Miracle-dz/UniFAR-FTUs huggingface dataset:
CESR Lattice for Two Beam Operations with Narrow Gap Undulators at CHESS

CESR has operated as a dedicated light source since the conclusion of colliding beam program in 2008. Two undulators with a 6.5mm-vertical gap were installed in Fall 2014, replacing a wiggler in the sextant of CESR that is the home to all CHESS beam lines. In order to operate narrow gap undulators with two beams, CESR pretzel lattice was redesigned so that e and e orbits are coincident in one machine sextant but separated in return arcs. In particular both e and e orbits are on axis through undulators. This “arc-pretzel” lattice has been the basis for undulator operation. To better understand the beam dynamics and improve machine performance, we developed many simulation tools: undulator modelling, injection tracking, etc. With installation of an additional quadrupole near undulators, the CESR lattice is further modified with a low beta waist in the insertion devices, allowing a more than two fold reduction of local beta functions. This reduction is anticipated to mitigate the effects of small aperture and undulator field errors and to enhance the x-ray brightness. The characterization of the lattice are compared with measurements of injection efficiency, tune scans, etc.
Multivariate Regression Analyses for Categorical Data

SUMMARY It is common to observe a vector of discrete and/or continuous responses in scientific problems where the objective is to characterize the dependence of each response on explanatory variables and to account for the association between the outcomes. The response vector can comprise repeated observations on one variable, as in longitudinal studies or genetic studies of families, or can include observations for different variables. This paper discusses a class of models for the marginal expectations of each response and for pairwise associations. The marginal models are contrasted with log-linear models. Two generalized estimating equation approaches are compared for parameter estimation. The first focuses on the regression parameters; the second simultaneously estimates the regression and association parameters. The robustness and efficiency of each is discussed. The methods are illustrated with analyses of two data sets from public health research.
Dihydroisotanshinone I and BMAL-SIRT1 Pathway in an In Vitro 6-OHDA-Induced Model of Parkinson’s Disease

Danshen has been widely used for the treatment of central nervous system diseases. We investigated the effect of dihydroisotanshinone I (DT), a compound extracted from Danshen, as well as the corresponding mechanisms in an in vitro-based 6-OHDA-induced Parkinson’s disease (PD) model. SH-SY5Y human neuroblastoma cell lines were pretreated with 6-hydroxydopamine (6-OHDA) and challenged with DT. Subsequently, the cell viability and levels of reactive oxygen species (ROS) and caspase-3 were analyzed. The effect of DT on the 6-OHDA-treated SH-SY5Y cells and the expression of the core circadian clock genes were measured using a real-time quantitative polymerase chain reaction. Our results indicated that DT attenuated the 6-OHDA-induced cell death in the SH-SY5Y cells and suppressed ROS and caspase-3. Moreover, DT reversed both the RNA and protein levels of BMAL1 and SIRT1 in the 6-OHDA-treated SH-SY5Y cells. Additionally, the SIRT1 inhibitor attenuated the effect of DT on BMAL1 and reduced the cell viability. The DT and SIRT1 activators activated SIRT1 and BMAL1, and then reduced the death of the SH-SY5Y cells damaged by 6-OHDA. SIRT1 silencing was enhanced by DT and resulted in a BMAL1 downregulation and a reduction in cell viability. In conclusion, our investigation suggested that DT reduces cell apoptosis, including an antioxidative effect due to a reduction in ROS, and regulates the circadian genes by enhancing SIRT1 and suppressing BMAL1. DT may possess novel therapeutic potential for PD in the future, but further in vivo studies are still needed.
Forecasting the Prospect of the Future Business Growth and Detecting Distorted Earnings in Public Insurance Industry: The Voice from Indonesia

Insurance firms nowadays become a highlight for society after some cases of payment failure in Indonesia occur such as Jiwasraya, and Bumi Putera. It may decrease public trust in the prospect of the insurance business in the future. This research aims to demonstrate the future growth of the insurance business performances in Indonesia and detect distorted earnings. Afterward, the future growth yielded by firms between distorted and non-distorted earnings is tested in this research. The sample was public insurance firms in Indonesia. Financial data were analyzed using Autoregressive Integrated Moving Average (ARIMA) for forecasting future growth, meanwhile, the nexus between variables was tested using a logistic regression model of panel data. The result found that the prospect of insurance firms will be growing with positive and negative values. They will pull through the ups and downs of business over the period of 2021 to 2027. This research further detected the distorted earnings in the insurance business performances and found that the future growth yielded by firms with distorted earnings has no difference from non-distorted earnings. The finding is addressed to the insurance companies as input. They need to prepare their business from now by increasing their performance. This finding is considered as the means of rebuilding their trust in the growth of the insurance business and providing the answer to society regarding the insurance business prospect in the future.
Reasonableness and Correctness for Operational Value-at-Risk

Calculating the amount of regulatory capital to cover unexpected losses due to operational events in the upcoming year has caused problems because of difficulties in fitting probability distributions to data. It is consequently difficult to judge an appropriate level of capital that reflects the risk profile of a financial institution. We provide theoretical and empirical analyses to link the calculated capital to the sum of losses using appropriate statistical approximations. We conclude that, in order to reasonably reflect the associated risk, the capital should be approximately half the sum of losses, with a wide bound for the ratio of capital to sum.
Multivariate hierarchical Bayesian models and choice of priors in the analysis of survey data

ABSTRACT Multivariate regression models, i.e. regression models where the left-hand side of the regression equation denotes a matrix of dependent variables, have long been developed. However, the statistical analysis of empirical data is usually restricted to multivariable regression methods with only one dependent variable. Within the framework of hierarchical Bayesian methods, the present study illustrates (i) how multivariate regression models offer new possibilities of information synthesis and theory testing in survey data analysis, and (ii) how sensitive the results are to different specifications of prior distributions. To this end, a large representative survey is utilized to specify two multivariate hierarchical Bayesian regression models which are calculated under two different prior distribution specifications. The estimation procedures and their implementation in R are described, convergence and predictive power analysis of each model are presented, and the advantages and disadvantages of multivariate regression methods are discussed. In general, the results obtained under each prior specification are to some extent similar, although differences were observed regarding model complexity, efficiency and predictive power. It is concluded that these methods facilitate the development and testing of complex research hypotheses, and are promising alternatives to a more efficient data analysis of large survey data sets.
Optimal navigation of autonomous systems through dynamic environments

The motion trajectory is obtained by solving an optimal control problem, which is translated into an optimization problem. Each trajectory will connect the current state of the autonomous system with the desired end state, which is imposed by the initial and final constraints. The autonomous system itself is represented by a kinematic vehicle model, which determines constraints on the velocity, acceleration,. Furthermore, there are anti-collision constraints which express that the shape of the autonomous system must not overlap with the shape of any obstacle. The proposed approach formulates these constraints by using the separating hyperplane theorem [1], which states that two non-intersecting convex sets can always be separated by a hyperplane. These constraints also contain a safety factor, which ensures that the system keeps a safe distance from all obstacles in the environment. Finally, the goal of the optimization problem is to minimize the motion time of the system.
The genome sequence of the Dark Spectacle, Abrostola triplasia (Linnaeus, 1758)

We present a genome assembly from an individual male Abrostola triplasia (the Dark Spectacle; Arthropoda; Insecta; Lepidoptera; Noctuidae). The genome sequence is 362.7 megabases in span. Most of the assembly is scaffolded into 31 chromosomal pseudomolecules, including the Z sex chromosome. The mitochondrial genome has also been assembled and is 15.34 kilobases in length. Gene annotation of this assembly on Ensembl identified 11,532 protein coding genes.
Relationship Between Cardio-metabolic Parameters in Diabetic Non-smokers, Current Smokers, and Quitters: A Systematic Review and Meta-analysis

Background: Smoking is associated with an increase in macrovascular and microvascular complications in people with diabetes. In addition to other concomitant vascular disturbances, it also appears to affect cardio-metric parameters. This may partly explain the acceleration of vascular complications in people with diabetes. This study aimed to investigate the relationship between smoking and its cessation in cardio-metric parameters in diabetes. Materials and Methods: Searches were conducted in Medline, Embase, and CINAHL. After screening 6866 studies, 14 observational studies with a total of 98978 participants with type 1 or type 2 diabetes were selected for the investigation. Results: Meta-analysis demonstrated that the mean difference of hemoglobin A1c (HBA1C) between non-smokers and smokers was 0.61% (95% confidence interval [CI] -0.88 to -0.33, P<0.001). The difference in low-density lipoprotein cholesterol between non-smokers and smokers was 0.11 mmol/L (95% CI -20.21 to 0.01, P=0.04). The difference in high-density lipoprotein cholesterol between non-smokers and smokers was 0.12 mmol/L (95% CI 0.08-0.15, P<0.001). However, there was no statistically significant difference in blood pressure (BP) between the two groups. The difference in HBA1C between quitters and continuing smokers was not statistically significant at 10.0. 10% (95 CI -0.42 to 0.21, P=0.53). Conversely, the results revealed that over 10 years, HBA1C was comparable between non-smokers and quitters. Conclusion: Smoking in patients with diabetes was widespread and should be the target of smoking cessation campaigns. Smoking cessation does not lead to an increase in HBA1C in the long term and may reduce vascular complications in diabetes by its desirable effect on the lipid profile.
Closing the gap in substance use disorder treatment through paraprofessional counselor training

ABSTRACT There is currently an insufficient supply of licensed substance use disorder counselors (SUDCs) to meet demand for services and the gap between supply and demand is projected to grow. Paraprofessional SUDCs offer one path to helping close this gap. The current study presents outcomes from a paraprofessional SUDC certificate program for Bachelor of Social Work (BSW) students. Student data (N = 66) were used to examine pre- to post-program changes in generalized SUDC knowledge and pre- to posttest changes in knowledge and self-efficacy from evidence-based workshops. Paired sample t-tests indicated that program participation significantly increased generalized SUDC knowledge. Participation in workshops significantly increased knowledge of motivational interviewing, integrative medicine, harm reduction, and grief and loss and self-efficacy related to integrative medicine and harm reduction. BSW students gain generalized and treatment-specific knowledge via participation in a SUDC certificate program. The deployment of paraprofessional SUDCs may provide one strategy to reduce treatment gaps
Automatic matching of buildings and corners

To update a portion of an existing cartographic database, the common practice is to relate a new data file to an existing file by means of survey control points that are included in both files. In the absence of such survey control points, well-defined points such as building corners can be used. This paper presents an algorithm to perform matching of common buildings and building corners in vector data files. The algorithm starts with a Fourier-based initial matching. A sequence of validity checks combined with robust estimation provides a complete recognition of common buildings. Matching of individual corner points is performed by using a similarity parameter, followed by a series of checks and validations. The two maps may have different scales, different coordinate systems, and no identifying cartographic labels. Experimental results have demonstrated the robustness of the algorithm.
Effectiveness of oxide trench array as a passive temperature compensation structure in AlN-on-silicon micromechanical resonators

This Letter presents the effectiveness of an oxide trench array (OTA) as a passive temperature compensation structure for aluminum nitride on silicon (AlN-on-Si) quasi-surface acoustic wave (SAW) micromechanical resonators over a wide temperature range. Two types of devices, namely, those with OTA and their reference counterparts without OTA, are designed, fabricated, and characterized over a wide temperature range of 360 °C. Experimental results show that the resonator with OTA has a first-order temperature coefficient of frequency (TCf1) at room temperature (20 °C) of 6.66 ppm/°C, which is lower than that of the reference device without OTA by 72% in magnitude. A high turnover temperature of 197 °C is achieved. Furthermore, the second-order temperature stability of the device has also improved. OTA is experimentally demonstrated to be an effective structure for passive temperature compensation, hence paving the way for using AlN-on-Si resonators as ultrasonic sensors or timing devices in ruggedized envi.
Effect of short-term abdominal breathing on heart rate variability as an indicator of emotional regulation in college student with internet gaming disorder

Abstract Online games are becoming increasingly popular, but a corresponding problem has emerged: Internet gaming disorder (IGD). IGD refers to problematic game use, where gaming results in problems with cognition and emotional regulation. Abdominal breathing (AB) is one method of psychophysiological reaction regulation. However, few studies have explored the effect of AB on gamers with IGD. In this study, we investigated the effects of short-term AB training (10 min) on the heart rate variability (HRV) of college students with IGD as they watched positive and negative online gaming videos. In total, 21 and 19 participants with low-risk IGD and high-risk IGD (HIGD), respectively, were included in the analysis. The results revealed that AB training was associated with increases in the natural logarithms of the total power and low-frequency HRV of the HIGD group during both video stimuli (p < .01). The difference in the natural logarithm of total power and low-frequency HRV between before and after AB was a predictor of IGD risk (area under the curve = 0.63 and 0.64, respectively). We find that short-term AB affects the HRV responses of college students with HIGD during game-related stimuli, particularly for negative games. These findings highlight the potential benefits of adding AB training to psychotherapies to improve the psychophysiological regulation of college students with IGD. Further studies should investigate the effect of long-term AB on the psychophysiological responses of those with IGD during gameplay.
Loan Fraud Users Detection in Online Lending Leveraging Multiple Data Views

In recent years, online lending platforms have been becoming attractive for micro-financing and popular in financial industries. However, such online lending platforms face a high risk of failure due to the lack of expertise on borrowers' creditworthness. Thus, risk forecasting is important to avoid economic loss. Detecting loan fraud users in advance is at the heart of risk forecasting. The purpose of fraud user (borrower) detection is to predict whether one user will fail to make required payments in the future. Detecting fraud users depend on historical loan records. However, a large proportion of users lack such information, especially for new users. In this paper, we attempt to detect loan fraud users from cross domain heterogeneous data views, including user attributes, installed app lists, app installation behaviors, and app-in logs, which compensate for the lack of historical loan records. However, it is difficult to effectively fuse the multiple heterogeneous data views. Moreover, some samples miss one or even more data views, increasing the difficulty in fusion. To address the challenges, we propose a novel end-to-end deep multiview learning approach, which encodes heterogeneous data views into homogeneous ones, generates the missing views based on the learned relationship among all the views, and then fuses all the views together to a comprehensive view for identifying fraud users. Our model is evaluated on a real-world large-scale dataset consisting of 401,978 loan records of 228,117 users from January 1, 2019, to September 30, 2019, achieving the state-of-the-art performance.
DOCUMENTATION OF CULTURAL HERITAGE THROUGH THE FUSION OF GEOMATIC TECHNIQUES. CASE STUDY OF THE CLOISTER OF "SANTO DOMINGO" (JAÉN, SPAIN)

Abstract. This study describes the methodology and results obtained for the modelling of the cloister of “Santo Domingo” located in the city of Jaén (Spain). The main objective of the application of geomatics techniques to this case was the graphic documentation of the site, the development of HBIM and the diffusion of the results. For this purpose, we developed a methodology based on the fusion of data obtained using several sensors, acquisition techniques and scales. In this sense, we used TLS, RPAS aerial photogrammetry and terrestrial photogrammetry using a spherical camera. As results, we obtained a 3D model with real texture and from this we developed several applications focused on the documentation and diffusion of the results, such as the BIM (HBIM), an interactive virtual tour and a 360 virtual tour available on a website. The proposed methodology has demonstrated its feasibility for performing this type of study due to the results and accuracy achieved with this application. We highlight the potential of current geomatic techniques for documenting this type of cultural heritage regardless of its structure and dimensions, selecting in each case the most appropriate methodology in relation to the scale of the products demanded. In addition, we also emphasize the need to use several techniques and the fusion of their partial products in order to obtain a complete product of the whole scene, taking advantage of the specificities of each one.
A new multi-source remote sensing image sample dataset with high resolution for flood area extraction: GF-FloodNet

ABSTRACT Deep learning algorithms show good prospects for remote sensing flood monitoring. They mostly rely on huge amounts of labeled data. However, there is a lack of available labeled data in actual needs. In this paper, we propose a high-resolution multi-source remote sensing dataset for flood area extraction: GF-FloodNet. GF-FloodNet contains 13388 samples from Gaofen-3 (GF-3) and Gaofen-2 (GF-2) images. We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it. Compare with other flood-related datasets, GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels, but also consists of multi-source remote sensing data. We thoroughly validate and evaluate the dataset using several deep learning models, including quantitative analysis, qualitative analysis, and validation on large-scale remote sensing data in real scenes. Experimental results reveal that GF-FloodNet has significant advantages by multi-source data. It can support different deep learning models for training to extract flood areas. There should be a potential optimal boundary for model training in any deep learning dataset. The boundary seems close to 4824 samples in GF-FloodNet. We provide GF-FloodNet at https://www.kaggle. com/datasets/pengliuair/gf-floodnet and https://pan.baidu. com/s/1vdUCGNAfFwG5UjZ9RLLFMQ? pwd=8v6o.
The role of the interference polynomial in the Euler deconvolution algorithm

The Euler deconvolution method belongs to the most popular semi-automated interpretation techniques in gravimetry and magnetometry. During its application on real data, it was realised that it can be performed only with the additional constant term B (background term), because of the regional field components. In the proposed contribution, the idea of the interference polynomial from the Werner deconvolution is introduced into the Euler deconvolution algorithm. Its role can be seen on two levels: a) it helps to deal with the influence of neighbouring anomalies, b) only thanks to this modification is it possible to obtain real solutions for the correct value of the structural index of a contact structure in gravimetry (N = -1). On the other hand, the proposed modification amplifies the instability of the method and either the smoothing of the input field or the stabilisation of its gradient evaluation must be realised.
Optimization procedure for a Talbot-Lau x-ray phase-contrast imaging system

In Talbot-Lau x-ray imaging, the fringe visibility provided by the interferometer is a crucial quality parameter to preserve high quality images at an acceptable dose level. The noise of the obtained differential phase signal and the dark-field image is directly influenced by the visibility. To optimize the performance of such an interferometer, we use wave-field simulations to investigate the effect of the phase grating G1. Therefore, we varied the grating parameters duty cycle and grating bar height. Each set of these parameters were evaluated for different propagation distances and for multiple x-ray spectra. In this multidimensional space the interferometer configuration with the highest visibility over a wide range of energies was selected to cover a multiple possible x-ray applications. We manufactured the optimized phase grating G1, the corresponding source grating G0 and analyzer grating G2 and compare the experimental results with the expected results obtained from simulations. The presented measurements show outstanding visibilities up to 50% using a broad x-ray spectrum. These measurements are in very good agreement to the simulation results. The achieved visibility is up to two times higher than for a standard-type setup. This enhancement results in high quality images at a reasonable dose level which we exemplarily demonstrate by imaging a foreign object in a pork trotter.