Miracle-dz/UniFAR-FTUs huggingface dataset:
Set-Theoretic and Type-Theoretic Ordinals Coincide

In constructive set theory, an ordinal is a hereditarily transitive set. In homotopy type theory (HoTT), an ordinal is a type with a transitive, wellfounded, and extensional binary relation. We show that the two definitions are equivalent if we use (the HoTT refinement of) Aczel’s interpretation of constructive set theory into type theory. Following this, we generalize the notion of a type-theoretic ordinal to capture all sets in Aczel’s interpretation rather than only the ordinals. This leads to a natural class of ordered structures which contains the type-theoretic ordinals and realizes the higher inductive interpretation of set theory. All our results are formalized in Agda.
Early Stages of Galilean Moon Formation in a Water-depleted Environment

A key feature of the Galilean satellite system is its monotonic decrease in bulk density with growing distance from Jupiter, indicating an ice mass fraction that is zero in the innermost moon Io and about half in the outer moons Ganymede and Callisto. Jupiter-formation models, and perhaps the Juno spacecraft water measurements, are consistent with the possibility that the Jovian system may have formed, at least partly, from ice-poor material. And yet, models of the formation of the Galilean satellites usually assume abundant water ice in the system. Here, we investigate the possibility that the Jovian circumplanetary disk was populated with ice-depleted chondritic minerals, including phyllosilicates. We show that the dehydration of such particles and the outward diffusion of the released water vapor allow condensation of significant amounts of ice in the formation region of Ganymede and Callisto in the Jovian circumplanetary disk. Our model predicts that Europa, Ganymede, and Callisto should have accreted little, if any, volatiles other than water ice, in contrast to the comet-like composition of Saturn’s moon Enceladus. This mechanism allows for the presence of ice-rich moons in water-depleted formation environments around exoplanets as well.
Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement

We commonly use agreement measures to assess the utility of judgements made by human annotators in Natural Language Processing (NLP) tasks. While inter-annotator agreement is frequently used as an indication of label reliability by measuring consistency between annotators, we argue for the additional use of intra-annotator agreement to measure label stability over time. However, in a systematic review, we find that the latter is rarely reported in this field. Calculating these measures can act as important quality control and provide insights into why annotators disagree. We propose exploratory annotation experiments to investigate the relationships between these measures and perceptions of subjectivity and ambiguity in text items.
Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy

Precise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereoelectroencephalography recordings of patients’ seizures. These key parameters together with their personalized model determine a given patient’s EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean, 0.028) than in non–seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy. Description Combining brain imaging data and machine learning workflows enables personalized brain models for potential patient management in epilepsy. Pinpointing the source of seizures Delineating the neural regions responsible for drug-resistant epilepsy is critical for the best surgery outcomes. Wang et al. present a digital workflow for mapping individual epileptogenic zone networks based on patient magnetic resonance imaging and stereoelectroencephalography recordings. The authors developed the model using simulated data, and then validate on a retrospective cohort of 53 patients with drug-resistant focal epilepsy. In a series of case studies, personalized models were used to retroactively estimate post-surgical patient outcomes that were then compared to actual surgical outcomes, demonstrating the potential clinical relevance of this toolset. —CAC
Host odour alpha-pinene increases or reduces response of Ips avulsus (Coleoptera: Curculionidae: Scolytinae) to its aggregation pheromone, depending on separation of release points

Abstract Monoterpenes in the resin of host trees are important host-location and -selection signals for conifer-infesting bark beetles and can increase or reduce responses to aggregation pheromones. Research on this interaction has been largely limited to collocated release points of the semiochemicals; however, sources are often separated in nature. I performed a trapping experiment to investigate whether distance between semiochemical release points influences how host monoterpene alpha-pinene affects Ips avulsus (Eichhoff) (Coleoptera: Curculionidae) response to its aggregation pheromone (ipsdienol and lanierone). High release (8 g/day) of alpha-pinene from a pheromone-baited trap caused a reduction in catches, but the same release of alpha-pinene from 4 m away significantly increased catches relative to pheromone alone. A low release (0.06 g/day) of alpha-pinene with either arrangement had no effect. Hence, a host odour released at rates sufficient to deter I. avulsus responses to its pheromone may nevertheless enhance attraction if the release point is sufficiently displaced from the pheromone source. The result suggests that, for I. avulsus, the semiochemical alpha-pinene may influence host finding and close-range assessment of host suitability in different ways. Operational lure formulations for some species of bark beetle might be improved by separating pheromone and host odour components or by adding a displaced host odour device.
Semiparametric Mixtures of Generalized Exponential Families

Abstract. A semiparametric mixture model is characterized by a non‐parametric mixing distribution 𝒬 (with respect to a parameter θ) and a structural parameter β common to all components. Much of the literature on mixture models has focused on fixing β and estimating 𝒬. However, this can lead to inconsistent estimation of both 𝒬 and the order of the model m. Creating a framework for consistent estimation remains an open problem and is the focus of this article. We formulate a class of generalized exponential family (GEF) models and establish sufficient conditions for the identifiability of finite mixtures formed from a GEF along with sufficient conditions for a nesting structure. Finite identifiability and nesting structure lead to the central result that semiparametric maximum likelihood estimation of 𝒬 and β fails. However, consistent estimation is possible if we restrict the class of mixing distributions and employ an information‐theoretic approach. This article provides a foundation for inference in semiparametric mixture models, in which GEFs and their structural properties play an instrumental role.
Outcomes of the Sequential Treatment of Unresectable Hepatocellular Carcinoma Using Lenvatinib

Background/Aim: The chemotherapeutic landscape for hepatocellular carcinomas (HCCs) has changed dramatically with the availability of several treatment options. This study aimed to assess the long-term outcomes of lenvatinib treatment and analyze its feasibility in the sequential treatment of HCCs. Patients and Methods: Eighty-five consecutive patients who received lenvatinib for unresectable HCCs were investigated retrospectively. Survival was assessed based on when the patients were first radiologically diagnosed with progressive disease. Among those with radiologically diagnosed stable or progressive disease at 3 months after lenvatinib administration, the cutoff α-fetoprotein (AFP) ratio (ratio of the AFP level after lenvatinib treatment to the pretreatment AFP level) that was predictive of survival was determined using receiver operating characteristic analysis. Results: The median survival time (MST) was significantly worse among patients diagnosed with progressive disease at 1 month after treatment than among those diagnosed at 2-3 or 3-4 months after treatment [MSTs at 1, 2-3, and 3-4 months: 2.2, 10.2, and 17.3 months, respectively (p<0.001)]. An AFP ratio of 1.36 (computed using the AFP level at 3 months after lenvatinib treatment) was significantly predictive of survival in patients with stable or progressive disease (26.3 vs. 11.3 months, p=0.0024). Conclusion: The prognosis of patients on lenvatinib who develop early progressive disease is dismal. Thus, their treatment should be ceased or switched. The 3-month AFP ratio of 1.36 may be a potentially useful cutoff for considering a switch to other treatments in patients radiologically diagnosed with stable or progressive disease.
Research on the Influence of Corporate Strategy on Tax Avoidance

This paper uses Chinese A-share listed companies from 2011 to 2020 as a research sample to explore the relationship between corporate strategy and tax avoidance. The research finds that the more radical the corporate strategy is, the higher the degree of tax avoidance is; Economic policy uncertainty will weaken the relationship between corporate strategy and tax avoidance. Economic policy uncertainty has a negative regulatory effect on the relationship between corporate strategy and tax avoidance. The results of this paper enlighten: on the one hand, the more radical the corporate strategy is, the more regulators should strengthen their tax supervision, which is conducive to the establishment of modern fiscal and financial system; On the other hand, in order to maintain the high-quality development of enterprise economy, policy makers should maintain the relative stability and continuity of economic policies.
Research on Consumer Satisfaction of Community Group Purchasing Platform Based on Entropy Method

The recurring outbreak of COVID-19 has witnessed community group purchasing return to the mainstay of national consumption. The paper conducted a questionnaire survey on consumer groups with experience in community group purchasing to explore the influencing factors of customer satisfaction in developing community group purchasing. The evaluation index system was determined based on the CCSI model, and the entropy weighting method was used to determine the index weights. Multidimensional regression analysis was used to verify the influencing factors of consumer satisfaction and their magnitude. The results show that expected satisfaction, perceived value, perceived quality, and platform image all have a positive influence on consumer satisfaction, and there is a meaningful relationship between consumer satisfaction and consumer loyalty. Multidimensional enhancement of consumer satisfaction can drive the sustainable development of community group purchasing.
Lean Service and BPM to Increase the Efficiency of an Operational Process in the Insurance Sector

One of the pillars of the world economy is the service sector, since in developed and developing countries it contributes 70% and 45% of GDP, respectively. For this reason, companies in this sector seek to innovate their processes, become more efficient and competitive. In this sense, Lean Service and BPM stand out for generating efficiency and competitiveness in Back Office processes, through a focus on optimizing the value of processes, eliminating activities and reducing waste. In this case study, the aim is to increase the efficiency of the process of issuing electronic receipts in a Peruvian Banking and Insurance company, through the implementation of Lean and BPM tools such as: electronic 5s, digital Poka Yoke, load balancing and BPMN. Our proposal model includes the aforementioned Lean Service and BPM tools, which together achieve the reduction of rework, reduction of time in operation and reduction of overtime, which translates into increased process efficiency. Although more and more companies are applying Lean tools in the Services sector, there are still few precedents for their application in the Banking and Insurance sector. In this sense, the novelty of our proposal is the application of a model that combines Lean and BPM tools, such as Poka Yoke and electronic 5s in a digital work environment, obtaining favorable results. For this reason, this research will contribute to the scientific community a new model to increase efficiency in companies in the insurance sector in Latin America.
ACTIVE CONTROL OF FLEXIBLE STRUCTURES USING A MINIMUM NUMBER OF COMPONENTS

Abstract This work deals with the active control of lightly damped flexible structures subject to any disturbance and using a minimum of information, treatment and active components. The theoretical, computational and experimental aspects are examined. An L.Q. R. (Linear Quadratic Regulator) method is used for computing the control by using a discrete state form of the actuator and structure mechanical model. Finite element modelling and classical modal reduction in the mathematical basis of the controlled modes are used for the state model. The mechanical design is optimized to minimize energy consumption and increase control efficiency. The modal reduction takes the possibilities of the numerical controller used into account, and the resulting effects on control and observance are studied. Numerical and experimental applications are achieved with a pin-connected–free beam supported by its actuator. The control and observance parameters are established in accordance with real-time performances so as to reduce spillover. Lastly, the system studied is detailed and the experimental and computed results are compared.
Influence of relative density distribution rules on the mechanical compression responses of additive manufactured Ti6Al4V graded lattice structures

Abstract To reveal the mapping mechanism between relative density distribution rules and compression responses of Graded lattice structures (GLSs), this work formed a series of lattice structures by establishing a theoretical model and adjusting distribution functions. To quantitatively describe the gradient variation rule, the degree of gradient (Dg ) was introduced. Combined with quasi-static compression experiment and numerical simulation, compression responses of lattice structures were analyzed. The results demonstrated that relative density distribution rules could adjust the compression responses and achieved the transformation of the failure mode, and Dg was negatively related to the mechanical properties and the energy absorption capacity. Therefore, using relative density distribution rules can predict the overall performance and provide a reference for the novel graded lattice structural design. Graphical Abstract
Ordinal Association in Contingency Tables: Some Interpretive Aspects

Abstract Two families of models for ordered contingency tables—Goodman's association models and canonical correlation models—are investigated and compared with respect to the interpretation of their parameters. We show that the two families of models actually refer to different kinds of ordinal association: stochastic order extremity for correlation models and stochastic order entropy for association models. This difference is related to the way the two models scale interaction. The scale difference is proven to be of substantial consequence, especially under strong association.
Three-dimensional imaging and quantification of mouse ovarian follicles via optical coherence tomography

Ovarian tissue cryopreservation has been successfully applied worldwide for fertility preservation. Correctly selecting the ovarian tissue with high follicle loading for freezing and reimplantation increases the likelihood of restoring ovarian function, but it is a challenging process. In this work, we explore the use of three-dimensional spectral-domain optical coherence tomography (SD-OCT) to identify different follicular stages, especially primary follicles, compare the identifications with H&E images, and measure the size and age-related follicular density distribution differences in mice ovaries. We use the thickness of the layers of granulosa cells to differentiate primordial and primary follicles from secondary follicles. The measured dimensions and age-related follicular distribution agree well with histological images and physiological aging. Finally, we apply attenuation coefficient map analyses to significantly improve the image contrast and the contrast-to-noise ratio (p < 0.001), facilitating follicle identification and quantification. We conclude that SD-OCT is a promising method to noninvasively evaluate ovarian follicles.
Using inbreeding to test the contribution of non-additive genetic effects to additive genetic variance: a case study in Drosophila serrata

Additive genetic variance, VA, is the key parameter for predicting adaptive and neutral phenotypic evolution. Changes in demography (e.g., increased close-relative inbreeding) can alter VA, but how depends on the, typically unknown, gene action and allele frequencies across many loci. For example, VA increases proportionally with the inbreeding coefficient when allelic effects are additive, but larger (or smaller) increases can occur when allele frequencies are unequal at causal loci with dominance effects. Here, we describe an experimental approach to assess the potential for rare, recessive alleles to inflate VA under inbreeding. Applying a powerful paired pedigree design in Drosophila serrata, we measured 11 wing traits on half-sibling families bred via either random or sibling mating, differing only in homozygosity (not allele frequency). Despite close inbreeding and substantial power to detect small VA, we detected no deviation from the expected additive effect of inbreeding on genetic (co)variances. Our results suggest the average dominance coefficient is very small relative to the additive effect, or that allele frequencies are relatively equal at loci affecting wing traits. We outline the further opportunities for this paired pedigree approach to reveal the characteristics of VA, providing insight into historical selection and future evolutionary potential.
In Silico Analysis of SARS-CoV-2 Spike Proteins of Different Field Variants

Background Coronaviruses belong to the group of RNA family of viruses which trigger diseases in birds, humans, and mammals, which can cause respiratory tract infections. The COVID-19 pandemic has badly affected every part of the world, and the situation in the world is getting worse with the emergence of novel variants. Our study aims to explore the genome of SARS-,CoV2 followed by in silico analysis of its proteins. Methods Different nucleotide and protein variants of SARS-Cov2 were retrieved from NCBI. Contigs & consensus sequences were developed to identify variations in these variants by using SnapGene. Data of variants that significantly differ from each other was run through Predict Protein software to understand changes produced in protein structure The SOPMA web server was used to predict the secondary structure of proteins. Tertiary structure details of selected proteins were analyzed using the online web server SWISS-MODEL. Findings Sequencing results shows numerous single nucleotide polymorphisms in surface glycoprotein, nucleocapsid, ORF1a, and ORF1ab polyprotein. While envelope, membrane, ORF3a, ORF6, ORF7a, ORF8, and ORF10 genes have no or few SNPs. Contigs were mto identifyn of variations in Alpha & Delta Variant of SARs-CoV-2 with reference strain (Wuhan). The secondary structures of SARs-CoV-2 proteins were predicted by using sopma software & were further compared with reference strain of SARS-CoV-2 (Wuhan) proteins. The tertiary structure details of only spike proteins were analyzed through the SWISS-MODEL and Ramachandran plot. By Swiss-model, a comparison of the tertiary structure model of SARS-COV-2 spike protein of Alpha & Delta Variant was made with reference strain (Wuhan). Alpha & Delta Variant of SARs-CoV-2 isolates submitted in GISAID from Pakistan with changes in structural and nonstructural proteins were compared with reference strain & 3D structure mapping of spike glycoprotein and mutations in amino acid were seen. Conclusion The surprising increased rate of SARS-CoV-2 transmission has forced numerous countries to impose a total lockdown due to an unusual occurrence. In this research, we employed in silico computational tools to analyze SARS-CoV-2 genomes worldwide to detect vital variations in structural proteins and dynamic changes in all SARS-CoV-2 proteins, mainly spike proteins, produced due to many mutations. Our analysis revealed substantial differences in functional, immunological, physicochemical, & structural variations in SARS-CoV-2 isolates. However real impact of these SNPs can only be determined further by experiments. Our results can aid in vivo and in vitro experiments in the future.
The bulk stress in a suspension of spherical particles of condensed phase in its slightly rarefied vapour gas

The rheological behaviour of a dilute suspension of spherical particles of condensed phase dispersed in its own slightly rarefied vapour gas is investigated on the basis of suspension theory (Batchelor 1970) and generalized slip-flow theory for a two-phase system of a gas and its condensed phase derived from the Boltzmann equation. The rarefaction of the gas and the phase-change process at the interfaces of the particles have the effect of reducing the Einstein coefficient of ϕ, volume fraction, in the expression for the effective viscosity in the suspension. In the case in which the pure rarefaction effect alone enters the problem, the coefficient is $\frac{5}{2}(1-2.702\,K)$, where K is the Knudsen number, a rarefaction parameter defined by K = l/L, l and L being respectively the mean free path of gas molecules and the radius of a spherical particle. When both the rarefaction and the phase-change process are taken into account, this becomes $\frac{5}{2} (1-3.533\,K)$. These modifications are not small, even at ordinary pressures, when the size of the particles is of the order of microns.