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Large language models (LLMs) have revolutionized many areas (e.g. natural language processing, software engineering, etc.) by achieving state-of-the-art performance on extensive downstream tasks. Aiming to achieve robust and general artificial intelligence, there has been a surge of interest in investigating the reasoning ability of the LLMs. Whereas the textual and numerical reasoning benchmarks adopted by previous works are rather shallow and simple, it is hard to conclude that the LLMs possess strong reasoning ability by merely achieving positive results on these benchmarks. Recent efforts have demonstrated that the LLMs are poor at solving sequential decision-making problems that require common-sense planning by evaluating their performance on the reinforcement learning benchmarks. In this work, we conduct an in-depth assessment of several state-of-the-art LLMs' reasoning ability based on the inductive logic programming (ILP) benchmark, which is broadly recognized as a representative and challenging measurement for evaluating logic program induction/synthesis systems as it requires inducing strict cause-effect logic to achieve robust deduction on independent and identically distributed (IID) and out-of-distribution (OOD) test samples. Our evaluations illustrate that compared with the neural program induction systems which are much smaller in model size, the state-of-the-art LLMs are much poorer in terms of reasoning ability by achieving much lower performance and generalization using either natural language prompting or truth-value matrix prompting., Comment: Accepted by The First International Workshop on Large Language Models for Code (ICSE 2024)

Human aging is invariably accompanied by a decline in renal function, a process potentially exacerbated by uremic toxins originating from gut microbes. Based on a registered household Chinese Guangxi longevity cohort (n = 151), we conducted comprehensive profiling of the gut microbiota and serum metabolome of individuals from 22 to 111 years of age and validated the findings in two independent East Asian aging cohorts (Japan aging cohort n = 330, Yunnan aging cohort n = 80), identifying unique age-dependent differences in the microbiota and serum metabolome. We discovered that the influence of the gut microbiota on serum metabolites intensifies with advancing age. Furthermore, mediation analyses unveiled putative causal relationships between the gut microbiota (Escherichia coli, Odoribacter splanchnicus, and Desulfovibrio piger) and serum metabolite markers related to impaired renal function (p-cresol, N-phenylacetylglutamine, 2-oxindole, and 4-aminohippuric acid) and aging. The fecal microbiota transplantation experiment demonstrated that the feces of elderly individuals could influence markers related to impaired renal function in the serum. Our findings reveal novel links between age-dependent alterations in the gut microbiota and serum metabolite markers of impaired renal function, providing novel insights into the effects of microbiota-metabolite interplay on renal function and healthy aging.

We employed the mechanical effect from 40 kHz ultrasound (US) to improve the delivery of riboflavin into corneal stroma for collagen crosslinking, which can benefit the treatment of keratoconus and other corneal ectasias. Experiments were conducted, first with porcine corneas ex vivo and then with New Zealand white rabbits in vivo, at varying mechanical index (MI) and sonication time. Results showed that 15 min of US applied on the cornea at MI = 0.8 in the presence of 0.5% of riboflavin solution enabled its delivery to deeper corneal stroma. Excessive heat was removed by a cooling setup to negate the thermal effect. The corneal absorption amount and penetration of riboflavin through cornea as detected by fluorotron, as well as the enhancement of corneal stiffness as measured by Young's modulus, were comparable to the conventional approach that requires complete corneal epithelium debridement. Histological analysis revealed minor exfoliation of superficial cell layers of corneal epithelium and loss of ZO-1 tight junctions immediately after US. Full recovery of the corneal epithelium and restoration of tight junctions occurred in 3-4 days. The study shows that low-intensity low-frequency ultrasound (LILF US) is a less invasive alternative to the conventional epithelium-off method for delivering riboflavin into the corneal stroma.

Human aging is invariably accompanied by a decline in renal function, a process potentially exacerbated by uremic toxins originating from gut microbes. Based on a registered household Chinese Guangxi longevity cohort (n = 151), we conducted comprehensive profiling of the gut microbiota and serum metabolome of individuals from 22 to 111 years of age and validated the findings in two independent East Asian aging cohorts (Japan aging cohort n = 330, Yunnan aging cohort n = 80), identifying unique age-dependent differences in the microbiota and serum metabolome. We discovered that the influence of the gut microbiota on serum metabolites intensifies with advancing age. Furthermore, mediation analyses unveiled putative causal relationships between the gut microbiota (Escherichia coli, Odoribacter splanchnicus, and Desulfovibrio piger) and serum metabolite markers related to impaired renal function (p-cresol, N-phenylacetylglutamine, 2-oxindole, and 4-aminohippuric acid) and aging. The fecal microbiota transplantation experiment demonstrated that the feces of elderly individuals could influence markers related to impaired renal function in the serum. Our findings reveal novel links between age-dependent alterations in the gut microbiota and serum metabolite markers of impaired renal function, providing novel insights into the effects of microbiota-metabolite interplay on renal function and healthy aging., Human aging is invariably accompanied by a decline in renal function, a process potentially exacerbated by uremic toxins originating from gut microbes. Based on a registered household Chinese Guangxi longevity cohort (n = 151), we conducted comprehensive profiling of the gut microbiota and serum metabolome of individuals from 22 to 111 years of age and validated the findings in two independent East Asian aging cohorts (Japan aging cohort n = 330, Yunnan aging cohort n = 80), identifying unique age-dependent differences in the microbiota and serum metabolome. We discovered that the influence of the gut microbiota on serum metabolites intensifies with advancing age. Furthermore, mediation analyses unveiled putative causal relationships between the gut microbiota (Escherichia coli, Odoribacter splanchnicus, and Desulfovibrio piger) and serum metabolite markers related to impaired renal function (p-cresol, N-phenylacetylglutamine, 2-oxindole, and 4-aminohippuric acid) and aging. The fecal microbiota transplantation experiment demonstrated that the feces of elderly individuals could influence markers related to impaired renal function in the serum. Our findings reveal novel links between age-dependent alterations in the gut microbiota and serum metabolite markers of impaired renal function, providing novel insights into the effects of microbiota-metabolite interplay on renal function and healthy aging.

Human aging is invariably accompanied by a decline in renal function, a process potentially exacerbated by uremic toxins originating from gut microbes. Based on a registered household Chinese Guangxi longevity cohort (n = 151), we conducted comprehensive profiling of the gut microbiota and serum metabolome of individuals from 22 to 111 years of age and validated the findings in two independent East Asian aging cohorts (Japan aging cohort n = 330, Yunnan aging cohort n = 80), identifying unique age-dependent differences in the microbiota and serum metabolome. We discovered that the influence of the gut microbiota on serum metabolites intensifies with advancing age. Furthermore, mediation analyses unveiled putative causal relationships between the gut microbiota (Escherichia coli, Odoribacter splanchnicus, and Desulfovibrio piger) and serum metabolite markers related to impaired renal function (p-cresol, N-phenylacetylglutamine, 2-oxindole, and 4-aminohippuric acid) and aging. The fecal microbiota transplantation experiment demonstrated that the feces of elderly individuals could influence markers related to impaired renal function in the serum. Our findings reveal novel links between age-dependent alterations in the gut microbiota and serum metabolite markers of impaired renal function, providing novel insights into the effects of microbiota-metabolite interplay on renal function and healthy aging., Human aging is invariably accompanied by a decline in renal function, a process potentially exacerbated by uremic toxins originating from gut microbes. Based on a registered household Chinese Guangxi longevity cohort (n = 151), we conducted comprehensive profiling of the gut microbiota and serum metabolome of individuals from 22 to 111 years of age and validated the findings in two independent East Asian aging cohorts (Japan aging cohort n = 330, Yunnan aging cohort n = 80), identifying unique age-dependent differences in the microbiota and serum metabolome. We discovered that the influence of the gut microbiota on serum metabolites intensifies with advancing age. Furthermore, mediation analyses unveiled putative causal relationships between the gut microbiota (Escherichia coli, Odoribacter splanchnicus, and Desulfovibrio piger) and serum metabolite markers related to impaired renal function (p-cresol, N-phenylacetylglutamine, 2-oxindole, and 4-aminohippuric acid) and aging. The fecal microbiota transplantation experiment demonstrated that the feces of elderly individuals could influence markers related to impaired renal function in the serum. Our findings reveal novel links between age-dependent alterations in the gut microbiota and serum metabolite markers of impaired renal function, providing novel insights into the effects of microbiota-metabolite interplay on renal function and healthy aging.

Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representations for link prediction. First, two complementary augmented views are derived from an incomplete graph structure through a bidirectional graph structure augmentation scheme. This augmentation scheme mitigates the potential information loss that is commonly associated with various data augmentation techniques involving raw graph data, such as edge perturbation, node removal, and attribute masking. Second, we propose a CGCL model that can learn invariant graph representations. A cross-view training scheme is proposed to train the proposed CGCL model. This scheme attempts to maximize the consistency information between one augmented view and the graph structure reconstructed from the other augmented view. Furthermore, we offer a comprehensive theoretical CGCL analysis. This paper empirically and experimentally demonstrates the effectiveness of the proposed CGCL method, achieving competitive results on graph datasets in comparisons with several state-of-the-art algorithms., Comment: 8 pages

Deep reinforcement learning (DRL) has revolutionized quantitative finance by achieving decent performance without significant human expert knowledge. Despite its achievements, we observe that the current state-of-the-art DRL models are still ineffective in identifying the market trend, causing them to miss good trading opportunities or suffer from large drawdowns when encountering market crashes. To tackle this limitation, a natural idea is to embed human expert knowledge regarding the market trend. Whereas, such knowledge is abstract and hard to be quantified. In this paper, we propose a universal neuro-symbolic tuning framework, called program sketch-based tuning (PST). Particularly, PST first proposes using a novel symbolic program sketch to embed the abstract human expert knowledge of market trends. Then we utilize the program sketch to tune a trained DRL policy according to the different market trend of the moment. Finally, in order to optimize this neural-symbolic framework, we propose a novel hybrid optimization method. Extensive evaluations on two popular quantitative trading tasks demonstrate that PST can significantly enhance the performance of previous state-of-the-art DRL strategies while being extremely lightweight.

Deep neural networks (DNNs) have made significant progress, but often suffer from fairness issues, as deep models typically show distinct accuracy differences among certain subgroups (e.g., males and females). Existing research addresses this critical issue by employing fairness-aware loss functions to constrain the last-layer outputs and directly regularize DNNs. Although the fairness of DNNs is improved, it is unclear how the trained network makes a fair prediction, which limits future fairness improvements. In this paper, we investigate fairness from the perspective of decision rationale and define the parameter parity score to characterize the fair decision process of networks by analyzing neuron influence in various subgroups. Extensive empirical studies show that the unfair issue could arise from the unaligned decision rationales of subgroups. Existing fairness regularization terms fail to achieve decision rationale alignment because they only constrain last-layer outputs while ignoring intermediate neuron alignment. To address the issue, we formulate the fairness as a new task, i.e., decision rationale alignment that requires DNNs' neurons to have consistent responses on subgroups at both intermediate processes and the final prediction. To make this idea practical during optimization, we relax the naive objective function and propose gradient-guided parity alignment, which encourages gradient-weighted consistency of neurons across subgroups. Extensive experiments on a variety of datasets show that our method can significantly enhance fairness while sustaining a high level of accuracy and outperforming other approaches by a wide margin.

We investigated the effects of interstitial N and C on the stacking fault energy (SFE) of an equiatomic CoCrNi medium entropy alloy. Results of computer modeling were compared to tensile deformation and electron microscopy data. Both N and C in solid solution increase the SFE of the face-centered cubic (FCC) alloy matrix at room temperature, with the former having a more significant effect by 240% for 0.5 at % N. Total energy calculations based on density functional theory (DFT) as well as thermodynamic modeling of the Gibbs free energy with the CALPHAD (CALculation of PHAse Diagrams) method reveal a stabilizing effect of N and C interstitials on the FCC lattice with respect to the hexagonal close-packed (HCP) CoCrNi-X (X: N, C) lattice. Scanning transmission electron microscopy (STEM) measurements of the width of dissociated 1/2 dislocations suggest that the SFE of CoCrNi increases from 22 to 42-44 mJ center dot m(-2) after doping the alloy with 0.5 at. % interstitial N. The higher SFE reduces the nucleation rates of twins, leading to an increase in the critical stress required to trigger deformation twinning, an effect which can be used to design load-dependent strain hardening response.

In this paper, a search for power-law fluctuations with fractality and intermittency analysis to explore the QCD phase diagram and the critical point is summarized. Experimental data on self-similar correlations and fluctuations with respect to the size of phase space volume in various high energy heavy-ion collisions are presented, with special emphasis on background subtraction and efficiency correction of the measurement. Phenomenological modelling and theoretical work on the subject are discussed. Finally, we highlight possible directions for future research., Comment: Invited brief review from MPLA, modified according to the journal requirement and a few typos corrected

High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 x 10-6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties., QC 20230215

Within the framework of intermittency analysis, a search for critical fluctuations is ongoing to locate the possible critical point in the quantum chromodynamics phase diagram. In this study, self-similar critical fluctuations from a critical Monte Carlo (CMC) model have been incorporated into the cascade ultrarelativistic quantum molecular dynamics (UrQMD) model. This hybrid UrQMD+CMC model exhibits a clear power-law behavior of scaled factorial moment for charged particles in Au+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 7.7-200 GeV. By comparing the UrQMD+CMC model results with those from the STAR experiment, it is found that the value of a calculated scaling exponent falls in the range of the experimental measurement when 1-2 \% signal of intermittency fluctuations is added into the UrQMD sample., Comment: 8 pages, 6 figures, published version

We investigated the effects of interstitial N and C on the stacking fault energy (SFE) of an equiatomic CoCrNi medium entropy alloy. Results of computer modeling were compared to tensile deformation and electron microscopy data. Both N and C in solid solution increase the SFE of the face-centered cubic (FCC) alloy matrix at room temperature, with the former having a more significant effect by 240% for 0.5 at % N. Total energy calculations based on density functional theory (DFT) as well as thermodynamic modeling of the Gibbs free energy with the CALPHAD (CALculation of PHAse Diagrams) method reveal a stabilizing effect of N and C interstitials on the FCC lattice with respect to the hexagonal close-packed (HCP) CoCrNi-X (X: N, C) lattice. Scanning transmission electron microscopy (STEM) measurements of the width of dissociated 1/2 dislocations suggest that the SFE of CoCrNi increases from 22 to 42-44 mJ center dot m(-2) after doping the alloy with 0.5 at. % interstitial N. The higher SFE reduces the nucleation rates of twins, leading to an increase in the critical stress required to trigger deformation twinning, an effect which can be used to design load-dependent strain hardening response.

High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10-6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Marcel Sluiter

The intricate processes of microbiota-gut-brain communication in modulating human cognition and emotion, especially in the context of mood disorders, have remained elusive. Here we performed faecal metagenomic, serum metabolomics and neuroimaging studies on a cohort of 109 unmedicated patients with depressed bipolar disorder (BD) patients and 40 healthy controls (HCs) to characterise the microbial-gut-brain axis in BD. Across over 12,000 measured metabolic features, we observed a large discrepancy (73.54%) in the serum metabolome between BD patients and HCs, spotting differentially abundant microbial-derived neuroactive metabolites including multiple B-vitamins, kynurenic acid, gamma-aminobutyric acid and short-chain fatty acids. These metabolites could be linked to the abundance of gut microbiota presented with corresponding biosynthetic potentials, including Akkermansia muciniphila, Citrobacter spp. (Citrobacter freundii and Citrobacter werkmanii), Phascolarctobacterium spp., Yersinia spp. (Yersinia frederiksenii and Yersinia aleksiciae), Enterobacter spp. (Enterobacter cloacae and Enterobacter kobei) and Flavobacterium spp. Based on functional neuroimaging, BD-related neuroactive microbes and metabolites were discovered as potential markers associated with BD-typical features of functional connectivity of brain networks, hinting at aberrant cognitive function, emotion regulation, and interoception. Our study combines gut microbiota and neuroactive metabolites with brain functional connectivity, thereby revealing potential signalling pathways from the microbiota to the gut and the brain, which may have a role in the pathophysiology of BD.

Despite achieving superior performance in human-level control problems, unlike humans, deep reinforcement learning (DRL) lacks high-order intelligence (e.g., logic deduction and reuse), thus it behaves ineffectively than humans regarding learning and generalization in complex problems. Previous works attempt to directly synthesize a white-box logic program as the DRL policy, manifesting logic-driven behaviors. However, most synthesis methods are built on imperative or declarative programming, and each has a distinct limitation, respectively. The former ignores the cause-effect logic during synthesis, resulting in low generalizability across tasks. The latter is strictly proof-based, thus failing to synthesize programs with complex hierarchical logic. In this paper, we combine the above two paradigms together and propose a novel Generalizable Logic Synthesis (GALOIS) framework to synthesize hierarchical and strict cause-effect logic programs. GALOIS leverages the program sketch and defines a new sketch-based hybrid program language for guiding the synthesis. Based on that, GALOIS proposes a sketch-based program synthesis method to automatically generate white-box programs with generalizable and interpretable cause-effect logic. Extensive evaluations on various decision-making tasks with complex logic demonstrate the superiority of GALOIS over mainstream baselines regarding the asymptotic performance, generalizability, and great knowledge reusability across different environments.

Deep learning has introduced significant improvements in many software analysis tasks. Although the Large Language Models (LLMs) based neural code models demonstrate commendable performance when trained and tested within the intra-project independent and identically distributed (IID) setting, they often struggle to generalize effectively to real-world inter-project out-of-distribution (OOD) data. In this work, we show that this phenomenon is caused by the heavy reliance on project-specific shortcuts for prediction instead of ground-truth evidence. We propose a Cond-Idf measurement to interpret this behavior, which quantifies the relatedness of a token with a label and its project-specificness. The strong correlation between model behavior and the proposed measurement indicates that without proper regularization, models tend to leverage spurious statistical cues for prediction. Equipped with these observations, we propose a novel bias mitigation mechanism that regularizes the model's learning behavior by leveraging latent logic relations among samples. Experimental results on two representative program analysis tasks indicate that our mitigation framework can improve both inter-project OOD generalization and adversarial robustness, while not sacrificing accuracy on intra-project IID data., Comment: Accepted by LREC-COLING 2024

Following publication of the original article [1], the authors reported an error in Fig. 1. The published Fig. 1B is an image of mRNA RT-qPCR analysis of Otogl levels in developing testes, rather than a protein quantification analysis image as labeled. We have attached a corrected version of Fig. 1B. The labels of F and G in the published Fig. 2 should be interchanged. Tis error does not change the scientific conclusions of the article in any way. The authors apologize for this error. The correct figures are presented below. The original article [1] has been updated.

Otogelin-like protein (encoded by Otogl) was highly structural similar to the gelforming mucin proteins. Although human OTOG mutations have been linked to deafness, the biological function of OTOGL in male germ cell development remains enigmatic. In screening 336 patients with non-obstructive azoospermia (NOA), OTOGL displays the high mutant ratio (13.99 %). Then, we examined the expression of OTOGL in developing mouse testes. Otogl mRNA and protein are continually expressed in postnatal developing testes from postnatal day 0 (P0) testes to P21 testes exhibiting a decreased trend with the age growth. We thus generated a global Otogl knockout mouse (KO) model using the CRISPR/Cas9 technology; however, Otogl KO mice displayed normal development and fertility. Further histological analysis of Otogl knockout mouse testes revealed that all types of spermatogenic cells are present in Otogl KO seminiferous tubules. Together, our study suggested that OTOGL is nonessential for male germ cell development and spermatogenesis.

Purpose : The corneal drug penetration is commonly detected by measuring fluorescence profile of labelled drug in corneal tissues by cryosection. However, such method requires sacrificing animals and the data is often debased by artefacts. Although ocular fluorophotometer may provide a better alternative, problems of Inner Filter Effect (IFE) and low resolution have been reported. This study reports a modified Fluorotron to circumvent this issue to allow quantitative measurement of both the absorption and penetration of riboflavin into the cornea. Using the new approach, the penetration depth of riboflavin by ultrasound-mediated delivery was characterized. Methods : The objective lens of ocular fluorophotometer was stabilised with plexiglass and the angle was increased between excitation and emission paths from 28 degree to 53 degrees that resulted in decreasing depth of focus to about 85 microns. The modified fluorophotometer was used to detect porcine eyes (Epi-on) which were soaked in 0.5% riboflavin for 30 minutes followed by washing and 0.025% riboflavin solution for 15 minutes by dropping different concentration riboflavin separately. The cryosection method was employed after ultrasound-mediated riboflavin delivery with different operating parameters of sonification which will be used to validate the result in modified fluorophotometer. Results : The riboflavin penetration profile of cornea which detected by the modified fluorophotometer can have a higher resolution and minimized IFE (Fig.1). We anticipate that the modified flurophotometer will expedite the optimization of new modalities, such as mediation by ultrasound, in corneal drug delivery. Conclusions : This modified Fluorotron fluorophotometer can detect the riboflavin penetration depth and concentration across the corneal sections with higher resolution and minimized IFE. The absorption and penetration of riboflavin in cornea by ultrasound-mediated delivery is distinguishable in cryosection fluorescen

Otogelin-like protein (encoded by Otogl) was highly structural similar to the gelforming mucin proteins. Although human OTOG mutations have been linked to deafness, the biological function of OTOGL in male germ cell development remains enigmatic. In screening 336 patients with non-obstructive azoospermia (NOA), OTOGL displays the high mutant ratio (13.99 %). Then, we examined the expression of OTOGL in developing mouse testes. Otogl mRNA and protein are continually expressed in postnatal developing testes from postnatal day 0 (P0) testes to P21 testes exhibiting a decreased trend with the age growth. We thus generated a global Otogl knockout mouse (KO) model using the CRISPR/Cas9 technology; however, Otogl KO mice displayed normal development and fertility. Further histological analysis of Otogl knockout mouse testes revealed that all types of spermatogenic cells are present in Otogl KO seminiferous tubules. Together, our study suggested that OTOGL is nonessential for male germ cell development and spermatogenesis.

Following publication of the original article [1], the authors reported an error in Fig. 1. The published Fig. 1B is an image of mRNA RT-qPCR analysis of Otogl levels in developing testes, rather than a protein quantification analysis image as labeled. We have attached a corrected version of Fig. 1B. The labels of F and G in the published Fig. 2 should be interchanged. Tis error does not change the scientific conclusions of the article in any way. The authors apologize for this error. The correct figures are presented below. The original article [1] has been updated.

Since its first emergence in 2004, the high-entropy alloy (HEA) concept has aimed at stabilizing single- or dual-phase multi-element solid solutions through high mixing entropy. Here, this strategy is changed and renders such massive solid solutions metastable, to trigger spinodal decomposition for improving the alloys’ magnetic properties. The motivation for starting from a HEA for this approach is to provide the chemical degrees of freedom required to tailor spinodal behavior using multiple components. The key idea is to form Fe-Co enriched regions which have an expanded volume (relative to unconstrained Fe-Co), due to coherency constraints imposed by the surrounding HEA matrix. As demonstrated by theory and experiments, this leads to improved magnetic properties of the decomposed alloy relative to the original solid solution matrix. In a prototype magnetic FeCoNiMnCu HEA, it is shown that the modulated structures, achieved by spinodal decomposition, lead to an increase of the Curie temperature by 48% and a simultaneous increase of magnetization by 70% at ambient temperature as compared to the homogenized single-phase reference alloy. The findings thus open a pathway for the development of advanced functional HEAs., (OLD) MSE-7

Exploration of the QCD phase diagram and the critical point is one of the main goals in current relativistic heavy-ion collisions. The QCD critical point is expected to belong to a three-dimensional (3D) Ising universality class. Machine learning techniques are found to be powerful in distinguishing different phases of matter and provide a new way on the study of phase diagram. We investigate phase transitions in the 3D cubic Ising model by using supervised learning methods. It is found that a 3D convolutional neural network can be trained to well predict physical quantities in different spin configurations. With a uniform neural network architecture, it can encode phases of matter and identify both the second- and the first-order phase transitions. The important features that discriminate different types of phase transitions in the classification processes are investigated. These findings could help to study and understand the QCD phase transitions in relativistic heavy-ion collisions., Comment: 7 pages, 6 figures, update introduction and discussions of the results

In order to estimate qualitatively the influence of nonequilibrium evolution in relativistic heavy ion collisions, we use the three dimensional Ising model with Metropolis algorithm to study the evolution from nonequilibrium to equilibrium on the phase boundary. The evolution of order parameter approaches its equilibrium value exponentially, the same as that given by Langevin equation. The average relaxation time is defined which is demonstrated to well represent the relaxation time in dynamical equations. It is shown that the average relaxation time at critical temperature diverges as the zth power of system size. The third and the fourth cumulants of order parameter during the nonequilibrium evolution could be either positive or negative, depending on the observation time, consistent with dynamical models at T > Tc. It is found that the nonequilibrium evolution at T > Tc lasts very short, and the influence is weaker than that at T < Tc. Those qualitative features are instructive to determine experimentally the critical point and the phase boundary of QCD., Comment: 8 pages, 5 figures; add fig.3 and up date the eq.(9-10), and contents

It is proposed that the $O(n)$ spin and geometrical percolation models can help to study the QCD phase diagram due to the universality properties of the phase transition. In this paper, correlations and fluctuations of various sizes of cluster in the Ising model are systematically studied. With a finite size system, we demonstrate how to use the finite size scaling and fixed point behavior to search for critical point. At critical point, the independency of system size is found from skewness and kurtosis of the maximum, second and third largest cluster and their correlations. It is similar to the Binder-ratio, which has provided a remarkable identification of the critical point. Through an explanation of the universal characteristic of skewness and kurtosis of the order parameter, a possible application to the relativistic heavy-ion collisions is also discussed., Comment: 12 pages, 5 figures. Accepted by IJMPE

In order to estimate qualitatively the influence of nonequilibrium evolution in relativistic heavy ion collisions, we use the three dimensional Ising model with Metropolis algorithm to study the evolution from nonequilibrium to equilibrium on the phase boundary. The evolution of order parameter approaches its equilibrium value exponentially, the same as that given by Langevin equation. The average relaxation time is defined which is demonstrated to well represent the relaxation time in dynamical equations. It is shown that the average relaxation time at critical temperature diverges as the zth power of system size. The third and the fourth cumulants of order parameter during the nonequilibrium evolution could be either positive or negative, depending on the observation time, consistent with dynamical models at T > Tc. It is found that the nonequilibrium evolution at T > Tc lasts very short, and the influence is weaker than that at T < Tc. Those qualitative features are instructive to determine experimentally the critical point and the phase boundary of QCD., Comment: 8 pages, 5 figures; add fig.3 and up date the eq.(9-10), and contents

Local density fluctuations near the QCD critical point has been suggested to exhibit a power-law behavior which can be probed by an intermittency analysis on scaled factorial moment (SFM) in relativistic heavy-ion collisions. The collision energy and centrality dependence of the second-order SFMs are systematically investigated in Au $+$ Au collisions at $\sqrt{s_{NN}}$ = 7.7, 11.5, 19.6, 27, 39, 62.4, and 200 GeV within the UrQMD model. We estimate the noncritical background in the measurement of intermittency and propose a cumulative variable method to effectively remove the background contributions. We further study the effect of particle detection efficiency by implementing the RHIC (STAR) experimental tracking efficiencies in the UrQMD events. A cell-by-cell method is proposed for experimental application of efficiency corrections on SFM. This work can provide a guidance of background subtraction and efficiency correction for the experimental measurement of intermittency in the search of the QCD critical point in heavy-ion collisions., Comment: Replaced with the published version

Radial flow can be directly extracted from the azimuthal distribution of mean transverse rapidity. We apply the event-plane method and the two-particle correlation method to estimate the anisotropic Fourier coefficient of the azimuthal distribution of mean transverse rapidity. Using the event sample generated by a multiphase transport model with string melting, we show that both methods are effective. For the two-particle correlation method to be reliable, the mean number of particles in an azimuthal bin must be above a certain threshold. Using these two methods, anisotropic radial flow can be estimated in a model-independent way in relativistic heavy-ion collisions., Comment: 8 pages, 4 figures

Otogelin-like protein (encoded by Otogl) was highly structural similar to the gelforming mucin proteins. Although human OTOG mutations have been linked to deafness, the biological function of OTOGL in male germ cell development remains enigmatic. In screening 336 patients with non-obstructive azoospermia (NOA), OTOGL displays the high mutant ratio (13.99 %). Then, we examined the expression of OTOGL in developing mouse testes. Otogl mRNA and protein are continually expressed in postnatal developing testes from postnatal day 0 (P0) testes to P21 testes exhibiting a decreased trend with the age growth. We thus generated a global Otogl knockout mouse (KO) model using the CRISPR/Cas9 technology; however, Otogl KO mice displayed normal development and fertility. Further histological analysis of Otogl knockout mouse testes revealed that all types of spermatogenic cells are present in Otogl KO seminiferous tubules. Together, our study suggested that OTOGL is nonessential for male germ cell development and spermatogenesis.

Purpose : The corneal drug penetration is commonly detected by measuring fluorescence profile of labelled drug in corneal tissues by cryosection. However, such method requires sacrificing animals and the data is often debased by artefacts. Although ocular fluorophotometer may provide a better alternative, problems of Inner Filter Effect (IFE) and low resolution have been reported. This study reports a modified Fluorotron to circumvent this issue to allow quantitative measurement of both the absorption and penetration of riboflavin into the cornea. Using the new approach, the penetration depth of riboflavin by ultrasound-mediated delivery was characterized. Methods : The objective lens of ocular fluorophotometer was stabilised with plexiglass and the angle was increased between excitation and emission paths from 28 degree to 53 degrees that resulted in decreasing depth of focus to about 85 microns. The modified fluorophotometer was used to detect porcine eyes (Epi-on) which were soaked in 0.5% riboflavin for 30 minutes followed by washing and 0.025% riboflavin solution for 15 minutes by dropping different concentration riboflavin separately. The cryosection method was employed after ultrasound-mediated riboflavin delivery with different operating parameters of sonification which will be used to validate the result in modified fluorophotometer. Results : The riboflavin penetration profile of cornea which detected by the modified fluorophotometer can have a higher resolution and minimized IFE (Fig.1). We anticipate that the modified flurophotometer will expedite the optimization of new modalities, such as mediation by ultrasound, in corneal drug delivery. Conclusions : This modified Fluorotron fluorophotometer can detect the riboflavin penetration depth and concentration across the corneal sections with higher resolution and minimized IFE. The absorption and penetration of riboflavin in cornea by ultrasound-mediated delivery is distinguishable in cryosection fluorescen

Following publication of the original article [1], the authors reported an error in Fig. 1. The published Fig. 1B is an image of mRNA RT-qPCR analysis of Otogl levels in developing testes, rather than a protein quantification analysis image as labeled. We have attached a corrected version of Fig. 1B. The labels of F and G in the published Fig. 2 should be interchanged. Tis error does not change the scientific conclusions of the article in any way. The authors apologize for this error. The correct figures are presented below. The original article [1] has been updated.

Since its first emergence in 2004, the high-entropy alloy (HEA) concept has aimed at stabilizing single- or dual-phase multi-element solid solutions through high mixing entropy. Here, this strategy is changed and renders such massive solid solutions metastable, to trigger spinodal decomposition for improving the alloys’ magnetic properties. The motivation for starting from a HEA for this approach is to provide the chemical degrees of freedom required to tailor spinodal behavior using multiple components. The key idea is to form Fe-Co enriched regions which have an expanded volume (relative to unconstrained Fe-Co), due to coherency constraints imposed by the surrounding HEA matrix. As demonstrated by theory and experiments, this leads to improved magnetic properties of the decomposed alloy relative to the original solid solution matrix. In a prototype magnetic FeCoNiMnCu HEA, it is shown that the modulated structures, achieved by spinodal decomposition, lead to an increase of the Curie temperature by 48% and a simultaneous increase of magnetization by 70% at ambient temperature as compared to the homogenized single-phase reference alloy. The findings thus open a pathway for the development of advanced functional HEAs., (OLD) MSE-7

Background: The human skin microbiota is considered to be essential for skin homeostasis and barrier function. Comprehensive analyses of its function would substantially benefit from a catalog of reference genes derived from metagenomic sequencing. The existing catalog for the human skin microbiome is based on samples from limited individuals from a single cohort on reference genomes, which limits the coverage of global skin microbiome diversity. Results: In the present study, we have used shotgun metagenomics to newly sequence 822 skin samples from Han Chinese, which were subsequently combined with 538 previously sequenced North American samples to construct an integrated Human Skin Microbial Gene Catalog (iHSMGC). The iHSMGC comprised 10,930,638 genes with the detection of 4,879,024 new genes. Characterization of the human skin resistome based on iHSMGC confirmed that skin commensals, such as Staphylococcus spp, are an important reservoir of antibiotic resistance genes (ARGs). Further analyses of skin microbial ARGs detected microbe-specific and skin site-specific ARG signatures. Of note, the abundance of ARGs was significantly higher in Chinese than Americans, while multidrug-resistant bacteria (“superbugs”) existed on the skin of both Americans and Chinese. A detailed analysis of microbial signatures identified Moraxella osloensis as a species specific for Chinese skin. Importantly, Moraxella osloensis proved to be a signature species for one of two robust patterns of microbial networks present on Chinese skin, with Cutibacterium acnes indicating the second one. Each of such “cutotypes” was associated with distinct patterns of data-driven marker genes, functional modules, and host skin properties. The two cutotypes markedly differed in functional modules related to their metabolic characteristics, indicating that host-dependent trophic chains might underlie their development. Conclusions: The development of the iHSMGC will facilitate further studies on the hu

Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data. Most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose a cross-lingual transfer learning framework, PLATO, for statistical type inference, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others, e.g., Python to JavaScript, Java to JavaScript, etc. PLATO is powered by a novel kernelized attention mechanism to constrain the attention scope of the backbone Transformer model such that the model is forced to base its prediction on commonly shared features among languages. In addition, we propose the syntax enhancement that augments the learning on the feature overlap among language domains. Furthermore, PLATO can also be used to improve the performance of the conventional supervised learning-based type inference by introducing cross-lingual augmentation, which enables the model to learn more general features across multiple languages. We evaluated PLATO under two settings: 1) under the cross-domain scenario that the target language data is not labeled or labeled partially, the results show that PLATO outperforms the state-of-the-art domain transfer techniques by a large margin, e.g., it improves the Python to TypeScript baseline by +5.40%@EM, +5.40%@weighted-F1, and 2) under the conventional monolingual supervised learning based scenario, PLATO improves the Python baseline by +4.40%@EM, +3.20%@EM (parametric).

Background: The human skin microbiota is considered to be essential for skin homeostasis and barrier function. Comprehensive analyses of its function would substantially benefit from a catalog of reference genes derived from metagenomic sequencing. The existing catalog for the human skin microbiome is based on samples from limited individuals from a single cohort on reference genomes, which limits the coverage of global skin microbiome diversity. Results: In the present study, we have used shotgun metagenomics to newly sequence 822 skin samples from Han Chinese, which were subsequently combined with 538 previously sequenced North American samples to construct an integrated Human Skin Microbial Gene Catalog (iHSMGC). The iHSMGC comprised 10,930,638 genes with the detection of 4,879,024 new genes. Characterization of the human skin resistome based on iHSMGC confirmed that skin commensals, such as Staphylococcus spp, are an important reservoir of antibiotic resistance genes (ARGs). Further analyses of skin microbial ARGs detected microbe-specific and skin site-specific ARG signatures. Of note, the abundance of ARGs was significantly higher in Chinese than Americans, while multidrug-resistant bacteria (“superbugs”) existed on the skin of both Americans and Chinese. A detailed analysis of microbial signatures identified Moraxella osloensis as a species specific for Chinese skin. Importantly, Moraxella osloensis proved to be a signature species for one of two robust patterns of microbial networks present on Chinese skin, with Cutibacterium acnes indicating the second one. Each of such “cutotypes” was associated with distinct patterns of data-driven marker genes, functional modules, and host skin properties. The two cutotypes markedly differed in functional modules related to their metabolic characteristics, indicating that host-dependent trophic chains might underlie their development. Conclusions: The development of the iHSMGC will facilitate further studies on the hu

We reveal the impact of magnetic ordering on stacking fault energy (SFE) and its influence on the deformation mechanisms and mechanical properties in a class of nonequiatomic quinary Mn-containing compositional complex alloys or high entropy alloys (HEAs). By combining ab initio simulation and experimental validation, we demonstrate magnetic ordering as an important factor in the activation and transition of deformation modes from planar dislocation slip to TWIP (twinning-induced plasticity) and/or TRIP (transformation-induced plasticity). A wide compositional space of Cr20MnxFeyCo20Niz(x+y+z=60, at. %) was probed by density-functional theory calculations to search for potential alloys displaying the TWIP/TRIP effects. Three selected promising HEA compositions with varying Mn concentrations were metallurgically synthesized, processed, and probed for microstructure, deformation mechanism, and mechanical property evaluation. The differences in the deformation modes of the probed HEAs are interpreted in terms of the computed SFEs and their dependence on the predicted magnetic state, as revealed by ab initio calculations and validated by explicit magnetic measurements. It is found that the Mn content plays a key role in the stabilization of antiferromagnetic configurations which strongly impact the SFEs and eventually lead to the prevalent deformation behavior., (OLD) MSE-7

We combined experimental investigations and theoretical calculations to unveil an abnormal magnetic behavior caused by addition of the nonmagnetic element Cu in face-centered-cubic FeNiCoMn-based high-entropy alloys (HEAs). Upon Cu addition, the probed HEAs show an increase of both Curie temperature and saturation magnetization in as-cast and homogenized states. Specifically, the saturation magnetization of the as-cast HEAs at room temperature increases by 77% and 177% at a Cu content of 11 and 20 at. %, respectively, compared to the as-cast equiatomic FeNiCoMn HEA without Cu. The increase in saturation magnetization of the as-cast HEAs is associated with the formation of an Fe-Co rich phase in the dendritic regions. For the homogenized HEAs, the magnetic state at room temperature transforms from paramagnetism to ferromagnetism after 20 at. % Cu addition. The increase of the saturation magnetization and Curie temperature cannot be adequately explained by the formation of Cu enriched zones according to atom probe tomography analysis. Ab initio calculations suggest Cu plays a pivotal role in the stabilization of a ferromagnetic ordering of Fe, and reveal an increase of the Curie temperature caused by Cu addition which agrees well with the experimental results. The underlying mechanism behind this phenomenon lies in a combined change in unit-cell volume and chemical composition and the related energetic stabilization of the magnetic ordering upon Cu alloying as revealed by theoretical calculations. Thus, the work unveils the mechanisms responsible for the Cu effect on the magnetic properties of FeNiCoMn HEAs, and suggests that nonmagnetic elements are also crucial to tune and improve magnetic properties of HEAs., (OLD) MSE-7

We combined experimental investigations and theoretical calculations to unveil an abnormal magnetic behavior caused by addition of the nonmagnetic element Cu in face-centered-cubic FeNiCoMn-based high-entropy alloys (HEAs). Upon Cu addition, the probed HEAs show an increase of both Curie temperature and saturation magnetization in as-cast and homogenized states. Specifically, the saturation magnetization of the as-cast HEAs at room temperature increases by 77% and 177% at a Cu content of 11 and 20 at. %, respectively, compared to the as-cast equiatomic FeNiCoMn HEA without Cu. The increase in saturation magnetization of the as-cast HEAs is associated with the formation of an Fe-Co rich phase in the dendritic regions. For the homogenized HEAs, the magnetic state at room temperature transforms from paramagnetism to ferromagnetism after 20 at. % Cu addition. The increase of the saturation magnetization and Curie temperature cannot be adequately explained by the formation of Cu enriched zones according to atom probe tomography analysis. Ab initio calculations suggest Cu plays a pivotal role in the stabilization of a ferromagnetic ordering of Fe, and reveal an increase of the Curie temperature caused by Cu addition which agrees well with the experimental results. The underlying mechanism behind this phenomenon lies in a combined change in unit-cell volume and chemical composition and the related energetic stabilization of the magnetic ordering upon Cu alloying as revealed by theoretical calculations. Thus, the work unveils the mechanisms responsible for the Cu effect on the magnetic properties of FeNiCoMn HEAs, and suggests that nonmagnetic elements are also crucial to tune and improve magnetic properties of HEAs., (OLD) MSE-7

We reveal the impact of magnetic ordering on stacking fault energy (SFE) and its influence on the deformation mechanisms and mechanical properties in a class of nonequiatomic quinary Mn-containing compositional complex alloys or high entropy alloys (HEAs). By combining ab initio simulation and experimental validation, we demonstrate magnetic ordering as an important factor in the activation and transition of deformation modes from planar dislocation slip to TWIP (twinning-induced plasticity) and/or TRIP (transformation-induced plasticity). A wide compositional space of Cr20MnxFeyCo20Niz(x+y+z=60, at. %) was probed by density-functional theory calculations to search for potential alloys displaying the TWIP/TRIP effects. Three selected promising HEA compositions with varying Mn concentrations were metallurgically synthesized, processed, and probed for microstructure, deformation mechanism, and mechanical property evaluation. The differences in the deformation modes of the probed HEAs are interpreted in terms of the computed SFEs and their dependence on the predicted magnetic state, as revealed by ab initio calculations and validated by explicit magnetic measurements. It is found that the Mn content plays a key role in the stabilization of antiferromagnetic configurations which strongly impact the SFEs and eventually lead to the prevalent deformation behavior., (OLD) MSE-7

Objective: Patients with renal failure suffer from symptoms caused by uraemic toxins, possibly of gut microbial origin, as deduced from studies in animals. The aim of the study is to characterise relationships between the intestinal microbiome composition, uraemic toxins and renal failure symptoms in human end-stage renal disease (ESRD). Design: Characterisation of gut microbiome, serum and faecal metabolome and human phenotypes in a cohort of 223 patients with ESRD and 69 healthy controls. Multidimensional data integration to reveal links between these datasets and the use of chronic kidney disease (CKD) rodent models to test the effects of intestinal microbiome on toxin accumulation and disease severity. Results: A group of microbial species enriched in ESRD correlates tightly to patient clinical variables and encode functions involved in toxin and secondary bile acids synthesis; the relative abundance of the microbial functions correlates with the serum or faecal concentrations of these metabolites. Microbiota from patients transplanted to renal injured germ-free mice or antibiotic-Treated rats induce higher production of serum uraemic toxins and aggravated renal fibrosis and oxidative stress more than microbiota from controls. Two of the species, Eggerthella lenta and Fusobacterium nucleatum, increase uraemic toxins production and promote renal disease development in a CKD rat model. A probiotic Bifidobacterium animalis decreases abundance of these species, reduces levels of toxins and the severity of the disease in rats. Conclusion: Aberrant gut microbiota in patients with ESRD sculpts a detrimental metabolome aggravating clinical outcomes, suggesting that the gut microbiota will be a promising target for diminishing uraemic toxicity in those patients. Trial registration number: This study was registered at ClinicalTrials.gov (NCT03010696).

Objective: Patients with renal failure suffer from symptoms caused by uraemic toxins, possibly of gut microbial origin, as deduced from studies in animals. The aim of the study is to characterise relationships between the intestinal microbiome composition, uraemic toxins and renal failure symptoms in human end-stage renal disease (ESRD). Design: Characterisation of gut microbiome, serum and faecal metabolome and human phenotypes in a cohort of 223 patients with ESRD and 69 healthy controls. Multidimensional data integration to reveal links between these datasets and the use of chronic kidney disease (CKD) rodent models to test the effects of intestinal microbiome on toxin accumulation and disease severity. Results: A group of microbial species enriched in ESRD correlates tightly to patient clinical variables and encode functions involved in toxin and secondary bile acids synthesis; the relative abundance of the microbial functions correlates with the serum or faecal concentrations of these metabolites. Microbiota from patients transplanted to renal injured germ-free mice or antibiotic-Treated rats induce higher production of serum uraemic toxins and aggravated renal fibrosis and oxidative stress more than microbiota from controls. Two of the species, Eggerthella lenta and Fusobacterium nucleatum, increase uraemic toxins production and promote renal disease development in a CKD rat model. A probiotic Bifidobacterium animalis decreases abundance of these species, reduces levels of toxins and the severity of the disease in rats. Conclusion: Aberrant gut microbiota in patients with ESRD sculpts a detrimental metabolome aggravating clinical outcomes, suggesting that the gut microbiota will be a promising target for diminishing uraemic toxicity in those patients. Trial registration number: This study was registered at ClinicalTrials.gov (NCT03010696).

To facilitate the understanding of Invar effects and design of FeNiCo-base Invar alloys characterized by low thermal expansion coefficient (TEC), we investigated the magnetic and thermal expansion behavior of an equiatomic prototype medium entropy alloy FeNiCo and a non-equiatomic (super Invar)Fe63Ni32Co5 (at. %)reference alloy by means of experiments and ab initio calculations. Both alloys consist of a single face-centered cubic phase with fully recrystallized microstructure in the homogenized state. Large spontaneous volume magnetostriction is observed in both alloys below their respective Curie temperatures. The Invar effect in the non-equiatomic Fe63Ni32Co5 alloy is of step-type with nearly zero TEC over a wide temperature range (from room temperature to 120 °C)below its Curie temperature. The equiatomic FeNiCo alloy shows a peak-type Invar effect in a very narrow temperature range (from ∼675 °C to ∼730 °C)with relatively low TECs. The equiatomic FeNiCo alloy shows both higher saturation magnetization and Curie temperature than the non-equiatomic Fe63Ni32Co5 alloy. The relationships among magnetic behavior, spontaneous volume magnetostriction and Invar effects for a wider array of metallic alloys are discussed mainly based on Masumoto's rule combined with Wohlfarth's itinerant electron theory. An Invar alloy search map is constructed based on the present results and available literature data to visualize the relationships among saturation magnetization, Curie temperature and thermal expansion coefficient for a wide range of Invar alloys. Based on this treasure map a design route for further developments of new Invar alloys by tuning their magnetic properties is discussed., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., (OLD) MSE-7

Higher cumulants of conserved charges are sensitive observables of quantum chromodynamics phase transitions. The sample of mixed events provides a background to estimate non-critical effects of cumulants. Four possible methods for constructing the sample of mixed events are suggested. The effectiveness of each method is examined. It is showed that the method of most random or least constrain is the best, rather than the conventional method.

Using the sample produced by the AMPT default model, we construct a corresponding mixed sample by the method of mixed events. The mixed sample provides an effective estimation for non-critical fluctuations which are caused by global and systematic effects. The dynamical cumulants of conserved charges are defined as the cumulants of the original sample minus the cumulants of the mixed sample. It is demonstrated that dynamical cumulants are subtracted statistical fluctuations, and centrality bin width or detection efficiency independent, in consistent with formulae corrected cumulants. Therefore, dynamical cumulants are helpful in obtaining critical fluctuations at the RHIC BES., Comment: arXiv admin note: text overlap with arXiv:1908.05465

The critical point is a fixed point in finite-size scaling. To quantify the behaviour of such a fixed point, we define, at a given temperature and scaling exponent ratio, the width of scaled observables for different sizes. The minimum of the width reveals the position of fixed point, its corresponding phase transition temperature, and scaling exponent ratio. The value of this ratio tells the nature of fixed point, which can be a critical point, a point of the first order phase transition line, or a point of the crossover region. To demonstrate the effectiveness of this method, we apply it to three typical samples produced by the 3D three-state Potts model. Results show the method to be more precise and effective than conventional methods. Finally, we discuss a possible application at the beam energy scan program of relativistic heavy-Ion collision., Comment: v3: final version. v2: references update. 7 pages, 4 figures. arXiv admin note: text overlap with arXiv:1805.04311

It is shown that intermittency, a self-similar correlation with respect to the size of the phase space volume, is sensitive to critical density fluctuations of baryon numbers in a system belonging to the three-dimensional (3D) Ising universality class. The relation between intermittency index and relative baryon density fluctuation is obtained. We thus suggest that measuring the intermittency in relativistic heavy-ion collisions could be used as a good probe of density fluctuations associated with the QCD critical phenomena. From recent preliminary results on neutron density fluctuations in central Au + Au collisions at $\sqrt{s_{NN}}$ = 7.7, 11.5, 19.6, 27, 39, 62.4 and 200 GeV at RHIC/STAR, the collision energy dependence of intermittency index is extracted and shows a non-monotonic behavior with a peak at around 20 - 27 GeV, indicating that the strength of intermittency becomes the largest in this energy region. The transport UrQMD model without implementing critical physics cannot describe the observed behavior., Comment: Replaced with the published version

To facilitate the understanding of Invar effects and design of FeNiCo-base Invar alloys characterized by low thermal expansion coefficient (TEC), we investigated the magnetic and thermal expansion behavior of an equiatomic prototype medium entropy alloy FeNiCo and a non-equiatomic (super Invar)Fe63Ni32Co5 (at. %)reference alloy by means of experiments and ab initio calculations. Both alloys consist of a single face-centered cubic phase with fully recrystallized microstructure in the homogenized state. Large spontaneous volume magnetostriction is observed in both alloys below their respective Curie temperatures. The Invar effect in the non-equiatomic Fe63Ni32Co5 alloy is of step-type with nearly zero TEC over a wide temperature range (from room temperature to 120 °C)below its Curie temperature. The equiatomic FeNiCo alloy shows a peak-type Invar effect in a very narrow temperature range (from ∼675 °C to ∼730 °C)with relatively low TECs. The equiatomic FeNiCo alloy shows both higher saturation magnetization and Curie temperature than the non-equiatomic Fe63Ni32Co5 alloy. The relationships among magnetic behavior, spontaneous volume magnetostriction and Invar effects for a wider array of metallic alloys are discussed mainly based on Masumoto's rule combined with Wohlfarth's itinerant electron theory. An Invar alloy search map is constructed based on the present results and available literature data to visualize the relationships among saturation magnetization, Curie temperature and thermal expansion coefficient for a wide range of Invar alloys. Based on this treasure map a design route for further developments of new Invar alloys by tuning their magnetic properties is discussed., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., (OLD) MSE-7