Miracle-dz/UniFAR-FTUs huggingface dataset:
Interbank network and banks' credit supply

This paper examines how the interbank network structure influences banks’ credit supply to the real economy. Using the dynamic UK interbank networks based on the quarterly evolutions of bilateral exposures from 2014 to 2021, we find evidence of both risk-sharing effect through the interbank core-periphery structure and liquidity-insurance effect within interbank lending communities. Core banks with high global centrality and banks in a larger community tend to lend more to non-financial firms. The effect of global centrality is still significant after controlling for the local effects including local centrality and community size. During the Covid-19 pandemic, the effect of risk sharing is mitigated whereas the effect of community lending is strengthened.
Two theories of endogenous money: an empirical study of Korea

Abstract This study tests Korea’s two endogenous money supply channels: the accommodationist and structuralist channels. The main analytic tools are the Fully Modified OLS, Maki’s structural break test, Error Correction Model, and Impulse Response Function (IRF) analysis. The primary purpose of this study is to propose analytical methods to compare the two endogenous money supply channels. The analytic models also consider the impact of fiscal policy and the current account balance on reserves. The test results reveal a unilateral causality from loan to reserve and a bilateral causal relationship between loan/reserve and Lf/deposit ratio. These results suggest that both accommodationist and structuralist channels coexist in Korea. However, the structuralist channel explains a relatively small portion of the endogenous money supply process. The IRF analysis does not provide evidence for the pressure on the interest rate spread exerted by financial institutions’ portfolio adjustment. This result can be attributed mainly to the central bank’s growing accommodative attitude toward the reserve demand.
The Influence of Semi-Empirical Control Valve Model Parameter Accuracy on Simulated Hydraulic System Responses

A parameter sensitivity analysis method is applied to an investigation of the importance of the identification accuracy of the parameter values describing the steady-state and dynamic behavior of three commonly used fluid power circuit components, i.e., single-stage pressure relief, reducing, and two-way flow control values. The component models are verified; these semi-empirical models take into account the most important continuous and discontinuous nonlinearities
3D Virtual reconstruction of asphalt mixture microstructure based on rigid body dynamic simulation

ABSTRACT This paper presents a virtual generation method for the heterogeneous microstructure of asphalt mixtures. The coarse aggregates used in the digital asphalt mixture were derived from real aggregates captured by a three-dimensional (3D) laser scanner. Then, a rigid-body dynamic simulation framework was proposed to generate a stable skeletal structure composed of coarse aggregates similar to those formed by compaction. Furthermore, FAM and air voids were generated based on the generated aggregate skeleton and the assumption of equal thickness of FAM film. To prove the reliability of the model, the air voids of the digital asphalt mixture samples were verified by comparison with real specimens. The generated digital specimens were also applied to finite element method (FEM) simulations to predict the dynamic modulus of the asphalt mixture and the simulated results were in good agreement with the experimental results. The method can be applied to study the spatial distribution of asphalt mixture components, the movement pattern of aggregate particles during compaction and numerical simulations at the microscopic scale.
Static Analysis of JNI Programs via Binary Decompilation

JNI programs are widely used thanks to the combined benefits of C and Java programs. However, because understanding the interaction behaviors between two different programming languages is challenging, JNI program development is difficult to get right and vulnerable to security attacks. Thus, researchers have proposed static analysis of JNI program source code to detect bugs and security vulnerabilities in JNI programs. Unfortunately, such source code analysis is not applicable to compiled JNI programs that are not open-sourced or open-source JNI programs containing third-party binary libraries. While JN-SAF, the state-of-the-art analyzer for compiled JNI programs, can analyze binary code, it has several limitations due to its symbolic execution and summary-based bottom-up analysis. In this paper, we propose a novel approach to statically analyze compiled JNI programs without their source code using binary decompilation. Unlike JN-SAF that analyzes binaries directly, our approach decompiles binaries and analyzes JNI programs with the decompiled binaries using an existing JNI program analyzer for source code. To decompile binaries to compilable C source code with precise JNI-interoperation-related types, we improve an existing decompilation tool by leveraging the characteristics of JNI programs. Our evaluation shows that the approach is precise as almost the same as the state-of-the-art JNI program analyzer for source code, and more precise than JN-SAF.
The AG Carinae nebula: abundant evidence for a red supergiant progenitor?

AG Carinae is a massive, evolved supergiant which is thought to be in transition from an 0 star to a Wolf-Rayet (WR) star and is currently identified as a luminous blue variable (LBV) with 10gLlL0 = 6.0. We present an abundance study of the ejecta nebula surrounding AG Car with the aim of elucidating the evolutionary history of the central star. Physical parameters and abundances are derived for five regions across the nebula from high spatial resolution spectroscopy obtained at the Anglo-Australian Telescope (AAT). We derive an average Te of 6350 ± 400 K, an ne of 820 ± 170 cm -3, and find that nitrogen (N) is enhanced by a factor of 4.5 ± 1.3 and that oxygen (0) is deficient by a factor of 15.1 ± 7.2. The derived abundances are compared with those determined for ejecta-type nebulae around WR stars and those predicted by hydrodynamical calculations and stellar evolutionary models. We find that the AG Car nebula is composed of mildly processed material that has not reached the CNO-equilibrium abundances predicted for LBV nebulae. The similarity of the AG Car nebular N abundance to WR nebulae leads us to suggest that the nebulae were ejected at the same evolutionary point, and have undergone no further chemical modification. For AG Car, this point appears to have occurred before the LBV phase because of the observed low N enrichment. Comparison of the observed N abundance with evolutionary model predictions indicates that the AG Car nebula may represent the hydrogen-rich (H-rich) envelope of a red supergiant (RSG). The problem of an RSG progenitor for AG Car is discussed and it is found that the LBV model of Stothers & Chin, incorporating a brief unstable RSG phase, is capable of explaining the observations. We conclude that despite its high luminosity, AG Car has probably experienced a brief RSG phase where it ejected its outer layers to form the currently observed nebula.
GLOBAL INSTABILITIES IN SPATIALLY DEVELOPING FLOWS: Non-Normality and Nonlinearity

The objective of this review is to critically assess the different approaches developed in recent years to understand the dynamics of open flows such as mixing layers, jets, wakes, separation bubbles, boundary layers, and so on. These complex flows develop in extended domains in which fluid particles are continuously advected downstream. They behave either as noise amplifiers or as oscillators, both of which exhibit strong nonlinearities (Huerre & Monkewitz 1990). The local approach is inherently weakly nonparallel and it assumes that the basic flow varies on a long length scale compared to the wavelength of the instability waves. The dynamics of the flow is then considered as a superposition of linear or nonlinear instability waves that, at leading order, behave at each streamwise station as if the flow were homogeneous in the streamwise direction. In the fully global context, the basic flow and the instabilities do not have to be characterized by widely separated length scales, and the dynamics is then viewed as the result of the interactions between Global modes living in the entire physical domain with the streamwise direction as an eigendirection. This second approach is more and more resorted to as a result of increased computational capability. The earlier review of Huerre & Monkewitz (1990) emphasized how local linear theory can account for the noise amplifier behavior as well as for the onset of a Global mode. The present survey first adopts the opposite point of view by demonstrating how fully global theory accounts for the noise amplifier behavior of open flows. From such a perspective, there is strong emphasis on the very peculiar nonorthogonality of linear Global modes, which in turn allows a novel interpretation of recent numerical simulations and experimental observations. The nonorthogonality of linear Global modes also imposes severe constraints on the extension of linear global theory to the fully nonlinear regime. When the flow is weakly nonparallel, this limitation is so severe that the linear Global mode theory is of little help. It is then much more appropriate to develop a fully nonlinear formulation involving the presence of a front separating the base state region from the bifurcated state region.
A Transformative Leadership Journey: Experiences of Graduates of the American Physical Therapy Association Fellowship in Education Leadership

Supplemental Digital Content is Available in the Text. Introduction. Physical therapy education has an acute need for effective leaders due to multiple factors, including increasing numbers of programs and retirement of experienced leaders. Recognizing the need for formal leadership education, the American Physical Therapy Association (APTA) developed a fellowship in physical therapy educational leadership in 2011. This study aimed to understand participants' perceptions of their leadership experience and the role of the APTA fellowship. Literature Review. A commonly reported approach to evaluating leadership education programs was designed by Kirkpatrick. Participants. Twenty-five fellowship graduates from 2012 to 2019 participated. Methods. We used a qualitative methodological approach incorporating a heuristic technique to gain an understanding of fellowship graduates' long-term perspectives. Semi-structured interviews were conducted via Zoom. Data were analyzed using thematic analysis. Results. Three themes were constructed using a journey metaphor: embarking on the journey, engaging in the journey, and realizing the effects of the journey. 1) Embarking on the journey included 2 subthemes: a) participants' personal aspirations as career planners, reluctant leaders, or explorers; and b) institutions' goals for a change in leadership or intentional succession planning. 2) Engaging in the journey included 3 subthemes: a) the fellowship learning environment builds a sense of community; b) consideration of leadership frames influences participants' thinking about leadership; and c) participants' institutional environment influences their learning. 3) Effects of the journey included 3 subthemes: a) personal growth as a leader including personal awareness and confidence; b) institutions' benefits from participants' leadership projects and increased engagement in institutional leadership; and c) physical therapy professions' gains from participants' seeking leadership positions in professional organizations. Discussion/Conclusions. The APTA Fellowship in Educational Leadership results in transformative change of participants as leaders. Findings offer guidance to individuals considering embarking on a leadership journey and institutions considering how to prepare and support emerging leaders in physical therapy education.
Page-Address Coalescing of Vector Gather Instructions for Efficient Address Translation

Vector gather instructions are available in various processors, which are essential for handling irregular memory accesses. Additionally, the processors support virtual memory that allows programmers not to consider the limitation of the physical memory space. To realize the virtual memory, the processors require address translation between virtual and physical addresses. When a vector gather instruction loads data elements distributed over the physical memory space, all virtual addresses must be translated one by one, causing many translations by accessing a Translation Lookaside Buffer (TLB). Hence, the TLB easily becomes a bottleneck in handling vector gather instructions. To relieve the bottleneck, this paper proposes an address coalescing method for the address translations of vector gather instructions by utilizing vector arithmetic units in the processor. The evaluation results show that the proposed method can achieve a 2x performance improvement in numerical and 1.08x in graph applications, which contain many vector gather instructions.
X-rays from G74.9 + 1.2 - An elderly Crab Nebula

The supernova remnant G74.9 + 1.2, which resembles the Crab Nebula in many of its radio properties, has been detected in 0.15-3 keV X-rays with the Imaging Proportional Counter on the Einstein Observatory. The intrinsic luminosity of the object is about 100 times weaker than the Crab Nebula in this band. The X-ray morphology is centrally brightened (not a shell) with a total extent about 5 arcmin (17 pc). The nearby, intense, compact radio source 2013 + 370 is also probably detected as an extended X-ray source. It is argued that G74.9 + 1.2 represents an object similar to the Crab Nebula but of age 4000-7000 years. If this is the case, the period of the pulsar or neutron star (whose loss of rotational energy fuels the nebula) is predicted to be 0.1-0.2 s.
Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors

This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained from questionnaires. A variety of classification and regression models were compared, including Long Short-Term Memory (LSTM) models. Additionally, the effects of different normalization methods and the impact of using different sensors were studied, and the way in which the results differed between the study subjects was analyzed. The results revealed that regression models generally performed better than classification models, with LSTM regression models achieving the best results. The normalization method called baseline reduction was found to be the most effective, and when used with an LSTM-based regression model it achieved high accuracy in detecting valence (mean square error = 0.43 and R2-score = 0.71) and arousal (mean square error = 0.59 and R2-score = 0.81). Moreover, it was found that even if all biosignals were not used in the training phase, reliable models could be obtained; in fact, for certain study subjects the best results were obtained using only a few of the sensors.
Artificial intelligence for materials research at extremes

Materials development is slow and expensive, taking decades from inception to fielding. For materials research at extremes, the situation is even more demanding, as the desired property combinations such as strength and oxidation resistance can have complex interactions. Here, we explore the role of AI and autonomous experimentation (AE) in the process of understanding and developing materials for extreme and coupled environments. AI is important in understanding materials under extremes due to the highly demanding and unique cases these environments represent. Materials are pushed to their limits in ways that, for example, equilibrium phase diagrams cannot describe. Often, multiple physical phenomena compete to determine the material response. Further, validation is often difficult or impossible. AI can help bridge these gaps, providing heuristic but valuable links between materials properties and performance under extreme conditions. We explore the potential advantages of AE along with decision strategies. In particular, we consider the problem of deciding between low-fidelity, inexpensive experiments and high-fidelity, expensive experiments. The cost of experiments is described in terms of the speed and throughput of automated experiments, contrasted with the human resources needed to execute manual experiments. We also consider the cost and benefits of modeling and simulation to further materials understanding, along with characterization of materials under extreme environments in the AE loop. Graphical abstract AI sequential decision-making methods for materials research: Active learning, which focuses on exploration by sampling uncertain regions, Bayesian and bandit optimization as well as reinforcement learning (RL), which trades off exploration of uncertain regions with exploitation of optimum function value. Bayesian and bandit optimization focus on finding the optimal value of the function at each step or cumulatively over the entire steps, respectively, whereas RL considers cumulative value of the labeling function, where the latter can change depending on the state of the system (blue, orange, or green).
Wave Propagation Speed and Equivalent Impedance of Dielectric-Coated Conductor Representing Lightning Return-Stroke Channel

In electromagnetic models of lightning return stroke, dielectric coating of channel-representing conductor is used to adjust the current wave propagation speed. In this letter, we examine dependences of both the speed and channel equivalent impedance on the electric permittivity and magnetic permeability of such coating. With increasing the permeability, the propagation speed of current wave decreases and the equivalent impedance increases significantly. In contrast, these parameters are not much influenced by the increase of permittivity. The reason for the difference has been explained in terms of the transmission-line theory.
Pan-genome and transcriptome analyses provide insights into genomic variation and differential gene expression profiles related to disease resistance and fatty acid biosynthesis in eastern black walnut (Juglans nigra)

Abstract Walnut (Juglans) species are used as nut crops worldwide. Eastern black walnut (EBW, Juglans nigra), a diploid, horticultural important woody species is native to much of eastern North America. Although it is highly valued for its wood and nut, there are few resources for understanding EBW genetics. Here, we present a high-quality genome assembly of J. nigra based on Illumina, Pacbio, and Hi-C technologies. The genome size was 540.8 Mb, with a scaffold N50 size of 35.1 Mb, and 99.0% of the assembly was anchored to 16 chromosomes. Using this genome as a reference, the resequencing of 74 accessions revealed the effective population size of J. nigra declined during the glacial maximum. A single whole-genome duplication event was identified in the J. nigra genome. Large syntenic blocks among J. nigra, Juglans regia, and Juglans microcarpa predominated, but inversions of more than 600 kb were identified. By comparing the EBW genome with those of J. regia and J. microcarpa, we detected InDel sizes of 34.9 Mb in J. regia and 18.3 Mb in J. microcarpa, respectively. Transcriptomic analysis of differentially expressed genes identified five presumed NBS-LRR (NUCLEOTIDE BINDING SITE-LEUCINE-RICH REPEAT) genes were upregulated during the development of walnut husks and shells compared to developing embryos. We also identified candidate genes with essential roles in seed oil synthesis, including FAD (FATTY ACID DESATURASE) and OLE (OLEOSIN). Our work advances the understanding of fatty acid bioaccumulation and disease resistance in nut crops, and also provides an essential resource for conducting genomics-enabled breeding in walnut.
Protein-Nanoparticle Interactions Govern the Interfacial Behavior of Polymeric Nanogels: Study of Protein Corona Formation at the Air/Water Interface

Biomedical applications of nanoparticles require a fundamental understanding of their interactions and behavior with biological interfaces. Protein corona formation can alter the morphology and properties of nanomaterials, and knowledge of the interfacial behavior of the complexes, using in situ analytical techniques, will impact the development of nanocarriers to maximize uptake and permeability at cellular interfaces. In this study we evaluate the interactions of acrylamide-based nanogels, with neutral, positive, and negative charges, with serum-abundant proteins albumin, fibrinogen, and immunoglobulin G. The formation of a protein corona complex between positively charged nanoparticles and albumin is characterized by dynamic light scattering, circular dichroism, and surface tensiometry; we use neutron reflectometry to resolve the complex structure at the air/water interface and demonstrate the effect of increased protein concentration on the interface. Surface tensiometry data suggest that the structure of the proteins can impact the interfacial properties of the complex formed. These results contribute to the understanding of the factors that influence the bio-nano interface, which will help to design nanomaterials with improved properties for applications in drug delivery.
Longitudinal outcomes of employment, income and housing for Flemish care leavers

Care leavers are more often unemployed, have lower annual earnings and live in inferior housing circumstances than their peers in the general population. International research has extensively studied and discussed these outcomes. However, in Flanders (the Dutch speaking part of Belgium), no previous quantitative studies are known. This study therefore explores and predicts outcomes on employment, income and housing circumstances of care leavers in Flanders and investigates if these outcomes improve in the years after leaving care. Associations with placement factors are explored to support recommendations to the clinical field. The current study involves 175 care-leavers (both family foster care (n = 105) and residential care (n = 70)). Data was collected using a self-reporting questionnaire touching multiple domains. The participants of the study (49 male, 126 female) are between 18 and 27 years old. Care leavers are employed at younger ages but are also more often unemployed than their peers from the general population, they earn significantly less than peers and are more often homeless. On all domains, no significant differences were found between foster- and residential care leavers. Placement characteristics are more declaratory than only the type of care. In the current study, most outcomes improve during the years after leaving care, this supports the theory of emerging adulthood. Although we cannot infer causal effects, care leavers who experienced fewer placement changes and received individual therapy during their time in care tend to have better outcomes in the current study. The same applies for care leavers who were prepared for leaving care and supported after leaving care. Policy makers should focus on increased support regarding breakdown prevention and individual mental health support during the time in care. Preparation for placement ending and aftercare should be provided.