and management of invasive fungal infections in Africa

Invasive fungal infections are a serious concern globally, especially in African settings which are typified by poorly funded and fragile healthcare systems. Low performance diagnostics, limited therapeutics and poor societal awareness of invasive fungal infections are some of the perennial challenges which have contributed to the unacceptably high death rates from these serious infections. However, recent advances have been recorded in fungal diagnostics and therapeutics development. Research into the development of vaccines to prevent fungal disease is beginning to yield promising results. Here we highlight key successes recorded and gaps in this journey and argue that national governments and relevant stakeholders need to do more to prioritise invasive fungal infections. Pragmatic and context-specific measures are proposed to mitigate the peculiar challenges Africa faces in tackling invasive fungal infections.
Growth of Firms in a Fragmented Cultural Industry: Italian Commercial Art Galleries' Competitive Strategies

The growth of cultural firms is important in developing local economies, enhancing employment, and improving urban sustainability, but it is difficult to achieve in fragmented industries that are populated by the smallest firms and offer a particularly unfavorable context for growth. The study takes a contingency perspective and contributes to both the literature on business strategy in fragmented industries and that on the growth of small and medium-sized enterprises (SMEs) by identifying the strategies implemented in a fragmented cultural industry, determining which of them are associated with a firm's expected growth, and finding the few firm-specific factors that are associated with growth. It also complements the extant literature on art galleries by looking at them from the understudied strategic perspective. Suggestions for practitioners and policy makers are included.
Barriers to and Facilitators of Antiretroviral Therapy Adherence in Nepal: A Qualitative Study

Patient's adherence is crucial to get the best out of antiretroviral therapy (ART). This study explores in-depth the barriers to and facilitators of ART adherence among Nepalese patients and service providers prescribing ART. Face-to-face semi-structured interviews were conducted with 34 participants. Interviews were audio-taped, transcribed, and translated into English before being analyzed thematically. ART-prescribed patients described a range of barriers for failing to adhere to ART. Financial difficulties, access to healthcare services, frequent transport blockades, religious/ritual obstacles, stigma and discrimination, and side-effects were the most-frequently discussed barriers whereas trustworthy health workers, perceived health benefits, and family support were the most-reported facilitators. Understanding barriers and facilitators can help in the design of an appropriate and targeted intervention. Healthcare providers should address some of the practical and cultural issues around ART whilst policy-makers should develop appropriate social policy to promote adherence among ART-prescribed patients.
Nanocarrier-Assisted Delivery of Metformin Boosts Remodeling of Diabetic Periodontal Tissue via Cellular Exocytosis-Mediated Regulation of Endoplasmic Reticulum Homeostasis

Endoplasmic reticulum (ER) dysfunction is a potential contributor to the impaired repair capacity of periodontal tissue in diabetes mellitus (DM) patients. Restoring ER homeostasis is thus critical for successful regenerative therapy of diabetic periodontal tissue. Recent studies have shown that metformin can modulate DM-induced ER dysfunction, yet its mechanism remains unclear. Herein, we show that high glucose elevates the intracellular miR-129-3p level due to exocytosis-mediated release failure and subsequently perturbs ER calcium homeostasis via downregulating transmembrane and coiled-coil domain 1 (TMCO1), an ER Ca2+ leak channel, in periodontal ligament stem cells (PDLSCs). This results in the degradation of RUNX2 via the ubiquitination-dependent pathway, in turn leading to impaired PDLSCs osteogenesis. Interestingly, metformin could upregulate P2X7R-mediated exosome release and decrease intracellular miR-129-3p accumulation, which restores ER homeostasis and thereby rescues the impaired PDLSCs. To further demonstrate the in vivo effect of metformin, a nanocarrier for sustained local delivery of metformin (Met@HALL) in periodontal tissue is developed. Our results demonstrate that compared to controls, Met@HALL with enhanced cytocompatibility and pro-osteogenic activity could boost the remodeling of diabetic periodontal tissue in rats. Collectively, our findings unravel a mechanism of metformin in restoring cellular ER homeostasis, enabling the development of a nanocarrier-mediated ER targeting strategy for remodeling diabetic periodontal tissue.
Hybrid energy coordination control strategy on associated gas power grid

This paper reviews the state-of-the-art in offshore oil and energy system design, develops a prototype hybrid marine renewable energy system and discusses an artificial intelligence algorithm to optimize the structure. A control strategy for the coordination of hybrid energy in an interconnected gas grid is developed. Numerical example results show that the optimization of the coordinated control strategy can provide a better balance between economy and reliability. (C) 2021 Published by Elsevier Ltd.