Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactions of fast ions with carbon nanotubes: Self-energy and stopping power

The interactions of charged particles with carbon nanotubes are studied by means of the linearized hydrodynamic theory for electronic excitations on the nanotube surface. General expressions are derived for the induced potential, the self-energy, and the stopping power for a charged particle moving paraxially in a nanotube. Numerical results are obtained showing the influence of the damping factor, the nanotube radius, and the particle position on its self-energy and the stopping power. Results for stopping power in the linearized hydrodynamic model are compared with those obtained by means of the dielectric formalism in random-phase approximation, showing a close agreement between the two approaches for high speeds of charged particles.
Resisting Best-Practice in Australian Practice-Based Jazz Doctorates

Recent research on practice-based doctorates in Australia has revealed an institutional preferencefor‘theorised’researchapproachesaimedatsituatingstudiesofpracticewithin establishedacademicparadigms.Inthisarticleweexaminehowtheaimofcommunicating with artistic peers steers the research design and the production of text-based artefacts for a group of practice-based doctoral students working on jazz topics (n = 11) away fromtheorisedapproachesandtowardswhatiscommonlyreferredtoasa‘commentary’ approach. Thisﬁndingrevealstheextenttowhichthevaluesofanartisticcommunitycan inﬂuencethescopeofwhatisdiscussedwithinpractice-baseddoctoratesandhighlights theneedforongoingdiscussionrelatedtohowsuchvaluesmightbestinterfacewithwhat institutionsviewasbest-practiceresearchframeworks.
Treatment of Fresh Meat, Fish and Products Thereof with Cold Atmospheric Plasma to Inactivate Microbial Pathogens and Extend Shelf Life

Assuring the safety of muscle foods and seafood is based on prerequisites and specific measures targeted against defined hazards. This concept is augmented by ‘interventions’, which are chemical or physical treatments, not genuinely part of the production process, but rather implemented in the framework of a safety assurance system. The present paper focuses on ‘Cold Atmospheric pressure Plasma’ (CAP) as an emerging non-thermal intervention for microbial decontamination. Over the past decade, a vast number of studies have explored the antimicrobial potential of different CAP systems against a plethora of different foodborne microorganisms. This contribution aims at providing a comprehensive reference and appraisal of the latest literature in the area, with a specific focus on the use of CAP for the treatment of fresh meat, fish and associated products to inactivate microbial pathogens and extend shelf life. Aspects such as changes to organoleptic and nutritional value alongside other matrix effects are considered, so as to provide the reader with a clear insight into the advantages and disadvantages of CAP-based decontamination strategies.
Facile Fabrication of SiO2/Zr Assisted with EDTA Complexed-Impregnation and Templated Methods for Crude Palm Oil to Biofuels Conversion via Catalytic Hydrocracking

Zr-containing SiO2 and their parent catalysts were fabricated with different methods using EDTA chelation and template-assist. The activity of the catalysts was explored in crude palm oil (CPO) hydrocracking, conducted under a continuous system micro-cylindrical reactor. The conversion features and the selectivity towards biofuel products were also examined. The physicochemical of catalysts, such as structure phase, functional groups, surface morphologies, acidity features, and particle size, were investigated. The study showed that the template method promoted the crystalline porous catalysts, whereas the chelate method initiated the non-porous structure. The catalysts’ acidity features of SiO2 and SiO2/Zr were affected by the preparation, which revealed that the EDTA chelate-assisted method provided higher acidity features compared with the template method. The CPO hydrocracking study showed that the SiO2/Zr-CEDTA provided the highest catalytic activity towards the hydrocracking process, with 87.37% of conversion attained with 66.29%. wt of liquid product. This catalyst exhibited selectivity towards bio-jet (36.88%), bio-diesel (31.43%), and bio-gasoline (26.80%). The reusability study revealed that the SiO2/Zr-CEDTA had better stability towards CPO conversion compared with SiO2/Zr-CEDTA, with a low decrease in catalyst performance at three consecutive runs.
SIO Flux Measurements of Variable Stars

We report measurements of total flux for six circumstellar SiO maser sources. Both polarizations were measured simultaneously for these highly polarized sources. We compare SiO curves with the infrared flux, especially the infrared minima, which are observed to coincide in phase with the optical minima. The comparisons of SiO and infrared flux at light minimum are the most stragithforward and indicate a characteristic phase lag for several of our sources. When interpreted in terms of observed physical motions associated with these stars, the SiO-infrared phase lag of 63--129 days can be used to infer a scale size on the order of 10/sup 13/ cm. The corresponding light-travel time is of the order of 10/sup 17/ cm, which is well outside the SiO line formation region.
PPDP: A Data Portal of Paris polyphylla for Polyphyllin Biosynthesis and Germplasm Resource Exploration

Paris polyphylla Smith is a perennial medicinal herb with records from around 2000 years ago. Polyphyllins are the main bioactive compounds of this herb, which are found to have remarkable effects on bacteriostatic, antiphlogistic, sedative, and antitumor. However, the market demand for P. polyphylla is sharply increasing, and the wild resources are threatened by plundering exploitation. Integrating molecular data of P. polyphylla can benefit the sustainable resource exploitation. Here, we constructed PPDP (Paris polyphylla Data Portal) to provide a data platform for polyphyllin biosynthesis and germplasm resource research. PPDP integrates related molecular data resources, functional genomics analysis, and morphological identification. The database provides abundant data (transcriptome, CDS, lncRNA, alternative splicing, gene family, SSR, and chloroplast genome) and practical analytical tools (network construction, heatmap of expression profiles, enrichment, and pathway search) with a user-friendly interface. So far, PPDP is the first biomolecular database for the genus Paris plants. In the future, we will gradually add genomic data and other necessary molecular biological information to improve the database.
The Effect of Nodalization Schemes on the Stability Characteristics of a Three Heated Channels under Supercritical Flow Condition

The present analysis is aimed at conducting node sensitivity analysis on the thermal–hydraulic performance of supercritical fluid in a three parallel channel configuration system. The heated channel was divided into different nodes and is examined under wide-ranging operating conditions. Firstly, the heated channel was divided into two nodes, like the two-phase flow system. In the second case, based on the correlation between the fluid properties, the heated channel was divided into three regions: heavy, mixture, and supercritical fluids. Finally, the channel was divided into N-nodes. Post the nodalization analysis, a non-linear analysis of three parallel channels was carried out under varied heat flux conditions. The analytical approximation functions were developed to capture the fluid flow dynamics. These functions were used to capture each node’s density, enthalpy, and velocity profiles under a wide range of operating conditions. The different flow instability (density wave oscillations and static) characteristics were observed at low pseudo- and relatively high subcooling numbers. In the density wave oscillations regime, out-of-phase oscillations and limit cycles are observed. A co-dimension parametric analysis with numerical simulations was carried out to confirm the obtained non-linear characteristics. Such analysis for parallel channel systems under supercritical working fluid flow conditions is missing in the literature which is limited to only linear stability analysis. This analysis can help to improve heat and mass transfer for designing efficient heated channel systems.
Psychological Dimensions and Their Inner Relationships of College Students’ Network Civilization

Network civilization is a product of the rapid development of the virtual world. This study aims to investigate the psychological structure of college students’ network civilization and to explore the role of value judgment and value identification between college students’ psychological perception and value selection. In this study, 1096 college students (511 men and 585 women) completed the anonymous questionnaire on network civilization. They completed the scales of psychological perception, value judgment, value identification, and value selection. The total scale and each subscale both had high Cronbach’s alphas (0.90–0.97), indicating good reliability. Results indicated the following: (1) Psychological perception and value selection are positively correlated. (2) Psychological perception improves college students’ value selection by enhancing their value judgment. (3) Psychological perception may positively affect college students’ value selection via value identification. (4) There is a chain-mediating role between psychological perception, value judgment, value identification, and value selection. These testimonies also contribute to and provide an empirical basis for guidance strategies for the cultivation of network civilization and moral education among college students.
A Second‐Order Explicit Pressure Projection Method for Eulerian Fluid Simulation

In this paper, we propose a novel second‐order explicit midpoint method to address the issue of energy loss and vorticity dissipation in Eulerian fluid simulation. The basic idea is to explicitly compute the pressure gradient at the middle time of each time step and apply it to the velocity field after advection. Theoretically, our solver can achieve higher accuracy than the first‐order solvers at similar computational cost. On the other hand, our method is twice and even faster than the implicit second‐order solvers at the cost of a small loss of accuracy. We have carried out a large number of 2D, 3D and numerical experiments to verify the effectiveness and availability of our algorithm.
Resolving Computational Challenges in Accelerating Electronic Structure Calculations using Machine Learning

Recent advances in use of machine learned surrogates to accelerate electronic struc-1 ture calculations provide exciting opportunities for materials modeling. While the 2 new models are extremely effective, the training of such models require millions 3 of samples for predicting the material properties for a configuration of atoms or 4 snapshot in a single temperature, atomic density pair. This results in excessively 5 high training costs when material properties for multiple snapshots at multiple 6 temperatures and densities are needed. We present a novel atom-centered de-7 composition of local density of states for supervision, which reduces the number 8 of samples for training and evaluation by orders of magnitude compared to past 9 approaches. Combined with a new model for learning atomic environment de-10 scriptions end-to-end, our approach allows resolving downstream quantities such 11 as band energy of melting point aluminum at a fraction of the cost of previous 12 state of the art, with matching or greater accuracy. We further demonstrate that 13 the new models generalize across multiple temperatures of Aluminum reducing 14 computational costs even further. Finally, in order to extend the approach even 15 further we devise an uncertainty metric to choose the next snapshot for training. We 16 demonstrate the efficacy of this metric using liquid and solid aluminum snapshots. 17
Predicting Immune Escape with Pretrained Protein Language Model Embeddings

Assessing the severity of new pathogenic variants requires an understanding of which mutations enable escape of the human immune response. Even single point mutations to an antigen can cause immune escape and infection by disrupting antibody binding. Recent work has modeled the effect of single point mutations on proteins by leveraging the information contained in large-scale, pretrained protein language models (PLMs). PLMs are often applied in a zero-shot setting, where the effect of each mutation is predicted based on the output of the language model with no additional training. However, this approach cannot appropriately model immune escape, which involves the interaction of two proteins—antibody and antigen— instead of one protein and requires making different predictions for the same antigenic mutation in response to different antibodies. Here, we explore several methods for predicting immune escape by building models on top of embeddings from PLMs. We evaluate our methods on a SARS-CoV-2 deep mutational scanning dataset and show that our embedding-based methods significantly outperform zero-shot methods, which have almost no predictive power. We also highlight insights gained into how best to use embeddings from PLMs to predict escape. Despite these promising results, simple statistical and machine learning baseline models that do not use pretraining perform comparably, showing that computationally expensive pretraining approaches may not be beneficial for escape prediction. Furthermore, all models perform relatively poorly, indicating that future work is necessary to improve escape prediction with or without pretrained embeddings1.
Metrics for Time-to-Event Prediction of Gaze Events

In this paper, we explore metrics for the evaluation of time-to-saccade problems. We define a new sampling strategy that takes the temporal nature of gaze data and time-to-saccade problems into account, avoiding samples of the same event in different datasets. This allows us to define novel error metrics for a more intuitive evaluation of predicted durations. The metrics are defined to evaluate the consistency of a predictor and the evaluation of the error over time. We evaluate our method using a state-of-the-art method for time-to-saccade prediction along with an average baseline on three different datasets.
Extended Abstract Track

While imbuing a model with invariance under symmetry transformations can improve data efficiency and predictive performance, most methods require specialised architectures and, thus, prior knowledge of the symmetries. Unfortunately, we don’t always know what symmetries are present in the data. Recent work has solved this problem by jointly learning the invariance (or the degree of invariance) with the model from the data alone. But, this work has focused on discriminative models. We describe a method for learning invariant generative models. We demonstrate that our method can learn a generative model of handwritten digits that is invariant to rotation.
The Status of Material Resources for Effective Teaching of Physics in Secondary Schools in Akwa Ibom State of Nigeria

The study investigated the status of material resources for the teaching and learning of physics in Akwa Ibom State Secondary Schools. It adopted survey design. A sample size of 1400 SS3 Physics students took part in the investigation. Mean was used in answering the three research questions raised to guide the investigation. Findings revealed that some resource materials are available and adequate for the teaching and learning of physics. The study showed a low frequency of use of the available resource materials. Recommendation made was that adequate provision of resource materials should be made by state government to schools for meaningful teaching and learning of physics. Physics teachers should make use of material resources during lessons.
Estimating illiquid asset class alpha and beta using secondary transaction prices

This paper introduces a novel method for estimating the true economic alpha and market beta of illiquid asset classes using secondary transaction prices. Furthermore, this approach can be used to measure the degree of stale pricing in the reported returns of such asset classes. We apply this methodology to private equity, venture capital, private real estate, and private natural resource asset classes. A significant degree of stale pricing was found with estimates of market beta significantly higher than those obtained using reported returns for all four of the asset classes considered. No asset class exhibited statistically significant positive alpha.
Exosomes Released by Influenza-Virus-Infected Cells Carry Factors Capable of Suppressing Immune Defense Genes in Naïve Cells

Abstract: Background Background: Exosomes are involved in intercellular communication and can transfer regulatory molecules between cells. Consequently, they can participate in host immune response regulation. For the influenza A virus (IAV), there is very limited information on changes in exosome composition during cell infection shedding light on the potential role of these extracellular membrane vesicles. Thus, the aim of our work was to study changes in exosomal composition following IAV infection of cells, as well as to evaluate their effect on uninfected cells. Methods: To characterize changes in the composition of cellular miRNAs and mRNAs of exosomes during IAV infection of A549 cells, NGS was used, as well as PCR to identify viral genes. Naïve A549 cells were stimulated with infected-cell-secreted exosomes for studying their activity. Changes in the expression of genes associated with the cell’s immune response were shown using PCR. The effect of exosomes on IAV replication was shown in MDCK cells using In-Cell ELISA and PCR of the supernatants. Results: A change in the miRNA composition (miR-21-3p, miR-26a-5p, miR-23a-5p, miR-548c-5p) and mRNA composition (RPL13A, MKNK2, TRIB3) of exosomes under the influence of the IAV was shown. Many RNAs were involved in the regulation of the immune response of the cell, mainly by suppressing it. After exosome stimulation of naïve cells, a significant decrease in the expression of genes involved in the immune response was shown (RIG1, IFIT1, MDA5, COX2, NFκB, AnxA1, PKR, IL6, IL18). When infecting MDCK cells, a significant decrease in nucleoprotein levels was observed in the presence of exosomes secreted by mock-infected cells. Viral levels in supernatants also decreased. Conclusions: Exosomes secreted by IAV-infected cells could reduce the immune response of neighboring intact cells, leading to more effective IAV replication. This may be associated both with regulatory functions of cellular miRNAs and mRNAs carried by exosomes, or with the presence of viral mRNAs encoding proteins with an immunosuppressive function.
A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization

Gradient descent-ascent (GDA) is a widely used algorithm for minimax optimization. However, GDA has been proved to converge to stationary points for nonconvex minimax optimization, which are suboptimal compared with local minimax points. In this work, we develop cubic regularization (CR) type algorithms that globally converge to local minimax points in nonconvex-strongly-concave minimax optimization. We first show that local minimax points are equivalent to second-order stationary points of a certain envelope function. Then, inspired by the classic cubic regularization algorithm, we propose an algorithm named Cubic-LocalMinimax for finding local minimax points, and provide a comprehensive convergence analysis by leveraging its intrinsic potential function. Specifically, we establish the global convergence of Cubic-LocalMinimax to a local minimax point at a sublinear convergence rate and characterize its iteration complexity. Also, we propose a GDA-based solver for solving the cubic subproblem involved in Cubic-LocalMinimax up to certain pre-defined accuracy, and analyze the overall gradient and Hessian-vector product computation complexities of such an inexact Cubic-LocalMinimax algorithm. Moreover, we propose a stochastic variant of Cubic-LocalMinimax for large-scale minimax optimization, and characterize its sample complexity under stochastic sub-sampling. Experimental results demonstrate faster convergence of our stochastic Cubic-LocalMinimax than some existing algorithms.
Preventive Effectiveness of Thoracic Side Airbags in Side-Impact Crashes Based on Korea In-Depth Accident Study (KIDAS) Database

Studies on the effectiveness of thoracic side airbags (tSABs) in preventing thoracic injuries is limited and conflicting. This retrospective observational study aims to evaluate the effectiveness of tSABs in side-impact crashes based on data for motor vehicle occupants (MVOs) who visited an emergency department in Korea. The data were obtained from the Korean In-Depth Accident Study (KIDAS) database for patients treated at Wonju Severance Christian Hospital between January 2011 and April 2020. Of the 3899 patients with road traffic injuries, data for 490 patients were used. The overall frequency of tSAB deployment in side-impact crashes was found to be 8.1%. In the multivariate analysis, elderly age, near-side impact, colliding with fixed objects, non-oblique force, and higher crush extent were found to be factors associated with higher thoracic injuries (Abbreviated Injury Scale ≥ 2). MVOs in crashes with tSAB deployment were at an increased risk of injury compared with MVOs in crashes with no deployment, but no statistical difference was observed [adjusted odds ratios (AORs): 1.65 (0.73–3.73)]. Further, the incidence of lung injury and rib fractures increased with tSAB activation (p < 0.05). These results demonstrate the limited capability of tSABs in preventing thoracic injuries in motor vehicle crashes.
PARTICLE ACCELERATION BY MAGNETIC RECONNECTION IN A TWISTED CORONAL LOOP

Photospheric motions may lead to twisted coronal magnetic fields which contain free energy that can be released by reconnection. Browning & Van der Linden suggested that such a relaxation event may be triggered by the onset of ideal kink instability. In the present work, we study the evolution of a twisted magnetic flux tube with zero net axial current following Hood et al. Based on the obtained magnetic and electric fields, proton and electron trajectories are calculated using the test-particle approach. We discuss resulting particle distributions and possible observational implications, for example, for small solar flares.