ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.
High-quality genome of the basidiomycete yeast Dioszegia hungarica PDD-24b-2 isolated from cloud water

The genome of the basidiomycete yeast Dioszegia hungarica strain PDD-24b-2 isolated from cloud water at the summit of puy de Dôme (France) was sequenced using a hybrid PacBio and Illumina sequencing strategy. The obtained assembled genome of 20.98 Mb and a GC content of 57% is structured in 16 large-scale contigs ranging from 90 kb to 5.56 Mb, and another 27.2 kb contig representing the complete circular mitochondrial genome. In total, 8,234 proteins were predicted from the genome sequence. The mitochondrial genome shows 16.2% cgu codon usage for arginine but has no canonical cognate tRNA to translate this codon. Detected transposable element (TE)-related sequences account for about 0.63% of the assembled genome. A dataset of 2,068 hand-picked public environmental metagenomes, representing over 20 Tbp of raw reads, was probed for D. hungarica related ITS sequences, and revealed worldwide distribution of this species, particularly in aerial habitats. Growth experiments suggested a psychrophilic phenotype and the ability to disperse by producing ballistospores. The high-quality assembled genome obtained for this D. hungarica strain will help investigate the behavior and ecological functions of this species in the environment.
Self-Attention-Based Conditional Variational Auto-Encoder Generative Adversarial Networks for Hyperspectral Classification

Hyperspectral classification is an important technique for remote sensing image analysis. For the current classification methods, limited training data affect the classification results. Recently, Conditional Variational Autoencoder Generative Adversarial Network (CVAEGAN) has been used to generate virtual samples to augment the training data, which could improve the classification performance. To further improve the classification performance, based on the CVAEGAN, we propose a Self-Attention-Based Conditional Variational Autoencoder Generative Adversarial Network (SACVAEGAN). Compared with CVAEGAN, we first use random latent vectors to obtain more enhanced virtual samples, which can improve the generalization performance. Then, we introduce the self-attention mechanism into our model to force the training process to pay more attention to global information, which can achieve better classification accuracy. Moreover, we explore model stability by incorporating the WGAN-GP loss function into our model to reduce the mode collapse probability. Experiments on three data sets and a comparison of the state-of-art methods show that SACVAEGAN has great advantages in accuracy compared with state-of-the-art HSI classification methods.
Microsatellite Loci of the Atlantic Horseshoe Crab (Limulus polyphemus) Reveal Inter-Localities Genetic Diversity in the Coastal Waters of the Eastern and Northern Yucatan Peninsula

The American horseshoe crab (Limulus polyphemus) is an economically and ecologically important species, which is currently categorized as endangered in Mexico. L. polyphemus, one of four extant horseshoe crab species that constitute the class Merostomata, is distributed along the Atlantic coastline of the USA from Alabama to Maine and has another population on the coastline of Campeche, Yucatan, and Quintana Roo in the Yucatan Peninsula, Mexico. In the present study, we evaluated the genetic diversity and genetic structure of four separated localities along the coast of the Yucatan peninsula (Champoton, CH; Isla Arena, IA; Rio Lagartos, RL; and Holbox Island, HI), using nine microsatellite-type molecular markers for this species. The aim of this study is to obtain a baseline of the current level of genetic diversity, which would allow the monitoring of important changes over time. Multilocus analyses revealed moderate levels of genetic diversity (He, 0.5230 to 0.6389) and genetic structure within the whole study area (FST 0.025). The population from RL showed limited gene flows, differing significantly from the other sampling sites. The genetic information obtained in this study can support the implementation of management and conservation programs for this species in Mexico.
Controlling inversion disorder in a stoichiometric spinel magnet

In the study of frustrated quantum magnets, it is essential to be able to control the nature and degree of site disorder during the growth process, as many measurement techniques are incapable of distinguishing between site disorder and frustration-induced spin disorder. Pyrochlore-structured spinel oxides can serve as model systems of geometrically frustrated three-dimensional quantum magnets; however, the nature of the magnetism in one well-studied spinel, ZnFe2O4, remains unclear. Here, we demonstrate simultaneous control of both stoichiometry and inversion disorder in the growth of ZnFe2O4 single crystals, directly yielding a revised understanding of both the collective spin behavior and lattice symmetry. Crystals grown in the stoichiometric limit with minimal site inversion disorder contravene all the previously suggested exotic spin phases in ZnFe2O4. Furthermore, the structure is confirmed on the [Formula: see text] space group with broken inversion symmetry that induces antiferroelectricity. The effective tuning of magnetic behavior by site disorder in the presence of robust antiferroelectricity makes ZnFe2O4 of special interest to multiferroic devices.
Phages, anti-CRISPR proteins, and drug-resistant bacteria: what do we know about this triad?

Phages are viruses that infect bacteria, relying on their genetic machinery to replicate. To survive the constant attack of phages, bacteria have developed diverse defense strategies to act against them. Nevertheless, phages rapidly co-evolve to overcome these barriers, resulting in a constant, and often surprising, molecular arms race. Thus, some phages have evolved protein inhibitors known as anti-CRISPRs (∼50-150 amino acids), which antagonize the bacterial CRISPR-Cas immune response. To date, around 45 anti-CRISPRs proteins with different mechanisms and structures have been discovered against the CRISPR-Cas type I and type II present in important animal and human pathogens such as Escherichia, Morganella, Klebsiella, Enterococcus, Pseudomonas, Staphylococcus, and Salmonella. Considering the alarming growth of antibiotic resistance, phage therapy, either alone or in combination with antibiotics, appears to be a promising alternative for the treatment of many bacterial infections. In this review, we illustrated the biological and clinical aspects of using phage therapy; furthermore, the CRISPR-Cas mechanism, and the interesting activity of anti-CRISPR proteins as a possible weapon to combat bacteria.
Assessing single-locus CRISPR/Cas9-based gene drive variants in the mosquito Aedes aegypti via single-generation crosses and modeling

The yellow fever mosquito Aedes aegypti is a major vector of arthropod-borne viruses, including dengue, chikungunya, and Zika viruses. A novel approach to mitigate arboviral infections is to generate mosquitoes refractory to infection by overexpressing antiviral effector molecules. Such an approach requires a mechanism to spread these antiviral effectors through a population, for example, by using CRISPR/Cas9-based gene drive systems. Critical to the design of a single-locus autonomous gene drive is that the selected genomic locus is amenable to both gene drive and appropriate expression of the antiviral effector. In our study, we used reverse engineering to target 2 intergenic genomic loci, which had previously shown to be highly permissive for antiviral effector gene expression, and we further investigated the use of 3 promoters (nanos, β2-tubulin, or zpg) for Cas9 expression. We then quantified the accrual of insertions or deletions (indels) after single-generation crossings, measured maternal effects, and assessed fitness costs associated with various transgenic lines to model the rate of gene drive fixation. Overall, MGDrivE modeling suggested that when an autonomous gene drive is placed into an intergenic locus, the gene drive system will eventually be blocked by the accrual of gene drive blocking resistance alleles and ultimately be lost in the population. Moreover, while genomic locus and promoter selection were critically important for the initial establishment of the autonomous gene drive, it was the fitness of the gene drive line that most strongly influenced the persistence of the gene drive in the simulated population. As such, we propose that when autonomous CRISPR/Cas9-based gene drive systems are anchored in an intergenic locus, they temporarily result in a strong population replacement effect, but as gene drive-blocking indels accrue, the gene drive becomes exhausted due to the fixation of CRISPR resistance alleles.
500 GHz-750 GHz Rectangular-Waveguide Vector-Network-Analyzer Calibrations

We develop an uncertainty analysis that captures the dominant sources of measurement error in state-of-the-art WM-380 (WR 1.5) rectangular-waveguide vector-network-analyzer measurements over the frequency range 500-750 GHz. We use the analysis to assess thru-reflect-line, thru-short-match, and thru-short-radiating-open calibrations. The comparison shows that thru-short-match and thru-short-radiating-open calibrations outperform thru-reflect-line calibrations, and that this is true even when multiple lines and optimal averaging are used to improve the thru-reflect-line calibrations.
Attributions for extreme weather events: science and the people

Both climate scientists and non-scientists (laypeople) attribute extreme weather events to various influences. Laypeople's attributions for these events are important as these attributions likely influence their views and actions about climate change and extreme events. Research has examined laypeople's attribution scepticism about climate change in general; however, few climate scientists are familiar with the processes underpinning laypeople's attributions for individual extreme events. Understanding these lay attributions is important for scientists to communicate their findings to the public. Following a brief summary of the way climate scientists calculate attributions for extreme weather events, we focus on cognitive and motivational processes that underlie laypeople's attributions for specific events. These include a tendency to prefer single-cause rather than multiple-cause explanations, a discounting of whether possible causes covary with extreme events, a preference for sufficient causes over probabilities, applying prevailing causal narratives, and the influence of motivational factors. For climate scientists and communicators who wish to inform the public about the role of climate change in extreme weather events, these patterns suggest several strategies to explain scientists' attributions for these events and enhance public engagement with climate change. These strategies include showing more explicitly that extreme weather events reflect multiple causal influences, that climate change is a mechanism that covaries with these events and increases the probability and intensity of many of these events, that human emissions contributing to climate change are controllable, and that misleading communications about weather attributions reflect motivated interests rather than good evidence.
Impact of a dam construction on the intertidal environment and free-living nematodes in the Ba Lai, Mekong Estuaries, Vietnam

The impact of high siltation and accumulation of organic and waste material in the intertidal of the dammed Ba Lai River in Vietnam as part of the Mekong estuarine system was investigated by means of marine free-living nematodes. Nutrients content (nitrate, ammonium, total phosphorus, total nitrogen), total suspended solids, total organic carbon, coliform, bacteria E. coli, pH, dissolved oxygen, total dissolved solids, methane and hydrogen sulfide concentration, and the nematode communities were characterized in sediment at selected stations along the river above and below the dam. Our results found elevated methane concentrations at the upstream side of the dam while hydrogen sulfide concentrations found to be highest in the downstream side of the dam. Furthermore, methane and hydrogen sulfide concentrations were correlated to nematode community characteristics such as trophic composition densities and genera composition. There was a clear difference between the communities above and below the dam. The discontinuous nematode community distribution indicated that the Ba Lai River is impacted by dam construction. Potentially the high deposition and eutrophication could turn the area into a methane-rich area related to predicted impact on nematodes.
Art of Generator Synchronizing

This paper presents a review of power-system synchronization, proper sync-check coordination, and fast synchronization methods. When paralleling two sources, it is crucial that the interconnecting circuit breaker be closed only when both sources are in voltage, frequency, and phase coincidence. An operator can synchronize manually or use one of the latest, state-of-the-art automatic synchronizers (ANSI/IEEE device 25A) and sync-check relays (ANSI/IEEE device 25) to automate closing. Generator and bus synchronization share most principles-with some important differences-for each type of synchronization. For generation plants, the method of generator synchronization selected depends greatly on the plant configuration and operating mode. For bus-line applications, the synchronizing method selected depends on the power-system "stiffness," motor loads, and whether a wye-delta transformer is located between the line and bus.
A hybrid approach to estimate the complex motions of clouds in sky images

Tracking the motion of clouds is essential to forecasting the weather and to predicting the short-term solar energy generation. Existing techniques mainly fall into two categories: variational optical flow, and block matching. In this paper, we summarize recent advances in estimating cloud motion using ground-based sky imagers and quantitatively evaluate state-of-the-art approaches. Then we propose a hybrid tracking framework to incorporate the strength of both block matching and optical flow models. To validate the accuracy of the proposed approach, we introduce a series of synthetic images to simulate the cloud movement and deformation, and thereafter comprehensively compare our hybrid approach with several representative tracking algorithms over both simulated and real images collected from various sites/imagers. The results show that our hybrid approach outperforms state-of-the-art models by reducing at least 30% motion estimation errors compared with the ground-truth motions in most of simulated image sequences. Moreover, our hybrid model demonstrates its superior efficiency in several real cloud image datasets by lowering at least 15% Mean Absolute Error (MAE) between predicted images and ground-truth images. (C) 2016 Elsevier Ltd. All rights reserved.
Highly Thermally Conductive Adhesion Elastomer Enhanced by Vertically Aligned Folded Graphene

Heat and stress transfer at an interface are crucial for the contact-based tactile sensing to measure the temperature, morphology, and modulus. However, fabricating a smart sensing material that combines high thermal conductivity, elasticity, and good adhesion is challenging. In this study, a composite is fabricated using a directional template of vertically aligned folded graphene (VAFG) and a copolymer matrix of poly-2-[[(butylamino)carbonyl]oxy]ethyl ester and polydimethylsiloxane, vinyl-end-terminated polydimethylsiloxane (poly(PBAx-ran-PDMS)). With optimized chemical cross-linking and supermolecular interactions, the poly(PBA-ran-PDMS)/VAFG exhibits high thermal conductivity (15.49 W m-1 K-1 ), an high elastic deformation, and an interfacial adhesion of up to 6500 N m-1 . Poly(PBA-ran-PDMS)/VAFG is highly sensitive to temperature and pressure and demonstrates a self-learning capacity for manipulator applications. The smart manipulator can distinguish and selectively capture unknown materials in the dark. Thermally conductive, elastic, and adhesive poly(PBA-ran-PDMS)/VAFG can be developed into core materials in intelligent soft robots.
Computational fluid dynamics simulation of forward osmosis (FO) membrane systems: Methodology, state of art, challenges and opportunities

Forward osmosis (FO) has attracted wide research attention owing to its potential application in seawater desalination. In the past decade, the research advancements in FO experimental studies have been accompanied by an increase in computational fluid dynamics (CFD) studies. With advanced CFD techniques, the complex flow patterns related to FO design and different operating conditions are now being studied and some solutions to existing problems are being suggested. Therefore, it is timely to review the CFD methodology, the detailed as-sumptions made and the mathematical models used for the FO processes. In addition, the problem of verification and validation is discussed in detail, together with the present state of art based on work over the last decade. Many FO CFD works have focused on varying membrane properties and operating conditions, while some have attempted to shed insight into the mechanisms that lead to flux enhancement. Finally, challenges and research opportunities related to FO CFD studies are provided.
Evaluating technological innovation impact: an empirical analysis of the offshore wind sector

This study analyzed the determinants that impact innovation on offshore wind energy (OSW) for a select group of countries, applying mixed-method approaches for a period between 2011 and 2021. The OSW sector witnessed cost reductions in recent years; therefore, this study analyzed how these factors impact technological innovation in the OSW sector. The fixed effects results proved trademark, carbon emissions, offshore wind capacity, and electricity from renewable energy, technical and scientific journals are significant and impact innovation regarding offshore wind energy. The maximum likelihood (MLE) coefficients are more robust than the restricted maximum likelihood (MREL) and better explained the significance of the variables in spurring OSW innovation. Ultimately, the interaction term "cross" came out significant in the analysis. It signified the importance of the interaction variables in scaling innovation. Similarly, the study forecasted OSW capacity additions to grow to more than 28GW by 2036, at a 48.8% growth rate, from the current over 55 G.W. capacity. Additionally, the infrastructure development of the OSW sector via a fitted line between total global offshore capacity and the development projects observed a negative relationship among these dual factors evaluated in the OSW, showing a decreasing trend of capacity additions among countries, as well as the fitted line relationship between total country OSW capacity and operational projects showed that China leads the globe with operational OSW projects. At the same time, Brazil is the new leader in the world regarding OSW capacity. The general analysis of the parameters of the OSW infrastructure plan showed the sector is declining regarding secured projects, development projects, operational projects, and capacities additions in some countries. However, global total capacity additions are on a steady path, declining a bit. This is attributable to the pandemic that slowed the global economy. This study will serve as a reference document for policy formulators regarding scaling up innovation for offshore wind energy.
Time-Dependent Deep Image Prior for Dynamic MRI

We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study of moving organs such as the heart. We introduce a generalized version of the deep-image-prior approach, which optimizes the weights of a reconstruction network to fit a sequence of sparsely acquired dynamic MRI measurements. Our method needs neither prior training nor additional data. In particular, for cardiac images, it does not require the marking of heartbeats or the reordering of spokes. The key ingredients of our method are threefold: 1) a fixed low-dimensional manifold that encodes the temporal variations of images; 2) a network that maps the manifold into a more expressive latent space; and 3) a convolutional neural network that generates a dynamic series of MRI images from the latent variables and that favors their consistency with the measurements in k-space. Our method outperforms the state-of-the-art methods quantitatively and qualitatively in both retrospective and real fetal cardiac datasets. To the best of our knowledge, this is the first unsupervised deep-learning-based method that can reconstruct the continuous variation of dynamic MRI sequences with high spatial resolution.
Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm

In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.