Miracle-dz/UniFAR-FTUs huggingface dataset:
Inland Water Body Surface Height Retrievals Using CYGNSS Delay Doppler Maps

CYGNSS satellites record the power of GNSS reflection signals in delay and Doppler shift bins. Myriad delay Doppler maps (DDMs) have been collected since 2016. Research has shown that reflection range delays can be derived from CYGNSS DDMs and their metadata for satellite altimetry. However, the performance of these DDMs in estimating inland water levels has not been thoroughly evaluated. This study leverages the coherent reflection-dominated DDMs from 2020 to 2022 to estimate water levels at five lakes. Compared to radar altimetric (RA) observations, the CYGNSS results have an overall bias of ~2.0 m, a root mean square difference (RMSD) of ~3.1 m, and an unbiased RMSD (ubRMSD) of ~2.4 m, when combining measurements from all study sites over the three-year period. During 2020–2021, the bias is lower at 1.1 m, the RMSD is ~1.9 m, and the ubRMSD is reduced to ~1.6 m. Smaller bias, RMSD, and ubRMSD values are also observed in 2022 nighttime data. However, the daytime data from 2022 show higher water level estimations compared to RA observations with a bias of 4.7 m, an RMSD of 6.1 m, and an ubRMSD of ~3.9 m. The CYGNSS overestimations are linked to an increase in ionospheric total electron content (TEC). This study demonstrates the feasibility of spaceborne GNSS-R for measuring inland water levels and highlights the usefulness of the current version of CYGNSS DDMs in monitoring reservoirs and inundation events characterized by substantial water level changes.
Image schemas in the Great Gatsby: A cognitive linguistic analysis of the protagonist’s psychological movement

Abstract Most research on image schema examined the meaning configuration of words connotation. However, previous studies of adjectives are meaningful in cognitive linguistics because they provide insight into how those adjectives are involved with psychological movement. In this sense, from the perspective of cognitive linguistics, one’s conceptualization and cognition are closely associated with their bodily experience and surroundings; adjectives are no exception. The varieties of transformations of image schemas lay the foundation for the conception and perception. Accordingly, this study is an investigation into the perception of adjectives and psychological activities associated with image schema. Data in this study come from adjectives in the novel The Great Gatsby, which were tagged and calculated with the help of software Python and the NLTK package. The results demonstrate that a significant amount of adjectives were used in the novel. Moreover, Nick’s psychological activities changed significantly from the SOURCE to the DESTINATION schematic stage, which is uncovered by his expression of adjectives. Finally, the results further suggest that transformations of image schema as BALANCE, CONTAINER, and PATH work together to motivate one’s linguistic expression of adjectives. The present findings have several implications for research on lexicons and literacy interpretation from cognitive linguistics.
Detection of characteristic eye points in non-ideal light conditions

Detection of characteristic eye points under challenging light conditions is a non-trivial task. Difficulty of this task increase even more when low resolution images are used. This article introduces an adaptive solution for detection of characteristic eye points in mentioned conditions. First, light normalization algorithm is performed. Then, face and eye detection followed by determination of eye status (namely: open or closed) is done. Finally, an adaptive method for pupil and eye corners detection is developed by comparing some existing methods. Experimental results show the outperformance of the proposed method.
Panoramic Radiography vs. CBCT in the Evaluation of the Maxillary Third Molar Roots

Background and Objectives: A comprehensive understanding of the position of third molar roots and adjacent structures, such as the maxillary sinus (MS), is essential for safe extractions. Diagnostic imaging plays a fundamental role in achieving accurate treatment planning. This study aimed to compare panoramic radiography (PR) and cone-beam computed tomography (CBCT) for the evaluation of maxillary third molar roots and their relationship with the MS. Materials and Methods: Two trained radiologists evaluated third molar images. The number of roots, morphology (fused/conical, divergent, dilacerated, or atypical), and their relationship with the MS in PR and CBCT were registered. Descriptive and inferential statistics were performed using the weighted Kappa test. Results: Regarding the number and morphology of the roots, Kappa values showed moderate (κ = 0.42) and fair agreement (κ = 0.38), respectively. Regarding the proximity with the MS, most of the roots showed close contact (30.6%), or 1/3 of root superimposition (35%), in PR evaluation, while in CBCT, the third molars were in contact with the MS floor (32%), and with alveolar domes (27.2%). Conclusions: PR is a moderately reliable image technique to identify the number of roots and root morphology of maxillary third molars. PR, however, does not provide any radiographic signs that clearly indicate the anatomical relationship between the maxillary third molar roots and the maxillary sinus detected in CBCT images.
Unlocking microalgal host—exploring dark-growing microalgae transformation for sustainable high-value phytochemical production

Microalgae have emerged as a promising, next-generation sustainable resource with versatile applications, particularly as expression platforms and green cell factories. They possess the ability to overcome the limitations of terrestrial plants, such non-arable land, water scarcity, time-intensive growth, and seasonal changes. However, the heterologous expression of interested genes in microalgae under heterotrophic cultivation (dark mode) remains a niche area within the field of engineering technologies. In this study, the green microalga, Chlorella sorokiniana AARL G015 was chosen as a potential candidate due to its remarkable capacity for rapid growth in complete darkness, its ability to utilize diverse carbon sources, and its potential for wastewater treatment in a circular bioeconomy model. The aims of this study were to advance microalgal genetic engineering via dark cultivation, thereby positioning the strain as promising dark-host for expressing heterologous genes to produce high-value phytochemicals and ingredients for food and feed. To facilitate comprehensive screening based on resistance, eleven common antibiotics were tested under heterotrophic condition. As the most effective selectable markers for this strain, G418, hygromycin, and streptomycin exhibited growth inhibition rates of 98%, 93%, and 92%, respectively, ensuring robust long-term transgenic growth. Successful transformation was achieved through microalgal cell cocultivation with Agrobacterium under complete darkness verified through the expression of green fluorescence protein and β-glucuronidase. In summary, this study pioneers an alternative dark-host microalgal platform, using, Chlorella, under dark mode, presenting an easy protocol for heterologous gene transformation for microalgal host, devoid of the need for expensive equipment and light for industrial production. Furthermore, the developed genetic transformation methodology presents a sustainable way for production of high-value nutrients, dietary supplements, nutraceuticals, proteins and pharmaceuticals using heterotrophic microalgae as an innovative host system.
Associations of loneliness with mental health and with social and physical activity among university students in Germany: results of the COVID-19 German student well-being study (C19 GSWS)

Introduction University students are at high risk for loneliness with a potential negative impact on health. The COVID-19 measures disrupted students’ academic routine and social life, which might have affected their perception of loneliness. This study investigated the prevalence of perceived loneliness among university students in Germany during the COVID-19 pandemic and its associations with mental health, behavioral outcomes, and sociodemographic characteristics. Methods COVID-19 German student well-being study (C19 GSWS) collected data from five German universities from October 27th to November 14th, 2021, resulting in a sample of 7,203 respondents. Associations of loneliness with depressive symptoms, anxiety, social and physical activity, as well as sociodemographic characteristics, were analyzed using multivariable logistic regressions. Results A total of 20.6% of students reported loneliness. Students with depressive or anxiety symptoms had more than eight- or sixfold odds, respectively, for reporting loneliness (depressive symptoms: OR = 8.29; 95% CI: 7.21–9.52; anxiety: OR = 6.48; 95% CI: 5.65–7.43) compared with students who did not report any symptoms. Students who were less physically active were more likely to experience loneliness compared with students who were more physically active (no moderate physical activity: OR = 1.39; 95% CI: 1.21–1.59; no vigorous physical activity: OR = 1.19; 95% CI: 1.04–1.36). We found no association between loneliness and social activity. However, loneliness was associated with being single (OR = 2.93; 95% CI: 2.55–3.36), living alone (OR = 1.31; 95% CI: 1.13–1.52), or having a temporary residency status in Germany (OR = 2.24; 95% CI: 1.65–3.04). Conclusion Our findings highlight the importance of loneliness as a relevant factor associated with health. Further research is needed to determine potential protective factors to tackle loneliness and to investigate how study conditions at higher education institutions may affect students’ perceived loneliness.
The response of an Al-10B4C coupled neutron detector based on PIPS technology to Cf-252

After decades of discussions, it has been firmly established that detectors made of silicon-based semiconductor materials can be effectively used for neutron detection by simply coating them with suitable substances. The incident thermal neutrons interact with the coating neutron-sensitive materials such as 10B and 6LiF, resulting in the production of secondary charged particles which can be effectively detected in the sequencial silicon substrate. In this article, the detector system was designed with a coupled neutron detector structure which combined a silicon detector with a 10B4C film in various forms. The 10B4C layer was deposited on the substract with electron beam evaporation method. Two kinds of structrue were discussed: (1) one was the direct contact neutron detector by depositing 10B4C directly onto the front surface of silicon-based detectors; (2) the other was the coupled neutron detectors by depositing 10B4C onto substrates made from different materials such as Al and glass which then coupled with silicon-based detectors. The responses of these neutron detectors to neutrons (Cf-252) were measured individually. It's showen that the detection capability of direct contact neutron detectors was lower than the coupled neutron detectors. For the coupled detectors, the detector by depositing 10B4C on the aluminum substrate was found to be superior than that by depositing 10B4C on the glass substrate.
‘Are we equal citizens?’: a critical discourse analysis (CDA) of language textbooks and minority faith learners’ insights in Pakistan

Language textbooks are discourse-led documents that contain official knowledge and construct specific learner subjectivities. Official knowledge embodies a worldview that serves the interests of dominant social group. In Pakistan, studies suggest that textbooks in general tend to distort the historical facts in favour of the national narrative. Using six language textbooks (Sindhi, Urdu, and English) taught at government-run schools in Sindh province of Pakistan, this study aims to understand how language is used to construct “Muslim identity”. Moreover, the study also considers minority faith language learners’ perspectives in order to understand their experiences and reactions. The analysis is informed by Fairclough’s (2001, 2003) writings on critical discourse analysis (CDA). The findings reveal that Muslim identity is constructed through a variety of discourses related to Islam. Furthermore, minority faith language learners perceive language learning spaces to be ideologically motivated, promoting a dominant religious identity narrative while disregarding diversity. The study recommends that the process of textbooks design should consider minority faiths to a considerable extent in the interest of inclusive and a sustainable society. The study has implications for wider global contexts, including Asia Pacific where language textbooks promote biases based on religion, gender, ethnicity, and language.
Multi-target Localization Based on an Improved FOCUSS Algorithm with Missing Information

The situation where the observation stations or the sensors cannot fully detect the targets is called missing information, which makes it difficult to locate targets. Due to its sparsity, Compressed Sensing (CS) can be applied to multi-target localization with missing information. An improved Focal Undetermined System Solver (IFOCUSS) algorithm is proposed, by dynamically updating the regularized factor that has a significant impact on the convergence of iteration processes. What’s more, a re-weighting method is adopted for the weighting matrix to improve convergence property and accelerate convergence, and a new method of choosing correct items from a sparse estimation vector is proposed to improve the positioning accuracy. Several simulations corroborate its notable reconstruction performance, which is illustrated by correctly estimating five targets with only 11 sensors on a moving station, achieving better convergence at lower signal-to-noise ratios (such as 12 dB) with about half the run time of Regularized FOCUSS (RFOCUSS) and being able to estimate two targets even with missing information up to 60%. Under the same conditions of missing information, IFOCUSS can estimate more targets with smaller positioning errors than RFOCUSS, achieving better convergence performance by eliminating almost all interference terms.
D ISCOVERY AND SNPS ANALYSES OF POPULATIONS OF JUNIPERUS MARITIMA IN THE OLYMPIC PENINSULA, A PLEISTOCENE REFUGIUM?

Trees from two populations of Juniperus commonly identified as J. scopulorum growing on the northeast side of the Olympic mountains were analyzed by SNPs from nrDNA and petN-psbM (cpDNA) and compared with J. scopulorum (UT), J. virginiana (TN) and J. maritima from Puget Sound. The trees of the Olympic Mountains were found to be J. maritima. The habitat of these newly investigated populations of J. maritima are atypical in that they occupy high elevation sites (600 - 1700 m), whereas most previous J. maritima sites are seaside. The Deer Park population (1700 m) plants are Krumholz due to the high winds and snowpack conditions. The Cordilleran ice sheet is mapped and the Olympic peninsula is proposed as the site of a Pleistocene refugium for J. maritima. Phytologia 92(1): 68-81 (April, 2010).
Can IR Images of the Water Surface Be Used to Quantify the Energy Spectrum and the Turbulent Kinetic Energy Dissipation Rate?

Near-surface oceanic turbulence plays an important role in the exchange of mass, momentum, and energy between the atmosphere and the ocean. The climate modifying the air–sea CO2 transfer rate varies linearly with the surface turbulent kinetic energy dissipation rate to the 1/4 power in a range of systems with different types of forcing, such as coastal oceans, river estuaries, large tidal freshwater rivers, and oceans. In the first part of this paper, we present a numerical study of the near-surface turbulent kinetic energy spectra deduced from a direct numerical simulation (DNS) compared to turbulent kinetic energy spectra deduced from idealized infrared (IR) images. The DNS temperature fields served as a surrogate for IR images from which we have calculated the underlying kinetic energy spectra. Despite the near-surface flow region being highly anisotropic, we demonstrated that modeled isotropic and homogeneous turbulence spectra can serve as an approximation to observed near-surface spectra within the inertial and dissipation ranges. The second part of this paper validates our numerical observations in a laboratory experiment. In this experiment, we compared the turbulent kinetic energy spectra near the surface, as measured using a submerged shear sensor with the spectra derived from infrared images collected from above the surface. The energy dissipation measured by the shear sensor was found to be within 20% of the dissipation value derived from the IR images. Numerically and experimentally, we have demonstrated that IR-based and remote measurement techniques of the aquatic near surface offer a potentially accurate and non-invasive way to measure near-surface turbulence, which is needed by the community to improve models of oceanic air–sea heat, momentum, and gas fluxes.
A Global Evaluation of Biodiversity Literacy in Zoo and Aquarium Visitors

The United Nations Strategic Plan for Biodiversity 2011–2020 is a key initiative within global efforts to halt and eventually reverse the loss of biodiversity. The very first target of this plan states that ‘by 2020, at the latest, people are aware of the values of biodiversity and the steps they can take to conserve and use it sustainably’. Zoos and aquariums worldwide, attracting more than 700 million visits every year, could potentially make a positive contribution to this target. However, a global evaluation of the educational impacts of visits to zoos and aquariums is entirely lacking in the existing literature. To address this gap, we conducted a large -scale impact evaluation study, using a pre - and post-visit repeated -measures survey design, to evaluate biodiversity literacy – biodiversity understanding and knowledge of actions to help protect biodiversity – in zoo and aquarium visitors. Our findings are based on the largest and most international study of zoo and aquarium visitors ever conducted worldwide; in total, more than 6,000 visitors to 30 zoos and aquariums around the globe participated in the study. The study’s main finding is that aggregate biodiversity understanding and knowledge of actions to help protect biodiversity both significantly increased over the course of zoo and aquarium visits. There was an increase from pre-visit (69.8%) to post‑visit (75.1%) in respondents demonstrating at least some positive evidence of biodiversity understanding. Similarly, there was an increase from pre‑visit (50.5%) to post‑visit (58.8%) in respondents that could identify a pro-biodiversity action that could be achieved at an individual level. This study provides the most compelling evidence to date that zoo and aquarium visits can contribute to increasing the number of people who understand biodiversity and know actions they can take to help protect biodiversity.
Climate modeling through radiative‐convective models

We present a review of the radiative-convective models that have been used in studies pertaining to the earth's climate. After familiarizing the reader with the theoretical background, modeling methodology, and techniques for solving the radiative transfer equation the review focuses on the published model studies concerning global climate and global climate change. Radiative-convective models compute the globally and seasonally averaged surface and atmospheric temperatures. The computed temperatures are in good agreement with the observed temperatures. The models include the important climatic feedback mechanism between surface temperature and H2O amount in the atmosphere. The principal weakness of the current models is their inability to simulate the feedback mechanism between surface temperature and cloud cover. It is shown that the value of the critical lapse rate adopted in radiative-convective models for convective adjustment is significantly larger than the observed globally averaged tropospheric lapse rate. The review also summarizes radiative-convective model results for the sensitivity of surface temperature to perturbations in (1) the concentrations of the major and minor optically active trace constituents, (2) aerosols, and (3) cloud amount. A simple analytical model is presented to demonstrate how the surface temperature in a radiative-convective model responds to perturbations.
Adaptive Fixed-Time Optimal Formation Control for Uncertain Nonlinear Multiagent Systems Using Reinforcement Learning

This article explores the application of reinforcement learning (RL) strategy to achieve an adaptive fixed-time (FxT) optimized formation control of uncertain nonlinear multiagent systems. The primary obstacle in this process is the difficulty in attaining FxT stability under the actor-critic setting due to intermediate estimation errors and generic system uncertainties. To overcome these challenges, the RL control algorithm is implemented using an identifier-actor-critic structure, where the identifier is utilized to address the system uncertainty involving unknown nonlinear dynamics and external disturbances. Furthermore, a novel quadratic function is introduced to establish the boundedness of the estimation error of the actor-critic learning law, which plays a pivotal role in the FxT stability analysis. Finally, a unified FxT optimized formation control strategy is developed, which guarantees the realization of the predetermined formation at a fixed time while optimizing the given performance measure. The effectiveness of the proposed control algorithm is verified through simulation of a team of marine surface vessels.
Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning

Background Prognostic prediction and the identification of prognostic factors are critical during the early period of atrial-fibrillation (AF)-related strokes as AF is associated with poor outcomes in stroke patients. Methods Two independent datasets, namely, the Korean Atrial Fibrillation Evaluation Registry in Ischemic Stroke Patients (K-ATTENTION) and the Korea University Stroke Registry (KUSR), were used for internal and external validation, respectively. These datasets include common variables such as demographic, laboratory, and imaging findings during early hospitalization. Outcomes were unfavorable functional status with modified Rankin scores of 3 or higher and mortality at 3 months. We developed two machine learning models, namely, a tree-based model and a multi-layer perceptron (MLP), along with a baseline logistic regression model. The area under the receiver operating characteristic curve (AUROC) was used as the outcome metric. The Shapley additive explanation (SHAP) method was used to evaluate the contributions of variables. Results Machine learning models outperformed logistic regression in predicting both outcomes. For 3-month unfavorable outcomes, MLP exhibited significantly higher AUROC values of 0.890 and 0.859 in internal and external validation sets, respectively, than those of logistic regression. For 3-month mortality, both machine learning models exhibited significantly higher AUROC values than the logistic regression for internal validation but not for external validation. The most significant predictor for both outcomes was the initial National Institute of Health and Stroke Scale. Conclusion The explainable machine learning model can reliably predict short-term outcomes and identify high-risk patients with AF-related strokes.