Miracle-dz/UniFAR-FTUs huggingface dataset:
On-Demand Centralized Resource Allocation for IoT Applications: AI-Enabled Benchmark

The development of emerging information technologies, such as the Internet of Things (IoT), edge computing, and blockchain, has triggered a significant increase in IoT application services and data volume. Ensuring satisfactory service quality for diverse IoT application services based on limited network resources has become an urgent issue. Generalized processor sharing (GPS), functioning as a central resource scheduling mechanism guiding differentiated services, stands as a key technology for implementing on-demand resource allocation. The performance prediction of GPS is a crucial step that aims to capture the actual allocated resources using various queue metrics. Some methods (mainly analytical methods) have attempted to establish upper and lower bounds or approximate solutions. Recently, artificial intelligence (AI) methods, such as deep learning, have been designed to assess performance under self-similar traffic. However, the proposed methods in the literature have been developed for specific traffic scenarios with predefined constraints, thus limiting their real-world applicability. Furthermore, the absence of a benchmark in the literature leads to an unfair performance prediction comparison. To address the drawbacks in the literature, an AI-enabled performance benchmark with comprehensive traffic-oriented experiments showcasing the performance of existing methods is presented. Specifically, three types of methods are employed: traditional approximate analytical methods, traditional machine learning-based methods, and deep learning-based methods. Following that, various traffic flows with different settings are collected, and intricate experimental analyses at both the feature and method levels under different traffic conditions are conducted. Finally, insights from the experimental analysis that may be beneficial for the future performance prediction of GPS are derived.
Non-Invasive Rodent Cardiac Output : Comparison of a Compact Clinical Monitor with Echocardiography Corresponding

Background: There are l imited options for non-invasive cardiac output (CO) measurement in rodents. This study aimed to compare a commercially available human CO monitor, USCOM® (USCOM Ltd, Sydney, Australia), with specialised rodent echocardiography for rat CO measurement. Methods: With institutional ethics committee approval, twenty-one anaesthetised, mechanically-ventilated male Sprague-Dawley rats (573±96g) were studied during refinement and study of an endotoxic shock model. Pulsed-wave Doppler echocardiography (15MHz rodent probe) was used to measure left ventricular (LV) outflow velocity and calculate stroke volume and CO. USCOM (v1.7; 2.2MHz) CO measurements followed each echocardiographic examination. USCOM CO was measured by combining continuous wave Doppler with predicted outflow tract diameter (OTD-U). Results: 21 paired measurements were analysed. Mean echocardiographic CO was 113mL/min (range 46–236). Mean USCOM CO was 245mL/min (range 75-553). Paired echocardiographic and USCOM measurements demonstrated significant correlations for heart rate (r=0.92, P≤0.0001) and CO (r=0.68, P=0.001). Bland Altman analysis of CO demonstrated mean bias of -131mL/min and precision of 52mL/min. Linear regression analysis yielded a simple correction factor for USCOM OTD estimation. Following application of this correction factor (0.68*OTD-U), mean bias improved to -0.1mL/min with precision of 38mL/min. Conclusions: USCOM (v1.7) is not interchangeable with pulsed-wave Doppler echocardiography for measuring rat CO. We propose a simple correction factor that should improve performance of this device in the rodent laboratory. Incorporation into a rat-specif ic algorithm should be evaluated prospectively across a range of potential applications.
Additional fluorine abundance determinations in evolved stars

We present new fluorine abundance measurements for a sample of carbon-rich asymptotic giant branch (AGB) stars and two other metal-poor evolved stars of Ba/CH types. The abundances are derived from IR, K-band, high-resolution spectra obtained using GEMINI-S/Phoenix and TNG/Giano-b. Our sample includes an extragalactic AGB carbon star belonging to the Sagittarius dSph galaxy. The metallicity of our stars ranges from [Fe/H] = 0.0 down to − 1.4 dex. The new measurements, together with those previously derived in similar stars, show that normal (N-type) and SC-type AGB carbon stars of near solar metallicity present similar F enhancements, discarding previous hints that suggested that SC-type stars have larger enhancements. These mild F enhancements are compatible with current chemical-evolution models pointing out that AGB stars, although relevant, are not the main sources of this element in the solar neighbourhood. Larger [F/Fe] ratios are found for lower-metallicity stars. This is confirmed by theory. We highlight a tight relation between the [F/⟨s⟩] ratio and the average s-element enhancement [⟨s⟩/Fe] for stars with [Fe/H] > −0.5, which can be explained by the current state-of-the-art low-mass AGB models assuming an extended 13C pocket. For stars with [Fe/H] < −0.5, discrepancies between observations and model predictions still exist. We conclude that the mechanism of F production in AGB stars needs further scrutiny and that simultaneous F and s-element measurements in a larger number of metal-poor AGB stars are needed to better constrain the models.
Improving Monte Carlo Evaluation with Offline Data

Most reinforcement learning practitioners evaluate their policies with online Monte Carlo estimators for either hyperparameter tuning or testing different algorithmic design choices, where the policy is repeatedly executed in the environment to get the average outcome. Such massive interactions with the environment are prohibitive in many scenarios. In this paper, we propose novel methods that improve the data efficiency of online Monte Carlo estimators while maintaining their unbiasedness. We first propose a tailored closed-form behavior policy that provably reduces the variance of an online Monte Carlo estimator. We then design efficient algorithms to learn this closed-form behavior policy from previously collected offline data. Theoretical analysis is provided to characterize how the behavior policy learning error affects the amount of reduced variance. Compared with previous works, our method achieves better empirical performance in a broader set of environments, with fewer requirements for offline data.
Factors Affecting the Synthesis of Bovine Serum Albumin Nanoparticles Using the Desolvation Method

Abstract Currently, protein-based nanoparticles are in high demand as drug delivery systems due to their exceptional qualities, including nontoxicity, nonantigenicity, and biodegradability. Other qualities include high nutritional value, abundance of renewable resources, excellent drug binding capacity, greater stability during storage and in vivo, as well as ease of upgrading during manufacture. Examples of protein suitable for this purpose include ovalbumin (OVA) derived from egg white, human serum albumin (HSA), and bovine serum albumin (BSA). To create albumin nanoparticles, six different processes have been investigated in depth and are frequently used in drug delivery systems. These included desolvation, thermal gelation, emulsification, NAB technology, self-assembly, and nanospray drying. Several experimental conditions in the synthesis of albumin nanoparticles can affect the physicochemical characterization. Therefore, this study aimed to provide an overview of various experimental conditions capable of affecting the physicochemical characteristics of BSA nanoparticles formed using the desolvation method. By considering the variation in optimal experimental conditions, a delivery system of BSA nanoparticles with the best physicochemical characterization results could be developed.
Active contours-driven registration method for the structure-informed segmentation of diffusion MR images

Current applications of whole-brain tractography to diffusion MRI data require highly precise delineations of anatomical structures, which are usually projected from T1 weighted images by registration. In this study, we propose regseg, which is a simultaneous segmentation and registration method that uses active contours without edges extracted from structural images. The contours evolve through a free-form deformation field supported by the B-spline basis to optimally map the contours onto the data in the target space. We tested the functionality of regseg using four digital phantoms warped with known and randomly generated deformations, where subvoxel accuracy was achieved. We then applied regseg to a registration/segmentation task using 16 real diffusion MRI datasets from the Human Connectome Project, which were warped by realistic and nonlinear distortions that are typically present in these data. We computed the misregistration error of the contours estimated by regseg with respect to their theoretical location using the ground truth, thereby obtaining a 95% CI of 0.56--0.66 mm distance between corresponding mesh vertices, which was below the 1.25 mm isotropic resolution of the images. We also compared the performance of our proposed method with a widely used registration tool, which showed that regseg outperformed this method in our settings.
Capturing the Influence of Conduction Velocity on Epicardial Activation Patterns Using Uncertainty Quantification

Individual variability in parameter settings, due to either user selection or disease states, can impact accuracy when simulating the electrical behavior of the heart. Here, we aim to test the impact of inevitable uncertainty in conduction velocities (CVs) on the output of simulations of cardiac propagation, given three stimulus locations on the left ventricular (LV) free wall. To understand the role of physiological variability in CV in simulations of cardiac activation, we generated detailed maps of the variability in propagation simulations by implementing bi-ventricular activation simulations and quantified the effects by deploying robust uncertainty quantification techniques based on polynomial chaos expansion (PCE). PCE allows efficient stochastic exploration with reduced computational demand by utilizing an emulator for the underlying forward model. Our results suggest that CV within healthy physiological ranges plays a small role in the activation times across all stimulation locations. However, we noticed differences in variation coefficients depending on the stimulation site, i.e., LV endocardium, midmyocardium, and epicardium. We observed low levels of variation in activation times near the earliest activation sites, whereas there was higher variation toward the termination sites. These results suggest that CV variability can play a role when simulating healthy and diseased states.
ESSPI as a Fast Tool for Load Prioritization on Microgrids Design

The Energy Storage System Priority Index (ESSPI) was originally presented in the past as a methodology to prioritize load. In this paper, the ESSPI is going to be used as a fast tool for load prioritization in microgrid design. The tool consists of the user inputting the load information based on the classification of the load. The inputs needed are the maximum critical recovery time and the energy consumption of the loads. Loads should be classified into different categories: critical load, essential load, discretionary load, non-essential load, and expendable load. The tool outputs would be the loads arranged based on their priority, the size, and the cost of the microgrid system depending on the user's desired investment for short-, mid-, and long-term investments.
Phocaeicola vulgatus alleviates diet-induced metabolic dysfunction-associated steatotic liver disease progression by downregulating histone acetylation level via 3-HPAA

ABSTRACT Diet-induced metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent metabolic disorder with limited effective interventions available. A novel approach to address this issue is through gut microbiota-based therapy. In our study, we utilized multi-omics analysis to identify Phocaeicola vulgatus (P. vulgatus) as a potential probiotic for the treatment of MASLD. Our findings from murine models clearly illustrate that the supplementation of P. vulgatus mitigates the development of MASLD. This beneficial effect is partly attributed to the metabolite 3-Hydroxyphenylacetic acid (3-HPAA) produced by P. vulgatus, which reduces the acetylation levels of H3K27 and downregulates the transcription of Squalene Epoxidase (SQLE), a rate-limiting enzyme in steroid biosynthesis that promotes lipid accumulation in liver cells. This study underscores the significant role of P. vulgatus in the development of MASLD and the critical importance of its metabolite 3-HPAA in regulating lipid homeostasis. These findings offer a promising avenue for early intervention therapy in the context of MASLD.
Macrophage Migration Inhibitory Factor in the Early Diagnosis of Endometriosis and Relationship to the Stage of the Disease

Background: Endometriosis is a chronic disease affecting women of reproductive age. Adolescent onset of symptoms is common. Macrophage migration inhibitory factor (MIF) is one of non-invasive blood biomarker that was found in endometriosis. Aim of the work: to evaluate the value of macrophage MIF in peripheral blood of women with and without endometriosis as a non-invasive early diagnostic method and correlate its level to the stage of the disease. Patient and methods: This observational case-control study was conducted in El-Hussein Hospital, Al-Azhar University, Cairo and private clinics. The study was approved by the local research ethics committee. During the period from May 2016 till July 2018. Results: Both groups were comparable as regard the incidence of infertility and the presence of chronic pelvic pain. The infertility was equal in both groups; it affected 96% of patients and control groups. The chronic pelvic pain was symmetrically present in both groups; it affected 70% of endometriosis and the control group. The level of MIF was 10.03±1.78 pg/ml in patients with endometriosis and 4.82±1.78 pg/ml in control group with significant P value (P <0.001). In our study, the level of MIF differed according to the stage of endometriosis, in stage I, it was 8.6±0.2 pg/ml, in stage II, it was 10.1±0.1 pg/ml, in stage III, it was 11.3±0.2 pg/ml and in stage IV, it was 14+0.0 pg/ml. Conclusion: MIF factor is a promising marker not only for noninvasive diagnosis of endometriosis but as a target for therapy also
Sex-related differences in Cushing's disease: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVES: Cushing's disease is a rare endocrine disorder. This review aimed to examine sex-specific differences in Cushing's disease. DESIGN AND SETTINGS: A meta-analysis was performed on published articles discussing the gender impact of Cushing's disease. METHODS: A systematic search was conducted to identify studies from Medline, Embase, CENTRAL and Scopus. Nine studies enrolling 1047 patients diagnosed with Cushing's disease were included in this meta-analysis. RESULTS: Male patients presented at a younger age (MD [mean difference]=−5.43; 95% CI [−5.78, −5.08]; P<.00001) than females. Male patients had a significantly higher prevalence of osteoporosis (RR [risk ratio]=1.75; 95% CI [1.36, 225]; P<.0001) and hypokalemia (RR=1.66; 95% CI [1.27, 2.16]; P=.0002). In addition, males had significantly higher rates of negative magnetic resonance imaging (RR=1.53; 95% CI [1.18, 2.0]; P=.002). No sex difference was observed in the prevalence of diabetes (RR=0.92; 95% CI [0.70, 1.22]); P=. 57) and dyslipidemia (RR=1.33; 95% CI [0.88, 2.0]; P=.17). CONCLUSION: Cushing's disease has a worse clinical presentation in males and more diagnostic difficulties compared to females.
Comparing Low- or Standard-Dose Alteplase in Endovascular Thrombectomy: Insights From a Nationwide Registry

BACKGROUND: Timely intravenous thrombolysis and endovascular thrombectomy are the standard reperfusion treatments for large vessel occlusion stroke. Currently, it is unknown whether a low-dose thrombolytic agent (0.6 mg/kg alteplase) can offer similar efficacy to the standard dose (0.9 mg/kg alteplase). METHODS: We enrolled consecutive patients in the multicenter Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke who had received combined thrombolysis (within 4.5 hours of onset) and thrombectomy treatment from January 2019 to April 2023. The choice of low- or standard-dose alteplase was based on the physician’s discretion. The outcomes included successful reperfusion (modified Thrombolysis in Cerebral Infarction score, 2b–3), symptomatic intracerebral hemorrhage, 90-day modified Rankin Scale score, and 90-day mortality. The outcomes between the 2 groups were compared using multivariable logistic regression and inverse probability of treatment weighting-adjusted analysis. RESULTS: Among the 2242 patients in the Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke, 734 (33%) received intravenous alteplase. Patients in the low-dose group (n=360) were older, had more women, more atrial fibrillation, and longer onset-to-needle time compared with the standard-dose group (n=374). In comparison to low-dose alteplase, standard-dose alteplase was associated with a lower rate of successful reperfusion (81% versus 87%; adjusted odds ratio, 0.63 [95% CI, 0.40–0.98]), a numerically higher incidence of symptomatic intracerebral hemorrhage (6.7% versus 3.9%; adjusted odds ratio, 1.81 [95% CI, 0.88–3.69]), but better 90-day modified Rankin Scale score (functional independence [modified Rankin Scale score, 0–2], 47% versus 31%; adjusted odds ratio, 1.91 [95% CI, 1.28–2.86]), and a numerically lower mortality rate (9% versus 15%; adjusted odds ratio, 0.73 [95% CI, 0.43–1.25]) after adjusting for covariates. Similar results were observed in the inverse probability of treatment weighting-adjusted models. The results were consistent across predefined subgroups and age strata. CONCLUSIONS: Despite the lower rate of successful reperfusion and higher risk of symptomatic intracerebral hemorrhage with standard-dose alteplase, standard-dose alteplase was associated with a better functional outcome in patients receiving combined thrombolysis and thrombectomy.
Clinical isolate characteristics and demographics of patients with C.jejuni and C.coli infections in Northern Israel, 2015–2021

C.coli is a significant cause of foodborne gastroenteritis worldwide, with the majority of cases attributed to C.jejuni. Although most clinical laboratories do not typically conduct antimicrobial susceptibility testing for C.coli, the rise in resistant strains has underscored the necessity for such testing and epidemiological surveillance. The current study presents clinical isolate characteristics and demographics of 221 patients with C.coli (coli and jejuni) infections in Northern Israel, between 2015 and 2021. Clinical and demographic data were collected from patient medical records. Susceptibility to erythromycin, tetracycline, ciprofloxacin, and gentamicin was assessed using the standard E-test. No significant correlations were found between bacterial species and patient ethnicity, patient gender, or duration of hospitalization. In contrast, significant differences were found between infecting species and patient age and age subgroup (P < 0.001). Furthermore, erythromycin resistance was observed in only 0.5% of the study population, while resistance to ciprofloxacin, tetracycline, and gentamicin was observed in 95%, 93%, and 2.3% of the population, respectively. The presented study underscores the need for routine surveillance of C.coli antibiotic resistance.
Novel idea of the theory and application of 3D volume fracturing for stimulation of shale gas reservoirs

Shale gas has become a preferably alternative energy resource and national strategic goal for many countries for its noticeable advantages, including huge reserves and potentials, green, and broad distribution. Successful exploitation and utilization of shale gas has profoundly impacted global economy, regional politics, and even military strategies around the world. China holds the largest amount of recoverable shale gas reserves in the world. The shale gas recoverable reserves of China rank firstly in the world. However, most of the shale gas formations in China are low-permeability deposit and in conditions of geological complex, tectonic active, and lack of water, which makes the exploitation of shale gas difficult using the current methods. It is imperative to renovate the methods and techniques for stimulation of shale gas formations and to establish theory and technology for achieving high-efficiency exploitation and utilization of shale gas in China. This paper briefly reviews the adopted methods, principles, intractable issues and challenges in reservoir fracturing. A promising reservoir stimulation idea, i.e., 3D volume fracturing, and its principles and technology, are proposed. The volume fracturing of shale rock is defined as a three-dimensional fracturing process from the mechanics point of view, taking into account the interaction, growth, bifurcation between internal pores, inherent joints or cracks, and artificial fractures of reservoir rock under complex geostresses. The volume fracturing of shale rock is a failure process featured by large scale, multi cracking, high strength, and intensive energy release. The paper preliminarily frames the 3D volume fracturing theory from the aspects of 3D fracture modes, growth mechanism, interaction between artificial fractures and structural cracks, crack networks and development, loading type, modes and media for realizing large-scale volume fracturing, visualization of 3D fracturing process and computing programs, and stimulation implementation. Based on the early laboratory and field tests, we discussed the key scientific issues and technical challenges in realizing this promising stimulation technology for shale gas reservoirs.
A Dynamic Surrogate Gradient Function for Memristive Spiking Neural Networks

Spiking Neural Networks (SNNs) have some dis-tinct advantages in addressing temporal data by simulating the information transmission mode of brain neurons. Due to the nondifferentiable behavior of spike emission, previous works try to replace the heaviside function with some static approximation functions to solve this problem. However, such static surrogate gradient approach can potentially introduce errors in gradient propagation. Therefore, in this paper, we first propose a dy-namic surrogate gradient function based on Tanh to reduce errors during gradient propagation. Furthermore, we propose a new synapse simulation method by using memristor crossbar arrays in SNNs to solve the memory wall problem. Finally, we conduct sufficient experiments on multiple datasets to verify the effectiveness of the dynamic surrogate gradient function in SNN. Experiment results show that our proposed algorithm can achieve high performance compared with the previous works.