Miracle-dz/UniFAR-FTUs huggingface dataset:
Integration of Deep Learning‐Based Inversion and Upscaled Mass‐Transfer Model for DNAPL Mass‐Discharge Estimation and Uncertainty Assessment

The challenges posed by high‐resolution characterization of dense nonaqueous phase liquid (DNAPL) source zone architecture (SZA) have motivated the development of simpler upscaled models that rely on domain‐averaged metrics to capture the average mass discharge downstream of source zones (SZ). However, SZA is highly irregular, making estimation of these domain‐averaged metrics from sparse borehole data extremely difficult. Poor estimation of SZ metrics means that upscaled models cannot reproduce the multistage effluent concentrations. Bayesian inversion methods can be used to obtain accurate estimates of SZ metrics and their uncertainties from sparse data, and from there, upscaled models can better reproduce multistage effluent concentrations. This work presents a framework for integrating a deep‐learning‐based 3D SZA inversion method named Convolutional Variational AutoEncoder—Ensemble Smoother with Multiple Data Assimilation (CVAE‐ESMDA) with a process‐based (PB) upscaled mass‐transfer model. This framework can utilize sparse SZ data to estimate directly mass discharge without multiphase modeling. First, CVAE‐ESMDA estimates the SZA by conditioning on sparse data, which is then used as input in the upscaled model for mass‐discharge estimation. We evaluated our framework on two real and 30 synthetic bench‐scale experiments, with significantly different SZAs and multistage effluent concentrations. The results demonstrate that the CVAE‐based inversion method captures the temporal variations in SZ metrics better than standard ordinary kriging. With the improved SZ metrics, the PB model more accurately reproduces the salient patterns of the multistage mass‐discharge profiles and associated uncertainty. This approach can be used to provide valuable input for risk‐based decision making in remediation applications.
Metabolomic Aspects of Conservative and Resistance-Related Elements of Response to Fusarium culmorum in the Grass Family

Abstract Background: Fusarium head blight (FHB) is a serious fungal disease affecting crop plants, causing substantial yield reductions and the production of mycotoxins in the infected grains. Achieving progress in the breeding of crops with increased resistance and maintaining a high yield is not possible without a thorough examination of the molecular basis of plant immunity responses. Methods: LC-MS-based metabolomics approaches powered by three-way ANOVA and the selec-tion of differentially accumulated metabolites (DAMs) were used for studying plant immunity. A correlation network and functional enrichment analysis were conducted on grains of barley and wheat genotypes that were resistant or susceptible to FHB, as well as on the model grass Brachypodium distachyon (Bd), as this is still poorly understood at the metabolomic level. Results: We selected common and genotype-specific DAMs in response to F. culmorum inoculation. The immunological reaction at the metabolomic level was strongly diversified between resistant and susceptible genotypes. DAMs that were common to all tested species from the porphyrin, flavonoid, and phenylpropanoid metabolic pathways were highly correlated, reflecting con-servativeness in the FHB response in the Poaceae family. Resistance-related DAMs belonged to different structural classes, including tryptophan-derived metabolites, pyrimidines, the amino acids proline and serine, as well as phenylpropanoids and flavonoids. The physiological re-sponse to F. culmorum of Bd was close to that of barley and wheat genotypes; however, metabo-lomic changes were strongly diversified. Conclusions: Combined targeted and untargeted metabolomics provides comprehensive knowledge about significant elements of plant immuni-ty that have the potential to be molecular biomarkers of enhanced resistance to FHB in the grass family. Thorough examination of the Bd metabolome in juxtaposition with diversified geno-types of barley and wheat facilitated its use as a model grass for plant–microbe interaction.
On the three-space-problem and the lifting of bounded sets,

We exhibit a general method to show that for several classes of Frechet spaces the Three-space-problem fails. This method works for instance for the class of distinguished Frechet spaces, for Frechet spaces with the density condition and also for dual Frechet spaces (which gives a negative answer to a question of D. Vogt). An example of a Banach space, which is not a dual Banach space but the strong dual of a DF-space, shows that there are two really different possibilities of defining the notion of a dual Frechet space. If in a Three-spaceproblem the corresponding quotient map is assumed to lift bounded sets, we obtain partial positive answers. Finally, we give this property of lifting bounded sets a special treatment.
Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

Abstract Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.
Current induced magnetization switching: quasi-static and dynamics

Summary form only given. The direct effect of the current on magnetization reversal is studied with different protocols on various electrodeposited magnetic nanowires of controlled morphology (diameter about 60 nm) (J.-E. Wegrowe et al, J. Appl. Phys. vol. 89, p. 7127, 2001). Three protocols were used: DC current injection, sub-microsecond pulse, and after-effect measurements with nanosecond resolution. The effect of the current in terms of barrier height is quantified (P. Guittienne, IEEE Trans. Mag. vol. 37, p. 2126, 2001). The three protocols are applied to two different structures: uniform Ni nanowires (6000 nm long) and two layer pseudo-spin valves with CPP geometry of Co(30 nm)/Cu(10 nm)/Co(10 nm).
Emergency Remote Teaching in Response to the COVID-19 Outbreak: Pedagogical Adjustments of Community College ESL Lecturers in Hong Kong

The COVID-19 pandemic has prompted the education sector to explore dramatically different teaching modes. This article is a study of emergency remote teaching (ERT) in a community college in Hong Kong. It investigates the challenges that a cohort of 50 ESL lecturers encountered and the strategic responses they adopted in relation to ERT. Documents such as the college newsletter, post-teaching reports and meeting records were used to analyze the language teachers’ context of adjustment, and one-on-one interviews were conducted with four participants from the cohort to explore their firsthand experiences. The analysis revealed the variety of challenges that the participants faced, including the development of technological skills, the preparation of online materials, the implementation of online assessment and marking, as well as interaction with students. In response to these challenges, the participants adopted diverse strategies to achieve pedagogical development in facilitating their teaching practices with students in synchronic online teaching. This article argues that language educators need to develop technological and online interactional competencies so that new pedagogical activities can be developed to enhance students’ learning. In addition, training sessions should be provided to support their pedagogical adaptation and development of new skills. Accordingly, it concludes that adjustment requires both individual and institutional investments.
The Mediator Role of Attitudes in Fish Choice Behavior: A Turkish Market Survey

Due to the dynamic nature of demand, it is becoming increasingly important for the fish industry to investigate the changing choice behaviors of consumers in the face of increasing demand. This research investigated the role of attitudes and socio-demographic characteristics, which are the main factors in the fish choice behavior of consumers and in fish consumption behavior. In this context, an ordered probit model was constructed to analyze the effect of attitudes and socio-demographic characteristics as independent variables on fish consumption and purchase intention as the dependent variables. In addition, descriptive statistics were also used to reveal the current preferences related to fish. The data required for the model and descriptive statistics were obtained from 421 participants using a cross-sectional consumer survey covering the main cities of the seven regions of Turkey. The results show that while consumers prefer fish more than red meat and less than poultry, they mostly buy fresh fish from fish markets. Moreover, taste, physical appearance, convenience, wild fish, and seller trust attitudes have a significant and positive relationship with the dependent variable (the frequency of fish purchase and consumption) and price has a negative and significant relationship. Moreover, an increase in education level has a positive and significant relationship with the frequency of fish consumption. The research results provide important suggestions and information for decision-makers in the fish industry to implement effective policies and meet the consumer expectations of producers and distributors in the fish industry. In addition, the current study provides guidance for future research.
Energy Performance Certificate Classes Rating Methods Tested with Data: How Does the Application of Minimum Energy Performance Standards to Worst-Performing Buildings Affect Renovation Rates, Costs, Emissions, Energy Consumption?

Energy renovations of the building stock are a paramount objective of the European Union (EU) to combat climate change. A tool for renovation progress monitoring is energy performance certificate (EPC) labelling. The present study tested the effect of different EPC label classifications on a national database, which comprises ~25,000 EPC values from apartment buildings, detached houses, office buildings, and educational, commercial, and service buildings. Analysing the EPC classes labelling resulting from four different EU methods, we estimated the annual renovation rates, costs, energy savings, and CO2 emissions reduction that would affect the national building stock if each of them was adopted, to fulfil the European Climate Target Plan by the year 2033. The ISO 52003-1:2017 two-point and one-point methods determined a very uneven distribution of renovation rates, from 0.45% to ~9%. Conversely, the Directive 15% recently proposed in COM/2021/802 with uniform rates determined smaller differences and standard deviation, not pushing renovations above 3.70%, namely a rate that once fine-tuned can stimulate realistic, yet effective renovation campaigns. The major differences in renovation rates provided by the studied methods show the need for a harmonized strategy such as the Directive proposal to enable achievement of European targets.
Noise-Robust Multimodal Audio-Visual Speech Recognition System for Speech-Based Interaction Applications

Speech is a commonly used interaction-recognition technique in edutainment-based systems and is a key technology for smooth educational learning and user–system interaction. However, its application to real environments is limited owing to the various noise disruptions in real environments. In this study, an audio and visual information-based multimode interaction system is proposed that enables virtual aquarium systems that use speech to interact to be robust to ambient noise. For audio-based speech recognition, a list of words recognized by a speech API is expressed as word vectors using a pretrained model. Meanwhile, vision-based speech recognition uses a composite end-to-end deep neural network. Subsequently, the vectors derived from the API and vision are classified after concatenation. The signal-to-noise ratio of the proposed system was determined based on data from four types of noise environments. Furthermore, it was tested for accuracy and efficiency against existing single-mode strategies for extracting visual features and audio speech recognition. Its average recognition rate was 91.42% when only speech was used, and improved by 6.7% to 98.12% when audio and visual information were combined. This method can be helpful in various real-world settings where speech recognition is regularly utilized, such as cafés, museums, music halls, and kiosks.
Optimised dry processing of protein concentrates from Australian pulses: A comparative study of faba bean, yellow pea and red lentil seed material

Milling and air classification settings were optimised for production of protein concentrates from Australian faba bean, yellow pea and red lentil seed material. Pulses were milled to flour of three progressively finer particle size distributions (D50 of 23–25, 16–18 and 13–14 μm) and air classified at classifier wheel speeds of 7080, 9600 and 10,200 rpm. Maximum protein concentration was reported for pulse flours (D50 of 13–14 μm) at 9600 rpm. Protein concentrations of 61.4, 58.1 and 61.0 g/100 g (db.), reflecting a fold increase in protein content of 1.9, 2.3 and 2.1, were reported for faba bean, yellow pea and red lentil, respectively. Protein, ash, fat and total dietary fibre contents were significantly higher in fine fractions (p < 0.05), compared with coarse fractions, resulting in protein concentrates with enhanced nutritional properties. Amino acid score (AAS) of protein concentrates highlighted deficiencies in sulphur‐containing amino acids, methionine and cysteine (MET + CYS), and tryptophan. Based on the lowest AAS (MET + CYS), protein concentrates were ranked highest for yellow pea (0.75), followed by faba bean (0.58) and red lentil (0.51). Phytochemical analysis demonstrated that bioactive constituents also co‐concentrated with protein (fine fraction), potentially leading to protein concentrates with enhanced health benefits. Shelf‐life assessment for the original flours and protein concentrates indicated the onset of rancidity after 3 months of storage. As fat content co‐concentrated with protein, the rancidity (%) scores were higher for protein concentrates compared with the original flours. This demonstrates the importance of developing effective treatments, suitable for dry processing, which can extend shelf‐life and stability of protein concentrate ingredients for domestic and export markets. The objective of this study was to increase the knowledge available on dry processing of protein concentrates from Australian pulses. The information generated from this study will look to inform future commercial scale processing operations.
Assessing Factors of Digital Entrepreneurial Intention among Young Generation

This study aims to identify the factors that influence youngsters’ intention to be involved in digital entrepreneurship. The study also examines the relationships between entrepreneurship education, personal attitude, and digital marketing literacy toward the digital entrepreneurial intention. A total of 151 respondents among students taking entrepreneurship subjects were collected and data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 27.0. Descriptive, correlation, and multiple regression analyses were used to analyze the data. Based on the stepwise method used, it was found that only personal attitude has a significant relationship with digital entrepreneurship intention among students. The personal attitude seems to have a strong relationship with digital entrepreneurial intention. This variable also was the best predictor variable in predicting digital entrepreneurial intention while educational support and digital marketing literacy were rejected. The finding is important to policymakers to formulate suitable policies to be flexible with the nature of young generations to be an entrepreneur. Based on the findings, the current curriculum and prospectus designed in the higher learning institution are beneficial and improve self-confidence and self-efficacy throughout the students’ life experiences. Hence, the Ministry of Higher Education can provide more grants and incentives to the universities to involve students in improving their digital entrepreneurial skills. In the future, the researchers might consider different genders and diversity of ethnicities in the model and add some other variables that affect digital entrepreneurial intention among the young generation such as personality factors, social factors, and risk-taking propensity.
An Exploration of Women Owned Home-based Business Through Institutional Theory Lens: A Case of Peshawar, Pakistan

This paper aims to explore the dynamics of women owned home-based businesses in the normative context of Peshawar. It helps to add to our understanding of how women create and manage their ventures and get legitimacy in a context where they live like second-class citizen with no rights. The study also highlights the impact of these ventures on their lives and household. A qualitative approach was used to explore the creation and management of women owned home-based businesses in the context of Peshawar. 20 women entrepreneurs were selected through purposive sampling technique. In-depth interviews were conducted to understand the lived experiences. Data was analyzed using thematic analysis. We have built on institutional theory to understand the creation and management of women owned home-based enterprises, which brought about unique insights into this ever-prevailing phenomenon. The findings of this study reveal that given the distinctive context of Peshawar, the role responsibilities of women are shaped through multiple socio-cultural and religious interpretations. Compliance to which, defines the grounds of negotiation for adopting an entrepreneurial career. This makes the venture creation a bounded phenomenon and highly dependent on the familial approval for which norms and values within normative context are negotiated. With familial approval, a home based women entrepreneur (HBWE) ensures her belongingness to the family mores. A legitimacy of HBWE is contingent upon the trust of the family members as an entrepreneur and home maker. At this level, she represents as role model by sharing control which enhances the cultural acceptability of home-based businesses (HBBs). This study has portrayed transition of HBWE through compliance, negotiating, belongingness, legitimacy, emancipation and empowerment.
TW HYA ASSOCIATION MEMBERSHIP AND NEW WISE-DETECTED CIRCUMSTELLAR DISKS

We assess the current membership of the nearby, young TW Hydrae association and examine newly proposed members with the Wide-field Infrared Survey Explorer (WISE) to search for infrared excess indicative of circumstellar disks. Newly proposed members TWA 30A, TWA 30B, TWA 31, and TWA 32 all show excess emission at 12 and 22 μm providing clear evidence for substantial dusty circumstellar disks around these low-mass, ∼8 Myr old stars that were previously shown to likely be accreting circumstellar material. TWA 30B shows large amounts of self-extinction, likely due to an edge-on disk geometry. We also confirm previously reported circumstellar disks with WISE and determine a 22 μm excess fraction of 42+10− 9% based on our results.
MAIN-SEQUENCE EFFECTIVE TEMPERATURES FROM A REVISED MASS–LUMINOSITY RELATION BASED ON ACCURATE PROPERTIES

The mass–luminosity ( M − L ? > ), mass–radius ( M − R ? > ), and mass–effective temperature ( M − T eff ? > ) diagrams for a subset of galactic nearby main-sequence stars with masses and radii accurate to ≤ 3 % ? > and luminosities accurate to ≤ 30 % ? > (268 stars) has led to a putative discovery. Four distinct mass domains have been identified, which we have tentatively associated with low, intermediate, high, and very high mass main-sequence stars, but which nevertheless are clearly separated by three distinct break points at 1.05, 2.4, and 7 M ⊙ ? > within the studied mass range of 0.38–32 M ⊙ ? > . Further, a revised mass–luminosity relation (MLR) is found based on linear fits for each of the mass domains identified. The revised, mass-domain based MLRs, which are classical ( L ∝ M &agr; ? > ), are shown to be preferable to a single linear, quadratic, or cubic equation representing an alternative MLR. Stellar radius evolution within the main sequence for stars with M > 1 M ⊙ ? > is clearly evident on the M − R ? > diagram, but it is not clear on the M − T eff ? > diagram based on published temperatures. Effective temperatures can be calculated directly using the well known Stephan–Boltzmann law by employing the accurately known values of M and R with the newly defined MLRs. With the calculated temperatures, stellar temperature evolution within the main sequence for stars with M > 1 M ⊙ ? > is clearly visible on the M − T eff ? > diagram. Our study asserts that it is now possible to compute the effective temperature of a main-sequence star with an accuracy of ∼6%, as long as its observed radius error is adequately small ( < 1 % ? > ) and its observed mass error is reasonably small ( < 6 % ? > ).
$\mathcal{L}$-classes on pseudomanifolds with one singular stratum

We study the index theorem and Chern character of an admissible pseudomanifold X y with one singular stratum. Under a condition on the link, we give a de Rham type realization of the Goresky-MacPherson-Siegel L -classes on X y in terms of curvature forms and eta invariant of the link.