Miracle-dz/UniFAR-FTUs huggingface dataset:
Universality of Osmotic Equation of State in Star Polymer Solutions

We experimentally measure the osmotic pressures of linear polymers and three-, four-, and eight-arm star polymers in a good solvent via membrane osmometry. These results reveal that the osmotic equations of state in the star polymer solutions are universally described by the same scaling function that describes linear polymer solutions. This universality is achieved by canceling increasing overlap concentrations and decreasing osmotic pressure, owing to the increased arm number. We further clarify the molar mass and arm number dependencies of the gyration radius and interpenetration factor, ensuring universality in star polymer solutions.
Development of optical immunosensors and their application to the analysis of human bone morphogenetic protein-7 (BMP-7)

In this study, a few optical immunosensors were developed to determine the concentration of BMP-7. Hydrophilic CdSe/ZnS quantum dots (QDs) were synthesized and conjugated to the antibody of BMP-7 (BMP-7Ab). The QDconjugated BMP-7Ab was used as a fluorescence probe at excitation and emission wavelengths of 470 nm and 585 nm, respectively. It was immobilized either on the bottom of the well of a 96-well microtiter plate or on the tip of an optical fiber. Two immunoassays, i.e. the direct and sandwich assays, were studied for their sensitivity. The sensitivity of the direct immunoassay was 1296.21, compared to 384.69 for the sandwich assay. The linear detection range was 0.0-1.0 ng/mL for both assays. Based on the results of the microtiter plate technique, the direct assay technique was used for the development of an optical fiber immunosensor. The optical fiber immunosensor has a linear detection range between 0.0 and 10.0 ng/mL with a detection limit of 0.413 ng/mL. The optical fiber immunosensor was applied to the sequential injection analysis for the automatic determination of BMP-7.
Dynamic finite element analysis of a magnetic hammer with a new composite mesh scheme

This paper presents a new formulation of axisymmetric upwind finite element method and a composite mesh scheme with a new sliding element. The advantage of this method is that the computing time can be considerably reduced in comparison with that of the conventional composite mesh schemes. Furthermore we achieve simplicity of the upwinding formulation and accurate calculation of the motional term in the eddy current problems.
The Ordered Matrix Dirichlet for State-Space Models

Many dynamical systems in the real world are naturally described by latent states with intrinsic orderings, such as"ally","neutral", and"enemy"relationships in international relations. These latent states manifest through countries' cooperative versus conflictual interactions over time. State-space models (SSMs) explicitly relate the dynamics of observed measurements to transitions in latent states. For discrete data, SSMs commonly do so through a state-to-action emission matrix and a state-to-state transition matrix. This paper introduces the Ordered Matrix Dirichlet (OMD) as a prior distribution over ordered stochastic matrices wherein the discrete distribution in the kth row stochastically dominates the (k+1)th, such that probability mass is shifted to the right when moving down rows. We illustrate the OMD prior within two SSMs: a hidden Markov model, and a novel dynamic Poisson Tucker decomposition model tailored to international relations data. We find that models built on the OMD recover interpretable ordered latent structure without forfeiting predictive performance. We suggest future applications to other domains where models with stochastic matrices are popular (e.g., topic modeling), and publish user-friendly code.
Curriculum Based Multi-Task Learning for Parkinson’s Disease Detection

There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson’s disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here we leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN). Typically, deep learning networks are trained by randomly selecting samples in each mini-batch. By contrast, curriculum learning is a training strategy that aims to boost classifier performance by starting with examples that are easier to classify. Here we define a curriculum to progressively increase the difficulty of the training data corresponding to the Hoehn and Yahr (H&Y) staging system for PD (total N=1,012; 653 PD patients, 359 controls; age range: 20.0-84.9 years). Even with our multi-task setting using pre-trained CNNs and transfer learning, PD classification based on T1-weighted (T1-w) MRI was challenging (ROC AUC: 0.59-0.65), but curriculum training boosted performance (by 3.9%) compared to our baseline model. Future work with multimodal imaging may further boost performance.
Enzyme kinetics simulation at the scale of individual particles

Enzyme-catalysed reactions involve two distinct timescales. There is a short timescale on which enzymes bind to substrate molecules to produce bound complexes, and a comparatively long timescale on which the complex is transformed into a product. The rate at which the substrate is converted into product is characteristically non-linear and is traditionally derived by applying singular perturbation theory to the system's governing equations. Central to this analysis is the assumption that complex formation is effectively instantaneous on the timescale over which significant substrate degradation occurs. This prevents accurate modelling of enzyme kinetics by many particle-based simulations of reaction-diffusion systems as they rely on proximity-based reaction conditions that do not correctly model the fast reactions associated with the complex on the long timescale. In this paper we derive a new proximity-based reaction condition that correctly incorporates the reactions that occur on the short timescale for a specific enzymatic system. We present proof of concept particle-based simulations and demonstrate that non-linear reaction rates typical of enzyme kinetics can be reproduced without needing to explicitly simulate reactions on the short timescale.
Third quantization of open quantum systems: Dissipative symmetries and connections to phase-space and Keldysh field-theory formulations

The connections between standard theoretical tools used to study open quantum systems can sometimes seem opaque. Whether it is a Lindblad master equation, the equation of motion for the Wigner function or a dissipative Keldysh action, features evident in one formalism are often masked in another. Here, we reformulate the technique of third quantization in a way that explicitly connects all three methods. We first show that our formulation reveals a fundamental dissipative symmetry present in all quadratic bosonic or fermionic Lindbladians. This symmetry can then be used to easily diagonalize these models, and provides a intuitive way to demonstrate the separation of dissipation and fluctations in linear systems. For bosons, we then show that the Wigner function and the characteristic function can be thought of as ''wavefunctions'' of the density matrix in the eigenbasis of the third-quantized superoperators we introduce. The field-theory representation of the time-evolution operator in this basis is then the Keldysh path integral. To highlight the utility of our approach, we apply our version of third quantization to a dissipative non-linear oscillator, and use it to obtain new exact results.
Revisiting Variable Ordering for Real Quantifier Elimination using Machine Learning

Cylindrical Algebraic Decomposition (CAD) is a key proof technique for formal verification of cyber-physical systems. CAD is computationally expensive, with worst-case doubly-exponential complexity. Selecting an optimal variable ordering is paramount to efficient use of CAD. Prior work has demonstrated that machine learning can be useful in determining efficient variable orderings. Much of this work has been driven by CAD problems extracted from applications of the MetiTarski theorem prover. In this paper, we revisit this prior work and consider issues of bias in existing training and test data. We observe that the classical MetiTarski benchmarks are heavily biased towards particular variable orderings. To address this, we apply symmetries to create a new dataset containing more than 41K MetiTarski challenges designed to remove bias. Furthermore, we evaluate issues of information leakage, and test the generalizability of our models on the new dataset.
Optimal local identifying and local locating-dominating codes

We introduce two new classes of covering codes in graphs for every positive integer $r$. These new codes are called local $r$-identifying and local $r$-locating-dominating codes and they are derived from $r$-identifying and $r$-locating-dominating codes, respectively. We study the sizes of optimal local 1-identifying codes in binary hypercubes. We obtain lower and upper bounds that are asymptotically tight. Together the bounds show that the cost of changing covering codes into local 1-identifying codes is negligible. For some small $n$ optimal constructions are obtained. Moreover, the upper bound is obtained by a linear code construction. Also, we study the densities of optimal local 1-identifying codes and local 1-locating-dominating codes in the infinite square grid, the hexagonal grid, the triangular grid, and the king grid. We prove that seven out of eight of our constructions have optimal densities.
Childrens understanding and use of inversion in arithmetic

In this presentation, I consider the origins and the extent of children’s understanding of the inverse relation between addition and subtraction. I argue that this understanding might have its origins in children’s informal experiences with physical matter but I also show that it is possible to improve children’s grasp of inversion through teaching. I also show that his teaching has beneficial effects on children’s solutions to sophisticated word problems in which the arithmetical operation that is need for the solution is not immediately obvious.
AVOIDING PERMUTATIONS AND THE NARAYANA NUMBERS

Abstract. We study 132 avoiding permutations that also avoid (2r+1)(2r+2)···12 but contain (2r−1)(2r)···12 pattern. We ﬁnd an identitybetween the number of these permutations and the Narayana number. Wealso present relations between 132 avoiding permutations and polygondissections. Finally, a generalization of these permutations is obtained. 1. IntroductionAvoiding permutations of certain patterns have been studied over the years.Especially, the number of avoiding permutations of pattern 132 with r descentsis the Narayananumber N(n,k) (See [3]). We study 132-avoidingpermutationsthat avoid another pattern but contain some other pattern.Let S n be the set of permutations on [n] := {1,2,. ,n}. For π ∈ S n and τ ∈ S k , we say that π contains τ-pattern if there exists a subsequence1 ≤ i 1 < i 2 < ··· < i k ≤ n such that (π i 1 π i 2 ···π i k ) is in the same relativeorder as τ. We say that π is τ-avoiding if such a subsequence does not exist.The set of all τ-avoiding permutations in S
Analysis of wavelength conversion using highly nondegenerate four-wave mixing in /spl lambda//4-shifted DFB lasers

We present a theoretical analysis of terahertz-range wavelength conversion using highly nondegenerate four-wave mixing in /spl lambda/=1.55 /spl mu/m, medium-coupling /spl lambda//4-shifted distributed feedback (DFB) lasers. Based on the coupled-wave approach, our model predicts up to /spl sim/2 THz probe-pump detuning with a -20 dB or higher conversion efficiency for quantum-well /spl lambda//4-shifted DFB. The influences of pump power and DFB coupling strength (/spl kappa/L) on wave mixing as well as the Bragg reflection effects are discussed. The predictions of the model are found to be in good agreement with recent experimental observations.
Si, Al distribution in micas; analysis by high-resolution 29 Si NMR spectroscopy

The analysis of 29Si MAR-NMR spectra of micas and vermiculite shows that the two main factors that govern the Si, Al distribution in the tetrahedral sheet of these minerals are: (i) Loewenstein's rule (avoidance of Al-O-Al linkages), and (ii) local balance of charges. In muscovite, phlogopite and vermiculite, Al is randomly distributed in hexagonal ring sites (meta and para dispositions equally favoured), but complying with the two above restrictions. In margarite Si and Al are observed to alternate strictly, in agreement with previous X-ray diffraction analysis.
The syntax of phonology : A radically substance-free approach

This thesis investigates the formal properties of phonological representation and computation. The starting point of the approach taken here is that these can and should be investigated independently of the effect that extraphonological factors, most notably phonetics, have on the shape of individual phonologies. In chapter 1, I summarise the conceptual and empirical arguments for a model of autonomous phonology. Then I discuss the differences between substancefree approaches to phonology, including the Concordia school (Hale & Reiss 2000a,b, 2003; Hale et al. 2007; Hale & Reiss 2008), the Toronto school (Dresher et al. 1994; Avery 1996; Dresher 1998; Avery & Rice 1989; Rice & Avery 1991; Piggott 1992; Rice 1993; Dresher 2001, 2003 inter alia), Element Theory (Harris 1990, 1994; Harris & Lindsey 1995; Harris 2005, 2006), the Parallel Structures Model (Moren 2003a,b, 2006) and radically substance-free phonology (Odden 2006, this thesis). The approach followed in this thesis is the most substance-free of the alternatives examined: neither phonological computation, nor phonological primes are innately connected to phonetic (or other extra-phonological) correlates. I discuss different formal aspects of phonological representations, and argue for a model using privative indexical features that can freely enter into feature geometrical dependency relations with one another. Finally, I summarise the most important properties of the architecture of radically substance-free phonology. Chapter 2 deals with integrating substance-free phonology and Optimality Theory (Prince & Smolensky 1993). I formalise featural identity constraints in a way that is compatible with a model using privative features and an unrestricted feature geometry. I also formalise Max and Dep constraints on features, and show how the model presented here can account for ‘fea-
On a t-Test for the Scale Parameter Based on Sample Percentiles

A Student's test of Ho:? = ? o against ? o of the scale parameter ? of the Normal, logistic, and Cauchys distributions, when the location parameter ? is known is based on a few sample percentiles selected from a large sample. Tables facilitating the computation of the test statistic are provided. The significance level and power of the test statistic for sample sizes ? 200 are simulated by the Monte Carlo method and compared with the significance level and power when it is assumed to have its asymptotic distribution.
A greedy genetic algorithm for continuous variables electromagnetic optimization problems

A greedy genetic algorithm for continuous variables electromagnetic optimization problems is presented. The presented algorithm is characterized by the use of a nonlinear simplex method as a principal optimizer, and of a greedy genetic algorithm to explore the search space, realizing a balance between diversity and a bias toward fitter individuals. The resulting algorithm merges the efficiency typical of calculus-based search with the robustness typical of random methods. A detailed comparison of the performance obtained implementing several strategies is presented, using an electromagnetic design test problem.
H ∞ Control for Suppressing Acoustic Modes of a Distributed Structure Using Cluster Sensing and Actuation

This research work presents an H ∞ control design based on cluster sensing and actuation to suppress acoustic modes of a distributed panel structure which is subjected to excitation with varying frequency. A state—space model of the planar structure is obtained based on cluster sensing and cluster actuation. Uncertainty sources are considered for robustness of the control system. The eigenvalues of the structure’s acoustic power modes are assumed to vary with the excitation frequency in order to consider sound radiation from the vibrating panel structure. The proposed controller design covers the uncertainties caused by varying disturbance frequencies and inhomogeneities of the structure.
Effects of introducing a gas into the free-electron laser

Many interesting applications of the free-electron laser (FEL) require the extension of the operating wavelength into the ultraviolet region of the spectrum. The introduction of a gas into the wiggler section of a FEL alters the phase velocity of the electromagnetic wave and so changes the synchronism condition relating wavelength to wiggler parameters and beam energy. This provides a means for tuning the frequency of an oscillator, and with the addition of 200 Torr of hydrogen gas the wavelength of a FEL operating in the near infrared without gas was reduced by 0.73 μm. The plasma generated from ionization of the hydrogen molecules by collisions with the electron beam diminished the oscillator gain, but this effect was eliminated by the addition of less than 0.1% of an electron attachment gas. Gain is also reduced by multiple scattering of the beam electrons, but this effect is not severe for a 1-m wiggler length. When hydrogen is used, a FEL with fixed wiggler parameters and electron energy can be tuned from the near infrared to ≈1200 A, and with helium the wavelength can be reduced to 600 A.
Piezoresistance in Quantized Conduction Bands in Silicon Inversion Layers

Piezoresistance measurements have been carried out on n‐type inversion layers on (100), (110), and (111) silicon at room temperature as a function of the gate voltage (perpendicular electric field). It has been shown that the values of the piezoresistance coefficients depend not only on the surface orientation but also on the magnitude of the gate voltage. The strongest surface field influence was observed in the (100) plane whereas no gate voltage dependence could be detected on (111) surfaces. The data obtained have been interpreted in terms of the quantization of energy levels in inversion layers. This quantization causes generally an occupation anisotropy of the valleys mostly pronounced for an electric field in the [100] direction and disappearing for a [111] direction of the field. Theoretical expressions for the (100) piezoresistance coefficients π11 and π12 have been derived taking into account the field dependences of the population effect and of the intervally scattering contribution, both affec.
Exoskeletal solutions to enable mobility with a lower leg fracture in austere environments

Abstract The treatment and evacuation of people with lower limb fractures in austere environments presents unique challenges that assistive exoskeletal devices could address. In these dangerous situations, independent mobility for the injured can preserve their vital capabilities so that they can safely evacuate and minimize the need for additional personnel to help. This expert view article discusses how different exoskeleton archetypes could provide independent mobility while satisfying the requisite needs for portability, maintainability, durability, and adaptability to be available and useful within austere environments. The authors also discuss areas of development that would enable exoskeletons to operate more effectively in these scenarios as well as preserve the health of the injured limb so that definitive treatment after evacuation will produce better outcomes.
1.2 ps pulses from passively mode-locked laser diode pumped Er-doped fibre ring laser

1.2 ps transform-limited pulses at 1557 nm have been produced by passively mode locking an Er-doped fibre laser. An InGaAs/GaAs-on-GaAs superlattice was used as a fast saturable absorber. The all-fibre laser is designed to be in a ring configuration. The low pump power requirement allowed pumping by a semiconductor laser diode.
Evaluation of Machine Learning Models for Detecting Disambiguation on Medical Abbreviations

The number of medical abbreviations in the world is due to the increasing number of diseases, technological advances in the medical field, research in the medical field, and the emergence of various drugs. A large number of medical abbreviations often have the same abbreviation but it has a different meaning. The similarity of these medical abbreviations often results in ambiguous abbreviations. The ambiguity of this abbreviation can be reduced by creating a system based on Artificial Intelligent (AI). In this paper, we have compared various models using Naive Bayes, LSTM, Logistic Regression, and SVM to get the best model for medical abbreviations disambiguation. The experimental results indicate that the highest model accuracy is obtained by LSTM model, which is at 97.21%.
PDGes: An Interpretable Detection Model for Parkinson’s Disease Using Smartphones

Parkinson’s disease (PD) is a neurodegenerative disorder that severely affects the motor system of patients. Early PD detection will greatly improve the quality of lives. However, existing automatic PD detection systems either rely on customized sensors or require users to perform special activities, using machine learning models whose prediction process is not understandable by medical professionals. In this article, we develop a non-disruptive PD detection system on smartphones based on interpretable prediction models. We design an application named PDGes to passively collect touchscreen and Inertial Measurement Unit data of users’ tapping and swiping actions on smartphones. Meaningful features that reflect finger dexterity, tremor, stiffness, and hand movement are extracted to build the prediction model. To better comprehend the decisions made by the model, we conduct a systematic analysis of feature importance to help validate the conformity of the model with clinical PD diagnosis. We collected data from 108 volunteers to evaluate the performance of PDGes. The experiment results show that PDGes achieves a detection accuracy of more than 94.5% on different smartphones.
The Effect of Fiber Bragg Grating (FBG) Sensors on Data Channel of Fiber Optic Communication (FOC) System

The advantages of optical fiber sensors, including fiber bragg grating (FBG) sensors, e.g. greater sensitivity, immunity to electromagnetic interference (EMI) and radio frequency interference (RFI), versatility, reliability, compatibility to optical communication and telemetry, reduced cost, reduced size, and reduced weight have been very well known. In particular, the FBG sensors have been widely utilized in many fields such as in structural health monitoring, oil and gas, civil industry, medical and space equipment. In this paper we develop a model of FBG sensors embedded fiber optic communication (FOC) system by using OptiSystem v.18. The bit error rate (BER) and Q-factor analysis as results of the effect of two FBG sensors on telecommunication channels, in which the sensors are integrated into a single fiber optic communication (FOC) with 80 km of fiber optic length. The results show that even with 2 sensors embedded in FOC system, the min BER and max Q factor of the system are still in good conditions with values of 4.35x 10-7 and 4.87 respectively.