Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein-based luminescent aerogels with elastic properties

ABSTRACT Gelatin is a famous gel-forming proteinaceous material with excellent hydrogelation properties. Herein, we report that protein nanofibrils (PNFs) can be employed to form hybrids with gelatin that can be converted to aerogels with attractive mechanical properties. Moreover, we are able to influence the gelation behavior of PNFs by mechanochemical processing. A combination of mechanochemistry and aqueous self-assembly is employed to prepare PNFs functionalized with hydrophobic dyes. These materials are then mixed with gelatin and converted into aerogels by freeze drying. We find that PNFs functionalized with PNFs lead to the formation of aerogels with more robust mechanical properties. Mechanochemical methodology as a green and scalable method can thus be employed for influencing the properties of protein-based aerogels. This represents a new and highly flexible and novel strategy for tuning both properties and functionality of protein materials. This work opens a simple and feasible way to produce nontoxic and biodegradable aerogel materials with favorable mechanical strength. GRAPHICAL ABSTRACT
Observing the Luminosity Increase and Roche Lobe Overflow of Planet Destruction

The destruction of planets by migration into the star will release significant amounts of energy and material, which will present opportunities to observational study planets in new ways. To observe planet destruction, it is important to understand the processes of how this energy and material is released as planets are destroyed. It is not known how fast the large amounts of energy and material are released, making it difficult to predict how observable planet destruction will be. There is a huge amount of energy made available by falling deep into the star's potential well: Simple calculations show that many of the currently known "hot Jupiters" can potentially produce events as luminous as a small nova if the energy is released fast enough. To observe these events, the important questions are how will this energy be released, and whether the energy will be released rapidly enough to create an event luminous enough to be found by transient surveys. Alternatively, if planet destruction is slowed by the inward migration alternating with periods of outward migration caused by Roche lobe overflow (RLOF), then the primary signature may be the effects of the release of large amounts of gas. The infall of this gas also may significantly contribute to the system's luminosity. The release of planetary gas may be a searchable signature of planet destruction. Signs of runaway RLOF and outward or alternating RLOF should be searched for. Observing planet destruction will provide a new window for study of exoplanets.
Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.
Controllability of Linear Fractional Systems with Delay in Control

This paper discusses the controllability of continuous-time linear fractional systems with control delay. The Atangana-Baleanu fractional derivative with the Caputo approach is used. First, the solution expression for a linear fractional system is obtained. Then, the corresponding fractional delay controllability Gramian matrix is defined, and its non-singularity as necessary and sufficient conditions for the controllability is proved. Finally, another equivalent condition based on the matrix rank formed by the coefficients matrices of the original system is provided that is much easier to check.
An Insight on Mud Behavior Upon Stepping

In this research we show a characterization of mud behavior under vertical stepping. We showed that mud stiffness can vary 45-fold and the energy spent to generate equivalent impulse can vary 2-fold depending on the mud water content, but also that stepping faster on mud leads to lower peak forces and higher energy consumption. Next, we showed that the peak force generated can be increased by 33% by changing the foot stiffness, but is reduced by 18% if stepping is repeated on the same spot. We then demonstrated how force control can be used to achieve identical force profiles on very different muds. These results will help to design mechanical parts or control strategies for legged robot locomotion on mud.
Valorization of ecological coal as energy source for smoking fish in the locality of Maga, Far North Region of Cameroon

Wood for smoking fish is increasingly rare in the locality of Maga where this activity is practiced intensely. The search for firewood to prepare meals and smoke fish has led to immense deforestation in the area. There is an urgent need to find alternative sources of this fuel. The objective of this work is to construct a fish smoking kiln operating with ecological coal made from rice husk. To achieve this, we have characterized the existing traditional and improved kiln by evaluating the evolution of temperature during smoking and the amount of fuel consumed per kilogram of smoked fish. Then the design of the cupboard kiln and its construction were made taking into account the type of fuel to be used. Its performance was evaluated. It appears from this work that the consumption of wood per unit of smoked fish varies with the type of the kiln. On average and in the locality of Maga, it takes 2.5 kg and 0.36 kg of wood to smoke 1 kg of catfish respectively with drum kiln and double-chamber brick kiln. When using ecological coal, 0.68 kg is sufficient to smoke 1 kg of fish. For the temperature fluctuation, it has been observed that it is most of the time of smoking above 100 °C on drum kiln while the temperature hardly reaches 100 °C with the rest of kiln. In addition, temperature is more stable in cupboard kiln fluctuating around 70 °C. These temperature intervals are quite sufficient for smoking fish. Knowing the potential production of biochar from rice husk in the locality of Maga, around 13,433tons of fish can be smoked just by valuing this type of agricultural residues. The surface area annually protected from deforestation is then estimated to 665 hectares.
Biological Sensing of Nitric Oxide in Macrophages and Atherosclerosis Using a Ruthenium-Based Sensor

Macrophage-derived nitric oxide (NO) plays a critical role in atherosclerosis and presents as a potential biomarker. We assessed the uptake, distribution, and NO detection capacity of an irreversible, ruthenium-based, fluorescent NO sensor (Ru-NO) in macrophages, plasma, and atherosclerotic plaques. In vitro, incubation of Ru-NO with human THP1 monocytes and THP1-PMA macrophages caused robust uptake, detected by Ru-NO fluorescence using mass-cytometry, confocal microscopy, and flow cytometry. THP1-PMA macrophages had higher Ru-NO uptake (+13%, p < 0.05) than THP1 monocytes with increased Ru-NO fluorescence following lipopolysaccharide stimulation (+14%, p < 0.05). In mice, intraperitoneal infusion of Ru-NO found Ru-NO uptake was greater in peritoneal CD11b+F4/80+ macrophages (+61%, p < 0.01) than CD11b+F4/80− monocytes. Infusion of Ru-NO into Apoe−/− mice fed high-cholesterol diet (HCD) revealed Ru-NO fluorescence co-localised with atherosclerotic plaque macrophages. When Ru-NO was added ex vivo to aortic cell suspensions from Apoe−/− mice, macrophage-specific uptake of Ru-NO was demonstrated. Ru-NO was added ex vivo to tail-vein blood samples collected monthly from Apoe−/− mice on HCD or chow. The plasma Ru-NO fluorescence signal was higher in HCD than chow-fed mice after 12 weeks (37.9%, p < 0.05). Finally, Ru-NO was added to plasma from patients (N = 50) following clinically-indicated angiograms. There was lower Ru-NO fluorescence from plasma from patients with myocardial infarction (−30.7%, p < 0.01) than those with stable coronary atherosclerosis. In conclusion, Ru-NO is internalised by macrophages in vitro, ex vivo, and in vivo, can be detected in atherosclerotic plaques, and generates measurable changes in fluorescence in murine and human plasma. Ru-NO displays promising utility as a sensor of atherosclerosis.
Endogeneity in Panel Data Models with Time-Varying and Time-Fixed Regressors: To IV or Not IV?

We analyse the problem of parameter inconsistency in panel data econometrics due to the correlation of exogenous variables with the error term. A common solution in this setting is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981). However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to systematically compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman- Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, the non-IV rival performs equally well or even better especially in terms of estimating variable coefficients for time- fixed regressors. Moreover, the non-IV method tends to have a smaller root mean square error (rmse) than both Hausman-Taylor models with perfect and imperfect knowledge about the underlying correlation between r.h. s variables and residual term. This indicates that it is generally more efficient. The results are roughly robust for various combinations in the time and cross-section dimension of the data.
Dynamical symmetry enhancement near massive IIA horizons

We prove that Killing horizons in massive IIA supergravity preserve an even number of supersymmetries, and that their symmetry algebra contains an sl ( 2 , R ) ? > subalgebra, confirming the conjecture of Gran et al (2013 J. High Energy Phys. JHEP11(2013)104). We also prove a new class of Lichnerowicz-type theorems for connections of the spin bundle whose holonomy is contained in a general linear group.
Privacy Policy Analysis with Sentence Classification

Privacy policies inform users of the data practices and access protocols employed by organizations and their digital counterparts. Research has shown that users often feel that these privacy policies are lengthy and complex to read and comprehend. However, it is critical for people to be aware of the data access practices employed by the organizations. Hence, much research has focused on automatically extracting privacy-specific artifacts from the policies, predominantly by using natural language classification tools. However, these classification tools are designed primarily for the classification of paragraphs or segments of the policies. In this paper, we report on our research where we identify the gap in classifying policies at a segment level, and provide an alternate definition of segment classification using sentence classification. To this aid, we train and evaluate sentence classifiers for privacy policies using BERT and XLNet. Our approach demonstrates improvements in prediction quality of existing models and hence, surpasses the current baselines for classification models, without requiring additional parameter and model tuning. Using our sentence classifiers, we also study topical structures in Alexa top 5000 website policies, in order to identify and quantify the diffusion of information pertaining to privacy-specific topics in a policy.
Similar Actions and Cooperation in the Centipede Played by Automata

This paper analyzes the evolutionary dynamics of a modified version of Rosenthal's ``centipede,'' whereby the players' strategies are represented by finite-state automata. In the framework considered, the automata required to implement different strategies may differ among them both with respect to their complexity, and with respect to their cost. This is due to the possibility that the players have to identify similar situations, and similar actions, across different information sets. This modification of the original game introduces an unstable equilibrium at which all strategies are represented in the populations of the players. With the unperturbed replicator dynamics, the system eventually converges to some point belonging to a continuum of rest-points that the present model shares with the evolutionary version of Rosenthal's original game. The transient phase--- characterized by fluctuations in the fractions of the two populations adopting different automata---can however be relatively long. Furthermore, numerical results point to the fact that small perturbations can turn the fluctuations characterizing the convergent paths of the unperturbed system into persistent phenomena. In this case, the players' payoffs can be significantly higher than those achievable at the Nash-equilibria.
Systolic Parallel Processing

The Systolic Mode of Parallel Processing. Introduction to the Underlying Concept. The Original Motivation: VSLI Implementation. The Present Trend: Efficient Algorithms for Massively Parallel Computers. A List of Known Applications. Defining and Expressing Systolic Arrays and Algorithms. Using Automata Notions. Defining Systolic Automata, Arrays, and Algorithms. Expressing Systolic Algorithms. Analysis and Comparison of Systolic Algorithms. Matrix-Vector and Matrix Multiplication. Introduction to Vectors and Matrices. Matrix-Vector Multiplication. Systolic Simulation of Feedforward Artificial Neural Networks. Matrix Multiplication. Solving Systems of Linear Algebraic Equations. Introduction to Linear Systems. Gaussian Elimination. Systolic Arrays for Triangularization and LU/QR Decomposition. Systolic Algorithms for Back Substitution. Systolic Implementation of Iterative Methods. Further Problems of Linear Algebra. Computing the Inverse of a Matrix. Generalized Elimination. Computing the Characteristic Polynomial. Matrix Transposition and Related Operations. Convolution and Linear Filters. Convolution, Correlation, FIR and IIR Filters. Semi-Systolic Realizations. Unidirectional Full-Systolic Arrays. Systolic Arrays with Bidirectional Data Flow. Bit-Level Systolic Convolver. Operations with Polynomials. Introduction. Multiplication of Polynomials and Integers. Division of Polynomials. Computing the Greatest Common Divisor. Polynomial Interpolation. Evaluation of Polynomials. Comparison Problems. Sorting. Selection and Running Order Statistics. Sorting and Order Statistics for Rank Filtering. A Data Structure: Priority Queue. Dynamic Programming and its Applications. Introduction. Implementing the Dynamic Programming Recurrence in a Two-Dimensional Systolic Array. Implementation in One-Dimensional Arrays. Further Dynamic Programming Recurrences. Computational Geometry. Convex Hull. Nearest-Neighbours Problems. Systematic Design of Systolic Algorithms. Dependence Graphs. Systolic Array Dependence Graphs. Extracting Systolic Algorithms from Dependence Graphs. Modifying the Properties of Systolic Algorithms. Partitioning of Systolic Algorithms. Partitioning, Algorithm Mapping, Design of Flexible Systolic Structures, Time Sharing. Application of c-Slow Automata to the Realization of Parallel Structures. Examples: Mapping Different Filter Banks onto the Same Fixed-Size Processor Array. A Summary of the Technique and Alternative Approaches. References and Additional Literature. Subject Index.
Morse theory and Lescop's equivariant propagator for 3-manifolds with $b_1=1$ fibered over $S^1$

For a 3-manifold $M$ with $b_1(M)=1$ fibered over $S^1$ and the fiberwise gradient $\xi$ of a fiberwise Morse function on $M$, we introduce the notion of amidakuji-like path (AL-path) on $M$. An AL-path is a piecewise smooth path on $M$ consisting of edges each of which is either a part of a critical locus of $\xi$ or a flow line of $-\xi$. Counting closed AL-paths with signs gives the Lefschetz zeta function of $M$. The "moduli space" of AL-paths on $M$ gives explicitly Lescop's equivariant propagator, which can be used to define $\mathbb{Z}$-equivariant version of Chern--Simons perturbation theory for $M$.
Paper 17: Follow-Up of Heterotopic Ossification (HO) After Platelet-Rich Plasma (PRP) for Groin Injuries

Objectives: A 2015 study of PRP for groin injuries in NFL players* alerted us to the possibility of PRP causing heterotopic ossification. This study of athletes seen from 2014 to 2019 provides a more comprehensive analysis of that observation. Methods: All athletes seen at one institution and identified at their first visit as having received PRP for a core muscle injury were followed and compared to core muscle injury patients who had not previously received PRP. HO was observed on MRI and intraoperatively and confirmed pathologically. All PRP patients were followed for at least two years. Results: PRP patients had a 40.7% occurrence of HO, compared to 0.7% in the non-PRP cohort (See Table). Three athletes who had PRP retired early from their sports due to the HO and scar issues. The occurrence of HO did not correlate with any technique of PRP administration. Twenty-two of 28 (78.6%) NFL players who received PRP developed HO, compared to 0 (0%) in 28 NFL randomly selected, age-, position- and injury matched controls. Three-month “success” rates were 67.9% and 96.4% respectively in the two groups (p<0.006) in terms of return to play at previously high-level levels of performance or greater, according to their own subjective assessments. Differences were mostly due to scar issues. Most “failure” patients in the PRP group had returned to play but not at their previously high levels. Conclusions: This longer-term, more comprehensive study confirms the previous preliminary analysis that caregivers should take a cautious approach to treatment with PRP for groin injuries. None None None Table 1. Univariable and multivariable logistic regression models predicting a final graft diameter of 8mm or more. Significant predictors are indicated in bold. 95%CI: 95% Confidence interval, BMI: body mass index.
Overview of the TurbSim Stochastic Inflow Turbulence Simulator: Version 1.10

The Turbsim stochastic inflow turbulence code was developed to provide a numerical simulation of a full-field flow that contains coherent turbulence structures that reflect the proper spatiotemporal turbulent velocity field relationships seen in instabilities associated with nocturnal boundary layer flows. This report provides the user with an overview of how the TurbSim code has been developed and some of the theory behind that development.
A Cross-Sectional Study on the Associations between Physical Activity Level, Depression, and Anxiety in Smokers and Ex-Smokers

Introduction: depression and anxiety is one of the most relevant public health problems. The link between smoking and depression has been demonstrated. Regular physical activity (PA) could act as a protector against mental health diseases. Objectives: (1) to explore the prevalence of depression and anxiety in relation to sex and to the condition of smoker and ex-smoker, (2) to study the differences in prevalence proportions according to the frequency and level of PA, and (3) to calculate the probability of presenting depression or anxiety according to the frequency and level of PA. Methods: a cross-sectional study was conducted, based on data extracted from the 2014 and 2020 Spanish European Health Survey and the 2017 Spanish National Health Survey. The sample included 9524, 7813, and 9079 participants, respectively. Descriptive analysis was performed (comparisons using a Chi-square test and z-test for independent proportions). Probability odds ratios of anxiety and depression were calculated according to PA. Results: women had a higher prevalence of depression and anxiety than men (p < 0.001–0.003). Higher levels of prevalence were observed in inactive people versus very active or active people (p < 0.001). Inactive people had a higher risk of depression and anxiety compared to very active people. Conclusion: inactive smokers and ex-smokers had higher levels of prevalence of depression and anxiety than active and very active people. Physical inactivity could increase the risk of suffering these mental disorders in this population. This could affect women more than men.
Understanding the Power of Clause Learning

Matrix factorization techniques have been frequently applied in information processing tasks. Among them, Non-negative Matrix Factorization (NMF) have received considerable attentions due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts-based in human brain. On the other hand, from geometric perspective the data is usually sampled from a low dimensional manifold embedded in high dimensional ambient space. One hopes then to find a compact representation which uncovers the hidden topics and simultaneously respects the intrinsic geometric structure. In this paper, we propose a novel algorithm, called Locality Preserving Non-negative Matrix Factorization (LPNMF), for this purpose. For two data points, we use KL-divergence to evaluate their similarity on the hidden topics. The optimal maps are obtained such that the feature values on hidden topics are restricted to be non-negative and vary smoothly along the geodesics of the data manifold. Our empirical study shows the encouraging results of the proposed algorithm in comparisons to the state-of-the-art algorithms on two large high-dimensional databases.