Miracle-dz/UniFAR-FTUs huggingface dataset:
Survival models for data arising from multiphase hazards, latent subgroups or subject to time-dependent treatment effects

Studies of time-to-event outcomes are among the most common in many areas of scientific research, particularly medicine. Ubiquitous in subject-area literature for this research, is the Cox model; a model which assumes that the instantaneous risk of failure is proportional for different groups of people with similar covariate values. The Cox model has become so pervasive in communicating results that the verification of this assumption is rarely mentioned in subject-area literature and alternative methods are even more rarely attempted. Unfortunately, the mechanisms leading to violation of this assumption can often be accounted for with alternative models yielding only slightly more complex interpretation than the standard Cox model. Motivated by a dataset capturing survival following coronary artery bypass graft surgery and another containing longitudinal tree growth and mortality, this thesis will describe, compare/contrast and provide interpretations of several models addressing specific pathologies which lead to violation of the proportional hazards assumption. In these models, the proportional hazards structure will be maintained in part, but be augmented to accommodate specific situations. Interpretations of these augmented proportional-hazards models will be a key element of the comparisons. Three different pathologies will be investigated, including complex (ie: multiphase) hazard functions, latent mixtures of individuals subject to distinct hazards, and effects of covariates which change over time either through a direct erosion of the effect or indirectly through complex longitudinal mechanisms. Additional scientific questions related to inference on duration of different risk phases or latent group membership will be enabled by these models. Related to these questions, a novel iii procedure incorporating covariate information into risk-phase durations will be presented. Further, through connections to the rapidly evolving field of joint modeling of longitudinal and time-to-event data, the utility of joint models to fully characterize the mechanisms underlying an overall, possibly time-dependent treatment effect will be explored. An application of joint models to interval-censored survival data including a novel, recursive, event-time imputation method exploiting the relationship between longitudinal data and the failure mechanism will be described.
The Finite Element Method

A bisulphite addition product of a polyisocyanate and/or polyisothiocyanate prepolymer, said addition product having on an average at least two isocyanate-bisulphite or isothiocyanate-bisulphite adduct groups per molecule, having no free isocyanate or isothiocyanate groups, and being freely water-soluble; said prepolymer being derived from a polyisocyanate and/or polyisothiocyanate and a polyhydroxy compound.
Introduction to Solid Mechanics

1. Fundamental Notions. 2. Stress. 3. Strain. 4. Introduction to Mechanical Properties of Solids. 5. One-Dimensional Problems. 6. Generalized Hooke's Law and Introduction to Energy Methods. 7. Plane Stress. 8. Plane Strain. 9. Failure Criteria. 10. Section Forces in Beams. 11. Stresses in Beams. 12. Deflection of Beams. 13. *Singularity Functions. 14. Torsion. 15. Three Dimensional Stress Properties at a Point. 16. Three-Dimensional Strain Relations at a Point. 17. Introduction to Elastic Stability. 18. * Energy Methods. 19. Introduction to Finite Elements. Appendices. Index.
Artificial Intelligence and Public Human Resource Management: Questions for Research and Practice

Advances in big data and artificial intelligence (AI), including machine learning (ML) and other cognitive computing technologies (CCT), have facilitated the development of human resource management (HRM) applications promising greater efficiency, economy, and effectiveness for public administration (Maciejewski, 2017) and better alignment with the modern, constantly evolving employment landscape. It is not surprising then that these advanced technologies are featured in proposals to elevate the government’s human capital. This article discusses current and emerging AI applications that stand to impact most (if not all) HRM functions and their prospects for elevating public human capital. In particular, this article (a) reviews the current state of the field with regards to AI and HRM, (b) discusses AI’s current and potential impact upon the core functional areas of HRM, (c) identifies the main challenges AI poses to such concerns as public values, equity, and traditional merit system principles, and (d) concludes by identifying research needs for public HRM scholarship and practice that highlight the growing role and influence of AI applications in the workplace.
Dynamic programming, tree-width and computation on graphical models

Computing on graphical models is a field of diverse interest today, due to the general applicability of these models. This thesis begins by giving background on the generalized Dynamic Programming (DP) method of performing inference computations (Chapter 1). We then (in Chapter 2) demonstrate explicity equivalences between different methods of computation and the importance of a parameter called tree-width. We go on to prove a novel method of bounding the tree-width of a graph by using maximum cardinality search. This gives a lower bound on the computational complexity of a graph with respect to standard methods. This bound can be quite weak or quite good. We provide experimental results demonstrating both cases. Chapter 3 is concerned with Coarse-to-Fine Dynamic Programming (CFDP), a method which can be faster than the standard methods, but requires special conditions. We prove theorems giving the complexity of CFDP for problems that meet certain criteria. These theoretical results are borne out with applications to specific problems.
Analysis of Fractional Splines Interpolation and Optimal Error Bounds

This paper presents a formulation and a study of three interpolatory fractional splines these are in the class of mα, m = 2, 4, 6, α = 0:5. We extend fractional splines function with uniform knots to approximate the solution of fractional equations. The developed of spline method is to analysis convergence fractional order derivatives and estimating error bounds. We propose spline fractional method to solve fractional differentiation equations. Numerical example is given to illustrate the applicability and accuracy of the methods.
Integrative Effect of Protective Structures and Irrigation Levels on Tomato Performance in Indian Hot-Arid Region

Protected cultivation is gaining momentum in (semi) arid regions to ameliorate the adverse environmental impacts on vegetable crops, besides ensuring high resource use efficiency in resource-limiting environments. Among the less techno-intensive protected cultivation structures, naturally ventilated polyhouses (NVP), insect-proof net houses (IPN) and shade net houses (SNH) are commercial structures in India. With the aim to find the best-protected structure, together with optimum irrigation level, for high yield and water productivity of the tomato crop, the most popular crop in hot arid regions, we evaluated tomato performance in low-tech protected structures (NVP, IPN and SNH) in interaction with three irrigation levels (100, 80 and 60% of crop evapotranspiration, ETc) during spring–summer of 2019 and 2020. The NVP was found superior to both the net house structures (IPN and SNH) for different performance indicators of tomatoes under investigation. The components of plant growth (leaf and stem dry mass) and fruit yield (fruit size, weight, yield), as well as fruit quality (total soluble solids, fruit dry matter and lycopene content) were higher in NVP, regardless of irrigation level. The yield as well as water productivity were significantly higher in NVP at 100% ETc. However, there was no statistical variation for water productivity between NVP and IPN. Microclimate parameters (temperature, relative humidity and photosynthetic active radiation) were markedly more congenial for tomato cultivation in NVP followed by IPN in relation to SNH. Consequently, plants’ physiological functioning with higher leaf relative water content (RWC) and lower leaf water potential concomitantly with better photosynthetic efficiency (chlorophyll fluorescence, Fv/Fm), was in NVP and IPN. Most growth and yield attributes were depressed with the decrease in water application rates; hence, deficit irrigation in these low-tech protected structures is not feasible. For tomato cultivation in resource-scarce arid regions, the combination of the normal rate of irrigation (100% ETc) and NVP was optimal for gaining high yield as well as water productivity as compared to net houses.
La gamificación en educación física: efectos sobre la motivación y el aprendizaje (Gamification in Physical Education: Evaluation of impact on motivation and motor learning)

Introducción: El objetivo del presente estudio fue evaluar los efectos de una Unidad Didáctica (UD) gamificada en comparación con una UD de metodología tradicional, sobre los niveles de motivación y de aprendizaje de los estudiantes de educación física. Material y método: 42 estudiantes (15 ± 0.4 años) distribuidos en dos clases de 3º de Educación Secundaria Obligatoria realizaron seis sesiones de una UD de judo. De forma aleatoria, se aplicó una metodología innovadora en una de las clases basada en la gamificación, y una metodología tradicional basada en el mando directo y la asignación de tareas en la otra clase. Se evaluó la motivación de los estudiantes pre- y post- intervención, y también se evaluó al final de la UD el aprendizaje motriz, y los aspectos actitudinales y sociales alcanzados por los estudiantes con una rúbrica. Resultados: la gamificación incrementó el aprendizaje de los estudiantes respecto al grupo de enseñanza tradicional, sin modificar los niveles de motivación. Conclusiones: los resultados obtenidos evidencian el gran potencial de la gamificación como herramienta para que los profesores de educación física logren un mayor aprendizaje en las unidades didácticas de iniciación deportiva. Palabras clave. metodologías activas; innovación educativa; judo; habilidades motoras; aprendizaje deportivo; deportes de combate. Abstract. Purpose: This study aimed to evaluate the impact of a gamified didactic unit (UD) compared to a traditional methodology UD on motivation and motor learning in physical education students. Methods: 42 students (15 ± 0.4 years) distributed in two classes in the 3rd year of the high school performed six judo sessions. Randomly, an innovative methodology based on gamification was applied in one class and a traditional methodology based on direct command and task assignment in the other. We evaluated the students' motivation before and after the intervention. And at the end of the UD, we assessed the students' motor learning and attitudinal and social aspects with a rubric. Results: gamification increased student learning compared to the traditional teaching group without modifying motivation levels. Conclusion: the results obtained show the great potential of gamification as a tool for physical education teachers to achieve more improvement in motor learning in their sports initiation students. Keywords. active methodologies; educational innovation; judo; motor skills; sports learning; combat sports.
The Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA), version 1.0 -- User manual

ZEBRA, the Zurich Extragalactic Bayesian Redshift Analyzer, is a tool for estimating redshifts and template types of galaxies using medium- and broad-band photometric data. ZEBRA employs novel techniques within the template-fitting approach to produce high-quality Maximum-Likelihood and Bayesian redshift estimates. This manuscript serves as a user guide to ZEBRA. It explains how to use ZEBRA, specifies input and output formats, and gives a short account of the available options. ZEBRA is a free and open-source software distributed under the GNU Public License 3 and available at this http URL . Several upgrades are currently being implemented in ZEBRA. An updated documentation will be provided at each new release. Any problems, comments and suggestions on the code and the manual should be sent via e-mail to zebra@phys.ethz. ch.
Quantum algorithm for optical-template recognition with noise filtering

We propose a probabilistic quantum algorithm that decides whether a monochrome picture matches a given template (or one out of a set of templates). As a major advantage to classical pattern recognition, the algorithm requires just a few incident photons and is thus suitable for very sensitive pictures (similar to the Elitzur-Vaidman problem). Furthermore, for a ${2}^{n}\ifmmode\times\else\texttimes\fi{}{2}^{m}$ image, $O(n+m)$ qubits are sufficient. Using the quantum Fourier transform, it is possible to improve the fault tolerance of the quantum algorithm by filtering out small-scale noise in the picture. For example images with $512\ifmmode\times\else\texttimes\fi{}512$ pixels, we have numerically simulated the unitary operations in order to demonstrate the applicability of the algorithm and to analyze its fault tolerance.
ASYMMETRIES IN SN 2014J NEAR MAXIMUM LIGHT REVEALED THROUGH SPECTROPOLARIMETRY

We present spectropolarimetric observations of the nearby Type Ia supernova SN 2014J in M82 over six epochs: +0, +7, +23, +51, +77, +109, and +111 days with respect to B-band maximum. The strong continuum polarization, which is constant with time, shows a wavelength dependence unlike that produced by linear dichroism in Milky Way dust. The observed polarization may be due entirely to interstellar dust or include a circumstellar scattering component. We find that the polarization angle aligns with the magnetic field of the host galaxy, arguing for an interstellar origin. Additionally, we confirm a peak in polarization at short wavelengths that would imply along the light of sight, in agreement with earlier polarization measurements. For illustrative purposes, we include a two-component fit to the continuum polarization of our +51-day epoch that combines a circumstellar scattering component with interstellar dust where scattering can account for over half of the polarization at 4000 Å. Upon removal of the interstellar polarization signal, SN 2014J exhibits very low levels of continuum polarization. Asymmetries in the distribution of elements within the ejecta are visible through moderate levels of time-variable polarization in accordance with the Si ii λ6355 absorption line. At maximum light, the line polarization reaches ∼0.6% and decreases to 1 week later. This feature also forms a loop on the plane, illustrating that the ion does not have an axisymmetric distribution. The observed polarization properties suggest that the explosion geometry of SN 2014J is generally spheroidal with a clumpy distribution of silicon.
Effect of a gamified digital platform in increasing learning about the prevention of metabolic syndrome, obesity, and type 2 diabetes mellitus

Introduction: Gamified digital platforms allow non-health related individuals to learn about the prevention, management and treatment of obesity, metabolic syndrome, and type 2 diabetes mellitus (T2DM). Objective: The purpose of this study was to evaluate whether a new gamified digital platform increases learning about the prevention of metabolic syndrome, obesity, and T2DM. Methods: An exploratory, randomized, cause-effect study was carried out in 150 children between 10 and 12 years of age. Three exploratory, cause-effect experiments were designed to evaluate each one of the following pathologies: T2DM, obesity and metabolic syndrome. For each experiment, two study groups of 25 individuals each were formed. The experimental group was asked to use the digital platform that contained the information on the pathology under study in an animated storytelling and playful way. The control group received written information about each pathology. The assessment was carried out by applying a validated questionnaire including basic questions about the three diseases. This test was performed before and after the intervention. Results: We found a significant improvement (p<0.05) in the post-intervention knowledge acquired within the experimental group about metabolic syndrome, obesity, and T2DM when using the digital platform and compared to the control group. Children in the control group significantly improved their scores after the intervention compared to their baseline ones. Conclusion: Gamified digital platforms have the potential to be a novel primary prevention method for metabolic diseases. The present study allows us to conclude that any validated learning instrument increases knowledge about metabolic diseases. However, gamified digital platforms significantly increase learning compared to other types of methods (written information).
Magnetism in strained graphene dots

We study the magnetization of square and hexagonal graphene dots. It is shown that two classes of hexagonal dots have a second-order phase transition at a critical Hubbard energy $U$, whose value is similar to the one in bulk graphene, albeit the dots do not have a density of states proportional to the absolute value of the energy, relatively to the Dirac point. Furthermore, we show that a particular class of hexagonal dots, having zigzag edges, does not exhibit zero-energy edge states. We also study the effect of uniaxial strain on the evolution of the magnetization of square dots and find that the overall effect is an enhancement of magnetization with strain. The enhancement can be as large as 100% for strain on the order of 20%. Additionally, stress induces a spatial displacement of the magnetization over the dot, moving it from the zigzag to the armchair edges.
PERSEPSI SISWA TERHADAP PENGGUNAAN GOOGLE CLASSROOM DALAM PEMBELAJARAN AKUNTANSI DENGAN PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL (TAM)

The Covid-19 pandemic since April 2020 in accordance with the Decree of the Minister of Education and Culture of Indonesia No. 4 of 2020 caused a paradigm shift in learning activities from conventional to online systems. SMKN 1 Jember is one of the schools that implement the system, especially in accounting subjects. This study aims to determine the perception of class X Accounting students on the use of Google Classroom in online accounting learning with the Technology Acceptance Model (TAM) approach. This type of research uses explanatory research with a quantitative approach. Data collection methods used are questionnaires, interviews, documents and observations. The data analysis method uses Structural Equation Modeling (SEM) with SmartPLS version 3.3. 2 data measurement. The results showed that H1: perceived ease of use on perceptions of usefulness in the use of Google Classroom had a significant effect of 69.5%, H2: perceived usefulness on attitudes to using Google Classroom had a significant effect of 88.8%, H3: perceived ease of use on Attitude to use Google Classroom has no significant effect at 7%, H4: Attitude to use Google Classroom has a significant effect at 71.7% and H5: intention to use Google Classroom has a significant effect at 84.2%. Of all the hypothesis testing has a significant effect. However, there is one hypothesis that does not provide usefulness and benefits in its use, then H3 shows a negative result. Therefore, further research is needed regarding the perception of ease of use on usage attitudes in the use of Google Classroom.
A commentary on the continued fraction by which the illustrious La Grange has expressed the binomial powers

Euler gives a continued fraction representation of (1+x)^n involving 1,3,5,7,. and n^2-1,n^2-4,n^3-9,. and squares of z, for x=2y and y=z/(1-z). He evaluates this continued fraction at z=t sqrt(-1), for ``vanishing'' n, and for infinite n.
IQGAP3 in clear cell renal cell carcinoma contributes to drug resistance and genome stability

Background Clear cell renal clear cell carcinoma (ccRCC) is resistant to most chemotherapeutic drugs and the molecular mechanisms have not been fully revealed. Genomic instability and the abnormal activation of bypass DNA repair pathway is the potential cause of tumor resistance to radiotherapy and chemotherapy. IQ-motif GTPase activating protein 3 (IQGAP3) regulates cell migration and intercellular adhesion. This study aims to analysis the effects of IQGAP3 expression on cell survival, genome stability and clinical prognosis in ccRCC. Methods Multiple bioinformatics analysis based on TCGA database and IHC analysis on clinical specimens were included. Quantitative real-time polymerase chain reaction (qRT-PCR) and western blot (WB) were used to determine protein expression level. MTT assay and 3D spheroid cell growth assay were used to assess cell proliferation and drug resistance in RNAi transfected ccRCC cells. Cell invasion capacity was evaluated by transwell assay. The influence of IQGAP3 on genome instability was revealed by micronuclei number and γ H2AX recruitment test. Results The highly expressed IQGAP3 in multiple subtypes of renal cell carcinoma has a clear prognostic value. Deletion of IQGAP3 inhibits cell growth in 3D Matrigel. IQGAP3 depletion lso increases accumulated DNA damage, and improves cell sensitivity to ionizing radiation and chemotherapeutic drugs. Therefore, targeting DNA damage repair function of IQGAP3 in tumorigenesis can provide ideas for the development of new targets for early diagnosis.
Phase diagram of the extended Bose–Hubbard model

By means of the density matrix renormalization group technique, we accurately determine the zero-temperature phase diagram of the one-dimensional extended Bose–Hubbard model with on-site and nearest-neighbor interactions. We analyze the scaling of the charge and of the neutral ground-state energy gaps, as well as of various order parameters. In this way we come to an accurate location of the boundaries between the superfluid and the insulating phases. In this last region, we are able to distinguish between the conventional Mott insulating and density-wave phases and the Haldane insulator phase displaying long-range string ordering, as originally predicted by Dalla Torre et al (2006 Phys. Rev. Lett. 97 260401).
Tools to Alleviate the Drug Resistance in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), an acid-fast bacillus that causes Tuberculosis (TB), is a pathogen that caused 1.5 million deaths in 2020. As per WHO estimates, another 4.1 million people are suffering from latent TB, either asymptomatic or not diagnosed, and the frequency of drug resistance is increasing due to intrinsically linked factors from both host and bacterium. For instance, poor access to TB diagnosis and reduced treatment in the era of the COVID-19 pandemic has resulted in more TB deaths and an 18% reduction in newly diagnosed cases of TB. Additionally, the detection of Mtb isolates exhibiting resistance to multiple drugs (MDR, XDR, and TDR) has complicated the scenario in the pathogen’s favour. Moreover, the conventional methods to detect drug resistance may miss mutations, making it challenging to decide on the treatment regimen. However, owing to collaborative initiatives, the last two decades have witnessed several advancements in both the detection methods and drug discovery against drug-resistant isolates. The majority of them belong to nucleic acid detection techniques. In this review, we highlight and summarize the molecular mechanism underlying drug resistance in Mtb, the recent advancements in resistance detection methods, and the newer drugs used against drug-resistant TB.