Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction between breaking-induced vortices and near-bed structures. Part 1. Experimental and theoretical investigation

Abstract The present work describes the vortex–vortex interactions observed during laboratory experiments, where a single regular water wave is allowed to travel over a discontinuous rigid bed promoting the generation of both near-bed and surface vortices. While near-bed vortices are generated by the flow separation occurring at the bed discontinuity, surface vortices are induced by the wave breaking in conjunction with a breaking-induced jet. A ‘backward breaking’ (previously observed in the case of solitary waves) occurs at the air–water interface downstream of the discontinuity and generates a surface anticlockwise vortex that interacts with the near-bed clockwise vortex. With the vortex–vortex interaction influenced by many physical mechanisms, a point-vortex model, by which vortices evolve under both self-advection (in relation to both free surface and seabed) and mutual interaction, has been implemented to separately investigate the vortex- and wave-induced dynamics. The available data indicate that both self-advection and mutual interaction are the governing mechanisms for the downward motion of the surface vortex, with the effect of the breaking-induced jet being negligible. The same two mechanisms, combined with the mean flow, are responsible for the almost horizontal and oscillating path of the near-bed vortex. The investigation of the vortex paths allow us to group the performed tests into three distinct classes, each characterized by a specific range of wave nonlinearity. The time evolution of the main variables characterizing the vortices (e.g. circulation, kinetic energy, enstrophy, radius) and their maximum values increase with the wave nonlinearity, such dependences being described by synthetic best-fit formulas.
Design and Development of a Technology-Agnostic NFR Testing Framework: Introducing the framework and discussing the future of load testing in Agile software development

Testing the non-functional requirements (NFR) of a system is particularly complicated and time-consuming. Challenges in this area are compounded when the system is developed under some offspring of Agile methodologies, which favor iterative development and rapid feedback from extensive testing. The authors of this paper build upon previous work investigating the common challenges and solutions cited in recent peer-reviewed research on this topic to design and build a tool consolidating many of the concepts found in this investigation. The tool is known as LuluPerfTest (LPT) and is an NFR testing framework meant to plug into continuous integration (CI) systems to run NFR tests configured with a JSON script. This allows developers and testers to build maintainable and minimally complex automated NFR test scripts. This study explains the challenges inherent in NFR testing in Agile software development and presents how LPT confronts those challenges. It aims to explain LPT and invite collaboration among other testing, verification, and validation researchers to create an open sources software (OSS) solution to the problems of NFR testing in Agile software development projects.
Ontology Enrichment with Text Extracted from Wikipedia

As biobanks require storing a large amount of data, the use of ontologies offer an effective solution to properly organise data and for data management. However, the specialised jargon embedded into an ontology, especially in the biomedical field, may constitute a difficulty for the people outside the proper domain. Our solution to this is to enhance ontology usability by automatically enriching an ontology. In this article we illustrate an enrichment process that allows us to expand in a controlled way the terms within an ontology. Our proposed enrichment process automatically adds in the ontological structure information extracted from external sources, in order to offer a more complete and clear knowledge of the domain and let users more easily query the ontology. Our enrichment process carefully selects from the wealth of information available in Wikipedia.
Cluster Analysis for Customer Segmentation with Open Banking Data

Segmenting customers into different groups using their characteristics and behaviors has always been an important topic. Customer segmentation can lead to better customer understanding and targeting, which in turn leads to more effective product tailoring and marketing strategies. Data mining methods are powerful techniques that can be used in customer segmentation to find customers with similar characteristics. Past research that evaluated different data mining techniques has often had drawbacks, such as using too time-consuming methods or conducting studies with smaller data sets. Density-based clustering algorithms for customer segmentation, such as the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) has only been examined by a few research papers. This study, which summarized the main findings of the unpublished dissertation of Bartels [2021], aimed to classify the segmentation of customers using a Recency, Frequency and Monetary Value (RFM) Model and the clustering techniques, K-Means and DBSCAN, to find groups of similarities and differences and to discover potential valuable and vulnerable customers. The data used was from Open Banking data sets, including anonymized transactions from various bank customers in the UK for three months in 2017 and mainly focused on different types of expenses. K-Means found three clusters each month that represent the most, medium, and least valuable customers. While the most valuable customers have the highest average values per attribute, the least valuable customers have the lowest average values. The found clusters were analyzed and evaluated to find potential vulnerable and valuable groups, which can help with future product tailoring and marketing, especially for unforeseen emergency circumstances such as a pandemic. K-Means outperformed DBSCAN, as the latter showed negative silhouette coefficients.
The Composable Finance Mosaic Bridge and Liquidity Forecasting

We introduce the forecasting technology behind Composable Finance's (Composable's)1 cross-layer cross-chain bridge Mosaic2 . Mosaic contains a network of vaults, one on each layer and chain, and we present our research into improving the operation and robustness of said bridge via liquidity forecasting of its associated vaults. Users utilize the bridge to transfer assets from a source network and source layer (a source vault) to a destination network and layer (a destination vault). However, if a vault is near depletion when a transfer is initiated the transfer can fail due to inefficient liquidity. Thus, critical to maximizing the user experience of Mosaic is being able to forecast accurately when the liquidity will be depleted from vaults supported by Mosaic. After introducing Mosaic, and without loss of generality, we forecast liquidity movements between three vaults: one on the layer 1 (L1) Ethereum mainnet, one on the L2 Polygon (POL) network, and one on the L2 Arbitrum (ARB) network. We demonstrate state of the art results from our statistical and AI methods that can consistently achieve low average errors (e.g., <2% liquidity) and high coverage (e.g., >80%) of the true liquidity levels in their 1-week-ahead forecast confidence intervals. As a baseline, we first develop autoregressive integrated moving average (ARIMA) models for forecasting. The ARIMA models are optimized at each time step and provide conservative confidence intervals that allow us to safely assess the times when vaults will reach critical liquidity values and therefore, prevent depletion. To further enhance our forecasting framework, we also develop a machine learning approach with Gaussian Processes that can further capture time series trends, past replenishment events, where large discontinuities are present. Liquidity data for our forecasting methodologies will be provided in two levels of fidelity. We first introduce our in-house Liquidity Simulation Environment (LSE) - a state machine capable of running hypothetical vault liquidity scenarios under certain assumed cross-layer trades/swaps. Next, we forecast real-world data obtained from a recent PoC three-vault bridge setup run by Composable, and we apply the LSE to that. Finally, we provide our thoughts on forecasting beyond the PoC - our work will be of interest to layer 1, layer 2, cross-layer cross-chain, and AI researchers working on DeFi and blockchain technologies.
Dynamic Appearance Augmentation Method that Enables Easy Prototyping of Masks for Performance

In this study, we propose a dynamic facial expression augmentation method that enables easy prototyping of masks for performance. The proposed method uses thermochromic ink, i.e., ink that changes color in response to change in temperature. We also propose a mechanism that allows the wearer to perceive the change in the color of the part of the mask drawn with thermochromic ink, by using the residual heat of the thermochromic ink. During performance, the wearer can check whether the mask is working without any trouble and without looking in a mirror and can therefore concentrate on the performance. We assume that a typical use of the system is in a theatrical performance such as a school play, where the performers make masks by hand in order to reproduce the characters in the story. The method is evaluated from four perspectives (cost, support for performance, diversity of expression, and change perception).
Modeling optimization of stencil computations via domain-level properties

Stencil computations are widely used in the scientific simulation domain, and their performance is critical to the overall efficiency of many large-scale numerical applications. Many optimization techniques, most of them varying strategies of tiling and parallelization, exist to systematically enhance the efficiency of stencil computations. However, the effectiveness of these optimizations vary significantly depending on the wide range of properties demonstrated by the different stencils. This paper studies several well-known optimization strategies for stencils and presents a new approach to effectively guide the composition of these optimizations, by modeling their interactions with four domain-level properties of stencils: spatial dimensionality, temporal order, order of accuracy, and directional dependence. When using our prediction model to guide optimizations for five real-world stencil applications, we were able to identify optimization strategies that outperformed two highly optimized stencil libraries by an average of 2.4x.
Removal of stigma: impact on property values

This paper determines the impact, stigma has on property values and how long the stigma remains after the Not in My Back Yard (NIMBY) structure has been removed. A quantitative analysis was undertaken, using a high voltage overhead transmission line (HVOTL) case study, to determine the effect on property values prior and post removal of the NIMBY structure. A repeat sales index in conjunction with the regression analysis determined the length of time; the stigma remained after removal of the NIMBY structure. The results show that while the NIMBY is in place the impact on value is confined to those properties in close proximity. This is in contradiction to the findings, where on removal of the NIMBY the property values of the whole neighbourhood improves with the stigma remaining for 3 to 4 years.
Mild-Intensity UV-A Radiation Applied Over a Long Duration Can Improve the Growth and Phenolic Contents of Sweet Basil

UV-A radiation (320–400 nm) is an abiotic stressor that may be used to enhance the production of beneficial secondary metabolites in crops such as leafy vegetables. However, tradeoffs between enhanced phytochemical contents and overall growth/yield reductions have been reported. The responses varied depending on the UV-A intensity, spectral peak, exposure time, species, and varieties. We quantified the changes in growth, morphology, photosynthesis, and phenolic contents of sweet basil grown under a base red/blue/green LED light with four supplemental UV-A intensity treatments (0, 10, 20, and 30 W·m−2) in an indoor environment over 14 days. The objective was to determine whether UV-A radiation could be utilized to improve both yield and quality of high-value sweet basil in a controlled production environment. Biomass harvested at 14 days after treatment (DAT) was highest under mild-intensity UV-A treatment of 10 W·m−2 and lowest under high-intensity UV-A treatment of 30 W·m−2. The total leaf area and the number of leaves were significantly lower under the 30 W·m−2 treatment than under the 10 and 20 W·m−2 treatments at 14 DAT. The maximum quantum efficiency of photosystem II (PSII) for photochemistry (Fv/Fm) showed a gradual decrease under the 20 and 30 W·m−2 treatments from 3 to 14 DAT, whereas Fv/Fm remained relatively constant under the 0 and 10 W·m−2 treatments over the entire 14 days. The leaf net photosynthesis rate showed a significant decrease of 17.4% in the 30 W·m−2 treatment compared to that in the 10 W·m−2 treatment at 14 DAT. Phenolic contents (PAL enzyme activity, total phenolic concentration, and antioxidant capacity) were the highest under the 20 W·m−2 treatment, followed by the 10, 30, and 0 W·m−2 treatments. Overall, our results indicate that the biomass production and accumulation of beneficial phenolic compounds in sweet basil varied depending on the intensity and duration of UV-A application. Mild UV-A radiation (10–20 W·m−2) can be a beneficial stressor to improve sweet basil yield and quality over relatively long-term cultivation.
Relationships Between Memory Impairments and Hippocampal Structure in Patients With Subcortical Ischemic Vascular Disease

Background and Purpose Patients with subcortical ischemic vascular disease (SIVD) suffer from memory disorders that are thought to be associated with the hippocampus. We aimed to explore changes in hippocampal subfields and the relationship between different hippocampal subfield volumes and different types of memory dysfunction in SIVD patients. Methods A total of 77 SIVD patients with cognitive impairment (SIVD-CI, n = 39) or normal cognition (HC-SIVD, n = 38) and 41 matched healthy controls (HCs) were included in this study. Memory function was measured in all subjects, and structural magnetic resonance imaging (MRI) was performed. Then, the hippocampus was segmented and measured by FreeSurfer 6.0 software. One-way ANOVA was used to compare the volume of hippocampal subfields among the three groups while controlling for age, sex, education and intracranial volume (ICV). Then, post hoc tests were used to evaluate differences between each pair of groups. Finally, correlations between significantly different hippocampal subfield volumes and memory scores were tested in SIVD patients. Results Almost all hippocampal subfields were significantly different among the three groups except for the bilateral hippocampal fissure (p = 0.366, p = 0.086, respectively.) and left parasubiculum (p = 0.166). Furthermore, the SIVD-CI patients showed smaller volumes in the right subiculum (p < 0.001), CA1 (p = 0.002), presubiculum (p = 0.002) and molecular layer of the hippocampus (p = 0.017) than the HC-SIVD patients. In addition, right subiculum volumes were positively related to Rey’s Auditory Verbal Learning Test (RAVLT) word recognition (r = 0.230, p = 0.050), reverse digit span test (R-DST) (r = 0.326, p = 0.005) and Rey–Osterrieth Complex Figure Test (ROCF) immediate recall (r = 0.247, p = 0.035) scores, right CA1 volumes were positively correlated with RAVLT word recognition (r = 0.261, p = 0.026), and right presubiculum volumes showed positive relationships with R-DST (r = 0.254, p = 0.030) and ROCF immediate recall (r = 0.242, p = 0.039) scores. Conclusion SIVD might lead to general reductions in volume in multiple hippocampal subfields. However, SIVD-CI patients showed atrophy in specific subfields, which might be associated with memory deficits.
Genomic Architecture and Evolution of the Cellulose synthase Gene Superfamily as Revealed by Phylogenomic Analysis

The Cellulose synthase superfamily synthesizes cellulose and different hemicellulosic polysaccharides in plant cell walls. While much has been discovered about the evolution and function of these genes, their genomic architecture and relationship with gene (sub-)functionalization and evolution remains unclear. By using 242 genomes covering plant evolution from green algae to eudicots, we performed a large-scale analysis of synteny, phylogenetic, and functional data of the CesA superfamily. Results revealed considerable gene copy number variation across species and gene families, and also two patterns – singletons vs. tandem arrays – in chromosomic gene arrangement. Synteny analysis revealed exceptional conservation of gene architecture across species, but also lineage-specific patterns across gene (sub-)families. Synteny patterns correlated with gene sub-functionalization into primary and secondary CesAs and distinct CslD functional isoforms. Furthermore, a genomic context shift of a group of cotton secondary CesAs was associated with peculiar properties of cotton fiber synthesis. Finally, phylogenetics suggested that primary CesA sequences appeared before the secondary CesAs, while phylogenomic analyses unveiled the genomic trace of the CslD duplication that initiated the CslF family. Our results describe in detail the genomic architecture of the CesA superfamily in plants, highlighting its crucial relevance for gene diversification and sub-functionalization, and for understanding their evolution.
The Moral Foundations of Desired Cultural Tightness

People vary on their desire for strict norms, and the moral underpinnings of these differences have yet to be explored. The current research examined whether and how moral beliefs held by individuals would affect the extent to which they want their country to be tight (i.e., having strict social norms) or loose (i.e., having more permissive social norms). In particular, the effects of the “binding” and “individualizing” foundations, which are moral beliefs focused on the importance of groups and individuals, respectively, were examined. We hypothesized that the binding foundations could predict people’s desire for cultural tightness. We also hypothesized that the perception that one’s society is threatened may drive this effect. Three studies were conducted using both cross-sectional (Studies 1 and 3) and two-wave (Study 2) designs. Demographic variables and participants’ political orientation effects were controlled. In Study 1, only the binding foundations significantly predicted higher desired tightness. In Study 2, binding foundations predicted desired tightness measured at follow-up. In Study 3, the positive effect of perceived threat on desired tightness via the binding foundations was confirmed. From additional within-paper analyses we also have some evidence of significant relationships, albeit unstable across studies, between desired tightness and individualizing foundations.
A Public Data Set With Ground Reaction Forces of Human Balance in Individuals With Parkinson's Disease

Citation: de Oliveira CEN, Ribeiro de Souza C, Treza RdC, Hondo SM, Los Angeles E, Bernardo C, Shida TKF, dos Santos de Oliveira L, Novaes TM, de Campos DdSF, Gisoldi E, Carvalho MdJ and Coelho DB (2022) A Public Data Set With Ground Reaction Forces of Human Balance in Individuals With Parkinson’s Disease. Front. Neurosci. 16:865882. doi: 10.3389/fnins.2022. 865882 A Public Data Set With Ground Reaction Forces of Human Balance in Individuals With Parkinson’s Disease
Moving Beyond DNA Sequence to Improve Plant Stress Responses

Plants offer a habitat for a range of interactions to occur among different stress factors. Epigenetics has become the most promising functional genomics tool, with huge potential for improving plant adaptation to biotic and abiotic stresses. Advances in plant molecular biology have dramatically changed our understanding of the molecular mechanisms that control these interactions, and plant epigenetics has attracted great interest in this context. Accumulating literature substantiates the crucial role of epigenetics in the diversity of plant responses that can be harnessed to accelerate the progress of crop improvement. However, harnessing epigenetics to its full potential will require a thorough understanding of the epigenetic modifications and assessing the functional relevance of these variants. The modern technologies of profiling and engineering plants at genome-wide scale provide new horizons to elucidate how epigenetic modifications occur in plants in response to stress conditions. This review summarizes recent progress on understanding the epigenetic regulation of plant stress responses, methods to detect genome-wide epigenetic modifications, and disentangling their contributions to plant phenotypes from other sources of variations. Key epigenetic mechanisms underlying stress memory are highlighted. Linking plant response with the patterns of epigenetic variations would help devise breeding strategies for improving crop performance under stressed scenarios.
Near Infrared Spectroscopy for Poor Grade Aneurysmal Subarachnoid Hemorrhage—A Concise Review

Delayed cerebral ischemia (DCI) disproportionately affects poor grade aneurysmal subarachnoid hemorrhage (aSAH) patients. An unreliable neurological exam and the lack of appropriate monitoring leads to unrecognized DCI, which in turn is associated with severe long-term deficits and higher mortality. Near Infrared Spectroscopy (NIRS) offers simple, continuous, real time, non-invasive cerebral monitoring. It provides regional cerebral oxygen saturation (c-rSO2), which reflects the balance between cerebral oxygen consumption and supply. Reports have demonstrated a good correlation with other cerebral oxygen and blood flow monitoring, and credible cerebrovascular reactivity indices were also derived from NIRS signals. Multiple critical c-rSO2 values have been reported in aSAH patients, based on various thresholds, duration, variation from baseline or cerebrovascular reactivity indices. Some were associated with vasospasm, some with DCI and others with clinical outcomes. However, the poor grade aSAH population has not been specifically studied and no randomized clinical trial has been published. The available literature does not support a specific NIRS-based intervention threshold to guide diagnostic or treatment in aSAH patients. We review herein the fundamental basic concepts behind NIRS technology, relationship of c-rSO2 to other brain monitoring values and their potential clinical interpretation. We follow with a critical evaluation of the use of NIRS in the aSAH population, more specifically its ability to diagnose vasospasm, to predict DCI and its association to outcome. In summary, NIRS might offer significant potential for poor grade aSAH in the future. However, current evidence does not support its use in clinical decision-making, and proper technology evaluation is required.