Miracle-dz/UniFAR-FTUs huggingface dataset:
A Flexible Observation-Driven Stationary Bivariate Negative Binomial INAR(1) with Non-homogeneous Levels of Over-dispersion

Abstract The existing bivariate integer-valued autoregressive process of order 1 (BINAR(1)) with negative binomial (NB) innovations is developed under stationary moment conditions and in particular under same level of over-dispersion index. In this paper, we propose a flexible BINAR(1) under NB innovations where the counting series are subject to two different levels of over-dispersion under same stationary moment condition. The unknown parameters of the new model are estimated using a generalized quasi-likelihood (QL) estimating equation. The performance of this estimation method is assessed through some numerical experiments under different time dimensions.
Assessment of Tilapia (Oreochromis spp.) Welfare in the Semi-Intensive and Intensive Culture Systems in Thailand

Simple Summary In this study, we evaluated welfare indicators at eight small-scale semi-intensive and intensive tilapia farms in Thailand, with a focus on four welfare categories: health, environment, behaviour, and nutrition. The results showed differences between the relative scores across all four welfare-indicator categories. Specifically, the behavioural assessments revealed poor welfare practices during the catching process on the tilapia farms, while the nutritional assessments showed differences in the feed conversion rates and K factor values. We identified correlations between the nutritional, environmental, and health indicators, which provided information about the critical welfare points in fish farming. By understanding welfare indicators and improving farming practices, farmers can produce healthier fish and improve their profits and the quality of the fish supplied to consumers. Taken together, our study provides valuable information that can be applied to the broader context of sustainable aquaculture and animal welfare. Abstract Welfare assessments have risen to prominence in the aquaculture industry, with increasing awareness of their significance among stakeholders in Thailand. In this study, we conducted a welfare assessment of tilapia (Oreochromis spp.) farms in Thailand, focusing on health, environmental, behavioural, and nutritional indicators. Comparing semi-intensive (earthen ponds) and intensive farming practices (cage culture), we found significant differences in the overall health score, particularly at farm F due to a disease outbreak (Kruskal–Wallis, p = 0.01). Skin and fin scores varied across farms, indicating their potential as indicators of tilapia health. Environmental assessments revealed differences in transparency between the two culturing systems (Mann–Whitney, p = 0.02). During the harvesting process, tilapia behaviours indicated poor welfare across all farms. However, no statistically significant difference in overall welfare scores was found between the two culturing systems. Correlations were observed between nutritional, environmental, and health indicators, with negative correlations between fish density and water transparency (r = −0.87, p = 0.02), presence of inhabitants (r = −0.78, p = 0.04), feeding behaviours (r = −0.78, p = 0.04), and swimming behaviours during capture (r = −0.98, p = 0.001). These findings provide valuable insights to enhance tilapia-farming practices and welfare in Thailand.
Novel (1-x)MgCu2Nb2O8 − xTiO2 composite ceramics with high Q×f and near-zero τ f

For the MCu2Nb2O8 (M=Ca, Mg, Zn or Ni) composite ceramics, it is still a difficult problem to achieve both high Q×f valule and near-zero temperature coefficient of resonant frequency (τ f ). In this paper, the (1-x)MgCu2Nb2O8 − xTiO2 (0 ≤ x ≤ 0.28) ceramics were fabricated by the two-step sintering (TSS) technique. Results of x-ray diffraction (XRD) and Raman spectroscopy proved that the ceramic samples were composite phases of MgNb2O6, CuO and TiO2. The chemical compositions and valences of Mg, Cu, Nb, O and Ti were investigated by x-ray photoelectron spectroscopy (XPS). Scanning electron microscopy (SEM) showed that proper TiO2 addition could promote growth of grains and increase the average grain size. Appropriate TiO2 content and enhanced densification contributed to the 0.84MgCu2Nb2O8 − 0.16TiO2 ceramics sintered at 935 °C with ε r = 18.75, Q×f = 34,398 GHz (f 0 = 7.8 GHz), and τ f = −0.56 ppm/°C, exhibiting potential applications in Low Temperature Co-fired Ceramic (LTCC) devices.
Spotlight on Plant Bromodomain Proteins

Simple Summary Bromodomains are protein modules able to read the chromatin epigenetic state and translate it into gene expression. Extensive research has been conducted on bromodomain-containing proteins in humans, particularly in the context of disease and cancer. However, in plants their functions remain far less understood. The focus of this review is to comprehensively organize plant bromodomain proteins into functional groups and elucidate what is currently known about their functions, with an emphasis on the model plant Arabidopsis. By systemically examining these proteins, we aim to shed light on their diverse roles, highlight members with yet undetermined functions, present similarities and differences with other eukaryotic homologs and pinpoint unresolved questions that present exciting opportunities for future research in this field. We anticipate that this review will contribute to a deeper understanding and inspire further investigations into the functions of these remarkable proteins in plants. Abstract Bromodomain-containing proteins (BRD-proteins) are the “readers” of histone lysine acetylation, translating chromatin state into gene expression. They act alone or as components of larger complexes and exhibit diverse functions to regulate gene expression; they participate in chromatin remodeling complexes, mediate histone modifications, serve as scaffolds to recruit transcriptional regulators or act themselves as transcriptional co-activators or repressors. Human BRD-proteins have been extensively studied and have gained interest as potential drug targets for various diseases, whereas in plants, this group of proteins is still not well investigated. In this review, we aimed to concentrate scientific knowledge on these chromatin “readers” with a focus on Arabidopsis. We organized plant BRD-proteins into groups based on their functions and domain architecture and summarized the published work regarding their interactions, activity and diverse functions. Overall, it seems that plant BRD-proteins are indispensable components and fine-tuners of the complex network plants have built to regulate development, flowering, hormone signaling and response to various biotic or abiotic stresses. This work will facilitate the understanding of their roles in plants and highlight BRD-proteins with yet undiscovered functions.
Sharp adaptive estimation for d-dimensional classes

In this paper, our aim is to estimate an anisotropic function in the framework of the Gaussian white noise model. We suppose that the function belongs to an unknown d-dimensional Holder class. We evaluate the performance of an estimator by the sup-norm risk. We find the exact asymptotics of the adaptive minimax risk and construct asymptotically adaptive estimators.
Intrinsic and extrinsic spin-orbit torques from first principles

This thesis attempts to shed light on the microscopic mechanisms underlying the current-induced magnetic torques in ferromagnetic heterostructures. We have developed first principles methods aiming at the accurate and efficient calculation of the so-called spin-orbit torques (SOTs) in magnetic thin films. The emphasis of this work is on the impurity-driven extrinsic SOTs. The main part of this thesis is dedicated to the development of a formalism for the calculation of the SOTs within the Korringa-Kohn-Rostoker (KKR) method. The impurity-induced transitions rates are obtained from first principles and their effect on transport properties is treated within the Boltzmann formalism. The developed formalism provides a mean to compute the SOTs beyond the conventional constant relaxation time approximation. We first apply our formalism to the investigation of FePt/Pt and Co/Cu bilayers in the presence of defects and impurities. Our results hint at a crucial dependence of the torque on the type of disorder present in the films, which we explain by a complex interplay of several competing Fermi surface contributions to the SOT. Astonishingly, specific defect distributions or doping elements lead respectively to an increase or a sign change of the torque, which can not be explained on the basis of simple models. We also compute the intrinsic SOT induced by electrical and thermal currents within the full potential linearized augmented plane-wave method. Motivated by recent experimental works, we then investigate the microscopic origin of the SOT in a Ag2Bi-terminated Ag film grown on ferromagnetic Fe(110). We find that the torque in that system can not be explained solely by the spin-orbit coupling in the Ag2Bi alloy, and instead results from the spin-orbit coupling in all regions of the film. Finally, we predict a large SOT in Fe/Ge bilayers and suggest that semiconductor substrates might be a promising alternative to heavy metals for the development of SOT-based magnetic random access memories. We show the strong dependence of the SOT on the stacking direction, thereby providing important guidelines for future experimental works. We also compute the sublattice-resolved SOTs in an antiferromagnetic Fe/Ge thin film and find a large anisotropy of the torkance tensor.
Evading the Adversary in Invariant Representation

Representations of data that are invariant to changes in specified nuisance factors are useful for a wide range of problems: removing potential bias in prediction problems, controlling the effects of known confounders, and disentangling meaningful factors of variation. Unfortunately, learning representations that exhibit invariance to arbitrary nuisance factors yet remain useful for other tasks is challenging. Existing approaches cast the trade-off between task performance and invariance in an adversarial way, using an iterative minimax optimization. We show that adversarial training is unnecessary and sometimes counter-productive by casting invariant representation learning for various tasks as a single information-theoretic objective that can be directly optimized. We demonstrate that this approach matches or exceeds performance of state-of-the-art adversarial approaches for learning fair representations and for generative modeling with controllable transformations.
Design Guidelines and Feedback Structure of Ring Oscillator PUF for Performance Improvement

The physical unclonable function (PUF) is a hardware security primitive that is used to generate secret keys or identity authentication for chips using random manufacturing process variation (MPV). The PUF based on ring oscillator (RO PUF) has been extensively studied in recent years because of its high robustness and ease of design. Although the performance has been optimized in previous studies, several uniqueness, reliability, and theoretical foundation concerns still remain. This article presents a transistor-level parameters-based quantitative theoretical model, which clearly reveals several design guidelines for improving the reliability of RO PUF. Furthermore, a PUF based on a feedback ring oscillator (RO) structure is proposed, which combined the RO topology and the drafting effect of XOR gates to enhance the uniqueness and reliability. The correctness of the theoretical model was verified by the SPICE simulation experiment result. And in the FPGA experiment result, the uniqueness and the reliability of feedback RO PUF using the same hardware resources on the same chip was superior to that of RO PUF. The theoretical research method of RO PUF used can be widely applied to other PUFs using ring topology and feedback RO PUF is a great substitute for RO PUF.
Sector connectors, specialists and scrappers: How cities use civic capital to compete in high-technology markets

This article uses three cities in the same Canadian province (Ontario): Toronto, Ottawa and Waterloo, to examine how regions compete in high-technology markets. We find that regions use civic capital to leverage new, technological windows of opportunity, but they do so in very different ways. Tracing Toronto’s evolution from a marketing hub for foreign multinationals into a centre for entrepreneurship, we illustrate how weak ties and cross-sectoral buzz created a ‘super connector’, scaling high-technology firms in a wide variety of areas. In Ottawa, task-specific cooperation in R&D, education and specialised infrastructure enabled the region to overcome the disadvantages of its small size as a ‘specialist’ in a single, capital-intensive niche, telecommunications equipment. Finally, entrepreneurs in Waterloo eschewed task-specific cooperation for peer-to-peer mentoring. By diffusing generic knowledge about how to circumvent the liabilities of smallness, mentoring networks enabled this ‘scrapper’ city to support smaller start-ups in a broad range of niches.
On finite groups with σ-subnormal Schmidt subgroups

ABSTRACT Let G be a finite group and σ = {σi|i∈I} be some partition of the set of all primes. We say that G is: σ-primary if G is a σi-group, for some i; σ-nilpotent if for some σ-primary groups . A subgroup A of G is called σ-subnormal in G if there is a subgroup chain such that either or is σ-primary for all i = 1,…,m. In this paper we prove that the set of all σ-subnormal (σ-nilpotent) subgroups of G forms a sublattice of the lattice of all subgroups of G and, being based on this result, we prove also that if every Schmidt subgroup of G is σ-subnormal in G, then the commutant subgroup G′ is σ-nilpotent.
Land cover classification using random forest with genetic algorithm-based parameter optimization

Abstract. Land cover classification based on remote sensing imagery is an important means to monitor, evaluate, and manage land resources. However, it requires robust classification methods that allow accurate mapping of complex land cover categories. Random forest (RF) is a powerful machine-learning classifier that can be used in land remote sensing. However, two important parameters of RF classification, namely, the number of trees and the number of variables tried at each split, affect classification accuracy. Thus, optimal parameter selection is an inevitable problem in RF-based image classification. This study uses the genetic algorithm (GA) to optimize the two parameters of RF to produce optimal land cover classification accuracy. HJ-1B CCD2 image data are used to classify six different land cover categories in Changping, Beijing, China. Experimental results show that GA-RF can avoid arbitrariness in the selection of parameters. The experiments also compare land cover classification results by using GA-RF method, traditional RF method (with default parameters), and support vector machine method. When the GA-RF method is used, classification accuracies, respectively, improved by 1.02% and 6.64%. The comparison results show that GA-RF is a feasible solution for land cover classification without compromising accuracy or incurring excessive time.
Identification of the material properties in nonuniform nanostructures

This paper is concerned with addressing two significant challenges arising from quantifying mechanical properties of nanomaterials, namely nonuniformity of the nanomaterial and the high noise level of measurements. For nonuniformity, an explicit solution is derived for the general Euler–Bernoulli equation in terms of the Green function for the Poisson equation. Then, by examining a stochastic source, the systematic error may be removed from measurements, which leads to more accurate estimation of mechanical properties. Based on Itô integral properties, three deterministic Fredholm integral equations can be deduced to extract the stiffness and the structure of the random source from measured data. To overcome ill-posedness and high nonlinearity in solving the Fredholm equations, a Tikhonov regularization method is developed with an a priori strategy of choosing the regularization parameter. Moreover, under a regularity assumption for the stiffness coefficient and structures of the random source, the convergence rate can be obtained in the sense of probability. Numerical examples are presented to illustrate the validity and effectiveness of the novel model and regularization method.
Overcoming the Limitation of Spin Statistics in Organic Light Emitting Diodes (OLEDs): Hot Exciton Mechanism and Its Characterization

Triplet harvesting processes are essential for enhancing efficiencies of fluorescent organic light-emitting diodes. Besides more conventional thermally activated delayed fluorescence and triplet-triplet annihilation, the hot exciton mechanism has been recently noticed because it helps reduce the efficiency roll-off and improve device stability. Hot exciton materials enable the conversion of triplet excitons to singlet ones via reverse inter-system crossing from high-lying triplet states and thereby the depopulation of long-lived triplet excitons that are prone to chemical and/or efficiency degradation. Although their anti-Kasha characteristics have not been clearly explained, numerous molecules with behaviors assigned to the hot exciton mechanism have been reported. Indeed, the related developments appear to have just passed the stage of infancy now, and there will likely be more roles that computational elucidations can play. With this perspective in mind, we review some selected experimental studies on the mechanism and the related designs and then on computational studies. On the computational side, we examine what has been found and what is still missing with regard to properly understanding this interesting mechanism. We further discuss potential future points of computational interests toward aiming for eventually presenting in silico design guides.
A STUDY OF MAST CELLS IN SURGICALLY RESECTED APPENDIX.

* Verma A 1 , Shinde R.M 2 , Ghatge R.M 1 , Gupta S 1 , Inamdar A.A 1 and Sujata N 1 . 1. Post Graduate student, Department of Pathology, D.Y. Patil Medical College, Kolhapur. 2. Associate Professor, Department of Pathology, D.Y. Patil Medical College, Kolhapur. Manuscript Info Abstract . Manuscript History
The enthralling tale of the formation and evolution of compact planetary systems

Of the myriad of insights into exoplanetary systems provided by the Kepler mission, one of the most intriguing new discoveries is that of a class of compact planetary systems which include Kepler-11, Kepler-32 and Kepler-90. In such systems, ensembles of several planets are found in very closely packed orbits (often within a few percent of an astronomical unit of one another). These systems present a challenge for traditional formation and migration scenarios, since these planets presumably formed at larger orbital radii before migrating inwards. In particular, it is difficult to understand how some planets in such systems could have migrated across strong mean-motion resonances without becoming trapped, and remaining relatively well-spaced. It is also difficult to explain how such systems remain dynamically cold, as resonant interactions tend to excite orbital eccentricity and lead to close encounters. I present a dynamical study of the formation of these systems, using an N-body method which incorporates a parametrized model of planet migration in a turbulent protoplanetary disc. The study explores a wide parameter space, and finds that under suitable conditions it is possible to form compact, close-packed planetary systems via traditional disc-driven migration, albeit with an over-abundance of mean-motion resonances. I then extend the study to include Jupiter-mass planets exterior to the compact systems, and find that the dynamical effect of these companions can significantly modify the resonant structure of the compact planets. Finally, I extend this work to twodimensional hydrodynamical simulations in an attempt to model type I migration self-consistently. In particular, I find that clearing of the disc by photoevaporation can halt migration of compact systems, and also discover that planet-disc interactions can under the right conditions break mean-motion resonances. The following chapters from this thesis have been published in the journal Monthly Notices of the Royal Astronomical Society: Chapter 3: Understanding the assembly of Kepler’s compact planetary systems, Hands T. O. , Alexander R. D. , Dehnen W. , 2014, MNRAS, 445, 749 Chapter 4: There might be giants: unseen Jupiter-mass planets as sculptors of tightly packed planetary systems, Hands T. O. , Alexander R. D. , 2016, MNRAS, 456, 4121 Chapter 5: Hands T. O. , Alexander R. D. , 2016, MNRAS, in prep
Impact of Psychological Conditions on Employee Engagement: Study Based on Regional Branches of ABC Insurance Company at Matale District

The concept of employee engagement has become increasingly important in the literature. Despite increased interest, there is still a scarcity of studies on employee engagement. Alternatively, employees in the insurance industry are required to work under tough conditions and in high-pressure scenarios. Past three-year records of ABC insurance company recorded significant increase in turnover rates and disengaged employees. The purpose of the current research was to study the impact of psychological conditions on employee engagement at regional branches of ABC insurance company at Matale district. In this study, psychological meaningfulness, safety, and availability were examined as dimensions of psychological conditions. Quantitative research methods were employed, and data were collected through a questionnaire. The sample size of 103 employees from three branches of ABC Insurance companies in the Matale district and the researcher utilized the simple random sampling technique. Data analysis was performed using SPSS 23.0 Package and MS Excel. Based on the findings of the study, a significant positive relationship was found between psychological conditions and employee engagement, indicating that psychological conditions had a significant impact on employee engagement with the greatest effect observed in the dimension of meaningfulness. Consequently, managers should take all three predicting psychological conditions into account and use a systematic approach. Accordingly, leading roles organizations should focus on job design to fulfil the work tasks of importance and values.
A likelihood-based approach for multivariate one-sided tests with missing data

ABSTRACT Inequality-restricted hypotheses testing methods containing multivariate one-sided testing methods are useful in practice, especially in multiple comparison problems. In practice, multivariate and longitudinal data often contain missing values since it may be difficult to observe all values for each variable. However, although missing values are common for multivariate data, statistical methods for multivariate one-sided tests with missing values are quite limited. In this article, motivated by a dataset in a recent collaborative project, we develop two likelihood-based methods for multivariate one-sided tests with missing values, where the missing data patterns can be arbitrary and the missing data mechanisms may be non-ignorable. Although non-ignorable missing data are not testable based on observed data, statistical methods addressing this issue can be used for sensitivity analysis and might lead to more reliable results, since ignoring informative missingness may lead to biased analysis. We analyse the real dataset in details under various possible missing data mechanisms and report interesting findings which are previously unavailable. We also derive some asymptotic results and evaluate our new tests using simulations.