Miracle-dz/UniFAR-FTUs huggingface dataset:
Cascaded Downscaling–Calibration Networks for Satellite Precipitation Estimation

Precipitation is a critical process in the terrestrial hydrological circulation, affecting climate change, water resource management, and agricultural production. Satellite-borne observations have prominent advantages in macro and mesoscopic quantitative precipitation estimation. Nevertheless, they are subject to low spatial resolution and inherent biases. Therefore, this study utilizes the surface–surface downscaling network and point–surface fusion network for fine-resolution and high-precision precipitation mapping over China. To deeply explore the complicated relationships between various ancillary factors, ground measurements, and satellite precipitation, an attention mechanism-based convolutional network (AMCN) is used for spatial downscaling and a geo-intelligent deep belief network (Geoi-DBN) is used for ground–satellite fusion. Experimental results indicate that cascaded networks toward two different objectives are superior to baseline methods, achieving $R^{2}$ and root mean square error (RMSE) of about 0.84 and 27.23 mm/month, respectively. Besides, the assistance of geo-intelligent items and ancillary factors contributes to fusion accuracy. This study provides an effective way for precipitation estimation over China.
Comprehensive Comparison of Two Color Varieties of Perillae Folium by GC-MS-Based Metabolomic Approach

Perillae Folium (PF), the leaf of Perilla frutescens (L.) Britt, is extensively used as culinary vegetable in many countries. It can be divided into two major varietal forms based on leaf color variation, including purple PF (Perilla frutescens var. arguta) and green PF (P. frutescens var. frutescens). The aroma of purple and green PF is discrepant. To figure out the divergence of chemical composition in purple and green PF, gas chromatography–tandem mass spectrometry (GC-MS) was applied to analyze compounds in purple and green PF. A total of 54 compounds were identified and relatively quantified. Multivariate statistical methods, including principal component analysis (PCA), orthogonal partial least-squares discrimination analysis (OPLS-DA) and clustering analysis (CA), were used to screen the chemical markers for discrimination of purple and green PF. Seven compounds that accumulated discrepantly in green and purple PF were characterized as chemical markers for the discrimination of the purple and green PF. Among these 7 marker compounds, limonene, shisool and perillaldehyde that from the same branch of the terpenoid biosynthetic pathway were with relatively higher contents in purple PF, while perilla ketone, isoegomaketone, tocopheryl and squalene on other branch pathways were higher in green PF. The results of the present study are expected to provide theoretical support for the development and utilization of PF resources.
Safety Monitoring System and Method for Unmanned Operation of Unmanned Aerial Vehicle Distribution Network Based on Learning Control Robot Technology

The continuous expansion of the scale of power grid construction has led to the emergence and development of the uninterrupted operation of the unmanned aerial vehicle (UAV) distribution network. However, in the actual power production process, due to the limitations of operating conditions and environment, power safety accidents are inevitable. With the support of intelligent technology, the monitoring effect and application level of the safety monitoring system for the uninterrupted operation of the UAV distribution network have been improved. Unified planning and scheduling were carried out throughout the job implementation process. The efficiency of equipment safety management has been strengthened, which is of great practical significance to the safe production of electric power. Based on the analysis of the environmental characteristics and implementation difficulties of the unmanned aerial vehicle distribution network, this paper studied the safety monitoring system and method by combining the high intelligence and high-efficiency characteristics of the learning control robot technology. The designed unmanned aerial vehicle distribution network safety monitoring system and method were tested. In order to understand the effect of the system intuitively, this paper analyzed from three aspects: system sensitivity, anti-interference, and data acquisition and management. It also contrasted with traditional monitoring systems. The test data showed that the sensitivity test value of the traditional monitoring system in the R1 circuit with the lowest voltage intensity was 71.3%. In the R4 circuit with the strongest voltage intensity, the test value only reached 84.4%. The test values of the system in this paper in the two types of circuits were 85.6% and 93.3%, respectively. In the data acquisition and management test, the acquisition and management rates under the highest numerical information could reach more than 97%. It can be seen that it has strong feasibility in actual operation.
New axes for the stellar mass fundamental plane

Abstract We argue that the stellar velocity dispersion observed in an elliptical galaxy is a good proxy for the halo velocity dispersion. As dark matter halos are almost completely characterized by a single scale parameter, the stellar velocity dispersion tells us the virial radius of the halo and the mass contained within. This permits non-dimensionalizing of the stellar mass and effective radius axes of the stellar mass fundamental plane by the virial radius and halo mass, respectively.
Spatial entanglement in optical parametric oscillators with photonic crystals

The effects of an intracavity photonic crystal in a multimode optical parametric oscillator are studied, with a special focus on quantum fluctuations. The capability to either stimulate or inhibit the spatial instability, lowering or increasing the parametric threshold, allows to control the intensity fluctuations and correlations. A significative quantum noise reduction and an increase of the range of squeezed quadratures are found above threshold where spatial Einstein-Podolsky-Rosen entanglement and inseparability are found.
The Loss of a Service Dog Through Retirement: Experiences and Impact on Human Partners

This study describes the experiences of service dog partners (N=22) whose service dog had retired within the previous five years. The participants were service dog partners who participated in a larger quantitative study (N=56) and agreed to a follow up interview. Guided by bereavement and human-animal bond theories and prior research, researchers coded the interviews using directed content analysis to identify prevalent themes. Findings underscore that the retirement of a service dog is poorly understood by others, and often results in disenfranchised grief about this ambiguous loss. The retirement process is difficult and often partners struggled with the need to quickly acquire another service dog and the subsequent impact this had on their ability to grieve. These results suggest that, given the unique nature of service dogs and the relationship between dog and partner, it is important that service dog organizations, mental health counselors and veterinarians are aware of the challenges and losses associated with the retirement of a service dog and offer appropriate tools and support to their clients during this challenging time.
Thin Tree Position

We introduce a method for creating a special type of tree, called a tree position, from a weighted graph. Leaves of the tree correspond to vertices of the original graph, and the tree edges contain information which can be used to partition these vertices. By repeatedly applying reducing operations to the tree position we arrive at a special type of tree position we call thin, and we show that partitions arising from thin tree positions have especially nice properties. The algorithm is based the topological notion of thin position for knots and 3-manifolds and builds on the previously defined idea of a Topological Intrinsic Lexicographic Order (TILO).
Extending the Coyote emulator to dark energy models with standard w0-wa parametrization of the equation of state

We discuss an extension of the Coyote emulator to predict non-linear matter power spectra of dark energy (DE) models with a scale factor dependent equation of state of the form w = w0+(1−a)wa. The extension is based on the mapping rule between non-linear spectra of DE models with constant equation of state and those with time varying one originally introduced in ref. [40]. Using a series of N-body simulations we show that the spectral equivalence is accurate to sub-percent level across the same range of modes and redshift covered by the Coyote suite. Thus, the extended emulator provides a very efficient and accurate tool to predict non-linear power spectra for DE models with w0-wa parametrization. According to the same criteria we have developed a numerical code that we have implemented in a dedicated module for the CAMB code, that can be used in combination with the Coyote Emulator in likelihood analyses of non-linear matter power spectrum measurements. All codes can be found at https://github.com/luciano-casarini/pkequal.
Bibliometric analysis of the literature on critical thinking: an increasingly important competence for higher education students

In recent years, interest in critical thinking(CT) has grown considerably. An evaluation of this research field and its challenges are provided in this paper. A bibliometric study was performed to analyse 1,295 papers on CT published in the last 50 years. The data were obtained from the WOS Core Collection database. The findings of this study improve the understanding of the CT domain by showing key studies, the main studies developing the field, key past studies and their influence in subsequent publications, emerging trends and potentially transformative ideas. Most publications and citations are from the last decade, reflecting the momentum of this concept over the period examined. The topic has also expanded geographically. Although the University of Iowa and the University of Alberta are the most prolific institutions, Asian universities have gained in prominence in recent years, as shown by the number of papers published. According to the analysis, the increase in the number of authors, publications and journals in this field and the rise in the number of publications written in collaboration with authors from different parts of the world are two trends that reflect the interest in CT as a way to understand the development of thinking skills.
Peran Advokasi Non-Governmental Organization Yayasan Inisiasi Alam Rehabilitasi Indonesia dalam Konservasi Primata Jenis Kukang di Indonesia

Isu-isu lingkungan dalam studi hubungan internasional tidak hanya berkutat pada peristiwa-persitiwa yang berkaitan dengan alam dan kelangsungan hidup manusia. Isu-isu lingkungan dalam studi hubungan internasional juga memberikan ruang bagi kajian konservasi flora dan fauna. Dalam penelitian ini, penulis berusaha untuk menjelaskan bagaimana peran aktor non-negara yang dalam hal ini YIARI dengan usahanya untuk melakukan advokasi dalam konservasi hewan primata jenis Kukang di Indonesia. Penulis dalam artikel ini menggunakan teori Jaringan Advokasi Transnasional untuk menjelaskan bagaimana YIARI memaksimalkan perannya untuk aktif dalam mengadvokasi isu konservasi kukang di Indonesia. Dalam artikel ini, penulis menemukan beberapa fakta bahwa strategi yang digunakan YIARI berhasil menunjukkan dampak positif dengan penurunan angka tren yang signifikan dalam fenomena eksploitasi kukang di Indonesia. Salah satu faktor yang menentukan keberhasilan advokasi YIARI adalah jejaring advokasi transnasional yang menghubungkan YIARI dengan sesama organisasi non-pemerintah yang sama-sama bergerak di bidang konservasi hewan sedunia. Selain itu penulis berargumen bahwa peran YIARI berhasil menggantikan peran pemerintah Indonesia yang tidak maksimal dalam penegakan hukum untuk menindak pelaku-pelaku eksploitasi hewan kukang.Kata Kunci: LSM, YIARI, Konservasi, Kukang, Jejaring Advokasi Transnasional
Geodesic deviations: modeling extreme mass-ratio systems and their gravitational waves

The method of geodesic deviations has been applied to derive accurate analytic approximations to geodesics in Schwarzschild spacetime. The results are used to construct analytic expressions for the source terms in the Regge–Wheeler and Zerilli–Moncrief equations, which describe the propagation of gravitational waves emitted by a compact massive object moving in the Schwarzschild background spacetime. The wave equations are solved numerically to provide the asymptotic form of the wave at large distances for a series of non-circular bound orbits with periastron distances up to the ISCO radius, and the power emitted in gravitational waves by the extreme mass-ratio binary system is computed. The results compare well with those of purely numerical approaches.
Covariate‐based cepstral parameterizations for time‐varying spatial error covariances

The difference between a mechanistic model of a spatio‐temporal process and associated observations can reasonably be represented by a random process with possible dependence structure in space and time. Often, such error processes are assumed to be stationary in time, so that a unique spatial error covariance structure is applicable for all time. However, model performance can be related to large‐scale time‐varying features, causing the error process to be nonstationary, leading to a time‐varying spatial covariance. Because of dimensionality constraints, one cannot estimate such time‐varying covariances reliably, and therefore they must be modeled. Such models for covariances should be able to accommodate influences from exogenous covariates. However, the inclusion of covariates in a standard covariance model is challenging due to the fact that the covariance must be positive definite. We mitigate this issue by modeling the time‐varying spatial dependence through the Fourier coefficients of the log spectrum, as originally developed for time series representations. The convenient feature of these so‐called “cepstral” models (or “exponential models”) is that the associated parameters are unrestricted yet still are guaranteed to produce a positive definite covariance. Thus, it is fairly straightforward to model these parameters as time‐varying random processes that depend on other covariates within the Bayesian hierarchical modeling paradigm. We illustrate the effectiveness of this model through various simulated examples and by applying it to differences from a long‐lead sea surface temperature forecast model. In this case, we consider the Pacific Decadal Oscillation and Southern Oscillation Index as possible factors that influence the error covariance. Copyright © 2014 John Wiley & Sons, Ltd.
How Problems Gain Importance and Become Contentious Issues through Agenda Setting

A significant number of problems, like poverty, crime, and lack of resources, face people in the world today. This 4-page fact sheet discusses how a problem draws political attention through agenda setting and becomes a public issue, discusses the role the media plays in agenda setting, and provides a brief commentary on the influence of interest groups on public issues. Written by Courtney T. Owens and Alexa J. Lamm, and published by the UF Department of Agricultural Education and Communication, December 2014. (Photo: iStock/Thinkstock)
Hybrid Data Fragmentation Using Genetic Killer Whale Optimization-Based Clustering Model

Effective storage structure is essential in distributed data processing systems as it determines the users' consistent, scalable and reliable data accessibility. A flexible distributed database system's major aspects are data fragmentation and subsequent allocation. An efficient data fragmentation technique must support both the horizontal and vertical categorization of the tuples so that the frequency and type of queries for the data statistics are learned along with the data pattern and the attributes that impact these patterns. However, most existing fragmentation methods are based only on the query frequency and type for the data statistics, leading to less effective analysis of the data patterns and attributes in large databases. To avoid the data loss and imperfect grouping of large databases, this paper presents a hybrid data fragmentation model using an advanced optimization-based clustering model for performing horizontal and vertical fragmentation. This proposed model, called Genetic Killer Whale Optimization based Clustering (GKWOC), is developed by integrating the Killer Whale Optimization (KWO) and Genetic Algorithms (GA) into the standard subspace clustering algorithm. This optimization-based hybrid fragmentation model performs fragmentation in terms of the tuples and the attributes. Since the databases can be high dimensional, subspace-based clustering can improvise good fragments for distributed databases and reduce the data access time for the users. This proposed model showed better fragmentation performance than the state-of-the-art methods during the evaluations, thus suitable for large distributed databases with competent access time and reduced error rates.
Manipulating Edge Majorana Fermions in Vortex State

A vortex in a model spinless px+ipy superconductor induces two Majorana fermions (MFs), one in the core and the other at the sample edge. In the present work, we show that edge MF can be generated, fused, transported, and braided easily by tuning gate voltages at point-like constriction junctions. Solving the time-dependent Bogoliubov-de Gennes equation, we demonstrate that the braiding of edge MFs obeys the non-Abelian statistics. The present setup is therefore a promising implementation for topological quantum computation, and has the advantage of easy manipulation and simple device structure.
Fermi-LAT data reprocessed with updated calibration constants

Four years into the mission, the understanding of the performance of the Fermi Large Area Telescope (LAT) and data analysis have increased enormously since launch. Thanks to a careful analysis of flight data, we were able to trace back some of the most significant sources of systematic uncertainties to using non-optimal calibration constants for some of the detectors. In this paper we report on a major effort within the LAT Collaboration to update these constants, to use them to reprocess the first four years of raw data, and to investigate the improvements observed for low- and high-level analysis. The Pass 7 reprocessed data, also known as P7REP data, are still being validated against the original Pass~7 (P7) data by the LAT Collaboration and should be made public, along with the corresponding instrument response functions, in the spring of 2013.
Reverse Genetics and Artificial Replication Systems of Borna Disease Virus 1

Borna disease virus 1 (BoDV-1) is a neurotropic RNA virus belonging to the family Bornaviridae within the order Mononegavirales. Whereas BoDV-1 causes neurological and behavioral disorders, called Borna disease (BD), in a wide range of mammals, its virulence in humans has been debated for several decades. However, a series of case reports in recent years have established the nature of BoDV-1 as a zoonotic pathogen that causes fatal encephalitis in humans. Although many virological properties of BoDV-1 have been revealed to date, the mechanism by which it causes fatal encephalitis in humans remains unclear. In addition, there are no effective vaccines or antiviral drugs that can be used in clinical practice. A reverse genetics approach to generating replication-competent recombinant viruses from full-length cDNA clones is a powerful tool that can be used to not only understand viral properties but also to develop vaccines and antiviral drugs. The rescue of recombinant BoDV-1 (rBoDV-1) was first reported in 2005. However, due to the slow nature of the replication of this virus, the rescue of high-titer rBoDV-1 required several months, limiting the use of this system. This review summarizes the history of the reverse genetics and artificial replication systems for orthobornaviruses and explores the recent progress in efforts to rescue rBoDV-1.
Phytochemicals and Bioactivities of Australian Native Lemon Myrtle (Backhousia citriodora) and Lemon-Scented Tea Tree (Leptospermum petersonii): A Comprehensive Review

ABSTRACT Through growing and weather conditions, Australian native herbs possess unique taste, flavour, and phytochemical content with wide bioactivity. Lemon myrtle and lemon-scented tea tree are Australian native herbs with a characteristic lemon flavour. The sensory and bioactive properties of herbs are influenced through a number of processes from harvest to use. Their cost effective processing and preparation while maintaining maximum sensory attributes and phytochemicals with the highest bioactivities are a priority towards determining their use. The review summarizes the recent research on the retention of polyphenols during pre-treatment or processing, various extraction parameters, extraction, bioactivities, isolation, and preparation or encapsulation. The limitations in existing research and direction for future research are also highlighted in this review. In general, the retention of polyphenols, antioxidant properties, and essential oil in lemon myrtle and lemon-scented tea tree leaves is significantly influenced by drying approaches, conditions, and pre-treatments. The extraction yield and stability of polyphenols and their various bioactivities including antioxidant, antimicrobial, anti-inflammatory can be varied with extraction solvent, applied method, and encapsulation operating conditions. Overall, further research is required in identification, isolation, and purification of individual bioactive compounds using sophisticated approaches. Finally, improvement in the overall process to a simple, cost-effective, and efficient one will benefit the end products and ultimately the native food industry, pharmaceuticals, and cosmetic industries.
High-energy neutrino fluxes from AGN populations inferred from X-ray surveys

High-energy neutrinos and photons are complementary messengers, probing violent astrophysical processes and structural evolution of the Universe. X-ray and neutrino observations jointly constrain conditions in active galactic nuclei (AGN) jets: their baryonic and leptonic contents, and particle production efficiency. Testing two standard neutrino production models for local source Cen A \citep{KT2008,BB2009}, we calculate the high-energy neutrino spectra of single AGN sources and derive the flux of high-energy neutrinos expected for the current epoch. Assuming that accretion determines both X-rays and particle creation, our parametric scaling relations predict neutrino yield in various AGN classes. We derive redshift-dependent number densities of each class, from {\it Chandra} and {\it Swift}/BAT X-ray luminosity functions \citep{SGB2008,ACS2009}. We integrate the neutrino spectrum expected from the cumulative history of AGN (correcting for cosmological and source effects, e.g. jet orientation and beaming). Both emission scenarios yield neutrino fluxes well above limits set by {\it IceCube} (by $\sim 4$--$10^6 \times$ at 1 PeV, depending on the assumed jet models for neutrino production). This implies that: (i) Cen A might not be a typical neutrino source as commonly assumed; (ii) both neutrino production models overestimate the efficiency; (iii) neutrino luminosity scales with accretion power differently among AGN classes and hence does not follow X-ray luminosity universally; (iv) some AGN are neutrino-quiet (e.g. below a power threshold for neutrino production); (v) neutrino and X-ray emission have different duty cycles (e.g. jets alternate between baryonic and leptonic flows); or (vi) some combination of the above.