Miracle-dz/UniFAR-FTUs huggingface dataset:
Injury-Transplantation Interval-Dependent Amelioration of Axonal Degeneration and Motor Deficit in Rats with Penetrating Traumatic Brain Injury

Penetrating traumatic brain injury (pTBI) is increasingly survivable, but permanently disabling as adult mammalian nervous system does not regenerate. Recently, our group demonstrated transplant location-dependent neuroprotection and safety of clinical trial–grade human neural stem cell (hNSC) transplantation in a rodent model of acute pTBI. To evaluate whether longer injury-transplantation intervals marked by chronic inflammation impede engraftment, 60 male Sprague-Dawley rats were randomized to three sets. Each set was divided equally into two groups: 1) with no injury (sham) or 2) pTBI. After either 1 week (groups 1 and 2), 2 weeks (groups 3 and 4), or 4 weeks after injury (groups 5 and 6), each animal received 0.5 million hNSCs perilesionally. A seventh group of pTBI animals treated with vehicle served as the negative control. All animals were allowed to survive 12 weeks with standard chemical immunosuppression. Motor capacity was assessed pre-transplant to establish injury-induced deficit and followed by testing at 8 and 12 weeks after transplantation. Animals were euthanized, perfused, and examined for lesion size, axonal degeneration, and engraftment. Compared to vehicle, transplanted groups showed a trend for reduced lesion size and axonal injury across intervals. Remote secondary axonal injury was significantly reduced in groups 2 and 4, but not in group 6. The majority of animals showed robust engraftment independent of the injury-transplant time interval. Modest amelioration of motor deficit paralleled the axonal injury trend. In aggregate, pTBI-induced remote secondary axonal injury was resolved by early, but not delayed, hNSC transplantation.
Interaction Based Credibility Analysis of News on Facebook Using Machine Learning Methodologies

Fake news and its dissemination are growing more popular as social media becomes more pervasive in our daily lives and Facebook users are particularly vulnerable. Numerous studies on detecting fake news have already been published. However, a study focusing on fake news in low resource languages like Bengali propagated through Facebook is rare due to data extraction challenges. In this paper, we work with fake news in the Bengali language and used various supervised machine learning algorithms to classify Facebook posts as fake, real, or satire and found XGBoost to generate the best outputs. Instead of the content, we trained our model with the interaction data of the posts to make it resilient against adversarial attacks. We observed that ensemble methods perform well with such social media metrics.
Effect of multiwalled carbon nanotubes on compressive behavior of concrete at elevated temperature for mass concreting

Abstract In this article, CNTs have been used in concrete, and the resulting properties are evaluated. Also, the CNT-reinforced concrete was exposed to elevated temperature, and the corresponding change in mechanical performance was investigated. Mechanical properties were studied, such as compressive and flexural strengths, stress-strain response, elastic modulus, compressive toughness, mass loss, and deterioration caused by exposure to elevated temperatures. Concrete specimens were heated to 200, 400, 600, and 800 °C at a heating rate of 5 °C/min and then tested for residual properties. CNT-reinforced concrete showed a higher compressive strength, elastic modulus, flexural strength, and compressive toughness than the control specimens. 0.15-CNT reinforced samples showed an increase of nearly 33% in Compressive strength and 50% in flexural strength. From the visual inspection, CNT-reinforced specimens showed less cracking at higher temperatures. Moreover, the effect of plastering concrete with CNTs was studied, which depicted better strength retention on exposure to fire. At 600 °C, 0.3 P-CNT plaster samples retained up to 43% of compressive strength and 46% of flexural strength, whereas control samples only retained up to 29% and 34% of their original strengths, respectively. Thus, it can be concluded that CNTs utilization in concrete is efficient for improved concrete fire resistance.
Fourier restriction theorems for curves with affine and Euclidean arclengths

Let M be a smooth manifold in . One may ask whether , the restriction of the Fourier transform of f to M makes sense for every f in . Since, for does not make sense pointwise, it is natural to introduce a measure σ on M and ask for an inequality for every f in (say) the Schwartz class. Results of this kind are called restriction theorems. An excellent survey article on this subject is to be found in Tomas[13].
Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast

This paper presents a data fusion framework to enhance the accuracy of air-pollutant forecast in the state of New South Wales (NSW), Australia using deep learning (DL) as a core model. Here, we propose a long short-term memory Bayesian neural network (LSTM-BNN) to improve performance of the predictive profiles via quantifying uncertainties and adjusting model parameters. For this, we develop a new inferring technique for kernel density estimation with subdivision tuning to ensure both forecast accuracy and computational efficiency with a limited number of samples from the prediction distributions. Moreover, a novel algorithm called spatially-adjusted multivariate imputation by chained equation is also developed to take into account spatial correlations between nearby air-quality stations for correctly imputing the incoming data, and hence, to enable forecasting at a local scale. The LSTM-BNN framework is evaluated with observed datasets collected from stations and modeling outputs generated by the Conformal Cubic Atmospheric Model - Chemical Transport Model (CCAM-CTM) currently used in NSW. The airborne pollutants under investigation are $PM_{2.5}$ and ozone, which frequently exceed the standards. The results obtained from data fusion with our framework demonstrated high performance of the proposed LSTM-BNN model in air-pollutant prediction with reductions of over 30% in root mean square error compared to CCAM-CTM and over 50 % in inferring time compared to a DL model with Gaussian-based inference. Accuracy and reliability of the proposed model were also achieved with air pollution forecast in various seasons and suburbs.
Multilevel and Longitudinal Modeling Using Stata, Second Edition

Multilevel and Longitudinal Modeling Using Stata, Second Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or employees in firms. Longitudinal data are also clustered with, for instance, repeated measurements on patients or several panel waves per survey respondent. Multilevel and longitudinal modeling can exploit the richness of such data and can disentangle processes operating at different levels. Assuming some knowledge of linear regression, this bestseller explains models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. The applications and exercises span a wide range of disciplines, making the book suitable for courses on multilevel and longitudinal modeling in the medical, social, and behavioral sciences and in applied statistics. This extensively revised second edition includes 3 new chapters, comprehensive updates for Stata 10, 38 new exercises, and 27 new data sets. The authors teach multilevel and longitudinal modeling at their universities and frequently hold workshops at international conferences. They have been developing a general modeling framework, GLLAMM, and Stata software gllamm for multilevel and latent variable modeling. This work has been published in their highly acclaimed book Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models and in many journals, including Biometrics, Psychometrika, Journal of Econometrics, and Journal of the Royal Statistical Society.
Assessment and Optimization of the Gamma-Weighted Two-Stream Approximation

Abstract The two primary foci of this note are to assess the ability of the multilayer gamma-weighted two-stream approximation (GWTSA) to compute domain-averaged solar radiative fluxes and to demonstrate how its execution time can be reduced with negligible impact on performance. In addition to the usual parameters needed by a 1D solar code, the GWTSA requires ν ∈ R+, which depends on both the horizontal mean and mean logarithm of cloud water content. Reduced central processing unit (CPU) time is realized by simply rounding ν to the nearest whole number, denoted as [ν]. The experiment reported on here uses 120 fields generated by a 2D cloud-resolving model simulation of an evolving tropical mesoscale convective cloud system. Benchmark calculations are provided by the independent column approximation (ICA), and results are also shown for the conventional two-stream model. The full GWTSA yields time- and domain-averaged broadband top-of-atmosphere albedo and surface absorptance values of 0.32 and 0.49, whic.
Multilayer Perceptron and Bayesian Neural Network-Based Elastic Implicit Full Waveform Inversion

We introduce and analyze the elastic implicit full waveform inversion (EIFWI) of seismic data, which uses neural networks to generate elastic models and perform full waveform inversion. EIFWI carries out inversion by linking two main networks: a neural network that generates elastic models and a recurrent neural network to perform the modeling. The approach is distinct from conventional waveform inversion in two key ways. First, it reduces reliance on accurate initial models relative to conventional FWI. Instead, it invokes general information about the target area, for instance, estimates of means and standard deviations of medium properties in the target area or, alternatively, well-log information in the target area. Second, iterative updating directly affects the weights in the neural network rather than the elastic model. Elastic models can be generated in the first part of the EIFWI process in either of two ways: through the use of a multilayer perceptron (MLP) network or a Bayesian neural network (BNN). Numerical testing is suggestive that the MLP-based EIFWI approach in principle builds accurate models in the absence of an explicit initial model, and the BNN-based EIFWI can give the uncertainty analysis for the prediction results.
THE INTERPLAY BETWEEN CLASSICAL ANALYSIS AND (NUMERICAL) LINEAR ALGEBRA — A TRIBUTE TO GENE H. GOLUB

Much of the work of Golub and his collaborators uses techniques of linear algebra to deal with problems in analysis, or employs tools from analysis to solve problems arising in linear algebra. Instances are described of such interdisciplinary work, taken from quadrature theory, orthogonal polynomials, and least squares problems on the one hand, and error analysis for linear algebraic systems, element-wise bounds for the inverse of matrices, and eigenvalue estimates on the other hand.
On the evidence for the effect of bubble interference on cavitation noise

Cavitation-noise measurements from an axisymmetric body with ‘controlled’ generation of cavitation are reported. The control was achieved by seeding artificial nuclei in the boundary layer by electrolysis. It was possible to alter the number density of nuclei by varying the electrolysis voltage, polarity and the geometry of the electrode. From the observed trend of cavitation-noise data it is postulated that there exists an ‘interference effect’ which influences cavitation noise. When the nucleus-number density is high and cavitation numbers are low this effect is strong. Under these conditions the properties of cavitation noise are found to differ considerably from those expected based on theories concerning noise from single-spherical-bubble cavitation.
Investigating and Supporting Patient and Caregiver Sensemaking in Complex Medical Decisions Using Participatory Design

Background. Patients and caregivers facing complex health decisions must make sense of unfamiliar, emotionally challenging information and experiences. For patients with hematological malignancy, bone marrow transplant (BMT) may be the best chance for a cure but has significant risk of morbidity and mortality. This study aimed to investigate and support patient and caregiver sensemaking as they consider BMT. Methods. Ten BMT patients and 5 caregivers engaged in remote participatory design (PD) workshops. Participants drew timelines of their memorable experiences leading up to BMT. Then, they used transparency paper to annotate their timelines and design improvements to this process. Results. Thematic analysis of drawings and transcripts revealed a 3-phase sensemaking process. In phase 1, participants were introduced to BMT and understood it as a possibility, not an inevitability. In phase 2, they focused on meeting prerequisites including remission and donor identification. Participants came to believe they needed transplant, consequently describing BMT not as a decision between viable options, but that transplant was their “only chance” for survival. In phase 3, participants attended an orientation detailing the extensive risks of transplant, leading to anxiety and doubt. Participants designed solutions that provided reassurance to those grappling with the life-altering impacts of transplant. Conclusions. For patients and caregivers navigating complex health decisions, sensemaking is a dynamic, ongoing process that affects expectations and emotional well-being. Interventions targeting reassurance alongside risk information can alleviate emotional impact and facilitate expectation development. The integration of PD and sensemaking methodologies enables participants to create holistic, tangible representations of experiences while empowering stakeholder engagement in intervention design. This method could be applied to other complex medical contexts to understand lived experiences and develop effective support interventions. Highlights Bone marrow transplant patients and caregivers experienced an evolving, emotionally challenging process of gradually understanding the transplant procedure and its risks. The solutions that participants designed centered on providing reassurance alongside risk information, suggesting future interventions could target emotional support as patients attempt to meet prerequisites and grapple with the risks of the potentially curative procedure. By viewing the challenges of complex medical decisions in terms of sensemaking and applying visual methods such as participatory design, researchers can facilitate expression of the dynamic, multifaceted, emotional components of experience and empower stakeholder involvement in intervention design.
An offline column extraction with Presep® PolyChelate resin and AG1‐X8 resin for the determination of the isotope compositions of low‐concentration lead in natural waters by the multi‐collector inductively coupled plasma mass spectrometry

A new high‐precision offline method for the determination of seawater lead (Pb) isotope ratios was developed, which simplifies and optimizes previously published protocols for the isotopic analysis of trace elements. The procedure involves initial separation of Pb from 250 to 500 mL of seawater by Presep® PolyChelate cation exchange resin and purification by AG1‐X8 anion exchange resin, and subsequently analyzed by Neptune multi‐collector ICP‐MS. For the extraction of Pb in seawater, the Presep® PolyChelate resin was characterized by a wide pH range and a faster adsorption rate, greatly reducing the volume of buffer solution required and significantly shortening the time required for the whole process. The total procedural Pb blank of this method is < 0.05% of the sample extract and the typical Pb recovery is ~ 100%. The method was evaluated by processing GEOTRACES intercalibration samples surface water (GSI, 206Pb/207Pb = 1.1744, 208Pb/206Pb = 2.0844) and GEOTRACES GP15 sample (14140#, 206Pb/207Pb = 1.1685, 208Pb/206Pb = 2.0960). Finally, this method was applied to riverine samples from the Changjiang and seawater samples from the East China Sea, and the in situ extraction was also applied in the Indian Ocean.
Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitors: Harms or Unexpected Benefits?

There is a need for innovative pharmaceutical intervention in light of the increasing prevalence of metabolic disease and cardiovascular disease. The kidneys’ sodium-glucose cotransporter 2 inhibitors (SGLT2) receptors are targeted to reduce glucose reabsorption by SGLT2. Patients with type 2 diabetes mellitus (T2DM) benefit the most from reduced blood glucose levels, although this is just one of the numerous physiological consequences. To establish existing understanding and possible advantages and risks for SGLT2 inhibitors in clinical practice, this article will explore the influence of SGLT2 inhibitors on six major organ systems. In addition, this literature review will discuss the benefits and potential drawbacks of SGLT2 inhibitors on various organ systems and their potential application in therapeutic settings.
Preliminary Study on the Rainfall-Runoff Inundation and Its Economic Lost at Bekasi River Basin, West Jawa

Flood disasters in Bekasi City almost occur every year, especially during high rainfall with a fairly long duration. Repeated flooding events due to extreme rainfall in Bekasi River Basin can be simulated using a distributed hydrological model. Rainfall-Runoff Inundation (RRI) model is a two-dimensional hydrological model capable of simulating rainfall-runoff and flood inundation simultaneously. The input data used in this study is extreme rainfall data derived from GSMaP satellite rainfall data, topography, and land derived from satellite remote sensing data. In this paper, we analyze the flood simulation in the Kali Bekasi watershed when extreme rainfall occurred on July 14, 15 and 16, 2022. On that date we found flooding in several areas including the Bekasi River Basin. From the results of the flood simulation data processing, it is then calculated how much economic loss due to the flood disaster occurred.
Spatial Panel Data Model with Error Dependence: A Bayesian Separable Covariance Approach

A hierarchical Bayesian model for spatial panel data is proposed. The idea behind the proposed method is to analyze spatially dependent panel data by means of a separable covariance matrix. Let us indicate the observations as yit, i = 1, . ,N regions and t = 1,. , T time, var(y), the covariance matrix of y is written as a Kronecker product of a purely spatial and a purely temporal covariance. On the one hand, the structure of separable covariances dramatically reduces the number of parameters, while on the other, the lack of a structured pattern for spatial and temporal covariances permits to capture possible unknown dependencies (both in time and space). The use of the Bayesian approach allows to overcome some of the difficulties of the classical (MLE or GMM based) approach. We present two illustrative examples: the estimation of cigarette price elasticity and of the determinants of the house price in 120 municipalities in the Province of Rome.
Object Detection Using Deep Learning, CNNs and Vision Transformers: A Review

Detecting objects remains one of computer vision and image understanding applications’ most fundamental and challenging aspects. Significant advances in object detection have been achieved through improved object representation and the use of deep neural network models. This paper examines more closely how object detection has evolved in the era of deep learning over the past years. We present a literature review on various state-of-the-art object detection algorithms and the underlying concepts behind these methods. We classify these methods into three main groups: anchor-based, anchor-free, and transformer-based detectors. Those approaches are distinct in the way they identify objects in the image. We discuss the insights behind these algorithms and experimental analyses to compare quality metrics, speed/accuracy tradeoffs, and training methodologies. The survey compares the major convolutional neural networks for object detection. It also covers the strengths and limitations of each object detector model and draws significant conclusions. We provide simple graphical illustrations summarising the development of object detection methods under deep learning. Finally, we identify where future research will be conducted.
Learning-Based Intelligent Reflecting Surface-Aided Cell-Free Massive MIMO Systems

Intelligent reflecting surface (IRS) is amenable to assisting cell-free wireless networks because of its low cost and high spectrum efficiency. However, solving the IRS optimization problem usually requires high computational complexity. Therefore, we propose a novel multi-agent reinforcement learning (MARL) based joint optimization scheme to improve the system performance. First, we model the MARL framework to portray the dynamic wireless communication environment and interactive agent entities, which interprets the relationship between reinforcement learning and communication strategies. Moreover, in an IRS-aided cell-free massive MIMO system, entries are collaborated by the CPU for joint optimization, and the actions are continuous. Thus we propose a multi-agent deep deterministic policy gradient scheme for jointly optimizing the user's power control and IRS's passive beamforming (MADDPG-JPA-PB). Via centralized training and decentralized execution, the system gradually converges into an optimized policy associated with the maximization of the success transmission rate. Simulation results verify that the performance of the proposed scheme outperforms various independent deep deterministic policy gradient (DDPG) schemes and traditional optimization methods.
The Estimation of Continuous Parameter Long-Memory Time Series Models

A class of univariate fractional ARIMA models with a continuous time parameter is developed for the purpose of modeling long-memory time series. The spectral density of discretely observed data is derived for both point observations (stock variables) and integral observations (flow variables). A frequency domain maximum likelihood method is proposed for estimating the longmemory parameter and is shown to be consistent and asymptotically normally distributed, and some issues associated with the computation of the spectral density are explored.
Protective Effect of the Phycobiliproteins from Arthrospira maxima on Indomethacin-Induced Gastric Ulcer in a Rat Model

Gastric ulcers (GU) constitute a disease with a global prevalence ≈ 8.09 million. Of their causes, non-steroidal anti-inflammatory drugs (NSAIDs) such as indomethacin (IND) rank as the second most frequent etiologic agent. The pathogenic process of gastric lesions is given by the overproduction of oxidative stress, promotion of inflammatory processes, and inhibition of prostaglandin synthesis. Spirulina Arthrospira maxima (SP) is a cyanobacterium with a wide variety of substances with high nutritional and health values such as phycobiliproteins (PBPs) that have outstanding antioxidant activity, anti-inflammatories effects, and accelerate the wound healing process. This study aimed to determine the protective effect of PBPs in GU induced by IND 40 mg/kg. Our results show that the PBPs protected against IND-induced damage with a dose-dependent effect. At a dose of 400 mg/kg, a marked decrease in the number of lesions is observed, as well as the recovery of the main markers of oxidative stress damage (MDA) and antioxidant species (SOD, CAT, GPx) at close to baseline levels. The evidence derived from the present investigation suggests that the antioxidant effect of PBPs, together with their reported anti-inflammatory effects to accelerate the wound healing process, is the most reliable cause of their antiulcerogenic activity in this GU model.