Previous Datasets Research Articles

Comparative Analysis of CNNs and Vision Transformers for Automatic Classification of Abandonment in Douro’s Vineyard Parcels

The Douro Demarcated Region is fundamental to local cultural and economic identity. Despite its importance, the region faces the challenge of abandoned vineyard plots, caused, among other factors, by the high costs of maintaining vineyards on hilly terrain. To solve this problem, the European Union (EU) offers subsidies to encourage active cultivation, with the aim of protecting the region’s cultural and environmental heritage. However, monitoring actively cultivated vineyards and those that have been abandoned presents considerable logistical challenges. With 43,843 vineyards spread over 250,000 hectares of rugged terrain, control of these plots is limited, which hampers the effectiveness of preservation and incentive initiatives. Currently, the EU only inspects 5 per cent of farmers annually, which results in insufficient coverage to ensure that subsidies are properly used and vineyards are actively maintained. To complement this limited monitoring, organisations such as the Instituto dos Vinhos do Douro e do Porto (IVDP) use aerial and satellite images, which are manually analysed to identify abandoned or active plots. To overcome these limitations, images can be analysed using deep learning methods, which have already shown great potential in agricultural applications. In this context, our research group has carried out some preliminary evaluations for the automatic detection of abandoned vineyards using deep learning models, which, despite showing promising results on the dataset used, proved to be limited when applied to images of the entire region. In this study, a new dataset was expanded to 137,000 images collected between 2018 and 2023, filling critical gaps in the previous datasets by including greater temporal and spatial diversity. Subsequently, a careful evaluation was carried out with various DL models. As a result, the ViT_b32 model demonstrated superior performance, achieving an average accuracy of 0.99 and an F1 score of 0.98, outperforming CNN-based models. In addition to the excellent results obtained, this dataset represents a significant contribution to advancing research in precision viticulture, providing a solid and relevant basis for future studies and driving the development of solutions applied to vineyard monitoring in the Douro Demarcated Region. These advances not only improve efficiency in detecting abandoned plots, but also contribute significantly to optimising the use of subsidies in the region.

Lack of emission lines in the optical spectra of SAX J1808.4–3658 during reflaring of the 2019 outburst

Aims. We present spectroscopy of the accreting X-ray binary and millisecond pulsar SAX J1808.4–3658. These observations are the first to be obtained during a reflaring phase. We collected spectroscopic data during the beginning of the reflaring of the 2019 outburst and compared them to previous datasets taken at different epochs, both of the same outburst and across the years. To this end, we also present spectra of the source taken during quiescence in 2007, one year before the next outburst. Methods. We made use of data taken by the Very Large Telescope (VLT) X-shooter spectrograph on August 31, 2019, three weeks after the outburst peak. For flux calibration, we used photometric data taken during the same night by the 1m telescopes from the Las Cumbres Observatory network that are located in Chile. We compare our spectra to the quiescent data taken by the VLT-FORS1 spectrograph in September, 2007. We inspected the spectral energy distribution by fitting our data with a multicolored accretion-disk model and sampled the posterior probability density function for the model parameters with a Markov chain Monte Carlo (MCMC) algorithm. Results. We find the optical spectra of the 2019 outburst to be unusually featureless, with no emission lines present despite the high resolution of the instrument. Fitting the UV-optical spectral energy distribution with a disk plus irradiated star model results in a very large value for the inner disk radius of ∼5130 ± 240 km, which could suggest that the disk was emptied of material during the outburst, possibly accounting for the emission-less spectra. Alternatively, the absence of emission lines could be due to a significant contribution of the jet emission at optical wavelengths.

Lightness modifications of the CIECAM16 and CIELAB based on the Helmholtz-Kohlrausch effect

This paper presents a variable-achromatic-color (VAC) lightness matching experiment conducted on a wide color gamut display. Based on the findings of this study, two models were proposed for the prediction of the Helmholtz-Kohlrausch (H-K) effect. The first method modifies High's hue-dependent function to account for the peak in red hues. The second method employs a lattice regression algorithm to construct a look-up table (LUT) based on the collected datasets. The model performance was evaluated by comparison with all other available H-K correction models and datasets. The results demonstrated that the two proposed models have achieved significant improvements in both the present and previous datasets.

Psychological impacts of the Gaza war on Palestinian young adults: a cross-sectional study of depression, anxiety, stress, and PTSD symptoms

BackgroundThe Gaza Strip has been embroiled in a violent military assault since October 2023, with an immense toll on the civilian population. Armed conflicts threaten the mental health of affected communities and survivors, and psychiatric morbidity increases with forced displacement and with severe and recurrent trauma. This study investigates the prevalence and predisposing factors of depression, anxiety, stress, and PTSD symptoms in a group of young adult students from the Gaza Strip during the war.MethodsA cross-sectional, internet-based survey recruited medical students from the Gaza Strip and used the DASS21, SWLS, and PCL-5 instruments. PTSD diagnosis required having a PCL-5 score ≥ 23 and fulfilling the DSM-5 criteria. Rates of depression, anxiety, stress, and life satisfaction were compared with a previous dataset collected in 2022. Finally, logistic regression models were fitted using R software to identify factors significantly associated with depression, anxiety, stress, and PTSD.ResultsThree hundred thirty-nine medical students participated. Most had been displaced several times, and the great majority had lost a relative, colleague, or friend. Also, a majority had lost their homes and income. 97.05% of participants suffered mild depressive symptoms or higher, while 84.37% and 90.56% reported mild anxiety and mild stress symptoms or higher, respectively. High levels of life dissatisfaction were also found, and 63.40% suffered from PTSD. Symptoms were significantly more prevalent than baseline rates. All participants with PTSD had at least one psychiatric comorbidity. Living in a shelter and having moderate stress symptoms or higher were significantly associated with depression. Being a female, losing a friend, having moderate stress symptoms or higher, and having PTSD predicted having moderate anxiety or higher. Having moderate or higher depression symptoms, moderate or higher anxiety symptoms, and PTSD predicted having moderate stress symptoms or higher. Finally, moderate or higher anxiety and stress symptoms predicted having PTSD.ConclusionThe study detected very high rates of psychiatric disorders among its population of young adult medical students and outlined a myriad of risk factors associated with higher comorbidity. Interventions are needed to prevent a brewing mental health crisis in the Gaza Strip.

Robust-DefReg: a robust coarse to fine non-rigid point cloud registration method based on graph convolutional neural networks

Abstract Point cloud registration is a critical process in computer vision and measurement science, aimed at determining transformations between corresponding sets of points for accurate spatial alignment. In particular, non-rigid registration involves estimating flexible transformations that map a source point cloud to a target point cloud, even under conditions of stretching, compression, or other complex deformations. This task becomes especially challenging when addressing measurement-specific issues like varying degrees of deformation, noise, and outliers, all of which can impact measurement accuracy and reliability. This paper introduces Robust-DefReg, a novel method for non-rigid point cloud registration that applies graph convolutional networks (GCNNs) within a coarse-to-fine registration framework. This end-to-end pipeline harnesses global feature learning to establish robust correspondences and precise transformations, enabling high accuracy across different deformation scales and noise levels. A key contribution of Robust-DefReg is its demonstrated resilience to various challenges, such as substantial deformations, noise, and outliers, factors often underreported in existing registration literature. In addition, we present SynBench, a comprehensive benchmark dataset specifically designed for evaluating non-rigid point cloud registration in realistic measurement scenarios. Unlike previous datasets, SynBench incorporates a range of challenges, making it a valuable tool for the fair assessment of registration methods in measurement applications. Experimental results on SynBench and additional datasets show that Robust-DefReg consistently outperforms state-of-the-art methods, offering higher registration accuracy and robustness, even with up to 45% outliers. SynBench and the Robust-DefReg source code are publicly accessible for further research and development at https://doi.org/10.11588/data/R9IKCF and https://github.com/m-kinz/Robust-DefReg, respectively.

A genomic approach to Porites (Anthozoa: Scleractinia) megadiversity from the Indo-Pacific

Porites corals are vital components of tropical reef ecosystems worldwide, serving as ecosystem engineers and hubs of biodiversity in shallow water coral reefs. Despite their ecological significance and the widespread use of Porites spp. as models for research, the richness and evolutionary relationships of species within the genus remain elusive. In this study, we analyzed genomic data from 330 colonies of Porites from 17 localities across the Indo-Pacific region based on the reduced representation genomic approach ezRAD. We retrieved 25,163 SNPs and provided a phylogenomic hypothesis for 29 nominal species and 10 unknown morphologies, recovering 15 deeply rooted molecular clades. Among these, 12 clades included samples corresponding to single distinct morphospecies. One did not match any nominal species. The remaining two clades comprised species complexes, which included various massive and encrusting morphologies commonly used in experimental biology. Within these complexes, we observed additional geographic or morphological structure, indicating complex evolutionary dynamics, possibly reflecting distinct species, isolated populations or hybridization. Additionally, a series of divergent samples underscored the importance of more sampling to define species boundaries and refine phylogenomic relationships. We also integrated our findings with previous phylogenetic datasets and their respective sampling localities, challenging traditional notions about Porites species geographic distributions. Overall, our findings indicate a need to revise past synonymies and to formally establish new species. A precise understanding of Porites species and their diversity and distributions is necessary for effective reef conservation and management.

Two distinct Dehalobacter metagenome-assembled genomes from anaerobic chloroform and dichloromethane degrading consortia.

Here we present two metagenomes and two Dehalobacter metagenome-assembled genomes from subcultures of an anaerobic chloroform and dichloromethane degrading microbial community used for bioremediation. Our objective was to assemble and curate the genome(s) of Dehalobacter, key biodegraders in the culture, through repeated sequencing and joint assembly with previous datasets.

Neuro Fuzzy in Predicting the Characteristics of Some Nanomaterials

Unveiling the impressive capabilities of the Adaptive Neuro-Fuzzy Inference System (ANFIS), this study effectively predicts key properties of engineered nanomaterials, opening doors to innovative applications across various industries. We initially investigate the cytotoxic effects of TiO2 and ZnO nanoparticles on immortalized human lung epithelial cells, employing ANFIS to establish correlations between nanoparticle size and behaviour in different media and the resulting cellular membrane damage, quantified by lactate dehydrogenase release. Next, to predict the compressive strength of geopolymers, analysing over previous experimental datasets focused on critical chemical ratios. This model demonstrates its capability to optimize formulations for enhanced mechanical performance in sustainable construction materials. Additionally, we apply ANFIS to evaluate the size of silver nanoparticles in montmorillonite/starch bio nanocomposites, identifying significant factors such as AgNO3 concentration. The ANFIS models achieved high accuracy across all applications, underscoring their utility in predicting material behaviour and optimizing formulations for improved performance and safety. Collectively, these findings illustrate the potential of ANFIS as a robust tool in nanomaterial research and development.

Immune checkpoints PD1/PDL1, TIM3/GAL9 and key immune mediators landscape reveal differential expression dynamics on imatinib response in chronic myeloid leukemia.

The immune system of chronic myeloid leukemia (CML) patients is severely impaired, hampering anti-tumor responses, and maximal immune recovery occurs after achieving deep molecular responses to tyrosine kinase inhibitors. This study aimed to discern the expression patterns of NCR2, IL2, IL4, EOMES, FOXP3, GATA3, RORGT, PD1/PDL1 and TIM3/GAL9, expanding our previous dataset up to 19 key immune mediators, during the initial year on imatinib. Gene expression dynamics were evaluated in 171 peripheral blood samples from 89 CML patients, including 43 longitudinally monitored individuals, and 52 healthy donors. Univariate and unsupervised analyses confirmed diminished expression of most studied immune mediators, except for TNF, ARG1 and IL4, differentiating between baseline and 3-month samples. Most of the studied mediators normalized along treatment, with a transient increase of TNF and IL6 levels at 3-months, especially in optimal responders (BCR::ABL1 < 0.1%). Univariate and multivariate analyses showed heightened ARG1 levels and a transition from PD1/PDL1 dominance at 3 months to TIM3/GAL9 at 12 months in non-optimal responders (BCR::ABL1 ≥ 0.1%). Our longitudinal design offers a deeper exploration of immune gene expression dynamics in CML patients on imatinib, highlighting its potential implications for therapy outcomes.

Predicting Feynman periods in ϕ4-theory

We present efficient data-driven approaches to predict the value of subdivergence-free Feynman integrals (Feynman periods) in ϕ4-theory from properties of the underlying Feynman graphs, based on a statistical examination of almost 2 million graphs. We find that the numbers of cuts and cycles determines the period to better than 2% relative accuracy. Hepp bound and Martin invariant allow for even more accurate predictions. In most cases, the period is a multi-linear function of the properties in question. Furthermore, we investigate the usefulness of machine-learning algorithms to predict the period. When sufficiently many properties of the graph are used, the period can be predicted with better than 0.05% relative accuracy.We use one of the constructed prediction models for weighted Monte-Carlo sampling of Feynman graphs, and compute the primitive contribution to the beta function of ϕ4-theory at L ∈ {13, … , 17} loops. Our results confirm the previously known numerical estimates of the primitive beta function and improve their accuracy. Compared to uniform random sampling of graphs, our new algorithm is 1000-times faster to reach a desired accuracy, or reaches 32-fold higher accuracy in fixed runtime.The dataset of all periods computed for this work, combined with a previous dataset, is made publicly available. Besides the physical application, it could serve as a benchmark for graph-based machine learning algorithms.

Global mapping of oil palm planting year from 1990 to 2021

Abstract. Oil palm is a controversial crop, primarily because it is associated with negative environmental impacts such as tropical deforestation. Mapping the crop and its characteristics, such as age, is crucial for informing public and policy discussions regarding these impacts. Oil palm has received substantial mapping efforts, but accurate and up-to-date oil palm maps of both extent and age are essential for monitoring impacts and informing concomitant debate. Here, we present a 10 m resolution global map of industrial and smallholder oil palm, developed using Sentinel-1 data for the years 2016–2021 and a deep learning model based on convolutional neural networks. In addition, we used Landsat-5, Landsat-7, and Landsat-8 to estimate the planting year from 1990 to 2021 at a 30 m spatial resolution. The planting year indicates the year of establishment of the current (as of 2021) oil palm plantation by means of either newly planted or replanted oil palm in an existing oil palm plantation. We validated the oil palm extent layer using 18 812 randomly distributed reference points. The accuracy of the planting-year layer was assessed using field data collected from 5831 industrial parcels and 1012 smallholder plantations distributed throughout the global oil palm growing area. We found oil palm plantations covering a total mapped area of 23.98 Mha, and our area estimates are 16.82 ± 0.19 Mha of industrial oil palm and 7.37 ± 0.25 Mha of smallholder oil palm worldwide. The producer's and user's accuracy are 91.0 ± 2.5 % and 91.8 ± 1.2 % for industrial plantations and 71.4 ± 0.7 % and 72.4 ± 1.8 % for smallholders; these values represent an improvement compared to a previous global oil palm dataset, particularly in terms of omission of oil palm. The overall mean error between the estimated planting year and the field data was −0.24 years, and the root-mean-square error was 2.65 years, but the agreement was lower for smallholders. Mapping the extent and planting year of smallholder plantations remains challenging, particularly for wild and sparsely planted oil palm, and future mapping efforts should focus on these specific types of plantations. The average oil palm plantation age was 14.1 years, and the area of oil palm over 20 years old was 6.28 Mha. Given that oil palm plantations are typically replanted after 25 years, our findings indicate that this area will require replanting within the coming decade, starting from 2021. Our dataset provides valuable input for optimal land use planning to meet the growing global demand for vegetable oils. The global oil palm extent layer for the year 2021 and the planting-year layer from 1990 to 2021 can be found at https://doi.org/10.5281/zenodo.13379129 (Descals, 2024a).

Investigation of the Cyclohexene Oxidation Mechanism Through the Direct Measurement of Organic Peroxy Radicals.

Monoterpenes, the second most abundant biogenic volatile organic compounds globally, are crucial in forming secondary organic aerosols, making their oxidation mechanisms vital for addressing climate change and air pollution. This study utilized cyclohexene as a surrogate to explore first-generation products from its ozonolysis through laboratory experiments and mechanistic modeling. We employed proton transfer reaction mass spectrometry with NH4+ ion sources (NH4+-CIMS) and a custom-built OH calibration source to quantify organic peroxy radicals (RO2) and closed-shell species. Under near-real atmospheric conditions in a Potential Aerosol Mass-Oxidation Flow Reactor, we identified 30 ozonolysis products, expanding previous data sets of low-oxygen compounds. Combined with simulations based on the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere and relevant literature, our results revealed that OH dominates over ozone in cyclohexene oxidation at typical atmospheric oxidant levels with H-abstraction contributing 30% of initial RO2 radicals. Highly oxidized molecules primarily arise from RO2 autoxidation initiated by ozone, and at least 15% of ozone oxidation products follow the overlooked nonvinyl hydroperoxides pathway. Gaps remain especially in understanding RO2 cross-reactions, and the structural complexity of monoterpenes further complicates research. As emissions decrease and afforestation increases, understanding these mechanisms becomes increasingly critical.

Contrasting the development of larval and adult body plans during the evolution of biphasic lifecycles in sea urchins.

Biphasic lifecycles are widespread among animals, but little is known about how the developmental transition between larvae and adults is regulated. Sea urchins are a unique system for studying this phenomenon because of the stark differences between their bilateral larval and pentaradial adult body plans. Here, we use single-cell RNA sequencing to analyze the development of Heliocidaris erythrogramma (He), a sea urchin species with an accelerated, non-feeding mode of larval development. The sequencing time course extends from embryogenesis to roughly a day before the onset of metamorphosis in He larvae, which is a period that has not been covered by previous datasets. We find that the non-feeding developmental strategy of He is associated with several changes in the specification of larval cell types compared to sea urchins with feeding larvae, such as the loss of a larva-specific skeletal cell population. Furthermore, the development of the larval and adult body plans in sea urchins may utilize largely different sets of regulatory genes. These findings lay the groundwork for extending existing developmental gene regulatory networks to cover additional stages of biphasic lifecycles.

Bias-aware face mask detection dataset

AbstractIn December 2019, a novel coronavirus (COVID-19) spread so quickly around the world that many countries had to set mandatory face mask rules in public areas to reduce the transmission of the virus. To monitor public adherence, researchers aimed to rapidly develop efficient systems that can detect faces with masks automatically. However, the lack of representative and novel datasets posed challenges for training efficient models. Early attempts to collect face mask datasets did not account for potential race, gender, and age biases. Therefore, the resulting models show inherent biases toward specific race groups, such as Asian or Caucasian. In this work, we present a novel face mask detection dataset that contains images posted on Twitter during the pandemic from around the world. Unlike previous datasets, the proposed Bias-Aware Face Mask Detection (BAFMD) dataset contains more images from underrepresented races and age groups to mitigate the problem of the face mask detection task. We perform experiments to investigate potential biases in widely used face mask detection datasets and illustrate that the BAFMD dataset yields models with better performance and generalization ability. The dataset is publicly available at https://github.com/Alpkant/BAFMD.

Podium to Publication: An Analysis of Papers Presented at the British Association of Paediatric Surgeons (BAPS) Congress (2009–19)

BackgroundThe British Association of Paediatric Surgeons (BAPS) annual congress is an opportunity to present clinical and scientific data that may influence future practice. The presentation of the Abstract is but the first step towards definitive publication in the medical literature. Our aim was to review what the success rate has been from previous congresses. MethodsAbstracts were reviewed for the 11-year-period 2009 to 2019. A MEDLINE search using keywords from title and authors’ names was used to identify subsequent publication. Comparison with a previous dataset [1] (1999-2008) was also carried out. Linear data were compared for trend using Mann-Whitney-U. P ≤ 0.05 was accepted as significant. Data were expressed as median (interquartile range). Results1061 abstracts were presented orally with 555 (52.3%) subsequently published. The number per year was 90 (81–106) compared to 81 (74–79) in 1999–2008 (P = 0.25), although with a higher number actually published thereafter [45 (32–54)% vs. 30 (27–35)%; P = 0.0003)]. Publication occurred in a wide range of journals (n = 99), with most being in the Journal of Pediatric Surgery (n = 251), albeit with a smaller proportion (49% vs. 69% formerly, P < 0.05). Overall, UK institutions contributed 52.6% (n = 558) of abstracts with 240 (43%) of them subsequently published at 19 (17–29)/year. The proportion of UK presentations had increased from 43% formerly (P = 0.016). Most UK presentations originated from institutions within London [41% vs. 45% formerly, P = 0.04]. There was a wide range of abstracts from institutions from outside of the UK (n = 49) with 38 (26–51) abstracts accepted each year and of these 62.4% were subsequently published at 20 (15–36)/year. ConclusionThere has been an increase in abstracts accepted for presentation over the period, together with a higher proportion actually reaching publication. A strong international contribution was noted which has also been maintained. London remains the primary source of abstracts and publications from within the UK, although there was a higher proportion from other UK centres noted over this period.Reference [1] Macdonald AL, Parsons C, Davenport M. Outcome of abstracts presented at the British Association of Paediatric Surgeons congresses (1999–2008). J Pediatr Surg. 2012; 47: 386-90. doi: 10.1016/j.jpedsurg.2011.09.043.

Sediment Dynamics in the Energetic Nearshore Zone: Acoustic Remote Sensing and Model Validation

AbstractAcoustic backscatter data were collected in the shallow nearshore environment under breaking and non‐breaking waves, using a multi‐frequency sonar at 0.5, 1.0, and 2.0 MHz. The data are used to develop and test an acoustic inverse model for measuring suspended sediment concentration (SSC) in the presence of bubbles generated by breaking waves. The model leverages the contrasting frequency dependence of acoustic scattering by bubbles and sand, to simultaneously estimate SSC and bubble void fraction. Validation against in situ sediment measurements shows the acoustic technique can recover sediment concentration with good accuracy in both breaking and non‐breaking waves, unlike existing algorithms which only perform well in non‐breaking waves. Finally, data from the experiment are used to validate existing theories for suspended sediment dynamics under breaking waves, for which few previous data sets exist.

Genome-wide investigation and characterization of heat shock transcription factors in papaya (Carica papaya)

Heat shock transcription factors (Hsf-s) have been considered one of the major regulatory proteins that play important roles in various biological processes during the growth and development of plants. Unfortunately, no comprehensive studies of the Hsf family in papaya (Carica papaya), a popular tropical fruit crop, have been performed, even the papaya genome assembly has been released recently. In this study, a total of 18 members of the Hsf family, namely CpHsf-s have been identified in papaya. Based on various computational tools, a systematic characterization of the CpHsf family, such as gene structure, physic-chemical features, categorization, and protein-protein interaction, has been conducted. We found that the physic-chemical properties of the CpHsf proteins were highly variable, while all proteins were hydrophilic and localized in the nucleus. Our classification indicated that the CpHsf proteins could be categorized into three groups, including HsfA, HsfB, and HsfC, and members in the same clade share similar gene structure. According to the previous RNA-Seq datasets, our re-analysis revealed that the CpHsf genes exhibited different expressions in various major organs or tissues during fruit ripening and under the ultra-low temperature stress. Taken together, our study could provide a list of candidate genes for further functional characterization towards the improvement of stress tolerance in papaya.

Norms for 718 Persian Words in Emotional Dimensions, Animacy, and Familiarity.

Research frequently uses words as stimuli to assess cognitive and psychological processes. However, various attributes of these words, such as their semantic and emotional aspects, could potentially confound study results if not properly controlled. This study aims to establish a reliable foundation for the semantic and emotional aspects of words for research in Persian. To this end, the present study provided norms for 718 Persian nouns in arousal, valence, familiarity, and animacy dimensions. The words were selected from a previous English dataset (Warriner et al. in Behav Res Methods 45(4):1191-1207, 2013), translated into Persian, and rated by a total of 463 native Persian-speaking participants. The ratings were obtained through an online questionnaire using a 9-point Likert scale for emotional dimensions (i.e., valence and arousal) and a 5-point Likert scale for semantic dimensions (i.e., familiarity and animacy). The reliability of the ratings was measured using the split-half method, and the result indicated a high consistency of ratings in all dimensions. To assess the relationship between the emotional and semantic dimensions, Pearson correlation coefficient was conducted. Gender differences were investigated through the Mann-Whitney U test, and significant differences were observed in all dimensions. These results are compared with findings from previous studies that were conducted in various languages.

Spontaneous ignition and flame propagation in hydrogen/methane wrinkled laminar flames at reheat conditions: Effect of pressure and hydrogen fraction

Direct numerical simulations (DNS) with detailed chemical kinetics and chemical explosive mode analysis (CEMA) are performed to investigate the effect of operating pressure and hydrogen–methane blending on laminar wrinkled flames at reheat combustion conditions. Building upon previous three-dimensional DNS datasets of reheat hydrogen flames, the present work considers two-dimensional configurations that render computationally-feasible simulations of high-pressure combustion and significantly more complex hydrocarbon chemistry. A geometrically-simplified representation of the reheat combustor is adopted and the thermodynamic states considered in the simulations are carefully chosen to mimic gas turbine operation conditions, which are constrained to achieve a target flame position and flame temperature. The two-dimensional DNS results are first assessed against the available three-dimensional data and then analyzed to extract the main trends exhibited by the reheat combustion process with respect to the fraction of the fuel consumed by spontaneous ignition and flame propagation, for increasing pressures and hydrogen fractions. Due to significant differences in the characteristic features of the velocity and thermal boundary layers between the three-dimensional and the two-dimensional configurations, a different global flame shape is observed (V-shaped flame vs W-shape flame) together with a ∼10% bias in the fraction of fuel consumed by spontaneous ignition. Crucially, results from the scaling studies reveal very clear trends with a significant decrease in the fuel consumption by spontaneous ignition for increasing pressures and hydrogen fractions, at the conditions investigated. Finally, this study highlights the important role that first-principle direct numerical simulations, even when conducted in computationally affordable two-dimensional simplified geometrical configurations, can play in obtaining detailed insights and quantitative trends useful for the characterization of complex reactive flows.Novelty and significanceThe reheat burners of the two-stage sequential gas turbine combustors are designed to stabilize flames primarily due to spontaneous ignition. However, prior numerical simulations revealed the presence of an additional assisted-ignition mode aided by recirculation zones. In this study, we used practically feasible two-dimensional direct numerical simulations of premixed hydrogen–air mixtures under lean conditions to quantify the roles of the two flame stabilization modes at different operating pressures. Achieving fundamental understanding of the effect of pressure and hydrogen fraction on flame stabilization is crucial to the development of two-stage sequential combustion and the present two-dimensional calculations provide important new insights. We show that at high pressures, both modes consume fuel to a similar extent. We also investigated the effect of methane blending on flame stabilization. This study also discusses how two-dimensional direct numerical simulations can be leveraged in the design optimization of reheat burners at low technology readiness levels.

Vigilant Attention During Cognitive and Language Processing in Aphasia.

Persons with aphasia (PWA) experience differences in attention after stroke, potentially impacting cognitive/language performance. This secondary analysis investigated physiologically measured vigilant attention during linguistic and nonlinguistic processing in PWA and control participants. To evaluate performance and attention in a language task, seven PWA read sentences aloud (linguistic task) and were compared to a previous data set of 10 controls and 10 PWA. To evaluate performance and attention in a language-independent task, 11 controls and nine PWA completed the Bivalent Shape Task (nonlinguistic task). Continuous electroencephalogram (EEG) data were collected during each session. A previously validated EEG algorithm classified vigilant-attention state for each experiment trial into high, moderate, distracted, or no attention. Dependent measures were task accuracy and amount of time spent in each attention state (measured by the number of trials). PWA produced significantly more errors than controls on the linguistic task, but groups performed similarly on the nonlinguistic task. During the linguistic task, controls spent significantly more time than PWA in a moderate-attention state, but no statistically significant differences were found between groups for other attention states. For the nonlinguistic task, amount of time controls and PWA spent in each attention state was more evenly distributed. When directly comparing attention patterns between linguistic and nonlinguistic tasks, PWA showed significantly more time in a high-attention state during the linguistic task as compared to the nonlinguistic task; however, controls showed no significant differences between linguistic and nonlinguistic tasks. This study provides new evidence that PWA experience a heightened state of vigilant attention when language processing demands are higher (during a linguistic task) than when language demands are lower (during a nonlinguistic task). Collectively, results of this study suggest that when processing language, PWA may allocate more attentional resources than when completing other kinds of cognitive tasks.