ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Benchmarking Optimization-Based Energy Disaggregation Algorithms

Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED.
Reflective Tomography Lidar Image Reconstruction for Long Distance Non-Cooperative Target

In the long-distance space target detection, the technique of laser reflection tomography (LRT) shows great power and attracts more attention for further study and real use. However, space targets are often non-cooperative, and normally a 360 degrees complete view of reflection projections cannot be obtained. Therefore, this article firstly introduces an improved LRT system design with more advanced laser equipment for long-distance non-cooperative detection to ensure the high quality of the lidar beam and the lidar projection data. Then, the LRT image reconstruction is proposed and focused on the laser image reconstruction method utilizing the total variation (TV) minimization approach based on the sparse algebraic reconstruction technique (ART) model, in order to reconstruct the laser image in a sparse or incomplete view of projections. At last, comparative experiments with the system are performed to validate the advantages of this method with the LRT system. In both near and far field experiments, the effectiveness and superiority of the proposed method are verified for different types of projection data through comparison to typical methods.
Artificial Reef Detection Method for Multibeam Sonar Imagery Based on Convolutional Neural Networks

Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods.
Amplified Gliosis and Interferon-Associated Inflammation in the Aging Brain following Diffuse Traumatic Brain Injury

Traumatic brain injury (TBI) is associated with chronic psychiatric complications and increased risk for development of neurodegenerative pathology. Aged individuals account for most TBI-related hospitalizations and deaths. Nonetheless, neurobiological mechanisms that underlie worsened functional outcomes after TBI in the elderly remain unclear. Therefore, this study aimed to identify pathways that govern differential responses to TBI with age. Here, adult (2 months of age) and aged (16-18 months of age) male C57BL/6 mice were subjected to diffuse brain injury (midline fluid percussion), and cognition, gliosis, and neuroinflammation were determined 7 or 30 d postinjury (dpi). Cognitive impairment was evident 7 dpi, independent of age. There was enhanced morphologic restructuring of microglia and astrocytes 7 dpi in the cortex and hippocampus of aged mice compared with adults. Transcriptional analysis revealed robust age-dependent amplification of cytokine/chemokine, complement, innate immune, and interferon-associated inflammatory gene expression in the cortex 7 dpi. Ingenuity pathway analysis of the transcriptional data showed that type I interferon (IFN) signaling was significantly enhanced in the aged brain after TBI compared with adults. Age prolonged inflammatory signaling and microgliosis 30 dpi with an increased presence of rod microglia. Based on these results, a STING (stimulator of interferon genes) agonist, DMXAA, was used to determine whether augmenting IFN signaling worsened cortical inflammation and gliosis after TBI. DMXAA-treated Adult-TBI mice showed comparable expression of myriad genes that were overexpressed in the cortex of Aged-TBI mice, including Irf7, Clec7a, Cxcl10, and Ccl5 Overall, diffuse TBI promoted amplified IFN signaling in aged mice, resulting in extended inflammation and gliosis.SIGNIFICANCE STATEMENT Elderly individuals are at higher risk of complications following traumatic brain injury (TBI). Individuals >70 years old have the highest rates of TBI-related hospitalization, neurodegenerative pathology, and death. Although inflammation has been linked with poor outcomes in aging, the specific biological pathways driving worsened outcomes after TBI in aging remain undefined. In this study, we identify amplified interferon-associated inflammation and gliosis in aged mice following TBI that was associated with persistent inflammatory gene expression and microglial morphologic diversity 30 dpi. STING (stimulator of interferon genes) agonist DMXAA was used to demonstrate a causal link between augmented interferon signaling and worsened neuroinflammation after TBI. Therefore, interferon signaling may represent a therapeutic target to reduce inflammation-associated complications following TBI.
Deep Learning Extraction of the Temperature-Dependent Parameters of Bulk Defects

Bulk defects in silicon solar cells are a key contributor to loss of efficiency. To detect and identify those defects, temperature- and injection-dependent lifetime spectroscopy is usually used, and the defect parameters are traditionally extracted using fitting methods to the Shockley-Read-Hall recombination statistics. In this study, we propose a deep learning-based extraction technique that is based on an alternative representation of the lifetime curves: lifetime mapping in the temperature and minority carrier concentration space. The deep learning approach successfully predicts all the defect parameters while addressing one of the main limitations of the traditional approach of locating the defect in the energy spectrum, which usually outputs two possible solutions. Furthermore, the approach is applied to temperature-dependent defect parameters where the traditional approach is not applicable, achieving satisfying levels of prediction of the defect parameters. Image representation and deep learning have the potential to bolster solar cell characterization techniques by extracting more insights from the characterization data.
Robotics Inspired Renewable Energy Developments: Prospective Opportunities and Challenges

The domain of Robotics is a good partner of renewable energy and is becoming critical to the sustainability and survival of the energy industry. The multi-disciplinary nature of robots offers precision, repeatability, reliability, productivity and intelligence, thus rendering their services in diversified tasks ranging from manufacturing, assembling, and installation to inspection and maintenance of renewable resources. This paper explores applications of real robots in four feasible renewable energy domains; solar, wind, hydro, and biological setups. In each case, existing state-of-the-art innovative robotic systems are investigated that have the potential to create a difference in the corresponding renewable sector in terms of reduced set-up time, lesser cost, improved quality, enhanced productivity and exceptional competitiveness in the global market. Instrumental opportunities and challenges of robot deployment in the renewable sector are also discussed with a brief case study of Saudi Arabia. It is expected that the wider dissemination of the instrumental role of robotics in renewable energy will contribute to further developments and stimulate more collaborations and partnerships between professionals of robotics and energy communities.
Visuo-tactile heading perception

Self-motion through an environment induces various sensory signals, i.e., visual, vestibular, auditory, or tactile. Numerous studies have investigated the role of visual and vestibular stimulation for the perception of self-motion direction (heading). Here, we investigated the rarely considered interaction of visual and tactile stimuli in heading perception. Participants were presented optic flow simulating forward self-motion across a horizontal ground plane (visual), airflow toward the participants' forehead (tactile), or both. In separate blocks of trials, participants indicated perceived heading from unimodal visual or tactile or bimodal sensory signals. In bimodal trials, presented headings were either spatially congruent or incongruent with a maximum offset between visual and tactile heading of 30°. To investigate the reference frame in which visuo-tactile heading is encoded, we varied head and eye orientation during presentation of the stimuli. Visual and tactile stimuli were designed to achieve comparable precision of heading reports between modalities. Nevertheless, in bimodal trials heading perception was dominated by the visual stimulus. A change of head orientation had no significant effect on perceived heading, whereas, surprisingly, a change in eye orientation affected tactile heading perception. Overall, we conclude that tactile flow is more important to heading perception than previously thought.NEW & NOTEWORTHY We investigated heading perception from visual-only (optic flow), tactile-only (tactile flow), or bimodal self-motion stimuli in different conditions varying in head and eye position. Overall, heading perception was body or world centered and non-Bayes optimal and revealed a centripetal bias. Although being visually dominated, tactile flow revealed a significant influence during bimodal heading perception.
A critical assessment of the potential and limitations of physicochemical analysis to advance knowledge on Levantine rock art

This paper offers an updated review of the variety of physicochemical analysis applied so far to Levantine rock art (Spain) to characterize the composition of the pigments, as well as the substrate and/or the natural coating covering these particular prehistoric paintings. This paper is part of a broader special issue evaluating the real contribution of scientific approaches to rock art research, assessing how they have improved our understanding of this particular heritage and the new research questions they open. In this context, and with a focus on Levantine rock art, our aim is to explore: 1. The guiding principles behind the different sorts of analysis conducted and published so far; 2. The non-invasive and invasive techniques applied to answer the research questions raised, and 3. If the result published as yet have met the expectations of rock art researchers. We also reflect on the potential, the limitations and the future developments of this sort of studies, as well as on the ethics and desirable protocols of applying invasive techniques to this UNESCO World Heritage listed archaeological remain. While the focus is Levantine rock art, the discussions raised by this paper and the experiences reported in relation to the various techniques used are of global interest, especially when dealing with open-air rock art.
FARNet: Fragmented affinity reasoning network of text instances for arbitrary shape text detection

Arbitrary shape text detection is a challenging task in scene text recognition. Driven by deep learning and large-scale data sets, the detection method based on connected component (CC) has increasingly gained popularity. However, there are still problems of unclear separation of text instances and incorrect component links. Thus, in this paper, the authors propose a novel component connection method, that is, Fragmented Affinity Reasoning Network of Text Instances (FARNet), for arbitrary shape text detection. The network consists of a Weighted Feature Fusion Pyramid Network (WFFPN), Text Fragments Subgraph (TFS), and Dense Graph Attention Network (DGAT), which can be trained end-to-end. The WFFPN is used to generate text fragments, TFS and DGAT jointly construct an affinity reasoning network. Since the neighbouring boundaries between text instances may blend them into a single instance, the core idea is to use the WFFPN to divide the text instance into a series of rectangular fragments, the affinity reasoning network infers the affinity between fragments and then links them to rebuild text instances. Extensive experiments on seven challenging datasets (ICDAR2015, MSRA-TD500, Totaltext, CTW-1500, ICDAR 2019MLT, ICDAR2019 ArT, and DAST-1500) demonstrate that the proposed text detector achieves state-of-the-art performance in both on polygon datasets and quadrilateral datasets. The code is available at .
Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition

In this letter, a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The technique decomposes the input image into the four frequency subbands by using DWT and estimates the singular value matrix of the low-low subband image, and, then, it reconstructs the enhanced image by applying inverse DWT. The technique is compared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as brightness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques.
Mapping of PET-measured aerosol deposition: a comparison study

Three-dimensional positron emission tomography (PET) can be used to assess the spatial distribution of inhaled aerosols, and with lung models of airway parameters this data can be converted into aerosol deposition information for each airway generation. Two alternative methods for extracting this generational data, the analytical and the arithmetic reconstruction technique (ART), are investigated in conjunction with two different airway branching models, monopodial and dichotomous, to determine pulmonary deposition in a canine model. All solutions revealed two regions with high deposition: the first 10 generations past the trachea (1-10) and the deepest lung generations (18-23). Post-imaging autoradiographic images were in agreement with the deposition pattern in the larger airway generations (1-10). In comparing the different techniques, the dichotomous lung model yielded large variance in mapped activity but similar concentrations because of variation in generation volumes, while ART showed a greater ability to discern small differences in deposition patterns between subjects. (C) 2005 Published by Elsevier Ltd.
Safety, Pharmacokinetics, and Pharmacodynamics of CC-90001 (BMS-986360), a c-Jun N-terminal Kinase Inhibitor, in Phase 1 Studies in Healthy Participants

CC-90001 selectively inhibits c-Jun N-terminal kinase (JNK), a stress-activated protein implicated in fibrosis. In 3 phase 1 trials evaluating CC-90001 pharmacokinetics, pharmacodynamics, and safety, healthy adults (N = 184) received oral CC-90001 in a single dose (10-720 mg) or multiple doses (30-480 mg once daily for 7-18 days) or placebo. CC-90001 was rapidly absorbed (median time to maximum concentration, 1-4 hours) and eliminated with a mean terminal elimination half-life of 12-28 hours. Steady state was reached on day 5, with a mean accumulation ratio of 1.5- to 2-fold following daily dosing. Exposure was similar in fed versus fasted participants and in Japanese versus non-Japanese participants. CC-90001 demonstrated dose- and exposure-dependent inhibition of JNK as determined by histopathological analysis of c-Jun phosphorylation in ultraviolet-irradiated skin. The most common treatment-emergent adverse events were nausea and headache; all were mild or moderate in intensity. Based on exposure-response analysis using high-quality electrocardiogram data, no clinically relevant QT prolongation liability for CC-90001 was observed. Overall, single- and multiple-dose CC-90001 were generally safe and well tolerated at the tested doses and demonstrated JNK pathway engagement. These results support further clinical evaluation of CC-90001.
Detection and localization of partial audio matches in various application scenarios

In this paper, we describe various application scenarios for archive management, broadcast/stream analysis, media search and media forensics which require the detection and accurate localization of unknown partial audio matches within items and datasets. We explain why they cannot be addressed with state-of-the-art matching approaches based on fingerprinting, and propose a new partial matching algorithm which can satisfy the relevant requirements. We propose two distinct requirement sets and hence two variants / settings for our proposed approach: One focusing on lower time granularity and hence lower computational complexity, to be able to deal with large datasets, and one focusing on fine-grain analysis for small datasets and individual items. Both variants are tested using distinct evaluation sets and methodologies and compared with a popular audio matching algorithm, thereby demonstrating that the proposed algorithm achieves convincing performance for the relevant application scenarios beyond the current state-of-the-art.
Effects of ultrasound pretreatment on functional property, antioxidant activity, and digestibility of soy protein isolate nanofibrils

Nanofibrils, an effective method to modulate the functional properties of proteins, can be promoted by ultrasound pretreatment. This study investigated the effect of ultrasound pretreatment on the structure, functional property, antioxidant activity and digestibility of soy protein isolate (SPI) nanofibrils. The results showed that high amplitude ultrasound had a significant effect on structure of SPI nanofibrils. SPI nanofibrils pretreated by 80% amplitude ultrasound showed a blueshift of the amide II band in Fourier transform infrared spectroscopy (FTIR), resulted in more tryptophan residues being buried and increased the crystallinity. Low amplitude ultrasound (20%) pretreatment significantly improved the solubility, emulsifying activity index (EAI) and water absorption capacity (WAC) of SPI nanofibrils, but 80% amplitude ultrasound pretreatment of SPI nanofibrils reduced emulsifying stability index (ESI). High amplitude ultrasound (60% and 80%) pretreatment of SPI nanofibrils improved the foaming capacity and foaming stability and decreased denaturation temperature. DPPH radical scavenging activity of SPI nanofibrils were significantly improved by ultrasound pretreatment. 20% amplitude ultrasound pretreatment improved DPPH, ABTS radical scavenging activity and ferric reducing antioxidant power of SPI nanofibrils. The digestion rate of 80% amplitude ultrasound-pretreated nanofibrils were consistently higher, and SPI nanofibrils pretreated by ultrasound were more fragmented and shorter after simulating gastrointestinal digestion. This study would expand the application of food-grade protein nanofibrils in the food industry.
Indoor air quality evaluation of two museums in a subtropical climate conditions

Indoor air quality can affect and influence the state of works of art exhibited inside museums. Museums, usually suffer badly from the generally higher levels of air pollution and the control of such contamination requires continual evaluation and the strict monitoring, using by using the most appropriate materials. The main aim of this paper was to characterize, analyze and determine indoor air pollutants in Cypriot-Archeological and Byzantine museum. Experimental measurements have been carried out concerning the measurements of CO, CO2, PM1, PM2.5, PM7, PM10, Relative Humidity (RH), O-3, NO, NO2, SO2, Benzene (BEN), Tulane (TOL), Ethylene (ETH), Xylene (XYL), NH3, H2S, for a period of 12 months. Considering the safety of exhibits in both museums and from the final results, the quality of air unfortunately cannot be described as satisfactory. The findings from this study are useful to policy makers in order to develop a new strategy plan for indoor air quality and especially for museums. (C) 2015 Elsevier Ltd. All rights reserved.
Dating Palaeolithic cave art: Why U-Th is the way to go

The chronology of European Upper Palaeolithic cave art is poorly known. Three chronometric techniques are commonly applicable: AMS C-14, TL and U-Th, and in recent years the efficacy of each has been the subject of considerable debate. We review here the use of the U-Th technique to date the formation of calcites that can be shown to have stratigraphic relationships to cave art. We focus particularly on two recent critiques of the method. By using specific examples from our own work using this method in Spain, we demonstrate how these critiques are highly flawed and hence misleading, and we argue that the U-Th dating of calcites is currently the most reliable of available chronometric techniques for dating cave art. Crown Copyright (C) 2015 Published by Elsevier Ltd. All rights reserved.
Blame it on her 'baby brain'? Investigating the contents of social stereotypes about pregnant women's warmth and competence

The Stereotype Content Model proposes that social stereotypes broadly exist along two dimensions: warmth and competence. This framework has been used to investigate the contents of stereotypes of gendered groups in a range of contexts. However, it has not been extensively applied to perceptions of pregnant women. This is important, given how pregnant women are typically framed by society to have 'baby brain' or reduced competence. Therefore, we investigated the contents of social stereotypes of pregnant women. In Study 1, participants (N = 590) rated a target group (pregnant women) and thirteen other comparison groups on perceptions of warmth (compassion, empathy and comfort) and competence (mathematics ability, logic and memory). Pregnant women were generally stereotyped to have low competence and high warmth, relative to other groups. Study 2 (N = 54) then descriptively investigated the wider contents of stereotypes related to pregnant women, new mothers, men and women using a trait generation task. Generated traits were coded within the dimensions of warmth and competence. This showed, again, that pregnant women were assigned traits related to warmth and poor competence. Taken together, these studies confirmed that perceptions of low competence and 'baby brain' in pregnancy is broadly held by a non-pregnant sample.