Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Urban Populations of a Gliding Mammal Vulnerable to Decline?

Simple Summary Arboreal mammals provide important ecological services, but they are threatened by many anthropogenic activities, including habitat isolation and fragmentation. We investigated the population dynamics of the Australian squirrel glider in a small and larger reference population over a 16 year period. Population modelling suggests that a decline occurred in the small population but not the larger one. External factors are implicated in the decline and warrant further investigation. Abstract Small populations are at high risk of extinction, and they are likely to need management intervention. Successful management, however, relies on sufficient long-term demographic data in order to determine whether apparent declines are natural fluctuations or the product of threatening processes. In this study, we monitored a small urban population of squirrel gliders (Petaurus norfolcensis) in Queensland, Australia, over a 16 year period. A reference population in a larger forest patch was also studied in order to investigate whether its demographic trends were similar. Using mark-recapture data to generate estimates of apparent survival and population size, we found evidence of a decline within the small population but not in the reference population over the monitoring period. We suggest that the influence of multiple factors may have led to the decline, but, ultimately, that the genetic condition of the small population may be responsible. Understanding demographic trends is an important context for management interventions of small populations, although causes of decline need to be identified for successful management. The squirrel glider provides a useful case study for small urban populations and particularly for arboreal mammals.
NPRC-I/O: An NoC-Based Real-Time I/O System With Reduced Contention and Enhanced Predictability

All systems rely on inputs and outputs (I/Os) to perceive and interact with their surroundings. In safety-critical systems, it is important to guarantee both the performance and time-predictability of I/O operations. However, with the continued growth of architectural complexity in modern safety-critical systems, satisfying such real-time requirements has become increasingly challenging due to complex I/O transaction paths and extensive hardware contention. In this article, we present a new Network-on-Chip (NoC)-based Predictable I/O system framework (NPRC-I/O) which reduces this contention and ensures the performance and time-predictability of I/O operations. Specifically, NPRC-I/O contains a programmable I/O command controller (NPRC-CC) and a run-time reconfigurable NoC ( $\text{R}^{2}$ NoC), which provides the capability to adjust I/O transaction paths at run time. Using this flexibility, we construct an end-to-end transmission latency analysis and an optimization engine that produces configurations for NPRC-I/O and the I/O traffic in a given system. The constructed analysis and optimization engine guarantee the timing of all hard real-time traffic while reducing the deadline misses of soft real-time traffic and overall transmission latency.
Bayesian calibration of strength parameters using hydrocode simulations of symmetric impact shock experiments of Al-5083

Predictive modeling of materials requires accurately parameterized constitutive models. Parameterizing models that describe dynamic strength and plasticity require experimentally probing materials in a variety of strain rate regimes. Some experimental protocols (e.g., plate impact) probe the constitutive response of a material using indirect measures such as free surface velocimetry. Manual efforts to parameterize constitutive models using indirect experimental measures often lead to non-unique optimizations without quantification of parameter uncertainty. This study uses a Bayesian statistical approach to find model parameters and to quantify the uncertainty of the resulting parameters. The technique is demonstrated by parameterizing the Johnson-Cook strength model for aluminum alloy 5083 by coupling hydrocode simulations and velocimetry measurements of a series of plate impact experiments. Simulation inputs and outputs are used to calibrate an emulator that mimics the outputs of the computationally intensive simulations. Varying the amount of experimental data available for emulator calibration showed clear differences in the degree of uncertainty and uniqueness of the resulting optimized Johnson-Cook parameters for Al-5083. The results of the optimization provided a numerical evaluation of the degree of confidence in model parameters and model performance. Given an understanding of the physical effects of certain model parameters, individual parameter uncertainty can be leveraged to quickly identify gaps in the physical domains covered by completed experiments.Predictive modeling of materials requires accurately parameterized constitutive models. Parameterizing models that describe dynamic strength and plasticity require experimentally probing materials in a variety of strain rate regimes. Some experimental protocols (e.g., plate impact) probe the constitutive response of a material using indirect measures such as free surface velocimetry. Manual efforts to parameterize constitutive models using indirect experimental measures often lead to non-unique optimizations without quantification of parameter uncertainty. This study uses a Bayesian statistical approach to find model parameters and to quantify the uncertainty of the resulting parameters. The technique is demonstrated by parameterizing the Johnson-Cook strength model for aluminum alloy 5083 by coupling hydrocode simulations and velocimetry measurements of a series of plate impact experiments. Simulation inputs and outputs are used to calibrate an emulator that mimics the outputs of the computationally inte.
Denoising algorithm in dual-wavelength images of retinal oximetry using variance stabilizing transform and cross dual domain filter

Image noise can dramatically affect image processing and hemoglobin oxygen saturation (SO2) calculation accuracy in non-invasive retinal oximetry. Recently, the denoising algorithm based on Variance stabilizing transform (VST) and dual domain filter (DDID) has been proposed to address this issue by our lab. Actually, dual-wavelength retinal images belong to multi-mode images, in order to maximize the use of complementary information between the dual-wavelength images, we further improve the algorithm. Firstly, noise parameters were also estimated by mixed Poisson-Gaussian (MPG) noise model. Secondly, a novel MPG denoising algorithm which we called VST+CDDID was proposed based on VST and cross dual domain filter. To evaluate the proposed algorithm, both simulative and real experiments have been carried out and the results show that the proposed method can effectively remove MPG noise and preserve edge details. Compared with current denoising methods based on single mode, such as VST+DDID and VST+ block-matching 3D filtering (BM3D), the proposed method shows great advantage in terms of visual quality and low-contrast detail. In conclusion, VST+CDDID can effectively use the complementary information between multi-mode images and combine the advantages of both cross bilateral filter in the time domain and Short-Time Fourier Transform (STFT) in the frequency domain. And it effectively restrained ringing effect by alternating iterative.
A Numerical Study of Passive Receptor-Mediated Endocytosis of Nanoparticles: The Effect of Mechanical Properties

In this work, a three-dimensional axisymmetric model with nanoparticle, receptor-ligand bonds and cell membrane as a system was used to study the quasi-static receptor-mediated endocytosis process of spherical nanoparticles in drug delivery. The minimization of the system energy function was carried out numerically, and the deformations of nanoparticle, receptor-ligand bonds and cell membrane were predicted. Results show that passive endocytosis may fail due to the rupture of receptor-ligand bonds during the wrapping process, and the size and rigidity of nanoparticles affect the total deformation energy and the terminal wrapping stage. Our results suggest that, in addition to the energy requirement, the success of passive endocytosis also depends on the maximum strength of the receptor-ligand bonds.
An assessment of potential thermostable D-Allulose epimerases obtained from genome mining using a computational simulation approach

: D-Allulose is a type of rare sugar with low-calorie and many health benefits, which shows a great prospect to be utilized for the food industries. The production of D-Allulose can be achieved biotechnologically using D-Allulose 3-Epimerase (DAEase) enzyme, which is an environmentally friendly approach compared to the chemical method. However, for the sustainable and large-scale production of D-Allulose, it is necessary to use the DAEase enzyme that has several characteristics, such as high thermal activity, high stability, and high specificity. The present work aims to discover new potential DAEase from thermophilic bacteria via a computational biology approach. Genome mining was first performed to obtain the relevant sequences from the database. The sequences were subsequently examined in order to ascertain their phylogenetic placements and identify the conserved motifs. Additionally, 3D modeling and Molecular Dynamic (MD) simulation of the protein were undertaken to explore the
A Heterogeneous Approach to Gapping

We argue that Gapping is a structurally heterogeneous phenomenon, supported only in vP and CP coordinate structures. This analysis thereby captures the Gapping scope ambiguity: the wide scope construal is the result of vP coordination, as in (2-a), and the distributive scope construal, the result of CP coordination, as in (2-b). It also makes several novel predictions, including that split scope in Gapping is impossible.
Power Flow Optimization and Control Strategy for Energy Router in Dual Mode Traction Power Supply System

A dual mode traction power supply system (TPSS), as a high-efficiency transportation approach, is composed of a mainline railway (AC traction power supply system) and an urban railway (DC traction power supply system). However, due to the neutral sections, the power from the two systems has been isolated, resulting in a low utilization rate of regenerative braking energy (RBE). The power flow from the two systems can not be managed cooperatively, causing high electricity bills. Therefore, this paper proposes a dual mode based energy router (DMER), which provides a path for energy interaction between two systems through multi-terminal converters. The proposed DMER integrates a hybrid energy storage system (HESS) and an energy feedback system (EFS) to recycle RBE. Moreover, a hierarchical control strategy is presented. The upper control layer is an optimized operation model, which takes the state of charge (SoC), energy conservation, and the other parameters as constraints to utilize more RBE and achieve the minimum daily operation cost of substations. The lower control layer identifies the control strategy for multiple converters to ensure the stable operation and dynamic response of DMER. Finally, the effectiveness of the topology and control strategy is verified using field load data and experiments.
Predicting Hepatocellular Carcinoma Risk in Chronic Hepatitis B Patients Receiving Finite Periods of Antiviral Therapy

Simple Summary Hepatocellular carcinoma (HCC) is a severe complication of chronic hepatitis B virus (HBV) infection. HCC can still develop in CHB patients undergoing antiviral therapy. Although several scoring systems for the prediction of HCC risk in CHB patients are available, very few of them are established for CHB patients receiving antiviral therapy. Our study developed a predictive scoring system for CHB patients on finite antiviral treatment, categorizing them into three risk groups based on a multivariate Cox proportional hazards model. Significant differences in HCC incidence were observed among these groups. Abstract PURPOSE: Hepatocellular carcinoma (HCC) is one of the most severe complications in chronic hepatitis B virus (HBV) infection. HCC can still develop in patients with chronic HBV (CHB) infection undergoing antiviral therapy. Several effective scoring systems for the prediction of HCC risk in CHB patients have been established. However, very few of them are designed for CHB patients receiving nucleos(t)ide analogues (NAs) therapy. Furthermore, none are available for HCC risk prediction in CHB patients receiving finite periods of antiviral therapy. METHODS: This study enrolled 790 consecutive treatment-naïve patients with CHB infection who had visited our liver clinics from 2008 to 2012 for pretreatment assessment before receiving antiviral therapies. The treatments were provided at finite periods according to the National Health Insurance Policy in Taiwan. The last follow-up date was 31 December 2021. We analyzed the virological and clinical factors in these 790 CHB patients receiving finite periods of NA treatments and identified the most significant risk factors for HCC to establish a novel predictive scoring system. By using stepwise selection in a multivariate Cox proportional hazards model, we divided the patients into three risk groups. RESULTS: Our predictive scoring system included five independent variables: genotype C (adjusted HR [aHR] = 2.23), NA-withdraw-related hepatitis relapse (aHR = 6.96), male (aHR = 4.19), liver cirrhosis (aHR = 11.14), and T1768A core promoter mutation (aHR = 3.21). This model revealed significant differences in HCC incidence among the three risk groups. The 5-year cumulative HCC risk significantly differed among the three risk groups (low risk: 1.33%, moderate risk: 4.99%, and high risk: 17.46%), with log-rank test p < 0.001. CONCLUSION: Our predictive scoring system is a promising tool for the prediction of HCC in CHB patients receiving finite NA treatments. Genotype C, NA-withdraw-related hepatitis relapse, male gender, liver cirrhosis, and the T1768A HBV core promoter mutation were significant independent risk factors.
Probability‐type, Shewhart‐type and EWMA control charts for individual observations from the two‐parameter Lindley distribution

Lindley distribution is a continuous distribution with various applications some of which are in medicine, genetics, epidemiology, biology, finance and actuarial sciences, ecology, meteorology, sociology, demography, agriculture, hydrology, geosciences, reliability and engineering, life testing and survival analysis, airborne systems and communications, environmental studies and modeling and describing of human mistakes, strikes, accidents, behavioural and emotional or IQ test scores and waiting times of customers in queues until service, etc. Due to its variety of applications, it appears to be important that control charts for detected shifts in a process should be constructed under the assumption that the quality characteristic of interest follows a Lindley‐related distribution. Here, we construct probability‐type, Shewhart‐type and EWMA control charts (and deal with the optimal choice of its parameters) for individual observations from the two‐parameter Lindley distribution, investigate and compare their performance and illustrate them using examples with both simulated and real data. The whole analysis reveals the superiority of using skewness correction for the construction of the control charts against not using it.
Comparison of Protein- or Amino Acid-Based Supplements in the Rehabilitation of Men with Severe Obesity: A Randomized Controlled Pilot Study

Background: Weight loss is associated with a reduction in all body compartments, including muscle mass (MM), and this effect produces a decrease in function and muscle strength. Our objective was to assess the impact of protein or amino acid supplements on MM loss in middle-aged men (age < 65 years) with severe obesity (BMI > 35 kg/m2) during weight loss. Materials and Methods: We conducted a single-site randomized controlled trial (Clinicaltrials.gov NCT05143398) with 40 in-patient male subjects with severe obesity. Participants underwent an intervention program consisting of a low-calorie balanced diet and structured physical activity. They were randomly assigned to 4-week treatment groups: (1) control (CTR, N = 10), (2) protein (P, N = 10), (3) branched-chain amino acid (BCAA, N = 10), and (4) essential amino acid mixture with tricarboxylic acid cycle intermediates (PD-E07, N = 10) supplementation. Results: Following 4 weeks of intervention, all groups showed similar reductions in body weight compared to baseline. When examining the delta values, a notable increase in muscle mass (MM) was observed in the PD-E07 intervention group [MM (kg): 2.84 ± 3.57; MM (%): 3.63 ± 3.14], in contrast to the CTR group [MM (kg): −2.46 ± 3.04; MM (%): −0.47 ± 2.28], with a statistical significance of p = 0.045 and p = 0.023, respectively. However, the MM values for the P group [MM (kg): −2.75 ± 5.98, p = 0.734; MM (%): −0.44 ± 4.02, p = 0.990] and the BCAA group [MM (kg): −1 ± 3.3, p = 0.734; MM (%): 0.34 ± 2.85, p = 0.956] did not exhibit a statistically significant difference when compared to the CTR group. Conclusions: Amino acid-based supplements may effectively mitigate the loss of MM typically observed during weight reduction. Further validation through large-scale studies is necessary.
Photodetection Characterization of SiPM Technologies for their Application in Scintillator based Neutron Detectors

Small-angle neutron scattering (SANS) experiments have become one of the most important techniques in the investigation of the properties of material on the atomic scale. Until 2001, nearly exclusively 3He-based detectors were used for neutron detection in these experiments, but due to the scarcity of 3He and its steeply rising price, researchers started to look for suitable alternatives. Scintillation based solid state detectors appeared as a prominent alternative. Silicon photomultipliers (SiPM), having single photon resolution, lower bias voltages compared to photomultiplier tubes (PMT), insensitivity to magnetic fields, low cost, possibility of modular design and higher readout rates, have the potential of becoming a photon detector of choice in scintillator based neutron detectors. The major concerns for utilizing the SiPM technology in this kind of applications are the increase in their noise performance and the decrease in their photon detection efficiency (PDE) due to direct exposure to neutrons. Here, a detailed comparative analysis of the PDE performance in the range between UV and NIR parts of the spectra for three different SiPM technologies, before and after irradiation with cold neutrons, has been carried out. For this investigation, one digital and two analog SiPM arrays were irradiated with 5Å wavelength cold neutrons and up to a dose of 6×1012 n/cm2 at the KWS-1 instrument of the Heinz Maier-Leibnitz Zentrum (MLZ) in Garching, Germany.
Detecting Anomalies Through Sequential Performance Analysis in Virtualized Environments

Virtualization enables cloud computing, allowing for server consolidation with cost reduction. It also introduces new challenges in terms of security and isolation, which are deterrents for the adoption of virtualization in critical systems. Virtualized systems tend to be very complex, and multi-tenancy is the norm, as the hypervisor manages the resources shared among virtual machines. This paper proposes a methodology that uses performance modeling for the detection of anomalies in virtualized environments that can be caused, for instance, by cyberattacks. Experiments are conducted to profile the system operation under normal conditions for its business transactions. The results are used to calibrate a performance model and to understand the impact of its parameters on the false positive probability. During operation, the system is monitored, and deviations are detected by applying a sequential analysis algorithm (the bucket algorithm). The methodology is evaluated using a representative cloud workload (TPCx-V), which was profiled during a set of controlled executions. We consider resource exhaustion anomalies to emulate the effects of attacks affecting the performance of the system. Our results show that the proposed approach is able to successfully detect anomalies, with a low number of false positives, and spot possible residual effects of anomalies on the system.
Delayed effect of Arctic stratospheric ozone on tropical rainfall

The tropical precipitation has a wide effect on the tropical economics and social life. Many studies made efforts to improve the tropical precipitation forecast using tropical climate factors. This study, based on observations, found that Arctic stratospheric ozone (ASO) could exert a significant effect on the tropical precipitation, i.e. there is more (less) rainfall over the eastern Pacific and less (more) precipitation over the western Pacific when the ASO anomalies are lower (larger) than normal. It is because a decrease (increase) in ASO could affect El Niño (La Niña) events and lead to a weakened (enhanced) Walker circulation. Time‐slice experiments confirmed that the ASO anomalies can force El Niño–Southern Oscillation‐like anomalies of tropical sea surface temperature and subsequent tropical precipitation anomalies. In addition, the ASO variations could also change the occurrence probability of extreme precipitation in the tropics. During the anomalously low (high) ASO events, there are more occurrences of heavier precipitation over the eastern Pacific (western Pacific) and of lighter precipitation over the western Pacific (eastern Pacific). Furthermore, the ASO variations lead tropical rainfall by approximately 21 months, suggesting that the ASO can serve as a potentially effective predictor of tropical rainfall.
Scattering center modelling and analysis of traffic objects

The combination of SAR (synthetic aperture radar) and FMCW (frequency modulation continuous wave) technology offers a high-accuracy, cost-effective imaging sensor for remote sensing. Instead of treating the target as a continuous object, this technology sees the target as several scattering points with certain amplitude which depend on the reflecting intensity. In this thesis, we implement two image reconstruction techniques: Gridding method (widely used in medical imaging) and unequally spaced FFTs (has advantages for arbitrary sampling geometries) to set up the scattering centers model. Then taking account of efficiency and saving time for further data process in self-driving area, only a few of the scattering centers having stronger amplitudes will be kept by doing sparse representation technology. Here, we choose the iterative soft thresholding algorithm based on l1-norm minimization to fulfill sparse representation. We then discuss the trade-off between the choice of threshold and the number of iterations for the optimization problem to converge.