Miracle-dz/UniFAR-FTUs huggingface dataset:
Social justice reasoning when students learn about social issues using multiple texts

ABSTRACT In this paper, I apply the Multiple Documents Text-Based Relevance Assessment and Content Extraction (MD-TRACE) model, to describe the types of cognitive processes that students engage to critically reason about social issues, as they are portrayed through mass media. In addition to examining such processes, I further consider the extent to which these are reflective of social justice reasoning, or students’ critical reasoning about social issues in ways that recognize and analyze inequities in society. Three studies are introduced to provide empirical examples of how cognitive processes, identified in the MD-TRACE, may function within the context of students’ reasoning about mass media. The processes examined include selection (Study 1), processing (Study 2), and integration (Study 3). Study 1 examines the types of perspectives that students propose seeking out in association with various social issues and the extent to which these perspectives may belong to marginalized groups (i.e., selection). Study 2 examines students’ critical reasoning about or abilities to critique two deliberately constructed texts during processing. Study 3 investigates students’ specific abilities to identify and critique narrative frames, or common reporting tropes, introduced across texts (i.e., integration). Together, these three studies serve as exemplars of students’ engagement in reasoning about mass media and social justice reasoning. They suggest that social justice reasoning involves, in part, students’ engagement in perspective taking, their application of prior knowledge to contextualize information in texts, and their recognition of common narrative frames across texts and the values that these uphold. Additional social justice reasoning strategies are suggested and directions for future research proposed.
Recommending tasks based on search queries and missions

Web search is an experience that naturally lends itself to recommendations, including query suggestions and related entities. In this article, we propose to recommend specific tasks to users, based on their search queries, such as planning a holiday trip or organizing a party. Specifically, we introduce the problem of query-based task recommendation and develop methods that combine well-established term-based ranking techniques with continuous semantic representations, including sentence representations from several transformer-based models. Using a purpose-built test collection, we find that our method is able to significantly outperform a strong text-based baseline. Further, we extend our approach to using a set of queries that all share the same underlying task, referred to as search mission, as input. The study is rounded off with a detailed feature and query analysis.
Distributed Power Control Algorithm for Cognitive Radio Networks Based on Geometric Programming

In order to improve spectrum efficiency in Cognitive Radio Networks (CRNs), a standard distributed optimal power control strategy with high Signal to Interference Plus Noise Ratio (SINR) based on maximization of data rate for each Second User (SU) under two constraints (maximum transmit power of each SU at all subcarriers and maximum tolerated interference power for Primary Users (PUs) at each subcarrier) is proposed in Orthogonal Frequency-division Multiplexing (OFDM) framework. Considering nonconvexity of our objective function, Geometric Programming (GP) is introduced to transform it into a convex optimization problem. Using Lagrange relaxation of the coupling constraints method and subgradient iterative algorithm in a distributed way, this dual problem is solved. To enhance the Quality of Service (QoS) requirements for SUs at each subcarrier, an improved algorithm is also presented. Numerical simulation results show that both the standard algorithm and the improved algorithm are all superior to the traditional IWFA in data rate of each SU, convergence speed, and computational complexity.
A multi-criteria decision-making model dealing with correlation among criteria for reservoir flood control operation

Flood control operation in a multi-reservoir system is a multi-criteria decision-making (MCDM) problem, in which the considered criteria are often correlated with each other. In this paper, we propose an MCDM model for reservoir flood control operation to deal with correlation among criteria. Considering the flood control safety of reservoirs and downstream protected regions, we establish the hierarchical structure of the criterion system. We use the principal component analysis method to eliminate the correlation, and transform the original criterion system into an independent comprehensive criterion system. The comprehensive decision matrix coupled with the weight vector obtained by the improved entropy weight method serves as the input to TOPSIS method, fuzzy optimum method, and fuzzy matter-element method, by which we determine the ranking order of the alternatives. We apply the proposed model to a cascade system of reservoirs at the Daduhe River basin in China. The results show that the dimensionality of the criterion system is reduced and the correlation among criteria is eliminated simultaneously, and the ranking order of the alternatives is reasonable. The proposed model provides an effective way to deal with correlation among criteria, and can be extended to wider applications in many other MCDM problems.
Gonad Ontogeny and Sex Differentiation in a Poeciliid, Gambusia holbrooki: Transition from a Bi- to a Mono-Lobed Organ

Simple Summary This study maps critical events of gonadogenesis in the live-bearing fish Gambusia holbrooki. Significantly, it provides the first evidence for primary gonochorism, the transition of an embryonically bi-lobed gonad to a mono-lobed organ, as occurs in adults and the dimorphically programmed onset of sex differentiation in this species. The outcomes are significant in basic biology and are applicable for developing control options for this notorious pest fish. Abstract Despite their uniqueness, the ontogeny and differentiation of the single-lobed gonads in the poeciliids are very poorly understood. To address this, we employed both cellular and molecular approaches to systematically map the development of the testes and ovary in Gambusia holbrooki from pre-parturition to adulthood, encompassing well over 19 developmental stages. The results show that putative gonads form prior to the completion of somitogenesis in this species, a comparatively early occurrence among teleosts. Remarkably, the species recapitulates the typical bi-lobed origin of the gonads during early development that later undergoes steric metamorphosis to form a single-lobed organ. Thereafter, the germ cells undergo mitotic proliferation in a sex-dependent manner before the acquisition of the sexual phenotype. The differentiation of the ovary preceded that of the testes, which occurred before parturition, where the genetic females developed meiotic primary oocytes stage I, indicating ovarian differentiation. However, genetic males showed gonial stem cells in nests with slow mitotic proliferation at the same developmental stage. Indeed, the first signs of male differentiation were obvious only post-parturition. The expression pattern of the gonadosoma markers foxl2, cyp19a1a, amh and dmrt1 in pre- and post-natal developmental stages were consistent with morphological changes in early gonad; they were activated during embryogenesis, followed by the onset of gonad formation, and a sex-dimorphic expression pattern concurrent with sex differentiation of the ovary (foxl2, cyp19a1a) and testes (amh and dmrt1). In conclusion, this study documents for the first time the underlying events of gonad formation in G. holbrooki and shows that this occurs relatively earlier than those previously described for ovi- and viviparous fish species, which may contribute to its reproductive and invasive prowess.
Asymptotics of Wiener Functionals and Applications to Mathematical Finance

In this thesis we study asymptotic expansions for option pricing with emphasis on small noise “singular perturbations” which are, as we shall see, better suited than the more popular small time asymptotics to approximate typical stochastic volatility models. In particular, we argue that analytic solutions are unlikely for more advanced models, and therefore numerical methods of calculation are required. The following are the main results of the thesis. We show that zeroth order implied volatility is given by the large deviation rate function under minimal assumptions. We then show a small noise sample path large deviations principle for a class of two dimensional positive diffusions of relevance to finance. We numerically calculate the large deviations rate function for an example process, Gatheral’s Double CEV model, and highlight the speed and accuracy of the approximation. We then investigate Yoshida-Watanabe asymptotic expansions and develop a Mathematica program to derive them automatically. Lastly, we develop a small noise asymptotic expansion for marginal densities of solutions of SDEs (joint work). Using this we determine the large strike implied volatility for the Stein-Stein model and the Schöbel and Zhu model by rescaling into a small noise problem.
Langlands parameters of derived functor modules and Vogan diagrams

Let $G$ be a linear reductive Lie group with finite center, let $K$ be a maximal compact subgroup, and assume that $\mathrm{rank } G = \mathrm {rank } K$. Let $\ g= l\oplus u$ be a $\theta$ stable parabolic subalgebra obtained by building $l$ from a subset of the compact simple roots and form $A_g(\lambda)$. Suppose $\Lambda=\lambda+2\delta( u\cap p)$ is $K$-dominant and the infinitesimal character, $\lambda+\delta$, of $A_{g}(\lambda)$ is nondominant due to a noncompact simple root. By interpreting these conditions on the level of Vogan diagrams, a conjecture by Knapp is (essentially) settled for the groups $G=SU(p,q),\, Sp(p,q)$, and $SO^*(2n)$, thereby determining the Langlands parameters of natural irreducible subquotient of $A_{ g}(\lambda)$. For the remaining classical groups, simple supplementary conditions are given under which the Langlands parameters may be determined.
A study in the near pressure field of co-axial subsonic jets

An experimental investigation of the near pressure field of unbounded subsonic jets has been performed. The near-field pressure was sampled, using linear and azimuthal arrays, on conical surfaces surrounding free jets generated by (1) a single axisymmetric nozzle, (2) a co-axial short-cowl nozzle, and (3) a co-axial short-cowl nozzle with serrations (the co-axial experiments were performed as part of the EU program, CoJeN (AST3-CT-2003-502790), where velocity and temperature-ratios were varied). The objective of the study is to better understand differences in the structure of the flows in terms of their sound production mechanisms. A model representation of the source mechanism associated with coherentstructures in the flow is considered, using both the pressure fluctuations themselves and the pressure-derivative source term from Curle’s acoustic analogy. A filtering operation is then applied in order to identify the structure of the radiating source field.
An Energy-Efficient Flexible Multi-Modal Wireless Sweat Sensing System Based on Laser Induced Graphene

Real-time sweat monitoring is vital for athletes in order to reflect their physical conditions, quantify their exercise loads, and evaluate their training results. Therefore, a multi-modal sweat sensing system with a patch-relay-host topology was developed, which consisted of a wireless sensor patch, a wireless data relay, and a host controller. The wireless sensor patch can monitor the lactate, glucose, K+, and Na+ concentrations in real-time. The data is forwarded via a wireless data relay through Near Field Communication (NFC) and Bluetooth Low Energy (BLE) technology and it is finally available on the host controller. Meanwhile, existing enzyme sensors in sweat-based wearable sports monitoring systems have limited sensitivities. To improve their sensitivities, this paper proposes a dual enzyme sensing optimization strategy and demonstrates Laser-Induced Graphene (LIG)-based sweat sensors decorated with Single-Walled Carbon Nanotubes (SWCNT). Manufacturing an entire LIG array takes less than one minute and costs about 0.11 yuan in materials, making it suitable for mass production. The in vitro test result showed sensitivities of 0.53 μA/mM and 3.9 μA/mM for lactate and glucose sensing, and 32.5 mV/decade and 33.2 mV/decade for K+ and Na+ sensing, respectively. To demonstrate the ability to characterize personal physical fitness, an ex vivo sweat analysis test was also performed. Overall, the high-sensitivity lactate enzyme sensor based on SWCNT/LIG can meet the requirements of sweat-based wearable sports monitoring systems.
Host Viral Load During Triple Coinfection of SARS-COV 2, Influenza Virus, and Syncytial Virus

The dynamics of the host viral load during triple coinfection between Sars-Cov 2, Influenza A, and the Syncytial virus is studied. To explore how the host cells, the infected cells, and the viral load behave as a function of time, numerical simulations are performed. Via numerical simulations, the differential equations are analyzed by considering initial conditions. Since the numerical simulations are performed by using the physiological parameters, the model system can genuinely help to understand the viral dynamics either in vivo or in vitro. The result obtained in this work depicts that a higher viral load is exhibited when the three viruses simultaneously infect the host cells than in a single infection case. This indicates that simultaneous infection with Sars-Cov 2, Influenza A, and Syncytial virus might be associated with a higher morbidity and mortality rate as these viruses by cooperating with each other facilitate a higher infection rate. Using physiological parameters, we further study the correlation between viral load, susceptible cells, and infected cells. As the three viral species cooperatively infect the host cells, the number of infected cells rises resulting in a higher viral load. Since Influenza A has a higher viral reproduction rate than the two types of virus, it dictates the overall dynamics as it has a competitive advantage. Since these viruses share the same resources, the virus production of a given virus can be affected by the other. The interference of these viruses relies on the initial dose, the order of infection, and the strain of the virus. We show that when Influenza A and Syncytial viruses are initiated a few days after Sars-Cov 2 establishes an infection, while SARS-CoV-2 thrives, and the other two viruses fail to establish an infection. The viruses with an initially higher dose have also a survival advantage. Furthermore, the effect of lymphocytes on the overall viral dynamics is explored via numerical simulations. Since SARS-CoV-2 targets several types of cells and organs, a higher SARS-CoV-2 viral load can be also associated with a higher morbidity and mortality rate.
An Everywhere Continuous Nowhere Differentiable Function

where g(x) = 1 + x for −2 ≤ x ≤ 0, g(x) = 1− x for 0 ≤ x ≤ 2 and g(x) has period 4. The function f(x) is continuous because it is the uniform limit of continuous functions. To show that it is not differentiable, take ∆x = ±2 k , choosing whichever sign makes x and x + ∆x be on the same linear segment of g(2 k x). We have 1. ∆g(2 n x) = 0 for n > k, since g(2 n x) has period 4 · 2 n . 2. |∆g(2 k x)| = 1. 3. |∆ ∑ k−1 n=1 g(2 n x)| ≤ (k−1)max|∆g(2 n x)| ≤ (k−1)2 k−1 2 k < 22 k−1 . Hence |∆f/∆x| ≥ 22 k − 22 k−1 which goes to infinity with k. The proof that the present example has the required property is simpler than that for any other example the author has seen. Weierstrass gave the example F (x) = ∑ ∞
Alleviating Exposure Bias for Neural Machine Translation via Contextual Augmentation and Self Distillation

In neural machine translation (NMT), most sequence-to-sequence (seq2seq) models are trained only with the teacher-forcing paradigm, where the ground truth history is used to predict the next ground truth word. At the inference stage, however, the decoder predicts the next token solely based on history generated from scratch. Both using ground truth history and predicting ground truth words potentially lead to exposure bias. On the one hand, to alleviate the issue of exposure bias caused by using ground truth history, we propose contextual augmentation by allowing substitution, insertion, and deletion of words. The contextual augmentation applies to target sequence to generate non-ground truth and natural history when predicting next words. On the other hand, to alleviate the exposure bias caused by predicting ground truth words, we further apply self distillation to guide the model to carry out optimization according to smoothed prediction distribution, i.e, enable the model to predict not only ground truth words, but also other potentially correct and reasonable words. Experimental results on WMT14 English $\leftrightarrow$ German and IWSLT14 German $\rightarrow$ English translation tasks demonstrate that our approach achieves significant improvements over Transformer on standard benchmarks. Detailed experimental analyses further reveal the effectiveness of our proposed approach in improving translation quality.
Band Reconstruction Using a Modified UNet for Sentinel-2 Images

Multispectral (MS) remote sensing images are of great interest for various applications, yet, quite often, an MS product exhibits one or more noisy bands, strip lines, or even missing bands, which leads to decreased confidence in the information it contains. Meeting this challenge, this article proposes a UNet-based neural network architecture to reconstruct a spectral band. The worst case scenario is considered, that of a missing band, the reconstruction being performed based on the available bands. Besides the comparison with state-of-the-art methods, both the qualitative and quantitative analyses are fulfilled considering several metrics: root-mean-square error, structural similarity index, signal-to-reconstruction error, peak-signal-to-noise ratio, and spectral angle mapper. The experiments focus on Sentinel-2 open data within the Copernicus program. Various patterns of urban areas, agricultural regions, and regions from North Pole or Kyiv, Ukraine are included in our dataset to prove the efficiency of band reconstruction regardless of land-cover diversity.
Size and Profitability of Enterprises in the Czech Republic in Relation to Working Capital Management

: The article is focused on the size and profitability of companies in the Czech Republic in relation to working capital management. The aim of the article is to find out whether and to what extent working capital and working capital management influence the size of companies and the profitability of companies operating in the manufacturing industry and the wholesale and retail sector. The generalized method of moments (GMM method) will be used to determine the goal of the article. Annual data for the period 2008 to 2021 of companies operating according to the CZ-NACE classification in the manufacturing industry and the retail and wholesale trade sectors will be included in the research. Using the GMM method, a statistically significant relationship between working capital management on the one hand and the profitability and size of companies on the other hand was demonstrated. Cash conversion cycle, current assets, short-term liabilities and working capital have an impact on the size and profitability of businesses in the Czech Republic.
A solvency study in non-life insurance: Part 1. Analyses of fire, windstorm, and glass claims

This is the first of two papers which report on a solvency study. The study is based on statistical analyses of policy and claims data of a portfolio of single-family houses and dwellings. This paper deals mainly with analyses of fire, windstorm, and glass liabilities. Claim frequencies and claim size distributions are estimated, and the results are used to derive moments of the annual claim amounts and to provide examples of solvency margin requirements for different classes of business. The second paper is devoted to a broader discussion of solvency margin requirements in non-life insurance.
Analysing the Effects of Different Types of Exercise on Dyspnoea and Fatigue in Adults through COPD-Systematic Review and Meta-Analysis of Randomised Clinical Trials

Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung condition, the main symptoms of which are dyspnoea and fatigue. Though exercise has been recommended for subjects with COPD, its benefits remain unclear. The aim of this study was to summarise, through a systematic review and meta-analysis, the available evidence on the effects of aerobic, resistance, stretching, and combined exercise on the main symptoms of COPD. Methods: Search was performed using the electronic databases PubMed and Web of Science. Randomised controlled trials (RCTs) with interventions based on aerobic, resistance and/or combined exercise published until July 2022 were identified. The effects were summarised based on standardised mean differences (95% confidence intervals) using random and fixed effect models. Results: Eight studies were selected, including a total of 375 subjects. The results obtained showed that resistance exercise, aerobic exercise and combined exercise seem to improve dyspnoea and fatigue symptoms in COPD subjects. Conclusions: In general, we can conclude that exercise-based interventions appear to improve the main COPD symptoms and may benefit quality of life in this population.
Incorporating Translation and Interpreting into the Business Language Classroom

This article presents a research-based proposal on how to integrate translation and interpreting into the business language classroom, focusing on both text-based and interactional approaches to language pedagogy in specialized domains. As a point of departure, the article first reviews the pedagogical possibilities of integrating translation and interpreting into the language classroom more generally. Then, the article specifically advocates for their integration in business language classrooms to support language learning outcomes while also developing what has been termed translation and interpreting literacy. Text-based and interactional approaches to pedagogy are suggested as potential ways the practice of translation and interpreting naturally dovetail with business language education. The article concludes with a call for additional research on these interlingual practices in business language studies to support domain-specific language learning that aligns with the professional realities encountered by students upon graduation.
The Effect of Superficial Oregano Essential Oil Application on the Quality of Modified Atmosphere-Packed Pork Loin

During meat storage, changes in the meat colour occur, making it less intensive and red. The present study was aimed at investigating the effect of oregano EO applied directly on the surface of fresh pork on its quality, with a special emphasis on the colour. In the study, an oregano essential oil in concentrations of 0.5% and 1.0% (v/v) was used on the surface of pork loins (1.5% v/w) packed in a modified atmosphere during 15-d storage at 4 °C. The application of oregano EO in the concentration of 1.0% increased lightness and hue and decreased redness compared to the control, whereas the concentration of 0.5% did not affect the pork colour. EO did not affect pH, free water content, purge and cooking losses, cooked meat juiciness and tenderness; however, it gave the meat a distinctive herbal aroma and taste. The antimicrobial effect of 1% EO was noted only on the 15th day. Therefore, the application of oregano essential oil is not recommended to protect the colour of raw pork nor to prolong its shelf-life; however, it might be used to obtain a new product with a specific herbal aroma and taste, with modifications in water-holding capacity of the meat.