ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Urban flood impact assessment: A state-of-the-art review

Flooding can cause major disruptions in cities, and lead to significant impacts on people, the economy and on the environment. These impacts may be exacerbated by climate and socio-economic changes. Resilience thinking has become an important way for city planners and decision makers to manage flood risks. Despite different definitions of resilience, a consistent theme is that flood resilient cities are impacted less by extreme flood events. Therefore, flood risk professionals and planners need to understand flood impacts to build flood resilient cities. This paper presents a state-of-the-art literature review on flood impact assessment in urban areas, detailing their application, and their limitations. It describes both techniques for dealing with individual categories of impacts, as well as methodologies for integrating them. The paper will also identify future avenues for progress in improving the techniques.
A Single-MOSFET Analog High Resolution-Targeted (SMART) Multiplier for Machine Learning Classification

Mixed-signal machine-learning classification has recently been demonstrated as an efficient alternative for classification with power expensive digital circuits. In this paper, a single-MOSFET analog multiplier is proposed for classifying high-dimensional input data into multi-class output space with less power and higher accuracy than state-of-the-art mixed-signal linear classifiers. A high-resolution (i.e., multi-bit) multiplication is facilitated within a single-MOSFET by feeding the features and feature weights into, respectively, the body and gate inputs. High-resolution classifier that considers the decisions of the individual predictors is designed at 180nm technology node and operates at 100MHz in near/subthreshold region. To evaluate the performance of the classifier, a reduced MNIST dataset is generated by downsampling the MNIST digit images from 784 features to 48 features. The system is simulated across a wide range of PVT variations, exhibiting average accuracy of 92% (2% improvement over state-of-the-art), energy consumption of 67.3 pJ per classification (over 8 times lower than state-of-the-art classifiers), area of 27,570 mu m(2) per binary classifier, and a stable response under PVT variations. Finally, to provide ground for future work on ultra-low-power deep and convolutional networks, scalability and robustness of the proposed multiplier is evaluated with a convolutional neural network on CIFAR-10. Similar classification accuracy with digital and SMART hardware has been observed. All the code and simulation files are available at an online public GitHub repository, https://github.com/faridken/SMART-Multiplier-for-ML.
Trends in American scientists' political donations and implications for trust in science

Scientists in the United States are more politically liberal than the general population. This fact has fed charges of political bias. To learn more about scientists' political behavior, we analyze publicly available Federal Election Commission data. We find that scientists who donate to federal candidates and parties are far more likely to support Democrats than Republicans, with less than 10 percent of donations going to Republicans in recent years. The same pattern holds true for employees of the academic sector generally, and for scientists employed in the energy sector. This was not always the case: Before 2000, political contributions were more evenly divided between Democrats and Republicans. We argue that these observed changes are more readily explained by changes in Republican Party attitudes toward science than by changes in American scientists. We reason that greater public involvement by centrist and conservative scientists could help increase trust in science among Republicans.
Development of an automated reconfigurable robotic airplane fuselage panel final assembly system using state-of-the-art automation technologies

Airspace companies regularly incorporate state-of-the-art automation technologies into their aircraft assembly systems to reduce lead times and manufacturing costs and improve product quality in their assembly plants. Rapid advances in assembly automation, robotics, material handling, measurement in the assembly processes, and systematic design methodologies require constant consideration of new applications and technologies in assembly systems' design studies for airplane structures such as fuselage panels. Such a design study is performed in this study to enable the formation of an automated, reconfigurable and robotic fuselage panel final assembly system. The authors' extensive review of the literature and catalogs reveals that stringer positioning robots, 3D projection devices for fastener position controls on the panels, reconfigurable fixtures for holding the panels, mobile refill friction stir spot joining robots to assemble clips to frames, robotic measurement systems, and modular crane for material handling can be used as state-of-the-art automation technologies in an automated and reconfigurable fuselage panel final assembly system. The proposed automated, reconfigurable, and robotic fuselage panel final assembly system provides an increase in its performance of adaptation level to the changes in the panel types, customers' order quantities, and suppliers' and customers' delivery dates.
Performance Evaluation of Full Search Equivalent Pattern Matching Algorithms

Pattern matching is widely used in signal processing, computer vision, and image and video processing. Full search equivalent algorithms accelerate the pattern matching process and, in the meantime, yield exactly the same result as the full search. This paper proposes an analysis and comparison of state-of-the-art algorithms for full search equivalent pattern matching. Our intention is that the data sets and tests used in our evaluation will be a benchmark for testing future pattern matching algorithms, and that the analysis concerning state-of-the-art algorithms could inspire new fast algorithms. We also propose extensions of the evaluated algorithms and show that they outperform the original formulations.
Multichannel magnetogastrogram: a clinical marker for pediatric chronic nausea

Chronic nausea is a widespread functional disease in children with numerous comorbidities. High-resolution electrogastrogram (HR-EGG) has shown sufficient sensitivity as a noninvasive clinical marker to objectively detect distinct gastric slow wave properties in children with functional nausea. We hypothesized that the increased precision of magnetogastrogram (MGG) slow wave recordings could provide supplementary information not evident on HR-EGG. We evaluated simultaneous pre- and postprandial MGG and HR-EGG recordings in pediatric patients with chronic nausea and healthy asymptomatic subjects, while also measuring nausea intensity and nausea severity. We found significant reductions in postprandial dominant frequency and normogastric power, and higher levels of postprandial bradygastric power in patients with nausea in both MGG and HR-EGG. MGG also detected significantly lower preprandial normogastric power in patients. A significant difference in the mean preprandial gastric slow wave propagation direction was observed in patients as compared with controls in both MGG (control: 180 ± 61°, patient: 34 ±72°; P < 0.05) and HR-EGG (control: 240 ± 39°, patient: 180 ± 46°; P < 0.05). Patients also showed a significant change in the mean slow wave direction between pre- and postprandial periods in MGG (P < 0.05). No statistical differences were observed in propagation speed between healthy subjects and patients in either MGG or HR-EGG pre/postprandial periods. The use of MGG and/or HR-EGG represents an opportunity to assess noninvasively the effects of chronic nausea on gastric slow wave activity. MGG data may offer the opportunity for further refinement of the more portable and economical HR-EGG in future machine-learning approaches for functional nausea.NEW & NOTEWORTHY Pediatric chronic nausea is a difficult-to-measure subjective complaint that requires objective diagnosis, clinical assessment, and individualized treatment plans. Our study demonstrates that multichannel MGG used in conjunction with custom HR-EGG detects key pathological signatures of functional nausea in children. This quantifiable measure may allow more personalized diagnosis and treatment in addition to minimizing the cost and potential radiation associated with current diagnostic approaches.
An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation

In this paper, we design a receiver that iteratively passes soft information between the channel estimation and data decoding stages. The receiver incorporates sparsity-based parametric channel estimation. State-of-the-art sparsity-based iterative receivers simplify the channel estimation problem by restricting the multipath delays to a grid. Our receiver does not impose such a restriction. As a result, it does not suffer from the leakage effect, which destroys sparsity. Communication at near capacity rates in high SNR requires a large modulation order. Due to the close proximity of modulation symbols in such systems, the grid-based approximation is of insufficient accuracy. We show numerically that a state-of-the-art iterative receiver with grid-based sparse channel estimation exhibits a bit-error-rate floor in the high SNR regime. On the contrary, our receiver performs very close to the perfect channel state information bound for all SNR values. We also demonstrate both theoretically and numerically that parametric channel estimation works well in dense channels, i.e., when the number of multipath components is large and each individual component cannot be resolved.
MobileFAN: Transferring deep hidden representation for face alignment

Facial landmark detection is a crucial prerequisite for many face analysis applications. Deep learning-based methods currently dominate the approach of addressing the facial landmark detection. However, such works generally introduce a large number of parameters, resulting in high memory cost. In this paper, we aim for a lightweight as well as effective solution to facial landmark detection. To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder. The proposed MobileFAN, with only 8% of the model size and lower computational cost, achieves superior or equivalent performance compared with state-of-the-art models. Moreover, by transferring the geometric structural information of a face graph from a large complex model to our proposed MobileFAN through feature-aligned distillation and feature-similarity distillation, the performance of MobileFAN is further improved in effectiveness and efficiency for face alignment. Extensive experiment results on three challenging facial landmark estimation benchmarks including COFW, 300W and WFLW show the superiority of our proposed MobileFAN against state-of-the-art methods. (C) 2019 Elsevier Ltd. All rights reserved.
Who cares? The grandmother kinship carers shouldering the burden within a gendered care economy

It is estimated that around half of all kinship carers in the UK are grandparents. International studies show that when broken down by gender, these carers are predominantly grandmothers. However, there is little research exploring the gender dimensions of kinship carers' experiences. Drawing on data from qualitative interviews with 27 grandparent kinship carers, this article highlights the gendered context in which the grandparents we spoke to found themselves. The grandparents - the majority of whom were grandmothers - described lives filled with multiple unpaid caring commitments and demands. We discuss the ways that gender norms, roles and stereotypes, alongside economic models and policies that invisiblise women's care work, shaped the experiences of the grandmothers who took part in our research. We argue that, despite their undeniable determination and commitment to love, nurture and care for their grandchildren in very difficult circumstances, and the money they are saving the state in doing so, grandmother kinship carers are penalized in multiple ways. Economically, emotionally, socially, physically and practically, grandmother kinship carers are unsupported and undervalued. We need a social, economic and cultural shift around the value of care and a redistribution of care work across genders. The situations of grandmother kinship carers need to be part of this shift, so that grandmothers who care for their grandchildren are no longer penalized, and all kinship carers are properly supported and valued.
Constraining the age of rock art by dating a rockfall event using sediment and rock-surface luminescence dating techniques

Optically stimulated luminescence (OSL) is used to determine the age of a rockfall event that removed part of the pictograph figures at the Great Gallery rock art panel in Canyonlands National Park, Utah, USA. Analyses from the outer millimeter of the buried surface of a rockfall boulder and quartz grains from the underlying sediment both provide consistent ages that also agree with an AMS radiocarbon age of a cottonwood leaf found immediately between the clast and underlying sediment. Measurement of the OSL signals as a function of depth into the surface of the boulder clearly shows that there is no detectable increase in the OSL signal to a depth of at least 3 mm suggesting that the OSL signal was fully reset to this depth before burial. Consistent OSL and radiocarbon ages for this rockfall event provide a minimum age of similar to 900 a for the Great Gallery, which is the type locality of Barrier Canyon Style rock art with a controversial and unknown origin. (C) 2012 Elsevier B.V. All rights reserved.
Solving Inverse Computational Imaging Problems Using Deep Pixel-Level Prior

Signal reconstruction is a challenging aspect of computational imaging as it often involves solving ill-posed inverse problems. Recently, deep feed-forward neural networks have led to state-of-the-art results in solving various inverse imaging problems. However, being task specific, these networks have to be learned for each inverse problem. On the other hand, a more flexible approach would be to learn a deep generative model once and then use it as a signal prior for solving various inverse problems. In this paper, we show that among the various state-of-the-art deep generative models, autoregressive models are especially suitable for our purpose for the following reasons. First, they explicitly model the pixel level dependencies and hence are capable of reconstructing low-level details, such as texture patterns and edges better. Second, they provide an explicit expression for the image prior, which can then he used for MAP-based inference along with the forward model. Third, they can model long range dependencies in images which make them ideal for handling global multiplexing as encountered in various compressive imaging systems. We demonstrate the efficacy of our proposed approach in solving three computational imaging problems: Single Pixel Camera, LiSens, and FlatCam. For both real and simulated cases, we obtain better reconstructions than the state-of-the-art methods in terms of perceptual and quantitative metrics.
An automated system for nucleic acid extraction from formalin-fixed paraffin-embedded samples using high intensity focused ultrasound technology

Formalin-fixed paraffin-embedded (FFPE) tissue samples are routinely used in prospective and retrospective studies. It is crucial to obtain high-quality nucleic acid (NA) from FFPE samples for downstream molecular analysis, such as quantitative polymerase chain reaction (PCR), Sanger sequencing, next-generation sequencing, and microarray, in both clinical diagnosis and basic research. The current NA extraction methods from FFPE samples using chemical solvent are tedious, environmentally unfriendly, and unamenable to automation or field deployment. We present a tool for NA extraction from FFPE samples using a high-intensity focused ultrasound (HIFU) technology. A cartridge strip containing reagents for FFPE sample deparaffinization and NA extraction and purification is operated by an automation tool consisting of a HIFU module, a liquid handling robot unit, and accessories including a thermal block and magnets. The HIFU module is a single concaved piezoelectric ceramic plate driven by a current-mode class-D power amplifier. Based on the ultrasonic cavitation effects, the HIFU module provides highly concentrated energy introducing paraffin emulsification and disintegration. The high quantity and quality of NA extracted using the reported system are evaluated by PCR and compared with the quantity and quality of NA extracted using the current standard methods.
RESEARCH ON ENVIRONMENTAL ART DESIGN BASED ON THE GUIDANCE OF ECOLOGICAL CONCEPTS

Based on the ecological concept, this article analyzed the environmental art design of Xuanzhou Ancient Town. It introduced the environmental art composition of Xuanzhou Ancient Town through the four aspects of geographical environment, economic industry, folk customs and traditional architecture. It was pointed out that because of the lack of unified development and management, there was currently a lack of overall planning for thc demolition and reconstruction of Xuanzhou Ancient Town's ancient buildings, the existing problems of the lack of protection of the ecological environment of ancient towns and the lack of complete public infrastructure. Finally, through the three aspects of design concept, design principle and design method, the environmental art design plan of Xuanzhou ancient town guided by ecological concept was proposed to protect the ancient buildings of Xuanzhou, protect the ecological environment of Xuanzhou ancient town, improve the artistry of the ancient town, and promote local travel industry development, thus pull the economic growth, realize the harmonious development of economic development and ecological protection, and achieve the effect of sustainable development.
A Novel Low Rank Smooth Flat-Field Correction Algorithm for Hyperspectral Microscopy Imaging

A flat-field correction method is proposed for multiple measured hyperspectral microscopy imaging in this paper. As the most crucial preprocessing process in quantitative microscopic analysis, flat-field correction solves the uneven illumination caused by vignetting in microscopic images, and guarantees the precision of spatial and spectral information in hyperspectral microscopic imaging. In order to carry out flat-field correction and extract uneven illumination among groups of hyperspectral microscopic data containing hundreds of bands simultaneously, two properties of vignetting have been exploited: i) low-rank property is reflected by little information contained in vignetting; ii) local smoothness can be observed as a gradual change in brightness of vignetting, which is typically equivalent to the sparseness in spatial frequency domain. Combining the two properties above, a novel Low Rank Smooth Flat-field Correction (LRSFC) model modified from common orthogonal basis extraction is proposed, while an optimization is solved based on alternating direction multiplier method (ADMM), obtaining a unique flat-field term with low-rank and smooth properties. Qualitative and quantitative experimental assessments indicate that LRSFC does not add extra cell texture to the extracted flat-field term, whose performance appears prior to other state-of-the-art flat-field correction methods.
Tumor-targeted miRNA nanomedicine for overcoming challenges in immunity and therapeutic resistance

miRNA are critical messengers in the tumor microenvironment (TME) that influence various processes leading to immune suppression, tumor progression, metastasis and resistance. Strategies to modulate miRNAs in the TME have important implications in overcoming these challenges. However, miR delivery to specific cells in the TME has been challenging. This review discusses nanomedicine strategies to achieve cell-specific delivery of miRNAs. The key goal of delivery is to activate the tumor immune landscape as well as to prevent chemotherapy resistance. Specifically, the use of hyaluronic acid-based nanoparticle miRNA delivery to the TME is discussed. The discussion is focused on miRNA-125b for reprogramming tumor-associated macrophages to overcome immunosuppression and miRNA-let-7b to overcome resistance to anticancer chemotherapeutics because both these miRNAs have been extensively evaluated for delivery with hyaluronic acid-based delivery systems.
"No Alcohol, No Risk. #FASD"- Twitter Activity on Alcohol and Pregnancy among Australian Organizations

Objective: Research has suggested that information communicated by public health and industry-funded organizations differ, as organizations linked to industry have tended to downplay risks with alcohol more broadly and pregnancy specifically. There is limited knowledge of how alcohol use in pregnancy and associated risks are communicated on social media in Australia. This study set out to describe communication of health risks associated with alcohol use during pregnancy on Twitter by Australian-based organizations and stakeholders. Methods: We searched for "alcohol" and "pregnancy", "pregnant", or "FASD" on Twitter accounts belonging to potentially relevant organizations, of which 17 had tweeted on the topic. Content analysis was undertaken on all tweets and summarized under eight themes. Results: A total of 347 tweets were identified, posted between 2010 and 2019 mainly by public health and disability nongovernmental organizations. Tweets generally focused on FASD, but other potential consequences of maternal alcohol use were infrequently mentioned and tended to be generic. We found infrequent mentions of direct advice around alcohol use during pregnancy and official guidelines. Overall, tweets reflected ongoing policy debates in Australia - including alcohol warning labeling, disability policy and increased activity was seen particularly before the second parliamentary inquiry into FASD. Conclusions: The limited number of tweets from relevant organizations over a nine-year period suggests focus has been on FASD while less discussion of alcohol use during pregnancy was evident. We identified an opportunity for more and consistent communication of trusted national health guidance.
Selection of Reference Genes for Normalization of qRT‒PCR Analysis in the Soybean Aphid Aphis glycines Matsumura (Hemiptera: Aphididae)

The soybean aphid Aphis glycines Matsumura is a predominant insect pest in Asia and North America and causes great losses to soybean. The release of genome data for A. glycines will facilitate gene function research in the future. However, suitable reference genes for A. glycines under various experimental conditions are scarce. To search for appropriate reference genes for A. glycines, nine candidate reference genes, including Act, α-Tub, β-Tub, RPS12, RPS18, RPL5, RPL27, EF1α, and Fer, were tested under six experimental conditions to evaluate their suitability for use in the normalization of qRT‒PCR data. Results showed that EF1α and RPS12 were optimal for the developmental stages of A. glycines, RPS18 and RPS12 were appropriate for wing dimorphism, β-Tub and RPS18 were suitable for different tissues and RPL5, and α-Tub could be used for normalization at different temperatures. β-Tub and EF1α could be proposed as reference genes for insecticide treatment, and RPL5 and RPS12 were found to be the most stable reference genes in different photoperiods. The results provide appropriate reference genes for analyzing gene expression in A. glycines and contribute to future research on the molecular physiology and biochemistry of A. glycines.