ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Vision-Based Topological Mapping and Navigation With Self-Organizing Neural Networks

Spatial mapping and navigation are critical cognitive functions of autonomous agents, enabling one to learn an internal representation of an environment and move through space with real-time sensory inputs, such as visual observations. Existing models for vision-based mapping and navigation, however, suffer from memory requirements that increase linearly with exploration duration and indirect path following behaviors. This article presents e-TM, a self-organizing neural network-based framework for incremental topological mapping and navigation. e-TM models the exploration trajectories explicitly as episodic memory, wherein salient landmarks are sequentially extracted as ``events'' from streaming observations. A memory consolidation procedure then performs a playback mechanism and transfers the embedded knowledge of the environmental layout into spatial memory, encoding topological relations between landmarks. Fusion adaptive resonance theory (ART) networks, as the building block of the two memory modules, can generalize multiple input patterns into memory templates and, therefore, provide a compact spatial representation and support the discovery of novel shortcuts through inferences. For navigation, e-TM applies a transfer learning paradigm to integrate human demonstrations into a pretrained locomotion network for smoother movements. Experimental results based on VizDoom, a simulated 3-D environment, have shown that, compared to semiparametric topological memory (SPTM), a state-of-the-art model, e-TM reduces the time costs of navigation significantly while learning much sparser topological graphs.
Nonlinear C-oss - V-DS Profile Based ZVS Range Calculation for Dual Active Bridge Converters

Generally, power electronic converters are designed to obtain the highest efficiency at rated power while they are most often operated under partial loading conditions. For dual active bridge converters, the zero-voltage-switching (ZVS) conditions can be impaired under light load situations. While load depending ZVS operation has been introduced by prior-art approaches, the nonlinear characteristic of the output capacitance in a power device is often not considered and its effect on operating boundaries of ZVS is neglected. In this letter, based on practical switching transients, an improved method of calculating the ZVS range is introduced. By taking into account the nonlinearity of output capacitance, the method is developed from a detailed analysis of real switching transients. A 2.5-kW prototype is built, and a comprehensive comparison with prior-art approaches is conducted to validate the accuracy of the proposed method.
Fast Control for Backlight Power-Saving Algorithm Using Motion Vectors from the Decoded Video Stream

Backlight power-saving algorithms can reduce the power consumption of the display by adjusting the frame pixels with optimal clipping points under some tradeoff criteria. However, the computation for the selected clipping points can be complex. In this paper, a novel algorithm is created to reduce the computation time of the state-of-the-art backlight power-saving algorithms. If the current frame is similar to the previous frame, it is unnecessary to execute the backlight powersaving algorithm for the optimal clipping points, and the derived clipping point from the previous frame can be used for the current frame automatically. In this paper, the motion vector information was used as the measurement of the similarity between adjacent frames, where the generation of the motion vector information requires no extra complexity since it is generated to reconstruct the decoded frame pixels before the display. The experiments showed that the proposed work can reduce the running time of the state-of-the-art methods by 25.21% to 64.22%, while the performances are maintained; the differences with the state-of-the-art methods in PSNR are only 0.02 similar to 1.91 dB, and those in power are only -0.001 similar to 0.008 W.
What Can We Learn from Rural Youth in British Columbia, Canada? Environment and Climate Change-Issues and Solutions

What can we learn from rural youth? was a youth-led arts-based participatory action research project carried out to understand and facilitate positive youth development in two rural communities in the province of British Columbia, Canada. Data was collected using photovoice, visual art, journal reflections, and group discussions. During the study, youth expressed a strong connection with nature for their development or wellbeing. Issues such as environmental degradation and climate change were identified as causes for concern. They discussed human responsibility for environmental stewardship both in their local communities and globally. Climate change hazards such as flood and fire, human action leading to environmental pollution, and human responsibility for environmental stewardship surfaced as issues for their development. Youth expressed a felt responsibility to act on climate change and to reduce the anthropogenic impact on the Earth. Based on youth voices, we conclude that attempts to engage youth in climate action without considering their psychosocial wellbeing, may overburden them.
Community Engagement via Mural Art to Foster a Sustainable Urban Environment

Community is a key element in a sustainable urban environment and possesses a capacity to become a major contributor in the development of it. Therefore, it is essential for communities to be active and engaged. The concept of a community and its engagement via mural art is addressed in this article. The aim of the research is to investigate the impact of mural art on community engagement and the contribution of communities to a sustainable urban environment. The research was based on materials obtained during the implementation of the "Murals for Communities" project. The aim of the project was to address the issues of community engagement in three cities with social disconnection: Waterford (Ireland), Heerlen (the Netherlands), and Kaunas (Lithuania). The engagement of the communities through mural art creation was initiated through community engagement workshops where the communities were encouraged to participate in collaborative actions resulting in the improvement of the urban environment and the strengthening of the communities themselves. In total, 54 community engagement workshops were organized and 18 murals were created in the three partner cities. The entire process of community engagement was a well-coordinated and structured framework of actors and events, where each of them played a significant role. The results of the research revealed that community engagement workshops were used as a successful tool for community bonding and strengthening. Hence, mural art is a successful tool for sustainable community development, and the process of mural creation with its various stages enables community members to be active participants of social interaction and developers of both a sustainable community and a sustainable urban environment.
LRRK1-mediated NDEL1 phosphorylation promotes cilia disassembly via dynein-2-driven retrograde intraflagellar transport

Primary cilia are antenna-like organelles that regulate growth and development via extracellular signals. However, the molecular mechanisms underlying cilia dynamics, particularly those regulating their disassembly, are not well understood. Here, we show that leucine-rich repeat kinase 1 (LRRK1) plays a role in regulating cilia disassembly. The depletion of LRRK1 impairs primary cilia resorption following serum stimulation in cultured cells. Polo-like kinase 1 (PLK1) plays an important role in this process. During ciliary resorption, PLK1 phosphorylates LRRK1 at the primary cilia base, resulting in its activation. We identified nuclear distribution protein nudE-like 1 (NDEL1), which is known to positively regulate cilia disassembly, as a target of LRRK1 phosphorylation. Whereas LRRK1 phosphorylation of NDEL1 on Ser-155 promotes NDEL1 interaction with the intermediate chains of cytoplasmic dynein-2, it is also crucial for triggering ciliary resorption through dynein-2-driven retrograde intraflagellar transport. These findings provide evidence that a novel PLK1-LRRK1-NDEL1 pathway regulates cilia disassembly.
RTV Silicone Rubber Pre-coated Ceramic Insulators for Transmission Lines

The paper gives the state-of-the-art on pre-coated ceramic insulators for transmission lines. The usage of pre-coated ceramic insulators has mainly been in coastal areas where sea salt causes flashovers of ceramic insulators. The experience to date has been very good everywhere with very few problems reported. Interest in the pre-coated insulator concept continues to be strong everywhere. The paper also provides guidelines to assist the user in the specification of pre-coated ceramic insulators.
IAN: The Individual Aggregation Network for Person Search

Person search in real-world scenarios is a new challenging computer version task with many meaningful applications. The challenge of this task mainly comes from: (1) unavailable bounding boxes for pedestrians and the model needs to search for the person over the whole gallery images; (2) huge variance of visual appearance of a particular person owing to varying poses, lighting conditions, and occlusions. To address these two critical issues in modern person search applications, we propose a novel Individual Aggregation Network (IAN) that can accurately localize persons by learning to minimize intra-person feature variations. IAN is built upon the state-of-the-art object detection framework, i.e., faster R-CNN, so that high-quality region proposals for pedestrians can be produced in an online manner. In addition, to relieve the negative effect caused by varying visual appearances of the same individual, IAN introduces a novel center loss that can increase the intra-class compactness of feature representations. The engaged center loss encourages persons with the same identity to have similar feature characteristics. Extensive experimental results on two benchmarks, i.e., CUHK-SYSU and PRW, well demonstrate the superiority of the proposed model. In particular, IAN achieves 77.23% mAP and 80.45% top-1 accuracy on CUHK-SYSU, which outperform the state-of-the-art by 1.7% and 1.85%, respectively. (C) 2018 Elsevier Ltd. All rights reserved.
Multicolor colorimetric assay for copper ion detection based on the etching of gold nanorods

An effective, selective, and multicolor colorimetric assay for Cu2+ detection based on the regulation of peroxidase-like nanozyme-mediated etching of gold nanorods (Au NRs) is proposed. Cu2+-creatinine complex is selected as the nanozyme that exhibits excellent peroxidase-like activity even in the case of low concentration of Cu2+, which can catalyze 3,3,5,5-tetramethylbenzidine (TMB) to produce oxidized TMB (TMB+) in the presence of hydrogen peroxide, and TMB+ is oxidized to generate TMB2+ after adding H+, and the TMB2+ can etch Au NRs. The determination of Cu2+ is achieved based on the blue shift of the longitudinal localized surface plasmon resonance peak of Au NRs. Under the optimal conditions, the developed colorimetric assay exhibits high sensitivity for the detection of Cu2+ (limit of detection is 0.034 μM) with a wide linear range of 0.05-4.0 μM (R2 = 0.987). The solution shows a rainbow-like color in response to the increase of Cu2+ concentration, which can realize the semi-quantitative detection of Cu2+ by naked eyes. In addition, the developed method exhibits excellent selectivity for Cu2+-detection. The established method was used for the determination of Cu2+ in lake water, soil, and normal human serum with satisfactory recovery of spiked samples.
Promoting the overall energy profit through using the liquid hydrolysate during microwave hydrothermal pretreatment of wheat straw as co-substrate for anaerobic digestion

Liquid hydrolysate (LH) derived from the microwave hydrothermal pretreatment (MHP) of wheat straw (WS) was anaerobically digested together with the solid residual to promote the overall energy profit. Different MHP temperatures (90, 120, 150, 180 °C) and retention times (10, 20, 40 min) were investigated. Increased MHP intensity generated plenty of VFAs (mainly acetate) and phenols in the LH, implying the double-side effect of LH on AD. The highest methane production of 227.92 mL CH4·gVS-1 Raw was obtained with MHP at 120 °C for 10 min, 21.53 % higher than the control. While, MHP at 180 °C for 40 min exhibited 29.02 % lower methane production (113.13 mL CH4·gVS-1 Raw) and 115.86 % longer lag phase (3.13 days) than the control. Butyrate fermentation endowed the treatment groups of 180 °C with resilience from the overload and inhibition. Methanosarcina was largely enriched by the abundant acetate in LH on the early stage of anaerobic digestion (AD), especially when with high MHP intensity. Increased abundance of Methanosaeta and Methanobacterium played a crucial role in maintaining methane production at the middle and later stage. The high number of species and evenness in methanogens community were beneficial for the startup of batch AD. Although negative net energy was obtained, the lower ratio of energy input and output compared with the most researches using the solid residual after MHP as the sole substrate for AD demonstrated the contribution of LH to the overall energy profit.
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging

Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper, we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions. Our method is able to generate a dense shadow-focused confidence map. In our method, a shadow-seg module is built to learn general shadow features for shadow segmentation, based on global image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is introduced to extend the obtained binary shadow segmentation to a reference confidence map. In addition, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This network is able to predict shadow confidence maps directly from input images during inference. We use evaluation metrics such as DICE, inter-class correlation, and so on, to verify the effectiveness of our method. Our method is more consistent than human annotation and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions. Furthermore, we demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion, and automated biometric measurements.
TAGNet: Learning Configurable Context Pathways for Semantic Segmentation

State-of-the-art semantic segmentation methods capture the relationship between pixels to facilitate contextual information exchange. Advanced methods utilize fixed pathways for context exchange, lacking the flexibility to harness the most relevant context for each pixel. In this paper, we present Configurable Context Pathways (CCPs), a novel model for establishing pathways for augmenting contextual information. In contrast to previous pathway models, CCPs are learned, leveraging configurable regions to form information flows between pairs of pixels. We propose TAGNet to adaptively configure the regions, which span over the entire image space, driven by the relationships between the remote pixels. Subsequently, the information flows along the pathways are updated gradually by the information provided by sequences of configurable regions, forming more powerful contextual information. We extensively evaluate the traveling, adaption, and gathering (TAG) stages of our network on the public benchmarks, demonstrating that all of the stages successfully improve the segmentation accuracy and help to surpass the state-of-the-art results. The code package is available at: https://github.com/dilincv/TAGNet.
FONT-SIR: Fourth-Order Nonlocal Tensor Decomposition Model for Spectral CT Image Reconstruction

Spectral computed tomography (CT) reconstructs images from different spectral data through photon counting detectors (PCDs). However, due to the limited number of photons and the counting rate in the corresponding spectral segment, the reconstructed spectral images are usually affected by severe noise. In this paper, we propose a fourth-order nonlocal tensor decomposition model for spectral CT image reconstruction (FONT-SIR). To maintain the original spatial relationships among similar patches and improve the imaging quality, similar patches without vectorization are grouped in both spectral and spatial domains simultaneously to form the fourth-order processing tensor unit. The similarity of different patches is measured with the cosine similarity of latent features extracted using principal component analysis (PCA). By imposing the constraints of the weighted nuclear and total variation (TV) norms, each fourth-order tensor unit is decomposed into a low-rank component and a sparse component, which can efficiently remove noise and artifacts while preserving the structural details. Moreover, the alternating direction method of multipliers (ADMM) is employed to solve the decomposition model. Extensive experimental results on both simulated and real data sets demonstrate that the proposed FONT-SIR achieves superior qualitative and quantitative performance compared with several state-of-the-art methods.
Galantamine derivatives with indole moiety: Docking, design, synthesis and acetylcholinesterase inhibitory activity

The inhibitors of acetylcholinesterase are the main therapy against Alzheimer's disease. Among them, galantamine is the best tolerated and the most prescribed drug. In the present study, 41 galantamine derivatives with known acetylcholinesterase inhibitory activities expressed as IC50 were selected from the literature and docked into a recombinant human acetylcholinesterase by GOLD. A linear relationship between GoldScores and pIC50 values was found and used to design and predict novel galantamine derivatives with indole moiety in the side chain. The four best predicted compounds were synthesized and tested for inhibitory activity. All of them were between 11 and 95 times more active than galantamine. The novel galantamine derivatives with indole moiety have dual site binding to the enzyme--the galantamine moiety binds to the catalytic anionic site and the indole moiety binds to peripheral anionic site. Additionally, the indole moiety of one of the novel inhibitors binds in a region, close to the peripheral anionic site of the enzyme, where the Ω-loop of amyloid beta peptide adheres to acetylcholinesterase. This compound emerges as a promising lead compound for multi-target anti-Alzheimer therapy not only because of the strong inhibitory activity, but also because it is able to block the amyloid beta deposition on acetylcholinesterase.
Asynchronous Reception of 2 RFID Tags

Commercial radio frequency identification (RFID) readers have to resolve collisions between tags, without sacrificing throughput. This work proposes a Viterbi joint sequence detector as well as a 2-symbol joint tag information detector that can resolve a collision between two tags in the physical layer. In sharp contrast to prior art, the proposed closed-form signal model takes into account the asynchrony level between the two collided tag responses, which is not uncommon with commercial, low-cost RFID tags that follow industry's Gen2 protocol. The asynchrony is considered as the time offset tau between the beginnings of the two tags' responses and is modeled through a derived shaping matrix that depends on the delayed tag information. Performance evaluation of the proposed detectors with simulated data under Ricean fading, as well as experimental data with software-defined radio, reveals improved performance compared to prior art, under various operating regimes. It is also shown that for different values of the parameter tau, BER does not present a monotonic behaviour. As a collateral dividend, it is found that clustering techniques on the filtered received signal should explicitly take into account the time offset tau, since the latter modifies the number of observed clusters.
Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem

Due to the explosion in restructuring of power markets within a deregulated economy, competitive power market needs to minimize their required generation reserve gaps. Efficient load forecasting for future demands can minimize the gap which will help in economic power generation, power operations, power construction planning and power distribution. Nowadays, neural networks are widely used for solving load forecasting problem due to its non-linear characteristics. Consequently, neural network is successfully combined with optimization techniques for finding optimal network parameters in order to reduce the forecasting error. In this paper, firstly a novel evolutionary algorithm based on follow the leader concept is developed and thereafter its performance is validated by COmparing Continuous Optimizers experimental framework on the set of 24 Black-Box Optimization Benchmarking functions with 12 state-of-art algorithms in 2-D, 3-D, 5-D, 10-D, and 20-D. The proposed algorithm outperformed all state-of-art algorithms in 20-D and ranked second in other dimensions. Further, the proposed algorithm is integrated with neural network for the proper tuning of network parameters to solve the real world problem of short term load forecasting. Through experiments on three real-world electricity load data sets namely New Pool England, New South Wales and Electric Reliability Council of Texas, we compared our proposed hybrid approach to baseline approaches and demonstrated its effectiveness in terms of predictive accuracy measures.
Node Reclamation and Replacement for Long-Lived Sensor Networks

When deployed for long-term tasks, the energy required to support sensor nodes' activities is far more than the energy that can be preloaded in their batteries. No matter how the battery energy is conserved, once the energy is used up, the network life terminates. Therefore, guaranteeing long-term energy supply has persisted as a big challenge. To address this problem, we propose a node reclamation and replacement (NRR) strategy, with which a mobile robot or human labor called mobile repairman (MR) periodically traverses the sensor network, reclaims nodes with low or no power supply, replaces them with fully charged ones, and brings the reclaimed nodes back to an energy station for recharging. To effectively and efficiently realize the strategy, we develop an adaptive rendezvous-based two-tier scheduling scheme (ARTS) to schedule the replacement/reclamation activities of the MR and the duty cycles of nodes. Extensive simulations have been conducted to verify the effectiveness and efficiency of the ARTS scheme.